From b81144868d05860707a345ee997847e21ff006c1 Mon Sep 17 00:00:00 2001 From: sezar543 Date: Mon, 27 Nov 2023 20:03:42 -0800 Subject: [PATCH 01/18] update readme --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 7fbf80b75..790653df7 100644 --- a/README.md +++ b/README.md @@ -2,3 +2,5 @@ Accompanying repo for the online course Deployment of Machine Learning Models. For the documentation, visit the [course on Udemy](https://www.udemy.com/deployment-of-machine-learning-models/?couponCode=TIDREPO). + +update \ No newline at end of file From 8a033bebf5d756bd8dea7f002b297cc2f5e06e18 Mon Sep 17 00:00:00 2001 From: sezar543 Date: Mon, 27 Nov 2023 20:08:07 -0800 Subject: [PATCH 02/18] update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 790653df7..99381ab60 100644 --- a/README.md +++ b/README.md @@ -3,4 +3,4 @@ Accompanying repo for the online course Deployment of Machine Learning Models. For the documentation, visit the [course on Udemy](https://www.udemy.com/deployment-of-machine-learning-models/?couponCode=TIDREPO). -update \ No newline at end of file +update2 \ No newline at end of file From 1664f2e58d5a8ea005a1afec33eefdef58f6a67e Mon Sep 17 00:00:00 2001 From: Reza Sadoughian Date: Tue, 17 Sep 2024 22:12:32 -0700 Subject: [PATCH 03/18] updated readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 99381ab60..4020341f1 100644 --- a/README.md +++ b/README.md @@ -3,4 +3,4 @@ Accompanying repo for the online course Deployment of Machine Learning Models. For the documentation, visit the [course on Udemy](https://www.udemy.com/deployment-of-machine-learning-models/?couponCode=TIDREPO). -update2 \ No newline at end of file +update3 \ No newline at end of file From 96b561cbded5bcdbc91e98cbfc2efd2bcb921062 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian Date: Tue, 17 Sep 2024 23:56:29 -0700 Subject: [PATCH 04/18] bump api version --- .circleci/config.yml | 4 ++-- section-07-ci-and-publishing/house-prices-api/app/__init__.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 037645ab2..6ada746d7 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -54,7 +54,7 @@ jobs: - run: name: Deploy to Railway App (You must set RAILWAY_TOKEN env var) command: | - cd section-07-ci-and-publishing/house-prices-api && railway up --detach + cd section-07-ci-and-publishing/house-prices-api && railway up --detach -s combative-rifle -e production section_07_test_and_upload_regression_model: <<: *defaults @@ -92,7 +92,7 @@ jobs: - run: name: Build and run Dockerfile (see https://docs.railway.app/deploy/dockerfiles) command: | - cd section-08-deploying-with-containers && railway up --detach + cd section-08-deploying-with-containers && railway up --detach -s combative-rifle -e production test_regression_model_py37: docker: diff --git a/section-07-ci-and-publishing/house-prices-api/app/__init__.py b/section-07-ci-and-publishing/house-prices-api/app/__init__.py index 3b93d0be0..27fdca497 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/__init__.py +++ b/section-07-ci-and-publishing/house-prices-api/app/__init__.py @@ -1 +1 @@ -__version__ = "0.0.2" +__version__ = "0.0.3" From aa939ce1622033982e68cc40cd331a300f4fbc81 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian Date: Wed, 18 Sep 2024 03:47:47 -0700 Subject: [PATCH 05/18] bump api version --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 4020341f1..ee21f4f1b 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -# Deployment of Machine Learning Models +# Deployment of Machine Learning Models updated Accompanying repo for the online course Deployment of Machine Learning Models. For the documentation, visit the [course on Udemy](https://www.udemy.com/deployment-of-machine-learning-models/?couponCode=TIDREPO). -update3 \ No newline at end of file +update2 \ No newline at end of file From fa025966380209a067f28476cfc26a1912680175 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian Date: Wed, 18 Sep 2024 03:51:16 -0700 Subject: [PATCH 06/18] bump api version --- section-07-ci-and-publishing/house-prices-api/app/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/section-07-ci-and-publishing/house-prices-api/app/__init__.py b/section-07-ci-and-publishing/house-prices-api/app/__init__.py index 27fdca497..81f0fdecc 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/__init__.py +++ b/section-07-ci-and-publishing/house-prices-api/app/__init__.py @@ -1 +1 @@ -__version__ = "0.0.3" +__version__ = "0.0.4" From 56dff378e835868bb80682a66d515fe9cfbf4a41 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian Date: Wed, 18 Sep 2024 23:31:53 -0700 Subject: [PATCH 07/18] bump api version --- section-07-ci-and-publishing/house-prices-api/app/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/section-07-ci-and-publishing/house-prices-api/app/__init__.py b/section-07-ci-and-publishing/house-prices-api/app/__init__.py index 81f0fdecc..b1a19e323 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/__init__.py +++ b/section-07-ci-and-publishing/house-prices-api/app/__init__.py @@ -1 +1 @@ -__version__ = "0.0.4" +__version__ = "0.0.5" From 280f79c5447c1b4d85c78600fdf95391c292016b Mon Sep 17 00:00:00 2001 From: sezar Date: Thu, 19 Sep 2024 01:59:08 -0700 Subject: [PATCH 08/18] bump api version2 --- .circleci/config.yml | 682 +- .dockerignore | 16 +- .gitignore | 280 +- Dockerfile | 44 +- LICENSE | 58 +- Makefile | 36 +- README.md | 10 +- assignment-section-05/MANIFEST.in | 34 +- assignment-section-05/README.md | 30 +- .../classification_model/VERSION | 2 +- .../classification_model/__init__.py | 34 +- .../classification_model/config.yml | 100 +- .../classification_model/config/core.py | 168 +- .../classification_model/pipeline.py | 128 +- .../classification_model/predict.py | 68 +- .../processing/data_manager.py | 210 +- .../processing/features.py | 52 +- .../processing/validation.py | 92 +- .../classification_model/train_pipeline.py | 74 +- assignment-section-05/mypy.ini | 26 +- assignment-section-05/pyproject.toml | 96 +- .../requirements/requirements.txt | 20 +- .../requirements/test_requirements.txt | 8 +- .../requirements/typing_requirements.txt | 8 +- assignment-section-05/setup.py | 140 +- assignment-section-05/tests/conftest.py | 52 +- assignment-section-05/tests/test_features.py | 32 +- .../tests/test_prediction.py | 54 +- assignment-section-05/tox.ini | 114 +- packages/ml_api/api/__init__.py | 8 +- packages/ml_api/api/app.py | 40 +- packages/ml_api/api/config.py | 140 +- packages/ml_api/api/controller.py | 178 +- packages/ml_api/api/validation.py | 310 +- packages/ml_api/diff_test_requirements.txt | 24 +- packages/ml_api/requirements.txt | 26 +- packages/ml_api/run.py | 20 +- packages/ml_api/run.sh | 4 +- packages/ml_api/test_data_predictions.csv | 1002 +- .../ml_api/tests/capture_model_predictions.py | 70 +- packages/ml_api/tests/conftest.py | 36 +- .../differential_tests/test_differential.py | 106 +- packages/ml_api/tests/test_controller.py | 158 +- packages/ml_api/tests/test_validation.py | 52 +- packages/ml_api/tox.ini | 62 +- packages/neural_network_model/MANIFEST.in | 32 +- packages/neural_network_model/config.yml | 8 +- .../neural_network_model/__init__.py | 14 +- .../neural_network_model/config/config.py | 76 +- .../neural_network_model/model.py | 158 +- .../neural_network_model/pipeline.py | 20 +- .../neural_network_model/predict.py | 134 +- .../processing/data_management.py | 260 +- .../neural_network_model/processing/errors.py | 12 +- .../processing/preprocessors.py | 100 +- .../neural_network_model/train_pipeline.py | 54 +- .../neural_network_model/requirements.txt | 34 +- packages/neural_network_model/setup.py | 158 +- .../neural_network_model/tests/conftest.py | 40 +- .../tests/test_predict.py | 34 +- packages/regression_model/MANIFEST.in | 30 +- .../regression_model/__init__.py | 34 +- .../regression_model/config/config.py | 210 +- .../regression_model/config/logging_config.py | 36 +- .../regression_model/pipeline.py | 100 +- .../regression_model/predict.py | 90 +- .../processing/data_management.py | 116 +- .../regression_model/processing/errors.py | 12 +- .../regression_model/processing/features.py | 68 +- .../processing/preprocessors.py | 328 +- .../regression_model/processing/validation.py | 60 +- .../regression_model/train_pipeline.py | 72 +- packages/regression_model/requirements.txt | 38 +- packages/regression_model/setup.py | 162 +- .../regression_model/tests/test_predict.py | 70 +- packages/regression_model/tox.ini | 50 +- scripts/fetch_kaggle_dataset.sh | 4 +- scripts/fetch_kaggle_large_dataset.sh | 20 +- scripts/input_test.json | 162 +- scripts/publish_model.sh | 86 +- ...hine-learning-pipeline-data-analysis.ipynb | 9338 ++++++++--------- ...earning-pipeline-feature-engineering.ipynb | 6280 +++++------ ...-learning-pipeline-feature-selection.ipynb | 1986 ++-- ...ine-learning-pipeline-model-training.ipynb | 2642 ++--- ...e-learning-pipeline-scoring-new-data.ipynb | 2838 ++--- ...feature-engineering-with-open-source.ipynb | 6716 ++++++------ .../07-feature-engineering-pipeline.ipynb | 3700 +++---- .../08-final-machine-learning-pipeline.ipynb | 2244 ++-- .../preprocessors.py | 108 +- .../preprocessors_bonus.py | 178 +- .../requirements.txt | 16 +- ...dicting-survival-titanic-assignement.ipynb | 1574 +-- ...predicting-survival-titanic-solution.ipynb | 2978 +++--- ...titanic-survival-pipeline-assignment.ipynb | 1464 +-- ...4-titanic-survival-pipeline-solution.ipynb | 1500 +-- .../MANIFEST.in | 34 +- section-05-production-model-package/mypy.ini | 26 +- .../pyproject.toml | 96 +- .../regression_model/VERSION | 2 +- .../regression_model/__init__.py | 34 +- .../regression_model/config.yml | 324 +- .../regression_model/config/core.py | 198 +- .../regression_model/pipeline.py | 230 +- .../regression_model/predict.py | 70 +- .../processing/data_manager.py | 110 +- .../regression_model/processing/features.py | 106 +- .../regression_model/processing/validation.py | 264 +- .../regression_model/train_pipeline.py | 66 +- .../requirements/requirements.txt | 20 +- .../requirements/test_requirements.txt | 8 +- .../requirements/typing_requirements.txt | 8 +- section-05-production-model-package/setup.py | 136 +- .../tests/conftest.py | 18 +- .../tests/test_features.py | 34 +- .../tests/test_prediction.py | 44 +- section-05-production-model-package/tox.ini | 108 +- .../house-prices-api/app/__init__.py | 2 +- .../house-prices-api/app/api.py | 98 +- .../house-prices-api/app/config.py | 140 +- .../house-prices-api/app/main.py | 116 +- .../house-prices-api/app/schemas/__init__.py | 4 +- .../house-prices-api/app/schemas/health.py | 14 +- .../house-prices-api/app/schemas/predict.py | 206 +- .../house-prices-api/app/tests/conftest.py | 42 +- .../house-prices-api/app/tests/test_api.py | 52 +- .../house-prices-api/mypy.ini | 8 +- .../house-prices-api/requirements.txt | 16 +- .../house-prices-api/test_requirements.txt | 12 +- .../house-prices-api/tox.ini | 116 +- .../house-prices-api/typing_requirements.txt | 10 +- .../house-prices-api/app/__init__.py | 2 +- .../house-prices-api/app/api.py | 98 +- .../house-prices-api/app/config.py | 140 +- .../house-prices-api/app/main.py | 116 +- .../house-prices-api/app/schemas/__init__.py | 4 +- .../house-prices-api/app/schemas/health.py | 14 +- .../house-prices-api/app/schemas/predict.py | 206 +- .../house-prices-api/app/tests/conftest.py | 42 +- .../house-prices-api/app/tests/test_api.py | 52 +- .../house-prices-api/mypy.ini | 8 +- .../house-prices-api/requirements.txt | 16 +- .../house-prices-api/test_requirements.txt | 12 +- .../house-prices-api/tox.ini | 114 +- .../house-prices-api/typing_requirements.txt | 10 +- .../model-package/MANIFEST.in | 34 +- .../model-package/mypy.ini | 26 +- .../model-package/publish_model.sh | 86 +- .../model-package/pyproject.toml | 96 +- .../model-package/regression_model/VERSION | 2 +- .../regression_model/__init__.py | 34 +- .../model-package/regression_model/config.yml | 324 +- .../regression_model/config/core.py | 198 +- .../regression_model/pipeline.py | 238 +- .../model-package/regression_model/predict.py | 70 +- .../processing/data_manager.py | 110 +- .../regression_model/processing/features.py | 106 +- .../regression_model/processing/validation.py | 264 +- .../regression_model/train_pipeline.py | 66 +- .../requirements/requirements.txt | 20 +- .../requirements/test_requirements.txt | 8 +- .../requirements/typing_requirements.txt | 8 +- .../model-package/setup.py | 136 +- .../model-package/tests/conftest.py | 18 +- .../model-package/tests/test_features.py | 34 +- .../model-package/tests/test_prediction.py | 44 +- .../model-package/tox.ini | 188 +- .../.dockerignore | 18 +- .../Dockerfile | 44 +- .../house-prices-api/app/__init__.py | 2 +- .../house-prices-api/app/api.py | 98 +- .../house-prices-api/app/config.py | 140 +- .../house-prices-api/app/main.py | 116 +- .../house-prices-api/app/schemas/__init__.py | 4 +- .../house-prices-api/app/schemas/health.py | 14 +- .../house-prices-api/app/schemas/predict.py | 206 +- .../house-prices-api/app/tests/conftest.py | 42 +- .../house-prices-api/app/tests/test_api.py | 52 +- .../house-prices-api/mypy.ini | 8 +- .../house-prices-api/requirements.txt | 20 +- .../house-prices-api/test_requirements.txt | 12 +- .../house-prices-api/tox.ini | 116 +- .../house-prices-api/typing_requirements.txt | 10 +- 182 files changed, 28983 insertions(+), 28983 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 6ada746d7..88d122d96 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1,341 +1,341 @@ -version: '2.1' -orbs: - node: circleci/node@5.1.0 - -defaults: &defaults - docker: - - image: cimg/python:3.11.1 - working_directory: ~/project - -prepare_venv: &prepare_venv - run: - name: Create venv - command: | - python -m venv venv - source venv/bin/activate - pip install --upgrade pip - -prepare_tox: &prepare_tox - run: - name: Install tox - command: | - pip install --user tox - -fetch_data: &fetch_data - run: - name: Set script permissions and fetch data - command: | - source venv/bin/activate - chmod +x ./scripts/fetch_kaggle_dataset.sh - ./scripts/fetch_kaggle_dataset.sh - -jobs: - section_07_test_app: - <<: *defaults - working_directory: ~/project/section-07-ci-and-publishing/house-prices-api - steps: - - checkout: - path: ~/project - - *prepare_tox - - run: - name: Runnning app tests - command: | - tox - - section_07_deploy_app_to_railway: - <<: *defaults - steps: - - checkout: - path: ~/project/ - - node/install: - node-version: '16.13' - - run: node --version - - run: npm i -g @railway/cli - - run: - name: Deploy to Railway App (You must set RAILWAY_TOKEN env var) - command: | - cd section-07-ci-and-publishing/house-prices-api && railway up --detach -s combative-rifle -e production - - section_07_test_and_upload_regression_model: - <<: *defaults - working_directory: ~/project/section-07-ci-and-publishing/model-package - steps: - - checkout: - path: ~/project - - *prepare_tox - - run: - name: Fetch the data - command: | - tox -e fetch_data - - run: - name: Test the model - command: | - tox - - run: - name: Publish model to Gemfury - command: | - tox -e publish_model - - - section_08_deploy_app_container_via_railway: - <<: *defaults - steps: - - setup_remote_docker: - # Supported versions: https://circleci.com/docs/2.0/building-docker-images/#docker-version - version: 20.10.18 - - checkout: - path: ~/project/ - - node/install: - node-version: '16.13' - - run: node --version - - run: npm i -g @railway/cli - - run: - name: Build and run Dockerfile (see https://docs.railway.app/deploy/dockerfiles) - command: | - cd section-08-deploying-with-containers && railway up --detach -s combative-rifle -e production - - test_regression_model_py37: - docker: - - image: circleci/python:3.7.6 - working_directory: ~/project/packages/regression_model - steps: - - checkout: - path: ~/project - - run: - name: Run tests with Python 3.7 - command: | - sudo pip install --upgrade pip - pip install --user tox - tox -e py37 - - test_regression_model_py38: - docker: - - image: circleci/python:3.8.0 - working_directory: ~/project/packages/regression_model - steps: - - checkout: - path: ~/project - - run: - name: Run tests with Python 3.8 - command: | - sudo pip install --upgrade pip - pip install --user tox - tox -e py38 - - test_ml_api_py37: - docker: - - image: circleci/python:3.7.6 - working_directory: ~/project/packages/ml_api - steps: - - checkout: - path: ~/project - - run: - name: Run API tests with Python 3.7 - command: | - sudo pip install --upgrade pip - pip install --user tox - tox -e py37 - - test_ml_api_py38: - docker: - - image: circleci/python:3.8.1 - working_directory: ~/project/packages/ml_api - steps: - - checkout: - path: ~/project - - run: - name: Run API tests with Python 3.8 - command: | - sudo pip install --upgrade pip - pip install --user tox - tox -e py38 - - train_and_upload_regression_model: - <<: *defaults - steps: - - checkout - - *prepare_venv - - run: - name: Install requirements - command: | - . venv/bin/activate - pip install -r packages/regression_model/requirements.txt - - *fetch_data - - run: - name: Train model - command: | - . venv/bin/activate - PYTHONPATH=./packages/regression_model python3 packages/regression_model/regression_model/train_pipeline.py - - run: - name: Publish model to Gemfury - command: | - . venv/bin/activate - chmod +x ./scripts/publish_model.sh - ./scripts/publish_model.sh ./packages/regression_model/ - - section_9_differential_tests: - <<: *defaults - steps: - - checkout - - *prepare_venv - - run: - name: Capturing previous model predictions - command: | - . venv/bin/activate - pip install -r packages/ml_api/diff_test_requirements.txt - PYTHONPATH=./packages/ml_api python3 packages/ml_api/tests/capture_model_predictions.py - - run: - name: Runnning differential tests - command: | - . venv/bin/activate - pip install -r packages/ml_api/requirements.txt - py.test -vv packages/ml_api/tests -m differential - - section_11_build_and_push_to_heroku_docker: - <<: *defaults - steps: - - checkout - - setup_remote_docker: - docker_layer_caching: true - - run: docker login --username=$HEROKU_EMAIL --password=$HEROKU_API_KEY registry.heroku.com - - run: - name: Setup Heroku CLI - command: | - wget -qO- https://cli-assets.heroku.com/install-ubuntu.sh | sh - - run: - name: Build and Push Image - command: | - make build-ml-api-heroku push-ml-api-heroku - - run: - name: Release to Heroku - command: | - heroku container:release web --app $HEROKU_APP_NAME - - section_12_publish_docker_image_to_aws: - <<: *defaults - working_directory: ~/project/packages/ml_models - steps: - - checkout - - setup_remote_docker - - run: - name: Publishing docker image to aws ECR - command: | - sudo pip install awscli - eval $(aws ecr get-login --no-include-email --region us-east-1) - make build-ml-api-aws tag-ml-api push-ml-api-aws - aws ecs update-service --cluster ml-api-cluster --service custom-service --task-definition first-run-task-definition --force-new-deployment - - section_13_train_and_upload_neural_network_model: - docker: - - image: circleci/python:3.6.4-stretch - working_directory: ~/project - steps: - - checkout - - *prepare_venv - - run: - name: Install requirements - command: | - . venv/bin/activate - pip install -r packages/neural_network_model/requirements.txt - - run: - name: Fetch Training data - 2GB - command: | - . venv/bin/activate - chmod +x ./scripts/fetch_kaggle_large_dataset.sh - ./scripts/fetch_kaggle_large_dataset.sh - - run: - name: Train model - command: | - . venv/bin/activate - PYTHONPATH=./packages/neural_network_model python3 packages/neural_network_model/neural_network_model/train_pipeline.py - - run: - name: Publish model to Gemfury - command: | - . venv/bin/activate - chmod +x ./scripts/publish_model.sh - ./scripts/publish_model.sh ./packages/neural_network_model/ - - -tags_only: &tags_only - filters: - branches: - ignore: /.*/ - tags: - only: /^.*/ - -workflows: - version: 2 - deploy_pipeline: - jobs: - - section_07_test_app - - section_07_deploy_app_to_railway: - requires: - - section_07_test_app - filters: - branches: - only: - - master - - demo - # upload after git tags are created - - section_07_test_and_upload_regression_model: - <<: *tags_only - - - section_08_deploy_app_container_via_railway: - filters: - branches: - only: - - master - - demo - -# test-all: -# jobs: -# - test_regression_model_py36 -# - test_regression_model_py37 -# - test_regression_model_py38 -# - test_ml_api_py36 -# - test_ml_api_py37 -# # - test_ml_api_py38 pending NN model update -# - section_9_differential_tests -# - train_and_upload_regression_model: -# requires: -# - test_regression_model_py36 -# - test_regression_model_py37 -# - test_regression_model_py38 -# - test_ml_api_py36 -# - test_ml_api_py37 -# - section_9_differential_tests -# filters: -# branches: -# only: -# - master -# - section_10_deploy_to_heroku: -# requires: -# - train_and_upload_regression_model -# filters: -# branches: -# only: -# - master -# - section_11_build_and_push_to_heroku_docker: -# requires: -# - train_and_upload_regression_model -# filters: -# branches: -# only: -# - master -# - section_12_publish_docker_image_to_aws: -# requires: -# - train_and_upload_regression_model -# filters: -# branches: -# only: -# - master -# - section_13_train_and_upload_neural_network_model: -# requires: -# - test_regression_model -# - test_ml_api -# - section_9_differential_tests -# - train_and_upload_regression_model -# filters: -# branches: -# only: -# - master +version: '2.1' +orbs: + node: circleci/node@5.1.0 + +defaults: &defaults + docker: + - image: cimg/python:3.11.1 + working_directory: ~/project + +prepare_venv: &prepare_venv + run: + name: Create venv + command: | + python -m venv venv + source venv/bin/activate + pip install --upgrade pip + +prepare_tox: &prepare_tox + run: + name: Install tox + command: | + pip install --user tox + +fetch_data: &fetch_data + run: + name: Set script permissions and fetch data + command: | + source venv/bin/activate + chmod +x ./scripts/fetch_kaggle_dataset.sh + ./scripts/fetch_kaggle_dataset.sh + +jobs: + section_07_test_app: + <<: *defaults + working_directory: ~/project/section-07-ci-and-publishing/house-prices-api + steps: + - checkout: + path: ~/project + - *prepare_tox + - run: + name: Runnning app tests + command: | + tox + + section_07_deploy_app_to_railway: + <<: *defaults + steps: + - checkout: + path: ~/project/ + - node/install: + node-version: '16.13' + - run: node --version + - run: npm i -g @railway/cli + - run: + name: Deploy to Railway App (You must set RAILWAY_TOKEN env var) + command: | + cd section-07-ci-and-publishing/house-prices-api && railway up --detach -s lavish-contentment -e production + + section_07_test_and_upload_regression_model: + <<: *defaults + working_directory: ~/project/section-07-ci-and-publishing/model-package + steps: + - checkout: + path: ~/project + - *prepare_tox + - run: + name: Fetch the data + command: | + tox -e fetch_data + - run: + name: Test the model + command: | + tox + - run: + name: Publish model to Gemfury + command: | + tox -e publish_model + + + section_08_deploy_app_container_via_railway: + <<: *defaults + steps: + - setup_remote_docker: + # Supported versions: https://circleci.com/docs/2.0/building-docker-images/#docker-version + version: 20.10.18 + - checkout: + path: ~/project/ + - node/install: + node-version: '16.13' + - run: node --version + - run: npm i -g @railway/cli + - run: + name: Build and run Dockerfile (see https://docs.railway.app/deploy/dockerfiles) + command: | + cd section-08-deploying-with-containers && railway up --detach -s lavish-contentment -e production + + test_regression_model_py37: + docker: + - image: circleci/python:3.7.6 + working_directory: ~/project/packages/regression_model + steps: + - checkout: + path: ~/project + - run: + name: Run tests with Python 3.7 + command: | + sudo pip install --upgrade pip + pip install --user tox + tox -e py37 + + test_regression_model_py38: + docker: + - image: circleci/python:3.8.0 + working_directory: ~/project/packages/regression_model + steps: + - checkout: + path: ~/project + - run: + name: Run tests with Python 3.8 + command: | + sudo pip install --upgrade pip + pip install --user tox + tox -e py38 + + test_ml_api_py37: + docker: + - image: circleci/python:3.7.6 + working_directory: ~/project/packages/ml_api + steps: + - checkout: + path: ~/project + - run: + name: Run API tests with Python 3.7 + command: | + sudo pip install --upgrade pip + pip install --user tox + tox -e py37 + + test_ml_api_py38: + docker: + - image: circleci/python:3.8.1 + working_directory: ~/project/packages/ml_api + steps: + - checkout: + path: ~/project + - run: + name: Run API tests with Python 3.8 + command: | + sudo pip install --upgrade pip + pip install --user tox + tox -e py38 + + train_and_upload_regression_model: + <<: *defaults + steps: + - checkout + - *prepare_venv + - run: + name: Install requirements + command: | + . venv/bin/activate + pip install -r packages/regression_model/requirements.txt + - *fetch_data + - run: + name: Train model + command: | + . venv/bin/activate + PYTHONPATH=./packages/regression_model python3 packages/regression_model/regression_model/train_pipeline.py + - run: + name: Publish model to Gemfury + command: | + . venv/bin/activate + chmod +x ./scripts/publish_model.sh + ./scripts/publish_model.sh ./packages/regression_model/ + + section_9_differential_tests: + <<: *defaults + steps: + - checkout + - *prepare_venv + - run: + name: Capturing previous model predictions + command: | + . venv/bin/activate + pip install -r packages/ml_api/diff_test_requirements.txt + PYTHONPATH=./packages/ml_api python3 packages/ml_api/tests/capture_model_predictions.py + - run: + name: Runnning differential tests + command: | + . venv/bin/activate + pip install -r packages/ml_api/requirements.txt + py.test -vv packages/ml_api/tests -m differential + + section_11_build_and_push_to_heroku_docker: + <<: *defaults + steps: + - checkout + - setup_remote_docker: + docker_layer_caching: true + - run: docker login --username=$HEROKU_EMAIL --password=$HEROKU_API_KEY registry.heroku.com + - run: + name: Setup Heroku CLI + command: | + wget -qO- https://cli-assets.heroku.com/install-ubuntu.sh | sh + - run: + name: Build and Push Image + command: | + make build-ml-api-heroku push-ml-api-heroku + - run: + name: Release to Heroku + command: | + heroku container:release web --app $HEROKU_APP_NAME + + section_12_publish_docker_image_to_aws: + <<: *defaults + working_directory: ~/project/packages/ml_models + steps: + - checkout + - setup_remote_docker + - run: + name: Publishing docker image to aws ECR + command: | + sudo pip install awscli + eval $(aws ecr get-login --no-include-email --region us-east-1) + make build-ml-api-aws tag-ml-api push-ml-api-aws + aws ecs update-service --cluster ml-api-cluster --service custom-service --task-definition first-run-task-definition --force-new-deployment + + section_13_train_and_upload_neural_network_model: + docker: + - image: circleci/python:3.6.4-stretch + working_directory: ~/project + steps: + - checkout + - *prepare_venv + - run: + name: Install requirements + command: | + . venv/bin/activate + pip install -r packages/neural_network_model/requirements.txt + - run: + name: Fetch Training data - 2GB + command: | + . venv/bin/activate + chmod +x ./scripts/fetch_kaggle_large_dataset.sh + ./scripts/fetch_kaggle_large_dataset.sh + - run: + name: Train model + command: | + . venv/bin/activate + PYTHONPATH=./packages/neural_network_model python3 packages/neural_network_model/neural_network_model/train_pipeline.py + - run: + name: Publish model to Gemfury + command: | + . venv/bin/activate + chmod +x ./scripts/publish_model.sh + ./scripts/publish_model.sh ./packages/neural_network_model/ + + +tags_only: &tags_only + filters: + branches: + ignore: /.*/ + tags: + only: /^.*/ + +workflows: + version: 2 + deploy_pipeline: + jobs: + - section_07_test_app + - section_07_deploy_app_to_railway: + requires: + - section_07_test_app + filters: + branches: + only: + - master + - demo + # upload after git tags are created + - section_07_test_and_upload_regression_model: + <<: *tags_only + + - section_08_deploy_app_container_via_railway: + filters: + branches: + only: + - master + - demo + +# test-all: +# jobs: +# - test_regression_model_py36 +# - test_regression_model_py37 +# - test_regression_model_py38 +# - test_ml_api_py36 +# - test_ml_api_py37 +# # - test_ml_api_py38 pending NN model update +# - section_9_differential_tests +# - train_and_upload_regression_model: +# requires: +# - test_regression_model_py36 +# - test_regression_model_py37 +# - test_regression_model_py38 +# - test_ml_api_py36 +# - test_ml_api_py37 +# - section_9_differential_tests +# filters: +# branches: +# only: +# - master +# - section_10_deploy_to_heroku: +# requires: +# - train_and_upload_regression_model +# filters: +# branches: +# only: +# - master +# - section_11_build_and_push_to_heroku_docker: +# requires: +# - train_and_upload_regression_model +# filters: +# branches: +# only: +# - master +# - section_12_publish_docker_image_to_aws: +# requires: +# - train_and_upload_regression_model +# filters: +# branches: +# only: +# - master +# - section_13_train_and_upload_neural_network_model: +# requires: +# - test_regression_model +# - test_ml_api +# - section_9_differential_tests +# - train_and_upload_regression_model +# filters: +# branches: +# only: +# - master diff --git a/.dockerignore b/.dockerignore index b9e54fcad..18506f7f4 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,9 +1,9 @@ -jupyter_notebooks* -*/env* -*/venv* -.circleci* -packages/regression_model -*.env -*.log -.git +jupyter_notebooks* +*/env* +*/venv* +.circleci* +packages/regression_model +*.env +*.log +.git .gitignore \ No newline at end of file diff --git a/.gitignore b/.gitignore index fae2064cf..39f9360be 100644 --- a/.gitignore +++ b/.gitignore @@ -1,140 +1,140 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -env39/ -env311/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ - -# pycharm -.idea/ - -# datafiles -packages/regression_model/regression_model/datasets/*.csv -packages/regression_model/regression_model/datasets/*.zip -packages/regression_model/regression_model/datasets/*.txt -train.csv -test.csv -raw.csv -data_description.txt -house-prices-advanced-regression-techniques.zip -sample_submission.csv -test_data_predictions.csv -v2-plant-seedlings-dataset/ -v2-plant-seedlings-dataset.zip - -# all logs -logs/ - -# trained models (will be created in CI) -section-05-production-model-package/regression_model/trained_models/*.pkl -packages/regression_model/regression_model/trained_models/*.pkl -packages/neural_network_model/neural_network_model/trained_models/*.pkl -packages/neural_network_model/neural_network_model/trained_models/*.h5 -*.h5 -packages/neural_network_model/neural_network_model/datasets/training_data_reference.txt -*.pkl - -.DS_Store - -kaggle.json -packages/ml_api/uploads/* +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +env39/ +env311/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# pycharm +.idea/ + +# datafiles +packages/regression_model/regression_model/datasets/*.csv +packages/regression_model/regression_model/datasets/*.zip +packages/regression_model/regression_model/datasets/*.txt +train.csv +test.csv +raw.csv +data_description.txt +house-prices-advanced-regression-techniques.zip +sample_submission.csv +test_data_predictions.csv +v2-plant-seedlings-dataset/ +v2-plant-seedlings-dataset.zip + +# all logs +logs/ + +# trained models (will be created in CI) +section-05-production-model-package/regression_model/trained_models/*.pkl +packages/regression_model/regression_model/trained_models/*.pkl +packages/neural_network_model/neural_network_model/trained_models/*.pkl +packages/neural_network_model/neural_network_model/trained_models/*.h5 +*.h5 +packages/neural_network_model/neural_network_model/datasets/training_data_reference.txt +*.pkl + +.DS_Store + +kaggle.json +packages/ml_api/uploads/* diff --git a/Dockerfile b/Dockerfile index bbba25c1a..ab8a620cd 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,23 +1,23 @@ -FROM python:3.6.4 - -# Create the user that will run the app -RUN adduser --disabled-password --gecos '' ml-api-user - -WORKDIR /opt/ml_api - -ARG PIP_EXTRA_INDEX_URL -ENV FLASK_APP run.py - -# Install requirements, including from Gemfury -ADD ./packages/ml_api /opt/ml_api/ -RUN pip install --upgrade pip -RUN pip install -r /opt/ml_api/requirements.txt - -RUN chmod +x /opt/ml_api/run.sh -RUN chown -R ml-api-user:ml-api-user ./ - -USER ml-api-user - -EXPOSE 5000 - +FROM python:3.6.4 + +# Create the user that will run the app +RUN adduser --disabled-password --gecos '' ml-api-user + +WORKDIR /opt/ml_api + +ARG PIP_EXTRA_INDEX_URL +ENV FLASK_APP run.py + +# Install requirements, including from Gemfury +ADD ./packages/ml_api /opt/ml_api/ +RUN pip install --upgrade pip +RUN pip install -r /opt/ml_api/requirements.txt + +RUN chmod +x /opt/ml_api/run.sh +RUN chown -R ml-api-user:ml-api-user ./ + +USER ml-api-user + +EXPOSE 5000 + CMD ["bash", "./run.sh"] \ No newline at end of file diff --git a/LICENSE b/LICENSE index f02d80abc..924fe1122 100644 --- a/LICENSE +++ b/LICENSE @@ -1,29 +1,29 @@ -BSD 3-Clause License - -Copyright (c) 2019, Soledad Galli and Christopher Samiullah. Deployment of Machine Learning Models, online course. -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +BSD 3-Clause License + +Copyright (c) 2019, Soledad Galli and Christopher Samiullah. Deployment of Machine Learning Models, online course. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/Makefile b/Makefile index 7fe16bef3..e37e509fb 100644 --- a/Makefile +++ b/Makefile @@ -1,18 +1,18 @@ -NAME=udemy-ml-api -COMMIT_ID=$(shell git rev-parse HEAD) - - -build-ml-api-heroku: - docker build --build-arg PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL} -t registry.heroku.com/$(NAME)/web:$(COMMIT_ID) . - -push-ml-api-heroku: - docker push registry.heroku.com/${HEROKU_APP_NAME}/web:$(COMMIT_ID) - -build-ml-api-aws: - docker build --build-arg PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL} -t $(NAME):$(COMMIT_ID) . - -push-ml-api-aws: - docker push ${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/$(NAME):$(COMMIT_ID) - -tag-ml-api: - docker tag $(NAME):$(COMMIT_ID) ${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/$(NAME):$(COMMIT_ID) +NAME=udemy-ml-api +COMMIT_ID=$(shell git rev-parse HEAD) + + +build-ml-api-heroku: + docker build --build-arg PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL} -t registry.heroku.com/$(NAME)/web:$(COMMIT_ID) . + +push-ml-api-heroku: + docker push registry.heroku.com/${HEROKU_APP_NAME}/web:$(COMMIT_ID) + +build-ml-api-aws: + docker build --build-arg PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL} -t $(NAME):$(COMMIT_ID) . + +push-ml-api-aws: + docker push ${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/$(NAME):$(COMMIT_ID) + +tag-ml-api: + docker tag $(NAME):$(COMMIT_ID) ${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/$(NAME):$(COMMIT_ID) diff --git a/README.md b/README.md index ee21f4f1b..39debcb6f 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -# Deployment of Machine Learning Models updated -Accompanying repo for the online course Deployment of Machine Learning Models. - -For the documentation, visit the [course on Udemy](https://www.udemy.com/deployment-of-machine-learning-models/?couponCode=TIDREPO). - +# Deployment of Machine Learning Models updated +Accompanying repo for the online course Deployment of Machine Learning Models. + +For the documentation, visit the [course on Udemy](https://www.udemy.com/deployment-of-machine-learning-models/?couponCode=TIDREPO). + update2 \ No newline at end of file diff --git a/assignment-section-05/MANIFEST.in b/assignment-section-05/MANIFEST.in index f17c22c78..cc1b6ef88 100644 --- a/assignment-section-05/MANIFEST.in +++ b/assignment-section-05/MANIFEST.in @@ -1,18 +1,18 @@ -include *.txt -include *.md -include *.pkl -recursive-include ./classification_model/* - -include classification_model/datasets/train.csv -include classification_model/datasets/test.csv -include classification_model/trained_models/*.pkl -include classification_model/VERSION -include classification_model/config.yml - -include ./requirements/requirements.txt -include ./requirements/test_requirements.txt -exclude *.log -exclude *.cfg - -recursive-exclude * __pycache__ +include *.txt +include *.md +include *.pkl +recursive-include ./classification_model/* + +include classification_model/datasets/train.csv +include classification_model/datasets/test.csv +include classification_model/trained_models/*.pkl +include classification_model/VERSION +include classification_model/config.yml + +include ./requirements/requirements.txt +include ./requirements/test_requirements.txt +exclude *.log +exclude *.cfg + +recursive-exclude * __pycache__ recursive-exclude * *.py[co] \ No newline at end of file diff --git a/assignment-section-05/README.md b/assignment-section-05/README.md index 2409cc1e1..c0872b7e6 100644 --- a/assignment-section-05/README.md +++ b/assignment-section-05/README.md @@ -1,16 +1,16 @@ -# Productionized Titanic Classification Model Package - -## Run With Tox (Recommended) -- Download the data from: https://www.openml.org/data/get_csv/16826755/phpMYEkMl -- Save the file as `raw.csv` in the classification_model/datasets directory -- `pip install tox` -- Make sure you are in the assignment-section-05 directory (where the tox.ini file is) then run the command: `tox` (this runs the tests and typechecks, trains the model under the hood). The first time you run this it creates a virtual env and installs -dependencies, so takes a few minutes. - -## Run Without Tox -- Download the data from: https://www.openml.org/data/get_csv/16826755/phpMYEkMl -- Save the file as `raw.csv` in the classification_model/datasets directory -- Add assignment-section-05 *and* classification_model paths to your system PYTHONPATH -- `pip install -r requirements/test_requirements` -- Train the model: `python classification_model/train_pipeline.py` +# Productionized Titanic Classification Model Package + +## Run With Tox (Recommended) +- Download the data from: https://www.openml.org/data/get_csv/16826755/phpMYEkMl +- Save the file as `raw.csv` in the classification_model/datasets directory +- `pip install tox` +- Make sure you are in the assignment-section-05 directory (where the tox.ini file is) then run the command: `tox` (this runs the tests and typechecks, trains the model under the hood). The first time you run this it creates a virtual env and installs +dependencies, so takes a few minutes. + +## Run Without Tox +- Download the data from: https://www.openml.org/data/get_csv/16826755/phpMYEkMl +- Save the file as `raw.csv` in the classification_model/datasets directory +- Add assignment-section-05 *and* classification_model paths to your system PYTHONPATH +- `pip install -r requirements/test_requirements` +- Train the model: `python classification_model/train_pipeline.py` - Run the tests `pytest tests` \ No newline at end of file diff --git a/assignment-section-05/classification_model/VERSION b/assignment-section-05/classification_model/VERSION index 8acdd82b7..84576eaa9 100644 --- a/assignment-section-05/classification_model/VERSION +++ b/assignment-section-05/classification_model/VERSION @@ -1 +1 @@ -0.0.1 +0.0.1 diff --git a/assignment-section-05/classification_model/__init__.py b/assignment-section-05/classification_model/__init__.py index 8cea86752..b38691e19 100644 --- a/assignment-section-05/classification_model/__init__.py +++ b/assignment-section-05/classification_model/__init__.py @@ -1,17 +1,17 @@ -import logging - -from classification_model.config.core import PACKAGE_ROOT, config - -# It is strongly advised that you do not add any handlers other than -# NullHandler to your library’s loggers. This is because the configuration -# of handlers is the prerogative of the application developer who uses your -# library. The application developer knows their target audience and what -# handlers are most appropriate for their application: if you add handlers -# ‘under the hood’, you might well interfere with their ability to carry out -# unit tests and deliver logs which suit their requirements. -# https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library -logging.getLogger(config.app_config.package_name).addHandler(logging.NullHandler()) - - -with open(PACKAGE_ROOT / "VERSION") as version_file: - __version__ = version_file.read().strip() +import logging + +from classification_model.config.core import PACKAGE_ROOT, config + +# It is strongly advised that you do not add any handlers other than +# NullHandler to your library’s loggers. This is because the configuration +# of handlers is the prerogative of the application developer who uses your +# library. The application developer knows their target audience and what +# handlers are most appropriate for their application: if you add handlers +# ‘under the hood’, you might well interfere with their ability to carry out +# unit tests and deliver logs which suit their requirements. +# https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library +logging.getLogger(config.app_config.package_name).addHandler(logging.NullHandler()) + + +with open(PACKAGE_ROOT / "VERSION") as version_file: + __version__ = version_file.read().strip() diff --git a/assignment-section-05/classification_model/config.yml b/assignment-section-05/classification_model/config.yml index 696a05035..a8f236ba8 100644 --- a/assignment-section-05/classification_model/config.yml +++ b/assignment-section-05/classification_model/config.yml @@ -1,51 +1,51 @@ -# Package Overview -package_name: regression_model - -# Data Files -raw_data_file: raw.csv -training_data_file: train.csv -test_data_file: test.csv - -# Variables -# The variable we are attempting to predict (sale price) -target: survived - -pipeline_name: titanic_classification_model -pipeline_save_file: titanic_classification_model_output_v - -features: - - pclass - - sex - - age - - sibsp - - parch - - fare - - cabin - - embarked - - title # generated from name - -# set train/test split -test_size: 0.1 - -# to set the random seed -random_state: 0 - -unused_fields: - - name - - ticket - - boat - - body - - home.dest - -numerical_vars: - - age - - fare - -categorical_vars: - - sex - - cabin - - embarked - - title - -cabin_vars: +# Package Overview +package_name: regression_model + +# Data Files +raw_data_file: raw.csv +training_data_file: train.csv +test_data_file: test.csv + +# Variables +# The variable we are attempting to predict (sale price) +target: survived + +pipeline_name: titanic_classification_model +pipeline_save_file: titanic_classification_model_output_v + +features: + - pclass + - sex + - age + - sibsp + - parch + - fare + - cabin + - embarked + - title # generated from name + +# set train/test split +test_size: 0.1 + +# to set the random seed +random_state: 0 + +unused_fields: + - name + - ticket + - boat + - body + - home.dest + +numerical_vars: + - age + - fare + +categorical_vars: + - sex + - cabin + - embarked + - title + +cabin_vars: - cabin \ No newline at end of file diff --git a/assignment-section-05/classification_model/config/core.py b/assignment-section-05/classification_model/config/core.py index 3f39d64f0..417728d14 100644 --- a/assignment-section-05/classification_model/config/core.py +++ b/assignment-section-05/classification_model/config/core.py @@ -1,84 +1,84 @@ -from pathlib import Path -from typing import Sequence - -from pydantic import BaseModel -from strictyaml import YAML, load - -import classification_model - -# Project Directories -PACKAGE_ROOT = Path(classification_model.__file__).resolve().parent -ROOT = PACKAGE_ROOT.parent -CONFIG_FILE_PATH = PACKAGE_ROOT / "config.yml" -DATASET_DIR = PACKAGE_ROOT / "datasets" -TRAINED_MODEL_DIR = PACKAGE_ROOT / "trained_models" - - -class AppConfig(BaseModel): - """ - Application-level config. - """ - - package_name: str - raw_data_file: str - pipeline_save_file: str - - -class ModelConfig(BaseModel): - """ - All configuration relevant to model - training and feature engineering. - """ - - target: str - unused_fields: Sequence[str] - features: Sequence[str] - test_size: float - random_state: int - numerical_vars: Sequence[str] - categorical_vars: Sequence[str] - cabin_vars: Sequence[str] - - -class Config(BaseModel): - """Master config object.""" - - app_config: AppConfig - model_config: ModelConfig - - -def find_config_file() -> Path: - """Locate the configuration file.""" - if CONFIG_FILE_PATH.is_file(): - return CONFIG_FILE_PATH - raise Exception(f"Config not found at {CONFIG_FILE_PATH!r}") - - -def fetch_config_from_yaml(cfg_path: Path = None) -> YAML: - """Parse YAML containing the package configuration.""" - - if not cfg_path: - cfg_path = find_config_file() - - if cfg_path: - with open(cfg_path, "r") as conf_file: - parsed_config = load(conf_file.read()) - return parsed_config - raise OSError(f"Did not find config file at path: {cfg_path}") - - -def create_and_validate_config(parsed_config: YAML = None) -> Config: - """Run validation on config values.""" - if parsed_config is None: - parsed_config = fetch_config_from_yaml() - - # specify the data attribute from the strictyaml YAML type. - _config = Config( - app_config=AppConfig(**parsed_config.data), - model_config=ModelConfig(**parsed_config.data), - ) - - return _config - - -config = create_and_validate_config() +from pathlib import Path +from typing import Sequence + +from pydantic import BaseModel +from strictyaml import YAML, load + +import classification_model + +# Project Directories +PACKAGE_ROOT = Path(classification_model.__file__).resolve().parent +ROOT = PACKAGE_ROOT.parent +CONFIG_FILE_PATH = PACKAGE_ROOT / "config.yml" +DATASET_DIR = PACKAGE_ROOT / "datasets" +TRAINED_MODEL_DIR = PACKAGE_ROOT / "trained_models" + + +class AppConfig(BaseModel): + """ + Application-level config. + """ + + package_name: str + raw_data_file: str + pipeline_save_file: str + + +class ModelConfig(BaseModel): + """ + All configuration relevant to model + training and feature engineering. + """ + + target: str + unused_fields: Sequence[str] + features: Sequence[str] + test_size: float + random_state: int + numerical_vars: Sequence[str] + categorical_vars: Sequence[str] + cabin_vars: Sequence[str] + + +class Config(BaseModel): + """Master config object.""" + + app_config: AppConfig + model_config: ModelConfig + + +def find_config_file() -> Path: + """Locate the configuration file.""" + if CONFIG_FILE_PATH.is_file(): + return CONFIG_FILE_PATH + raise Exception(f"Config not found at {CONFIG_FILE_PATH!r}") + + +def fetch_config_from_yaml(cfg_path: Path = None) -> YAML: + """Parse YAML containing the package configuration.""" + + if not cfg_path: + cfg_path = find_config_file() + + if cfg_path: + with open(cfg_path, "r") as conf_file: + parsed_config = load(conf_file.read()) + return parsed_config + raise OSError(f"Did not find config file at path: {cfg_path}") + + +def create_and_validate_config(parsed_config: YAML = None) -> Config: + """Run validation on config values.""" + if parsed_config is None: + parsed_config = fetch_config_from_yaml() + + # specify the data attribute from the strictyaml YAML type. + _config = Config( + app_config=AppConfig(**parsed_config.data), + model_config=ModelConfig(**parsed_config.data), + ) + + return _config + + +config = create_and_validate_config() diff --git a/assignment-section-05/classification_model/pipeline.py b/assignment-section-05/classification_model/pipeline.py index c20abd660..35bbfec93 100644 --- a/assignment-section-05/classification_model/pipeline.py +++ b/assignment-section-05/classification_model/pipeline.py @@ -1,64 +1,64 @@ -# for encoding categorical variables -from feature_engine.encoding import OneHotEncoder, RareLabelEncoder - -# for imputation -from feature_engine.imputation import ( - AddMissingIndicator, - CategoricalImputer, - MeanMedianImputer, -) -from sklearn.linear_model import LogisticRegression -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler - -from classification_model.config.core import config -from classification_model.processing.features import ExtractLetterTransformer - -titanic_pipe = Pipeline( - [ - # impute categorical variables with string missing - ( - "categorical_imputation", - CategoricalImputer( - imputation_method="missing", - variables=config.model_config.categorical_vars, - ), - ), - # add missing indicator to numerical variables - ( - "missing_indicator", - AddMissingIndicator(variables=config.model_config.numerical_vars), - ), - # impute numerical variables with the median - ( - "median_imputation", - MeanMedianImputer( - imputation_method="median", variables=config.model_config.numerical_vars - ), - ), - # Extract letter from cabin - ( - "extract_letter", - ExtractLetterTransformer(variables=config.model_config.cabin_vars), - ), - # == CATEGORICAL ENCODING ====== - # remove categories present in less than 5% of the observations (0.05) - # group them in one category called 'Rare' - ( - "rare_label_encoder", - RareLabelEncoder( - tol=0.05, n_categories=1, variables=config.model_config.categorical_vars - ), - ), - # encode categorical variables using one hot encoding into k-1 variables - ( - "categorical_encoder", - OneHotEncoder( - drop_last=True, variables=config.model_config.categorical_vars - ), - ), - # scale - ("scaler", StandardScaler()), - ("Logit", LogisticRegression(C=0.0005, random_state=0)), - ] -) +# for encoding categorical variables +from feature_engine.encoding import OneHotEncoder, RareLabelEncoder + +# for imputation +from feature_engine.imputation import ( + AddMissingIndicator, + CategoricalImputer, + MeanMedianImputer, +) +from sklearn.linear_model import LogisticRegression +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler + +from classification_model.config.core import config +from classification_model.processing.features import ExtractLetterTransformer + +titanic_pipe = Pipeline( + [ + # impute categorical variables with string missing + ( + "categorical_imputation", + CategoricalImputer( + imputation_method="missing", + variables=config.model_config.categorical_vars, + ), + ), + # add missing indicator to numerical variables + ( + "missing_indicator", + AddMissingIndicator(variables=config.model_config.numerical_vars), + ), + # impute numerical variables with the median + ( + "median_imputation", + MeanMedianImputer( + imputation_method="median", variables=config.model_config.numerical_vars + ), + ), + # Extract letter from cabin + ( + "extract_letter", + ExtractLetterTransformer(variables=config.model_config.cabin_vars), + ), + # == CATEGORICAL ENCODING ====== + # remove categories present in less than 5% of the observations (0.05) + # group them in one category called 'Rare' + ( + "rare_label_encoder", + RareLabelEncoder( + tol=0.05, n_categories=1, variables=config.model_config.categorical_vars + ), + ), + # encode categorical variables using one hot encoding into k-1 variables + ( + "categorical_encoder", + OneHotEncoder( + drop_last=True, variables=config.model_config.categorical_vars + ), + ), + # scale + ("scaler", StandardScaler()), + ("Logit", LogisticRegression(C=0.0005, random_state=0)), + ] +) diff --git a/assignment-section-05/classification_model/predict.py b/assignment-section-05/classification_model/predict.py index eb2990bb3..d25de563b 100644 --- a/assignment-section-05/classification_model/predict.py +++ b/assignment-section-05/classification_model/predict.py @@ -1,34 +1,34 @@ -import typing as t - -import pandas as pd - -from classification_model import __version__ as _version -from classification_model.config.core import config -from classification_model.processing.data_manager import load_pipeline -from classification_model.processing.validation import validate_inputs - -pipeline_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" -_titanic_pipe = load_pipeline(file_name=pipeline_file_name) - - -def make_prediction( - *, - input_data: t.Union[pd.DataFrame, dict], -) -> dict: - """Make a prediction using a saved model pipeline.""" - - data = pd.DataFrame(input_data) - validated_data, errors = validate_inputs(input_data=data) - results = {"predictions": None, "version": _version, "errors": errors} - - if not errors: - predictions = _titanic_pipe.predict( - X=validated_data[config.model_config.features] - ) - results = { - "predictions": predictions, - "version": _version, - "errors": errors, - } - - return results +import typing as t + +import pandas as pd + +from classification_model import __version__ as _version +from classification_model.config.core import config +from classification_model.processing.data_manager import load_pipeline +from classification_model.processing.validation import validate_inputs + +pipeline_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" +_titanic_pipe = load_pipeline(file_name=pipeline_file_name) + + +def make_prediction( + *, + input_data: t.Union[pd.DataFrame, dict], +) -> dict: + """Make a prediction using a saved model pipeline.""" + + data = pd.DataFrame(input_data) + validated_data, errors = validate_inputs(input_data=data) + results = {"predictions": None, "version": _version, "errors": errors} + + if not errors: + predictions = _titanic_pipe.predict( + X=validated_data[config.model_config.features] + ) + results = { + "predictions": predictions, + "version": _version, + "errors": errors, + } + + return results diff --git a/assignment-section-05/classification_model/processing/data_manager.py b/assignment-section-05/classification_model/processing/data_manager.py index 550eebdfc..82d43e54d 100644 --- a/assignment-section-05/classification_model/processing/data_manager.py +++ b/assignment-section-05/classification_model/processing/data_manager.py @@ -1,105 +1,105 @@ -import logging -import re -from pathlib import Path -from typing import Any, List, Union - -import joblib -import numpy as np -import pandas as pd -from sklearn.pipeline import Pipeline - -from classification_model import __version__ as _version -from classification_model.config.core import DATASET_DIR, TRAINED_MODEL_DIR, config - -logger = logging.getLogger(__name__) - - -# float type for np.nan -def get_first_cabin(row: Any) -> Union[str, float]: - try: - return row.split()[0] - except AttributeError: - return np.nan - - -def get_title(passenger: str) -> str: - """Extracts the title (Mr, Ms, etc) from the name variable.""" - line = passenger - if re.search("Mrs", line): - return "Mrs" - elif re.search("Mr", line): - return "Mr" - elif re.search("Miss", line): - return "Miss" - elif re.search("Master", line): - return "Master" - else: - return "Other" - - -def pre_pipeline_preparation(*, dataframe: pd.DataFrame) -> pd.DataFrame: - # replace question marks with NaN values - data = dataframe.replace("?", np.nan) - - # retain only the first cabin if more than - # 1 are available per passenger - data["cabin"] = data["cabin"].apply(get_first_cabin) - - data["title"] = data["name"].apply(get_title) - - # cast numerical variables as floats - data["fare"] = data["fare"].astype("float") - data["age"] = data["age"].astype("float") - - # drop unnecessary variables - data.drop(labels=config.model_config.unused_fields, axis=1, inplace=True) - - return data - - -def _load_raw_dataset(*, file_name: str) -> pd.DataFrame: - dataframe = pd.read_csv(Path(f"{DATASET_DIR}/{file_name}")) - return dataframe - - -def load_dataset(*, file_name: str) -> pd.DataFrame: - dataframe = pd.read_csv(Path(f"{DATASET_DIR}/{file_name}")) - transformed = pre_pipeline_preparation(dataframe=dataframe) - - return transformed - - -def save_pipeline(*, pipeline_to_persist: Pipeline) -> None: - """Persist the pipeline. - Saves the versioned model, and overwrites any previous - saved models. This ensures that when the package is - published, there is only one trained model that can be - called, and we know exactly how it was built. - """ - - # Prepare versioned save file name - save_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" - save_path = TRAINED_MODEL_DIR / save_file_name - - remove_old_pipelines(files_to_keep=[save_file_name]) - joblib.dump(pipeline_to_persist, save_path) - - -def load_pipeline(*, file_name: str) -> Pipeline: - """Load a persisted pipeline.""" - - file_path = TRAINED_MODEL_DIR / file_name - return joblib.load(filename=file_path) - - -def remove_old_pipelines(*, files_to_keep: List[str]) -> None: - """ - Remove old model pipelines. - This is to ensure there is a simple one-to-one - mapping between the package version and the model - version to be imported and used by other applications. - """ - do_not_delete = files_to_keep + ["__init__.py"] - for model_file in TRAINED_MODEL_DIR.iterdir(): - if model_file.name not in do_not_delete: - model_file.unlink() +import logging +import re +from pathlib import Path +from typing import Any, List, Union + +import joblib +import numpy as np +import pandas as pd +from sklearn.pipeline import Pipeline + +from classification_model import __version__ as _version +from classification_model.config.core import DATASET_DIR, TRAINED_MODEL_DIR, config + +logger = logging.getLogger(__name__) + + +# float type for np.nan +def get_first_cabin(row: Any) -> Union[str, float]: + try: + return row.split()[0] + except AttributeError: + return np.nan + + +def get_title(passenger: str) -> str: + """Extracts the title (Mr, Ms, etc) from the name variable.""" + line = passenger + if re.search("Mrs", line): + return "Mrs" + elif re.search("Mr", line): + return "Mr" + elif re.search("Miss", line): + return "Miss" + elif re.search("Master", line): + return "Master" + else: + return "Other" + + +def pre_pipeline_preparation(*, dataframe: pd.DataFrame) -> pd.DataFrame: + # replace question marks with NaN values + data = dataframe.replace("?", np.nan) + + # retain only the first cabin if more than + # 1 are available per passenger + data["cabin"] = data["cabin"].apply(get_first_cabin) + + data["title"] = data["name"].apply(get_title) + + # cast numerical variables as floats + data["fare"] = data["fare"].astype("float") + data["age"] = data["age"].astype("float") + + # drop unnecessary variables + data.drop(labels=config.model_config.unused_fields, axis=1, inplace=True) + + return data + + +def _load_raw_dataset(*, file_name: str) -> pd.DataFrame: + dataframe = pd.read_csv(Path(f"{DATASET_DIR}/{file_name}")) + return dataframe + + +def load_dataset(*, file_name: str) -> pd.DataFrame: + dataframe = pd.read_csv(Path(f"{DATASET_DIR}/{file_name}")) + transformed = pre_pipeline_preparation(dataframe=dataframe) + + return transformed + + +def save_pipeline(*, pipeline_to_persist: Pipeline) -> None: + """Persist the pipeline. + Saves the versioned model, and overwrites any previous + saved models. This ensures that when the package is + published, there is only one trained model that can be + called, and we know exactly how it was built. + """ + + # Prepare versioned save file name + save_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" + save_path = TRAINED_MODEL_DIR / save_file_name + + remove_old_pipelines(files_to_keep=[save_file_name]) + joblib.dump(pipeline_to_persist, save_path) + + +def load_pipeline(*, file_name: str) -> Pipeline: + """Load a persisted pipeline.""" + + file_path = TRAINED_MODEL_DIR / file_name + return joblib.load(filename=file_path) + + +def remove_old_pipelines(*, files_to_keep: List[str]) -> None: + """ + Remove old model pipelines. + This is to ensure there is a simple one-to-one + mapping between the package version and the model + version to be imported and used by other applications. + """ + do_not_delete = files_to_keep + ["__init__.py"] + for model_file in TRAINED_MODEL_DIR.iterdir(): + if model_file.name not in do_not_delete: + model_file.unlink() diff --git a/assignment-section-05/classification_model/processing/features.py b/assignment-section-05/classification_model/processing/features.py index fb7c629c8..50e730ccc 100644 --- a/assignment-section-05/classification_model/processing/features.py +++ b/assignment-section-05/classification_model/processing/features.py @@ -1,26 +1,26 @@ -from sklearn.base import BaseEstimator, TransformerMixin - - -class ExtractLetterTransformer(BaseEstimator, TransformerMixin): - # Extract first letter of variable - - def __init__(self, variables): - - if not isinstance(variables, list): - raise ValueError("variables should be a list") - - self.variables = variables - - def fit(self, X, y=None): - # we need this step to fit the sklearn pipeline - return self - - def transform(self, X): - - # so that we do not over-write the original dataframe - X = X.copy() - - for feature in self.variables: - X[feature] = X[feature].str[0] - - return X +from sklearn.base import BaseEstimator, TransformerMixin + + +class ExtractLetterTransformer(BaseEstimator, TransformerMixin): + # Extract first letter of variable + + def __init__(self, variables): + + if not isinstance(variables, list): + raise ValueError("variables should be a list") + + self.variables = variables + + def fit(self, X, y=None): + # we need this step to fit the sklearn pipeline + return self + + def transform(self, X): + + # so that we do not over-write the original dataframe + X = X.copy() + + for feature in self.variables: + X[feature] = X[feature].str[0] + + return X diff --git a/assignment-section-05/classification_model/processing/validation.py b/assignment-section-05/classification_model/processing/validation.py index 7ac1870b0..78933713d 100644 --- a/assignment-section-05/classification_model/processing/validation.py +++ b/assignment-section-05/classification_model/processing/validation.py @@ -1,46 +1,46 @@ -from typing import List, Optional, Tuple, Union - -import numpy as np -import pandas as pd -from pydantic import BaseModel, ValidationError - -from classification_model.config.core import config -from classification_model.processing.data_manager import pre_pipeline_preparation - - -def validate_inputs(*, input_data: pd.DataFrame) -> Tuple[pd.DataFrame, Optional[dict]]: - """Check model inputs for unprocessable values.""" - - pre_processed = pre_pipeline_preparation(dataframe=input_data) - validated_data = pre_processed[config.model_config.features].copy() - errors = None - - try: - # replace numpy nans so that pydantic can validate - MultipleTitanicDataInputs( - inputs=validated_data.replace({np.nan: None}).to_dict(orient="records") - ) - except ValidationError as error: - errors = error.json() - - return validated_data, errors - - -class TitanicDataInputSchema(BaseModel): - pclass: Optional[int] - name: Optional[str] - sex: Optional[str] - age: Optional[int] - sibsp: Optional[int] - parch: Optional[int] - ticket: Optional[int] - fare: Optional[float] - cabin: Optional[str] - embarked: Optional[str] - boat: Optional[Union[str, int]] - body: Optional[int] - # TODO: rename home.dest, can get away with it now as it is not used - - -class MultipleTitanicDataInputs(BaseModel): - inputs: List[TitanicDataInputSchema] +from typing import List, Optional, Tuple, Union + +import numpy as np +import pandas as pd +from pydantic import BaseModel, ValidationError + +from classification_model.config.core import config +from classification_model.processing.data_manager import pre_pipeline_preparation + + +def validate_inputs(*, input_data: pd.DataFrame) -> Tuple[pd.DataFrame, Optional[dict]]: + """Check model inputs for unprocessable values.""" + + pre_processed = pre_pipeline_preparation(dataframe=input_data) + validated_data = pre_processed[config.model_config.features].copy() + errors = None + + try: + # replace numpy nans so that pydantic can validate + MultipleTitanicDataInputs( + inputs=validated_data.replace({np.nan: None}).to_dict(orient="records") + ) + except ValidationError as error: + errors = error.json() + + return validated_data, errors + + +class TitanicDataInputSchema(BaseModel): + pclass: Optional[int] + name: Optional[str] + sex: Optional[str] + age: Optional[int] + sibsp: Optional[int] + parch: Optional[int] + ticket: Optional[int] + fare: Optional[float] + cabin: Optional[str] + embarked: Optional[str] + boat: Optional[Union[str, int]] + body: Optional[int] + # TODO: rename home.dest, can get away with it now as it is not used + + +class MultipleTitanicDataInputs(BaseModel): + inputs: List[TitanicDataInputSchema] diff --git a/assignment-section-05/classification_model/train_pipeline.py b/assignment-section-05/classification_model/train_pipeline.py index 5c83a97f3..d85a844c5 100644 --- a/assignment-section-05/classification_model/train_pipeline.py +++ b/assignment-section-05/classification_model/train_pipeline.py @@ -1,37 +1,37 @@ -from sklearn.model_selection import train_test_split - -from classification_model.config.core import config -from classification_model.pipeline import titanic_pipe -from classification_model.processing.data_manager import load_dataset, save_pipeline - - -def run_training() -> None: - """ - Train the model. - - Training data can be found here: - https://www.openml.org/data/get_csv/16826755/phpMYEkMl - """ - - # read training data - data = load_dataset(file_name=config.app_config.raw_data_file) - - # divide train and test - X_train, X_test, y_train, y_test = train_test_split( - data[config.model_config.features], # predictors - data[config.model_config.target], - test_size=config.model_config.test_size, - # we are setting the random seed here - # for reproducibility - random_state=config.model_config.random_state, - ) - - # fit model - titanic_pipe.fit(X_train, y_train) - - # persist trained model - save_pipeline(pipeline_to_persist=titanic_pipe) - - -if __name__ == "__main__": - run_training() +from sklearn.model_selection import train_test_split + +from classification_model.config.core import config +from classification_model.pipeline import titanic_pipe +from classification_model.processing.data_manager import load_dataset, save_pipeline + + +def run_training() -> None: + """ + Train the model. + + Training data can be found here: + https://www.openml.org/data/get_csv/16826755/phpMYEkMl + """ + + # read training data + data = load_dataset(file_name=config.app_config.raw_data_file) + + # divide train and test + X_train, X_test, y_train, y_test = train_test_split( + data[config.model_config.features], # predictors + data[config.model_config.target], + test_size=config.model_config.test_size, + # we are setting the random seed here + # for reproducibility + random_state=config.model_config.random_state, + ) + + # fit model + titanic_pipe.fit(X_train, y_train) + + # persist trained model + save_pipeline(pipeline_to_persist=titanic_pipe) + + +if __name__ == "__main__": + run_training() diff --git a/assignment-section-05/mypy.ini b/assignment-section-05/mypy.ini index d6984fd7a..44edacac6 100644 --- a/assignment-section-05/mypy.ini +++ b/assignment-section-05/mypy.ini @@ -1,14 +1,14 @@ -[mypy] -warn_unreachable = False -warn_unused_ignores = True -follow_imports = skip -show_error_context = True -warn_incomplete_stub = True -ignore_missing_imports = True -check_untyped_defs = True -cache_dir = /dev/null -# Allow defining functions without any types. -disallow_untyped_defs = False -warn_redundant_casts = True -warn_unused_configs = True +[mypy] +warn_unreachable = False +warn_unused_ignores = True +follow_imports = skip +show_error_context = True +warn_incomplete_stub = True +ignore_missing_imports = True +check_untyped_defs = True +cache_dir = /dev/null +# Allow defining functions without any types. +disallow_untyped_defs = False +warn_redundant_casts = True +warn_unused_configs = True strict_optional = True \ No newline at end of file diff --git a/assignment-section-05/pyproject.toml b/assignment-section-05/pyproject.toml index 31a46cadd..29227b4db 100644 --- a/assignment-section-05/pyproject.toml +++ b/assignment-section-05/pyproject.toml @@ -1,48 +1,48 @@ -[build-system] -requires = [ - "setuptools>=42", - "wheel" -] -build-backend = "setuptools.build_meta" - -[tool.pytest.ini_options] -minversion = "2.0" -addopts = "-rfEX -p pytester --strict-markers" -python_files = ["test_*.py", "*_test.py"] -python_classes = ["Test", "Acceptance"] -python_functions = ["test"] -# NOTE: "doc" is not included here, but gets tested explicitly via "doctesting". -testpaths = ["tests"] -xfail_strict = true -filterwarnings = [ - "error", - "default:Using or importing the ABCs:DeprecationWarning:unittest2.*", - # produced by older pyparsing<=2.2.0. - "default:Using or importing the ABCs:DeprecationWarning:pyparsing.*", - "default:the imp module is deprecated in favour of importlib:DeprecationWarning:nose.*", - # distutils is deprecated in 3.10, scheduled for removal in 3.12 - "ignore:The distutils package is deprecated:DeprecationWarning", - # produced by python3.6/site.py itself (3.6.7 on Travis, could not trigger it with 3.6.8)." - "ignore:.*U.*mode is deprecated:DeprecationWarning:(?!(pytest|_pytest))", - # produced by pytest-xdist - "ignore:.*type argument to addoption.*:DeprecationWarning", - # produced on execnet (pytest-xdist) - "ignore:.*inspect.getargspec.*deprecated, use inspect.signature.*:DeprecationWarning", - # pytest's own futurewarnings - "ignore::pytest.PytestExperimentalApiWarning", - # Do not cause SyntaxError for invalid escape sequences in py37. - # Those are caught/handled by pyupgrade, and not easy to filter with the - # module being the filename (with .py removed). - "default:invalid escape sequence:DeprecationWarning", - # ignore use of unregistered marks, because we use many to test the implementation - "ignore::_pytest.warning_types.PytestUnknownMarkWarning", -] - -[tool.black] -target-version = ['py311'] - -[tool.isort] -profile = "black" -line_length = 100 -lines_between_sections = 1 -skip = "migrations" +[build-system] +requires = [ + "setuptools>=42", + "wheel" +] +build-backend = "setuptools.build_meta" + +[tool.pytest.ini_options] +minversion = "2.0" +addopts = "-rfEX -p pytester --strict-markers" +python_files = ["test_*.py", "*_test.py"] +python_classes = ["Test", "Acceptance"] +python_functions = ["test"] +# NOTE: "doc" is not included here, but gets tested explicitly via "doctesting". +testpaths = ["tests"] +xfail_strict = true +filterwarnings = [ + "error", + "default:Using or importing the ABCs:DeprecationWarning:unittest2.*", + # produced by older pyparsing<=2.2.0. + "default:Using or importing the ABCs:DeprecationWarning:pyparsing.*", + "default:the imp module is deprecated in favour of importlib:DeprecationWarning:nose.*", + # distutils is deprecated in 3.10, scheduled for removal in 3.12 + "ignore:The distutils package is deprecated:DeprecationWarning", + # produced by python3.6/site.py itself (3.6.7 on Travis, could not trigger it with 3.6.8)." + "ignore:.*U.*mode is deprecated:DeprecationWarning:(?!(pytest|_pytest))", + # produced by pytest-xdist + "ignore:.*type argument to addoption.*:DeprecationWarning", + # produced on execnet (pytest-xdist) + "ignore:.*inspect.getargspec.*deprecated, use inspect.signature.*:DeprecationWarning", + # pytest's own futurewarnings + "ignore::pytest.PytestExperimentalApiWarning", + # Do not cause SyntaxError for invalid escape sequences in py37. + # Those are caught/handled by pyupgrade, and not easy to filter with the + # module being the filename (with .py removed). + "default:invalid escape sequence:DeprecationWarning", + # ignore use of unregistered marks, because we use many to test the implementation + "ignore::_pytest.warning_types.PytestUnknownMarkWarning", +] + +[tool.black] +target-version = ['py311'] + +[tool.isort] +profile = "black" +line_length = 100 +lines_between_sections = 1 +skip = "migrations" diff --git a/assignment-section-05/requirements/requirements.txt b/assignment-section-05/requirements/requirements.txt index f3783b618..1576eadbf 100644 --- a/assignment-section-05/requirements/requirements.txt +++ b/assignment-section-05/requirements/requirements.txt @@ -1,11 +1,11 @@ -# We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) -# to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small -# updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. -numpy>=1.21.0,<2.0.0 -pandas>=1.3.5,<2.0.0 -pydantic>=1.8.1,<2.0.0 -scikit-learn>=1.1.3,<2.0.0 -strictyaml>=1.3.2,<2.0.0 -ruamel.yaml>=0.16.12,<1.0.0 -feature-engine>=1.0.2,<2.0.0 +# We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) +# to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small +# updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. +numpy>=1.21.0,<2.0.0 +pandas>=1.3.5,<2.0.0 +pydantic>=1.8.1,<2.0.0 +scikit-learn>=1.1.3,<2.0.0 +strictyaml>=1.3.2,<2.0.0 +ruamel.yaml>=0.16.12,<1.0.0 +feature-engine>=1.0.2,<2.0.0 joblib>=1.0.1,<2.0.0 \ No newline at end of file diff --git a/assignment-section-05/requirements/test_requirements.txt b/assignment-section-05/requirements/test_requirements.txt index e69019391..b080f909c 100644 --- a/assignment-section-05/requirements/test_requirements.txt +++ b/assignment-section-05/requirements/test_requirements.txt @@ -1,4 +1,4 @@ --r requirements.txt - -# testing requirements -pytest>=7.2.0,<8.0.0 +-r requirements.txt + +# testing requirements +pytest>=7.2.0,<8.0.0 diff --git a/assignment-section-05/requirements/typing_requirements.txt b/assignment-section-05/requirements/typing_requirements.txt index 667cc2e4d..59619752c 100644 --- a/assignment-section-05/requirements/typing_requirements.txt +++ b/assignment-section-05/requirements/typing_requirements.txt @@ -1,5 +1,5 @@ -# repo maintenance tooling -black>=22.12.0,<23.0.0 -flake8>=6.0.0,<7.0.0 -mypy>=0.991,<1.0.0 +# repo maintenance tooling +black>=22.12.0,<23.0.0 +flake8>=6.0.0,<7.0.0 +mypy>=0.991,<1.0.0 isort>=5.11.4,<6.0.0 \ No newline at end of file diff --git a/assignment-section-05/setup.py b/assignment-section-05/setup.py index fa2e43e4e..eeb0a78b9 100644 --- a/assignment-section-05/setup.py +++ b/assignment-section-05/setup.py @@ -1,71 +1,71 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from pathlib import Path - -from setuptools import find_packages, setup - -# Package meta-data. -NAME = 'tid-titanic-classification-model' -DESCRIPTION = "Example Titanic dataset classification model package from Train In Data." -URL = "https://github.com/trainindata/deploying-machine-learning-models" -EMAIL = "christopher.samiullah@protonmail.com" -AUTHOR = "ChristopherGS" -REQUIRES_PYTHON = ">=3.7.0" - - -# The rest you shouldn't have to touch too much :) -# ------------------------------------------------ -# Except, perhaps the License and Trove Classifiers! -# Trove Classifiers: https://pypi.org/classifiers/ -# If you do change the License, remember to change the -# Trove Classifier for that! -long_description = DESCRIPTION - -# Load the package's VERSION file as a dictionary. -about = {} -ROOT_DIR = Path(__file__).resolve().parent -REQUIREMENTS_DIR = ROOT_DIR / 'requirements' -PACKAGE_DIR = ROOT_DIR / 'classification_model' -with open(PACKAGE_DIR / "VERSION") as f: - _version = f.read().strip() - about["__version__"] = _version - - -# What packages are required for this module to be executed? -def list_reqs(fname="requirements.txt"): - with open(REQUIREMENTS_DIR / fname) as fd: - return fd.read().splitlines() - -# Where the magic happens: -setup( - name=NAME, - version=about["__version__"], - description=DESCRIPTION, - long_description=long_description, - long_description_content_type="text/markdown", - author=AUTHOR, - author_email=EMAIL, - python_requires=REQUIRES_PYTHON, - url=URL, - packages=find_packages(exclude=("tests",)), - package_data={"classification_model": ["VERSION"]}, - install_requires=list_reqs(), - extras_require={}, - include_package_data=True, - license="BSD-3", - classifiers=[ - # Trove classifiers - # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers - "License :: OSI Approved :: MIT License", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: Implementation :: CPython", - "Programming Language :: Python :: Implementation :: PyPy", - ], +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +from pathlib import Path + +from setuptools import find_packages, setup + +# Package meta-data. +NAME = 'tid-titanic-classification-model' +DESCRIPTION = "Example Titanic dataset classification model package from Train In Data." +URL = "https://github.com/trainindata/deploying-machine-learning-models" +EMAIL = "christopher.samiullah@protonmail.com" +AUTHOR = "ChristopherGS" +REQUIRES_PYTHON = ">=3.7.0" + + +# The rest you shouldn't have to touch too much :) +# ------------------------------------------------ +# Except, perhaps the License and Trove Classifiers! +# Trove Classifiers: https://pypi.org/classifiers/ +# If you do change the License, remember to change the +# Trove Classifier for that! +long_description = DESCRIPTION + +# Load the package's VERSION file as a dictionary. +about = {} +ROOT_DIR = Path(__file__).resolve().parent +REQUIREMENTS_DIR = ROOT_DIR / 'requirements' +PACKAGE_DIR = ROOT_DIR / 'classification_model' +with open(PACKAGE_DIR / "VERSION") as f: + _version = f.read().strip() + about["__version__"] = _version + + +# What packages are required for this module to be executed? +def list_reqs(fname="requirements.txt"): + with open(REQUIREMENTS_DIR / fname) as fd: + return fd.read().splitlines() + +# Where the magic happens: +setup( + name=NAME, + version=about["__version__"], + description=DESCRIPTION, + long_description=long_description, + long_description_content_type="text/markdown", + author=AUTHOR, + author_email=EMAIL, + python_requires=REQUIRES_PYTHON, + url=URL, + packages=find_packages(exclude=("tests",)), + package_data={"classification_model": ["VERSION"]}, + install_requires=list_reqs(), + extras_require={}, + include_package_data=True, + license="BSD-3", + classifiers=[ + # Trove classifiers + # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers + "License :: OSI Approved :: MIT License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: Implementation :: PyPy", + ], ) \ No newline at end of file diff --git a/assignment-section-05/tests/conftest.py b/assignment-section-05/tests/conftest.py index 8e3fd46ad..468ef00ea 100644 --- a/assignment-section-05/tests/conftest.py +++ b/assignment-section-05/tests/conftest.py @@ -1,26 +1,26 @@ -import logging - -import pytest -from sklearn.model_selection import train_test_split - -from classification_model.config.core import config -from classification_model.processing.data_manager import _load_raw_dataset - -logger = logging.getLogger(__name__) - - -@pytest.fixture -def sample_input_data(): - data = _load_raw_dataset(file_name=config.app_config.raw_data_file) - - # divide train and test - X_train, X_test, y_train, y_test = train_test_split( - data, # predictors - data[config.model_config.target], - test_size=config.model_config.test_size, - # we are setting the random seed here - # for reproducibility - random_state=config.model_config.random_state, - ) - - return X_test +import logging + +import pytest +from sklearn.model_selection import train_test_split + +from classification_model.config.core import config +from classification_model.processing.data_manager import _load_raw_dataset + +logger = logging.getLogger(__name__) + + +@pytest.fixture +def sample_input_data(): + data = _load_raw_dataset(file_name=config.app_config.raw_data_file) + + # divide train and test + X_train, X_test, y_train, y_test = train_test_split( + data, # predictors + data[config.model_config.target], + test_size=config.model_config.test_size, + # we are setting the random seed here + # for reproducibility + random_state=config.model_config.random_state, + ) + + return X_test diff --git a/assignment-section-05/tests/test_features.py b/assignment-section-05/tests/test_features.py index c3f88101b..97686674a 100644 --- a/assignment-section-05/tests/test_features.py +++ b/assignment-section-05/tests/test_features.py @@ -1,16 +1,16 @@ -from classification_model.config.core import config -from classification_model.processing.features import ExtractLetterTransformer - - -def test_temporal_variable_transformer(sample_input_data): - # Given - transformer = ExtractLetterTransformer( - variables=config.model_config.cabin_vars, # cabin - ) - assert sample_input_data["cabin"].iat[6] == "E12" - - # When - subject = transformer.fit_transform(sample_input_data) - - # Then - assert subject["cabin"].iat[6] == "E" +from classification_model.config.core import config +from classification_model.processing.features import ExtractLetterTransformer + + +def test_temporal_variable_transformer(sample_input_data): + # Given + transformer = ExtractLetterTransformer( + variables=config.model_config.cabin_vars, # cabin + ) + assert sample_input_data["cabin"].iat[6] == "E12" + + # When + subject = transformer.fit_transform(sample_input_data) + + # Then + assert subject["cabin"].iat[6] == "E" diff --git a/assignment-section-05/tests/test_prediction.py b/assignment-section-05/tests/test_prediction.py index 76965698a..a3981dc26 100644 --- a/assignment-section-05/tests/test_prediction.py +++ b/assignment-section-05/tests/test_prediction.py @@ -1,27 +1,27 @@ -""" -Note: These tests will fail if you have not first trained the model. -""" - -import numpy as np -from sklearn.metrics import accuracy_score - -from classification_model.predict import make_prediction - - -def test_make_prediction(sample_input_data): - # Given - expected_no_predictions = 131 - - # When - result = make_prediction(input_data=sample_input_data) - - # Then - predictions = result.get("predictions") - assert isinstance(predictions, np.ndarray) - assert isinstance(predictions[0], np.int64) - assert result.get("errors") is None - assert len(predictions) == expected_no_predictions - _predictions = list(predictions) - y_true = sample_input_data["survived"] - accuracy = accuracy_score(_predictions, y_true) - assert accuracy > 0.7 +""" +Note: These tests will fail if you have not first trained the model. +""" + +import numpy as np +from sklearn.metrics import accuracy_score + +from classification_model.predict import make_prediction + + +def test_make_prediction(sample_input_data): + # Given + expected_no_predictions = 131 + + # When + result = make_prediction(input_data=sample_input_data) + + # Then + predictions = result.get("predictions") + assert isinstance(predictions, np.ndarray) + assert isinstance(predictions[0], np.int64) + assert result.get("errors") is None + assert len(predictions) == expected_no_predictions + _predictions = list(predictions) + y_true = sample_input_data["survived"] + accuracy = accuracy_score(_predictions, y_true) + assert accuracy > 0.7 diff --git a/assignment-section-05/tox.ini b/assignment-section-05/tox.ini index 37829355f..cdb56591f 100644 --- a/assignment-section-05/tox.ini +++ b/assignment-section-05/tox.ini @@ -1,58 +1,58 @@ -# Tox is a generic virtualenv management and test command line tool. Its goal is to -# standardize testing in Python. We will be using it extensively in this course. - -# Using Tox we can (on multiple operating systems): -# + Eliminate PYTHONPATH challenges when running scripts/tests -# + Eliminate virtualenv setup confusion -# + Streamline steps such as model training, model publishing - - -[tox] -envlist = test_package, checks -skipsdist = True - -[testenv] -install_command = pip install {opts} {packages} - -[testenv:test_package] -deps = - -rrequirements/test_requirements.txt - -setenv = - PYTHONPATH=. - PYTHONHASHSEED=0 - -commands= - python classification_model/train_pipeline.py - pytest \ - -s \ - -vv \ - {posargs:tests/} - - -[testenv:train] -envdir = {toxworkdir}/test_package - -deps = - {[testenv:test_package]deps} - -setenv = - {[testenv:test_package]setenv} -commands= - python classification_model/train_pipeline.py - - -[testenv:checks] -envdir = {toxworkdir}/checks -deps = - -r{toxinidir}/requirements/typing_requirements.txt -commands = - flake8 classification_model tests - isort classification_model tests - black classification_model tests - {posargs:mypy classification_model} - - -[flake8] -exclude = .git,env +# Tox is a generic virtualenv management and test command line tool. Its goal is to +# standardize testing in Python. We will be using it extensively in this course. + +# Using Tox we can (on multiple operating systems): +# + Eliminate PYTHONPATH challenges when running scripts/tests +# + Eliminate virtualenv setup confusion +# + Streamline steps such as model training, model publishing + + +[tox] +envlist = test_package, checks +skipsdist = True + +[testenv] +install_command = pip install {opts} {packages} + +[testenv:test_package] +deps = + -rrequirements/test_requirements.txt + +setenv = + PYTHONPATH=. + PYTHONHASHSEED=0 + +commands= + python classification_model/train_pipeline.py + pytest \ + -s \ + -vv \ + {posargs:tests/} + + +[testenv:train] +envdir = {toxworkdir}/test_package + +deps = + {[testenv:test_package]deps} + +setenv = + {[testenv:test_package]setenv} +commands= + python classification_model/train_pipeline.py + + +[testenv:checks] +envdir = {toxworkdir}/checks +deps = + -r{toxinidir}/requirements/typing_requirements.txt +commands = + flake8 classification_model tests + isort classification_model tests + black classification_model tests + {posargs:mypy classification_model} + + +[flake8] +exclude = .git,env max-line-length = 90 \ No newline at end of file diff --git a/packages/ml_api/api/__init__.py b/packages/ml_api/api/__init__.py index ad56c24c1..73c37af98 100644 --- a/packages/ml_api/api/__init__.py +++ b/packages/ml_api/api/__init__.py @@ -1,4 +1,4 @@ -from api.config import PACKAGE_ROOT - -with open(PACKAGE_ROOT / 'VERSION') as version_file: - __version__ = version_file.read().strip() +from api.config import PACKAGE_ROOT + +with open(PACKAGE_ROOT / 'VERSION') as version_file: + __version__ = version_file.read().strip() diff --git a/packages/ml_api/api/app.py b/packages/ml_api/api/app.py index 40abdb55f..3908e3e56 100644 --- a/packages/ml_api/api/app.py +++ b/packages/ml_api/api/app.py @@ -1,20 +1,20 @@ -from flask import Flask - -from api.config import get_logger - - -_logger = get_logger(logger_name=__name__) - - -def create_app(*, config_object) -> Flask: - """Create a flask app instance.""" - - flask_app = Flask('ml_api') - flask_app.config.from_object(config_object) - - # import blueprints - from api.controller import prediction_app - flask_app.register_blueprint(prediction_app) - _logger.debug('Application instance created') - - return flask_app +from flask import Flask + +from api.config import get_logger + + +_logger = get_logger(logger_name=__name__) + + +def create_app(*, config_object) -> Flask: + """Create a flask app instance.""" + + flask_app = Flask('ml_api') + flask_app.config.from_object(config_object) + + # import blueprints + from api.controller import prediction_app + flask_app.register_blueprint(prediction_app) + _logger.debug('Application instance created') + + return flask_app diff --git a/packages/ml_api/api/config.py b/packages/ml_api/api/config.py index 3ca849c99..81fda84f3 100644 --- a/packages/ml_api/api/config.py +++ b/packages/ml_api/api/config.py @@ -1,70 +1,70 @@ -import logging -from logging.handlers import TimedRotatingFileHandler -import pathlib -import os -import sys - -PACKAGE_ROOT = pathlib.Path(__file__).resolve().parent.parent - -FORMATTER = logging.Formatter( - "%(asctime)s — %(name)s — %(levelname)s —" - "%(funcName)s:%(lineno)d — %(message)s") -LOG_DIR = PACKAGE_ROOT / 'logs' -LOG_DIR.mkdir(exist_ok=True) -LOG_FILE = LOG_DIR / 'ml_api.log' -UPLOAD_FOLDER = PACKAGE_ROOT / 'uploads' -UPLOAD_FOLDER.mkdir(exist_ok=True) - -ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg']) - - -def get_console_handler(): - console_handler = logging.StreamHandler(sys.stdout) - console_handler.setFormatter(FORMATTER) - return console_handler - - -def get_file_handler(): - file_handler = TimedRotatingFileHandler( - LOG_FILE, when='midnight') - file_handler.setFormatter(FORMATTER) - file_handler.setLevel(logging.WARNING) - return file_handler - - -def get_logger(*, logger_name): - """Get logger with prepared handlers.""" - - logger = logging.getLogger(logger_name) - - logger.setLevel(logging.INFO) - - logger.addHandler(get_console_handler()) - logger.addHandler(get_file_handler()) - logger.propagate = False - - return logger - - -class Config: - DEBUG = False - TESTING = False - CSRF_ENABLED = True - SECRET_KEY = 'this-really-needs-to-be-changed' - SERVER_PORT = 5000 - UPLOAD_FOLDER = UPLOAD_FOLDER - - -class ProductionConfig(Config): - DEBUG = False - SERVER_ADDRESS: os.environ.get('SERVER_ADDRESS', '0.0.0.0') - SERVER_PORT: os.environ.get('SERVER_PORT', '5000') - - -class DevelopmentConfig(Config): - DEVELOPMENT = True - DEBUG = True - - -class TestingConfig(Config): - TESTING = True +import logging +from logging.handlers import TimedRotatingFileHandler +import pathlib +import os +import sys + +PACKAGE_ROOT = pathlib.Path(__file__).resolve().parent.parent + +FORMATTER = logging.Formatter( + "%(asctime)s — %(name)s — %(levelname)s —" + "%(funcName)s:%(lineno)d — %(message)s") +LOG_DIR = PACKAGE_ROOT / 'logs' +LOG_DIR.mkdir(exist_ok=True) +LOG_FILE = LOG_DIR / 'ml_api.log' +UPLOAD_FOLDER = PACKAGE_ROOT / 'uploads' +UPLOAD_FOLDER.mkdir(exist_ok=True) + +ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg']) + + +def get_console_handler(): + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setFormatter(FORMATTER) + return console_handler + + +def get_file_handler(): + file_handler = TimedRotatingFileHandler( + LOG_FILE, when='midnight') + file_handler.setFormatter(FORMATTER) + file_handler.setLevel(logging.WARNING) + return file_handler + + +def get_logger(*, logger_name): + """Get logger with prepared handlers.""" + + logger = logging.getLogger(logger_name) + + logger.setLevel(logging.INFO) + + logger.addHandler(get_console_handler()) + logger.addHandler(get_file_handler()) + logger.propagate = False + + return logger + + +class Config: + DEBUG = False + TESTING = False + CSRF_ENABLED = True + SECRET_KEY = 'this-really-needs-to-be-changed' + SERVER_PORT = 5000 + UPLOAD_FOLDER = UPLOAD_FOLDER + + +class ProductionConfig(Config): + DEBUG = False + SERVER_ADDRESS: os.environ.get('SERVER_ADDRESS', '0.0.0.0') + SERVER_PORT: os.environ.get('SERVER_PORT', '5000') + + +class DevelopmentConfig(Config): + DEVELOPMENT = True + DEBUG = True + + +class TestingConfig(Config): + TESTING = True diff --git a/packages/ml_api/api/controller.py b/packages/ml_api/api/controller.py index 4e683b2dc..6af2abed5 100644 --- a/packages/ml_api/api/controller.py +++ b/packages/ml_api/api/controller.py @@ -1,89 +1,89 @@ -from flask import Blueprint, request, jsonify -from regression_model.predict import make_prediction -from regression_model import __version__ as _version -from neural_network_model.predict import make_single_prediction -import os -from werkzeug.utils import secure_filename - -from api.config import get_logger, UPLOAD_FOLDER -from api.validation import validate_inputs, allowed_file -from api import __version__ as api_version - -_logger = get_logger(logger_name=__name__) - - -prediction_app = Blueprint('prediction_app', __name__) - - -@prediction_app.route('/health', methods=['GET']) -def health(): - if request.method == 'GET': - _logger.info('health status OK') - return 'ok' - - -@prediction_app.route('/version', methods=['GET']) -def version(): - if request.method == 'GET': - return jsonify({'model_version': _version, - 'api_version': api_version}) - - -@prediction_app.route('/v1/predict/regression', methods=['POST']) -def predict(): - if request.method == 'POST': - # Step 1: Extract POST data from request body as JSON - json_data = request.get_json() - _logger.debug(f'Inputs: {json_data}') - - # Step 2: Validate the input using marshmallow schema - input_data, errors = validate_inputs(input_data=json_data) - - # Step 3: Model prediction - result = make_prediction(input_data=input_data) - _logger.debug(f'Outputs: {result}') - - # Step 4: Convert numpy ndarray to list - predictions = result.get('predictions').tolist() - version = result.get('version') - - # Step 5: Return the response as JSON - return jsonify({'predictions': predictions, - 'version': version, - 'errors': errors}) - - -@prediction_app.route('/predict/classifier', methods=['POST']) -def predict_image(): - if request.method == 'POST': - # Step 1: check if the post request has the file part - if 'file' not in request.files: - return jsonify('No file found'), 400 - - file = request.files['file'] - - # Step 2: Basic file extension validation - if file and allowed_file(file.filename): - filename = secure_filename(file.filename) - - # Step 3: Save the file - # Note, in production, this would require careful - # validation, management and clean up. - file.save(os.path.join(UPLOAD_FOLDER, filename)) - - _logger.debug(f'Inputs: {filename}') - - # Step 4: perform prediction - result = make_single_prediction( - image_name=filename, - image_directory=UPLOAD_FOLDER) - - _logger.debug(f'Outputs: {result}') - - readable_predictions = result.get('readable_predictions') - version = result.get('version') - - # Step 5: Return the response as JSON - return jsonify( - {'readable_predictions': readable_predictions[0], - 'version': version}) +from flask import Blueprint, request, jsonify +from regression_model.predict import make_prediction +from regression_model import __version__ as _version +from neural_network_model.predict import make_single_prediction +import os +from werkzeug.utils import secure_filename + +from api.config import get_logger, UPLOAD_FOLDER +from api.validation import validate_inputs, allowed_file +from api import __version__ as api_version + +_logger = get_logger(logger_name=__name__) + + +prediction_app = Blueprint('prediction_app', __name__) + + +@prediction_app.route('/health', methods=['GET']) +def health(): + if request.method == 'GET': + _logger.info('health status OK') + return 'ok' + + +@prediction_app.route('/version', methods=['GET']) +def version(): + if request.method == 'GET': + return jsonify({'model_version': _version, + 'api_version': api_version}) + + +@prediction_app.route('/v1/predict/regression', methods=['POST']) +def predict(): + if request.method == 'POST': + # Step 1: Extract POST data from request body as JSON + json_data = request.get_json() + _logger.debug(f'Inputs: {json_data}') + + # Step 2: Validate the input using marshmallow schema + input_data, errors = validate_inputs(input_data=json_data) + + # Step 3: Model prediction + result = make_prediction(input_data=input_data) + _logger.debug(f'Outputs: {result}') + + # Step 4: Convert numpy ndarray to list + predictions = result.get('predictions').tolist() + version = result.get('version') + + # Step 5: Return the response as JSON + return jsonify({'predictions': predictions, + 'version': version, + 'errors': errors}) + + +@prediction_app.route('/predict/classifier', methods=['POST']) +def predict_image(): + if request.method == 'POST': + # Step 1: check if the post request has the file part + if 'file' not in request.files: + return jsonify('No file found'), 400 + + file = request.files['file'] + + # Step 2: Basic file extension validation + if file and allowed_file(file.filename): + filename = secure_filename(file.filename) + + # Step 3: Save the file + # Note, in production, this would require careful + # validation, management and clean up. + file.save(os.path.join(UPLOAD_FOLDER, filename)) + + _logger.debug(f'Inputs: {filename}') + + # Step 4: perform prediction + result = make_single_prediction( + image_name=filename, + image_directory=UPLOAD_FOLDER) + + _logger.debug(f'Outputs: {result}') + + readable_predictions = result.get('readable_predictions') + version = result.get('version') + + # Step 5: Return the response as JSON + return jsonify( + {'readable_predictions': readable_predictions[0], + 'version': version}) diff --git a/packages/ml_api/api/validation.py b/packages/ml_api/api/validation.py index c143263a4..5635e4143 100644 --- a/packages/ml_api/api/validation.py +++ b/packages/ml_api/api/validation.py @@ -1,155 +1,155 @@ -import typing as t - -from marshmallow import Schema, fields -from marshmallow import ValidationError - -from api import config - - -class InvalidInputError(Exception): - """Invalid model input.""" - - -SYNTAX_ERROR_FIELD_MAP = { - '1stFlrSF': 'FirstFlrSF', - '2ndFlrSF': 'SecondFlrSF', - '3SsnPorch': 'ThreeSsnPortch' -} - - -class HouseDataRequestSchema(Schema): - Alley = fields.Str(allow_none=True) - BedroomAbvGr = fields.Integer() - BldgType = fields.Str() - BsmtCond = fields.Str() - BsmtExposure = fields.Str(allow_none=True) - BsmtFinSF1 = fields.Float() - BsmtFinSF2 = fields.Float() - BsmtFinType1 = fields.Str() - BsmtFinType2 = fields.Str() - BsmtFullBath = fields.Float() - BsmtHalfBath = fields.Float() - BsmtQual = fields.Str(allow_none=True) - BsmtUnfSF = fields.Float() - CentralAir = fields.Str() - Condition1 = fields.Str() - Condition2 = fields.Str() - Electrical = fields.Str() - EnclosedPorch = fields.Integer() - ExterCond = fields.Str() - ExterQual = fields.Str() - Exterior1st = fields.Str() - Exterior2nd = fields.Str() - Fence = fields.Str(allow_none=True) - FireplaceQu = fields.Str(allow_none=True) - Fireplaces = fields.Integer() - Foundation = fields.Str() - FullBath = fields.Integer() - Functional = fields.Str() - GarageArea = fields.Float() - GarageCars = fields.Float() - GarageCond = fields.Str() - GarageFinish = fields.Str(allow_none=True) - GarageQual = fields.Str() - GarageType = fields.Str(allow_none=True) - GarageYrBlt = fields.Float() - GrLivArea = fields.Integer() - HalfBath = fields.Integer() - Heating = fields.Str() - HeatingQC = fields.Str() - HouseStyle = fields.Str() - Id = fields.Integer() - KitchenAbvGr = fields.Integer() - KitchenQual = fields.Str() - LandContour = fields.Str() - LandSlope = fields.Str() - LotArea = fields.Integer() - LotConfig = fields.Str() - LotFrontage = fields.Float(allow_none=True) - LotShape = fields.Str() - LowQualFinSF = fields.Integer() - MSSubClass = fields.Integer() - MSZoning = fields.Str() - MasVnrArea = fields.Float() - MasVnrType = fields.Str(allow_none=True) - MiscFeature = fields.Str(allow_none=True) - MiscVal = fields.Integer() - MoSold = fields.Integer() - Neighborhood = fields.Str() - OpenPorchSF = fields.Integer() - OverallCond = fields.Integer() - OverallQual = fields.Integer() - PavedDrive = fields.Str() - PoolArea = fields.Integer() - PoolQC = fields.Str(allow_none=True) - RoofMatl = fields.Str() - RoofStyle = fields.Str() - SaleCondition = fields.Str() - SaleType = fields.Str() - ScreenPorch = fields.Integer() - Street = fields.Str() - TotRmsAbvGrd = fields.Integer() - TotalBsmtSF = fields.Float() - Utilities = fields.Str() - WoodDeckSF = fields.Integer() - YearBuilt = fields.Integer() - YearRemodAdd = fields.Integer() - YrSold = fields.Integer() - FirstFlrSF = fields.Integer() - SecondFlrSF = fields.Integer() - ThreeSsnPortch = fields.Integer() - - -def _filter_error_rows(errors: dict, - validated_input: t.List[dict] - ) -> t.List[dict]: - """Remove input data rows with errors.""" - - indexes = errors.keys() - # delete them in reverse order so that you - # don't throw off the subsequent indexes. - for index in sorted(indexes, reverse=True): - del validated_input[index] - - return validated_input - - -def validate_inputs(input_data): - """Check prediction inputs against schema.""" - - # set many=True to allow passing in a list - schema = HouseDataRequestSchema(strict=True, many=True) - - # convert syntax error field names (beginning with numbers) - for dict in input_data: - for key, value in SYNTAX_ERROR_FIELD_MAP.items(): - dict[value] = dict[key] - del dict[key] - - errors = None - try: - schema.load(input_data) - except ValidationError as exc: - errors = exc.messages - - # convert syntax error field names back - # this is a hack - never name your data - # fields with numbers as the first letter. - for dict in input_data: - for key, value in SYNTAX_ERROR_FIELD_MAP.items(): - dict[key] = dict[value] - del dict[value] - - if errors: - validated_input = _filter_error_rows( - errors=errors, - validated_input=input_data) - else: - validated_input = input_data - - return validated_input, errors - - -def allowed_file(filename): - return '.' in filename and \ - filename.rsplit('.', 1)[1].lower() in config.ALLOWED_EXTENSIONS +import typing as t + +from marshmallow import Schema, fields +from marshmallow import ValidationError + +from api import config + + +class InvalidInputError(Exception): + """Invalid model input.""" + + +SYNTAX_ERROR_FIELD_MAP = { + '1stFlrSF': 'FirstFlrSF', + '2ndFlrSF': 'SecondFlrSF', + '3SsnPorch': 'ThreeSsnPortch' +} + + +class HouseDataRequestSchema(Schema): + Alley = fields.Str(allow_none=True) + BedroomAbvGr = fields.Integer() + BldgType = fields.Str() + BsmtCond = fields.Str() + BsmtExposure = fields.Str(allow_none=True) + BsmtFinSF1 = fields.Float() + BsmtFinSF2 = fields.Float() + BsmtFinType1 = fields.Str() + BsmtFinType2 = fields.Str() + BsmtFullBath = fields.Float() + BsmtHalfBath = fields.Float() + BsmtQual = fields.Str(allow_none=True) + BsmtUnfSF = fields.Float() + CentralAir = fields.Str() + Condition1 = fields.Str() + Condition2 = fields.Str() + Electrical = fields.Str() + EnclosedPorch = fields.Integer() + ExterCond = fields.Str() + ExterQual = fields.Str() + Exterior1st = fields.Str() + Exterior2nd = fields.Str() + Fence = fields.Str(allow_none=True) + FireplaceQu = fields.Str(allow_none=True) + Fireplaces = fields.Integer() + Foundation = fields.Str() + FullBath = fields.Integer() + Functional = fields.Str() + GarageArea = fields.Float() + GarageCars = fields.Float() + GarageCond = fields.Str() + GarageFinish = fields.Str(allow_none=True) + GarageQual = fields.Str() + GarageType = fields.Str(allow_none=True) + GarageYrBlt = fields.Float() + GrLivArea = fields.Integer() + HalfBath = fields.Integer() + Heating = fields.Str() + HeatingQC = fields.Str() + HouseStyle = fields.Str() + Id = fields.Integer() + KitchenAbvGr = fields.Integer() + KitchenQual = fields.Str() + LandContour = fields.Str() + LandSlope = fields.Str() + LotArea = fields.Integer() + LotConfig = fields.Str() + LotFrontage = fields.Float(allow_none=True) + LotShape = fields.Str() + LowQualFinSF = fields.Integer() + MSSubClass = fields.Integer() + MSZoning = fields.Str() + MasVnrArea = fields.Float() + MasVnrType = fields.Str(allow_none=True) + MiscFeature = fields.Str(allow_none=True) + MiscVal = fields.Integer() + MoSold = fields.Integer() + Neighborhood = fields.Str() + OpenPorchSF = fields.Integer() + OverallCond = fields.Integer() + OverallQual = fields.Integer() + PavedDrive = fields.Str() + PoolArea = fields.Integer() + PoolQC = fields.Str(allow_none=True) + RoofMatl = fields.Str() + RoofStyle = fields.Str() + SaleCondition = fields.Str() + SaleType = fields.Str() + ScreenPorch = fields.Integer() + Street = fields.Str() + TotRmsAbvGrd = fields.Integer() + TotalBsmtSF = fields.Float() + Utilities = fields.Str() + WoodDeckSF = fields.Integer() + YearBuilt = fields.Integer() + YearRemodAdd = fields.Integer() + YrSold = fields.Integer() + FirstFlrSF = fields.Integer() + SecondFlrSF = fields.Integer() + ThreeSsnPortch = fields.Integer() + + +def _filter_error_rows(errors: dict, + validated_input: t.List[dict] + ) -> t.List[dict]: + """Remove input data rows with errors.""" + + indexes = errors.keys() + # delete them in reverse order so that you + # don't throw off the subsequent indexes. + for index in sorted(indexes, reverse=True): + del validated_input[index] + + return validated_input + + +def validate_inputs(input_data): + """Check prediction inputs against schema.""" + + # set many=True to allow passing in a list + schema = HouseDataRequestSchema(strict=True, many=True) + + # convert syntax error field names (beginning with numbers) + for dict in input_data: + for key, value in SYNTAX_ERROR_FIELD_MAP.items(): + dict[value] = dict[key] + del dict[key] + + errors = None + try: + schema.load(input_data) + except ValidationError as exc: + errors = exc.messages + + # convert syntax error field names back + # this is a hack - never name your data + # fields with numbers as the first letter. + for dict in input_data: + for key, value in SYNTAX_ERROR_FIELD_MAP.items(): + dict[key] = dict[value] + del dict[value] + + if errors: + validated_input = _filter_error_rows( + errors=errors, + validated_input=input_data) + else: + validated_input = input_data + + return validated_input, errors + + +def allowed_file(filename): + return '.' in filename and \ + filename.rsplit('.', 1)[1].lower() in config.ALLOWED_EXTENSIONS diff --git a/packages/ml_api/diff_test_requirements.txt b/packages/ml_api/diff_test_requirements.txt index 37ebe9b56..e223a2c07 100644 --- a/packages/ml_api/diff_test_requirements.txt +++ b/packages/ml_api/diff_test_requirements.txt @@ -1,13 +1,13 @@ ---extra-index-url=${PIP_EXTRA_INDEX_URL} - -# api -flask>=1.1.1,<1.2.0 - -# schema validation -marshmallow==2.17.0 - -# Set this to the previous model version -regression-model==2.0.19 - -# temporarily necessary as we update sklearn +--extra-index-url=${PIP_EXTRA_INDEX_URL} + +# api +flask>=1.1.1,<1.2.0 + +# schema validation +marshmallow==2.17.0 + +# Set this to the previous model version +regression-model==2.0.19 + +# temporarily necessary as we update sklearn joblib>=0.14.1,<0.15.0 \ No newline at end of file diff --git a/packages/ml_api/requirements.txt b/packages/ml_api/requirements.txt index 39a8feec1..005a09005 100644 --- a/packages/ml_api/requirements.txt +++ b/packages/ml_api/requirements.txt @@ -1,14 +1,14 @@ ---extra-index-url=${PIP_EXTRA_INDEX_URL} - -# api -flask>=1.1.1,<1.2.0 - -# schema validation -marshmallow==2.17.0 - -# Install from gemfury -regression-model==2.0.20 -neural_network_model==0.1.1 - -# Deployment +--extra-index-url=${PIP_EXTRA_INDEX_URL} + +# api +flask>=1.1.1,<1.2.0 + +# schema validation +marshmallow==2.17.0 + +# Install from gemfury +regression-model==2.0.20 +neural_network_model==0.1.1 + +# Deployment gunicorn==19.9.0 \ No newline at end of file diff --git a/packages/ml_api/run.py b/packages/ml_api/run.py index 7f60a072a..f96b716df 100644 --- a/packages/ml_api/run.py +++ b/packages/ml_api/run.py @@ -1,10 +1,10 @@ -from api.app import create_app -from api.config import DevelopmentConfig, ProductionConfig - - -application = create_app( - config_object=ProductionConfig) - - -if __name__ == '__main__': - application.run() +from api.app import create_app +from api.config import DevelopmentConfig, ProductionConfig + + +application = create_app( + config_object=ProductionConfig) + + +if __name__ == '__main__': + application.run() diff --git a/packages/ml_api/run.sh b/packages/ml_api/run.sh index f579e6b1a..60554f79b 100644 --- a/packages/ml_api/run.sh +++ b/packages/ml_api/run.sh @@ -1,3 +1,3 @@ -#!/usr/bin/env bash -export IS_DEBUG=${DEBUG:-false} +#!/usr/bin/env bash +export IS_DEBUG=${DEBUG:-false} exec gunicorn --bind 0.0.0.0:5000 --access-logfile - --error-logfile - run:application \ No newline at end of file diff --git a/packages/ml_api/test_data_predictions.csv b/packages/ml_api/test_data_predictions.csv index d1117a25b..34624aac6 100644 --- a/packages/ml_api/test_data_predictions.csv +++ b/packages/ml_api/test_data_predictions.csv @@ -1,501 +1,501 @@ -,predictions,version -0,143988.30704997465,0.2.0 -1,116598.08159580332,0.2.0 -2,130128.90560814076,0.2.0 -3,113470.10675716968,0.2.0 -4,159022.48121448176,0.2.0 -5,139861.32732907546,0.2.0 -6,227118.89767805065,0.2.0 -7,91953.99400144782,0.2.0 -8,225573.26579772323,0.2.0 -9,125802.8602526304,0.2.0 -10,137481.49149643493,0.2.0 -11,124990.09839895074,0.2.0 -12,133270.15609091,0.2.0 -13,192143.4530280595,0.2.0 -14,123206.5594461486,0.2.0 -15,201801.77975634683,0.2.0 -16,198027.98470170778,0.2.0 -17,185664.94305866087,0.2.0 -18,146728.39264190392,0.2.0 -19,152443.1572738422,0.2.0 -20,197054.58979409203,0.2.0 -21,146781.9115319493,0.2.0 -22,138838.0050135225,0.2.0 -23,259997.45200360558,0.2.0 -24,220904.18524276977,0.2.0 -25,162760.6578114075,0.2.0 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+492,140830.26421534023,0.2.0 +493,96630.01740632812,0.2.0 +494,146497.76662391485,0.2.0 +495,161384.411998765,0.2.0 +496,122294.75296565886,0.2.0 +497,187349.35839738324,0.2.0 +498,139773.34125411394,0.2.0 +499,151158.00827612064,0.2.0 diff --git a/packages/ml_api/tests/capture_model_predictions.py b/packages/ml_api/tests/capture_model_predictions.py index 19a71142a..fb15794a8 100644 --- a/packages/ml_api/tests/capture_model_predictions.py +++ b/packages/ml_api/tests/capture_model_predictions.py @@ -1,35 +1,35 @@ -""" -This script should only be run in CI. -Never run it locally or you will disrupt the -differential test versioning logic. -""" - -import pandas as pd - -from regression_model.predict import make_prediction -from regression_model.processing.data_management import load_dataset - -from api import config - - -def capture_predictions() -> None: - """Save the test data predictions to a CSV.""" - - save_file = 'test_data_predictions.csv' - test_data = load_dataset(file_name='test.csv') - - # we take a slice with no input validation issues - multiple_test_input = test_data[99:600] - - predictions = make_prediction(input_data=multiple_test_input) - - # save predictions for the test dataset - predictions_df = pd.DataFrame(predictions) - - # hack here to save the file to the regression model - # package of the repo, not the installed package - predictions_df.to_csv(f'{config.PACKAGE_ROOT}/{save_file}') - - -if __name__ == '__main__': - capture_predictions() +""" +This script should only be run in CI. +Never run it locally or you will disrupt the +differential test versioning logic. +""" + +import pandas as pd + +from regression_model.predict import make_prediction +from regression_model.processing.data_management import load_dataset + +from api import config + + +def capture_predictions() -> None: + """Save the test data predictions to a CSV.""" + + save_file = 'test_data_predictions.csv' + test_data = load_dataset(file_name='test.csv') + + # we take a slice with no input validation issues + multiple_test_input = test_data[99:600] + + predictions = make_prediction(input_data=multiple_test_input) + + # save predictions for the test dataset + predictions_df = pd.DataFrame(predictions) + + # hack here to save the file to the regression model + # package of the repo, not the installed package + predictions_df.to_csv(f'{config.PACKAGE_ROOT}/{save_file}') + + +if __name__ == '__main__': + capture_predictions() diff --git a/packages/ml_api/tests/conftest.py b/packages/ml_api/tests/conftest.py index 3134a9b4d..af2e95416 100644 --- a/packages/ml_api/tests/conftest.py +++ b/packages/ml_api/tests/conftest.py @@ -1,18 +1,18 @@ -import pytest - -from api.app import create_app -from api.config import TestingConfig - - -@pytest.fixture -def app(): - app = create_app(config_object=TestingConfig) - - with app.app_context(): - yield app - - -@pytest.fixture -def flask_test_client(app): - with app.test_client() as test_client: - yield test_client +import pytest + +from api.app import create_app +from api.config import TestingConfig + + +@pytest.fixture +def app(): + app = create_app(config_object=TestingConfig) + + with app.app_context(): + yield app + + +@pytest.fixture +def flask_test_client(app): + with app.test_client() as test_client: + yield test_client diff --git a/packages/ml_api/tests/differential_tests/test_differential.py b/packages/ml_api/tests/differential_tests/test_differential.py index acabf724d..b0bb67c60 100644 --- a/packages/ml_api/tests/differential_tests/test_differential.py +++ b/packages/ml_api/tests/differential_tests/test_differential.py @@ -1,53 +1,53 @@ -import math - -from regression_model.config import config as model_config -from regression_model.predict import make_prediction -from regression_model.processing.data_management import load_dataset -import pandas as pd -import pytest - - -from api import config - - -@pytest.mark.differential -def test_model_prediction_differential( - *, - save_file: str = 'test_data_predictions.csv'): - """ - This test compares the prediction result similarity of - the current model with the previous model's results. - """ - - # Given - # Load the saved previous model predictions - previous_model_df = pd.read_csv(f'{config.PACKAGE_ROOT}/{save_file}') - previous_model_predictions = previous_model_df.predictions.values - - test_data = load_dataset(file_name=model_config.TESTING_DATA_FILE) - multiple_test_input = test_data[99:600] - - # When - current_result = make_prediction(input_data=multiple_test_input) - current_model_predictions = current_result.get('predictions') - - # Then - # diff the current model vs. the old model - assert len(previous_model_predictions) == len( - current_model_predictions) - - # Perform the differential test - for previous_value, current_value in zip( - previous_model_predictions, current_model_predictions): - - # convert numpy float64 to Python float. - previous_value = previous_value.item() - current_value = current_value.item() - - # rel_tol is the relative tolerance – it is the maximum allowed - # difference between a and b, relative to the larger absolute - # value of a or b. For example, to set a tolerance of 5%, pass - # rel_tol=0.05. - assert math.isclose(previous_value, - current_value, - rel_tol=model_config.ACCEPTABLE_MODEL_DIFFERENCE) +import math + +from regression_model.config import config as model_config +from regression_model.predict import make_prediction +from regression_model.processing.data_management import load_dataset +import pandas as pd +import pytest + + +from api import config + + +@pytest.mark.differential +def test_model_prediction_differential( + *, + save_file: str = 'test_data_predictions.csv'): + """ + This test compares the prediction result similarity of + the current model with the previous model's results. + """ + + # Given + # Load the saved previous model predictions + previous_model_df = pd.read_csv(f'{config.PACKAGE_ROOT}/{save_file}') + previous_model_predictions = previous_model_df.predictions.values + + test_data = load_dataset(file_name=model_config.TESTING_DATA_FILE) + multiple_test_input = test_data[99:600] + + # When + current_result = make_prediction(input_data=multiple_test_input) + current_model_predictions = current_result.get('predictions') + + # Then + # diff the current model vs. the old model + assert len(previous_model_predictions) == len( + current_model_predictions) + + # Perform the differential test + for previous_value, current_value in zip( + previous_model_predictions, current_model_predictions): + + # convert numpy float64 to Python float. + previous_value = previous_value.item() + current_value = current_value.item() + + # rel_tol is the relative tolerance – it is the maximum allowed + # difference between a and b, relative to the larger absolute + # value of a or b. For example, to set a tolerance of 5%, pass + # rel_tol=0.05. + assert math.isclose(previous_value, + current_value, + rel_tol=model_config.ACCEPTABLE_MODEL_DIFFERENCE) diff --git a/packages/ml_api/tests/test_controller.py b/packages/ml_api/tests/test_controller.py index e45179b14..47a4e1865 100644 --- a/packages/ml_api/tests/test_controller.py +++ b/packages/ml_api/tests/test_controller.py @@ -1,79 +1,79 @@ -import io -import json -import math -import os - -from neural_network_model.config import config as ccn_config -from regression_model import __version__ as _version -from regression_model.config import config as model_config -from regression_model.processing.data_management import load_dataset - -from api import __version__ as api_version - - -def test_health_endpoint_returns_200(flask_test_client): - # When - response = flask_test_client.get('/health') - - # Then - assert response.status_code == 200 - - -def test_version_endpoint_returns_version(flask_test_client): - # When - response = flask_test_client.get('/version') - - # Then - assert response.status_code == 200 - response_json = json.loads(response.data) - assert response_json['model_version'] == _version - assert response_json['api_version'] == api_version - - -def test_prediction_endpoint_returns_prediction(flask_test_client): - # Given - # Load the test data from the regression_model package - # This is important as it makes it harder for the test - # data versions to get confused by not spreading it - # across packages. - test_data = load_dataset(file_name=model_config.TESTING_DATA_FILE) - post_json = test_data[0:1].to_json(orient='records') - - # When - response = flask_test_client.post('/v1/predict/regression', - json=json.loads(post_json)) - - # Then - assert response.status_code == 200 - response_json = json.loads(response.data) - prediction = response_json['predictions'] - response_version = response_json['version'] - assert math.ceil(prediction[0]) == 112476 - assert response_version == _version - - -def test_classifier_endpoint_returns_prediction(flask_test_client): - # Given - # Load the test data from the neural_network_model package - # This is important as it makes it harder for the test - # data versions to get confused by not spreading it - # across packages. - data_dir = os.path.abspath(os.path.join(ccn_config.DATA_FOLDER, os.pardir)) - test_dir = os.path.join(data_dir, 'test_data') - black_grass_dir = os.path.join(test_dir, 'Black-grass') - black_grass_image = os.path.join(black_grass_dir, '1.png') - with open(black_grass_image, "rb") as image_file: - file_bytes = image_file.read() - data = dict( - file=(io.BytesIO(bytearray(file_bytes)), "1.png"), - ) - - # When - response = flask_test_client.post('/predict/classifier', - content_type='multipart/form-data', - data=data) - - # Then - assert response.status_code == 200 - response_json = json.loads(response.data) - assert response_json['readable_predictions'] +import io +import json +import math +import os + +from neural_network_model.config import config as ccn_config +from regression_model import __version__ as _version +from regression_model.config import config as model_config +from regression_model.processing.data_management import load_dataset + +from api import __version__ as api_version + + +def test_health_endpoint_returns_200(flask_test_client): + # When + response = flask_test_client.get('/health') + + # Then + assert response.status_code == 200 + + +def test_version_endpoint_returns_version(flask_test_client): + # When + response = flask_test_client.get('/version') + + # Then + assert response.status_code == 200 + response_json = json.loads(response.data) + assert response_json['model_version'] == _version + assert response_json['api_version'] == api_version + + +def test_prediction_endpoint_returns_prediction(flask_test_client): + # Given + # Load the test data from the regression_model package + # This is important as it makes it harder for the test + # data versions to get confused by not spreading it + # across packages. + test_data = load_dataset(file_name=model_config.TESTING_DATA_FILE) + post_json = test_data[0:1].to_json(orient='records') + + # When + response = flask_test_client.post('/v1/predict/regression', + json=json.loads(post_json)) + + # Then + assert response.status_code == 200 + response_json = json.loads(response.data) + prediction = response_json['predictions'] + response_version = response_json['version'] + assert math.ceil(prediction[0]) == 112476 + assert response_version == _version + + +def test_classifier_endpoint_returns_prediction(flask_test_client): + # Given + # Load the test data from the neural_network_model package + # This is important as it makes it harder for the test + # data versions to get confused by not spreading it + # across packages. + data_dir = os.path.abspath(os.path.join(ccn_config.DATA_FOLDER, os.pardir)) + test_dir = os.path.join(data_dir, 'test_data') + black_grass_dir = os.path.join(test_dir, 'Black-grass') + black_grass_image = os.path.join(black_grass_dir, '1.png') + with open(black_grass_image, "rb") as image_file: + file_bytes = image_file.read() + data = dict( + file=(io.BytesIO(bytearray(file_bytes)), "1.png"), + ) + + # When + response = flask_test_client.post('/predict/classifier', + content_type='multipart/form-data', + data=data) + + # Then + assert response.status_code == 200 + response_json = json.loads(response.data) + assert response_json['readable_predictions'] diff --git a/packages/ml_api/tests/test_validation.py b/packages/ml_api/tests/test_validation.py index d34d86c72..46e4ca809 100644 --- a/packages/ml_api/tests/test_validation.py +++ b/packages/ml_api/tests/test_validation.py @@ -1,26 +1,26 @@ -import json - -from regression_model.config import config -from regression_model.processing.data_management import load_dataset - - -def test_prediction_endpoint_validation_200(flask_test_client): - # Given - # Load the test data from the regression_model package. - # This is important as it makes it harder for the test - # data versions to get confused by not spreading it - # across packages. - test_data = load_dataset(file_name=config.TESTING_DATA_FILE) - post_json = test_data.to_json(orient='records') - - # When - response = flask_test_client.post('/v1/predict/regression', - json=json.loads(post_json)) - - # Then - assert response.status_code == 200 - response_json = json.loads(response.data) - - # Check correct number of errors removed - assert len(response_json.get('predictions')) + len( - response_json.get('errors')) == len(test_data) +import json + +from regression_model.config import config +from regression_model.processing.data_management import load_dataset + + +def test_prediction_endpoint_validation_200(flask_test_client): + # Given + # Load the test data from the regression_model package. + # This is important as it makes it harder for the test + # data versions to get confused by not spreading it + # across packages. + test_data = load_dataset(file_name=config.TESTING_DATA_FILE) + post_json = test_data.to_json(orient='records') + + # When + response = flask_test_client.post('/v1/predict/regression', + json=json.loads(post_json)) + + # Then + assert response.status_code == 200 + response_json = json.loads(response.data) + + # Check correct number of errors removed + assert len(response_json.get('predictions')) + len( + response_json.get('errors')) == len(test_data) diff --git a/packages/ml_api/tox.ini b/packages/ml_api/tox.ini index 50e82033a..9b1dace97 100644 --- a/packages/ml_api/tox.ini +++ b/packages/ml_api/tox.ini @@ -1,32 +1,32 @@ -[tox] -envlist = py36, py37, py38 -skipsdist = True - - -[testenv] -install_command = pip install --pre {opts} {packages} -deps = - -rrequirements.txt - -passenv = - PIP_EXTRA_INDEX_URL - KERAS_BACKEND - -setenv = - PYTHONPATH=. - -commands = - pytest \ - -s \ - -v \ - -m "not differential" \ - {posargs:tests} - - -# content of pytest.ini -[pytest] -markers = - integration: mark a test as an integration test. - differential: mark a test as a differential test. -filterwarnings = +[tox] +envlist = py36, py37, py38 +skipsdist = True + + +[testenv] +install_command = pip install --pre {opts} {packages} +deps = + -rrequirements.txt + +passenv = + PIP_EXTRA_INDEX_URL + KERAS_BACKEND + +setenv = + PYTHONPATH=. + +commands = + pytest \ + -s \ + -v \ + -m "not differential" \ + {posargs:tests} + + +# content of pytest.ini +[pytest] +markers = + integration: mark a test as an integration test. + differential: mark a test as a differential test. +filterwarnings = ignore::DeprecationWarning \ No newline at end of file diff --git a/packages/neural_network_model/MANIFEST.in b/packages/neural_network_model/MANIFEST.in index f9aca5b03..60f2ed58d 100644 --- a/packages/neural_network_model/MANIFEST.in +++ b/packages/neural_network_model/MANIFEST.in @@ -1,17 +1,17 @@ -include *.txt -include *.md -include *.cfg -include *.pkl -recursive-include ./neural_network_model/*.py - -include neural_network_model/trained_models/*.pkl -include neural_network_model/trained_models/*.h5 -include neural_network_model/VERSION -include neural_network_model/datasets/test_data/Black-grass/1.png -include neural_network_model/datasets/test_data/Charlock/1.png - -include ./requirements.txt -exclude *.log - -recursive-exclude * __pycache__ +include *.txt +include *.md +include *.cfg +include *.pkl +recursive-include ./neural_network_model/*.py + +include neural_network_model/trained_models/*.pkl +include neural_network_model/trained_models/*.h5 +include neural_network_model/VERSION +include neural_network_model/datasets/test_data/Black-grass/1.png +include neural_network_model/datasets/test_data/Charlock/1.png + +include ./requirements.txt +exclude *.log + +recursive-exclude * __pycache__ recursive-exclude * *.py[co] \ No newline at end of file diff --git a/packages/neural_network_model/config.yml b/packages/neural_network_model/config.yml index a939e5708..5a91996e1 100644 --- a/packages/neural_network_model/config.yml +++ b/packages/neural_network_model/config.yml @@ -1,4 +1,4 @@ -MODEL_NAME: ${MODEL_NAME:cnn_model} -PIPELINE_NAME: ${PIPELINE_NAME:cnn_pipe} -CLASSES_PATH: ${CLASSES_PATH:False} -IMAGE_SIZE: $(IMAGE_SIZE:150} +MODEL_NAME: ${MODEL_NAME:cnn_model} +PIPELINE_NAME: ${PIPELINE_NAME:cnn_pipe} +CLASSES_PATH: ${CLASSES_PATH:False} +IMAGE_SIZE: $(IMAGE_SIZE:150} diff --git a/packages/neural_network_model/neural_network_model/__init__.py b/packages/neural_network_model/neural_network_model/__init__.py index b6c968d56..890cf8a19 100644 --- a/packages/neural_network_model/neural_network_model/__init__.py +++ b/packages/neural_network_model/neural_network_model/__init__.py @@ -1,7 +1,7 @@ -import os - -from neural_network_model.config import config - - -with open(os.path.join(config.PACKAGE_ROOT, 'VERSION')) as version_file: - __version__ = version_file.read().strip() +import os + +from neural_network_model.config import config + + +with open(os.path.join(config.PACKAGE_ROOT, 'VERSION')) as version_file: + __version__ = version_file.read().strip() diff --git a/packages/neural_network_model/neural_network_model/config/config.py b/packages/neural_network_model/neural_network_model/config/config.py index 4d8b173d7..94adec2a5 100644 --- a/packages/neural_network_model/neural_network_model/config/config.py +++ b/packages/neural_network_model/neural_network_model/config/config.py @@ -1,38 +1,38 @@ -# The Keras model loading function does not play well with -# Pathlib at the moment, so we are using the old os module -# style - -import os - -PWD = os.path.dirname(os.path.abspath(__file__)) -PACKAGE_ROOT = os.path.abspath(os.path.join(PWD, '..')) -DATASET_DIR = os.path.join(PACKAGE_ROOT, 'datasets') -TRAINED_MODEL_DIR = os.path.join(PACKAGE_ROOT, 'trained_models') -DATA_FOLDER = os.path.join(DATASET_DIR, 'v2-plant-seedlings-dataset') - -# MODEL PERSISTING -MODEL_NAME = 'cnn_model' -PIPELINE_NAME = 'cnn_pipe' -CLASSES_NAME = 'classes' -ENCODER_NAME = 'encoder' - -# MODEL FITTING -IMAGE_SIZE = 150 # 50 for testing, 150 for final model -BATCH_SIZE = 10 -EPOCHS = int(os.environ.get('EPOCHS', 1)) # 1 for testing, 10 for final model - - -with open(os.path.join(PACKAGE_ROOT, 'VERSION')) as version_file: - _version = version_file.read().strip() - -MODEL_FILE_NAME = f'{MODEL_NAME}_{_version}.h5' -MODEL_PATH = os.path.join(TRAINED_MODEL_DIR, MODEL_FILE_NAME) - -PIPELINE_FILE_NAME = f'{PIPELINE_NAME}_{_version}.pkl' -PIPELINE_PATH = os.path.join(TRAINED_MODEL_DIR, PIPELINE_FILE_NAME) - -CLASSES_FILE_NAME = f'{CLASSES_NAME}_{_version}.pkl' -CLASSES_PATH = os.path.join(TRAINED_MODEL_DIR, CLASSES_FILE_NAME) - -ENCODER_FILE_NAME = f'{ENCODER_NAME}_{_version}.pkl' -ENCODER_PATH = os.path.join(TRAINED_MODEL_DIR, ENCODER_FILE_NAME) +# The Keras model loading function does not play well with +# Pathlib at the moment, so we are using the old os module +# style + +import os + +PWD = os.path.dirname(os.path.abspath(__file__)) +PACKAGE_ROOT = os.path.abspath(os.path.join(PWD, '..')) +DATASET_DIR = os.path.join(PACKAGE_ROOT, 'datasets') +TRAINED_MODEL_DIR = os.path.join(PACKAGE_ROOT, 'trained_models') +DATA_FOLDER = os.path.join(DATASET_DIR, 'v2-plant-seedlings-dataset') + +# MODEL PERSISTING +MODEL_NAME = 'cnn_model' +PIPELINE_NAME = 'cnn_pipe' +CLASSES_NAME = 'classes' +ENCODER_NAME = 'encoder' + +# MODEL FITTING +IMAGE_SIZE = 150 # 50 for testing, 150 for final model +BATCH_SIZE = 10 +EPOCHS = int(os.environ.get('EPOCHS', 1)) # 1 for testing, 10 for final model + + +with open(os.path.join(PACKAGE_ROOT, 'VERSION')) as version_file: + _version = version_file.read().strip() + +MODEL_FILE_NAME = f'{MODEL_NAME}_{_version}.h5' +MODEL_PATH = os.path.join(TRAINED_MODEL_DIR, MODEL_FILE_NAME) + +PIPELINE_FILE_NAME = f'{PIPELINE_NAME}_{_version}.pkl' +PIPELINE_PATH = os.path.join(TRAINED_MODEL_DIR, PIPELINE_FILE_NAME) + +CLASSES_FILE_NAME = f'{CLASSES_NAME}_{_version}.pkl' +CLASSES_PATH = os.path.join(TRAINED_MODEL_DIR, CLASSES_FILE_NAME) + +ENCODER_FILE_NAME = f'{ENCODER_NAME}_{_version}.pkl' +ENCODER_PATH = os.path.join(TRAINED_MODEL_DIR, ENCODER_FILE_NAME) diff --git a/packages/neural_network_model/neural_network_model/model.py b/packages/neural_network_model/neural_network_model/model.py index ef31d213b..400e4a702 100644 --- a/packages/neural_network_model/neural_network_model/model.py +++ b/packages/neural_network_model/neural_network_model/model.py @@ -1,79 +1,79 @@ -# for the convolutional network -from keras.models import Sequential -from keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Flatten -from keras.optimizers import Adam -from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint -from keras.wrappers.scikit_learn import KerasClassifier - -from neural_network_model.config import config - - -def cnn_model(kernel_size=(3, 3), - pool_size=(2, 2), - first_filters=32, - second_filters=64, - third_filters=128, - dropout_conv=0.3, - dropout_dense=0.3, - image_size=50): - - model = Sequential() - model.add(Conv2D( - first_filters, - kernel_size, - activation='relu', - input_shape=(image_size, image_size, 3))) - model.add(Conv2D(first_filters, kernel_size, activation = 'relu')) - model.add(MaxPooling2D(pool_size=pool_size)) - model.add(Dropout(dropout_conv)) - - model.add(Conv2D(second_filters, kernel_size, activation='relu')) - model.add(Conv2D(second_filters, kernel_size, activation ='relu')) - model.add(MaxPooling2D(pool_size=pool_size)) - model.add(Dropout(dropout_conv)) - - model.add(Conv2D(third_filters, kernel_size, activation='relu')) - model.add(Conv2D(third_filters, kernel_size, activation ='relu')) - model.add(MaxPooling2D(pool_size=pool_size)) - model.add(Dropout(dropout_conv)) - - model.add(Flatten()) - model.add(Dense(256, activation="relu")) - model.add(Dropout(dropout_dense)) - model.add(Dense(12, activation="softmax")) - - model.compile(Adam(lr=0.0001), - loss='binary_crossentropy', - metrics=['accuracy']) - - return model - - -checkpoint = ModelCheckpoint(config.MODEL_PATH, - monitor='acc', - verbose=1, - save_best_only=True, - mode='max') - -reduce_lr = ReduceLROnPlateau(monitor='acc', - factor=0.5, - patience=2, - verbose=1, - mode='max', - min_lr=0.00001) - -callbacks_list = [checkpoint, reduce_lr] - -cnn_clf = KerasClassifier(build_fn=cnn_model, - batch_size=config.BATCH_SIZE, - validation_split=10, - epochs=config.EPOCHS, - verbose=1, # progress bar - required for CI job - callbacks=callbacks_list, - image_size=config.IMAGE_SIZE - ) - - -if __name__ == '__main__': - model = cnn_model() - model.summary() +# for the convolutional network +from keras.models import Sequential +from keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Flatten +from keras.optimizers import Adam +from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint +from keras.wrappers.scikit_learn import KerasClassifier + +from neural_network_model.config import config + + +def cnn_model(kernel_size=(3, 3), + pool_size=(2, 2), + first_filters=32, + second_filters=64, + third_filters=128, + dropout_conv=0.3, + dropout_dense=0.3, + image_size=50): + + model = Sequential() + model.add(Conv2D( + first_filters, + kernel_size, + activation='relu', + input_shape=(image_size, image_size, 3))) + model.add(Conv2D(first_filters, kernel_size, activation = 'relu')) + model.add(MaxPooling2D(pool_size=pool_size)) + model.add(Dropout(dropout_conv)) + + model.add(Conv2D(second_filters, kernel_size, activation='relu')) + model.add(Conv2D(second_filters, kernel_size, activation ='relu')) + model.add(MaxPooling2D(pool_size=pool_size)) + model.add(Dropout(dropout_conv)) + + model.add(Conv2D(third_filters, kernel_size, activation='relu')) + model.add(Conv2D(third_filters, kernel_size, activation ='relu')) + model.add(MaxPooling2D(pool_size=pool_size)) + model.add(Dropout(dropout_conv)) + + model.add(Flatten()) + model.add(Dense(256, activation="relu")) + model.add(Dropout(dropout_dense)) + model.add(Dense(12, activation="softmax")) + + model.compile(Adam(lr=0.0001), + loss='binary_crossentropy', + metrics=['accuracy']) + + return model + + +checkpoint = ModelCheckpoint(config.MODEL_PATH, + monitor='acc', + verbose=1, + save_best_only=True, + mode='max') + +reduce_lr = ReduceLROnPlateau(monitor='acc', + factor=0.5, + patience=2, + verbose=1, + mode='max', + min_lr=0.00001) + +callbacks_list = [checkpoint, reduce_lr] + +cnn_clf = KerasClassifier(build_fn=cnn_model, + batch_size=config.BATCH_SIZE, + validation_split=10, + epochs=config.EPOCHS, + verbose=1, # progress bar - required for CI job + callbacks=callbacks_list, + image_size=config.IMAGE_SIZE + ) + + +if __name__ == '__main__': + model = cnn_model() + model.summary() diff --git a/packages/neural_network_model/neural_network_model/pipeline.py b/packages/neural_network_model/neural_network_model/pipeline.py index d8f68a6cc..aa2c0043e 100644 --- a/packages/neural_network_model/neural_network_model/pipeline.py +++ b/packages/neural_network_model/neural_network_model/pipeline.py @@ -1,10 +1,10 @@ -from sklearn.pipeline import Pipeline - -from neural_network_model.config import config -from neural_network_model.processing import preprocessors as pp -from neural_network_model import model - - -pipe = Pipeline([ - ('dataset', pp.CreateDataset(config.IMAGE_SIZE)), - ('cnn_model', model.cnn_clf)]) +from sklearn.pipeline import Pipeline + +from neural_network_model.config import config +from neural_network_model.processing import preprocessors as pp +from neural_network_model import model + + +pipe = Pipeline([ + ('dataset', pp.CreateDataset(config.IMAGE_SIZE)), + ('cnn_model', model.cnn_clf)]) diff --git a/packages/neural_network_model/neural_network_model/predict.py b/packages/neural_network_model/neural_network_model/predict.py index 56869268c..3405af5f7 100644 --- a/packages/neural_network_model/neural_network_model/predict.py +++ b/packages/neural_network_model/neural_network_model/predict.py @@ -1,67 +1,67 @@ -import logging - -import pandas as pd - -from neural_network_model import __version__ as _version -from neural_network_model.processing import data_management as dm - -_logger = logging.getLogger(__name__) -KERAS_PIPELINE = dm.load_pipeline_keras() -ENCODER = dm.load_encoder() - - -def make_single_prediction(*, image_name: str, image_directory: str): - """Make a single prediction using the saved model pipeline. - - Args: - image_name: Filename of the image to classify - image_directory: Location of the image to classify - - Returns - Dictionary with both raw predictions and readable values. - """ - - image_df = dm.load_single_image( - data_folder=image_directory, - filename=image_name) - - prepared_df = image_df['image'].reset_index(drop=True) - _logger.info(f'received input array: {prepared_df}, ' - f'filename: {image_name}') - - predictions = KERAS_PIPELINE.predict(prepared_df) - readable_predictions = ENCODER.encoder.inverse_transform(predictions) - - _logger.info(f'Made prediction: {predictions}' - f' with model version: {_version}') - - return dict(predictions=predictions, - readable_predictions=readable_predictions, - version=_version) - - -def make_bulk_prediction(*, images_df: pd.Series) -> dict: - """Make multiple predictions using the saved model pipeline. - - Currently, this function is primarily for testing purposes, - allowing us to pass in a directory of images for running - bulk predictions. - - Args: - images_df: Pandas series of images - - Returns - Dictionary with both raw predictions and their classifications. - """ - - _logger.info(f'received input df: {images_df}') - - predictions = KERAS_PIPELINE.predict(images_df) - readable_predictions = ENCODER.encoder.inverse_transform(predictions) - - _logger.info(f'Made predictions: {predictions}' - f' with model version: {_version}') - - return dict(predictions=predictions, - readable_predictions=readable_predictions, - version=_version) +import logging + +import pandas as pd + +from neural_network_model import __version__ as _version +from neural_network_model.processing import data_management as dm + +_logger = logging.getLogger(__name__) +KERAS_PIPELINE = dm.load_pipeline_keras() +ENCODER = dm.load_encoder() + + +def make_single_prediction(*, image_name: str, image_directory: str): + """Make a single prediction using the saved model pipeline. + + Args: + image_name: Filename of the image to classify + image_directory: Location of the image to classify + + Returns + Dictionary with both raw predictions and readable values. + """ + + image_df = dm.load_single_image( + data_folder=image_directory, + filename=image_name) + + prepared_df = image_df['image'].reset_index(drop=True) + _logger.info(f'received input array: {prepared_df}, ' + f'filename: {image_name}') + + predictions = KERAS_PIPELINE.predict(prepared_df) + readable_predictions = ENCODER.encoder.inverse_transform(predictions) + + _logger.info(f'Made prediction: {predictions}' + f' with model version: {_version}') + + return dict(predictions=predictions, + readable_predictions=readable_predictions, + version=_version) + + +def make_bulk_prediction(*, images_df: pd.Series) -> dict: + """Make multiple predictions using the saved model pipeline. + + Currently, this function is primarily for testing purposes, + allowing us to pass in a directory of images for running + bulk predictions. + + Args: + images_df: Pandas series of images + + Returns + Dictionary with both raw predictions and their classifications. + """ + + _logger.info(f'received input df: {images_df}') + + predictions = KERAS_PIPELINE.predict(images_df) + readable_predictions = ENCODER.encoder.inverse_transform(predictions) + + _logger.info(f'Made predictions: {predictions}' + f' with model version: {_version}') + + return dict(predictions=predictions, + readable_predictions=readable_predictions, + version=_version) diff --git a/packages/neural_network_model/neural_network_model/processing/data_management.py b/packages/neural_network_model/neural_network_model/processing/data_management.py index 675362ca0..3dd028023 100644 --- a/packages/neural_network_model/neural_network_model/processing/data_management.py +++ b/packages/neural_network_model/neural_network_model/processing/data_management.py @@ -1,130 +1,130 @@ -import logging -import os -import typing as t -from glob import glob -from pathlib import Path - -import pandas as pd -from keras.models import load_model -from keras.wrappers.scikit_learn import KerasClassifier -from sklearn.externals import joblib -from sklearn.model_selection import train_test_split -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import LabelEncoder - -from neural_network_model import model as m -from neural_network_model.config import config - -_logger = logging.getLogger(__name__) - - -def load_single_image(data_folder: str, filename: str) -> pd.DataFrame: - """Makes dataframe with image path and target.""" - - image_df = [] - - # search for specific image in directory - for image_path in glob(os.path.join(data_folder, f'{filename}')): - tmp = pd.DataFrame([image_path, 'unknown']).T - image_df.append(tmp) - - # concatenate the final df - images_df = pd.concat(image_df, axis=0, ignore_index=True) - images_df.columns = ['image', 'target'] - - return images_df - - -def load_image_paths(data_folder: str) -> pd.DataFrame: - """Makes dataframe with image path and target.""" - - images_df = [] - - # navigate within each folder - for class_folder_name in os.listdir(data_folder): - class_folder_path = os.path.join(data_folder, class_folder_name) - - # collect every image path - for image_path in glob(os.path.join(class_folder_path, "*.png")): - tmp = pd.DataFrame([image_path, class_folder_name]).T - images_df.append(tmp) - - # concatenate the final df - images_df = pd.concat(images_df, axis=0, ignore_index=True) - images_df.columns = ['image', 'target'] - - return images_df - - -def get_train_test_target(df: pd.DataFrame): - """Split a dataset into train and test segments.""" - - X_train, X_test, y_train, y_test = train_test_split(df['image'], - df['target'], - test_size=0.20, - random_state=101) - - X_train.reset_index(drop=True, inplace=True) - X_test.reset_index(drop=True, inplace=True) - - y_train.reset_index(drop=True, inplace=True) - y_test.reset_index(drop=True, inplace=True) - - return X_train, X_test, y_train, y_test - - -def save_pipeline_keras(model) -> None: - """Persist keras model to disk.""" - - joblib.dump(model.named_steps['dataset'], config.PIPELINE_PATH) - joblib.dump(model.named_steps['cnn_model'].classes_, config.CLASSES_PATH) - model.named_steps['cnn_model'].model.save(str(config.MODEL_PATH)) - - remove_old_pipelines( - files_to_keep=[config.MODEL_FILE_NAME, config.ENCODER_FILE_NAME, - config.PIPELINE_FILE_NAME, config.CLASSES_FILE_NAME]) - - -def load_pipeline_keras() -> Pipeline: - """Load a Keras Pipeline from disk.""" - - dataset = joblib.load(config.PIPELINE_PATH) - - build_model = lambda: load_model(config.MODEL_PATH) - - classifier = KerasClassifier(build_fn=build_model, - batch_size=config.BATCH_SIZE, - validation_split=10, - epochs=config.EPOCHS, - verbose=2, - callbacks=m.callbacks_list, - # image_size = config.IMAGE_SIZE - ) - - classifier.classes_ = joblib.load(config.CLASSES_PATH) - classifier.model = build_model() - - return Pipeline([ - ('dataset', dataset), - ('cnn_model', classifier) - ]) - - -def load_encoder() -> LabelEncoder: - encoder = joblib.load(config.ENCODER_PATH) - - return encoder - - -def remove_old_pipelines(*, files_to_keep: t.List[str]) -> None: - """ - Remove old model pipelines, models, encoders and classes. - - This is to ensure there is a simple one-to-one - mapping between the package version and the model - version to be imported and used by other applications. - """ - do_not_delete = files_to_keep + ['__init__.py'] - for model_file in Path(config.TRAINED_MODEL_DIR).iterdir(): - if model_file.name not in do_not_delete: - model_file.unlink() +import logging +import os +import typing as t +from glob import glob +from pathlib import Path + +import pandas as pd +from keras.models import load_model +from keras.wrappers.scikit_learn import KerasClassifier +from sklearn.externals import joblib +from sklearn.model_selection import train_test_split +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import LabelEncoder + +from neural_network_model import model as m +from neural_network_model.config import config + +_logger = logging.getLogger(__name__) + + +def load_single_image(data_folder: str, filename: str) -> pd.DataFrame: + """Makes dataframe with image path and target.""" + + image_df = [] + + # search for specific image in directory + for image_path in glob(os.path.join(data_folder, f'{filename}')): + tmp = pd.DataFrame([image_path, 'unknown']).T + image_df.append(tmp) + + # concatenate the final df + images_df = pd.concat(image_df, axis=0, ignore_index=True) + images_df.columns = ['image', 'target'] + + return images_df + + +def load_image_paths(data_folder: str) -> pd.DataFrame: + """Makes dataframe with image path and target.""" + + images_df = [] + + # navigate within each folder + for class_folder_name in os.listdir(data_folder): + class_folder_path = os.path.join(data_folder, class_folder_name) + + # collect every image path + for image_path in glob(os.path.join(class_folder_path, "*.png")): + tmp = pd.DataFrame([image_path, class_folder_name]).T + images_df.append(tmp) + + # concatenate the final df + images_df = pd.concat(images_df, axis=0, ignore_index=True) + images_df.columns = ['image', 'target'] + + return images_df + + +def get_train_test_target(df: pd.DataFrame): + """Split a dataset into train and test segments.""" + + X_train, X_test, y_train, y_test = train_test_split(df['image'], + df['target'], + test_size=0.20, + random_state=101) + + X_train.reset_index(drop=True, inplace=True) + X_test.reset_index(drop=True, inplace=True) + + y_train.reset_index(drop=True, inplace=True) + y_test.reset_index(drop=True, inplace=True) + + return X_train, X_test, y_train, y_test + + +def save_pipeline_keras(model) -> None: + """Persist keras model to disk.""" + + joblib.dump(model.named_steps['dataset'], config.PIPELINE_PATH) + joblib.dump(model.named_steps['cnn_model'].classes_, config.CLASSES_PATH) + model.named_steps['cnn_model'].model.save(str(config.MODEL_PATH)) + + remove_old_pipelines( + files_to_keep=[config.MODEL_FILE_NAME, config.ENCODER_FILE_NAME, + config.PIPELINE_FILE_NAME, config.CLASSES_FILE_NAME]) + + +def load_pipeline_keras() -> Pipeline: + """Load a Keras Pipeline from disk.""" + + dataset = joblib.load(config.PIPELINE_PATH) + + build_model = lambda: load_model(config.MODEL_PATH) + + classifier = KerasClassifier(build_fn=build_model, + batch_size=config.BATCH_SIZE, + validation_split=10, + epochs=config.EPOCHS, + verbose=2, + callbacks=m.callbacks_list, + # image_size = config.IMAGE_SIZE + ) + + classifier.classes_ = joblib.load(config.CLASSES_PATH) + classifier.model = build_model() + + return Pipeline([ + ('dataset', dataset), + ('cnn_model', classifier) + ]) + + +def load_encoder() -> LabelEncoder: + encoder = joblib.load(config.ENCODER_PATH) + + return encoder + + +def remove_old_pipelines(*, files_to_keep: t.List[str]) -> None: + """ + Remove old model pipelines, models, encoders and classes. + + This is to ensure there is a simple one-to-one + mapping between the package version and the model + version to be imported and used by other applications. + """ + do_not_delete = files_to_keep + ['__init__.py'] + for model_file in Path(config.TRAINED_MODEL_DIR).iterdir(): + if model_file.name not in do_not_delete: + model_file.unlink() diff --git a/packages/neural_network_model/neural_network_model/processing/errors.py b/packages/neural_network_model/neural_network_model/processing/errors.py index b92425437..b5e75f063 100644 --- a/packages/neural_network_model/neural_network_model/processing/errors.py +++ b/packages/neural_network_model/neural_network_model/processing/errors.py @@ -1,6 +1,6 @@ -class BaseError(Exception): - """Base package error.""" - - -class InvalidModelInputError(BaseError): - """Model input contains an error.""" +class BaseError(Exception): + """Base package error.""" + + +class InvalidModelInputError(BaseError): + """Model input contains an error.""" diff --git a/packages/neural_network_model/neural_network_model/processing/preprocessors.py b/packages/neural_network_model/neural_network_model/processing/preprocessors.py index 37f813c19..889120d15 100644 --- a/packages/neural_network_model/neural_network_model/processing/preprocessors.py +++ b/packages/neural_network_model/neural_network_model/processing/preprocessors.py @@ -1,50 +1,50 @@ -import numpy as np -import cv2 -from keras.utils import np_utils -from sklearn.preprocessing import LabelEncoder -from sklearn.base import BaseEstimator, TransformerMixin - - -class TargetEncoder(BaseEstimator, TransformerMixin): - - def __init__(self, encoder=LabelEncoder()): - self.encoder = encoder - - def fit(self, X, y=None): - # note that x is the target in this case - self.encoder.fit(X) - return self - - def transform(self, X): - X = X.copy() - X = np_utils.to_categorical(self.encoder.transform(X)) - return X - - -def _im_resize(df, n, image_size): - im = cv2.imread(df[n]) - im = cv2.resize(im, (image_size, image_size)) - return im - - -class CreateDataset(BaseEstimator, TransformerMixin): - - def __init__(self, image_size=50): - self.image_size = image_size - - def fit(self, X, y=None): - return self - - def transform(self, X): - X = X.copy() - tmp = np.zeros((len(X), - self.image_size, - self.image_size, 3), dtype='float32') - - for n in range(0, len(X)): - im = _im_resize(X, n, self.image_size) - tmp[n] = im - - print('Dataset Images shape: {} size: {:,}'.format( - tmp.shape, tmp.size)) - return tmp +import numpy as np +import cv2 +from keras.utils import np_utils +from sklearn.preprocessing import LabelEncoder +from sklearn.base import BaseEstimator, TransformerMixin + + +class TargetEncoder(BaseEstimator, TransformerMixin): + + def __init__(self, encoder=LabelEncoder()): + self.encoder = encoder + + def fit(self, X, y=None): + # note that x is the target in this case + self.encoder.fit(X) + return self + + def transform(self, X): + X = X.copy() + X = np_utils.to_categorical(self.encoder.transform(X)) + return X + + +def _im_resize(df, n, image_size): + im = cv2.imread(df[n]) + im = cv2.resize(im, (image_size, image_size)) + return im + + +class CreateDataset(BaseEstimator, TransformerMixin): + + def __init__(self, image_size=50): + self.image_size = image_size + + def fit(self, X, y=None): + return self + + def transform(self, X): + X = X.copy() + tmp = np.zeros((len(X), + self.image_size, + self.image_size, 3), dtype='float32') + + for n in range(0, len(X)): + im = _im_resize(X, n, self.image_size) + tmp[n] = im + + print('Dataset Images shape: {} size: {:,}'.format( + tmp.shape, tmp.size)) + return tmp diff --git a/packages/neural_network_model/neural_network_model/train_pipeline.py b/packages/neural_network_model/neural_network_model/train_pipeline.py index 13110b145..86cea636f 100644 --- a/packages/neural_network_model/neural_network_model/train_pipeline.py +++ b/packages/neural_network_model/neural_network_model/train_pipeline.py @@ -1,27 +1,27 @@ -from sklearn.externals import joblib - -from neural_network_model import pipeline as pipe -from neural_network_model.config import config -from neural_network_model.processing import data_management as dm -from neural_network_model.processing import preprocessors as pp - - -def run_training(save_result: bool = True): - """Train a Convolutional Neural Network.""" - - images_df = dm.load_image_paths(config.DATA_FOLDER) - X_train, X_test, y_train, y_test = dm.get_train_test_target(images_df) - - enc = pp.TargetEncoder() - enc.fit(y_train) - y_train = enc.transform(y_train) - - pipe.pipe.fit(X_train, y_train) - - if save_result: - joblib.dump(enc, config.ENCODER_PATH) - dm.save_pipeline_keras(pipe.pipe) - - -if __name__ == '__main__': - run_training(save_result=True) +from sklearn.externals import joblib + +from neural_network_model import pipeline as pipe +from neural_network_model.config import config +from neural_network_model.processing import data_management as dm +from neural_network_model.processing import preprocessors as pp + + +def run_training(save_result: bool = True): + """Train a Convolutional Neural Network.""" + + images_df = dm.load_image_paths(config.DATA_FOLDER) + X_train, X_test, y_train, y_test = dm.get_train_test_target(images_df) + + enc = pp.TargetEncoder() + enc.fit(y_train) + y_train = enc.transform(y_train) + + pipe.pipe.fit(X_train, y_train) + + if save_result: + joblib.dump(enc, config.ENCODER_PATH) + dm.save_pipeline_keras(pipe.pipe) + + +if __name__ == '__main__': + run_training(save_result=True) diff --git a/packages/neural_network_model/requirements.txt b/packages/neural_network_model/requirements.txt index ffac1feac..4752f6397 100644 --- a/packages/neural_network_model/requirements.txt +++ b/packages/neural_network_model/requirements.txt @@ -1,18 +1,18 @@ -# production requirements -pandas==0.23.4 -numpy==1.13.3 -scikit-learn==0.19.0 -Keras==2.1.3 -opencv-python==4.0.0.21 -h5py==2.9.0 -Theano==0.9.0 - -# packaging -setuptools==40.6.3 -wheel==0.32.3 - -# testing requirements -pytest==4.0.2 - -# fetching datasets +# production requirements +pandas==0.23.4 +numpy==1.13.3 +scikit-learn==0.19.0 +Keras==2.1.3 +opencv-python==4.0.0.21 +h5py==2.9.0 +Theano==0.9.0 + +# packaging +setuptools==40.6.3 +wheel==0.32.3 + +# testing requirements +pytest==4.0.2 + +# fetching datasets kaggle==1.5.1.1 \ No newline at end of file diff --git a/packages/neural_network_model/setup.py b/packages/neural_network_model/setup.py index dd4e4d6a6..e5750d04f 100644 --- a/packages/neural_network_model/setup.py +++ b/packages/neural_network_model/setup.py @@ -1,79 +1,79 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import io -import os -from pathlib import Path - -from setuptools import find_packages, setup - - -# Package meta-data. -NAME = 'neural_network_model' -DESCRIPTION = 'Train and deploy neural network model.' -URL = 'your github project' -EMAIL = 'your_email@email.com' -AUTHOR = 'Your name' -REQUIRES_PYTHON = '>=3.6.0' - - -# What packages are required for this module to be executed? -def list_reqs(fname='requirements.txt'): - with open(fname) as fd: - return fd.read().splitlines() - - -# The rest you shouldn't have to touch too much :) -# ------------------------------------------------ -# Except, perhaps the License and Trove Classifiers! -# If you do change the License, remember to change the -# Trove Classifier for that! - -here = os.path.abspath(os.path.dirname(__file__)) - -# Import the README and use it as the long-description. -# Note: this will only work if 'README.md' is present in your MANIFEST.in file! -try: - with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f: - long_description = '\n' + f.read() -except FileNotFoundError: - long_description = DESCRIPTION - - -# Load the package's __version__.py module as a dictionary. -ROOT_DIR = Path(__file__).resolve().parent -PACKAGE_DIR = ROOT_DIR / NAME -about = {} -with open(PACKAGE_DIR / 'VERSION') as f: - _version = f.read().strip() - about['__version__'] = _version - - -# Where the magic happens: -setup( - name=NAME, - version=about['__version__'], - description=DESCRIPTION, - long_description=long_description, - long_description_content_type='text/markdown', - author=AUTHOR, - author_email=EMAIL, - python_requires=REQUIRES_PYTHON, - url=URL, - packages=find_packages(exclude=('tests',)), - package_data={'neural_network_model': ['VERSION']}, - install_requires=list_reqs(), - extras_require={}, - include_package_data=True, - license='MIT', - classifiers=[ - # Trove classifiers - # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers - 'License :: OSI Approved :: MIT License', - 'Programming Language :: Python', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: Implementation :: CPython', - 'Programming Language :: Python :: Implementation :: PyPy' - ], -) +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import io +import os +from pathlib import Path + +from setuptools import find_packages, setup + + +# Package meta-data. +NAME = 'neural_network_model' +DESCRIPTION = 'Train and deploy neural network model.' +URL = 'your github project' +EMAIL = 'your_email@email.com' +AUTHOR = 'Your name' +REQUIRES_PYTHON = '>=3.6.0' + + +# What packages are required for this module to be executed? +def list_reqs(fname='requirements.txt'): + with open(fname) as fd: + return fd.read().splitlines() + + +# The rest you shouldn't have to touch too much :) +# ------------------------------------------------ +# Except, perhaps the License and Trove Classifiers! +# If you do change the License, remember to change the +# Trove Classifier for that! + +here = os.path.abspath(os.path.dirname(__file__)) + +# Import the README and use it as the long-description. +# Note: this will only work if 'README.md' is present in your MANIFEST.in file! +try: + with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f: + long_description = '\n' + f.read() +except FileNotFoundError: + long_description = DESCRIPTION + + +# Load the package's __version__.py module as a dictionary. +ROOT_DIR = Path(__file__).resolve().parent +PACKAGE_DIR = ROOT_DIR / NAME +about = {} +with open(PACKAGE_DIR / 'VERSION') as f: + _version = f.read().strip() + about['__version__'] = _version + + +# Where the magic happens: +setup( + name=NAME, + version=about['__version__'], + description=DESCRIPTION, + long_description=long_description, + long_description_content_type='text/markdown', + author=AUTHOR, + author_email=EMAIL, + python_requires=REQUIRES_PYTHON, + url=URL, + packages=find_packages(exclude=('tests',)), + package_data={'neural_network_model': ['VERSION']}, + install_requires=list_reqs(), + extras_require={}, + include_package_data=True, + license='MIT', + classifiers=[ + # Trove classifiers + # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers + 'License :: OSI Approved :: MIT License', + 'Programming Language :: Python', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: Implementation :: CPython', + 'Programming Language :: Python :: Implementation :: PyPy' + ], +) diff --git a/packages/neural_network_model/tests/conftest.py b/packages/neural_network_model/tests/conftest.py index 90aa8aa79..53d0e662f 100644 --- a/packages/neural_network_model/tests/conftest.py +++ b/packages/neural_network_model/tests/conftest.py @@ -1,20 +1,20 @@ -import pytest -import os - -from neural_network_model.config import config - - -@pytest.fixture -def black_grass_dir(): - test_data_dir = os.path.join(config.DATASET_DIR, 'test_data') - black_grass_dir = os.path.join(test_data_dir, 'Black-grass') - - return black_grass_dir - - -@pytest.fixture -def charlock_dir(): - test_data_dir = os.path.join(config.DATASET_DIR, 'test_data') - charlock_dir = os.path.join(test_data_dir, 'Charlock') - - return charlock_dir +import pytest +import os + +from neural_network_model.config import config + + +@pytest.fixture +def black_grass_dir(): + test_data_dir = os.path.join(config.DATASET_DIR, 'test_data') + black_grass_dir = os.path.join(test_data_dir, 'Black-grass') + + return black_grass_dir + + +@pytest.fixture +def charlock_dir(): + test_data_dir = os.path.join(config.DATASET_DIR, 'test_data') + charlock_dir = os.path.join(test_data_dir, 'Charlock') + + return charlock_dir diff --git a/packages/neural_network_model/tests/test_predict.py b/packages/neural_network_model/tests/test_predict.py index 020fba5ab..bbf7e784f 100644 --- a/packages/neural_network_model/tests/test_predict.py +++ b/packages/neural_network_model/tests/test_predict.py @@ -1,17 +1,17 @@ -from neural_network_model import __version__ as _version -from neural_network_model.predict import (make_single_prediction) - - -def test_make_prediction_on_sample(charlock_dir): - # Given - filename = '1.png' - expected_classification = 'Charlock' - - # When - results = make_single_prediction(image_directory=charlock_dir, - image_name=filename) - - # Then - assert results['predictions'] is not None - assert results['readable_predictions'][0] == expected_classification - assert results['version'] == _version +from neural_network_model import __version__ as _version +from neural_network_model.predict import (make_single_prediction) + + +def test_make_prediction_on_sample(charlock_dir): + # Given + filename = '1.png' + expected_classification = 'Charlock' + + # When + results = make_single_prediction(image_directory=charlock_dir, + image_name=filename) + + # Then + assert results['predictions'] is not None + assert results['readable_predictions'][0] == expected_classification + assert results['version'] == _version diff --git a/packages/regression_model/MANIFEST.in b/packages/regression_model/MANIFEST.in index 26d14ca59..04cf47ebf 100644 --- a/packages/regression_model/MANIFEST.in +++ b/packages/regression_model/MANIFEST.in @@ -1,16 +1,16 @@ -include *.txt -include *.md -include *.cfg -include *.pkl -recursive-include ./regression_model/* - -include regression_model/datasets/train.csv -include regression_model/datasets/test.csv -include regression_model/trained_models/*.pkl -include regression_model/VERSION - -include ./requirements.txt -exclude *.log - -recursive-exclude * __pycache__ +include *.txt +include *.md +include *.cfg +include *.pkl +recursive-include ./regression_model/* + +include regression_model/datasets/train.csv +include regression_model/datasets/test.csv +include regression_model/trained_models/*.pkl +include regression_model/VERSION + +include ./requirements.txt +exclude *.log + +recursive-exclude * __pycache__ recursive-exclude * *.py[co] \ No newline at end of file diff --git a/packages/regression_model/regression_model/__init__.py b/packages/regression_model/regression_model/__init__.py index b2ed75243..05f09904c 100644 --- a/packages/regression_model/regression_model/__init__.py +++ b/packages/regression_model/regression_model/__init__.py @@ -1,17 +1,17 @@ -import logging - -from regression_model.config import config -from regression_model.config import logging_config - - -VERSION_PATH = config.PACKAGE_ROOT / 'VERSION' - -# Configure logger for use in package -logger = logging.getLogger(__name__) -logger.setLevel(logging.DEBUG) -logger.addHandler(logging_config.get_console_handler()) -logger.propagate = False - - -with open(VERSION_PATH, 'r') as version_file: - __version__ = version_file.read().strip() +import logging + +from regression_model.config import config +from regression_model.config import logging_config + + +VERSION_PATH = config.PACKAGE_ROOT / 'VERSION' + +# Configure logger for use in package +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) +logger.addHandler(logging_config.get_console_handler()) +logger.propagate = False + + +with open(VERSION_PATH, 'r') as version_file: + __version__ = version_file.read().strip() diff --git a/packages/regression_model/regression_model/config/config.py b/packages/regression_model/regression_model/config/config.py index 5644a9fec..61de860e9 100644 --- a/packages/regression_model/regression_model/config/config.py +++ b/packages/regression_model/regression_model/config/config.py @@ -1,105 +1,105 @@ -import pathlib - -import regression_model - -import pandas as pd - - -pd.options.display.max_rows = 10 -pd.options.display.max_columns = 10 - - -PACKAGE_ROOT = pathlib.Path(regression_model.__file__).resolve().parent -TRAINED_MODEL_DIR = PACKAGE_ROOT / "trained_models" -DATASET_DIR = PACKAGE_ROOT / "datasets" - -# data -TESTING_DATA_FILE = "test.csv" -TRAINING_DATA_FILE = "train.csv" -TARGET = "SalePrice" - - -# variables -FEATURES = [ - "MSSubClass", - "MSZoning", - "Neighborhood", - "OverallQual", - "OverallCond", - "YearRemodAdd", - "RoofStyle", - "MasVnrType", - "BsmtQual", - "BsmtExposure", - "HeatingQC", - "CentralAir", - "1stFlrSF", - "GrLivArea", - "BsmtFullBath", - "KitchenQual", - "Fireplaces", - "FireplaceQu", - "GarageType", - "GarageFinish", - "GarageCars", - "PavedDrive", - "LotFrontage", - # this one is only to calculate temporal variable: - "YrSold", -] - -# this variable is to calculate the temporal variable, -# can be dropped afterwards -DROP_FEATURES = "YrSold" - -# numerical variables with NA in train set -NUMERICAL_VARS_WITH_NA = ["LotFrontage"] - -# categorical variables with NA in train set -CATEGORICAL_VARS_WITH_NA = [ - "MasVnrType", - "BsmtQual", - "BsmtExposure", - "FireplaceQu", - "GarageType", - "GarageFinish", -] - -TEMPORAL_VARS = "YearRemodAdd" - -# variables to log transform -NUMERICALS_LOG_VARS = ["LotFrontage", "1stFlrSF", "GrLivArea"] - -# categorical variables to encode -CATEGORICAL_VARS = [ - "MSZoning", - "Neighborhood", - "RoofStyle", - "MasVnrType", - "BsmtQual", - "BsmtExposure", - "HeatingQC", - "CentralAir", - "KitchenQual", - "FireplaceQu", - "GarageType", - "GarageFinish", - "PavedDrive", -] - -NUMERICAL_NA_NOT_ALLOWED = [ - feature - for feature in FEATURES - if feature not in CATEGORICAL_VARS + NUMERICAL_VARS_WITH_NA -] - -CATEGORICAL_NA_NOT_ALLOWED = [ - feature for feature in CATEGORICAL_VARS if feature not in CATEGORICAL_VARS_WITH_NA -] - - -PIPELINE_NAME = "lasso_regression" -PIPELINE_SAVE_FILE = f"{PIPELINE_NAME}_output_v" - -# used for differential testing -ACCEPTABLE_MODEL_DIFFERENCE = 0.05 +import pathlib + +import regression_model + +import pandas as pd + + +pd.options.display.max_rows = 10 +pd.options.display.max_columns = 10 + + +PACKAGE_ROOT = pathlib.Path(regression_model.__file__).resolve().parent +TRAINED_MODEL_DIR = PACKAGE_ROOT / "trained_models" +DATASET_DIR = PACKAGE_ROOT / "datasets" + +# data +TESTING_DATA_FILE = "test.csv" +TRAINING_DATA_FILE = "train.csv" +TARGET = "SalePrice" + + +# variables +FEATURES = [ + "MSSubClass", + "MSZoning", + "Neighborhood", + "OverallQual", + "OverallCond", + "YearRemodAdd", + "RoofStyle", + "MasVnrType", + "BsmtQual", + "BsmtExposure", + "HeatingQC", + "CentralAir", + "1stFlrSF", + "GrLivArea", + "BsmtFullBath", + "KitchenQual", + "Fireplaces", + "FireplaceQu", + "GarageType", + "GarageFinish", + "GarageCars", + "PavedDrive", + "LotFrontage", + # this one is only to calculate temporal variable: + "YrSold", +] + +# this variable is to calculate the temporal variable, +# can be dropped afterwards +DROP_FEATURES = "YrSold" + +# numerical variables with NA in train set +NUMERICAL_VARS_WITH_NA = ["LotFrontage"] + +# categorical variables with NA in train set +CATEGORICAL_VARS_WITH_NA = [ + "MasVnrType", + "BsmtQual", + "BsmtExposure", + "FireplaceQu", + "GarageType", + "GarageFinish", +] + +TEMPORAL_VARS = "YearRemodAdd" + +# variables to log transform +NUMERICALS_LOG_VARS = ["LotFrontage", "1stFlrSF", "GrLivArea"] + +# categorical variables to encode +CATEGORICAL_VARS = [ + "MSZoning", + "Neighborhood", + "RoofStyle", + "MasVnrType", + "BsmtQual", + "BsmtExposure", + "HeatingQC", + "CentralAir", + "KitchenQual", + "FireplaceQu", + "GarageType", + "GarageFinish", + "PavedDrive", +] + +NUMERICAL_NA_NOT_ALLOWED = [ + feature + for feature in FEATURES + if feature not in CATEGORICAL_VARS + NUMERICAL_VARS_WITH_NA +] + +CATEGORICAL_NA_NOT_ALLOWED = [ + feature for feature in CATEGORICAL_VARS if feature not in CATEGORICAL_VARS_WITH_NA +] + + +PIPELINE_NAME = "lasso_regression" +PIPELINE_SAVE_FILE = f"{PIPELINE_NAME}_output_v" + +# used for differential testing +ACCEPTABLE_MODEL_DIFFERENCE = 0.05 diff --git a/packages/regression_model/regression_model/config/logging_config.py b/packages/regression_model/regression_model/config/logging_config.py index 68e9e95a0..ead7bf169 100644 --- a/packages/regression_model/regression_model/config/logging_config.py +++ b/packages/regression_model/regression_model/config/logging_config.py @@ -1,18 +1,18 @@ -import logging -import sys - - -# Multiple calls to logging.getLogger('someLogger') return a -# reference to the same logger object. This is true not only -# within the same module, but also across modules as long as -# it is in the same Python interpreter process. - -FORMATTER = logging.Formatter( - "%(asctime)s — %(name)s — %(levelname)s —" "%(funcName)s:%(lineno)d — %(message)s" -) - - -def get_console_handler(): - console_handler = logging.StreamHandler(sys.stdout) - console_handler.setFormatter(FORMATTER) - return console_handler +import logging +import sys + + +# Multiple calls to logging.getLogger('someLogger') return a +# reference to the same logger object. This is true not only +# within the same module, but also across modules as long as +# it is in the same Python interpreter process. + +FORMATTER = logging.Formatter( + "%(asctime)s — %(name)s — %(levelname)s —" "%(funcName)s:%(lineno)d — %(message)s" +) + + +def get_console_handler(): + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setFormatter(FORMATTER) + return console_handler diff --git a/packages/regression_model/regression_model/pipeline.py b/packages/regression_model/regression_model/pipeline.py index 2cafd9845..f46665d40 100644 --- a/packages/regression_model/regression_model/pipeline.py +++ b/packages/regression_model/regression_model/pipeline.py @@ -1,50 +1,50 @@ -from sklearn.linear_model import Lasso -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import MinMaxScaler - -from regression_model.processing import preprocessors as pp -from regression_model.processing import features -from regression_model.config import config - -import logging - - -_logger = logging.getLogger(__name__) - - -price_pipe = Pipeline( - [ - ( - "categorical_imputer", - pp.CategoricalImputer(variables=config.CATEGORICAL_VARS_WITH_NA), - ), - ( - "numerical_inputer", - pp.NumericalImputer(variables=config.NUMERICAL_VARS_WITH_NA), - ), - ( - "temporal_variable", - pp.TemporalVariableEstimator( - variables=config.TEMPORAL_VARS, reference_variable=config.DROP_FEATURES - ), - ), - ( - "rare_label_encoder", - pp.RareLabelCategoricalEncoder(tol=0.01, variables=config.CATEGORICAL_VARS), - ), - ( - "categorical_encoder", - pp.CategoricalEncoder(variables=config.CATEGORICAL_VARS), - ), - ( - "log_transformer", - features.LogTransformer(variables=config.NUMERICALS_LOG_VARS), - ), - ( - "drop_features", - pp.DropUnecessaryFeatures(variables_to_drop=config.DROP_FEATURES), - ), - ("scaler", MinMaxScaler()), - ("Linear_model", Lasso(alpha=0.005, random_state=0)), - ] -) +from sklearn.linear_model import Lasso +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import MinMaxScaler + +from regression_model.processing import preprocessors as pp +from regression_model.processing import features +from regression_model.config import config + +import logging + + +_logger = logging.getLogger(__name__) + + +price_pipe = Pipeline( + [ + ( + "categorical_imputer", + pp.CategoricalImputer(variables=config.CATEGORICAL_VARS_WITH_NA), + ), + ( + "numerical_inputer", + pp.NumericalImputer(variables=config.NUMERICAL_VARS_WITH_NA), + ), + ( + "temporal_variable", + pp.TemporalVariableEstimator( + variables=config.TEMPORAL_VARS, reference_variable=config.DROP_FEATURES + ), + ), + ( + "rare_label_encoder", + pp.RareLabelCategoricalEncoder(tol=0.01, variables=config.CATEGORICAL_VARS), + ), + ( + "categorical_encoder", + pp.CategoricalEncoder(variables=config.CATEGORICAL_VARS), + ), + ( + "log_transformer", + features.LogTransformer(variables=config.NUMERICALS_LOG_VARS), + ), + ( + "drop_features", + pp.DropUnecessaryFeatures(variables_to_drop=config.DROP_FEATURES), + ), + ("scaler", MinMaxScaler()), + ("Linear_model", Lasso(alpha=0.005, random_state=0)), + ] +) diff --git a/packages/regression_model/regression_model/predict.py b/packages/regression_model/regression_model/predict.py index 7e4ed3d67..acde9c0cd 100644 --- a/packages/regression_model/regression_model/predict.py +++ b/packages/regression_model/regression_model/predict.py @@ -1,45 +1,45 @@ -import numpy as np -import pandas as pd - -from regression_model.processing.data_management import load_pipeline -from regression_model.config import config -from regression_model.processing.validation import validate_inputs -from regression_model import __version__ as _version - -import logging -import typing as t - - -_logger = logging.getLogger(__name__) - -pipeline_file_name = f"{config.PIPELINE_SAVE_FILE}{_version}.pkl" -_price_pipe = load_pipeline(file_name=pipeline_file_name) - - -def make_prediction(*, input_data: t.Union[pd.DataFrame, dict], - ) -> dict: - """Make a prediction using a saved model pipeline. - - Args: - input_data: Array of model prediction inputs. - - Returns: - Predictions for each input row, as well as the model version. - """ - - data = pd.DataFrame(input_data) - validated_data = validate_inputs(input_data=data) - - prediction = _price_pipe.predict(validated_data[config.FEATURES]) - - output = np.exp(prediction) - - results = {"predictions": output, "version": _version} - - _logger.info( - f"Making predictions with model version: {_version} " - f"Inputs: {validated_data} " - f"Predictions: {results}" - ) - - return results +import numpy as np +import pandas as pd + +from regression_model.processing.data_management import load_pipeline +from regression_model.config import config +from regression_model.processing.validation import validate_inputs +from regression_model import __version__ as _version + +import logging +import typing as t + + +_logger = logging.getLogger(__name__) + +pipeline_file_name = f"{config.PIPELINE_SAVE_FILE}{_version}.pkl" +_price_pipe = load_pipeline(file_name=pipeline_file_name) + + +def make_prediction(*, input_data: t.Union[pd.DataFrame, dict], + ) -> dict: + """Make a prediction using a saved model pipeline. + + Args: + input_data: Array of model prediction inputs. + + Returns: + Predictions for each input row, as well as the model version. + """ + + data = pd.DataFrame(input_data) + validated_data = validate_inputs(input_data=data) + + prediction = _price_pipe.predict(validated_data[config.FEATURES]) + + output = np.exp(prediction) + + results = {"predictions": output, "version": _version} + + _logger.info( + f"Making predictions with model version: {_version} " + f"Inputs: {validated_data} " + f"Predictions: {results}" + ) + + return results diff --git a/packages/regression_model/regression_model/processing/data_management.py b/packages/regression_model/regression_model/processing/data_management.py index 0357e1219..65bcc044f 100644 --- a/packages/regression_model/regression_model/processing/data_management.py +++ b/packages/regression_model/regression_model/processing/data_management.py @@ -1,58 +1,58 @@ -import pandas as pd -import joblib -from sklearn.pipeline import Pipeline - -from regression_model.config import config -from regression_model import __version__ as _version - -import logging -import typing as t - - -_logger = logging.getLogger(__name__) - - -def load_dataset(*, file_name: str) -> pd.DataFrame: - _data = pd.read_csv(f"{config.DATASET_DIR}/{file_name}") - return _data - - -def save_pipeline(*, pipeline_to_persist) -> None: - """Persist the pipeline. - Saves the versioned model, and overwrites any previous - saved models. This ensures that when the package is - published, there is only one trained model that can be - called, and we know exactly how it was built. - """ - - # Prepare versioned save file name - save_file_name = f"{config.PIPELINE_SAVE_FILE}{_version}.pkl" - save_path = config.TRAINED_MODEL_DIR / save_file_name - - remove_old_pipelines(files_to_keep=[save_file_name]) - joblib.dump(pipeline_to_persist, save_path) - _logger.info(f"saved pipeline: {save_file_name}") - - -def load_pipeline(*, file_name: str) -> Pipeline: - """Load a persisted pipeline.""" - - file_path = config.TRAINED_MODEL_DIR / file_name - trained_model = joblib.load(filename=file_path) - return trained_model - - -def remove_old_pipelines(*, files_to_keep: t.List[str]) -> None: - """ - Remove old model pipelines. - - This is to ensure there is a simple one-to-one - mapping between the package version and the model - version to be imported and used by other applications. - However, we do also include the immediate previous - pipeline version for differential testing purposes. - """ - do_not_delete = files_to_keep + ['__init__.py'] - for model_file in config.TRAINED_MODEL_DIR.iterdir(): - if model_file.name not in do_not_delete: - model_file.unlink() +import pandas as pd +import joblib +from sklearn.pipeline import Pipeline + +from regression_model.config import config +from regression_model import __version__ as _version + +import logging +import typing as t + + +_logger = logging.getLogger(__name__) + + +def load_dataset(*, file_name: str) -> pd.DataFrame: + _data = pd.read_csv(f"{config.DATASET_DIR}/{file_name}") + return _data + + +def save_pipeline(*, pipeline_to_persist) -> None: + """Persist the pipeline. + Saves the versioned model, and overwrites any previous + saved models. This ensures that when the package is + published, there is only one trained model that can be + called, and we know exactly how it was built. + """ + + # Prepare versioned save file name + save_file_name = f"{config.PIPELINE_SAVE_FILE}{_version}.pkl" + save_path = config.TRAINED_MODEL_DIR / save_file_name + + remove_old_pipelines(files_to_keep=[save_file_name]) + joblib.dump(pipeline_to_persist, save_path) + _logger.info(f"saved pipeline: {save_file_name}") + + +def load_pipeline(*, file_name: str) -> Pipeline: + """Load a persisted pipeline.""" + + file_path = config.TRAINED_MODEL_DIR / file_name + trained_model = joblib.load(filename=file_path) + return trained_model + + +def remove_old_pipelines(*, files_to_keep: t.List[str]) -> None: + """ + Remove old model pipelines. + + This is to ensure there is a simple one-to-one + mapping between the package version and the model + version to be imported and used by other applications. + However, we do also include the immediate previous + pipeline version for differential testing purposes. + """ + do_not_delete = files_to_keep + ['__init__.py'] + for model_file in config.TRAINED_MODEL_DIR.iterdir(): + if model_file.name not in do_not_delete: + model_file.unlink() diff --git a/packages/regression_model/regression_model/processing/errors.py b/packages/regression_model/regression_model/processing/errors.py index b92425437..b5e75f063 100644 --- a/packages/regression_model/regression_model/processing/errors.py +++ b/packages/regression_model/regression_model/processing/errors.py @@ -1,6 +1,6 @@ -class BaseError(Exception): - """Base package error.""" - - -class InvalidModelInputError(BaseError): - """Model input contains an error.""" +class BaseError(Exception): + """Base package error.""" + + +class InvalidModelInputError(BaseError): + """Model input contains an error.""" diff --git a/packages/regression_model/regression_model/processing/features.py b/packages/regression_model/regression_model/processing/features.py index ab5b368b6..5543df772 100644 --- a/packages/regression_model/regression_model/processing/features.py +++ b/packages/regression_model/regression_model/processing/features.py @@ -1,34 +1,34 @@ -import numpy as np -from sklearn.base import BaseEstimator, TransformerMixin - -from regression_model.processing.errors import InvalidModelInputError - - -class LogTransformer(BaseEstimator, TransformerMixin): - """Logarithm transformer.""" - - def __init__(self, variables=None): - if not isinstance(variables, list): - self.variables = [variables] - else: - self.variables = variables - - def fit(self, X, y=None): - # to accomodate the pipeline - return self - - def transform(self, X): - X = X.copy() - - # check that the values are non-negative for log transform - if not (X[self.variables] > 0).all().all(): - vars_ = self.variables[(X[self.variables] <= 0).any()] - raise InvalidModelInputError( - f"Variables contain zero or negative values, " - f"can't apply log for vars: {vars_}" - ) - - for feature in self.variables: - X[feature] = np.log(X[feature]) - - return X +import numpy as np +from sklearn.base import BaseEstimator, TransformerMixin + +from regression_model.processing.errors import InvalidModelInputError + + +class LogTransformer(BaseEstimator, TransformerMixin): + """Logarithm transformer.""" + + def __init__(self, variables=None): + if not isinstance(variables, list): + self.variables = [variables] + else: + self.variables = variables + + def fit(self, X, y=None): + # to accomodate the pipeline + return self + + def transform(self, X): + X = X.copy() + + # check that the values are non-negative for log transform + if not (X[self.variables] > 0).all().all(): + vars_ = self.variables[(X[self.variables] <= 0).any()] + raise InvalidModelInputError( + f"Variables contain zero or negative values, " + f"can't apply log for vars: {vars_}" + ) + + for feature in self.variables: + X[feature] = np.log(X[feature]) + + return X diff --git a/packages/regression_model/regression_model/processing/preprocessors.py b/packages/regression_model/regression_model/processing/preprocessors.py index 47326120f..e5899822e 100644 --- a/packages/regression_model/regression_model/processing/preprocessors.py +++ b/packages/regression_model/regression_model/processing/preprocessors.py @@ -1,164 +1,164 @@ -import numpy as np -import pandas as pd -from sklearn.base import BaseEstimator, TransformerMixin - -from regression_model.processing.errors import InvalidModelInputError - - -class CategoricalImputer(BaseEstimator, TransformerMixin): - """Categorical data missing value imputer.""" - - def __init__(self, variables=None) -> None: - if not isinstance(variables, list): - self.variables = [variables] - else: - self.variables = variables - - def fit(self, X: pd.DataFrame, y: pd.Series = None) -> "CategoricalImputer": - """Fit statement to accomodate the sklearn pipeline.""" - - return self - - def transform(self, X: pd.DataFrame) -> pd.DataFrame: - """Apply the transforms to the dataframe.""" - - X = X.copy() - for feature in self.variables: - X[feature] = X[feature].fillna("Missing") - - return X - - -class NumericalImputer(BaseEstimator, TransformerMixin): - """Numerical missing value imputer.""" - - def __init__(self, variables=None): - if not isinstance(variables, list): - self.variables = [variables] - else: - self.variables = variables - - def fit(self, X, y=None): - # persist mode in a dictionary - self.imputer_dict_ = {} - for feature in self.variables: - self.imputer_dict_[feature] = X[feature].mode()[0] - return self - - def transform(self, X): - X = X.copy() - for feature in self.variables: - X[feature].fillna(self.imputer_dict_[feature], inplace=True) - return X - - -class TemporalVariableEstimator(BaseEstimator, TransformerMixin): - """Temporal variable calculator.""" - - def __init__(self, variables=None, reference_variable=None): - if not isinstance(variables, list): - self.variables = [variables] - else: - self.variables = variables - - self.reference_variables = reference_variable - - def fit(self, X, y=None): - # we need this step to fit the sklearn pipeline - return self - - def transform(self, X): - X = X.copy() - for feature in self.variables: - X[feature] = X[self.reference_variables] - X[feature] - - return X - - -class RareLabelCategoricalEncoder(BaseEstimator, TransformerMixin): - """Rare label categorical encoder""" - - def __init__(self, tol=0.05, variables=None): - self.tol = tol - if not isinstance(variables, list): - self.variables = [variables] - else: - self.variables = variables - - def fit(self, X, y=None): - # persist frequent labels in dictionary - self.encoder_dict_ = {} - - for var in self.variables: - # the encoder will learn the most frequent categories - t = pd.Series(X[var].value_counts() / np.float(len(X))) - # frequent labels: - self.encoder_dict_[var] = list(t[t >= self.tol].index) - - return self - - def transform(self, X): - X = X.copy() - for feature in self.variables: - X[feature] = np.where( - X[feature].isin(self.encoder_dict_[feature]), X[feature], "Rare" - ) - - return X - - -class CategoricalEncoder(BaseEstimator, TransformerMixin): - """String to numbers categorical encoder.""" - - def __init__(self, variables=None): - if not isinstance(variables, list): - self.variables = [variables] - else: - self.variables = variables - - def fit(self, X, y): - temp = pd.concat([X, y], axis=1) - temp.columns = list(X.columns) + ["target"] - - # persist transforming dictionary - self.encoder_dict_ = {} - - for var in self.variables: - t = temp.groupby([var])["target"].mean().sort_values(ascending=True).index - self.encoder_dict_[var] = {k: i for i, k in enumerate(t, 0)} - - return self - - def transform(self, X): - # encode labels - X = X.copy() - for feature in self.variables: - X[feature] = X[feature].map(self.encoder_dict_[feature]) - - # check if transformer introduces NaN - if X[self.variables].isnull().any().any(): - null_counts = X[self.variables].isnull().any() - vars_ = { - key: value for (key, value) in null_counts.items() if value is True - } - raise InvalidModelInputError( - f"Categorical encoder has introduced NaN when " - f"transforming categorical variables: {vars_.keys()}" - ) - - return X - - -class DropUnecessaryFeatures(BaseEstimator, TransformerMixin): - def __init__(self, variables_to_drop=None): - self.variables = variables_to_drop - - def fit(self, X, y=None): - return self - - def transform(self, X): - # encode labels - X = X.copy() - X = X.drop(self.variables, axis=1) - - return X +import numpy as np +import pandas as pd +from sklearn.base import BaseEstimator, TransformerMixin + +from regression_model.processing.errors import InvalidModelInputError + + +class CategoricalImputer(BaseEstimator, TransformerMixin): + """Categorical data missing value imputer.""" + + def __init__(self, variables=None) -> None: + if not isinstance(variables, list): + self.variables = [variables] + else: + self.variables = variables + + def fit(self, X: pd.DataFrame, y: pd.Series = None) -> "CategoricalImputer": + """Fit statement to accomodate the sklearn pipeline.""" + + return self + + def transform(self, X: pd.DataFrame) -> pd.DataFrame: + """Apply the transforms to the dataframe.""" + + X = X.copy() + for feature in self.variables: + X[feature] = X[feature].fillna("Missing") + + return X + + +class NumericalImputer(BaseEstimator, TransformerMixin): + """Numerical missing value imputer.""" + + def __init__(self, variables=None): + if not isinstance(variables, list): + self.variables = [variables] + else: + self.variables = variables + + def fit(self, X, y=None): + # persist mode in a dictionary + self.imputer_dict_ = {} + for feature in self.variables: + self.imputer_dict_[feature] = X[feature].mode()[0] + return self + + def transform(self, X): + X = X.copy() + for feature in self.variables: + X[feature].fillna(self.imputer_dict_[feature], inplace=True) + return X + + +class TemporalVariableEstimator(BaseEstimator, TransformerMixin): + """Temporal variable calculator.""" + + def __init__(self, variables=None, reference_variable=None): + if not isinstance(variables, list): + self.variables = [variables] + else: + self.variables = variables + + self.reference_variables = reference_variable + + def fit(self, X, y=None): + # we need this step to fit the sklearn pipeline + return self + + def transform(self, X): + X = X.copy() + for feature in self.variables: + X[feature] = X[self.reference_variables] - X[feature] + + return X + + +class RareLabelCategoricalEncoder(BaseEstimator, TransformerMixin): + """Rare label categorical encoder""" + + def __init__(self, tol=0.05, variables=None): + self.tol = tol + if not isinstance(variables, list): + self.variables = [variables] + else: + self.variables = variables + + def fit(self, X, y=None): + # persist frequent labels in dictionary + self.encoder_dict_ = {} + + for var in self.variables: + # the encoder will learn the most frequent categories + t = pd.Series(X[var].value_counts() / np.float(len(X))) + # frequent labels: + self.encoder_dict_[var] = list(t[t >= self.tol].index) + + return self + + def transform(self, X): + X = X.copy() + for feature in self.variables: + X[feature] = np.where( + X[feature].isin(self.encoder_dict_[feature]), X[feature], "Rare" + ) + + return X + + +class CategoricalEncoder(BaseEstimator, TransformerMixin): + """String to numbers categorical encoder.""" + + def __init__(self, variables=None): + if not isinstance(variables, list): + self.variables = [variables] + else: + self.variables = variables + + def fit(self, X, y): + temp = pd.concat([X, y], axis=1) + temp.columns = list(X.columns) + ["target"] + + # persist transforming dictionary + self.encoder_dict_ = {} + + for var in self.variables: + t = temp.groupby([var])["target"].mean().sort_values(ascending=True).index + self.encoder_dict_[var] = {k: i for i, k in enumerate(t, 0)} + + return self + + def transform(self, X): + # encode labels + X = X.copy() + for feature in self.variables: + X[feature] = X[feature].map(self.encoder_dict_[feature]) + + # check if transformer introduces NaN + if X[self.variables].isnull().any().any(): + null_counts = X[self.variables].isnull().any() + vars_ = { + key: value for (key, value) in null_counts.items() if value is True + } + raise InvalidModelInputError( + f"Categorical encoder has introduced NaN when " + f"transforming categorical variables: {vars_.keys()}" + ) + + return X + + +class DropUnecessaryFeatures(BaseEstimator, TransformerMixin): + def __init__(self, variables_to_drop=None): + self.variables = variables_to_drop + + def fit(self, X, y=None): + return self + + def transform(self, X): + # encode labels + X = X.copy() + X = X.drop(self.variables, axis=1) + + return X diff --git a/packages/regression_model/regression_model/processing/validation.py b/packages/regression_model/regression_model/processing/validation.py index 73e8f2151..a332be46a 100644 --- a/packages/regression_model/regression_model/processing/validation.py +++ b/packages/regression_model/regression_model/processing/validation.py @@ -1,30 +1,30 @@ -from regression_model.config import config - -import pandas as pd - - -def validate_inputs(input_data: pd.DataFrame) -> pd.DataFrame: - """Check model inputs for unprocessable values.""" - - validated_data = input_data.copy() - - # check for numerical variables with NA not seen during training - if input_data[config.NUMERICAL_NA_NOT_ALLOWED].isnull().any().any(): - validated_data = validated_data.dropna( - axis=0, subset=config.NUMERICAL_NA_NOT_ALLOWED - ) - - # check for categorical variables with NA not seen during training - if input_data[config.CATEGORICAL_NA_NOT_ALLOWED].isnull().any().any(): - validated_data = validated_data.dropna( - axis=0, subset=config.CATEGORICAL_NA_NOT_ALLOWED - ) - - # check for values <= 0 for the log transformed variables - if (input_data[config.NUMERICALS_LOG_VARS] <= 0).any().any(): - vars_with_neg_values = config.NUMERICALS_LOG_VARS[ - (input_data[config.NUMERICALS_LOG_VARS] <= 0).any() - ] - validated_data = validated_data[validated_data[vars_with_neg_values] > 0] - - return validated_data +from regression_model.config import config + +import pandas as pd + + +def validate_inputs(input_data: pd.DataFrame) -> pd.DataFrame: + """Check model inputs for unprocessable values.""" + + validated_data = input_data.copy() + + # check for numerical variables with NA not seen during training + if input_data[config.NUMERICAL_NA_NOT_ALLOWED].isnull().any().any(): + validated_data = validated_data.dropna( + axis=0, subset=config.NUMERICAL_NA_NOT_ALLOWED + ) + + # check for categorical variables with NA not seen during training + if input_data[config.CATEGORICAL_NA_NOT_ALLOWED].isnull().any().any(): + validated_data = validated_data.dropna( + axis=0, subset=config.CATEGORICAL_NA_NOT_ALLOWED + ) + + # check for values <= 0 for the log transformed variables + if (input_data[config.NUMERICALS_LOG_VARS] <= 0).any().any(): + vars_with_neg_values = config.NUMERICALS_LOG_VARS[ + (input_data[config.NUMERICALS_LOG_VARS] <= 0).any() + ] + validated_data = validated_data[validated_data[vars_with_neg_values] > 0] + + return validated_data diff --git a/packages/regression_model/regression_model/train_pipeline.py b/packages/regression_model/regression_model/train_pipeline.py index 4af6ec172..8388796fa 100644 --- a/packages/regression_model/regression_model/train_pipeline.py +++ b/packages/regression_model/regression_model/train_pipeline.py @@ -1,36 +1,36 @@ -import numpy as np -from sklearn.model_selection import train_test_split - -from regression_model import pipeline -from regression_model.processing.data_management import load_dataset, save_pipeline -from regression_model.config import config -from regression_model import __version__ as _version - -import logging - - -_logger = logging.getLogger(__name__) - - -def run_training() -> None: - """Train the model.""" - - # read training data - data = load_dataset(file_name=config.TRAINING_DATA_FILE) - - # divide train and test - X_train, X_test, y_train, y_test = train_test_split( - data[config.FEATURES], data[config.TARGET], test_size=0.1, random_state=0 - ) # we are setting the seed here - - # transform the target - y_train = np.log(y_train) - - pipeline.price_pipe.fit(X_train[config.FEATURES], y_train) - - _logger.info(f"saving model version: {_version}") - save_pipeline(pipeline_to_persist=pipeline.price_pipe) - - -if __name__ == "__main__": - run_training() +import numpy as np +from sklearn.model_selection import train_test_split + +from regression_model import pipeline +from regression_model.processing.data_management import load_dataset, save_pipeline +from regression_model.config import config +from regression_model import __version__ as _version + +import logging + + +_logger = logging.getLogger(__name__) + + +def run_training() -> None: + """Train the model.""" + + # read training data + data = load_dataset(file_name=config.TRAINING_DATA_FILE) + + # divide train and test + X_train, X_test, y_train, y_test = train_test_split( + data[config.FEATURES], data[config.TARGET], test_size=0.1, random_state=0 + ) # we are setting the seed here + + # transform the target + y_train = np.log(y_train) + + pipeline.price_pipe.fit(X_train[config.FEATURES], y_train) + + _logger.info(f"saving model version: {_version}") + save_pipeline(pipeline_to_persist=pipeline.price_pipe) + + +if __name__ == "__main__": + run_training() diff --git a/packages/regression_model/requirements.txt b/packages/regression_model/requirements.txt index 919b75917..124991e19 100644 --- a/packages/regression_model/requirements.txt +++ b/packages/regression_model/requirements.txt @@ -1,19 +1,19 @@ -# We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) -# to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small -# updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. - -# Model Building Requirements -numpy>=1.18.1,<1.19.0 -pandas>=0.25.3,<0.26.0 -scikit-learn>=0.22.1,<0.23.0 -joblib>=0.14.1,<0.15.0 - -# testing requirements -pytest>=5.3.2,<6.0.0 - -# packaging -setuptools>=41.4.0,<42.0.0 -wheel>=0.33.6,<0.34.0 - -# fetching datasets -kaggle>=1.5.6,<1.6.0 +# We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) +# to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small +# updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. + +# Model Building Requirements +numpy>=1.18.1,<1.19.0 +pandas>=0.25.3,<0.26.0 +scikit-learn>=0.22.1,<0.23.0 +joblib>=0.14.1,<0.15.0 + +# testing requirements +pytest>=5.3.2,<6.0.0 + +# packaging +setuptools>=41.4.0,<42.0.0 +wheel>=0.33.6,<0.34.0 + +# fetching datasets +kaggle>=1.5.6,<1.6.0 diff --git a/packages/regression_model/setup.py b/packages/regression_model/setup.py index 264c47805..7a78147a6 100644 --- a/packages/regression_model/setup.py +++ b/packages/regression_model/setup.py @@ -1,81 +1,81 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import io -import os -from pathlib import Path - -from setuptools import find_packages, setup - - -# Package meta-data. -NAME = 'regression_model' -DESCRIPTION = 'Regression model for using in the Train In Data online course "Deployment of Machine Learning Models".' -URL = 'https://github.com/trainindata/deploying-machine-learning-models' -EMAIL = 'christopher.samiullah@protonmail.com' -AUTHOR = 'ChristopherGS' -REQUIRES_PYTHON = '>=3.6.0' - - -# Packages that are required for this module to be executed -def list_reqs(fname='requirements.txt'): - with open(fname) as fd: - return fd.read().splitlines() - - -# The rest you shouldn't have to touch too much :) -# ------------------------------------------------ -# Except, perhaps the License and Trove Classifiers! -# If you do change the License, remember to change the -# Trove Classifier for that! - -here = os.path.abspath(os.path.dirname(__file__)) - -# Import the README and use it as the long-description. -# Note: this will only work if 'README.md' is present in your MANIFEST.in file! -try: - with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f: - long_description = '\n' + f.read() -except FileNotFoundError: - long_description = DESCRIPTION - - -# Load the package's __version__.py module as a dictionary. -ROOT_DIR = Path(__file__).resolve().parent -PACKAGE_DIR = ROOT_DIR / 'regression_model' -about = {} -with open(PACKAGE_DIR / 'VERSION') as f: - _version = f.read().strip() - about['__version__'] = _version - - -# Where the magic happens: -setup( - name=NAME, - version=about['__version__'], - description=DESCRIPTION, - long_description=long_description, - long_description_content_type='text/markdown', - author=AUTHOR, - author_email=EMAIL, - python_requires=REQUIRES_PYTHON, - url=URL, - packages=find_packages(exclude=('tests',)), - package_data={'regression_model': ['VERSION']}, - install_requires=list_reqs(), - extras_require={}, - include_package_data=True, - license='BSD 3', - classifiers=[ - # Trove classifiers - # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers - 'License :: OSI Approved :: MIT License', - 'Programming Language :: Python', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Programming Language :: Python :: Implementation :: CPython', - 'Programming Language :: Python :: Implementation :: PyPy' - ], -) +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import io +import os +from pathlib import Path + +from setuptools import find_packages, setup + + +# Package meta-data. +NAME = 'regression_model' +DESCRIPTION = 'Regression model for using in the Train In Data online course "Deployment of Machine Learning Models".' +URL = 'https://github.com/trainindata/deploying-machine-learning-models' +EMAIL = 'christopher.samiullah@protonmail.com' +AUTHOR = 'ChristopherGS' +REQUIRES_PYTHON = '>=3.6.0' + + +# Packages that are required for this module to be executed +def list_reqs(fname='requirements.txt'): + with open(fname) as fd: + return fd.read().splitlines() + + +# The rest you shouldn't have to touch too much :) +# ------------------------------------------------ +# Except, perhaps the License and Trove Classifiers! +# If you do change the License, remember to change the +# Trove Classifier for that! + +here = os.path.abspath(os.path.dirname(__file__)) + +# Import the README and use it as the long-description. +# Note: this will only work if 'README.md' is present in your MANIFEST.in file! +try: + with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f: + long_description = '\n' + f.read() +except FileNotFoundError: + long_description = DESCRIPTION + + +# Load the package's __version__.py module as a dictionary. +ROOT_DIR = Path(__file__).resolve().parent +PACKAGE_DIR = ROOT_DIR / 'regression_model' +about = {} +with open(PACKAGE_DIR / 'VERSION') as f: + _version = f.read().strip() + about['__version__'] = _version + + +# Where the magic happens: +setup( + name=NAME, + version=about['__version__'], + description=DESCRIPTION, + long_description=long_description, + long_description_content_type='text/markdown', + author=AUTHOR, + author_email=EMAIL, + python_requires=REQUIRES_PYTHON, + url=URL, + packages=find_packages(exclude=('tests',)), + package_data={'regression_model': ['VERSION']}, + install_requires=list_reqs(), + extras_require={}, + include_package_data=True, + license='BSD 3', + classifiers=[ + # Trove classifiers + # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers + 'License :: OSI Approved :: MIT License', + 'Programming Language :: Python', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + 'Programming Language :: Python :: Implementation :: CPython', + 'Programming Language :: Python :: Implementation :: PyPy' + ], +) diff --git a/packages/regression_model/tests/test_predict.py b/packages/regression_model/tests/test_predict.py index 3c7147f89..49855b928 100644 --- a/packages/regression_model/tests/test_predict.py +++ b/packages/regression_model/tests/test_predict.py @@ -1,35 +1,35 @@ -import math - -from regression_model.predict import make_prediction -from regression_model.processing.data_management import load_dataset - - -def test_make_single_prediction(): - # Given - test_data = load_dataset(file_name='test.csv') - single_test_input = test_data[0:1] - - # When - subject = make_prediction(input_data=single_test_input) - - # Then - assert subject is not None - assert isinstance(subject.get('predictions')[0], float) - assert math.ceil(subject.get('predictions')[0]) == 112476 - - -def test_make_multiple_predictions(): - # Given - test_data = load_dataset(file_name='test.csv') - original_data_length = len(test_data) - multiple_test_input = test_data - - # When - subject = make_prediction(input_data=multiple_test_input) - - # Then - assert subject is not None - assert len(subject.get('predictions')) == 1451 - - # We expect some rows to be filtered out - assert len(subject.get('predictions')) != original_data_length +import math + +from regression_model.predict import make_prediction +from regression_model.processing.data_management import load_dataset + + +def test_make_single_prediction(): + # Given + test_data = load_dataset(file_name='test.csv') + single_test_input = test_data[0:1] + + # When + subject = make_prediction(input_data=single_test_input) + + # Then + assert subject is not None + assert isinstance(subject.get('predictions')[0], float) + assert math.ceil(subject.get('predictions')[0]) == 112476 + + +def test_make_multiple_predictions(): + # Given + test_data = load_dataset(file_name='test.csv') + original_data_length = len(test_data) + multiple_test_input = test_data + + # When + subject = make_prediction(input_data=multiple_test_input) + + # Then + assert subject is not None + assert len(subject.get('predictions')) == 1451 + + # We expect some rows to be filtered out + assert len(subject.get('predictions')) != original_data_length diff --git a/packages/regression_model/tox.ini b/packages/regression_model/tox.ini index ed418416f..657766196 100644 --- a/packages/regression_model/tox.ini +++ b/packages/regression_model/tox.ini @@ -1,25 +1,25 @@ -[tox] -envlist = py36, py37, py38 - - -[testenv] -install_command = pip install --pre {opts} {packages} -whitelist_externals = unzip -deps = - -rrequirements.txt - -passenv = - KAGGLE_USERNAME - KAGGLE_KEY - -setenv = - PYTHONPATH=. - -commands = - kaggle competitions download -c house-prices-advanced-regression-techniques -p regression_model/datasets/ - unzip -o regression_model/datasets/house-prices-advanced-regression-techniques.zip -d regression_model/datasets - python regression_model/train_pipeline.py - pytest \ - -s \ - -v \ - {posargs:tests} +[tox] +envlist = py36, py37, py38 + + +[testenv] +install_command = pip install --pre {opts} {packages} +whitelist_externals = unzip +deps = + -rrequirements.txt + +passenv = + KAGGLE_USERNAME + KAGGLE_KEY + +setenv = + PYTHONPATH=. + +commands = + kaggle competitions download -c house-prices-advanced-regression-techniques -p regression_model/datasets/ + unzip -o regression_model/datasets/house-prices-advanced-regression-techniques.zip -d regression_model/datasets + python regression_model/train_pipeline.py + pytest \ + -s \ + -v \ + {posargs:tests} diff --git a/scripts/fetch_kaggle_dataset.sh b/scripts/fetch_kaggle_dataset.sh index 455b9c970..2bc8f325b 100755 --- a/scripts/fetch_kaggle_dataset.sh +++ b/scripts/fetch_kaggle_dataset.sh @@ -1,3 +1,3 @@ -#!/usr/bin/env bash - +#!/usr/bin/env bash + kaggle competitions download -c house-prices-advanced-regression-techniques -p packages/regression_model/regression_model/datasets/ \ No newline at end of file diff --git a/scripts/fetch_kaggle_large_dataset.sh b/scripts/fetch_kaggle_large_dataset.sh index e83841e99..50bb70b2f 100755 --- a/scripts/fetch_kaggle_large_dataset.sh +++ b/scripts/fetch_kaggle_large_dataset.sh @@ -1,11 +1,11 @@ -#!/usr/bin/env bash - -TRAINING_DATA_URL="vbookshelf/v2-plant-seedlings-dataset" -NOW=$(date) - -kaggle datasets download -d $TRAINING_DATA_URL -p packages/neural_network_model/neural_network_model/datasets/ && \ -unzip packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset.zip -d packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset && \ -echo $TRAINING_DATA_URL 'retrieved on:' $NOW > packages/neural_network_model/neural_network_model/datasets/training_data_reference.txt && \ -mkdir -p "./packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset/Shepherds Purse" && \ -mv -v "./packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset/Shepherd’s Purse/"* "./packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset/Shepherds Purse" +#!/usr/bin/env bash + +TRAINING_DATA_URL="vbookshelf/v2-plant-seedlings-dataset" +NOW=$(date) + +kaggle datasets download -d $TRAINING_DATA_URL -p packages/neural_network_model/neural_network_model/datasets/ && \ +unzip packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset.zip -d packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset && \ +echo $TRAINING_DATA_URL 'retrieved on:' $NOW > packages/neural_network_model/neural_network_model/datasets/training_data_reference.txt && \ +mkdir -p "./packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset/Shepherds Purse" && \ +mv -v "./packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset/Shepherd’s Purse/"* "./packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset/Shepherds Purse" rm -rf "./packages/neural_network_model/neural_network_model/datasets/v2-plant-seedlings-dataset/Shepherd’s Purse" \ No newline at end of file diff --git a/scripts/input_test.json b/scripts/input_test.json index bee61b12a..8297ebddc 100644 --- a/scripts/input_test.json +++ b/scripts/input_test.json @@ -1,82 +1,82 @@ -[{ - "Id": 1461, - "MSSubClass": 20, - "MSZoning": "RH", - "LotFrontage": 80.0, - "LotArea": 11622, - "Street": "Pave", - "Alley": null, - "LotShape": "Reg", - "LandContour": "Lvl", - "Utilities": "AllPub", - "LotConfig": "Inside", - "LandSlope": "Gtl", - "Neighborhood": "NAmes", - "Condition1": "Feedr", - "Condition2": "Norm", - "BldgType": "1Fam", - "HouseStyle": "1Story", - "OverallQual": 5, - "OverallCond": 6, - "YearBuilt": 1961, - "YearRemodAdd": 1961, - "RoofStyle": "Gable", - "RoofMatl": "CompShg", - "Exterior1st": "VinylSd", - "Exterior2nd": "VinylSd", - "MasVnrType": "None", - "MasVnrArea": 0.0, - "ExterQual": "TA", - "ExterCond": "TA", - "Foundation": "CBlock", - "BsmtQual": "TA", - "BsmtCond": "TA", - "BsmtExposure": "No", - "BsmtFinType1": "Rec", - "BsmtFinSF1": 468.0, - "BsmtFinType2": "LwQ", - "BsmtFinSF2": 144.0, - "BsmtUnfSF": 270.0, - "TotalBsmtSF": 882.0, - "Heating": "GasA", - "HeatingQC": "TA", - "CentralAir": "Y", - "Electrical": "SBrkr", - "1stFlrSF": 896, - "2ndFlrSF": 0, - "LowQualFinSF": 0, - "GrLivArea": 896, - "BsmtFullBath": 0.0, - "BsmtHalfBath": 0.0, - "FullBath": 1, - "HalfBath": 0, - "BedroomAbvGr": 2, - "KitchenAbvGr": 1, - "KitchenQual": "TA", - "TotRmsAbvGrd": 5, - "Functional": "Typ", - "Fireplaces": 0, - "FireplaceQu": null, - "GarageType": "Attchd", - "GarageYrBlt": 1961.0, - "GarageFinish": "Unf", - "GarageCars": 1.0, - "GarageArea": 730.0, - "GarageQual": "TA", - "GarageCond": "TA", - "PavedDrive": "Y", - "WoodDeckSF": 140, - "OpenPorchSF": 0, - "EnclosedPorch": 0, - "3SsnPorch": 0, - "ScreenPorch": 120, - "PoolArea": 0, - "PoolQC": null, - "Fence": "MnPrv", - "MiscFeature": null, - "MiscVal": 0, - "MoSold": 6, - "YrSold": 2010, - "SaleType": "WD", - "SaleCondition": "Normal" +[{ + "Id": 1461, + "MSSubClass": 20, + "MSZoning": "RH", + "LotFrontage": 80.0, + "LotArea": 11622, + "Street": "Pave", + "Alley": null, + "LotShape": "Reg", + "LandContour": "Lvl", + "Utilities": "AllPub", + "LotConfig": "Inside", + "LandSlope": "Gtl", + "Neighborhood": "NAmes", + "Condition1": "Feedr", + "Condition2": "Norm", + "BldgType": "1Fam", + "HouseStyle": "1Story", + "OverallQual": 5, + "OverallCond": 6, + "YearBuilt": 1961, + "YearRemodAdd": 1961, + "RoofStyle": "Gable", + "RoofMatl": "CompShg", + "Exterior1st": "VinylSd", + "Exterior2nd": "VinylSd", + "MasVnrType": "None", + "MasVnrArea": 0.0, + "ExterQual": "TA", + "ExterCond": "TA", + "Foundation": "CBlock", + "BsmtQual": "TA", + "BsmtCond": "TA", + "BsmtExposure": "No", + "BsmtFinType1": "Rec", + "BsmtFinSF1": 468.0, + "BsmtFinType2": "LwQ", + "BsmtFinSF2": 144.0, + "BsmtUnfSF": 270.0, + "TotalBsmtSF": 882.0, + "Heating": "GasA", + "HeatingQC": "TA", + "CentralAir": "Y", + "Electrical": "SBrkr", + "1stFlrSF": 896, + "2ndFlrSF": 0, + "LowQualFinSF": 0, + "GrLivArea": 896, + "BsmtFullBath": 0.0, + "BsmtHalfBath": 0.0, + "FullBath": 1, + "HalfBath": 0, + "BedroomAbvGr": 2, + "KitchenAbvGr": 1, + "KitchenQual": "TA", + "TotRmsAbvGrd": 5, + "Functional": "Typ", + "Fireplaces": 0, + "FireplaceQu": null, + "GarageType": "Attchd", + "GarageYrBlt": 1961.0, + "GarageFinish": "Unf", + "GarageCars": 1.0, + "GarageArea": 730.0, + "GarageQual": "TA", + "GarageCond": "TA", + "PavedDrive": "Y", + "WoodDeckSF": 140, + "OpenPorchSF": 0, + "EnclosedPorch": 0, + "3SsnPorch": 0, + "ScreenPorch": 120, + "PoolArea": 0, + "PoolQC": null, + "Fence": "MnPrv", + "MiscFeature": null, + "MiscVal": 0, + "MoSold": 6, + "YrSold": 2010, + "SaleType": "WD", + "SaleCondition": "Normal" }] \ No newline at end of file diff --git a/scripts/publish_model.sh b/scripts/publish_model.sh index 9a1cad78a..b479b1428 100755 --- a/scripts/publish_model.sh +++ b/scripts/publish_model.sh @@ -1,44 +1,44 @@ -#!/bin/bash - -# Building packages and uploading them to a Gemfury repository - -GEMFURY_URL=$GEMFURY_PUSH_URL - -set -e - -DIRS="$@" -BASE_DIR=$(pwd) -SETUP="setup.py" - -warn() { - echo "$@" 1>&2 -} - -die() { - warn "$@" - exit 1 -} - -build() { - DIR="${1/%\//}" - echo "Checking directory $DIR" - cd "$BASE_DIR/$DIR" - [ ! -e $SETUP ] && warn "No $SETUP file, skipping" && return - PACKAGE_NAME=$(python $SETUP --fullname) - echo "Package $PACKAGE_NAME" - python "$SETUP" sdist bdist_wheel || die "Building package $PACKAGE_NAME failed" - for X in $(ls dist) - do - curl -F package=@"dist/$X" "$GEMFURY_URL" || die "Uploading package $PACKAGE_NAME failed on file dist/$X" - done -} - -if [ -n "$DIRS" ]; then - for dir in $DIRS; do - build $dir - done -else - ls -d */ | while read dir; do - build $dir - done +#!/bin/bash + +# Building packages and uploading them to a Gemfury repository + +GEMFURY_URL=$GEMFURY_PUSH_URL + +set -e + +DIRS="$@" +BASE_DIR=$(pwd) +SETUP="setup.py" + +warn() { + echo "$@" 1>&2 +} + +die() { + warn "$@" + exit 1 +} + +build() { + DIR="${1/%\//}" + echo "Checking directory $DIR" + cd "$BASE_DIR/$DIR" + [ ! -e $SETUP ] && warn "No $SETUP file, skipping" && return + PACKAGE_NAME=$(python $SETUP --fullname) + echo "Package $PACKAGE_NAME" + python "$SETUP" sdist bdist_wheel || die "Building package $PACKAGE_NAME failed" + for X in $(ls dist) + do + curl -F package=@"dist/$X" "$GEMFURY_URL" || die "Uploading package $PACKAGE_NAME failed on file dist/$X" + done +} + +if [ -n "$DIRS" ]; then + for dir in $DIRS; do + build $dir + done +else + ls -d */ | while read dir; do + build $dir + done fi \ No newline at end of file diff --git a/section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb b/section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb index df3c3c9f1..e23b25d2d 100644 --- a/section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb +++ b/section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb @@ -1,4669 +1,4669 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Machine Learning Pipeline - Data Analysis\n", - "\n", - "In the following notebooks, we will go through the implementation of each of the steps in the Machine Learning Pipeline. \n", - "\n", - "We will discuss:\n", - "\n", - "1. **Data Analysis**\n", - "2. Feature Engineering\n", - "3. Feature Selection\n", - "4. Model Training\n", - "5. Obtaining Predictions / Scoring\n", - "\n", - "\n", - "We will use the house price dataset available on [Kaggle.com](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data). See below for more details.\n", - "\n", - "===================================================================================================\n", - "\n", - "## Predicting Sale Price of Houses\n", - "\n", - "The aim of the project is to build a machine learning model to predict the sale price of homes based on different explanatory variables describing aspects of residential houses.\n", - "\n", - "\n", - "### Why is this important? \n", - "\n", - "Predicting house prices is useful to identify fruitful investments or to determine whether the price advertised for a house is over or under-estimated.\n", - "\n", - "\n", - "### What is the objective of the machine learning model?\n", - "\n", - "We aim to minimise the difference between the real price and the price estimated by our model. We will evaluate model performance with the:\n", - "\n", - "1. mean squared error (mse)\n", - "2. root squared of the mean squared error (rmse)\n", - "3. r-squared (r2).\n", - "\n", - "\n", - "### How do I download the dataset?\n", - "\n", - "**Instructions also in the lecture \"Download Dataset\" in section 1 of the course**\n", - "\n", - "- Visit the [Kaggle Website](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data).\n", - "\n", - "- Remember to **log in**.\n", - "\n", - "- Scroll down to the bottom of the page, and click on the link **'train.csv'**, and then click the 'download' blue button towards the right of the screen, to download the dataset.\n", - "\n", - "- The download the file called **'test.csv'** and save it in the directory with the notebooks.\n", - "\n", - "\n", - "\n", - "**Note the following:**\n", - "\n", - "- You need to be logged in to Kaggle in order to download the datasets.\n", - "- You need to accept the terms and conditions of the competition to download the dataset\n", - "- If you save the file to the directory with the jupyter notebook, then you can run the code as it is written here." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Analysis\n", - "\n", - "Let's go ahead and load the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# to handle datasets\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# for plotting\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "# for the yeo-johnson transformation\n", - "import scipy.stats as stats\n", - "\n", - "# to display all the columns of the dataframe in the notebook\n", - "pd.pandas.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1460, 81)\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 1 60 RL 65.0 8450 Pave NaN Reg \n", - "1 2 20 RL 80.0 9600 Pave NaN Reg \n", - "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", - "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", - "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", - "\n", - " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", - "0 Lvl AllPub Inside Gtl CollgCr Norm \n", - "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", - "2 Lvl AllPub Inside Gtl CollgCr Norm \n", - "3 Lvl AllPub Corner Gtl Crawfor Norm \n", - "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", - "\n", - " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", - "0 Norm 1Fam 2Story 7 5 2003 \n", - "1 Norm 1Fam 1Story 6 8 1976 \n", - "2 Norm 1Fam 2Story 7 5 2001 \n", - "3 Norm 1Fam 2Story 7 5 1915 \n", - "4 Norm 1Fam 2Story 8 5 2000 \n", - "\n", - " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", - "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", - "1 1976 Gable CompShg MetalSd MetalSd None \n", - "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", - "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", - "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", - "\n", - " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", - "0 196.0 Gd TA PConc Gd TA No \n", - "1 0.0 TA TA CBlock Gd TA Gd \n", - "2 162.0 Gd TA PConc Gd TA Mn \n", - "3 0.0 TA TA BrkTil TA Gd No \n", - "4 350.0 Gd TA PConc Gd TA Av \n", - "\n", - " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", - "0 GLQ 706 Unf 0 150 856 \n", - "1 ALQ 978 Unf 0 284 1262 \n", - "2 GLQ 486 Unf 0 434 920 \n", - "3 ALQ 216 Unf 0 540 756 \n", - "4 GLQ 655 Unf 0 490 1145 \n", - "\n", - " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", - "0 GasA Ex Y SBrkr 856 854 0 \n", - "1 GasA Ex Y SBrkr 1262 0 0 \n", - "2 GasA Ex Y SBrkr 920 866 0 \n", - "3 GasA Gd Y SBrkr 961 756 0 \n", - "4 GasA Ex Y SBrkr 1145 1053 0 \n", - "\n", - " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", - "0 1710 1 0 2 1 3 \n", - "1 1262 0 1 2 0 3 \n", - "2 1786 1 0 2 1 3 \n", - "3 1717 1 0 1 0 3 \n", - "4 2198 1 0 2 1 4 \n", - "\n", - " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", - "0 1 Gd 8 Typ 0 NaN \n", - "1 1 TA 6 Typ 1 TA \n", - "2 1 Gd 6 Typ 1 TA \n", - "3 1 Gd 7 Typ 1 Gd \n", - "4 1 Gd 9 Typ 1 TA \n", - "\n", - " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", - "0 Attchd 2003.0 RFn 2 548 TA \n", - "1 Attchd 1976.0 RFn 2 460 TA \n", - "2 Attchd 2001.0 RFn 2 608 TA \n", - "3 Detchd 1998.0 Unf 3 642 TA \n", - "4 Attchd 2000.0 RFn 3 836 TA \n", - "\n", - " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", - "0 TA Y 0 61 0 0 \n", - "1 TA Y 298 0 0 0 \n", - "2 TA Y 0 42 0 0 \n", - "3 TA Y 0 35 272 0 \n", - "4 TA Y 192 84 0 0 \n", - "\n", - " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", - "0 0 0 NaN NaN NaN 0 2 2008 \n", - "1 0 0 NaN NaN NaN 0 5 2007 \n", - "2 0 0 NaN NaN NaN 0 9 2008 \n", - "3 0 0 NaN NaN NaN 0 2 2006 \n", - "4 0 0 NaN NaN NaN 0 12 2008 \n", - "\n", - " SaleType SaleCondition SalePrice \n", - "0 WD Normal 208500 \n", - "1 WD Normal 181500 \n", - "2 WD Normal 223500 \n", - "3 WD Abnorml 140000 \n", - "4 WD Normal 250000 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load dataset\n", - "data = pd.read_csv('train.csv')\n", - "\n", - "# rows and columns of the data\n", - "print(data.shape)\n", - "\n", - "# visualise the dataset\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1460, 80)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# drop id, it is just a number given to identify each house\n", - "data.drop('Id', axis=1, inplace=True)\n", - "\n", - "data.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The house price dataset contains 1460 rows, that is, houses, and 80 columns, i.e., variables. \n", - "\n", - "79 are predictive variables and 1 is the target variable: SalePrice\n", - "\n", - "## Analysis\n", - "\n", - "**We will analyse the following:**\n", - "\n", - "1. The target variable\n", - "2. Variable types (categorical and numerical)\n", - "3. Missing data\n", - "4. Numerical variables\n", - " - Discrete\n", - " - Continuous\n", - " - Distributions\n", - " - Transformations\n", - "\n", - "5. Categorical variables\n", - " - Cardinality\n", - " - Rare Labels\n", - " - Special mappings\n", - " \n", - "6. Additional Reading Resources\n", - "\n", - "## Target\n", - "\n", - "Let's begin by exploring the target distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# histogran to evaluate target distribution\n", - "\n", - "data['SalePrice'].hist(bins=50, density=True)\n", - "plt.ylabel('Number of houses')\n", - "plt.xlabel('Sale Price')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the target is continuous, and the distribution is skewed towards the right.\n", - "\n", - "We can improve the value spread with a mathematical transformation." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's transform the target using the logarithm\n", - "\n", - "np.log(data['SalePrice']).hist(bins=50, density=True)\n", - "plt.ylabel('Number of houses')\n", - "plt.xlabel('Log of Sale Price')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now the distribution looks more Gaussian." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Variable Types\n", - "\n", - "Next, let's identify the categorical and numerical variables" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "44" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let's identify the categorical variables\n", - "# we will capture those of type *object*\n", - "\n", - "cat_vars = [var for var in data.columns if data[var].dtype == 'O']\n", - "\n", - "# MSSubClass is also categorical by definition, despite its numeric values\n", - "# (you can find the definitions of the variables in the data_description.txt\n", - "# file available on Kaggle, in the same website where you downloaded the data)\n", - "\n", - "# lets add MSSubClass to the list of categorical variables\n", - "cat_vars = cat_vars + ['MSSubClass']\n", - "\n", - "# number of categorical variables\n", - "len(cat_vars)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# cast all variables as categorical\n", - "data[cat_vars] = data[cat_vars].astype('O')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "35" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now let's identify the numerical variables\n", - "\n", - "num_vars = [\n", - " var for var in data.columns if var not in cat_vars and var != 'SalePrice'\n", - "]\n", - "\n", - "# number of numerical variables\n", - "len(num_vars)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Missing values\n", - "\n", - "Let's go ahead and find out which variables of the dataset contain missing values." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PoolQC 0.995205\n", - "MiscFeature 0.963014\n", - "Alley 0.937671\n", - "Fence 0.807534\n", - "FireplaceQu 0.472603\n", - "LotFrontage 0.177397\n", - "GarageType 0.055479\n", - "GarageYrBlt 0.055479\n", - "GarageFinish 0.055479\n", - "GarageQual 0.055479\n", - "GarageCond 0.055479\n", - "BsmtExposure 0.026027\n", - "BsmtFinType2 0.026027\n", - "BsmtFinType1 0.025342\n", - "BsmtCond 0.025342\n", - "BsmtQual 0.025342\n", - "MasVnrArea 0.005479\n", - "MasVnrType 0.005479\n", - "Electrical 0.000685\n", - "dtype: float64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# make a list of the variables that contain missing values\n", - "vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 0]\n", - "\n", - "# determine percentage of missing values (expressed as decimals)\n", - "# and display the result ordered by % of missin data\n", - "\n", - "data[vars_with_na].isnull().mean().sort_values(ascending=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our dataset contains a few variables with a big proportion of missing values (4 variables at the top). And some other variables with a small percentage of missing observations.\n", - "\n", - "This means that to train a machine learning model with this data set, we need to impute the missing data in these variables.\n", - "\n", - "We can also visualize the percentage of missing values in the variables as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot\n", - "\n", - "data[vars_with_na].isnull().mean().sort_values(\n", - " ascending=False).plot.bar(figsize=(10, 4))\n", - "plt.ylabel('Percentage of missing data')\n", - "plt.axhline(y=0.90, color='r', linestyle='-')\n", - "plt.axhline(y=0.80, color='g', linestyle='-')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of categorical variables with na: 16\n", - "Number of numerical variables with na: 3\n" - ] - } - ], - "source": [ - "# now we can determine which variables, from those with missing data,\n", - "# are numerical and which are categorical\n", - "\n", - "cat_na = [var for var in cat_vars if var in vars_with_na]\n", - "num_na = [var for var in num_vars if var in vars_with_na]\n", - "\n", - "print('Number of categorical variables with na: ', len(cat_na))\n", - "print('Number of numerical variables with na: ', len(num_na))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "num_na" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Alley',\n", - " 'MasVnrType',\n", - " 'BsmtQual',\n", - " 'BsmtCond',\n", - " 'BsmtExposure',\n", - " 'BsmtFinType1',\n", - " 'BsmtFinType2',\n", - " 'Electrical',\n", - " 'FireplaceQu',\n", - " 'GarageType',\n", - " 'GarageFinish',\n", - " 'GarageQual',\n", - " 'GarageCond',\n", - " 'PoolQC',\n", - " 'Fence',\n", - " 'MiscFeature']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cat_na" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Relationship between missing data and Sale Price\n", - "\n", - "Let's evaluate the price of the house in those observations where the information is missing. We will do this for each variable that shows missing data." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def analyse_na_value(df, var):\n", - "\n", - " # copy of the dataframe, so that we do not override the original data\n", - " # see the link for more details about pandas.copy()\n", - " # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.copy.html\n", - " df = df.copy()\n", - "\n", - " # let's make an interim variable that indicates 1 if the\n", - " # observation was missing or 0 otherwise\n", - " df[var] = np.where(df[var].isnull(), 1, 0)\n", - "\n", - " # let's compare the median SalePrice in the observations where data is missing\n", - " # vs the observations where data is available\n", - "\n", - " # determine the median price in the groups 1 and 0,\n", - " # and the standard deviation of the sale price,\n", - " # and we capture the results in a temporary dataset\n", - " tmp = df.groupby(var)['SalePrice'].agg(['mean', 'std'])\n", - "\n", - " # plot into a bar graph\n", - " tmp.plot(kind=\"barh\", y=\"mean\", legend=False,\n", - " xerr=\"std\", title=\"Sale Price\", color='green')\n", - "\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's run the function on each variable with missing data\n", - "\n", - "for var in vars_with_na:\n", - " analyse_na_value(data, var)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In some variables, the average Sale Price in houses where the information is missing, differs from the average Sale Price in houses where information exists. This suggests that data being missing could be a good predictor of Sale Price." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Numerical variables\n", - "\n", - "Let's go ahead and find out what numerical variables we have in the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of numerical variables: 35\n" - ] - }, - { - "data": { - "text/html": [ - "
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LotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSold
065.084507520032003196.0706015085685685401710102131802003.025480610000022008
180.0960068197619760.0978028412621262001262012031611976.0246029800000052007
268.0112507520012002162.0486043492092086601786102131612001.026080420000092008
360.0955075191519700.0216054075696175601717101031711998.03642035272000022006
484.0142608520002000350.0655049011451145105302198102141912000.038361928400000122008
\n", - "
" - ], - "text/plain": [ - " LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd \\\n", - "0 65.0 8450 7 5 2003 2003 \n", - "1 80.0 9600 6 8 1976 1976 \n", - "2 68.0 11250 7 5 2001 2002 \n", - "3 60.0 9550 7 5 1915 1970 \n", - "4 84.0 14260 8 5 2000 2000 \n", - "\n", - " MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF \\\n", - "0 196.0 706 0 150 856 856 \n", - "1 0.0 978 0 284 1262 1262 \n", - "2 162.0 486 0 434 920 920 \n", - "3 0.0 216 0 540 756 961 \n", - "4 350.0 655 0 490 1145 1145 \n", - "\n", - " 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath \\\n", - "0 854 0 1710 1 0 2 \n", - "1 0 0 1262 0 1 2 \n", - "2 866 0 1786 1 0 2 \n", - "3 756 0 1717 1 0 1 \n", - "4 1053 0 2198 1 0 2 \n", - "\n", - " HalfBath BedroomAbvGr KitchenAbvGr TotRmsAbvGrd Fireplaces \\\n", - "0 1 3 1 8 0 \n", - "1 0 3 1 6 1 \n", - "2 1 3 1 6 1 \n", - "3 0 3 1 7 1 \n", - "4 1 4 1 9 1 \n", - "\n", - " GarageYrBlt GarageCars GarageArea WoodDeckSF OpenPorchSF \\\n", - "0 2003.0 2 548 0 61 \n", - "1 1976.0 2 460 298 0 \n", - "2 2001.0 2 608 0 42 \n", - "3 1998.0 3 642 0 35 \n", - "4 2000.0 3 836 192 84 \n", - "\n", - " EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold \n", - "0 0 0 0 0 0 2 2008 \n", - "1 0 0 0 0 0 5 2007 \n", - "2 0 0 0 0 0 9 2008 \n", - "3 272 0 0 0 0 2 2006 \n", - "4 0 0 0 0 0 12 2008 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('Number of numerical variables: ', len(num_vars))\n", - "\n", - "# visualise the numerical variables\n", - "data[num_vars].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Temporal variables\n", - "\n", - "We have 4 year variables in the dataset:\n", - "\n", - "- YearBuilt: year in which the house was built\n", - "- YearRemodAdd: year in which the house was remodeled\n", - "- GarageYrBlt: year in which a garage was built\n", - "- YrSold: year in which the house was sold\n", - "\n", - "We generally don't use date variables in their raw format. Instead, we extract information from them. For example, we can capture the difference in years between the year the house was built and the year the house was sold." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'YrSold']" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# list of variables that contain year information\n", - "\n", - "year_vars = [var for var in num_vars if 'Yr' in var or 'Year' in var]\n", - "\n", - "year_vars" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "YearBuilt [2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 1965 2005 1962 2006\n", - " 1960 1929 1970 1967 1958 1930 2002 1968 2007 1951 1957 1927 1920 1966\n", - " 1959 1994 1954 1953 1955 1983 1975 1997 1934 1963 1981 1964 1999 1972\n", - " 1921 1945 1982 1998 1956 1948 1910 1995 1991 2009 1950 1961 1977 1985\n", - " 1979 1885 1919 1990 1969 1935 1988 1971 1952 1936 1923 1924 1984 1926\n", - " 1940 1941 1987 1986 2008 1908 1892 1916 1932 1918 1912 1947 1925 1900\n", - " 1980 1989 1992 1949 1880 1928 1978 1922 1996 2010 1946 1913 1937 1942\n", - " 1938 1974 1893 1914 1906 1890 1898 1904 1882 1875 1911 1917 1872 1905]\n", - "\n", - "YearRemodAdd [2003 1976 2002 1970 2000 1995 2005 1973 1950 1965 2006 1962 2007 1960\n", - " 2001 1967 2004 2008 1997 1959 1990 1955 1983 1980 1966 1963 1987 1964\n", - " 1972 1996 1998 1989 1953 1956 1968 1981 1992 2009 1982 1961 1993 1999\n", - " 1985 1979 1977 1969 1958 1991 1971 1952 1975 2010 1984 1986 1994 1988\n", - " 1954 1957 1951 1978 1974]\n", - "\n", - "GarageYrBlt [2003. 1976. 2001. 1998. 2000. 1993. 2004. 1973. 1931. 1939. 1965. 2005.\n", - " 1962. 2006. 1960. 1991. 1970. 1967. 1958. 1930. 2002. 1968. 2007. 2008.\n", - " 1957. 1920. 1966. 1959. 1995. 1954. 1953. nan 1983. 1977. 1997. 1985.\n", - " 1963. 1981. 1964. 1999. 1935. 1990. 1945. 1987. 1989. 1915. 1956. 1948.\n", - " 1974. 2009. 1950. 1961. 1921. 1900. 1979. 1951. 1969. 1936. 1975. 1971.\n", - " 1923. 1984. 1926. 1955. 1986. 1988. 1916. 1932. 1972. 1918. 1980. 1924.\n", - " 1996. 1940. 1949. 1994. 1910. 1978. 1982. 1992. 1925. 1941. 2010. 1927.\n", - " 1947. 1937. 1942. 1938. 1952. 1928. 1922. 1934. 1906. 1914. 1946. 1908.\n", - " 1929. 1933.]\n", - "\n", - "YrSold [2008 2007 2006 2009 2010]\n", - "\n" - ] - } - ], - "source": [ - "# let's explore the values of these temporal variables\n", - "\n", - "for var in year_vars:\n", - " print(var, data[var].unique())\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As expected, the values are years.\n", - "\n", - "We can explore the evolution of the sale price with the years in which the house was sold:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Median House Price')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot median sale price vs year in which it was sold\n", - "\n", - "data.groupby('YrSold')['SalePrice'].median().plot()\n", - "plt.ylabel('Median House Price')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There has been a drop in the value of the houses. That is unusual, in real life, house prices typically go up as years go by.\n", - "\n", - "Let's explore a bit further. \n", - "\n", - "Let's plot the price of sale vs year in which it was built" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Median House Price')" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot median sale price vs year in which it was built\n", - "\n", - "data.groupby('YearBuilt')['SalePrice'].median().plot()\n", - "plt.ylabel('Median House Price')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that newly built / younger houses tend to be more expensive.\n", - "\n", - "Could it be that lately older houses were sold? Let's have a look at that." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this, we will capture the elapsed years between the Year variables and the year in which the house was sold:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def analyse_year_vars(df, var):\n", - " \n", - " df = df.copy()\n", - " \n", - " # capture difference between a year variable and year\n", - " # in which the house was sold\n", - " df[var] = df['YrSold'] - df[var]\n", - " \n", - " df.groupby('YrSold')[var].median().plot()\n", - " plt.ylabel('Time from ' + var)\n", - " plt.show()\n", - " \n", - " \n", - "for var in year_vars:\n", - " if var !='YrSold':\n", - " analyse_year_vars(data, var)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the plots, we see that towards 2010, the houses sold had older garages, and had not been remodelled recently, that might explain why we see cheaper sales prices in recent years, at least in this dataset.\n", - "\n", - "We can now plot instead the time since last remodelled, or time since built, and sale price, to see if there is a relationship." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def analyse_year_vars(df, var):\n", - " \n", - " df = df.copy()\n", - " \n", - " # capture difference between a year variable and year\n", - " # in which the house was sold\n", - " df[var] = df['YrSold'] - df[var]\n", - " \n", - " plt.scatter(df[var], df['SalePrice'])\n", - " plt.ylabel('SalePrice')\n", - " plt.xlabel(var)\n", - " plt.show()\n", - " \n", - " \n", - "for var in year_vars:\n", - " if var !='YrSold':\n", - " analyse_year_vars(data, var)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that there is a tendency to a decrease in price, with older houses. In other words, the longer the time between the house was built or remodeled and sale date, the lower the sale Price. \n", - "\n", - "Which makes sense, cause this means that the house will have an older look, and potentially needs repairs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Discrete variables\n", - "\n", - "Let's go ahead and find which variables are discrete, i.e., show a finite number of values" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of discrete variables: 13\n" - ] - } - ], - "source": [ - "# let's male a list of discrete variables\n", - "discrete_vars = [var for var in num_vars if len(\n", - " data[var].unique()) < 20 and var not in year_vars]\n", - "\n", - "\n", - "print('Number of discrete variables: ', len(discrete_vars))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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OverallQualOverallCondBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageCarsPoolAreaMoSold
07510213180202
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27510213161209
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" - ], - "text/plain": [ - " OverallQual OverallCond BsmtFullBath BsmtHalfBath FullBath HalfBath \\\n", - "0 7 5 1 0 2 1 \n", - "1 6 8 0 1 2 0 \n", - "2 7 5 1 0 2 1 \n", - "3 7 5 1 0 1 0 \n", - "4 8 5 1 0 2 1 \n", - "\n", - " BedroomAbvGr KitchenAbvGr TotRmsAbvGrd Fireplaces GarageCars PoolArea \\\n", - "0 3 1 8 0 2 0 \n", - "1 3 1 6 1 2 0 \n", - "2 3 1 6 1 2 0 \n", - "3 3 1 7 1 3 0 \n", - "4 4 1 9 1 3 0 \n", - "\n", - " MoSold \n", - "0 2 \n", - "1 5 \n", - "2 9 \n", - "3 2 \n", - "4 12 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let's visualise the discrete variables\n", - "\n", - "data[discrete_vars].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These discrete variables tend to be qualifications (Qual) or grading scales (Cond), or refer to the number of rooms, or units (FullBath, GarageCars), or indicate the area of the room (KitchenAbvGr).\n", - "\n", - "We expect higher prices, with bigger numbers.\n", - "\n", - "Let's go ahead and analyse their contribution to the house price.\n", - "\n", - "MoSold is the month in which the house was sold." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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mJdTX17Ny5coS1mjh6H5sSqklY8+ePeRyOXK5HMlkkn379vHoo48CcO7cOVpbW0tbwTJ3++23s2HDBnK5HDU1NaWuzoLRwKaUWjJeeOEFe0F2Pp/nnXfe4dFHHyWVSnHixAn6+/uprq6mo6OD9evXl7i25SkQCJS6CgtOA5tSasm48847efnll3E6nfh8PtavX08sFuPdd98llUrh9Xqpq6sjmUxSVVVFW1vb5Z9U3XB0jE0ptWScOXPGvh0IBMhms9TW1rJy5Uq8Xi8A4+PjxONxRkdHS1VNtcRpi00ptWScP39+xvHw8DDLli1jcnJyRrkxhtra2sWsmioj2mJTSi0Zy5cvJ51OU1j9Ax0dHXR0dBAIBFi5ciVOp5Oamho2bdpUsTP61LWbd4tNRFYB640xvxQRP+Ayxkxe7nFKKTUfx44dY8eOHRw7dgyHw0FNTQ1/9md/RkNDA/feey8DAwN4PB7a2trweDylrq5awuYV2ETkjynskVYPrKWQl/Hv0EXQSqkrMD4+zuDgIIFAgPb2dhyOQqdRPB6nu7ubd999FyjMiEwkErzyyits27aNmpqaip6erq6v+XZFfh74EBAFMMacBpYtVKWUUpVnYGCAX//613R2dnLkyBHeeust+77i5qLvvPOOXWaM4fnnn1/0eqryN9/AljLGpIsHIuJigbebUUpVlq6urhnHg4ODJBIJAGpra6mpqSEUCtn3e71empsv3koxl8vR1dXFu+++Szgcvuh+dbHJyUnS6fTlT6wQ8x1je0VEvgb4ReQBCsmHf7Zw1VJKVZpit2ORiGAlSQfg7rvvJp1O4/V68Xg8eDweBgcHL3qeAwcOcPz4cWKxGDU1NfzWb/2WZiS5hGQyyRtvvEE0GsXhcLBp0ybWrl1b6motuPm22L4CjADHgD+hkIn/3y9UpZRSlWfdunUzgtvKlSvx+Xz2scfjoaOjg2AwaE8OuXAD4KmpKQ4ePEhvby/j4+OcO3eOV155ZXFeQBk6ffo00WgUKIxbnjhxgmQyWeJaLbz5ttj8wJPGmL8HEBGnVRZfqIoppSpLY2MjH/nIRxgeHiYQCFwUtICLtlG58FhELup+1IXalxaPz/yITqVSvP/++7S2ttLU1DSjxVxJ5ttie5FCICvyA7+8/tVRamkaHR3lC1/4AmNjY6WuSlnz+/00NjbS0NBwVY+vqqqa0e3ocDjo6Oi4TrWrPC0tLfbtqakpTp48SXd3N2+88caMiTqVZr4tNp8xZqp4YIyZEpGqBaqTUiWXTCZJpVLU1NQgIvYeYXv27OHP//zPS129shSJRDh48CCJRAK3280dd9zBsmUzJ1ffe++9vPzyy/bxhz/84Rn3iwi//du/zSuvvEIqlaK+vp6dO3cuRvWviyeeeILOzs5FvWY0GmVqaopoNIrT6eTAgQP2fe3t7XR3d5NOp3nsscfI5/PU1NQQDAYXtY4XWrduHV/84hev+vHzDWwxEbnDGPM2gIhsAxJXfVWllrDTp0/z/vvvY4whGAyyfv16e4+wvXv3snv37qtucdzIjh8/bs+CzGQyHD16lPvvv39Gd1gxH+SljqHwYfy7v/u7RCIR6urqymqxdmdnJycPH6bl8qdeV1VAfHKSxAUzI8fHxjAiRCMR8tZEnTFgWU0NXrd7kWtZcPF0oSs338D2JeCfRKQfEKAF+PR1uL5SS0oymbSDGhS6b/7Lf/kv9nE+n9dW21WKxWIzjhOJBPl8HqfTaZe9+uqrM8751a9+xde+9rWLnsvn882YeFJOWoDPsfhjWxNeH++lM/ZxrdvNZpebwWSSswaYVqfl6Qwd7tJ8YfjedVhJNq8xNmPMQWAT8P8C/p/ATcaYt+Z+lFLlJ5FI2EGs6Ne//rW9gDiTyfCLX/yiFFUre9PHe6Aw49HhcNDZ2cmvf/1r3nnnHXbt2jXjnAcffHAxq1jRQh4Pt9bW0ub3syYQYFN1NQC+aV8sivyzlJWTOVtsIvJRY8w+EfmdC+7aICIYY360gHVTatGFQiGqqqpmzCZ74IEHeP3118lkMrjdbv2wvUqbN2/G5XIxOjqK3+9n8+bNnDlzhhMnTgCFdFvF/dXS6TSJRIKGhgZOnjzJxo0bK3YG32Kqdrmods382A+53bT4fAxaywDqPR6aZukCLieX64rcBewDfnuW+wyggU1VFBHh7rvv5tSpUyQSCdra2rj77rvZv38/UJiFt3v37hLXsjw5nU42bNjA5OQkg4ODjIyMMDk5SVXVB/PQnnvuOdLpNFNTU/aYZn19PX6/n1WrVpWw9pVtTSBAu9+PMQZvmbfW4DKBzRjzdRFxAHuNMU8vUp2UKqmqqiq2bt06o+zhhx/m2Wef5eGHH9aJI9egq6uL4eFhoJAaa2BggLa2Nnu87N1338VvfcBCYcIJFJZbaGBbWB5H5exidtlXYozJA//uai8gIk4ReUdEfm4drxaRN0SkU0T+t4h4rHKvddxp3d8x7Tm+apW/LyIfm1b+kFXWKSJfmVY+6zWUulq7d+/mtttu09baNTDGcOrUKbq6uuz1gNMz/DudTvx+P+5ps/GMMRhjGBsbY9++fbz++uscO3aMffv2sW/fPs6dO1eS16KWtvmG6F+KyL8VkRUiUl/8medj/ww4Me34PwLfNsasA8aBz1nlnwPGrfJvW+chIjcDnwE2Aw8B/90Klk7gvwEPAzcDv2+dO9c1lFIlcvToUYaHhxkZGeHMmTP09/cTCAT45Cc/yX333ccDDzxAe3s7TqeTQCCAiFBbW4uIEI/HicVinDt3jmeeeYZoNEosFuPo0aOaCFldZL6B7dMUtq75FfCW9XPocg8SkXbgt4D/YR0L8FHgn61T9gCPWrcfsY6x7r/fOv8R4IfGmJQxpgvoBO60fjqNMWetnQd+CDxymWsodVWmL9C+kDGGiYmJGyIH39XK5/P09vbS2NjIypUrqaqqIplMsmbNGgYHB4nH4/T09DAxMUE+n8fhcFBbW0swGKSlpcVeEhCNRsnlckxN2fkiGBkZKdXLUkvUvNaxGWNWX+Xz/xcK3ZjV1nEDMGGMyVrHvUCbdbsNOG9dLysiEev8NuCDpfIzH3P+gvKdl7mGUldsdHT0kgu0E4kE+/fvJxaLISJs2rSJdevWlbjGS4+I4HQ6yefztLS00NLSQl9fH6dOnWJwcJC+vj5uvvlm+vr6yOVydvdkJpMhn8/T3d2Ny+XC7/cjIvj9H2T4m77VjVJwmRabiOwUkSMiMiUi+0Xkpvk+sYh8AhheyuvdRORxETkkIofK8Vuf5i9cHHv27LlogXbRqVOn7IXHxhhOnjypLbdZiAgbN260j2OxmD0bcmBggFwux+DgIMlkkmg0SjKZJJlMMjg4SG9vL6lUiv7+fgYHB9m1axd+vx+n08m6detm3bNN3dgu12L7b8C/pdAF+UkKLbCPzfWAaT4EfFJEPg74gBrgb4CQiLisFlU70Ged3wesAHqtjUxrKWR3KZYXTX/MbOVjc1xjBmPMd4HvAmzfvr3sNk6d3j32J3/yJ6TTaUKhkK73uc5eeOGFixZoFzOPFFNEFRljSCaTZZsVYyGtXr2axsZGIpEIAC+//DJnz55lZGSEYDBIPp8nk8nYXyKK72U4HGbjxo1kMhmcTie33Xab3WK+cI83peDyY2wOY8wL1vjWPwEX7zNxCcaYrxpj2o0xHRQmf+wzxvwB8BLwKeu03cBPrdvPWsdY9+8zhb/wZ4HPWLMmVwPrgTeBg8B6awakx7rGs9ZjLnWNijG9e+yf/umf+MlPfsKvf/1rOzmsun4eeOABe6behQu0ly9fPuPcQCBAbW3totavXGSzWc6cOcO7777L22+/zenTpxkdHbXH35qamnC5XLhcLrLZLMlkEmMMJ06cIBqN4na7cTgcuFwuHA6HBjV1SZf7ywiJyO8Uf2Y5vhpfBv5cRDopjId9zyr/HtBglf85hc1NMca8CzwNvAf8C/B5Y0zOao39G+B5CrMun7bOnesaFaPYPZbNZonH4+zbtw8obAF/9uzZEteusuzevdtuBV+4QHvlypVs2bKFpqYmVq5cyd13360t5ks4efIk58+fJ51O2xtgZrNZGhsbueeee1i3bh0tLS1UV1eTz+dxuVxUVVVRU1NDT08PAMFgcMb4mrq8nDGcnppif3iMtyfGGb8gEXIlulxX5CvMzDoy/XjemUeMMS8DL1u3z1KY0XjhOUng9y7x+L8A/mKW8uco7OZ9Yfms16gkxe6xfD5PNpvlnXfe4dFHHwXQMZ7rrLGxcc4F2itXrmTlypUlql35KG4Imk6nOX78OGfPnmXNmjW0trYyPj5OKpUikUjg9/vtmZEul4utW7cSjUYREaampvjlL3/JmjVruPnmmy9zRQXQl0gwYvXiJHN5Tk1Nsa2uDlcFfwG7XOaRP1qsiqgr88ADD/Dcc89hjMHj8XD77bfb9xXz7anrZ/fu3XR3d+sC7WsQCoWYnJyks7OTWCxGNpslHA4zMjKCy+Wy98Bzu904nU5cVk5Dp9NJdXU1XV1dxGIxampq7HHMFStWzFjQrS42mc3OOM4ZQzybpaaC37d5dVKLSLOIfE9E9lrHN4uILnouoWL3mIhQV1fHZz/7WVpaWtixY8dFmzcqtRTcdNNN1NfXEw6HiUajNDY2Mjg4yMDAgD0LMpVKYYyxp/a73W5uuukmIpEIw8PDxGIxTpw4wY9//GOeeeYZfvazn+k6tsu4MOmxU4Qq13x3LCtP8x19/T6FsaziSPkpCnu0qRIpdo+JCJ/4xCfYtWsXO3bsuGhrEHV9zLZAOxqNcuTIEQ4fPmzP9FOX5vV6+dCHPsSaNWvYvHkzuVyORCJBNpvF4/HQ399PPp+/aHxt9erV9kSRfD7PiRMn6O/vZ2JigiNHjnDo0GVzRdzQ2vx+mrxeHCL4nU42BoMV3Q0J899otNEY87SIfBXsBdS5BayXmgftHlscsy3Qrqqq4rXXXiNrdfP09fVx3333EQgESlzbpW/t2rWMjIxQX19PLpfD6/XS3t7OxMQEUJh5Ov19dDgctLa2ks/nOXv2LA6Hg2XLluFwOMjn8/bEEjU7pwjrg0HWl7oii2i+gS0mIg0UJowgIncB+hW1xBobG/nbv/3bUlej4s22QPvRRx+1g1qxvL+/n/Xrb6SPj6uzefNmEokEVVVVjI2NEY/H8fl85HI5/H6/PZ2/qJjRZXBwkObmZtasWUN1dbV9vy7QVheab2D7cwrrydaKyGsU1rN9au6HKFUZZlug/elPf/qi87xlvjnjtXriiSfo7Oyc17nxeJxIJEIkEsHlchEOh+2p/Ol0GofDQSAQwOVy8ZnPfIaenh4ymQwejwcRweVy2a294eFhe7nLbNatW8cXv/jF6/UyVRmYb67It0VkF7AREOB9Y0xmQWum1BJRnIE6fQft1tZWGhsb7SnsdXV1Ohv1ClRVVVFVVUVrayvxeBwRweFw0NzcTF9fn/1Form5mfPnzzM1NYXb7SaTyVBbW2vvdF4MfkpNN+dfxByLsDeICMYY3UFbVbzdu3ezd+9e4IMF2g6Hg7vvvpvx8XGMMdTXz3cXp8p1ta2iaDTKvn377Az+X/3qVxERmpub+eQnP8nrr7/O8PAwIkJ7ezsbN27kIx/5iCabVpd0ua86vz3HffNeoK1UOZtrgXZdXV0Ja1Z+RkZGGBgYIBAIsGrVKt555x1725qJiQkymQzBYJDJyUkeeughstkswWCQsbExcrkckUiEhoYGbR2rOekCbaXmQWegXru+vj7efvttAHK5HC+++CLxeJyamhqampowxpBKpWhqaqKpqYmqqiomJydZv349Pp+PSCTC5s2b+djHPqZptRbBZCbDUCqFQ4RWnw+/1aIuB/PunBaR36Kwi7WdttwY842FqJRSS43OQL12586ds293dXVx5swZAoEAY2NjpNNpvF4v6Wl5DIsptFpaWrjjjjtwOp186EMf0iUV11kmn8d9QULpkVSKF0eGSeXyBFxO2vx+doTqcJVJ4ul5BTYR+TugCvgIhd2wP0Uhw75SSs1LMfXVxMQE/f39VFdXY43V8+6779pbLsViMeLxOD/5yU9YuXIlxhjq6urYtWsXDofDnsSjrk0sm+XU1BSJXA6f08HGYDUBayLOwfEwsWxhqXIkk8VBknAgw7Iymfk73/B7jzHms8C4MeY/AHcDGxauWkqpSrNmzRref/99Tp48SW9vL/F4nIaGBs6fP09PTw8ul4tVq1YxNjbG2NiYHejOnj1Ld3c3b775Ji+88AK/+MUv5r2sQF3amViMRK4QvJK5PJ2xKQBSuRzZC3anjOVyuMsoW8l8uyKLuynGRWQ5EAZaF6ZKSqlKFIvFWLNmDdFolIaGBt566y1isRhDQ0MEg0FSqRSHDx8mnU6Tz+cZHBzkxIkTAJw9e5aenh7uueceO61Wa2urdkteg1huZnLkYgvN7XDQ7PUQyWRI5/MANHk8hMqolTzfFtvPRSQE/H+Bt4Au4AcLVSmllppTp07x8MMPa0vhGqTTadxuNw0NDQQCAZqbm8lkMjQ0NJDL5RgeHmZoaAgRsbOSjI2N2UmRu7q67LRbAFNTU6V7MRXgwkBVPHaIsLG6hpuqg6zw+7m1poaPLFtWVvsMzhnYRGSHiLQYY75pjJkAgsAx4J+Aby9C/ZRaFMlkktOnT/P+++8Tj8cvuv9b3/oWsViMb3zj4vlSxcS9am6tra32WrVMJsPQ0BA+n4+pqSl6e3sZHBykv78fl8tFdXU1oVCI6upqGhoacDgcRCIRJicnAewAqS5vPJ3m1OQkvYm43QIDWBsI0uj14HE4aPB4WBcM2veF3G6219Xz0WXLuLO+Hm+ZTBopulxX5P8P+E0AEfkw8JfAF4CtwHfRtFqqAmQyGV599VV7g9auri527dplTyk/deoU3d3dAHR3d9PZ2WkvDj516hSdnZ0YY1i9erVufjmHQCDAhz70Ibq7u8lkMqxatQoR4dy5c1RVVdlb1XR1dZFOp7ntttvIZDJ2dheHw0EikaCpqYlNmzZpxpF5eC8a5Z3IBMlcHpcIawMBttXV4Xc68TgcbAhWz/n4ct0F4HJ/GU5jTNi6/Wngu8aYZ4BnROTwgtZMqUVS3A+sKJPJ0Nvbayc0/ta3vjXj/G984xs89dRTjI2N8f7779vlZ86cob6+XrcOmkNtbS1btmyhtrYWESEcDhOJROxNR4vps7LZLCJCfX29vZt2TU0Ng4ODNDQ0EAqFSv1SlrxoJkNvIkEyV2ilZY1hJJ1iMJlktTU2mc7n6YnHiedyhNxu2v1+HGUazKa7bGATEZcxJgvcDzx+BY9Vqiw4ZulmcU5bjFpsrV14XBzvSafTDAwMkEqlqK6u1sA2D42NjeTzeYaGhohGo5w4cYLBwUFisRiZTAZjDAcOHKCmpoZsNmtnHGloaODYsWOcO3cOt9vN1q1bqa2tLfXLWZJyxlxUljfWFi2W9ycn7R22p7JZ8sbQUQETci4XnH4AvCIioxRmRr4KICLr0G1rVIVobW3lzJkzRKNRoJCgt7293b6/vb2d3t5eexxt5cqVAPYYz/vvv08iUZg43NfXR09Pj32OmmlwcJCDBw8yPDzM6dOnGRwcJBqNMjk5STgcJp/Pk8vlMMYwMjJCJpMhEAiQz+eZmJhg2bJl/PSnP2VgYIC6ujpCoRBf/epX6ejoKPVLm5fe3l4mge9xcdC53ozbxaDHzWgqSdb6263zuBnxenBjyOXz9Gdn5rJ3pVO0BqoWvG5zGQCmenuv6Tkul1LrL0TkRQpT+39hjP0VwEFhrE2psud0Orn33nsZHBzEGENzc/OM8Zv29nZOnjxJKpUCYNOmTQCEQiHWrl3LkSNH8Hq9tLS0UFNTQ19fnwa2Wbzyyis888wzHDt2jFgsxvLly4lEIoyNjeFwOAgGg8TjcXufO5/Px7Jly6ivr2dsbIza2loymQxnz56192ObmJjgJz/5CV/60pdK+MqWJhFhWW0tXrebSDyO3+2moaYGt9Ub4RDB6XCQmzahxFVGabPmctnuRGPMgVnKTi1MdZQqDYfDwfLly2e977XXXrODGsCbb77JyMgITU1NrF27lq1bt2Kmdfv4fL7ZnuaGlk6n+elPf8r58+eZmJgglUrR09NDKBQilUrZ29gUZ6S6XC6ampoIBoOEQiFqamrsVnFxI9J0Oo3H4yEWi5XypV2R9vZ2JkZH+RyLM46VMHA8kyHjdEHesCyR/GD2owhjgQCdUzFyxuBxOLipKkBgkep2Kd/DEJrWY3I1dJxMqcuYvlM2gDGGaDRKU1MTXq+XjRs38v7772OMwe/3s2GDJuW5UC6XY2RkBBEhk8mQSqVwuVx0dHSQz+dJpVKEQiG8Xi8jIyOEQiHuuusuurq6GBsbw+12c9dddxEMBjl48CATExOICH6/n/vuu6/UL2/J6k8myOQ/+NI1nEqx3O+nymqZNXi81NZ5SOVyVDmdZbVWbS4LFthExAf8CvBa1/lnY8zXRWQ18EOggcJi739ljEmLiBd4CtgGjAGfNsZ0W8/1VeBzQA74ojHmeav8IeBvACfwP4wxf2mVz3qNhXqtqrI98MAD/PjHP7aPb7/9dpYtW2Yfr1+/nvb2dhKJBKFQaNbJKDc6v99PW1sbr7/+OoFAgEQigcPhoL6+nttuu42RkRFGRkYYHR3l8OHDZDIZenp6uP3228nlcoyPj3P06FHGxsbI5/PU1NRQU1PDtm3buPXWW0v98pas6UENIJ7L0huP015VZQc3l7UjeSVZyP+BKeCjxpgtFNa9PSQidwH/Efi2MWYdME4hYGH9HrfKv22dh4jcDHyGws4CDwH/XUScIuIE/hvwMHAz8PvWucxxDaWu2Je+9CVqampwuVy43W6+/OUv22M8RX6/n/r6eg1qc/jEJz5BR0cHy5cvZ8uWLaxcuZLa2lrWrl1LXV0dv/Ebv0FDQwMulwuv14vH47HH1Yr7uKVSKYwxrFq1iptvvpmNGzfO2BFAzTQ9afFwKsVAMsVIKsXhiQnG0qk5HlneFux/oSko5rxxWz8G+Cjwz1b5HuBR6/Yj1jHW/fdLoV38CPBDY0zKGNMFdAJ3Wj+dxpizVmvsh8Aj1mMudQ2lrlhjYyOf+MQnqK2t5bHHHmPz5s2lrlJZam1t5dFHH2Xnzp00Njbi9/sZGxujv7+ftrY2hoeHaWlpscfVstksr776Kt3d3WSzWVwuF1VVVXi9XiKRCOl0Gp/PpxlILiGVzzOcSpHK5YhmMuSMYWWV3+5u7E0kLvMM5WtBv15aLavDwDDwAnAGmLDWxQH0AsWtcNuA8wDW/REKXYl2+QWPuVR5wxzXuLB+j4vIIRE5NDIycg2vVFW63/u93yMQCPDYY4+Vuipla8WKFdTW1pJMJvF4POTzebxeL4ODgwDs3LmTdevW0dzcTDKZJJ1Ok06nyWazuN1uNm/ejNvtxhhDPB5ncnKSnTt3Vsy40PV2ZmqKcDqN1+kk4HKRMXnc8sFHfn7hVxyUzIIGNmNMzhizFWin0MLatJDXu1LGmO8aY7YbY7Y3NTWVujpqiTHG2N1cP/vZz4jH4zz77LMlrlX58ng83HfffdTX1+N2u6mpqeHUqVMcOHCAY8eOMT4+jsPhIJ/PMzU1Zc9EHRgYsGdSFgNacfLJz372M83TeQmRaWvUnCIzghpAawXP3l2UEUNjzISIvERhH7fQtGwm7UCfdVofsALoFREXUEthEkmxvGj6Y2YrH5vjGkrNy+joKO+8846dauvnP/85xhj27t3L7t27tfvrKhhjGB0dJZVK4fF48Hg8DA4OkkwmefPNN+nv7+djH/sYoVAIl8tFJBKxs7w0NTXh8Xjwer324vlYLMbY2BgDAwO0tc3aKXNDCzhdTE2b0bsuEGC5308smyVUZtvQXKkFa7GJSJO11Q0i4gceAE4AL/FB8uTdwE+t289ax1j377MWhD8LfEZEvNZsx/UUdu8+CKwXkdUi4qEwweRZ6zGXuoZSl2WMmRHUfvzjH9tbpOTzefbs2TPXw9UlHD58mDfeeIPJyUmMMQwPD+P3+0mlUqRSKc6cOcORI0dIJBIkEglOnDjB2NgY4XCYkZER4vE4IyMjJBIJotEo8XicxsZGMpnM5S9+A1oXCNgzH/1OJ+uCQZq8XjoCgYoOarCwXZGtwEsicpRCEHrBGPNz4MvAn4tIJ4XxsO9Z538PaLDK/xz4CoAx5l3gaeA94F+Az1tdnFng3wDPUwiYT1vnMsc1lLqsVCo1Iyny4cOH7W6xTCbDL37xi1JVrWwlEgl6e3sREUKhEIODg5w9e5azZ88yMTHB2NgY2WyW4eFhuru77UXXxYXv0WiUo0ePMjQ0xNmzZ8nlclRXV5PP52lt1T2PZ1PlcrE1FOLO+npuD4UIVNiU/rks2Cs1xhwFbp+l/CyF8bYLy5PA713iuf4C+ItZyp8DnpvvNZSaD5/PR3V1tb3310033cTx48cxxiAiPPjggyWuYfmZnpklnU6TSCSIxWJEIhEymQxTU1NEIhF8Ph9DQ0NkMhk7q78xhlwuRzqdJpVKMTU1hdfr5ZZbbrEXdatLK9etZ66FLrpRahY7duxg2bJlnDt3jtWrV5NOp5mYmCCfz/PJT36y1NUrO1VVVbS0tBAOhzl9+jTJZJJEIoHL5bIn6UQiEbq6ukgkEuRyOXsDV2MMDocDr9fL1NQU0WiUnp4ejhw5wnvvvVfql6aWIA1sSs0iEAiwZs0aVq1axcmTJ4nH40QiEcLhMD/4wQ9KXb2ytG3bNqqrq/F4PHbLq5j/UUTI5XIkEgkymQyZTAaxMmJUV1cjIiQSCZLJJPl8nmw2S19fn92FqdR0GtiUuoRUKkU2m+XNN9+0JzjEYjF+9rOflbpqZevIkSMcPnyY3t5eu1XmdrtxOp3k83l7Kn8ul8Pj8RAKhQgEAjQ2NuK2JjwU0z8ZY8hkMjP2zlMKNLApdUnFhcLJZNL+EM7lcjMmlqj5O336NG+88QZDQ0OkUimCwSBVVVUEg0FEBBGxxzGLyZKNMbhcLlatWkVHRwd+v99etxaJRBgcHLQXeCtVdONMk6kgg4ODvP/+++RyOTo6OlizZk2pq1SRpu/QPDk5aW+XUl9fX+qqlaX9+/cTiUQwxhCLxRARAoEAy5YtwxhDMpkkHo/bgSubzdoTeGKxGDU1NTQ2NhKLxQgGg9TW1uL1ejl48KCOe6oZNLCVmXg8zqFDh+xZZu+++649MK+uXjqdticutLW1UcxE09zcjM/nm9F60DGdq1PM95hIJGbsu5bNZvF4PMTj8Rlr0orvuYiQzWZxOp2sWLGCcDhsd08Gg0Fdx6YuooFtkT3xxBN0dnZe9eMnJycZHR0FsH+vXr2axsbGa6rXunXr+OIXv3hNz1HO9u/fTzQaBeD8+fPs2LGDlpYW6uvrue+++9i3bx9QaMU99NBDpaxq2Vq9ejVNTU0cPXqUXC6H2+1mamrKzvWYy+XI5XIXPa6qqop8Po/H42HZsmVEIhF7DK6hoYHt27cv9ktRS5wGtjLj8Xjs28U8hrqO59pMTEzYQa2ou7ubSCTC2NgYDoeDUCgEgNPp1K1prlIgEGBsbAwRwev1Ul1dzcTEBOFwGKfTedH2M8Uxt6mpKdLpNPF4nK6uLlasWEFDQwMtLS38wR/8AatWrSrRK6osQ8kko+k0XoeDdr8fXxlPytHAtsiuR6uos7OTU6dO8Z3vfIdgMMiTTz6pGc6vgXuW9ELnzp2juOPDr371K0SEYDAIwCuvvMLXvvY1AJLJJOPj49TW1lJVVbV4lS4z+Xye/fv343K5EBHS6TThcNhuoU1fkF0cY3O73fj9fvuLhMPhIJvNMjExwW233UZVVZV2Q14nw6kkZ6xsLwCRTIY7QqGy/VzRwFaG1q1bx5o1a3juuedwOBxl+8e3VAQCATo6OuyEux6PZ8YHZk1NDaOjowQCAUTE7vYdHBzkjTfesP8NbrvtNlauXFmKl3BNrrV7fD5yuRwvv/wy0WjUXjpRDGTARRn6i3/TIkIsFsPlctlptiYnJ/n5z39OTU0NnZ2d19wNP1+V3F0/kprZWk7l80xls1SXaU5JDWxlyuFwaJfYdXTrrbeycuVKEokEDQ0N/OxnP6Ovr4/a2lrC4fCMLxD9/f1MTU3xwx/+kLGxMbxeL+vWrePEiRNlGdg6Ozt55913ILSw1wknwkSiEXLk7ECWJ1/YfvhCUrhvKjEF+ULgE4fgcruIp+JMpaYIJAI4ljk4nzo/yxNcZxMLf4lSG0omERHq3G7cDgce7YpUqvzV1tZSW1trZ5gfHBykt7eXdDpNXV2dfV4ul+Po0aP2uFwqleLs2bNs2bLFnsVXdkKQv29h9zWrDlUT/mmYbD4LbgoBLQ+Idbv42wHiFnL5HCZjwGmdB+SyORxOB7gg5UvRm+2l/SPtOD0L+yHseLlyv0QmcjnG02mmclnSeUMkk+bDjU14y/iLc/nWXKkFkMlkOH/+vN0VWewyu3AcLhKJMH1z2rGxMSKRCEeOHLloIooq8NR6MDkDaQqBKk8hiDmlENSc1g+Qz+QLQc0AWT4IgtZPLpEjMZogNZIi1hub7XJqnkZSKRwirKkK0FFVRUfVB9vdlCsNbEpdIJPJ2FujFNddRaPRGRnqGxsbaWtrY/Xq1VRVVZHL5ZicnOTkyZO89tpr9jY3qsDkDZH3Irir3DhcDsQl4KTQ+hIKx3kgxwcB7KInKdyfz1rJkfOG1GQKk5+tL1PNl2vaeKbf6cTtcOAsx16HabQrUqlp3G43dXV1dsb58+fPY4yxM2DU1NQgImzZsgWn04nf7ycajeL3++nt7aW3t5fVq1czNDRUluNtCyWXyiEuQRwCbnDgKIy1kf8goM0nPhns1pvD7cDpduJv8S9o3SvdMq+XwVSSZK7wbSLoclFvLStK5XL0JBIkcznqPR6W+3xl0dWugU2paVKpFE1NTdTU1BAOh2lvb8fpdOJ0Oslms2QyGTweDx6PhzvuuINEImFvROp0OhER+vv78fl8pX4pS4rT68QVdJHL5Mgn83brV4xgMB+Mr82HgCvgwtfgo2FrA56g5/KPUZfkcjjYWhtiPJNGKEweKQavY9EIXfE4iWwOv8vJjlAdHYFAiWt8eRrYlLJks1leffVVEokELS0t9Pf3U1VVhdvttsfYstks+XyeSCSCiPDqq6/aC7mj0Sitra3U19fPGH9TIA4hn84jbsHpd5LP5Mmlc4WgNt/WWpEBIwZvyEvD1oaFqvINxSFCg2dmood4LseZWIypbHGtYZaj0YgGNqXKydDQkL0/WCgU4u6772Z8fNweQ0skEkxOTuJwOPjhD39ITU0NwWCQ1tZWe7+wtWvXct9995VFd81iyyVyuPwuTMYUJpEUs2fN1Vqb7b58oSyTyDB+fJyG2xsKY3XquvKIEM/OTHGWzi/szNnrRQObUpbp+3olEgkGBgZoamqyy4vBKp/P09vbSyAQYNWqVaxcuZLq6mpisRj33nsvHR0dpaj+kuet95I5kiEzlSGXzM0+QWQ6B4XAdnH6SPKpPOmJNMmJJOnxNL5G7fq93lwOBx2BKrpicQzgcQgryyS7jgY2pSzLli0jHo9z+vRpurq6WL58OStWrLB3c75QTU2Nnem/rq6O1atXs3bt2sWudtlw+pw4PU7SJn1xUBMQr2DSZuZ9l2jJ5RI5UqMpYj0xGm7R7siFsqOunqDLxWQmS63bxcbqmlJXaV40sCllOXfuHGNjY/T09NDT00MqleL222+3U2y53W4ymYyds3DNmjXceeedDA4O4vP5dBbkZWTjWcQt5FOzZxsxOavQwQfBbY4eXZM3pCfShZmWakEEXS521NWTzufxlNGCbQ1sSlk6Ozs5f/48ExMTJJNJzp07x8svv0wqlaKqqsoOaIlEgvr6epxOJ8FgkJtvvrnUVS8LDo+D1Ghq9mA1fSF20VxdldbaN4fbgcNdPh+45aqcghosYGATkRXAU0AzhT/X7xpj/kZE6oH/DXQA3cBjxphxKfT1/A3wcSAO/KEx5m3ruXYD/9566m8ZY/ZY5duA7wN+4Dngz4wx5lLXWKjXqpauK0nw29PTw5kzZ+wkvZlMhpGRETKZjJ1l3uv14vF4eOWVV/jVr37Fnj17rnhH7UpOpjsXb8iLOAWHy0E+O0vUusKZkflcYdmAu6Y8E/UuZYlcjlw+j9PhwF+GWUgWMgxngf+3MeZm4C7g8yJyM/AV4EVjzHrgResY4GFgvfXzOPAdACtIfR3YCdwJfF1Eion7vgP88bTHFXeAvNQ1lLrI+Pg43d3dpNPpQkYLY+zWmNvtxuv1kkwmiUQipNNpkskkw8PDTExMkEgkdOuUeXLXuHH5r+K79GwtPAGXz4W/2U98IH7NdbvR5IxhMJnkXDxObNqO8HljeC8a5cXhYZ7u6+P/PzjA4YmJspkNWbRgLTZjzAAwYN2eFJETQBvwCHCfddoe4GXgy1b5U6awcvOAiIREpNU69wVjTBhARF4AHhKRl4EaY8wBq/wp4FFg7xzXKHvxeByHw6ELgOfpci2jwcFBnnvuOc6fP08ul2PLli309PTYOyd4PB62bt3K3/3d3wGF9//RRx8lFArR19fHzTffzE033cSKFSvYunXrQr+cspaJZvA1+EhNXGG6sektuWnT/911blxVLrKx7GyPWrIGge9dUfP0+huejJKyvpBNJuLU1dTQ6nYTSyUZTSUZTyULNcxkeDuVpD7hom6R1q8Ncu0bTSzKGJuIdAC3A28AzVbQg8JraLZutwHT95/otcrmKu+dpZw5rnFhvR6n0Dpc8gP/uVyOQ4cOMTw8jIjorsHXycDAAN3d3RhjGBkZobe3l2AwyMqVK/F6vaRSKaqrq3G5XCSTSZYvX8769esZGhqitrYWl6vwX+j8+fO0t7cv2t5g5SiXyCEIDp+DXCx3ZV2PRcXH5CAxmMBX5yskUS4T69atK3UVSKfTuPv6KHbgRvv7iYkQ2rqVfDiMb2QE3/gHIze+6mr8DQ2EWloWpX4hrv19WvDAJiJB4BngS8aY6PRp09Z42IJ+dZnrGsaY7wLfBdi+ffuc9ViMzRjnEolECIfDM8ri8Tgej2dJjdeU2/iR2+3GGEMsFmN8fNzO5O9yuWhvb2dwcJBsNkt9fT09PT3s2rWLQCBAe3s7Z8+eZWRkhMnJSeLxOOfPn+fWW29l8+bNVzzuVkq9vb0QWfitWXwTPnKDOUzUIA75YBbkVcrH8qTOphCv4Ohd4MkNE9Brei972uUshf8bIyMj/OhHPwIKyby///3vEwgEeOKJJ5iYmOBXv/oVhw8ftpe53HbbbWzfvr2s1mcuaGATETeFoPaPxpgfWcVDItJqjBmwuhqHrfI+YMW0h7dbZX180K1YLH/ZKm+f5fy5rnHVOjs7eefYe+SrSvOBFZ+MkIzP3J4j6HXixc1bZwZLUqcLOeLhy5+0xGzcuJEVK1bw2muvMTY2ZmcTgUKW/9tvv521a9fy9NNPc9NNN7F161Z7W5vx8XFisRiJRIJ4PE53dzfd3d0cPnyYxx9/HK/Xe5mr31gCtQF8QR+R0cg1B7WidDxNOp2+/IkKKORCfeedd+wvckNDQ+TzeQKBAFNTU4RCIe666y6qq6vp6emhsbGRTZs2lVVQg4WdFSnA94ATxpi/nnbXs8Bu4C+t3z+dVv5vROSHFCaKRKzA9Dzwf02bMPIg8FVjTFhEoiJyF4Uuzs8Cf3uZa1yTfFU9yZs/cT2e6sqvnZgi3X/aPhZxkF15E3nX0kkA63vv56WuwhVzu91s27aNgYEBstksNTU11NXV0dTUxJYtW9iyZQuTk5M4nU7S6TRVVVVUVVVx8uRJXC4XDoeDXC5nb1MTi8UYHh7myJEj3HnnnSV+dfPT3t7OiIws+EajAG5xF75+zpJN5Gpk8hnym/Lkdyxs3R0vO2hva7/8iUtcX18fqVSKdevWEYlESKVSGGMYHh7mpZdeoqmpiR07dvCRj3yk1FW9JgvZYvsQ8K+AYyJy2Cr7GoVg87SIfA44Bzxm3fcchan+nRSm+/8RgBXAvgkctM77RnEiCfCnfDDdf6/1wxzXKFsuf5Cq5g7SkVHE4cAbasaxhIJaOctkMtxxxx0EAgHee+89+vv72blzJ/fccw8A7733HmNjY2QyGf7X//pfjIyMkEgkaGhowO/3U11dbW9r47G2+8iX2SyyxZDP5En0JjDZ6zj64CiM3anLS6VSDA0NEY1GqampIRQKEY1G7XWaUOim7Onpoa2tzf5bLkcLOSvy11w6b8D9s5xvgM9f4rmeBJ6cpfwQcMss5WOzXaPceYJ1eIJ1lz9RzVs4HKarq4szZ84QDoepr69ndHSU9957j7a2NhobGzl37hw+n4+hoSHGx8ft1ll/fz9ut5tNmzaRTqcZHR2lsbHRTsWlZhKXkAqnCguBrhdHIWCquU1MTPD666+TSqXo6uqipqaG1atXXzTDenJykn379rFs2TJqa2vZtm0bgTLI5n8hzTyibljxeJz9+/fj9XqZnJykq6uL4eFhnE4nZ8+e5dVXX2X16tWMjY1x6tQppqamyOfzZDIZAoEAPp+PfD7P6tWrWb9+PWNjY1RVVREKhWhra7t8BW4w+VyefO76BiFBFnY1boU4ffq0vSP8Lbfcwvj4OJs2baK1tZUf/OAHGGMwxnD27FnWrFkDFCasHT9+nJ07d5a49ldOA5u6YRUHzovjbMeOHSORSNj7rXk8HoaGhgiHw4yMjADgcrnsWZQiQjAYJJlM0tzcTC6XIxQK4fV6mZiYIBQKlfYFLjGp0dT1384nj+aKnIdiFp1EIkEoFKKxsZHW1laCwSAtLS1EIhHq6+tZtWoV1dXV9uNGR0d56623mJiYoKGhgc2bN9t7Ey5lGtjUDas4rpDJZJicnGRgYMBeUlEMbqOjoySTSXvMLJvNYoyxJ41kMhnef/99XnnlFbLZLGvXriWRSPDRj360ZK9rqYr1xq7vmjMHOFwOsvHyWqBdClNTU3R1dQGFpATbtm2zZ//6fD58Ph+/8Ru/gTGGiYkJ+3EjIyP23348Hiefz3PHHXcsev2vlAY2dcNqampiamqKl156ib179xKNRsnlcuRyOaqqqkgmk2QyGXK5DyYnGGMQEZxOJ/l8nqmpKd555x1yuRzBYJBTp07h9/vLr8U2sfDr2KRHcE+5cXqc5NLXYcJHHshAcCy44HVngg/SPywR811bm8vl6OnpsdPBOZ1O3n33Xfbt2wcUuimhsMYuk8kwNjZGOp3G5/PZG+sWORyOOZNDLJV1rBrY1A1raGiIrq4uDhw4QDgcJpvN2rkii9vTQCGYFTkcDowx5HI5u9VWvB2LxXA4HEQiESKRSKle1hVbrGwYw+5hOk0nvflexsbG7L3srpbD4aAmUMO9W+5d+BRzbUsja8jVKHb/ejwee6bj9GDl9/vt2263m5ZpGUZSqdSMf6dymSmpgU3dsAYHBzly5AhjY2MzuhtFxA5sxZ/ifcXWm4jYSZPhg+n9xV0B2tvLZ83TYn3DTiaTHDhwgBdffJE9e/YQiUSIRqNX9BwigsPhsCfp/OZv/iZf+cpX7AkPN5Ir+Xc7ceKE3bpzOBzs2LGDZcuWXfZx4XCYt99+m0QiQTAYZPv27TPG4JYqDWwVwhhDLlnITOLyB0tcm/JQVVVFPB4nkUjMKDfGkEoVJjoUJ4tcqDiLbLpsNovH42HTpk00NOiuzhfK5/NUV1czODhIMBgkl8sRj8evqOVWfN9TqRSTk5McP358Rlexmt1NN91Ec3Mzk5OTNDU12ePLl1NfX8/9999POp0uq0w6GtgqgMnnifWfJpsqbN/h8gUILF+HiM6Dnsvq1avt6fsXLqguThC50nRNXq+XW265xR6LUx84ePAgp0+f5sSJE/T29tpdv5fi8Xgu+f7n83m8Xi+JRIJXXnmF9evXL1S1K0Z9ff1V5TAVkbIKaqCBrSJkpsbtoAaQTcbITE3gqS6fRLylMDw8zLJlyy754VocO5utdXYpiUSCiYkJDWoXSCQSnDlzhmeeeYYzZ84QixV6Fy58791uN7lczv4wLQa3Ylk+n0dEqKqqYvny5QQCAc6dO1eKl6SWMA1sS1g6OkYmFsHp8eEOhMhlkjg9fpxe/4zz8rmLN7o0ufKdAr1YOyn09fWxb9++i7oip7vS1Fjj4+P86Ec/slNsLbSlMgvtcrxeL8eOHaOrq4tYLDbjy0JxlmlxUk6R0+nE6XTa3cHJZNIe7yyuJQyFQtx6662lellqidLAtkSlJoZIjPUDkAwPkI1H8dYXZiv56lvx1X0wc8kdqCM1PoQxxckPDtyB0KLX+Xrp7Ozk1PG3WRlc2LGTwbMDxCYj826NzVciPEiy++DlT7xGPVPOBb/G9eJwOKivryedTtspyaa3aqd/gSi2yooTeIpjavl83p444vF4qKqqYsOGDdx1112L/nrU0qaBbZ56e3txxCOLlsE+NTaMxxpUj09FIJvDFT1X+I9+XvA2tcz4YPBmMqQShe4dX1UA5+nzsz7vQnLEx+jtvT4txZXBHP9++9R1ea5L+U+RKQ5e56AmwMZQhq/cPolrgTfA/Nah8pok9OEPf5if/OQn9PX1zfplwul02uOaxW5HKGyMmclk7FZeJpOhtraWu+++m3vvvZdM5uIeC3Vj08C2RDkcTnLFbLHGIPLBN9zif/Dpgc3lduNyh0pQ0/JV7XVwnVMX4hDwuR0MTWZoC5XHmp/FctNNN11yHZQxxp5VCtiTeorZXYqBrvgTj8d56623ALj99tsX7TWo8qCBbZ7a29sZSrkWbT82ScbIDJzB5HMQiyBAJlALFLL8p5s7FqUeV8L33s9pb1+c7eOvhzWNC7Oo1+2A/ILuC19+jDG8/fbbNDc34/V67YXt04mIXVZcPzi9NVbsliyeNzAwwOnTpxkcHGTTpk2L+nrU0qaBbYly+QLUrNpMNhnD6faRS8fJxKM4PX48NbpG6nqo9TvxuiBxHefZeBwQjudZFiyf8a/FUExP1tjYiMPhuGjWqIjgdrtnjLUVuysdDgdOp9Ne71bc4NXj8dg7PSs1nQa2JUwcTtxVNQA43J6ynhByJXp7e4lNOhd8DGkobHA4XXCNqZ2mS+bhRFj4i0MBPO6F/e91btJJoLd3Qa9xvfh8PqqqquwZjcUJIsXg5XQ6ZxwXA1wx9ZPb7UZE7B9jDC6Xi5qaGt37Tl1EA5u6YdVV+/F4XMRS1y+wGQOxVIZ4Kr3gga2ciAgbN25kaGiIpqYmcrmcPTvS5XLZY2oul2tGd6Tb7bZ/e71eQqEQyWSSbDbL6tWr+c3f/E1uueWivYbVDU7/56klp729nWR2YMFnRaazeUbOOXj+BFyvhQUeB6ysMfxW6wQf2biwOzt/61AQXxnlpPT5fNTX19v7fxVbbR6Ph2Qyicfjwefz2VsD+f1+nE6nva1KQ0MDPp/PbrXdeeedfPzjH7+qbBqqsmnOJXXDiiRyLA/58F3H4bA8UO1xUuPXMbYL1dfXU1NTQ39/P8lkklwuRz6fJ5VKYYyxN7AsjqktW7aM2tpa2trauOWWW/B4PMTjccbGxjDGsGrVKjuDiVLTaYtN3bD8HgfprCF3HZebuR3gcAgbmhd4G5UyNDw8zPbt2zl58iRTU1O4XC57Kr/L5cLlctmbX3q9XrZu3UoymaSurs7O51lMTr1q1Srq6uro7OzUMTZ1EQ1sFSyfSZFNxXF5AzjcuqbqQrk8hGO56zo1P+CBuiqHdoXMIh6P28mPGxsb6e/vR0QIBoMEg0Gi0Sh+vx8Roba2lqamJlatWoXH46Grq4vly5czPDxMJpNhamqKkydPsmPHjlK/LLUEaWCrUOnJMInhHgwGQfA3r8ITrCt1teatZ2rhZ0WORWKcjfvImSvbE2wukaRwYsLDt4/VXrfnvJSeKScbFvwq14/f76e7u9ue+RgMBonFYng8HrxeL4FAgJqaGrLZrD39f3R0lDVr1tDS0sLp06cZHx9nYmKCuro6JiYm7BaeUtNpYLsCjnh40VJqzYckCx/Ixldz0X3JkSHc+Q+mROT73sLX2Lyg9XHEw8C1L9BerJ2K3cPDxHsm8foK+7JdF04XoZYOfB0L35LYQHnt6uxyuexWlzGG6upqwuEwLpcLj8dDY2MjHo+HoaEhwuEw4XCYiYkJ+vr6yOfzjIyMkMvlCAQC9vH03Z+VKlqwwCYiTwKfAIaNMbdYZfXA/wY6gG7gMWPMuBRWa/4N8HEgDvyhMeZt6zG7gX9vPe23jDF7rPJtwPcBP/Ac8GfGGHOpa1zr61mKHyCnTxcyyK9fe3Ew6XYmZ+TjczgcrFq10FlBWq7L+7RY2er379/Pf/7P/5n333+fU6dOXfHeaxfyeDw0NTXx4Q9/mL/+67/WrWsu0NDQwKZNmxgaGqKqqorx8XGmpqYIBoNkMhlSqZQ98zGXy9HT00M+nyeZLPwtx+NxJicnaWtro7q62t6oVKkLLWSL7fvAfwWemlb2FeBFY8xfishXrOMvAw8D662fncB3gJ1WkPo6sB0wwFsi8qwVqL4D/DHwBoXA9hCwd45rXJOluDVIsU5PPPHERfcdP36crq4u+3jt2rXcfPPNi1a3cuD3+6murqajo4N4PM7Zs2ev6PHTFxQXBYNBamtry27H4cXgcDj4nd/5Herr63nllVeoq6vD5XLZ427xeBy3243P5yOZTNLU1MTIyIg9C9Ln81FTU0MmkyEYDLJu3Tp7JqVS0y1YYDPG/EpEOi4ofgS4z7q9B3iZQtB5BHjKFD4lDohISERarXNfMMaEAUTkBeAhEXkZqDHGHLDKnwIepRDYLnWNG8rmzZuprq5mfHyc+vp6nTk2ixUrVrBs2TJGR0ftD87iomFgzu1spi8khg8y02cyGXvMSM00MTHB8ePHyWaz3H///UxNTfHSSy8Rj8dJp9NMTU3hdDrJ5/NUV1fbmUoymQzpdJqGhgacTiehUIi2tjY2bNjAqlWrSv2y1BK02GNszcaYAev2IFAc9GkDpu+z0muVzVXeO0v5XNe4iIg8DjwOsHLlyit9LUtacUq0/se/tIaGBu6//37efvttRGTGeE0ul5uR3qmY4snpdNoLh9PptL2YePqOz8FgkNHRURobG0vyupYiYwwHDx6kr6+Ps2fPYoyhtrbW3l9tamqKhoYGGhsbicViBAIB+/3r7u4mHo/jcrnYtm0bK1eu5N5776WtrY1QKFTaF6aWpJLNSrZaZwuaA/1y1zDGfNcYs90Ys72pqWkhq6KWqHQ6TU9PD8PDw0xNTZHJZMhkMrjdblyuwve+6S234k7PxRae1+vF7Xbb42urV6+mra2N4eHhUr2kJSkej5NMJunp6SGVSjExMWF3MaZSKWKxGOFw2O6OXLduHffeey/RaJSmpiZWrFhBOp1mdHSUpqYmli9frkFNXdJiB7Yhq4sR63fxf38fML2vrN0qm6u8fZbyua6h1AzGGP7hH/6BSCRi7/1VzIaRzWbt1tj0wDZ9n7B8Po/L5bLTPi1fvpxPfepTdtZ59QG/34/b7SYcDtPd3c3w8DCdnZ3k83lWrFhBS0sLjY2NrFmzhkwmQzabxev1smzZMpxOJxMTE6xfv56VK1fi8Xg4ffp0qV+SWsIWO7A9C+y2bu8Gfjqt/LNScBcQsboTnwceFJE6EakDHgSet+6Lishd1ozKz17wXLNdQ6kZzp8/z+jo6IyM81AIeMVuxgv3DCuWFVt2TqeTxsZGNmzYQHt7O+Pj46xYsYL2MsrhuBgcDgfbt2/H6SykGgsEAtTW1pLL5Whra6O5uZnGxkZcLhdut5uRkRHeeOMNksmknW0knU7b3ZPZ67gjg6o8Cznd/wcUJnE0ikgvhdmNfwk8LSKfA84Bj1mnP0dhqn8nhen+fwRgjAmLyDeBg9Z53yhOJAH+lA+m+++1fpjjGkrNkEgkEBG7hTAfImLvJ1ZM8VRsrRW7y1auXKlT/WfR2NjIQw89RGdnJ4lEAqfTSTgcpq2tjWw2SyqVYvPmzZw6dYpkMkk8HieVSpHL5aiqqsIYYyc81rFjNZeFnBX5+5e46/5ZzjXA5y/xPE8CT85Sfgi4aL8KY8zYbNdQ6kLFmXbLly8nFovN2OTyUlwuF4FAgFQqZc+EjMVinD59momJCTo6Ovj1r3/NJz/5yUV4BeVn7dq1JJNJ+3jnzp1UV1dz88034/P5aGxs5IUXXgCws/8Hg0FWrlzJmjVrqK6uZvv27eiYuJqLZh5RNyyfz8eKFStwOp0MDAwQjUYv2ujyQsUWQ7FF5vF4ZnRbplIpwuEwQ0NDNDcvbKaXcrRmzRp71mhtbS3Lly+/qHXr9/uJx+Pccsst9r/HnXfeic/n06Cm5kUDm7phrVmzho6ODkZGRmhvb+fMmTNAYYzN4/GQzWbJ5/O43W7S6TROp5OdO3cyNTVFf38/tbW19kSSYjYMv99PfX09k5OTGtguYdmyZSxbtuyS91dXV5PJZGhtbSWbzeL3+6mqqqKjo0ODmpoXDWzqhuV0OvmDP/gDotEodXV1eDweBgcHiUQiOBwOO72T2+1mbGyMlpYWvva1r+Hz+XjmmWd48803GRkZoaOjg/b2dqqrqwmFQjgcjjk/uNXcRISGhgYefvhhoNBlXEyKDJBMJgmHw9TW1hIIBEpZVbVEaWCrENFoFBHRaeZXqLGxkQ996ENEo1GSySSnT5/m/Pnz+Hw+QqEQTqeTtWvXMjg4SDAYpL6+np6eHtasWUMulyObzTI0NISI0NjYyG233cYtt9xCTc3FianV1fF4PthyaXBwkLfeesvuKt6yZUvFJVdQ104DW5kzxrB//35GR0cBaGlpYfv27Torb56KrYODBw+SyWRYtWoVgUCARCJBNpvF5XKRzWaJRCJks1lOnjzJwMAA6XQah8OBx+MhFApRX19Pa2srGzZsoLW1tdQvq2KdOHFixvjniRMnWLFihf69qxl0P8QyNzU1ZQc1KHyjHRwcLGGNysvAwACHDh3C4/HQ0NBAS0sLoVAIv99v534Mh8NEIhFOnz5NV1cXPp/PTr+VSCRIp9P2mrajR48SDocvc1WVzWYZGBi44vcqk8lc9Dxz5fRUNyYNbGVutvVXiUSiBDUpT2+88QanTp3i9OnTdkqtXbt2sWrVKjtTfzKZZHBwkHw+zw9/+EOWLVvGpz/9ae677z6WL1+Oz+ejoaGBqqoqgBlfNNTFYrEY+/bt49ChQ7z22mu88847837shd2O7e3tOBz6MaZm0q7IMhcIBHA4HDOS9La0LPS+a+XjiSeeoLOz85L3Hz16lP7+fvvY4XAwNTXFmTNnGBsbI5FI2IuE8/k84+Pj/MM//APPPfcc6XSa7u5uu9Xh8/mora2lubnZDnKzWbdu3ZLcBulaXe69LhodHWVycnJGWVtbmz2WVkyXdan3aHJykmQyidfrpbq6+rLdkJX6fqtL08BW5jweD3fddRddXV2ICGvWrJnzQ1XNJCL2WjQoBCcofGGYnJwkl8sRjUYJBAKEQiFCoRDhcJjm5mYmJiZwu90EAgFisRjJZJLW1lZ9/y9jtjWC08sutyt2dXW1TpJSc9LAVgEaGhpoaGgodTWWpMt9U3/ppZd46aWXyOVyOBwO1q5dyyOPPILT6eTZZ59lcnKSb3/723g8Hurq6hARUqkUTzzxBAcPHrTHM4sfzPfddx+1tbUL/rqWovm2iop5IItjY9XV1ezatUsngKjrRgObuqHdeeedTE1NMT4+TiAQoL29nbq6OgB++7d/m/Pnz7N37157JqTH42H16tX09fUxOTnJ0NAQTU1NOBwO6uvrb9igdiWampq4++676evrw+fz0dHRoUFNXVca2JaofD5PIpGgqqpK/9MvoEAgwMc+9jGGhobweDwzFlbX1tYSDAbZtWsXTz5ZSFfqcDjYsGEDf//3f4/L5aKxsZF0Om1vgKnmR3sZ1ELSwLYEjY2N8dZbb9mZ43fs2KGbKi4gj8fDihUrZr1vYGCAn/zkJ/ZxMpnkf/7P/8ljjz1GLpfj+PHj1NXV4ff7yWQybNy4cZFqrZS6FA1si2w+M8fOnz8/Yxq/x+Ohra3tovMuN3vsSujMsdnlcrkZa61yuZy9nCIejzMyMoLf7yeXy3Hq1Cnq6uo0nZZSJaYLQJYYY8xFa9MuXJRa5Pf7LzuDTF2b1tbWGeukXC6X3T2cTCYREVavXm2fMz4+XqqqKqUs2mJbZPNpFb355psMDQ3Zx21tbdxxxx0LWS11CR6Ph40bN9Ld3Q0Uxt2qq6u5/fbbCYfDZDKZGa1pHTdSqvRE09EUbN++3Rw6dKjU1QAKLbT33nuPiYkJGhoa2LRpEy6XfgcplVOnTvGv//W/to+ffPJJ1q1bB8C5c+c4ffo0xhjWrl3LmjVrSlVNpW5Es86s00/LJcjtdrNly5ZSV0NZiomNBwYGWL58uR3UAFatWsWqVatKWDul1IV0jE2pefjmN79JIBDgW9/6VqmropS6DO2KtCylrkillFLzMmtXpLbYlFJKVRQNbEoppSpKxQY2EXlIRN4XkU4R+Uqp66OUUmpxVGRgExEn8N+Ah4Gbgd8XkZtLWyullFKLoSIDG3An0GmMOWuMSQM/BB4pcZ2UUkotgkoNbG3A+WnHvVbZDCLyuIgcEpFDIyMji1Y5pZRSC+eGXqBtjPku8F0AERkRkXMlrtLVaARGS12JG4S+14tH3+vFVa7v978YYx66sLBSA1sfMH0fknar7JKMMU0LWqMFIiKHjDHbS12PG4G+14tH3+vFVWnvd6V2RR4E1ovIahHxAJ8Bni1xnZRSSi2CimyxGWOyIvJvgOcBJ/CkMebdEldLKaXUIqjIwAZgjHkOeK7U9VgE3y11BW4g+l4vHn2vF1dFvd+aK1IppVRFqdQxNqWUUjcoDWxKKaUqiga2MqW5MBePiDwpIsMicrzUdal0IrJCRF4SkfdE5F0R+bNS16lSiYhPRN4UkSPWe/0fSl2n60XH2MqQlQvzFPAAhawqB4HfN8a8V9KKVSgR+TAwBTxljLml1PWpZCLSCrQaY94WkWrgLeBR/du+/kREgIAxZkpE3MCvgT8zxhwocdWumbbYypPmwlxExphfAeFS1+NGYIwZMMa8bd2eBE4wSzo8de1MwZR16LZ+KqKlo4GtPM0rF6ZS5UxEOoDbgTdKXJWKJSJOETkMDAMvGGMq4r3WwKaUWnJEJAg8A3zJGBMtdX0qlTEmZ4zZSiHt4J0iUhFd7RrYytMV58JUqlxY4z3PAP9ojPlRqetzIzDGTAAvARclFC5HGtjKk+bCVBXJmtDwPeCEMeavS12fSiYiTSISsm77KUxGO1nSSl0nGtjKkDEmCxRzYZ4AntZcmAtHRH4A7Ac2ikiviHyu1HWqYB8C/hXwURE5bP18vNSVqlCtwEsicpTCl+UXjDE/L3Gdrgud7q+UUqqiaItNKaVURdHAppRSqqJoYFNKKVVRNLAppZSqKBrYlFJKVRQNbEotMSKSmzbV/bCVWupS5/6hiPxX6/b/KSL/1rr9fRHpsh5/UkS+Po/r/qGILJ923C0ijdfhJSm1qFylroBS6iIJK83Rtfo/jDH/LCI+4D0RecoY0zXH+X8IHAf6r8O1lSoZbbEpVQamt55EZLuIvHwFD/dZv2PW4/8/InJQRI6LyHel4FPAduAfrVae33rMF0TkbRE5JiKbrtfrUWohaWBTaunxT+uG/PE1PM9fWZnbe4EfGmOGrfL/aozZYe0t5wc+YYz5Z+AQ8AfGmK3GmIR17qgx5g7gO8C/vYa6KLVoNLAptfQkrOCy1Rjz/7iG5/k/rC7NFuB+EbnHKv+IiLwhIseAjwKb53iOYhLit4COa6iLUotGA5tS5SHLB/9ffXOdeCFrM8mXgd+wxtv+O/ApY8ytwN9f5vlS1u8cOiavyoQGNqXKQzewzbr9u1fyQBFxATuBM3wQxEatPc8+Ne3USaD62qqpVOlpYFOqPPwH4G9E5BCF1tN8FMfYjgLHgB9Z+279PYXZj89TyOpe9H3g7y6YPKJU2dHs/koppSqKttiUUkpVFA1sSimlKooGNqWUUhVFA5tSSqmKooFNKaVURdHAppRSqqJoYFNKKVVR/m/Fh3uWlnzw6gAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for var in discrete_vars:\n", - " # make boxplot with Catplot\n", - " sns.catplot(x=var, y='SalePrice', data=data, kind=\"box\", height=4, aspect=1.5)\n", - " # add data points to boxplot with stripplot\n", - " sns.stripplot(x=var, y='SalePrice', data=data, jitter=0.1, alpha=0.3, color='k')\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For most discrete numerical variables, we see an increase in the sale price, with the quality, or overall condition, or number of rooms, or surface.\n", - "\n", - "For some variables, we don't see this tendency. Most likely that variable is not a good predictor of sale price." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Continuous variables\n", - "\n", - "Let's go ahead and find the distribution of the continuous variables. We will consider continuous variables to all those that are not temporal or discrete." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of continuous variables: 18\n" - ] - } - ], - "source": [ - "# make list of continuous variables\n", - "cont_vars = [\n", - " var for var in num_vars if var not in discrete_vars+year_vars]\n", - "\n", - "print('Number of continuous variables: ', len(cont_vars))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LotFrontageLotAreaMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchMiscVal
065.08450196.07060150856856854017105480610000
180.096000.097802841262126200126246029800000
268.011250162.04860434920920866017866080420000
360.095500.0216054075696175601717642035272000
484.014260350.0655049011451145105302198836192840000
\n", - "
" - ], - "text/plain": [ - " LotFrontage LotArea MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF \\\n", - "0 65.0 8450 196.0 706 0 150 \n", - "1 80.0 9600 0.0 978 0 284 \n", - "2 68.0 11250 162.0 486 0 434 \n", - "3 60.0 9550 0.0 216 0 540 \n", - "4 84.0 14260 350.0 655 0 490 \n", - "\n", - " TotalBsmtSF 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea GarageArea \\\n", - "0 856 856 854 0 1710 548 \n", - "1 1262 1262 0 0 1262 460 \n", - "2 920 920 866 0 1786 608 \n", - "3 756 961 756 0 1717 642 \n", - "4 1145 1145 1053 0 2198 836 \n", - "\n", - " WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch MiscVal \n", - "0 0 61 0 0 0 0 \n", - "1 298 0 0 0 0 0 \n", - "2 0 42 0 0 0 0 \n", - "3 0 35 272 0 0 0 \n", - "4 192 84 0 0 0 0 " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let's visualise the continuous variables\n", - "\n", - "data[cont_vars].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# lets plot histograms for all continuous variables\n", - "\n", - "data[cont_vars].hist(bins=30, figsize=(15,15))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The variables are not normally distributed. And there are a particular few that are extremely skewed like 3SsnPorch, ScreenPorch and MiscVal.\n", - "\n", - "Sometimes, transforming the variables to improve the value spread, improves the model performance. But it is unlikely that a transformation will help change the distribution of the super skewed variables dramatically.\n", - "\n", - "We can apply a Yeo-Johnson transformation to variables like LotFrontage, LotArea, BsmUnfSF, and a binary transformation to variables like 3SsnPorch, ScreenPorch and MiscVal.\n", - "\n", - "Let's go ahead and do that." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# first make a list with the super skewed variables\n", - "# for later\n", - "\n", - "skewed = [\n", - " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", - " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# capture the remaining continuous variables\n", - "\n", - "cont_vars = [\n", - " 'LotFrontage',\n", - " 'LotArea',\n", - " 'MasVnrArea',\n", - " 'BsmtFinSF1',\n", - " 'BsmtUnfSF',\n", - " 'TotalBsmtSF',\n", - " '1stFlrSF',\n", - " '2ndFlrSF',\n", - " 'GrLivArea',\n", - " 'GarageArea',\n", - " 'WoodDeckSF',\n", - " 'OpenPorchSF',\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Yeo-Johnson transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Let's go ahead and analyse the distributions of the variables\n", - "# after applying a yeo-johnson transformation\n", - "\n", - "# temporary copy of the data\n", - "tmp = data.copy()\n", - "\n", - "for var in cont_vars:\n", - "\n", - " # transform the variable - yeo-johsnon\n", - " tmp[var], param = stats.yeojohnson(data[var])\n", - "\n", - " \n", - "# plot the histograms of the transformed variables\n", - "tmp[cont_vars].hist(bins=30, figsize=(15,15))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For LotFrontage and MasVnrArea the transformation did not do an amazing job. \n", - "\n", - "For the others, the values seem to be spread more evenly in the range.\n", - "\n", - "Whether this helps improve the predictive power, remains to be seen. To determine if this is the case, we should train a model with the original values and one with the transformed values, and determine model performance, and feature importance. But that escapes the scope of this course.\n", - "\n", - "Here, we will do a quick visual exploration here instead:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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OKz04NMwVd+7g8jNPqDrTyMYn9nDCZd/kldf31z8pj3rM6+8bYc2W/nF/9/RpJc448QhW/+DZ0cGZ/QODXLJ6G5es3kbvBHMey18C+gcG6QoHWSa9ZpovIjD+akCec2VnqV3qKVI0OkdEpNU6Mi2kHtVSL3726hClikTtelc5LH8QfH7twxz32Xu4ZPU2RtzpqpEAXs7hvuCUo6tuFw2s03InNmiddsBU7tr+wphZT6ImkvMYzTGG/YMso68ZTc9ZtW4X557UO5qW0tNdotRV/X8BycvEF20Binapp0iR6BwRkSJQz3UN1XqeRxwO7Z7KtAOmVp13+e1HH8rGJ/aMe/4Ug1de28es5XePKXdgeMQTZzUpW7VuFxuXL+bGB56hjhnwGvZ8OLiumspUl7SqDVCMTgsYTc9Zs6V/TJ53mtk12mUBl3app0gRGOgcEZHCUHBdw7Ils7mkygqFA3uH2HrZqaP343K0k3q/R7x6mkatQLb8uhecfDQ3bHqmxtbpTStNYe/QyLjyRvOc41QGwrVeN+44VQbyaaf1a5e5qtulniKt9tTKM1pdBRGRUUoLqWHpgt7EWTNgfH5f0vR4eZjZ083arf1s2Lk7k9frMuOjpxzNn55zYmJKQrVjEa1XNXHTzDUyxzho8JKIiIgUS0f2XNe7iMzlZ57Aslu3M1QxV19pio3L72tmsFdeNGaiwXt3qSt2Gr2klIS4YxF9rVo5j0lfQJL+L92lLg4qTYldtEaDl0RERKRIOjK4TgpGk8rLQWV0qe3KVRbLklIcauVPNyqL17zqnGA1xwVfuHfM3/f+eUewYedunh8YHJ07tjIXuGdaCXd4eXBoXBCelANdbZBob3j8KmcLAQq3gqKIiIhIpY4MrhsRzX8tB42Xrt7GFXfuGBNcLpozY9w0dt2lLs49qZebHni26hR7WetNkctcTvOo7I0eGBwak8cdnV87TS5wtfnBk76A9PZ0s3H54qqvqwF+IiIiUmQKrhMk9bpWBo3RVIXyDBbnntQ72uMbfW7fMYelmps5jhl1zwiyaM6MmgMd3YOANSnNI6qemUCS5ge/ZPW20WnzovtM0wvd7AF+aWYfEREREYlScB2jWq9rtSnjIAgg737oBaYdMBUH/v3lV7lk9TZWrdvFsiWzueqcuSy7ZRsxk3FU1UiH940pZhAZGBzi5RQLy5RVpnR8fu3Doz3yXWacf/JRXLl0btXc84HBYH7w6dNKDOwdn05SBNXeA0Wqp4iIiBSLgusYSb2u5V7MWl7aOzTaox1dDOXS1dt44xsOqDuwblSaeLzLjF859KBUU+wBdJemjM7hPe2ArjGL1Ay7c8OmZ3hq9y9qTq83NOJMO2DqmGkMi6Tae0DBtYiIiCTJbSo+M7vOzF40s0ciZavMbKeZPWRmt5tZT8Jznzazh81sm5ltzquOcdZu7U8MCssD+BrlwE9//nrDz8/DsDvLlswet7phkr1DI6NT6CWt/rjxiT0smjOj6sqUkH5mlejKjAtXrm9oBch6JdVNU//JZNau7baISJHkOc/19cBpFWX3AW9z9xOBx4DPVnn+Inef7+59OdUvVvnSf5yZPd25roQ4fVpp3HLqE9Vdqv4v7u3pZumCXlZ9cN6YOax7uks1n1vNhp27ueqcufRWmSovzTR6cXNiN7rEej2S6pbV1H+t+MIgksL1tGG7rfNHRIokt+Da3e8H9lSU3evu+8K7m4Aj89p/o5LyqUtdwZzW9eQn12vrZaey6kPzqgak9Qjmrz6Rhccdlvh4eRDh0gW9bL3sVJ5eeQZPrzyDbZefyqsTyF95fmCQpQt62bh8MVefNz9xUZpaqqVn5BmgLlsyu+E619KqLwwitbRru63zR0SKpJUrNP4e8G8Jjzlwr5ltMbOLqr2ImV1kZpvNbPPu3dmsVBjn4AOmsnRBb26Llhz/xoOZf8W9XLJ6W+r852q6zDj3pCA3+MFnXh73eHdpCgeVpnDp6m2JgelE/tboc5cu6B3txTaC3vK4RWviJKVh9A8Mjh6rPALUidS5lmpfGEQKbsLtdh5tts4fESmSlgxoNLPPAfuAGxM2eZe795vZG4H7zGxn2KMyjrtfA1wD0NfXl1vSRrnHetmS2Q1PpweMm4Ku7McvvjKh+lUadmfNln7ufuiF2LoODo0wGPZMJ82EkeZvnWIwUvHnxPXwNjqNXq2BkVETHXAYN/VerXm3G6F8bmlHWbXbebXZOn9EpCia3nNtZhcC7wcucI/PYHb3/vD3i8DtwDuaVsEEh3aXRoOvwaFhuqx2bvTTK8/g6vPmj+n9PPiA5n2fGRwajl0yPGnbyp6fuN7bj55y9Jj7f/Hh+eP+xqx6eCE+PaOaRj9gm5mqkXc+t0jW2qHd1vkjIkXR1J5rMzsN+GPg3e6+N2Gbg4Ep7v7z8PapwBeaWM1YP39tH5es3jZ6v9ZKiwcfEASElT22xy6/O5f6ZSEuMK3W41zZ0/vV8+ZnPk1d5XLrtbq5Gv2AbebUe3FXBFqxlLsWyZE02qHdrnb+6H0uIs2WW3BtZjcB7wEON7PngMsJRpkfSHDJEGCTu19sZjOBa939dOBNwO3h41OBf3H3b+ZVz7SGK3MfqjDgSx+YG/tYPWkOWejpLvHavpFUaSzVAtPKD6jKZd7zXGQlGuAvXLk+8fhNJEBtZqpG5ReGVnzga5Gc7E2GIK5d2+2kq2V6n4tIkjzb7NyCa3c/P6b4HxK2fR44Pbz9JDAvr3o1Q7WMkWVLZnPp6m2pFniZcD2AFWedAMBnbt5etbe9Vs9P5QfUjZueGfc3lJc3L69GmceHV1Ie+PRpJS4/84SG95n0pSevS83NXsq9khbJydZkCeLatd1OOsZ6n4tInLzb7FbOFjJpjTiJI9c3/2RPUwJrCIbul4O4r3x43rjc5fJ3gFp50nEfUNX+hjzzlePywK8+bz5bLzt1QidEnlPvFZEGVWZLM8AUk97nIhIn7zZby5/npNx4Ry87lLqM12NmCokyI7OFasyCHO9Du0uYMToQc9id3jougTTyQZRn71D5C0P52F6aQW95EVI1mqnZPfWTnYK4YtL7XETi5N1mK7jOyRQzZlUMXqwVWEN2gXX0tQYiC98Mu2PAojkzUg9U7JlWSj3rSFSegUUel3RanarRTEUZVDlZKIgrJr3PRSRO3m12qrQQM3uXmf1ueHuGmR2byd4nsVqzibSSAzdueiY2bSNuSrqkVSmnWDBgMkmegYUuw09MnovkdKKipRWpzQ7ofS4icfJus2v2XJvZ5UAfMBv4R6AE3AAszKQG0hIOsWkbcUFr0kQpIx70ik8rTWFvxVLp9b5J6x21q8vwE9dJPfV5K1JakdrssfQ+F5FKebfZadJCPgAsAB6EYIS4mb0hk71LS8UFoo0Ep5WBtQHnnpT+A62RFA9dhpeiKVAQpzZbRKSGPNvsNGkhr4crcjmMLhYgk0BcIJpFcOrAhp27U2/fSIpH0S7DixSI2mwRkRZKE1zfbGZfA3rM7PeBbwFfz7dakrekQLTe5caT1NMD3kiKh3IpRRKpzRYRaaGawbW7/zlwK7CGIIfvMnf/q7wrJtkodRlXnzefq8+bnyoQrQxap08rUZpSZVWcBPX0gCdtW+s1li7oZePyxXz1vPkAXLp6GwtXrs9lfm2RdqE2W0SktdIMaDwW+K673xfe7zazWe7+dN6Vk7HqnQO7y4xVH5w3GkSn7dWtzEOqtfR5pXrTMyYyXdZkWRlPJCtqs0VEWivNgMZbgHdG7g+HZb+WS40kUT2BdXepK7Z3uhwo9w8Mpl5QJi7pv++Yw0YD7vIiNQN7h0aD7/LiLmlG4E5k1K6WNxYZR222iEgLpQmup7r76+U77v66mR2QY50kA3GzdVT28pbn4m6ktzdplG2jPcmNjtrVlHwi46jNFhFpoTQDGneb2VnlO2Z2NvAf+VVJsrD6h8+Oyz2O6+Uty2oBlkYXd1m7tZ+FK9dz7PK768qbbjRfW2QSU5stItJCaXquLwZuNLO/JpjC+Fngd3KtlUzY0LBzxZ07xvQG1+rNbbS3N5qTnZS5Uu210/R2Jy0yo+WNRcZRmy0i0kI1g2t3fwI4xcwOCe//IvdaSSZe2jvEwpXrWTRnBht27k4MfMtq9fbGBbjAuOC23teulTedJvguwsp4IkWgNltEpLUSg2sz+6i732Bmn64oB8Dd/yLnukkG+gcGuWHTMzW3K3VZ1d7epAD3wKlTagbWtXqS41ZahP293bWC7yxWWap3+XWRolGbLSJSDNV6rsuremnZ3A5w8AFTE2cWeX5gkCnhzCJRg0PDVQNrg5qB6tqt/RjE9qqXe7vzHrSo6fxkklCbLSJSAInBtbt/zcy6gJ+5+1ebWCdpgZcHh8bcT5pZJK3enm42Ll9cc7tV63bFBtYGo73dM3u6Y3u3sxq0qOn8ZDJQmy0iUgxVc67dfdjMzgfUUOdsisFIffFrpqYd0FWzpzrOFAsWqxmKVL6eAYVJvc/O/l7jvActajo/pcVMFmqz4+n9LSLNlGa2kI3hqPPVwCvlQnd/MLdadaBWBtYAr7w+zLJbto8GyWl7qkcc3J2e7hIvDw7V/cGV1CvdG+mVznvQYt4940X/YFdazKTTcW322q39VVPP9P4WkWZKE1zPD39/IVLmQO1r/tJWhhqM8B14fd8wT608o+7nJvVKL5ozg4Ur148JSNOkmTQiz57xdvhgV1rMpDM//N0xbXa1c0rvbxFptjTB9YfcXQsQCN2lrsQBjHuHRsaVpemxjeuVXjRnBmu29DctIM2zZ7wdPtiVFjPpdFybXe2c0vtbRJqt2lR8ZwLXAUNmNgJ82N2/17SaSSGYQTlDJM20e2X19NhWTqW3cOX6pgekWUznF6cdPtjzTouR5uj0NjvpnNL7W0Sardry518CfsPdZwLnAlfV88Jmdp2ZvWhmj0TKVpnZTjN7yMxuN7OehOeeZma7zOxxM1tez37z0F3qanUVWqI0xZg6xUbvD1TMKBJlNnYJ88/cvL2hZdChPQLStNphefZlS2aPe49rlcu2NKE2G9q73U46p/T+FpFmqxZc73P3nQDu/gD1z516PXBaRdl9wNvc/UTgMeCzlU8Kp5L6G+B9wFuB883srXXuOzO9Pd1cdc5czGpvm4Vm7aeWLjMOOWgqQ8Pp8rDf+ebD+OxtD9MfLoGeNCAyTYBcpIA0+oVh4cr1rN3aX9fzm/nB3mhdly7o5apz5tLb042x/z1flLQVSW2ibTa0absdHadR+f7X+1tEmq1azvUbK1b6GnO/1mpf7n6/mc2qKLs3cncT8MGYp74DeNzdnwQws28AZwM/qra/PHSZsWjOjGAu5ibN5tGs/dQy4s5Le5N7qrvCqfq6zDj/5KPYsHN3qpSRNAFy3lPvpZXFYMRmLc8+0brmlRYjTTWhNjvcpi3b7XNP6q06TkPvbxFppmrB9dcZ2/NReX+ifo9gqqhKvcCzkfvPAScnvYiZXQRcBHD00UdnWL2g9zXN0uGT0cyebv795Vdje6C7zHjiqtPHlB27/O6ar5k2QG5WQFpLVoMRm/HB3g4DJyV3ebfZkEG7nUebHfflXu9/EWmVais0XpHXTs3sc8A+4MaJvpa7XwNcA9DX11eQft/2Vg6CL1m9LfbxYXcWrlw/JuBNGjTUZcaIe90BcpqANO/5o9sp97ud6ir5yLPNhuza7TzabL3/RaRIquVc58LMLgTeD1zgHpsE0Q8cFbl/ZFgmNRx8QBc93aWGnttlNi4fsbdKCkf5sms5rzEpt/grH57HUyvPYOPyxZkGvuU0iHKOd2V9slCk3O9a2qmu0n6K3m7r/S8iRdLU4NrMTgP+GDjL3fcmbPZD4HgzO9bMDgA+AtzRrDq2s1eHRnh5cIjenm4WHndYXc8dcR8XBC9bMpvSlOQRltHZP5o9aKhaGkRW2mmWgXaqq7SXdmi39f4XkSJJs4hMQ8zsJuA9wOFm9hxwOcEo8wOB+yyYFmOTu19sZjOBa939dHffZ2afBNYBXcB17r4jr3pOJuX86P6BQfa88joLjzuM7z2xhzTXXZN6eMYvDTNW9LJrMwcNNeMycFFyv9Nop7pKcbVru633v4gUSc3g2szeBPwpMNPd3xdOr/Tr7v4P1Z7n7ufHFMc+x92fB06P3L8HuKdW3fJw9XnzE3ON28ng0DAbn9gzpswgNtBO6uG54s4dDNdYEv3QBtNQJqpZC0O00ywD7VRXyU+jbTa0b7sNev+LSHGkSQu5nqA3YmZ4/zHgkpzq03K1co3b2TuPO2z0b+sKJ9Sulr5RbSq+slde35dpnnNaugwskuh6OqjNFhEpmjRpIYe7+81m9lmA8PJfujWw29SyJbO5dPW2VOkURVCec7qWTU++NG4KvSRpA+ahYW/JdFe6DCySqOPabBGRIkkTXL9iZr9MmFFgZqcAL+daqxZbuqCXWzY/My6tooh6w6CyctGVOGkCcNg/E0da9eQ5V06ft2jODDbs3N1QgKzLwCKxOq7NFhEpkjTB9acJRn0fZ2YbgRnEr9A1aazd2s+mp15qdTVqKqdBVPbiJoXQXSnXVo+biaOatHnOcasIRhfp6R8YZNkt24H0KyCKyDgd12aLiBRJzeDa3R80s3cDswnGxO1y99rJuG0szUC+PB3/xoPZ+/pI7IC9st6KXt5oL+7n1z4cu7Lk+ScfNa4sTrWe6FKXMTS8/9jUk+ecJmgfGnFW3LFDwbVIgzqxzRYRKZLE4NrMzkl46FfNDHe/Lac6tVyagXx5evzFV/jqefMBxqV7dJe6xgxAjFul8MqlcwG46YFnx6SCbNi5m7Vb+2sGrkkzcZQD+kbznNOmjwwMKg4QqVcnt9kiIkVSref6zCqPOaCGOidO0Mu7cfliIHnQXlyaRTlX+sqlc+k75rDEx6sFxHE53NEUlEZ7lZOCdhHJhNpsEZECSAyu3f13m1mRIunpLrW897Tcy1sOZss91Jeu3saqdbtGe5CTVilcuqC35uNJ8pqJI+3Ay+nTWjN3tkg76+Q2W0SkSFKt0GhmZwAnAAeVy9z9C3lVqtVWnHVCyxeSiQ4SjOuhrjZVYP/AIMcuv7vq4wtXrq8aOOcxE0dl0H5od4mfv7ZvTH57qcu4/MwTMt2vSKfptDZbRKRI0qzQ+PfANGARcC3BqPMf5Fyvlin3ELdS5SDBuB7oWsMtaz1eTs/oHxjkktXbWHHHDlacdULuAwkrg/a4nHENZhRpXKe12SIiRZOm5/qd7n6imT3k7leY2VeAf8u7Yq2SJm0hD92lKbw6NBIbYFYbCJi0pHm9BgaHUuVjZ01zVYtkrqPabBGRokmz/Hk5sttrZjOBIeCI/KrUWq0IrIP9juAEPcmfuXk7n1+7fxGXavNIO0Fgnk0dhlveay8iE9ZRbbaISNGkicruMrMeYBXwIPA08C851mnS6jLDqL2Yy7A7N2x6ZjTAXrZkNtWeMTg0klkd61ltsZa1W/tZuHI9xy6/m4Ur16deUl1EJkRttohIC9UMrt39i+4+4O5rgGOAOe5+Wf5VK47enm4+esrRE3qN0hTjKx+ex1Mrz2Ak5TLkNz3wLBCkTlxwytFVA+zE/XYZpSnpn5l2tcVayoMw+8MVI8vTACrAFsmX2mwRkdZKDK7N7NfM7Fci938HuBn4opkd1ozKtUJ3qWvc/WVLZtN3zGGUutIHqdHp5Hq6S6z60LzR3OK0AWx0AZgrl84dXVgmrd6eblZ9cB6rPjSP3p5ujP1fFOKmu6tntcVaqk0DKCLZ69Q2W0SkaKr1XH8NeB3AzH4TWAn8M/AycE3+VWuNc0/qHROIlldDXLVu15hlv6vp7elm62WncvV58+nt6eblwSFWrds12mu7bMnscUF8nMr0kXoG/vX2dLNx+eLRAYMbly/mqZVnsHH5Yq5cOndM/Sr/1iwkpZdkmXYiImN0ZJstIlI01WYL6XL3PeHt84BrwsuMa8xsW+41a5ENO3ezcfnicYu2pF1ZsNRlLJozgwVfuHfMMurluakvWb2N3p5uzj2pl7u2v1B1sZoDp9q45crTLHBjkKoHOs+ZOpJWY8wq7URExunINltEpGiq9Vx3mVk5+P4tYH3ksVSLz7Sj5wcGY/OF03rHrOms2dI/JrAuK/d79w8MsmZLPyvOOmG09zjO3qGRcXnKK846oWYOtdPc6fTixPXOZ5l2IiLjdGSbLSJSNNWC65uA/21m/0owtdN3AczsPxFcZpyUZvZ0x+YLp/X9J/ekem50GfKNyxfz9MozYoPsyjzlpQt6R3Ooq2n17BxLF/Ry1Tlzc0s7EZFxOrLNFhEpmsTeDHf/kpl9m2B+1HvdR0fXTQE+1YzKtcKyJbO5dAJLn4/UsaJLZf5x2jzlcjpH5bLoUeXZOcrbt4IWiBFpnk5ts0VEiqbqVHzuvsndb3f3VyJlj7n7g/lXrTWWLuhtWl5w5X6S9ptUHu0djqPZOUQ6Sye22SIiRZPN0n6TTJrZPOqdP7pSXP5xI3nK5bSSpJpodg4RERGR5lFwHaMyX7inu8S0yBLj06eVYueP7ukeP3c04fM/esrRo1PrdZlx7knjUyYmkqdcb6+3iIiIiGQvtxHkZnYd8H7gRXd/W1j2IWAF8BbgHe6+OeG5TwM/B4aBfe7el1c9k4zPa96/xPir4e3KnOK4HOjuUhfvn3cEa7b0jy4KM+zOmi399B1z2Ojzy1P/PT8wyMyebr563vy68pWXLZkdu2/NziEiabV7uy0iUgR59lxfD5xWUfYIcA5wf4rnL3L3+a1uoOtZaTCp53nDzt1VXyOLpcI1O4eIZOB6JkG7LSLSSrn1XLv7/WY2q6LsUQCzxnOVm63elQbjZshImn2k/BrVAvh6gmPNziEiEzFZ2m0RkVYqas61A/ea2RYzu6jahmZ2kZltNrPNu3fvzrQSa7f2MyXhA6WeXOZa+dBZLBW+dms/C1eu59jld7d8jmsR6Uip2u0822wRkSIoanD9Lnd/O/A+4L+Z2W8mbeju17h7n7v3zZgxI7MKlFM1hn38xNX15jLXmgVkooMRs0grERGZoFTtdl5ttohIURQyuHb3/vD3i8DtwDuaXYekVRq7zOrOZa6VD50UfC+aMyNVb3Q9eeEiInkoQrstIlIEueVcN8rMDgamuPvPw9unAl9odj2SUjJG3BvKa66WD10uj84WsmjODNZs6R8NmqutuJhFWkktlbOZLFsyO/f87lbsU0TqV5R2W0SkCPKciu8m4D3A4Wb2HHA5sAf4K2AGcLeZbXP3JWY2E7jW3U8H3gTcHg6emQr8i7t/M696JpnZ001/THCa17zRlcH3wpXrUw9ynEhd0wSwlVMMNmNp9VbsU6TTtXu7LSJSBHnOFnJ+wkO3x2z7PHB6ePtJYF5e9Uqr1fNG19MbXauuSQF02gA2q9lM6tGKfYp0unZvt0VEiqBwaSFFEZeq0cy0hHp6o6vVtVoAnTaAbUbaSdrX1nLuIiIiUmQKrqto5bzRi+bM4IZNz8SWx0mqa7UAOm0A2+wUmVbtU0RERGSiCjlbiMCGnfHzvyaVJ6kWQCcFqod2l8bMUrJozoyqUwnmodb0hSIiIiJFpOC6QKILwcT12kL9aRHV5tCOC2BLU4xXXt83Zs7sNVv6Ofek3qYura7l3EVERKQdKS2kICpzo5PUmxZRbbBjXK723tf38dLeoTGvMTg0zIadu9m4fHFd+54oLecuIiIi7UbBdQaymI85adGaqEbSImoNzKwMYI9dfnfs62ggoYiIiEhtCq4nKKv5mKsFrwYTmq2knh5gDSQUERERaZxyricoq6XHk4LX3p5unlp5BhuXL25KioQGEoqIiIg0TsH1BGU1H3NRgloNJBQRERFpnNJC6lSZX90zrTRuACDUn0bR6kVrKuuiYFpERESkfgqu6xCXX12aYpS6jKFhH92u0R5nBbUiIiIi7U1pIXWIy68eGnEOPmCq0ihERERERD3X9UjKo355cIhtl5/a5NqIiIiISNGo57oO1VY7FBERERFRcF2HoszoISIiIiLF1JHB9fRppbrKyzRNnYhIazTabouINFtH5ly/9Yg3sPGJPbHltWhGDxGR5ptIuy0i0kwd2XMd10BXKxcRkdZSuy0i7aIjg2sRERERkTwouBYRERERyYiCaxERERGRjCi4FhERERHJSEcG15rSSUSkvajdFpF2kVtwbWbXmdmLZvZIpOxDZrbDzEbMrK/Kc08zs11m9riZLc+6bq8NDddVLiLSCdRui4hMXJ4919cDp1WUPQKcA9yf9CQz6wL+Bngf8FbgfDN7a5YV2zs0Ule5iEiHuB612yIiE5JbcO3u9wN7KsoedfddNZ76DuBxd3/S3V8HvgGcnVM1RUQkpHZbRGTiiphz3Qs8G7n/XFgWy8wuMrPNZrZ59+7duVdORETGSd1uq80WkcmuiMF1Xdz9Gnfvc/e+GTNmtLo6IiJShdpsEZnsihhc9wNHRe4fGZaJiEgxqd0WEQkVMbj+IXC8mR1rZgcAHwHuyHIHmtJJRCRTardFREJ5TsV3E/B9YLaZPWdmHzezD5jZc8CvA3eb2bpw25lmdg+Au+8DPgmsAx4Fbnb3HVnW7RevDtVVLiLSCdRui4hM3NS8Xtjdz0946PaYbZ8HTo/cvwe4J6eqkTRzk2Z0EpFOpnZbRGTiipgWIiIiIiLSlhRci4iIiIhkpCODa6uzXEREWkvttoi0i44Mrr3OchERaS212yLSLjoyuO6y+L6OpHIREWkttdsi0i46Mrg+/+Sj6ioXEZHWUrstIu0it6n4iuzKpXMBuOmBZxl2p8uM808+arRcRESKRe22iLQLc588GWt9fX2+efPmVldDRKRuZrbF3ftaXY9mUpstIu2qWpvdkWkhIiIiIiJ5UHAtIiIiIpIRBdciIiIiIhlRcC0iIiIikhEF1yIiIiIiGVFwLSIiIiKSEQXXIiIiIiIZUXAtIiIiIpIRBdciIiIiIhnpyOXPAdZu7WfVul08PzDIzJ5uli2ZzdIFva2uloiIJFC7LSLtoCOD67Vb+1l2y3aGRoKl3/sHBll2y3YANdQiIgWkdltE2kVHpoWsuGPHaANdNjTirLhjR4tqJCIi1ajdFpF20ZHB9cDgUF3lIiLSWmq3RaRddGRwLSIiIiKSh44MrqeV4v/spHIREWkttdsi0i5ya5XM7Doze9HMHomUHWZm95nZj8Pf0xOeO2xm28KfO7Ku24GlrrrKRUQ6gdptEZGJy/Mr//XAaRVly4Fvu/vxwLfD+3EG3X1++HNW1hUb2JuQu5dQLiLSIa5H7baIyITkFly7+/3Anoris4F/Cm//E7A0r/1XM7Onu65yEZFOoHZbRGTimp2s9iZ3fyG8/e/AmxK2O8jMNpvZJjNbWu0FzeyicNvNu3fvTlWJRXNm1FUuItLBMm23G2mzQe22iLSPlo0EcXcHPOHhY9y9D/ht4GozO67K61zj7n3u3jdjRrpG9u6HXqirXEREsmm3G2mzQe22iLSPZgfXPzWzIwDC3y/GbeTu/eHvJ4HvAAuyrMRLCTl6SeUiIh1M7baISB2aHVzfAXwsvP0x4F8rNzCz6WZ2YHj7cGAh8KOm1VBERKLUbouI1CHPqfhuAr4PzDaz58zs48BK4D+b2Y+B94b3MbM+M7s2fOpbgM1mth3YAKx090wb6Z7uUl3lIiKdQO22iMjETc3rhd39/ISHfitm283AJ8Lb3wPm5lUvgBVnncCyW7YzNLI/dbA0xVhx1gl57lZEpNDUbouITFxuwXWRLV3QC8Cqdbt4fmCQmT3dLFsye7RcRESKRe22iLSLjgyuIWio1SiLiLQPtdsi0g5aNhWfiIiIiMhko+BaRERERCQjCq5FRERERDKi4FpEREREJCMKrkVEREREMmLuXnurNmFmu4Gf1Pm0w4H/yKE6jVBdxitKPaA4dSlKPUB1idNoPY5x9xlZV6bIGmyzoTj/62raoY6gemapHeoIqmdWEtvsSRVcN8LMNrt7X6vrAapLkesBxalLUeoBqkuR6zGZtcMxboc6guqZpXaoI6iezaC0EBERERGRjCi4FhERERHJiIJruKbVFYhQXcYrSj2gOHUpSj1AdYlTlHpMZu1wjNuhjqB6Zqkd6giqZ+46PudaRERERCQr6rkWEREREcmIgmsRERERkYx0dHBtZqeZ2S4ze9zMlue8r6PMbIOZ/cjMdpjZ/xeWH2Zm95nZj8Pf08NyM7P/GdbtITN7ew516jKzrWZ2V3j/WDN7INznajM7ICw/MLz/ePj4rAzr0GNmt5rZTjN71Mx+vVXHxMwuDf83j5jZTWZ2ULOOiZldZ2YvmtkjkbK6j4OZfSzc/sdm9rGM6rEq/P88ZGa3m1lP5LHPhvXYZWZLIuUTPrfi6hJ57DNm5mZ2eHg/t2NSrS5m9qnw2Owwsy9HynM7LpNZreNT7bxLOuYtquenLWjrHzKzb5vZMZHHhs1sW/hzR4vreaGZ7Y7U5xORxyZ83mRUx69G6veYmQ1EHmvKsazWFoWP59r+ZFjPC8L6PWxm3zOzeZHHng7Lt5nZ5hbX8z1m9nLkf3tZ5LH2aEPdvSN/gC7gCeDNwAHAduCtOe7vCODt4e03AI8BbwW+DCwPy5cDfxbePh34N8CAU4AHcqjTp4F/Ae4K798MfCS8/ffAH4S3/xD4+/D2R4DVGdbhn4BPhLcPAHpacUyAXuApoDtyLC5s1jEBfhN4O/BIpKyu4wAcBjwZ/p4e3p6eQT1OBaaGt/8sUo+3hufNgcCx4fnUldW5FVeXsPwoYB3B4iOH531MqhyXRcC3gAPD+29sxnGZrD9pjk/SeZd0zFtYz0XAtPD2H0TbB+AXBTqeFwJ/HfPcTM6bLOpYsf2ngOtacCxj26LI47m2PxnW853l/QPvI/IZCjxN2J4W4Hi+hzAumcj7pZU/ndxz/Q7gcXd/0t1fB74BnJ3Xztz9BXd/MLz9c+BRgoDubIIAk/D30vD22cA/e2AT0GNmR2RVHzM7EjgDuDa8b8Bi4NaEupTreCvwW+H2E63DoQQn2T8AuPvr7j5Ai44JMBXoNrOpwDTgBZp0TNz9fmBPRXG9x2EJcJ+773H3l4D7gNMmWg93v9fd94V3NwFHRurxDXd/zd2fAh4nOK8yObcSjgnAV4E/BqKjsXM7JlXq8gfASnd/LdzmxUhdcjsuk1ia45N03iUd85bU0903uPve8G70nGmmibzfMjlvcqjj+cBNOdSjqiptUVmu7U9W9XT374X1gNa9L9MczyRt04Z2cnDdCzwbuf9cWJa78FLmAuAB4E3u/kL40L8Db2pS/a4mCFBGwvu/DAxEgqjo/kbrEj7+crj9RB0L7Ab+0YL0lGvN7GBacEzcvR/4c+AZgqD6ZWALzT8mUfUeh2a8p3+PoIemJfUws7OBfnffXvFQK47JrwK/EaYn/G8z+7UW1mUySHN8ks67Zh7bevf1cfafMwAHmdlmM9tkZktzqF9Z2nqeG6YK3GpmR9X53GbVkTC15lhgfaS4WceylnY85yvflw7ca2ZbzOyiFtUp6tfNbLuZ/ZuZnRCWFfl4jjG11RXoNGZ2CLAGuMTdfxbt7HR3N7Pc50Y0s/cDL7r7FjN7T977q2IqwaWhT7n7A2b2lwTpD6OaeEymE3wDPhYYAG4hxx6GejXrOFRjZp8D9gE3tmj/04A/IUhTKYKpBJd7TwF+DbjZzN7c2ipJkZjZR4E+4N2R4mPcvT98r6w3s4fd/YnW1JA7gZvc/TUz+68EVwUWt6gutXwEuNXdhyNlRTqWbcPMFhEE1++KFL8rPJZvBO4zs51hD3MrPEjwv/2FmZ0OrAWOb1FdGtLJPdf9BLmbZUeGZbkxsxJBYH2ju98WFv+0nNoQ/i5fWs6zfguBs8zsaYLLKouBvyS4lFX+whXd32hdwscPBf5vBvV4DnjO3R8I799KEGy34pi8F3jK3Xe7+xBwG8FxavYxiar3OOR2fMzsQuD9wAXuXg7ym12P4wi+/GwP37tHAg+a2a+0oC4QvH9vCy8F/4DgKtDhLarLZJDm+CSdd808tqn2ZWbvBT4HnFVOHYLRq2S4+5PAdwiuYraknu7+fyN1uxY4Ke1zm1XHiI9QkRLSxGNZS9uc82Z2IsH/+mx3H/3MihzLF4HbyS+tqiZ3/5m7/yK8fQ9QsmDweuGOZyIvQOJ3K34Iep2eJPiwLifGn5Dj/gz4Z+DqivJVjB209uXw9hmMHSDxg5zq9R72D2i8hbGD9/4wvP3fGDuI6OYM9/9dYHZ4e0V4PJp+TICTgR0EudZG0IPzqWYeE2AWYwfM1XUcCHpRnyIYODM9vH1YBvU4DfgRMKNiuxMYO4jsSYIBJ5mdW5V1qXjsafYPaMz1mCQcl4uBL4S3f5XgcqU147hMxp80xyfpvEs65i2s5wKCgVfHV5RPZ/8A2MOBH5PTgKyU9TwicvsDwKbwdmbnzUTrGG43JzzfrRXHMtxHtbYo9/Yno3oeTTAe4Z0V5QcDb4jc/h5wWgvr+Svl/zVBkP9MeGzbpg1teQVa+scHI3wfCxvBz+W8r3cR5DQ9BGwLf04nyBf8dtgwfKt84oVvpL8J6/Yw0JdTvd7D/uD6zcAPwpPvlkjDdVB4//Hw8TdnuP/5wObwuKwNG6CWHBPgCmAn8Ajwvwg+qJtyTAh6ZF4Ahgh6RD/eyHEgyIl+PPz53Yzq8ThB4Fh+3/59ZPvPhfXYBbwvy3Mrri4Vjz/N/uA6t2NS5bgcANwQvl8eBBY347hM5p+44wN8gaD3t+p5l3TMW1TPbwE/jZwzd4Tl7wzfn9vD3x9vcT2vIuhU2A5sAOZEnjvh8yaLOob3VxAMHo4+r2nHMuH8vxi4OHw81/Ynw3peC7wUeV9uDsvfHB7H7eH7Ie94qFY9Pxl5X24i8mUg7v1SxB8tfy4iIiIikpFOzrkWEREREcmUgmsRERERkYwouBYRERERyYiCaxERERGRjCi4FhEREZFJxcyuM7MXzeyRFNt+2sx+FK5W+u1wRdDyY0eb2b1m9mi4zaxar6fgWnJlZkea2b+a2Y/N7Akz+0szOyBh25lmdmuK17zHzHoarM8KM/ujhHI3s/8UKbskLOurcx/vNrPvV5RNNbOfmtnMBup8iZm9amaH1vtcEelsZvbLZrYt/Pl3M+uP3I9tiyewrznh6241s+OyfO066vCduDY7LH/GIssim9laM/tFA/u43Myuqiibb2aPNljntWa2qZHnSlXXk36l5a0EUyieSLCg3Zcjj/0zsMrd30Iw7/aLMc8fQ8G15CZsxG4D1rr78QQLbRwCfClm26nu/ry7f7DW67r76e4+kHV9CeYn/Ujk/ocI5tqs13eBI6PffAlWgNzh7s+neYHIqpAA5wM/BM5Jsa2IyCgPVmGc7+7zCRbC+mr5vru/nnH7sZRgifIFnmIZcgs0Mw4ZIFh5l7CD5ogGX+cm4LyKsnErSFZTPu5hPU4CDg2XcU/cVurjwfLte6JlZnacmX3TzLaY2XfNbE647QZ33xtutolg9UfM7K3AVHe/L9zuF5HtEim4ljwtBl51938EcPdh4FLg98xsmpldaGZ3mNl64NtmNqt8+SZ8/ObwEsztZvZAuTfCzJ42s8PD7R81s6+b2Y7wsk13uM3vm9kPzWy7ma0xs2kp6rsWODt8/nHAy8B/lB80s78zs83hvq6IlK+MXE76c3cfAW5mbKA+2vCGPSh/ZmY/MLPHzOw3wvIxxyNSj0OAzxME2cRta2YHW3AJ7Adhr1H575gVNiAPhj/vTPOPE5HJy8yuN7O/N7MHgC+b2TvM7Pth2/E9M5sdbnehmd0WBiM/NrMvh+Vd4Ws8YmYPm9mlZnY6cAnwB2a2Idzu0+E2j5jZJWHZLDPbZWb/TLAA02+Y2c7w9R4zsxvN7L1mtjHc5zvC5yW1cd1m9o3ws+B2oLvKn/4N9rfL5xB0/pSPySEWpAM8GP5N5dc/2MzuDj9LHjGz89z9MeAlMzs58tofZn8b/wsz+1L4nE1m9qa44x6px50VdYv7H8UGhWZ2Zvj5uNXMvlXelyS6BviUu58E/BHwtzHbfJxgxU0IOgUHwvNgq5mtMrOumntp9So2+pm8P8B/J+glqSzfCpwIXEiwOlN55cFZhMuhhm/6r4W33wbsI1z1inBlvnD7fcD8sPxm4KPh7V+O7O/K8GSCYKWvP4qp04pwn7eF+/sc8DHgO5H9luvZFZafSLCK4i72L9XaE/7uA7aGtw8kuIxUfv53gK+Et08HvhXeHnM8wrLPAf+D4IvwT4A3xW0L/Gnkb+8hWMHqYILl3A8Ky48nXJFLP/rRT+f9RNq564G7CJeIB36JoHcOgqtsa8LbFxIsN30oweqYPwGOIuhpvS/yuj3R1w9vn0RwNfBggg6CHQTLws8CRoBTwu3K7fjcsJ3bAlxHsOrh2QRXPqu1cZ8GrgvLTyTyWVHxt38HOJlgNeAu4N5w378IH58K/FJ4+3CCFRUNOBf4euR1Dg1//xHh5xvBkuebI9s4cGZ4+8vA58PbY457WHYf8BsEQdzDkfLK/9G3gePD2ycD68Pb09n/+fMJws8W/Ywex1nsjysOAQbZv0LlNuDRiu0/StBzXV6N+YMEHW1vDt8ja0ixGqguNUir3efue2LK3wX8JYC7P2JmDyU8/yl33xbe3kJwIgG8zcyuJGiEDwHWpaxPufdgCfBbwO9GHvuwmV1EcIIdAbwV+BHwKvAPZnYXQWOIu28Oe0JmA28BHqj4O8s9JtE6w/jjcT7wAXcfMbM1BKkqfx2z7anAWbY/n/wg4GjgeeCvzWw+MEzQgIuI3OLB1UQIgud/MrPjCQLDUmS7b7v7ywBm9iPgGIJA+c1m9lfA3QSBaqV3Abe7+yvhc28jCCLvAH7i7tEc46fc/eFwux3hPt3MHmZ/+5jUxv0m8D8B3P2hKp8VELSB/4egje9296ctkoIN/KmZ/SZB8N8LvIngC8JXzOzPgLvc/bvh9quB75nZZxifEvI64WcBQRv/nyOPjR73sJf5eOD/hH/vkJm9zd0fiW5rZocQLPd+S6S+B4a/jwRWm9kRwAHAU1X+/k43BRjwIEVqHDN7L0GH1rvd/bWw+Dlgm7s/GW6zluDL1D/U2pFIXn5E0Hsxysx+iaBBfDwsemWC+3gtcnsYRr8wXg980t3nAlcQNMRp3AX8F+AZd/9ZudDMjiXoqfgtDwY83E3QI7yPYIDDrcD7gW9GXusmgkY3LhevXO9onSFyPMxsLkHDe5+ZPR2+zvlx2xL2sPj+XMqj3f1RgjScnwLzCHrTMx3AJCJtK9p+fBHY4O5vA85kbHs5ro1195cI2pTvABcD105g35X7GIncH2F/+5jUxtXrGwTB+M0V5RcAM4CTwuDrpwRt/GPA2wmC7CvN7DIAd3+WIJB9N0Hv9urIaw152O1JlTaeIJVkOvBU2MbPIr6NHw0KIz9vCR/7K+Cvw8+6/0r6z7qOE36mP2VmH4LRnP954e0FwNeAs9w9OmDxh0CPmc0I7y8miG2qUnAtefo2MM3MfgeCPD3gK8D1XntAwEaChqc8oGBunft+A/CCmZUIGs1Uwnr9/4wfdPlLBA3dy2Fvw/vCuh1CcJnwHoJAdl7kOTcRXGJaDPxrnfWHoJFd4e6zwp+ZwEwbO1CybB3wKQu7NcKGAoIeqRc8yAP/LwSXQ0VEog4F+sPbF9ba2MwOB6a4+xqC8SBvj9nsu8BSC8bPHAx8ICxrVFIbdz/w22HZ2whSQ6r5LnAV4zs8DgVedPchM1tE0EOPBTM87XX3G4BVjP1bbwK+Cjzp7s818DedD5xWbuMJOqM+UrlRtaCQsf+7jzVQh0nLzG4Cvg/MNrPnzOzjBPHAx81sO8EVmLPDzVcRXOW+xYIZb+6A0bFif0Qwtulhgi95X6+1b6WFSG7Cy1wfAP7WzMp5w/cAf5Li6X9LcJnyR8BOgpPg5Tp2/z+AB4Dd4e831FHvb8SUbTezrWFdniUI/glf91/N7CCCk+7Tkec8amavAFvKl0br9BGCnOyo28Pyn1aUfxG4GnjIgtH3TxH0pP8tsCb8gvNNJn6lQEQmny8TtLefJ7gqV0sv8I+2f6aPz1Zu4O4Pmtn1wA/ComvdfaulmCM4QVIb93dhXR4FHiVIw0gU9ij/ecxDNwJ3hgHUZoK2HoKOnVVmNgIMAX8Qec4tBL3gn6r3jwmPwzEE+b3luj1lZi9XDJQsuwD4u/B/VCLogd9OkOd+i5m9BKwHjq23LpOVu5+f8NC46fnc/b1VXuc+an9pG6OcBC9SKGEvd8ndX7VgxoxvAbPd/fUWV01EREQkkXqupaimARvCtA4D/lCBtYiIiBSdeq5FRERERDKiAY0iIiIiIhlRcC0iIiIikhEF1yIiIiIiGVFwLSIiIiKSEQXXIiIiIiIZ+X8BQxbHenhRRgAAAABJRU5ErkJggg==\n", 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zZkzrwf2Pvuj7BXUGeDpTFK7buGdy0Iipl4Ajk4koKnHEbcq2dtZ5tzs3+1VnV6oTNfnYVMvNHJycriwL8bNk7aamp/Jx6s2cqYGuXj+KdRv3hHq+JGb7aDR5fY8Irh3eiXu2l2vqqq9eP4qr1o+iWMgjn5MpZSMcmUxEFJ9OWG68Ve46b3fujfr9aHdubvRqQ3fvmMy5XvvGHJwsNq7rlMcqGLprBwDzMqF+Aa3ZJWCd50yjcVXPie6d22OVKvI9gr7ePMYOVbs2yBMRxcmde4q9ebxy+OjkIPSsL3Hd6olCHMuvu/cpbdydWfX6evO47qIFmfwcdAo2rj1UJxSrN+zy/GB6fYGvWj+K67+7C9ddtKCpwX31z5lGjc6iqxOK3unTMPK589uyP0RE3aQ+T3gN7szqIDZTw3jbsweweff+QA3uqAfWZyEvm/ROn5a5z0CnYePawFQq4fUFBqxA5/dF9DvzNT2nI98DVCe8RwSnSRrP7omIOkGjPOHIYhw2NYzdV0wb9URHPbA+yPt92ptm4pevHPE80UlSFj8DnaYrBzS2wu9DW6mOGwcWHFcwTxfV6Itw7DF55ERS3bAGOHiCiCio4ZEylqzdhFNW3Y8lazc1XFUwaINJgUDPlyamYzNNL+fFlH96RJp6L4K834eOTCDBRZGNmIuTx8a1D68vZKMP7bgq8j1TG9ivHjmKa4d3egbTRs958FA10WXNHb329D/A1JHMHDxBRBRMM8t2h2kwpWEZ8DDCHJup0es1LR9g5eT69yLIiU2QfSqPVRpOCBAnr6485uJ0YOPax1X2Ut7uL57pC+woFQs49pip1TbVcWtQoFcwbfScaXHInvLqppWLcePKxVxalYioCc0s2+2VJ/I5QdFwVbSVZcDD9qq3yuvYTFPRmRq9zrR8XleP3e9F0BObIHk57uXMG1EAHzx3LnNxCnVlzXUh3xN4btT6Oi/nQ7t6w64pZ6zOGePV60c9n8t0ictZIvyaex8NNWdrj7y+3KnbklNn4Wf7Xo5lIAaXNieiJJjidiGfvT6iZuqDndzjzF5xXCEPEWDMp963mdrbOGbdaKT+2JzVDt3TvwKNe2UH+0vG/Fseq+CUVfdDPPKm18BHv1zv7Itfjl1y6izPFY/zPTI5w0sUuMx5OmUvKkUg7KID9T0Ag/0ljF53Pm4y9N6GvcQ1PFLG6g27puyXwD9xHFfIT561Aq+fRT/zqwreNve44AcYUis9IkREzeikRWSaXXhrsL+ELauW4saVi/Ha0QkcPFT1HYszp1gI3QvdTK96FJxje3rtcmxZtRQ3DC5saoEYv/dQ4d0hBdSeiDjv2VXrR/GyR8O6rzePNSsWoq/XPJbqmV9VPPPz9GnRNruYj9OpK3uum+HVA+DuyXYbWjY/1BQ+V/n0dPf4XHY6eKiK27fuw2+9aSYEr08kXx6roNzCaOGS3Wuwefd+4/NwNDIRUXO8ckSYWtkgM1kU8jmcd/rs0L3QUc+60QpTjvUTNv86ir15LFm7CeWxSs3MXF5t8d7pVtPplcNHjc9XHqt45udXj4S/olws5PHuRScYl1dnPk6fruy5bkaY3min9iuIRheHGn0RFcCTL70ayUwiM6fnJuvcBk6ehS2rlk6eddfjaGQioua0umy3X2PK/Xybd+8P3QvdbK96WtS/t0G9cvjoZGdSo3z6wlgF6zbuaVjeEUV+njk9h5crVWzevd9YX5+Vv003Yc91AGF6FJwVnVrpOU6K05B3926cd/psz7Pl806f3dZ9IyLqJM30yjrmFAueOaZULNSMhTHVH/s1zlvtVU9C/eqO7vyUE2k425YAoeqg5xQLbestduflnMdMZADzcRrF1rgWkVsAvBvAS6r6VnvbOgAXATgCYC+Aj6rqmMdjnwHwGwDjAI6q6kBc+9lIqW5VqGuHd+KOR57DuCpyIjj3LX145lcVvDBWQSHfMzmjRhSskcqaSE1ho96Nzbv3t3FviKgdOiVuZ0Ery30HbQCbGuF+PZ1egwvDLkUe1PBIGdd/d9fkIizFQh6rL65dtrvR+3Tt8M4pi824O4QaNawbDUz0uv/QsvnGgY5xGjecADAfp0+cZSG3ArigbttDAN6qqmcC+DmAa3wef56qLk4qQBfyOdy0cjG2rFpa07C+beu+yS/ruCq27D0wOaVPlA1rALj0rBKOSXCKPr/abdZ4EXWkW5HhuJ0Vzcxz7Ra0rMRrOrkgvdD1gwvjalgP3b2jZnXDsUoVV60fRf/nH8TwSLnh+zQ8Uq5pWDfDeR+DEFh5ebC/hIRn4avBfJw+sfVcq+rDIjKvbtuDrptbAVwW1+u3IifiGajueOS5yF5j5vQcJhS+Z8xOD3kascaLqPNkOW5nid+MHEEbskHKStrZCx3Wuo17UB33zm8HD1Vxzb07MWNaj+/7tG7jnpYa1n29+cn3IsggSAVw29Z92Lx7f6qWPGc+Tp8ka64/BmC94XcK4EERUQDfUNWbTU8iIlcCuBIA5s6dG8mOjat6XpaKsqGbz/X4jv519sM9ajkt0l5/R0SxaTluxxGzsybqGTn8Sidaqe2OgmnfGh1rpTpubOw6j221x9ZJ6e6TkCDjpepnFEkS83E6JdK4FpHPAjgK4HbDXd6pqmUReROAh0Rkt6o+7HVHO4DfDAADAwORfNYFVkAY7C9Zl67u2hHppO+AdfnLr2HtSMOX162+Bp2IukNUcTuOmJ0VTkPTdNDN9EAmsehLUPX5szxWwdBdOwCY68GDcN6nVp4DAF6uVGsmIQiz4mJaPrhcQCad2j4Vn4h8BNaAmQ+oencFq2rZ/v8lAPcBOLttOwjrS+MM5lu9YVfkDes0KxULxonxnZHo/CITdZcsxO20c9cPe2m2BzKpRV+C8Mqf1QnF6g27MLRsPvK58IXL+R6ZfJ9My6bPCLpQi1jrTDh/k7SWYZqUigXm45Rqa+NaRC4A8OcALlbVQ4b7zBSRNzg/AzgfwGPt20uLc7mp3aOBk5QTwZZVS3HdRQuaGgTTSNiVwogoeVmK22nmt/BLqVjApWdZNcRh42OaFn2pZ8qfY5WqVTN92SJjZ46p2X3sMdNqSl4uPas02eOcE8EHzp2L144Gm1wgK21pa7Xm5nIy824yYmtci8gdAH4MYL6IPC8iHwfwVQBvgHXJcFREvm7fd46IPGA/9M0A/q+I7ADwEwD3q+r349pPk24cIDCuiiVrNwFASwsceGl1dDwRxS/rcTvNTI1dgdUDe8/2csP46NVQyvKiL4P9JYx87nw8s3Y5blq5uCbnmNq9Y66BhMMjZdyzvVwzg9c/PGIutxSx3u8UTfQRyDH5Hlx6Vil0TmbeTU6cs4Vc4bH57wz3fQHAhfbPvwCwKK79CsqZlH3m9FxTy5VmVXmsgqvWj6KvN4/rLlow5cvb7NysUYyOJ6J4ZT1up5nfnNNB4qOptvrSs0q4Z3s5lYu+mPLnzOmNp5jt6817zsjhPmnwet/8qjgFwPvPnRtovFOaVKoTuG3rPvT15nHjysWT48GWrN3km4uZd5PD5c8NnEnZ87nOfIvyOZnsKfDiTIXkPsNt5Sw4zZcuiYjiNrRsPvJ1K+w59cNB4qOpobR59/7IrzRGxZQ/67d75ZZXDh/1rMkuj1Um58EOO5hxQqOdUjcOuR4xLnPu5OVrh3cGysXMu8nh8ucGzofv5Q6tua6Oa8Nph+rPcFs5C25mpTAioo5S31a0bweJj6YGUXmskvh0eyam/Fm/3Su3VCcUxUIeIpjSg33wUBVDd+9oajq8tA9aHJ9Q37Feleq45xoYXrmYeTc5ndktG4GiPciikz+EzlydftwBvZWz4GZXCiMi6gRei6Y4nRxB4qMpFzlTx6ZR0HpwUyfPWKWK3unefYDVcU3NdHjtZjpBqM/FzLvJCdS4FpF3ishH7Z9ni8gp8e5W8pzPrteHs5M0Ck7uINjKwJmgy/USUeu6MWankXsAoqkB+YLd89woPg4tm+/ZGeKeOjZtvPJnvkdw6MjRmkGZpvmlcyIsYfBger/qczHzbnIaloWIyHUABgDMB/C/AOQB3AZgSby7liznspXzIVy9YVdXTcsHTD3DHVo2f8oSsWHOgtN66ZKok3RrzE6b+gGIJk6DqH6pcqfB7J527qr1o57PkdYGaP0xHVfI49UjRyfLPJxaYVNP7LgqSi0uFNNpCvlcqEGszLvJCFJz/R4A/QB+BlgjxJ35TDuZ+wxwsN+af7SbGtc5kZqFCNxf0GZmCyGitunKmO2l2dmNoni9HpFA9b3OzFRBVlo0NTTTXL7ozh1L1m6akkcr1XGIeM853debx9Cy+bGskpxVM6b1YODkWRg4eRZzcYoFaVwfUVUVEQUmFwjoeM5lK+dDm9aegTgIXq/pqg/wPAsmSr2ujNn12r0seP3rBR04d/+jL+KGwYWBBoy3evWwWVGdpJjyqOmtUn39b3XNvY+iUg22OEwnG6tYM4asWbEQW1YtTXp3yCBIzfWdIvINAEUR+WMA/wzgm/HuVvIOHqrWTHFTNKwi1Ynq41xaltIlokC6MmbXa/ey4H4rMPo5eKiKJWs3+dZkO5KooY1yIZKwPey1s4pkbemX+DAnp1/DnmtV/bKI/CGAX8Oq4fucqj4U+56lSKU6jhnTelDI55oKnu3WzPREjXRTzz1RljFmW9o9x28rz+vM3OQVt70GqbXz6mGUC5GYet57BJ6LzTidWs2euKRRVPmZOTndGvZc26PM/0VVh1T1z2AtcTsv9j1LmbFKNRNfbgHwgXPnwjCYuGlprukjotcxZlvavSx4q8+rmNo3m4Zp0/zm1/brvfZaqt3U825abMYpF4mjIWmacSNOUeZn5uR0C1IWchcAd6HTuL2NUkgBDJw8y7jCUyO9+R7Oi0mUbYzZaP8cv87AxFYokLpp0/wacabyEL9SksH+EoaWzcecYmFyVhTTZAFjFatkJo6hjF9576IYntWfwlr9udV1bJiT0y/IgMZpqnrEuaGqR0Rkeoz7RC0QoKWR1ZXqBG5cudhz8Eq7R94TUVMYszF1Gri4Y9bm3ftbfo5SsZC6QWpepRyOSnUc139315T3tFG9e/1AU79SiTim4XM6jpOY5q/Z1yvZJyPO5xiwZl9hPk6nII3r/SJysapuAAARuQTAL+PdLWqWAi1NWTSnWPCs6Wv3yHsiahpjtq2d9cmtNtLS2hvpvH+mObYPHqri2uGduGFw4eQ2v3p3r4a3UxLTrsn2FNbxFAt55HMyZeXMtKk/6WI+Tr8gZSGfAPAXIrJPRJ4D8N8B/Em8u0VJ8Avu7R55T0RNY8xOQE8LdbRpKQExGewvoeRTHnL71n015SGmUpLjCnnjSYii/XXQY5UqxlPesPbKy8zH6dewca2qe1X1XABnAPhtVX2Hqj4V/65RnAr5HD547tzA9X3NDmohovZizE5GsxcM+3rz2LJq6WTs9RoImAZ+ver1S7B71bsDwK8PmxdiKxULmGi1GLkJaZs5O9cjKBbyvnnZLx9TOhjLQkTkg6p6m4h8um47AEBV/2fM+0YxKRnqs5yg7lXDNcenNq3dl6NY+000FWN2NjlLgQPpvtw/2F/C6g27jIMP6+fj3vbsAdy+dV9NqYfpBMTpnV23cU9XNxCLhTxWX7yg5m/tlZdN+Vjs+zf7WWFujY5fz7WzqtcbDP8og/rseUOvXj9a0yvSaKEAU08E0N7LUVEuaEDUYRizM8hdCpH2y/2rL15gXMqlvhRk8+79gWuonZKaKGZcybLfHD5ak5tN+e6802d7/h3qryCEwdwaLWPPtap+Q0RyAH6tqje2cZ9SId8jLQ0MTKuDh6qTPSXuXpFGCwU0GtTSrgnto1zQgKiTdHvMTtrM6TnPhVAacS+T3u6Fb8Iy9Uh71QWH2edXj4zjmnt3Ysa0IMPAOpfzWXBy84xpPZ75zu/EpdnPCnNrtHw/yao6DuCKNu1Lqqy7fNFkL28nc748QYK636CWdk1on/bkQ5Skbo7ZSdMm64XdPdftXvimGTcMLsSNKxfXjNe59KwS1m3cU1MnHnafK9VxY8lJN/J7P14Yq0Sei5lboxXkNHGLiHxVRH5HRN7m/It9z1LgcDVtQx3i4dRXeanf3u6FGRrtT6PtRF2oa2N2kg41mS/cPddh42tSgx8H+0vYsmopnl67HEPL5uOe7eWacoKr1o/i4KuvIZ9r/yqI3cCph44yFzO3RitI43oxgAUAPg/gK/a/L8e4T6mwesOuTCx37mfmdO8a6XrOFzVfN5dUvkemfFFNy9e267JR0o17ogxYjC6M2Wn2zNrlvtPMNVoe3Cu+pqVG1qucALBONtI+zV2SguTnmdNzU2qrnXwXdS5mbo1WkEVkLlfVrluAoBMuT1UC1P8JXINI6r/FhlzQzoUZvF4baN+qa0QZ1JUxO2nFQt4zbxQLVnnhuE/ZSP2sIEHiWVpqZP3KBrrj2m9zguTnSnW8prZaAFx61uufjyhzMXNrtPym4rsIwC0AqiIyAeC9qvqjtu0ZtSxIYFMA92wv4/5HX5yySlV1XFM5mCHJxj1RWjFmJ+vdi07AbVv3eW4HGi+1HbZhnJYaWb9pWpMmAiQwdXYgQfJz/ZwKCmsWlrgwt0bHryzkiwB+R1XnALgUwJowTywit4jISyLymGvbOhHZLSKPish9IlI0PPYCEdkjIk+JyKowrxuV3nz3jFquVMdr5lp142AGosxoKWYD2Y/bSTI1epztQaaZCxNv/Wpk21mL7TdNa9LS2rBuBXNyNvi1II+q6m4AUNVHEH6e1FsBXFC37SEAb1XVMwH8HMA19Q+yp5L6GwDvgrXC2BUickbI125Zs4NTOg0HMxAFk4KV9VqN2UDG43aSGvUkB+lxDBNvTTWy550+O9Ja7Eaf68H+Ei49i72d7cKcHJ04Y7ZfzfWb6lb6qrndaLUvVX1YRObVbXvQdXMrgMs8Hno2gKdU9RcAICLfAXAJgMf9Xo9aUyzk8drRiZoaPg5mIAomJSvrtRSz7fswbjfpOEPN9XF2zXWjHsew8dZUIxtlLXaQz/XwSBn3bOdCI+3AnByduGO2X+P6m6jt+ai/3aqPAVjvsb0E4DnX7ecBnGN6EhG5EsCVADB37twId6+7vHvRCRg4eRYHMxA1ISWDy+KO2UAEcbtTY7ZpMhBnu19tcqnJeOtVI3t1hAt9NVoxstuXK2+HmdNzOHRknDk5YnHHbL8VGq9v+dkNROSzAI4CuL3V51LVmwHcDAADAwMdWGHVHpt378cNg+2bUo+ok6RhcFmcMRuILm53asweM4xbcbYPLZtf01MGWD2RUU9lamrEN1NOYPr8Or18QaarzYlgQhXF3rxxbA+ZFXunY9fnlya9Gx0n7pjd9lF7IvIRAO8G8AH1XtKqDOAk1+0T7W0UI/cHKgW1o0SZ0ukLMDBuN9boM9CuNQK8arEFVoM4bDw3HVNOJPA6EG84ZhpuXLkYI587Hx88d65phlcyeGGswpwcg7hjdlsb1yJyAYA/B3Cxqh4y3O2nAE4TkVNEZDqA9wHY0K597FbOByotCxMQZUknL8DAuB1MkM+Ae2XDLauWxnKl0N2IB6yGtXM2FDaemxrqfnN21xurVDF01w5cO7wTm3fvhwLoYQs7sOMKeebkGMQds2NrXIvIHQB+DGC+iDwvIh8H8FVYNYAPicioiHzdvu8cEXkAAFT1KIBPAdgI4AkAd6rqrrj2k2o/UI1q7IhoqqRXLo0K43bz0vQZcBrxpWIB9c3gMPHcmQnE3RZWGNcXM6pOKG7bum+yXKV+/mbyVsjnIALm5BjE/X1tuEKjiLwZwP8AMEdV32VPr/R2Vf07v8ep6hUemz0fo6ovALjQdfsBAA802jdqnXN5z/mipqF2lCiL0rIAQ7MxG2DcblVaPgOOKOK509vs5jSw2UaOj5ObTeU3zMmti/P7GqTn+lZYvRFz7Ns/B3BVLHtDbdMDIJ+Tyct7zqWmYm/e8/6dUjtK1AVuBWN2Rwpbexu2rtTr+U2NOIXVAKTo5XqkYekNc3K6BWlcH6+qd8JerdO+/BdsJAOlhnvFyWIhj+N681OWO69Ux6GKjq0dJeoSjNkdKOx4mOGRMg4dOTpluymeD4+UMXTXjprnH7prh7HDBQhXe03ecj0yWWaTE8HM6TmMN6ibYU5Ov4ZlIQBeFZE3wr4CJCLnAng51r2iSPX15jHyufNrtp2y6n7P+75cqeLGlYs53zVRdjFmd6Aw8/LWL5DhKBbyWH3xAs/7X71+dEqZR3VCcbg6jkI+F3h2EAouJ4KvXL6o5u9hys2AVYrDnJwNQRrXn4Y16vtUEdkCYDa8V+iiFCrkc7juogVTtvvNhZq2ukEiCoUxuwOFqZ/2aogDwMwZ04wNcVNfaaU6gZvsDhcuGBOtr7x30ZS/hyk3l4oFbFnF+a6zomFZiKr+DMB/BPAOAH8CYIGqPhr3jlHrpvUIZkzrwdXrR6fU53Xy1GFE3YwxuzOFqZ+OoiHu5sw+0o4K6x5YqxJ2upyAubmDGXuuRWSF4Vf/XkSgqvfGtE8UkaMTirGKtSKWU58H1I6QZfkHUWdgzO5sphUevRpdYVZpbDTrRJ+r5tpvCfeoTAB49cg4eqSzp+xzhjwxN3cmv7KQi3x+pwAYqDOmvj6P5R9EHYUxu4OFaXRF0RAHrMF27rJCr+eNSyc3rOsxN3ceY+NaVT/azh2h9uDcmESdiTG78wVtdLXaEHfU1406j1+9YdfkVVGKBnNzZwkyoBEishzAAgDHONtU9fNx7RTFp/6y4PBImZefiDoMY3Z6tSvmhm2If+bOHVOm1qtO6JTZSJzndY6Dgxyj4c7NzMvZ13BAo73U7UoA/xXWTDCXAzg55v2iGNRfFgw7byoRpR9jdnqlNeYO9pcwYZiz2tSjOthfwnmnz45zt7qGOzen9TNC4QRZROYdqvohAAdV9XoAbwfw7+PdLYpaqVjAmhULJ89+h0fK+MydO4zzphJRZjFmp5TfXNVJC7uaIwDc8chzce1O18iJ1OTm1Rt2pfYzQsEFKQtxTlsPicgcAL8CcEJ8u0RRE6BmfkznzNi0uhZrv4gyjTE7pcJMkdfO0oCwqzk6uEJj6yZUazq9TLXszMvZEqRx/T0RKQJYB+BnsEadfzPOnaJo1fc8NJrX1K+ngohSjzE7pYJOkVe/wmL9dG1R8lvN8d2LTsC6jXtw9frRmga+0/Cn1rn/9n7vKfNytjRsXKvqF+wf7xGR7wE4RlW5lG5GePU8+J0Bc7J6omxjzE6voFPkhVnqvBVOeaBXD7QIcM/28pQG/rZnD9Rsp+bV/+39cjPzcrb4LSLzHwA8p6r/at/+EIBLATwrIqtV9UCb9pECyIlgXBV9vXmoAi9XqsZLiabek/raLyLKDsbs9As6RV6Y8pGw3LN8CGBc9vzgoanlCZXqOO545LmG5SB9vXm8cvgoqt00WbWHkp1rg+ZnU27u680zL2eMX8/1NwD8AQCIyO8CWAtr9PliADcDuCzunaPGSsVCTT11EKbeEzasiTKNMTsDgkyRF2aFxTDqS0Caafr6Nazd+ajbp+qLMje7F/KhbPBrXOdcPR0rAdysqvfAutQ4GvueUUPNlnBweVWijsSY3SHCrLAYRqPxNu7XmjGtJ/RCMeWxCk695gGMq6Jk5xUAGLp7B6rjtY3yfI9g5dkndWSJCXMz+TauRWSaqh4F8PsArgz4OIqRcxmvFOJLZxp1zi8sUUdhzO4QcTWygpSV9AiwZsVCAMDV60dD9247PdtOjfaaFQux7rJFuP67uyZLTUSsBWo2796PS88q4b6flfHqkXQ3sMX+v9H70debx3UXLWBu7nJ+AfcOAP9HRH4Ja2qnfwEAEfktABwck4CSx2htZxT3eafPxubd+6d8Sds56pyIEsWY3UHiaGSZyk3c/t0xr9f3XrV+1Hg/v3pthzMIc8uqpcZ89A+P7EMaS7NnTs/VNPhNuyj2G1Gfd5es3TSZj73yMwDm5g5mbFyr6hdF5Aew5kd9UHWy0KoHVh0ftZF7rmqvAHXb1n2T93V/Sds16pyIksWYTY14lZvUe9lVClIyNMadjp4gNdXlsQquHd6JGwYXeuajNDasAeBwdSLYHRV4eu3yyZtB8/OMaT3MzR3M91Khqm712Pbz+HaHTOrnwmxUo+Z8SeMcdU5E6cKYTX7c5SamRrE713g1xgVWA3Hdxj2BG9hO4zJLeSfoAjlh15EArPxsuk+W3iMyC7L8eVcp5HNJ74KnV187iuGRMoDgXz7nEpQXTkhPRNR9BvtL2LJqKW5auXhKvqsfiDfYX8KaFQtRsvOFuxTE6YE97/TZgfLmHY8819a8U79PuR4x3NNbThrf3znRWLJ2U+j8bMLc3BnYuK6zZsXCQF+qdhurVHHNvTsxPFIO/OVzarsaBVAiIuou7oazwCr18JqO1WmMl4qFKXXHleo4Nu/eX9MANxlX9cxHcSgW8lOO7Q0zvC/U9+Z7puxTvkdwTH5q8yjfI+jrzQPwPtEIk5/7evPMzR0stsa1iNwiIi+JyGOubZeLyC4RmRCRAZ/HPiMiO0VkVES2xbWPXgb7S5gIeDmoVaYmfLGQn/wCuzmlHkEClPMlDRpAiYiyGrepOU7D+em1yycHHZr4lRg6z/PM2uXGzqmcyGQ+8spvUXr1yFEAqDm2lw3TCh6qTqBSHZ/c72IhDwimzF5SLOSx7vJFGPnc+cYTjTD5+bqLFjA3d7A4p2e6FcBXAfy9a9tjAFbAWuygkfNU9Zcx7FdDQUZUR8HUhJ85Y1rDQAbUTtNkmi0EiGfUORF1pFuR0bhN8Qq6sM0V55xUM4DPvR2w8tG6jXs8V4AUANNyMmVO7LCq44qr1o9ONnYBoMdeJdFkXBWFfM6aJtDj9WfOmDaZR6POz9R5Ymtcq+rDIjKvbtsTACApLLtwCzKiOk7OF9AvkLHBTERRy3LcpngFXdjmhkFrjmxnmfScCK4456TJ7YB/XfK6yxa1PAWgozxWwdBdOwAJNkAx6EBD5mdqJK011wrgQRHZLiJX+t1RRK4UkW0ism3//v2RvLhXKUWvR/1VXFgrTUQZFChuxxGzKX5hSgxvGFyIvWsuxDNrl2Pvmgtxw+DCybmfT1l1P3oMJ2pzigUM9peM9dulYgFPr13esL7brTqhLfeEO/vmYH6mRtK6atc7VbUsIm8C8JCI7FbVh73uqKo3A7gZAAYGBiIrlnafeV47vNPzMlcc3LXSAJdBJaLMCBS344rZFL8wPbLu1QeLvXm8cvgoqvak1l69yO7GaaNe8jivLhcLebx2dMK3h575mRpJZeNaVcv2/y+JyH0Azgbg2biO2/BIGbcbGtaFfA9mzZyB8lgFPdLcZPj5HsGxx0zD2KEqa6WJKLPSFLcpWfULqXjVVwPWIMcJVWMdslfj1Wm0O4MQg85HXa9HrNevuhJ3IZ/D6osXGF/bjfmZ/KSucS0iMwH0qOpv7J/PB/D5JPZleKSMz9y5w1jfdbg6gXlvtGqvmmlYFwt5rL54Ab+gRJRpaYrb1B7unun6BmiQhVQAYEK1ZnVDN3fjdXikjNUbdk2pxW62YW29NpDrsfLwyxXvzi2iZsU5Fd8dAH4MYL6IPC8iHxeR94jI8wDeDuB+Edlo33eOiDxgP/TNAP6viOwA8BMA96vq9+PaTxPnzLvRl3fL3gNNv8ZYpYrP3LkD1w7vbPo5iIiikvW4Te3h5MfyWAWK2nmegeALqRxXyE/WYbsXYql/raG7dmDMMJVeK6rjiur4BHpEUB6rMB9TZOKcLeQKw6/u87jvCwAutH/+BYBFce1XI9cO75wc5dxIFMWC46qT9dzu0dRERO2W1bhNrXPnPq8ZPty8eqadeZ4H+0uBprPN9whePXJ0stHsNNDdr/HCWKXhFHqtcs9nzXxMUUnrbCGJuW3rvli/yCZ3PPJc21+TiIjIGbTv5D6nkWnqxfWb5xnwnk0jnxMUC/nJmUaOPWbalFk8KtVxrN6wq6ZXPEg+7uvNR7qyMvMxtSp1NdfdKokGPRERkakxedvWfbht674pPdlB5nkGpg4KdG8zZbxmyj+Wn3kCBk6eFdkMIszH1Co2rlMiyrNuIiKiIIZHyg0bk/XlEkEWlKmfTaN+BpEobd69f7Lhv27jnkArLJeKBWMjn/mYWsWykJRwloZ1c0+6bxrsQURE1AynwRuU08MdZkEZR9AZRJrhlKMM9pewZdVSFAt53/sLgC2rluID5871/L1XPnZjbqZG2HOdgN58D147qr4DR+rP8t2DPThFEBERtSpsg9fdwx12nuegM4g0Y07dio0vNygtce4fZKn2eszNFAQb1zEQ+M8ksuKsExuORG40GpuIiKgVYRu8rZRLBJlBpBley44fV8j71m4fOnIUwyNlDPaXcMPgwlAzgzA3UxAsC4mYAMZLTY57tpcbXkZqNBqbiIioFfU9vo5C3rtp0Khcwo/XDCLNyPXUzjriVY7S6Bzg4KFqzbzcYTA3UxBsXNfx+vIvOXUWSoYgVK/Ym294Fuyc5foxBT3TdiIiojC8GryFfA5rVpyJD547d7KnOieCD547t6W5n5067VYHC45PKGbOmIan1y7HllVLPXuLxwzLrbsFycNemJspCDau6ziDNIDXL4E986sKzjt9NvI9jYNCkC810Pgs1xT06i9/ERERNcNvYOINgwuxd82FeGbtcuxdcyEGTp7V8iC+wf4SvvLeRS33YDfKn0Ebus30NjM3UxCsua7jnAXXD1i4Z3sZK88+Cbdv3edbT+18qft68zjo09Bu9OU3zRPKmi4iIopKkIGJUQ7iq89tgPcYJaeTy28+bZOhZfNx9frRhqsoN9PbzNxMQbBx7cE0YGHz7v24ceVi41yd7rPX6y5agKG7d0xZgar+fn7CjsYmIiKKWtSD+Ny5zWv+a3eObDSftun5tz17wLczrJXeZuZmaoSNaw9+AxbcZ63lsQpyIhhXRanu7DXo/YiIiNIszkF8QXqCm+klvmFwIQZOnjX52OMKeYhYpZvsbaa4sXHtIcjSrkG+lDy7JSKirGuUE1vllytbyaPMwZQUDmj0wAELREREFuZEonDYc+2BAxaIiIgszIlE4XRl49o0k0dfb37yZ15OIiJKjyBxm+LDnEgUXFeWhZxxwhs8t79yuNrU3J1ERBQvU9w2be8EwyPllueWJqL268qe6x/tPeC5vTqBpufuJCKi+Jjitml71kU5tzQRtVdX9lz7TSzf7JKoREQUH1PcbrRQSFb5zS1NROnWlY3rRqKYu5OIiKhZcc4tTUTxYuPaQ1RzdxIRETXDlIeYn4jSrytrrv3ENXfn8EiZ0xgREVEgQ8vmN7X0N5kxD1O7dGXjumRYbSongjUrFob6sgX5snJgChFRa0xxu9ShPbmcW9oSVYOYeZjaKbayEBG5RUReEpHHXNsuF5FdIjIhIgM+j71ARPaIyFMisirqfRtaNh+5HqnZlusRfOW9iwJ9yZzpkeatuh9Xrx9FeawCxetf1vrpkjgwhYiyIItxu5N7cgf7S9iyaimeXrscW1Yt7dhGoGnKQadB3CjHBsE8TO0UZ831rQAuqNv2GIAVAB42PUhEcgD+BsC7AJwB4AoROSPKHdv27AGMT9SOMR+fUGx7tvGUTu4vOzB1pLrXl5UDU4goI25FB8ZtSi+/BnSUDWLmYWqn2MpCVPVhEZlXt+0JABARz8fYzgbwlKr+wr7vdwBcAuDxqPbtjkeeM26/YXBhzbb6S1KvvnZ0ype9Xv2XdY7hciYHphBRmnRK3KbkhC3j8GtAR9kgZh6mdkrjbCElAO4o+ry9zZOIXCki20Rk2/79+wO9wLh6z4xav93rjHqsMnX53Xr1X9ahZfNRyOdqtnFgChF1kMBxu5mYDQSP25ScZso4/BrQUc6YwjxM7ZTGxnUoqnqzqg6o6sDs2bMDPSZn6IGp3+51Rt2I15d1sL+ENSsWolQsQGANwAk7cJKIqBM0E7OB4HGbktNMGYdfAzrKBjHzMLVTGmcLKQM4yXX7RHtbZK445yTctnWf53a3oJeeBFbtdcnnEthgf4lfYiLqVKmJ25ScZso4/KYcjHrGFOZhapc0Nq5/CuA0ETkFVnB+H4D3R/kCAyfP8gzSAyfPqrltqtHq682jd/q0rp4eiYjIJTVxm5LTTF1zowY0G8SURbE1rkXkDgC/B+B4EXkewHUADgD4awCzAdwvIqOqukxE5gD4lqpeqKpHReRTADYCyAG4RVV3Rblvf3Hvo8bt7i+x6Yz6uosW8MtORB2nE+I2JafZhW/YgKZOE+dsIVcYfnWfx31fAHCh6/YDAB6IaddwqDoRaDsn8SeibtIJcZuSw5xJZEljWUiq8IyaiIgoGOZMog6YLYSIiIiIKC26snFtmriJEzoREaUT4zYRZUVXNq5NSw5wKQIionRi3CairOjKxjUXIyAiyhbGbSLKiq5sXJsWHeBiBERE6cS4TURZ0ZWzhdwwuBAAcMcjz2FcFTkRXHHOSZPbiYgoXRi3iSgrRLVzKtYGBgZ027ZtSe8GEVFoIrJdVQeS3o92Yswmoqzyi9ldWRZCRERERBQHNq6JiIiIiCLCxjURERERUUTYuCYiIiIiiggb10REREREEWHjmoiIiIgoImxcExERERFFhI1rIiIiIqKIsHFNRERERBSRrlz+HACGR8pYt3EPXhirYE6xgKFl8zHYX0p6t4iIyIBxm4iyoCsb18MjZQzdtQPVCWvp9/JYBUN37QAABmoiohRi3CairOjKspDVG3ZNBmhHdUKxesOuhPaIiIj8MG4TUVZ0ZeN6rFINtZ2IiJLFuE1EWdGVjWsiIiIiojh0ZeO6N+992KbtRESULMZtIsqK2KKSiNwiIi+JyGOubbNE5CERedL+v8/w2HERGbX/bYh632bkc6G2ExF1A8ZtIqLWxXnKfyuAC+q2rQLwA1U9DcAP7NteKqq62P53cdQ7NnbIULtn2E5E1CVuBeM2EVFLYmtcq+rDAA7Ubb4EwLftn78NYDCu1/czp1jw3K4AlqzdhOGRcnt3iIgoBbIYt03bO9XwSBlL1m7CKavuZ74iSql2F6u9WVVftH/+VwBvNtzvGBHZJiJbRWTQ7wlF5Er7vtv2798faCeGls1HwXApsTxWwTX37owkYDEIElEHiDRuNxOzAe+4XcjnMLRsfuDnyLrhkTKuuXcnymMVKKLNV52G+ZeSlNhIEFVVWJ3FXk5W1QEA7wdwk4ic6vM8N6vqgKoOzJ49O9BrD/aXsGbFQpQMPR6V6jjWbdwT6LlMGASJqNNEEbebidlAbdwWAKViAWtWLOyqBWTWbdyDSnW8ZlsU+arTMP9S0tq9QuO/icgJqvqiiJwA4CWvO6lq2f7/FyLyQwD9APZGuSOD/SUM9pdwyqr7PTPFC2OVlp7fLwh2UzIgosxLXdzuVqa81Gq+6jTMv5S0dvdcbwDwYfvnDwP4x/o7iEifiMywfz4ewBIAj8e1Q3HV8TEIElGHSF3c7lasOw+G+ZeSFudUfHcA+DGA+SLyvIh8HMBaAH8oIk8C+AP7NkRkQES+ZT/0twFsE5EdADYDWKuqsQXpuOr4GASJKGuyEre7FevOg2H+paTFVhaiqlcYfvX7HvfdBuCP7J9/BGBhXPtVz7lEtG7jHrwwVsGcYgFDy+a3fOloaNl8XHPvzppLUwyCRJRmWYnb3SqufNVpmH8pae2uuU6lOOr4GASJiChq3V53HgTzLyWNjesYMQgSERG1H/MvJSmxqfiIiIiIiDoNG9dERERERBFh45qIiIiIKCJsXBMRERERRYSNayIiIiKiiIiq1+Lf2SQi+wE8G/JhxwP4ZQy70048hnTohGMAOuM4sngMJ6vq7KR3op2ajNlANv++JjyW9OmU4wB4LHEyxuyOalw3Q0S2qepA0vvRCh5DOnTCMQCdcRydcAxk1kl/Xx5L+nTKcQA8lqSwLISIiIiIKCJsXBMRERERRYSNa+DmpHcgAjyGdOiEYwA64zg64RjIrJP+vjyW9OmU4wB4LIno+pprIiIiIqKosOeaiIiIiCgibFwTEREREUWkqxvXInKBiOwRkadEZFXS++MmIreIyEsi8phr2ywReUhEnrT/77O3i4j8lX0cj4rI21yP+bB9/ydF5MNtPoaTRGSziDwuIrtE5E+zdhwicoyI/EREdtjHcL29/RQRecTe1/UiMt3ePsO+/ZT9+3mu57rG3r5HRJa16xjs186JyIiIfC+L+2+//jMislNERkVkm70tM58lal2aY3ZYXp/nrAiTn9LOcCyrRaRs/21GReTCJPcxqLA5N818jiUbfxtV7cp/AHIA9gJ4C4DpAHYAOCPp/XLt3+8CeBuAx1zbvgRglf3zKgD/v/3zhQD+CYAAOBfAI/b2WQB+Yf/fZ//c18ZjOAHA2+yf3wDg5wDOyNJx2PtyrP1zHsAj9r7dCeB99vavA/jP9s+fBPB1++f3AVhv/3yG/RmbAeAU+7OXa+Pf4tMA/gHA9+zbmdp/ex+eAXB83bbMfJb4r+W/f6pjdhPHM+XznJV/CJGf0v7PcCyrAfxZ0vvWxLGEyrlp/udzLJn423Rzz/XZAJ5S1V+o6hEA3wFwScL7NElVHwZwoG7zJQC+bf/8bQCDru1/r5atAIoicgKAZQAeUtUDqnoQwEMALoh9522q+qKq/sz++TcAngBQytJx2Pvyin0zb/9TAEsB3G04BufY7gbw+yIi9vbvqOprqvo0gKdgfQZjJyInAlgO4Fv2bcnS/jeQmc8StSzVMbubhMxPqWY4lkxqIuemls+xZEI3N65LAJ5z3X4e6f/DvVlVX7R//lcAb7Z/Nh1Lao7RLi/oh9Xzm6njsEsqRgG8BKsxthfAmKoe9difyX21f/8ygDci2WO4CcCfA5iwb78R2dp/hwJ4UES2i8iV9rZMfZaoJZ32t/P6PGeZ6buYVZ+yS8puyUIZRb2AOTcT6o4FyMDfppsb15mm1rWSTMyjKCLHArgHwFWq+mv377JwHKo6rqqLAZwIq/fs9GT3KDgReTeAl1R1e9L7EoF3qurbALwLwH8Rkd91/zILnyUiF9/Pc5Z1wHfxawBOBbAYwIsAvpLo3oSU9Zzr5nEsmfjbdHPjugzgJNftE+1tafZv9qVt2P+/ZG83HUvixygieVhfjNtV9V57c+aOAwBUdQzAZgBvh1VmMM1jfyb31f79cQB+heSOYQmAi0XkGViX0ZcC+EtkZ/8nqWrZ/v8lAPfBOtHJ5GeJmtJRfzvD5znLTN/FzFHVf7M7VSYAfBMZ+tuEzLmp5nUsWfnbdHPj+qcAThNr1oTpsAZvbUh4nxrZAMCZ3eDDAP7Rtf1D9gwJ5wJ42b4EtBHA+SLSZ186Od/e1hZ2re7fAXhCVf+n61eZOQ4RmS0iRfvnAoA/hFX7tRnAZYZjcI7tMgCb7J6CDQDeJ9ZsHKcAOA3AT+Lef1W9RlVPVNV5sD7jm1T1A1nZf4eIzBSRNzg/w/oMPIYMfZaoZVmM2Z58Ps9ZZvouZo7TELW9Bxn52zSRc1PLdCyZ+du0c/Rk2v7BmlHg57BqaD+b9P7U7dsdsC55VGHVFn4cVu3rDwA8CeCfAcyy7ysA/sY+jp0ABlzP8zFYg8+eAvDRNh/DO2FdfnoUwKj978IsHQeAMwGM2MfwGIDP2dvfAqtx+RSAuwDMsLcfY99+yv79W1zP9Vn72PYAeFcCn6nfw+uzhWRq/+393WH/2+V8X7P0WeK/SD4HqY3ZIY/D8/OclX9h8lPa/xmO5X/bceNRWA3TE5Lez4DHEirnpvmfz7Fk4m/D5c+JiIiIiCLSzWUhRERERESRYuOaiIiIiCgibFwTEREREUWEjWsiIiIiooiwcU1EREREFBE2ril2InKiiPyjiDwpIntF5C/teWq97jtHRO4O8JwPOPNPN7E/q0XkzwzbyyIyKiK7ReRrItLyd0RErhKRXtftZ0Rkp/06oyLyjiDHIyLnisgj9mOeEJHV9vaPiMh+1/P9vb39chHZJSITIjLQ6nEQUTaJyBtd8eFfXXFu1BSLW3it0+3nHRGRU6N87hD78EOvmGdv3+OKoS0vOy8iRRH5pOv2PBGpuN7fURGZGzCvfczODY+KyGMicom9/VYRedr1fP/N3v5FEXlORF5p9TgoWtMa34WoefZE8PcC+JqqXiIiOQA3A/gigKG6+05T1Rfw+uImRqp6YRz7C+BGVf2y3ah+GMB/hLXgSiuuAnAbgEOubeep6i9dt4Mcz7cBvFdVd9jv43zX79ar6qfq7v8YgBUAvhF+l4moU6jqr2AtFw37pPwVVf2y83s79h6N6OUGAdytqjcEubOdI0StFffa4QOquk1EZgHYKyK3quqRFp6vCOCTAP7WtW2vqi6uu59vXhORE2GtJfA2VX1ZrGW/Z7vuMqSq9Q307wL4Kqz5qylF2HNNcVsK4LCq/i8AUNVxAFcD+JiI9Nq9rhtEZBOAH9hn/Y8BgP37O0XkcRG5z+61HbB/94yIHG/f/wkR+abdS/ugWCspQkT+WER+KiI7ROQed+9xANNhLahy0H6u/2bvx6Mi8h1722oR+baI/IuIPCsiK0TkS3bPw/dFJG/3MMwBsFlEjI30IMcD4E2wFjuAWsu/Pu53AKr6hKruCXHMRNQl7N7Qr4vIIwC+JCJni8iP7R7nH4nIfPt+HxGRe+2Y9qSIfMnenrOf4zE75l0tIhfC6kz4z068E5FP2/d5TESusrfNs3uQ/x5WJ8DviHW18FYR+bmI3C4ifyAiW+zXPNt+3EwRuUVEfmLvp9OzWxCR79ix8z4ABTR2LIBXAYx7HYv9vD8UkRtFZJv93P/Bfi+eFBHn5GEtgFPF6lFeZ3iv3XnN8/2EFd9/A+AVAFDVV1T1ab8DUNWtaq08SynDnmuK2wIA290bVPXXIrIPwG/Zm94G4ExVPSAi81x3/SSAg6p6hoi8FdYKTV5OA3CFqv6xiNwJ4FJYPcX3quo3AcAOhB8H8NcN9vdqEfkggJMB/JOqOq+5CsApqvqa1JZvnArgPABnAPgxgEtV9c/tAL9cVf9KRD6NqT3Vm0VkHMBrqnpOwOO5EcAeEfkhgO8D+LaqHrYfs1JE3mn//JfOyQwRkY8TAbxDVcdF5N8B+B1VPSoifwDgf8CKPYDV690P4DVYMeivYTUGS6r6VsAqj1DVMRH5OuyecRE5C8BHAZwDa9XUR0Tk/8DqtDgNwIdVdasd938LwOWwVlD9KYD3w1ql72IAfwGrR/yzADap6sfsOPwTEflnAH8C4JCq/raInAngZz7HfLuIvGa//lX2sZ9Vfyyu+x9R1QER+VNYy4afBeAArF7vG2Hlhrc6PdX2sZwqIqP247cAqG90e72fOwD8G4CnReQHsPLXd12PWSci19o//ydV3elzjJQw9lxTGjykqgc8tr8TwHcAQFUfg7XcqZenXY3g7QDm2T+/1e5V3gngA7Aa+o3caAfJNwGYKSLvs7c/CisofxCA+/LpP6lqFdZyrDlYjV7Yt+fB7DxVXezRsDYej6p+HsAAgAdhJZ7vux6z3n6+xWxYE1FAd9lXEwHgOAB32T2sN6I2Xv5AVV+2T+Yfh9X58AsAbxGRvxaRCwD82uP53wngPlV9VVVfgVUi+Dv2755V1a2u+z6tqjvt8pBd9msqamPp+QBW2Q3XH8K6ujgXwO/C6oCAqj4Kc64ArLKQM+3H/ZmINDqWDfb/OwHsUtUXVfU1+zEnGV5jryse/xeP3095P+2/wwWwykd+DuBGscfV2IZcz8mGdcqxcU1xexzWmf4ku4dkLoCn7E2vtvgar7l+HsfrV2RuBfApVV0I4HpYgTgQu8H8fVhBGwCWA/gbWL3sPxUR5zVes+8/AaBqJwMAmEDzV4ZMxwNV3auqXwPw+wAWicgbm3wNIiJ37P0CgM127+1FqI2XU2KSqh4EsAhWI/cTAL7VwmvXv8aE67Y7lgqsq4NOI3Ouqj4R8nUBAKq6H1YP9zkNjsW9H/X7GGmMV8tPVHUNgPfh9SsHlDFsXFPcfgCgV0Q+BFh1egC+AuBWVT3k+0jrctp77cedAWBhyNd+A4AXRSQPq+c6MBERAEtgXfrrAXCSqm4G8N9h9fAcG+LpfmPvS0tEZLm9X4B1SXMcwFirz0tEBCuule2fP9LoziJyPIAeVb0HwLWwOh7q/QuAQbHGz8wE8B57W7M2AvivThwUkX57+8OwrubBLiE8M8D+98Iqzdgb8FhMoorvc0TE/bqLATzb6vNSMti4pljZPbnvAXC5iDwJ63LXYVg1dI38LYDZIvI4gBtgXSp8OcTL/38AHoHVSN8d8DFX25ccH4NV5vG39v+32eUlIwD+SlXHQuzHzQC+Lz4DGgP6T7Dq80YB/G9YlzfHTXcWkfeIyPMA3g7gfhHZ2OLrE1Hn+hKANSIygmA9siUAP7Tj0W0Arqm/g6r+DNYVxJ/AisXfUtWRFvbxCwDyAB4VkV32bQD4GoBjReQJAJ9H3TifOrfb+7wdVifP9iDHYmLPxLLFHgzpOaAxoDyAL4s1sHMUwEoAf+r3ALEG0D8PqwPr+boyEkqQvH4Vmyhd7F7uvKoeFmu+1H8GML/FaZOIiIiIYsPZQijNemHNqpGHVWv3STasiYiIKM3Yc01EREREFBHWXBMRERERRYSNayIiIiKiiLBxTUREREQUETauiYiIiIgiwsY1EREREVFE/h9pXlluUhxDZwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's plot the original or transformed variables\n", - "# vs sale price, and see if there is a relationship\n", - "\n", - "for var in cont_vars:\n", - " \n", - " plt.figure(figsize=(12,4))\n", - " \n", - " # plot the original variable vs sale price \n", - " plt.subplot(1, 2, 1)\n", - " plt.scatter(data[var], np.log(data['SalePrice']))\n", - " plt.ylabel('Sale Price')\n", - " plt.xlabel('Original ' + var)\n", - "\n", - " # plot transformed variable vs sale price\n", - " plt.subplot(1, 2, 2)\n", - " plt.scatter(tmp[var], np.log(tmp['SalePrice']))\n", - " plt.ylabel('Sale Price')\n", - " plt.xlabel('Transformed ' + var)\n", - " \n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By eye, the transformations seems to improve the relationship only for LotArea.\n", - "\n", - "Let's try a different transformation now. Most variables contain the value 0, and thus we can't apply the logarithmic transformation, but we can certainly do that for the following variables:\n", - "\n", - " [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]\n", - " \n", - " So let's do that and see if that changes the variable distribution and its relationship with the target.\n", - " \n", - " ### Logarithmic transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Let's go ahead and analyse the distributions of these variables\n", - "# after applying a logarithmic transformation\n", - "\n", - "tmp = data.copy()\n", - "\n", - "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n", - "\n", - " # transform the variable with logarithm\n", - " tmp[var] = np.log(data[var])\n", - " \n", - "tmp[[\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]].hist(bins=30)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The distribution of the variables are now more \"Gaussian\" looking.\n", - "\n", - "Let's go ahead and evaluate their relationship with the target." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's plot the original or transformed variables\n", - "# vs sale price, and see if there is a relationship\n", - "\n", - "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n", - " \n", - " plt.figure(figsize=(12,4))\n", - " \n", - " # plot the original variable vs sale price \n", - " plt.subplot(1, 2, 1)\n", - " plt.scatter(data[var], np.log(data['SalePrice']))\n", - " plt.ylabel('Sale Price')\n", - " plt.xlabel('Original ' + var)\n", - "\n", - " # plot transformed variable vs sale price\n", - " plt.subplot(1, 2, 2)\n", - " plt.scatter(tmp[var], np.log(tmp['SalePrice']))\n", - " plt.ylabel('Sale Price')\n", - " plt.xlabel('Transformed ' + var)\n", - " \n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The transformed variables have a better spread of the values, which may in turn, help make better predictions.\n", - "\n", - "## Skewed variables\n", - "\n", - "Let's transform them into binary variables and see how predictive they are:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for var in skewed:\n", - " \n", - " tmp = data.copy()\n", - " \n", - " # map the variable values into 0 and 1\n", - " tmp[var] = np.where(data[var]==0, 0, 1)\n", - " \n", - " # determine mean sale price in the mapped values\n", - " tmp = tmp.groupby(var)['SalePrice'].agg(['mean', 'std'])\n", - "\n", - " # plot into a bar graph\n", - " tmp.plot(kind=\"barh\", y=\"mean\", legend=False,\n", - " xerr=\"std\", title=\"Sale Price\", color='green')\n", - "\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There seem to be a difference in Sale Price in the mapped values, but the confidence intervals overlap, so most likely this is not significant or predictive." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Categorical variables\n", - "\n", - "Let's go ahead and analyse the categorical variables present in the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of categorical variables: 44\n" - ] - } - ], - "source": [ - "print('Number of categorical variables: ', len(cat_vars))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MSZoningStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinType2HeatingHeatingQCCentralAirElectricalKitchenQualFunctionalFireplaceQuGarageTypeGarageFinishGarageQualGarageCondPavedDrivePoolQCFenceMiscFeatureSaleTypeSaleConditionMSSubClass
0RLPaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2StoryGableCompShgVinylSdVinylSdBrkFaceGdTAPConcGdTANoGLQUnfGasAExYSBrkrGdTypNaNAttchdRFnTATAYNaNNaNNaNWDNormal60
1RLPaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1StoryGableCompShgMetalSdMetalSdNoneTATACBlockGdTAGdALQUnfGasAExYSBrkrTATypTAAttchdRFnTATAYNaNNaNNaNWDNormal20
2RLPaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2StoryGableCompShgVinylSdVinylSdBrkFaceGdTAPConcGdTAMnGLQUnfGasAExYSBrkrGdTypTAAttchdRFnTATAYNaNNaNNaNWDNormal60
3RLPaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2StoryGableCompShgWd SdngWd ShngNoneTATABrkTilTAGdNoALQUnfGasAGdYSBrkrGdTypGdDetchdUnfTATAYNaNNaNNaNWDAbnorml70
4RLPaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2StoryGableCompShgVinylSdVinylSdBrkFaceGdTAPConcGdTAAvGLQUnfGasAExYSBrkrGdTypTAAttchdRFnTATAYNaNNaNNaNWDNormal60
\n", - "
" - ], - "text/plain": [ - " MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope \\\n", - "0 RL Pave NaN Reg Lvl AllPub Inside Gtl \n", - "1 RL Pave NaN Reg Lvl AllPub FR2 Gtl \n", - "2 RL Pave NaN IR1 Lvl AllPub Inside Gtl \n", - "3 RL Pave NaN IR1 Lvl AllPub Corner Gtl \n", - "4 RL Pave NaN IR1 Lvl AllPub FR2 Gtl \n", - "\n", - " Neighborhood Condition1 Condition2 BldgType HouseStyle RoofStyle RoofMatl \\\n", - "0 CollgCr Norm Norm 1Fam 2Story Gable CompShg \n", - "1 Veenker Feedr Norm 1Fam 1Story Gable CompShg \n", - "2 CollgCr Norm Norm 1Fam 2Story Gable CompShg \n", - "3 Crawfor Norm Norm 1Fam 2Story Gable CompShg \n", - "4 NoRidge Norm Norm 1Fam 2Story Gable CompShg \n", - "\n", - " Exterior1st Exterior2nd MasVnrType ExterQual ExterCond Foundation BsmtQual \\\n", - "0 VinylSd VinylSd BrkFace Gd TA PConc Gd \n", - "1 MetalSd MetalSd None TA TA CBlock Gd \n", - "2 VinylSd VinylSd BrkFace Gd TA PConc Gd \n", - "3 Wd Sdng Wd Shng None TA TA BrkTil TA \n", - "4 VinylSd VinylSd BrkFace Gd TA PConc Gd \n", - "\n", - " BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2 Heating HeatingQC \\\n", - "0 TA No GLQ Unf GasA Ex \n", - "1 TA Gd ALQ Unf GasA Ex \n", - "2 TA Mn GLQ Unf GasA Ex \n", - "3 Gd No ALQ Unf GasA Gd \n", - "4 TA Av GLQ Unf GasA Ex \n", - "\n", - " CentralAir Electrical KitchenQual Functional FireplaceQu GarageType \\\n", - "0 Y SBrkr Gd Typ NaN Attchd \n", - "1 Y SBrkr TA Typ TA Attchd \n", - "2 Y SBrkr Gd Typ TA Attchd \n", - "3 Y SBrkr Gd Typ Gd Detchd \n", - "4 Y SBrkr Gd Typ TA Attchd \n", - "\n", - " GarageFinish GarageQual GarageCond PavedDrive PoolQC Fence MiscFeature \\\n", - "0 RFn TA TA Y NaN NaN NaN \n", - "1 RFn TA TA Y NaN NaN NaN \n", - "2 RFn TA TA Y NaN NaN NaN \n", - "3 Unf TA TA Y NaN NaN NaN \n", - "4 RFn TA TA Y NaN NaN NaN \n", - "\n", - " SaleType SaleCondition MSSubClass \n", - "0 WD Normal 60 \n", - "1 WD Normal 20 \n", - "2 WD Normal 60 \n", - "3 WD Abnorml 70 \n", - "4 WD Normal 60 " - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let's visualise the values of the categorical variables\n", - "data[cat_vars].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Number of labels: cardinality\n", - "\n", - "Let's evaluate how many different categories are present in each of the variables." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# we count unique categories with pandas unique() \n", - "# and then plot them in descending order\n", - "\n", - "data[cat_vars].nunique().sort_values(ascending=False).plot.bar(figsize=(12,5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All the categorical variables show low cardinality, this means that they have only few different labels. That is good as we won't need to tackle cardinality during our feature engineering lecture.\n", - "\n", - "## Quality variables\n", - "\n", - "There are a number of variables that refer to the quality of some aspect of the house, for example the garage, or the fence, or the kitchen. I will replace these categories by numbers increasing with the quality of the place or room.\n", - "\n", - "The mappings can be obtained from the Kaggle Website. One example:\n", - "\n", - "- Ex = Excellent\n", - "- Gd = Good\n", - "- TA = Average/Typical\n", - "- Fa =\tFair\n", - "- Po = Poor" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# re-map strings to numbers, which determine quality\n", - "\n", - "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", - "\n", - "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", - " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", - " 'GarageQual', 'GarageCond',\n", - " ]\n", - "\n", - "for var in qual_vars:\n", - " data[var] = data[var].map(qual_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4, 'Missing': 0, 'NA': 0}\n", - "\n", - "var = 'BsmtExposure'\n", - "\n", - "data[var] = data[var].map(exposure_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", - "\n", - "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n", - "\n", - "for var in finish_vars:\n", - " data[var] = data[var].map(finish_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", - "\n", - "var = 'GarageFinish'\n", - "\n", - "data[var] = data[var].map(garage_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n", - "\n", - "var = 'Fence'\n", - "\n", - "data[var] = data[var].map(fence_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# capture all quality variables\n", - "\n", - "qual_vars = qual_vars + finish_vars + ['BsmtExposure','GarageFinish','Fence']" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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II9x33320tbUtWhmxeXNsQgi/EKI4+x54GDgOPAtkXftO4GeZ988Cn8pER94FBDPTiS8CDwshyjNBIw8DL2b2TQkh7spEQ37qgs+a7RoLipSSd955h/PnzxMIBDh8+DB9fenZ00IvTKhQKAqfnTt3smHDhkU1WoP5HbHVAm8KIY4A+4DnpJQvAF8Btgsh2oFtmW2A54FOoAP4J+APAKSU48BfAfszry9l2sgc88+Zc84BuzPtl7rGgjI5OUk0Gp3R1t+fHlTu2rXLzgcxTVON2hQKRd6xWOuxKa3IDPOhFRmLxXjllVdmVNxdunQpGzZs4NFHH53h9BwOB0899RRer5elS5ficCh9aoVCobgCSityofF6vaxcudIubeH3+2lrawPgvvvus49LJpM0NjbS1dXFyZMn2bt3b07sVSgUikJAObZ5ZuXKlWzbto377ruPBx98EK/Xe9Ex2VyQLBMTEwSDwYUyUaFQKK6Ls2fPsmPHDjo6OnJtygyUY1sAPB4PZWVl9sgN4I033rDfCyE4ceLEjHPUVKRCoVgMjI2N8cYbb7Bnzx5Onjw5Y2nly1/+MpFIhC996Ut2WyAQoKuri0gkkgtzAVVoNGds376d5557DsMwKCoqYtWqVfa+JUuW4Pf7c2idQqFQpPUh9+3bh2EYAJw7dw6Px0Nraytnz56lu7sbgO7ubjo6OojFYnabEII77riDmpqaBbdbjdhyxM6dO+3kRrfbzRe/+EVuu+02tm7dysaNG3NrnEKhUJCO7M46tSxZlaYvf/nLM9q/8IUv0NPTY29LKXM2RakcW46oqqqakdxYX19PU1PTogubVSgUNy/FxcX2A3iW0tJSAHtklqWnp4cLo+xzFXWvHFsOWazJjQqFQgHp2aSNGzficrkAqKurY8mSJQwODtLY2Djj2NbW1ovali1btmC2Tueq89iEEEuBNinly0IIL+CQUobm1boFZD7y2BQKhaIQkFJiWRZjY2Ps378fy7I4e/Ys3//+920h5K9+9ats2rSJ/v5+wuEwtbW1lJfPeynM689jE0L8HvBD4BuZpibgpzfELIVCoVAsaoQQ6LrOqVOnME2T0dFR3nrrLUKhkD3d+PrrryOEoKmpiVWrVi2EU7skVzsV+YfAPcAUgJSyHVj4UJcCQ6n7KxSKfCKVStHZ2UlnZyeHDx8mEonYebgvvvhijq17n6t1bAkpZTK7IYRwMM/lZm4GvvWtb3Hw4EG+/e1v59oUhUKhuCJ1dXWMj6eler1eL1JKpqamsCyL2tpZy17mhKt1bK8LIf4b4BVCbAf+Dfj5/JlV+Bw/fpx//dd/ZXJyku9+97sXRRgpFArFYmPJkiW43W6mpqbsitqJRIJgMMjAwEBujZvG1Tq2vwACwDHgs6SV+P9yvowqdCzL4qmnnrLnpk3T5Ktf/WqOrVIoFIpLYxgG7777LmVlZSSTSRwOB1JKXC4XlmVRXFycaxNtrlZ5xAt8S0r5TwBCCD3TFr3sWYpZMQyDAwcO2ImPhmHw9ttv59gqhUKhuDSBQIBEIkFzczPxeBzDMHC5XLb83/DwMJZlMTQ0hGma1NXV4XQ6c2Lr1Y7YXiHtyLJ4gZdvvDk3By6Xi3vuucf+QjgcDh588MEcW6VQKBSXxu12Y5omsViMlpYWXC4Xuq4D6XuY0+nkzTff5L333uPw4cO89tprFwm8LxRXlccmhDgspdx4pbZ8ZqHz2Pr7+/nYxz5GPB7H7/fzs5/9jOrq6gW7vkKhUFzIU089dUkZrGg0Snt7O8FgkFQqhZQSt9uNy+VC0zSKiorsnLYs5eXllJWVzWhbsWIFf/zHf3yjTJ5TPbaIEGKT/UlCbAZiN8Kqm5XGxkY+/vGPU1ZWxpNPPqmcmkKhWLRIKRkdHcXj8aBpGi6Xi5KSEuLxOOFwmKmpKQzDIBab6RYsy8qJvVe7xvafgH8TQgyQ9pB1wCfmy6ibhccff5yXX36Zj3zkI7k2RaFQKC45kjIMg927d3P+/HmGhoYAcDqdfP/73yeVSnHnnXfy67/+65w8eZI1a9ag6zoOh4MPfOADOalUclWOTUq5XwixCrgl03RGSpmaP7NuDn7+858TjUZ59tln+fznP8/Q0BBdXV1omsaKFSuUILJCoVgUOBwOqqqqGB4ettvKy8vx+/0kEgkeffRRdF1n1apVtLW14XA4WLJkCT6fLzf2Xm6nEOKDUspXhRAfu2DXSiEEUsofz6NtBc3o6Ci7d+9GSsnu3bt54oknOHny5Iz9Dz74YM6+GAqFQjGdTZs24fF4ME2TUChEMBhE0zRKS0vtUVltbS1r167NsaVXXmO7P/Pz8VleH55HuwqeXbt22XlslmXx9NNPz9hvWRYjIyO5ME2hUCguwu12c9ttt/Hwww8TjUaJRCLouo6maYRCIVpaWtiyZUuuzQSuMGKTUn5BCKEBu6WUP1ggm24K9uzZQyqVns1NpVLs3buXe++9d8Yxqoq2QqFYTMTjcQ4ePEgqlcLhcJBKpXC5XDQ1NbF+/fpcm2dzxahIKaUF/JfrvYAQQhdCHBJC/CKzvUwI8a4QokMI8X0hhCvT7s5sd2T2t0z7jP+aaT8jhHhkWvujmbYOIcRfTGuf9RqLie3bt9vJi06nk8cff5y6ujp7f3Nzs4qUVCgUi4pwOIzD4WBycpLjx48zNDREf38/p0+f5uzZszkrLHohVxvu/7IQ4k+FEEuEEBXZ11We+x+BU9O2/wb4qpRyBTAB/E6m/XeAiUz7VzPHIYRYA3wSWAs8CvyvjLPUga8BO4A1wG9mjr3cNRYN04uLCiH49Kc/ze23385DDz3E9u3bufXWW3NonUKhUFxMeXk5ExMThMNhxsbGiEQiTE1NEYlEOHPmDD09Pbk2Ebh6x/YJ0qVrfgW8l3ldMZtZCNEEfAj458y2AD5IurYbwC7go5n3T2S2yex/KHP8E8AzUsqElLIL6ADuyLw6pJSdmcoDzwBPXOEai4aioiLKyspIpVI0NDTYEZA+n++iJEeFQqHIJclkkkAggGmaNDY2YpomNTU1FBUVUVJSwsDAAGNjYxw+fJhoNPdKi1cb7n+99b3/jvQ0ZlYdsxKYlFIame0+IFtLvBHozVzPEEIEM8c3Anunfeb0c3ovaL/zCtdYFExOTrJ79246OjowDIMzZ84wNjamwvsVCsWiY2hoiDfffJOJiQk7CduyLDRNsyW1AoEAe/fupaamBsuyePDBBxdvoVEhxJ1CiCNCiLAQ4h0hxOqr/WAhxIeBESnle3O2cp4QQnxGCHFACHEgEAgs2HU7OjrYs2ePvR2Px/mnf/qnBbu+QqFQXC1vvvkmR48e5fz587z99tt0dHSwYsUKNE1DCIHL5WJycpJkMolpmhw7dowzZ87k1OYrTUV+DfhT0qOgvyU9Arta7gE+IoToJj1N+EHg74GyTKFSgCagP/O+H1gCdiHTUmBsevsF51yqfewy15iBlPJpKeUWKeWWhQzUME2Tw4cPz1D3f/llpSldqKhK6Yp8pru72w4KSaVSTExM0NraymOPPUZLSwttbW2sWLGCmpoaAEZGRnj33XdzWp/tSo5Nk1Luyaxv/Rtw1Xd/KeV/lVI2SSlbSAd/vCql/H8BvwSezBy2E/hZ5v2zmW0y+1+V6d/ms8AnM1GTy4A2YB+wH2jLREC6Mtd4NnPOpa6xKGhpaWHjxo1oWvrX73a7eeyxx3JslWK+2LVrF0ePHmXXrl1XPlihWGRUVVXZ730+H6FQiFOnTnHmzBkikQg9PT2cPn2aM2fOcOrUKcbGxnA4HLz33ns5K6B8JcdWJoT4WPY1y/b18OfA54UQHaRHgt/MtH8TqMy0f550cVOklCeAHwAngReAP5RSmpk1tD8CXiQddfmDzLGXu8aioLa2lt///d/HMAyEEHg8nhlRkrFYjFAolEMLFTeKCxVmjh8/zssvv8xzzz3H0aNHcyYSq7jxFOrI/OGHH6apqYny8nKam5tZt24dFRUVuFwuQqEQUkoqKysxDINoNMrKlStpamoC4Pz58zmx+UrBI6+TVhmZbVsCVyWpJaV8DXgt876TdETjhcfEgd+4xPl/Dfz1LO3Pk67mfWH7rNdYLASDQQ4ePIimaUgpmZqaYmRkhMrKSk6cOEFnZyeQDq298847c1asTzF3pivMpFIp/u7v/o4nnngCgJ6eHoqKimhtbc2liYobxPSR+ec//3m7PVttOjtDk280NDTw8Y9/nL1793LkyBG8Xi+NjY309/eTTCZJJBJomkZ1dTU+n49Vq1bZfXW5cpNCfNnftJTy05d5/V8LZWSh0d/fz/e+9z0Mw8A0TaSU/OVf/iXBYNB2agATExM5G8orbgzTFWayqg3TmZiYyIVZihvMhSPzsbExW1HoxRdf5MUXX1w0OV7Xw+TkJIZh0NjYSDgc5uzZs5imiWmaQLpW28TEBMuWLSMQCHDy5EmGhoZoa2vLib1X9QghhKgVQnxTCLE7s71GCLHokp7zBcuy6OrqIhaLEY1Gicfj9Pf3z5r/sRhyQhTXz3SFGa/Xy6ZNm2bsr6i4Wp0DxWLmQu3XXbt2ce7cObLR1oZhcOzYMeLxeC7NvC4sy+LAgQMcP36c8fFxli5dSklJCTU1NdTW1iKEYHh4GJfLxeDgIB6Ph/r6+ouqASwkVzs2/jbptayGzPZZ0jXaFNdBMpnE7Xbb21JKiouLqaqqumjasb6+fqHNU9xAdu7cSVozIF3640/+5E/w+Xw4HA6WLVtGS0tLbg1U3BAu1H596aWXmJqamnGMlJJwOJwL8+bEqVOnGB0dZXJykhMnTnD06FHq6+t54IEHbBHk+vp6LMsiGo0SCAQoLy/H4XDYtdsWmqt1bFUZEWQL0gnUgDlvVhU4Ukp8Pp/98nq9pFIpnE4nW7dupaGhgerqajZv3myH0Cryk6qqKnbs2IEQgh07drB27VoeeughduzYwbp162ynV2gUaiDFpbhQ+/Xhhx++6H/X6XTmNGn5ehkaGqKqqorx8XGCwSCjo6OEQiF0XUfXdaSUaJpGeXk5pmmSTCbtcxdlPbZpRIQQlaQDRhBC3AUE582qAmNgYICTJ0+STCZZsmSJrYSdXW/RdZ3t27cDUFJSwubNm3NpruIGs3PnTrq7u2dEvhYqUkqSyeQlAykKlZ07d7J7924ANE1j586dVFRUkEwm6e/vx+PxsGrVKlupI58oKiqip6eHuro6qqurcblc1NbWMjw8TElJCZZlsXz5cluJxOPxMDY2Rl1dXc5qs12tY/s86Xyy5UKIt0jnsz15+VMU8H7AQHb+vbu7m6KiIpYsWcKJEyfQNA2Px1OwT+6K9KjtH/7hH3JtxrwzPj7OwYMHGR4e5rvf/S4ej4fdu3ezc+fOgpeLy47Mn332WXbs2GH3d+XKlaxcuTLH1s2NNWvW0NubVi90uVy0traiaRqVlZV4vV6klCxdupRIJEJVVRV+v9+els1VJOhVXVVKeZB00dGtwGeBtVLKo/NpWKEwOTl5USmHiYkJDh48SFFRET6fD03T+NWvfpUjCxWKG8Phw4eJxWK88sormKZJNBq1AyluBnbu3MmGDRsKbmReXFzMRz/6UW699VZqa2vxeDyUlJTQ3NxMbW0tNTU1PPTQQ3ziE5+grKwMp9OJz+cjlUrNiPJeSK6kFTk9GfsjwC3ASuDxOSRo31SUlZVdNBorLy9n+/btOBzpAbPD4eDhhx/OhXkKxQ3BsiwikQiALRdnmqYdSHEzkB2ZF+Lo9ODBg+i6jsPhIB6P09DQgBACIQRFRUUsX7581py1rGzgQnOlqcjHL7PvqhO0b2Y8Hg+bNm2y19iam5tpaWmZMSev63rBPeUpbi6yU1NjY2Ns3LiR9957D13X7UAKRf4SDoc5deoUnZ2dhEIhRkZGOH/+PJs3byYajdoBIpWVlSQSCQYGBvD7/VRXV7N06dKc2HxZxyal/PRCGVLINDQ00NDQMKOtqqqKrVu38tprr7F169aCfMpT3Fxs2rSJEydO8OEPf5gTJ07gdDrtQApF/iKlpKenB8uyCAQCpFIphoaGGB4e5ty5czQ1NWEYhn3M+Pg4AwMDVFVV5SxP82qDRxBCfIh0FWu7CqaU8kvzYVQhMjExwYkTJ4jFYtTX17NmzRrOnDmDlJL29vZcm6dQzBmPx8PmzZvZvHkzXV1dFwVSKPITv99PUVERo6OjJJNJhBCYpsm5c+cIBoPous6bb76JaZq0t7djmiZOp5P33nuPLVu20Ni48OUwr8qxCSH+N+ADHiRdDftJ0gr7iqvAsiz27dtn53ecPn2aF198kePHj6NpGqlUyq5xpFAUAjdTikOhE41G8Xq9RKPRGYEhQ0NDBINBwuEw//RP/8SmTZtmKKtomkZfX19OHNvVxmJulVJ+CpiQUn4RuJt0EIniKpiampqRtHj+/Hm++93vAu8vun/xi1/MlXkKxQ2nkAMpbjaOHj1KeXk5paWlhMNh2tvb6e/v5/Dhw8TjcaLRKJOTk5w/f55z587R1dVFNBqloaEBr9ebE5uv1rHFMj+jQogGwACU1tNVUlRUZEdAQvoJKBh8P789qx0ppeTUqVO89NJLvP7664yMjOTCXIVCobAZGhri1KlTjIyM2EoyK1asIBgMEo/HsSyLVCrFiRMnME2TYDDIwMAAxcXFLF++PCc2X61j+4UQogz4f4D3gC7ge/NlVKHhcDi47bbb8HjSy5PLli2bEUyi6zrNzc2cP3+ejo4OEokEU1NT7N+/n0QikSuzFQqFgmQyiWEYBAIBIpGIHcJfXV2Nw+HA6/WSTCYJhUI0NzezevVq6uvr8fv9+P3+nNh8pTy224UQdVLKv5JSTgJFwDHg34CvLoB9BUNdXR3btm3j9ttvp6mpicrKSizLwul0UlxcTFtb20W6epZlqbImCoUip7S2tjIwMMDY2BhTU1OMj48TCASorq6muroaKSW6ruN2uxkaGkLXdfx+/4xZqoXmSiO2bwBJACHEB4CvZNqCwNPza1rhceLECb72ta/xzW9+k7179xKJRGwB0XfffZfS0tIZxwshLmpTKBSKhWRiYoJwOMzk5CSRSITx8XGOHDlCfX09LpcLp9PJ3XffzS233EIymSQYDFJcXJxTzdsruVRdSjmeef8J4Gkp5Y+AHwkhDs+rZXnIU089RUdHx4w2y7KYnJwkHo/T3d1NLBYjFouhaRrxeJxkMkkymaSqqoq/+7u/Y3R0lHA4jKZpVFRUXKTasGLFCv74j/94IbulmCOjo6N88Ytf5H/8j/+hgikUeUUqlSIajeL3++3QfsuySCQS9PX1YZom8XicgYEB2traSCQSNDQ08Bu/8Rs5rWRwpRGbLoTIOr+HgFen7cvdODOPyJZ6SCQSjIyM0NvbSyAQIBaL2dWzpZTU1dUhhKC6upqWlhaam5spLi7OtfmKG0BW6f4b3/jGTVM49mYrW1OoZNX6s+tsqVSKZDJJPB6no6OD4eFhgsEgBw8eZP/+/ezbt4+zZ8/yta99jXfffTdndl/JOX0PeF0IMUo6MvINACHEClTZmouYbST14osvkkwmGRsbY2BgAMuybO3IWCyGw+Hg13/91/nzP//zhTZXsQCMjo7y/PPPEwwG+cEPfsDSpUtZs2YNt912W65Nu2HE43FcLtcMJfebrWxNoaJpGkVFRViWhcvlIhqNUlpaSiQSYXR0lGAwiBCC/v5+QqEQFRUVvP766zQ0NHDmzBkSiQQf+MAHFtzuK0lq/bUQ4hXSof0vyfdl6jXgc/NtXCHg9/ttx+bz+airq7PX1UKhEPX19fzu7/5urs1UzBO7du0iFouRSqVwOBy8+uqrlJSU0NTURHV1da7NmxORSIQDBw4wNTWF2+3mtttuo7q6mtHRUXbv3o2U8qYpW1OoJJNJpqam2LJlC16vl8OHDzM2NoaUklgshmVZQPrhxjRNPB4PPp+PUCiEx+Ph2LFjbNy4kZKSkgW1+4rh/lLKvVLKn0gpI9PazmZK2SiuwLp163C73dTU1OBwONB1HZ/Ph8fjoaamhtWrV6t/+gJmz549dsqGYRgcOnQIoCCmJE+cOMHU1BQAiUSCQ4cOIaVk165ddqmmm6lsTSGSSCSwLIvq6mq8Xi8ej8cO97csCymlPQPlcDgwTRO3242UEr/fj9vtzonCf26qwN1ElJWVsW3bNp544gmeeOIJGhoa8Pv9rF279qaMeLzZ1l62b99u5/Jk8xl1Xae2tjbHls2dUCg0YzuRSJBMJtmzZ49daPJmKltTiBQXFxMIBDh+/Djt7e0MDAyQSCRIpVJ2NXBN06irq2PDhg2sXr0al8tFfX09y5cvp7W1NSdBJPMWACKE8AC/AtyZ6/xQSvkFIcQy4BmgknSy97+XUiaFEG7gO8BmYAz4hJSyO/NZ/xX4HcAE/lhK+WKm/VHg7wEd+Gcp5Vcy7bNeY776eiU0TaOkpIRVq1bR0tJi5689/fTTFxUhLRTi8TinT59mamqKmpoaVq5ciaZpN93aS7Y8UXFxMZZl8Zu/+Zts3rzZTtbPZ2pqauju7ra3S0tLcbvdbN++neeff55UKqXK1uQ5sViMkpISpJRMTU3ZTs2yLBwOB0IIdF23lfxvv/12brvtNnw+nx0Id2E9yoVgPkdsCeCDUspbgY3Ao0KIu4C/Ab4qpVwBTJB2WGR+TmTav5o5DiHEGuCTpCsLPAr8LyGELoTQga8BO4A1wG9mjuUy18gpzc3Ndo0qwL7ZFSIHDhygt7eXYDBIe3s7p0+ftgMppJQ8//zzN8Woraqqih07duB2u/nkJz/Jtm3bchoGfSNZs2YNy5Ytw+/3U19fz5YtW4C0M8/ezFTZmvwmlUrhdrspKSnB6/Xi8/nQNA2Xy4XX66W8vNyO3h4dHQXSI/dNmzbR1tZm3+sWmnlzbDJNOLPpzLwk8EHgh5n2XcBHM++fyGyT2f+QSP93PAE8I6VMSCm7gA7gjsyrQ0rZmRmNPQM8kTnnUtfIKdnR2vnz55mamiIcDnP+/Hlef/11u/pwIZBIJC5STBkaGmLXrl32fHsqlWLXrl309fVx9OhRent7C3b0unPnTjZs2FBwN3hd11m3bh0f/OAH2bJli11wMuvMhRA3VdmaQpxmLykpoaysjJKSEvx+P7W1tVRWVlJeXk5zc7NdNdvhcJBKpewqJpOTkzm1e17X2DIjq8PACLAHOAdMSimzq4l9QLamQSPQC5DZHyQ9lWi3X3DOpdorL3ONC+37jBDigBDiQCAQmENPr45Dhw6haRrNzc10dXXZN/+pqSmOHTs279dfKJxO50Vl4ouKinjppZds5yWl5Ec/+hGHDh2iq6uL1157jbfeeqsgndvNqHRfqM78UliWxTe+8Q2OHDnCN77xjVybc0O566672LZtG/fccw/19fVUV1dTVVXF0qVLcblceDweO78tGo0yNDSU8/iBeXVsUkpTSrkRaCI9wlo1n9e7VqSUT0spt0gpt8x36HW2siyAaZokEgk7Ws40TTo7OxkcHCSRSNDb20t/f3/eTlNqmsaGDRvsaQifz8eaNWuoqqqacZzL5cIwDI4fP87Zs2fZs2cPe/fuLUjndrNxszjz/v5+XnrpJZ555hl+/OMfI6XkxRdf5NSpU/T19dlBNPmM0+lk3bp1rFq1Cq/XS1FREeXl5VRWVtrTkIZhEAwGCQQC9vp6LqIhsyyIeoiUclII8UvSddzKhBCOzIiqCejPHNYPLAH6MmonpaSDSLLtWaafM1v72GWukTP6+vo4f/48qVSKxsZGioqKcDqdWJbFiRMn8Pv9vPPOO3R1dbF8+XJ0XaekpIT77rtvRuJrvlBfX09NTQ2xWAy/348QgoGBgRnHjI+PMzIyYhcn1HWd0dFRRkZGCiJqUFHYJBIJDh8+jGVZ7N69m3g8jhACwzD4n//zf/Lxj38cj8fDvffem7O6ZDeKrFak0+m0Q/jPnTtHPB7H7XZTXFxspzUNDg7y4x//mM7OTlauXMm6desWPIBk3u6YQojqTKkbhBBeYDtwCvgl6QrcADuBn2XeP5vZJrP/1UxC+LPAJ4UQ7ky0Yxvp6t37gTYhxDIhhIt0gMmzmXMudY2c0Nvba+cv9fT0cOjQITZu3EhVVRWxWAyPx4PL5eLo0aMMDQ0xOjpqRyENDg7m0vQ5oes6RUVFl/xS+3y+GaPSpqYmgBlFWQuBQlx7UUAwGLS/v4cPHwbeT3l47733MAzD1ojNd7KRkP39/QwPD3P69Gm6u7uJRqN2LuPU1BQnT57kzTffpLOzk1AoRHd3d07uYfM5FKgHfimEOEraCe2RUv4C+HPg80KIDtLrYd/MHP9NoDLT/nngLwCklCeAHwAngReAP8xMcRrAHwEvknaYP8gcy2WukRMGBwc5d+4cQ0ND9o0+Go3aCY7nzp2zvzDHjh1j3759HDx4kKGhobydjpyNbdu2zdh+/PHH+bVf+zVWrFjBhg0bqK6uxu12U1dXlyML5040GmViYmLGdOr0FAdF4VBWVmbncmXJ/m9HIhH27t3L4cOH6e7uzvspybKyMjo6OnC73YyOjjI1NUU0GiUWi2EYhq19a1kWkUjEnpYEbMe3kMzbVKSU8ihwkSCelLKT9Hrbhe1x4Dcu8Vl/Dfz1LO3PA89f7TVyyfnz5+2RmNvt5vTp06RSKaampjhx4gQVFRVIKQkEAnZ0WXYar1D47Gc/aweQaJrGZz/7WSorK/m1X/s1enp60HWdlpaWnIUIz5WTJ09y7tw5IC2ltnXrVsLhsJKXKlBcLhdbtmzh1KlTVFRUEA6HMU0TAI/HQ09PD0IIWltbefPNN7n//vvzcllBSsmJEycIBoN2rqJhGESjUQzDwDRNTNPE4XBgGAZTU1OEQiFOnz5NZWUlW7duXXCblUL/AlBfX8/4+DipVAohBMlkkomJCYaGhgiFQnZgSV1dHX6/n8nJSerr69E0jdOnT+fkizEfVFVV0djYSF9fHw0NDVRWVmKaJiUlJaxfvz7X5s2JcDhsOzVI6yieO3eOF1544SJ5qZshMb1QsSzLjmiurKykpaWFmpoaNE2zRzO6rhONRu0cL4fDQTgcZnR0lJqamgW1d7ZSWtfK8PAwvb29jI6OEgqFCIVCJJNJEokEUkosy+LUqVNYloVpmjidTiYnJxkYGKC3t9eepp0L11quSzm2BUBKyaZNmxgYGEDXdcLhMF1dXYyPj2OaJkIIHA4HkUgEXdftfwin05mTYfx8kQ0MARgYGODZZ5+1i6lu2rSJoqKiHFt4/WQDYKYTi8VmlZdSji1/OXbsGF1dXXaeVigUYsOGDfbsi5SSVCqFaZpUVFTg8/nswJELpy0Xgo6ODo4fOUKx6/pu9VJKOnp6SCRThMJhEokkiXg8XW6LtKN3OZ1gWZiGgSUtpKWh4yQRiTA5PEiPNOfUh1Dy2qMrlWObZ6SUtLe3c/bsWQzDoKqqCo/HQ1tbG3v37iWRSLBkyRKklBQVFVFdXU0kEsEwDFauXJnzfJAbyXRx3FAoxM9+9jM++tGPEgwGOXLkCPfcc0+OLbx+Kioq8Hq9xGIxu62xsfGmlZcqxOKqUkp6eno4efKk/XceGhpi9erV9v5s/qbL5cLv97Ny5UqEEFRVVeXs91DscnBH7fWp3cSSSUZ7BYOJGC4piVsGDk2gCR1TWliknYiOxJASBwKPrlHk0Kn0ONlUV8WS6rkp7ewbnrjyQReQfxO+ecahQ4d4/fXXMU2T48eP8+yzz3L69GlKS0spLi7G5XJRXFxMWVkZS5YsQdd1li5ditfrZXR0lLa2tlx34YYxffQSj8ftSFEg50oFc0XTNLZu3crSpUupra1l8+bN1NfX37TyUt/61rfYu3cvX/7ylzl//nyuzbkhCCEIhUIzHl5M06Svr8/OSYW0U3O73fz2b/82d955J3fccQd33XVXLkyeM5qmEUsmsUwLXddACiwJugZOXUd3aFhIUqaBlBLTskgaJqaUOJ0O6irKcmN3Tq56E3Hy5EmklITDYbtoXyqV4vDhw9TW1uL3+yktLWXLli20traybds2tm7dyvLly1mxYkVBRUVu377dDgzxer0zim1emLydj/h8PjZs2MAdd9xBQ0MDcHPKS42OjvJv//ZvRKNRXn31Vd544405r/MsFpYvX24/qAghWLJkCaZpomka8XiccDhMPB7HMAx6enoAqK2tzYkQ8I1AACU+L26Hg1AsXXPNtEziKQOkRJoWhmmhoeHQdRy6hgCKPW5cDgf9o+M5sVtNRc4zWcHb6SU+dF1neHiYwcFBO0ry/Pnz1NXVUVFRQTAYpKqqCqfTWVAqHDt37uSnP/0pkUgEl8vFk08+iaZpVFRU5H3wyOXYuXMn3d3dN81o7Zvf/KY9qkmlUvzkJz+xVeIXOnjiRrNp0yZGR0cZHx+nqKgIn89HTU0N9fX1hEIhO3Bk+fLljI6OMjo6ihDCztHMN1wOB8VeD8O6hmGZoAlcQkdKiBsGLl1HkxJLSkzLxJKAEARjMarLiomnUiRSKdwLHOmsRmzzzH333Ud9fT2lpaVEo1E0TWN8fJzJyUnC4TCWZdHU1MTSpUsZGBigvb2d9vZ2jhw5gpQy76ssTyeRSNDW1kYymWTt2rWUlJTwyCOPcPvttxdEGRdFmldeecV+H4vFOHjwIBMTE7z77rsXqc/kGy6Xi23btnH33Xezbt067r//fjt3UQiBpmlommYr3UNaditfsSxJsc9LbWkpLt2JU9NwO53pl+7AqetoQiNppEiaFpZlkUwZTIYjjE2FSRpmTlIc1IhtnikpKeGzn/0sW7du5dvf/jbd3d10d3fjdruJxWKkUimGhobw+Xx0d3czMDBAJBKxS4Es9imMqw0nNk2T9vZ2+vv7MQyD/v5+vvKVr9DY2HiRYPKFXGuo72Ljwhp0PT09DA4O4vV6WblyZV7LLQ0NDdHT00MkEkEIYesITk5OEgqFME2TsrIye8Ry/vx5e5o2X8kGf2WRUtqOLPv/On2GJp//voj0WlpTVQXBaJSB8SCGaYCQaLrAsCzcLgfxVAqBRGjp/icNk8lIlGA0ikM5tsIjHA6zd+9egsEgJSUltLW1MTY2hmGkF1uj0SgnT57E6/UyMjJiJzyWl5fz6quv8vDDD9tRV/lMIBAgFovZYfHxeByn05mXCavXwujo6IwE7Q9+8IP09r5flGJ8fJwHHnhg0T/AzEYgEGD//v3EYjGOHz+OpmnceuutnD17Fp/PR2lpKZZl2Q4PuOJDTD7i8/m45ZZbOHv2LJqm4ff7bfUcv9+f1wFgmhBUlRQzPBmkrqyUYCSGKR343S6i8SQJwyBppNA0gYZIh/tLga6DQ9OYCEdIGsaCT0UqxzbPnD59mlgshsvlwuFwcPr0aYaHh+1cF6fTSV1dHbFYzH653W7i8TiaptHZ2bmoHdvVjKRSqRQvvPACkUiEr371q1iWxQc/+EGefPJJ1qxZc8Xz85ldu3bZAUCmafKtb32L7du32/vD4TBTU1N5mdaRnVbMSoiZpkkwGETTNJLJpK0En8XlcuX1TX46wWCQU6dO2Vqv999/PydOnMDn81FUVMQ//uM/0tjYSGlpaV4+tEynsriIqWgMBDRWlhFLpkiZBi6nAwuJYZq4HA4kEsMUWJbE63JT5POSSBlEk0nl2AqNUChEX18f586dY3h4GL/fb4sfh8NhO0pwaGiIVCpFKpVCSkl9fT1VVVWUlZXltgM3AF3X7Sd1v99v16ELh8MYhoHDUbhfwz179sworvrOO+9wzz332LJpmqbl7fpitg/Zv20ikSAWixEKhezvrd/vxzAMbr/9dqqqqgrib21ZFnv37iWZTDI1NcVPf/pTO/l6YmKCxsZG1q5dm2MrbxyWJUmkDDSh0Ts6TjJlYElJPJXCME0cWiYS0u0hbqQwLYtirwddCEr9vpzYnP/fskVOZ2cnP/3pT+1gkdraWpYuXUoymWR4eBhN0wgGg/Y0pKZpGIaBx+Nh+/btrFu3LtddmDOaprFu3ToOHDhAOBy2n9yHh4dpb29f1CPS60VKiRCC++67jxdffBHLspiammLt2rW0t7dTUlJCa2srq1atwu1259rc66KlpYXOzk6Gh4c5fPgwExMT7Nu3j1gshhACIQQul4vm5ua8FLa+1PpxPB63Fev7+/sZGhpC13VbBDgWi131mnA+rB8LASnTpCcQIBSLkUilSBgm0kprvpq6hYYgZZmZ1AAnXo8Ln8tFXXkpRTl4cFOObR4JBoMMDg4Si8Vsxe9QKER9fT1tbW2cPn3abss+2aZSKXRdp6KigmXLluXlFNVsNDY24nA4KC8vx+l02lNUwWAwx5bdWCzL4tixY/T19eFwOGwJsXg8nQMkpWTDhg1EIhHuuOOOvA5/dzqdeL1eqqurKS0ttf+WpmnaArlutxvTNBkeHi6YGnvTRbqTyaTtxBOJBJZl2SPXvA4amYYQAg2IJZKYliRpWJjW+/qn0gIEmcRtHRCUO/xUlZawvLYGZw6kxJRjm0d0Xae7u5uJiQlGR0eJRqMEg0HcbjerV68mFouRSCTQdd3+h3C73RQVFVFRUcGrr77K+vXr83aq6kKycmLTk84LKZ0B0vX2skobyWSSX/7ylxQXF9t9Pnny5Ixginwmu6aWFfYOhUKkUinC4TC6rtvFJ4PBIAcPHmT79u15NRV5uZHU+fPnOXHiBK+++ipnzpwhFApx5swZnE4nn/vc5ygvL2fbtm0FERxlWRIpIBJPEk+mMC4QjTABIdPJ3AkjPU05FY9zb8Mt+D25mY3In29ZHqLrOn6/33ZolmXhdrsZHx/npZdeIh6Pk0gk7DWnbDG/7FOulJJQKFQwjk3XdWpraxkfH8fr9dLY2Ehra2uuzbqhZKXBshJL2aAKl8tlq6FDOmQ839VWslXeDcMgmUyiaZq91mSaJqlUyg6cMgyDcDhcEGvGAM3Nzei6bo/Ms1GhZWVlnDlzhuXLlxOPx+11yHxmNBQiHIsTSyYxzEsLGqfltAwEEI0nIIfiEsqxzSNOp5Pa2lrKysoYGRnB4XDgcDgIBAKMjY3ZdYyyRUd1XcfpdJJKpTh27Bhr164tmBtBFo/HQ0NDw0VFRwsFXdc5duyYrbzR0NAwYwSzfv16li1bRmtra0E8zW/atImDBw/i9/uxLAu/329P1QkhME2TWCyGZVkUFxfn2NobS1dXF3V1dZSVlTE2NsapU6dwOp3E43E6Ojo4fPgw8XichoYGbrnllryNjozEE3QOB+zpx0thkX6Ik7oDv8fF+cA4FcW5mZnI//+sRYzD4cDtduN2u3E4HEgp7Sf5bKXdbCn57EK7y+WyazslEom8Lbp5s2GaJm+99Rbvvfcevb29DA8P43a78Xg8dvFJl8tFQ0MDK1euJBQKzVrqJt8oLi62a4+tWLGCuro6PB4PHo8Hl8uFEAK/309NTU3e3tgvxfTRd0NDA06nk3A4TF9fH6dPn+bcuXNEIhHa29vp7OzMsbXXj5SSiXAES1rp+cYLyDaZlkTKdLBJKBbn/OgYwWh0QW3NohzbPJJMJpFSctddd7FmzRp0XbdD391ud3rhVUpKS0vx+Xz2dI7X68Xr9VJZWVkQN7+bgd7eXsbHx0kkElRVVVFbW0tzczOdnZ22YwN4+eWX2bNnD/v27ePll1+ekaydrxw/fpyysjKqq6txuVyEQiG7AKWmaUSjUTo7O9mzZw+BQCDX5t4wpk+jl5eX43a7bT1MKSW/+MUveOWVVxgeHraDiPKRaDKFBAzTYvqgTfC+U5OALkDXNVKGiWlJHLrG4Pgk8UxFj4VETUXOI+FwmMbGRsbGxqiqqqK+vp5IJEJlZSWJRAIhhB0BmVUEzz7lZteg8jUUPEs8HseyrIJYa7gc2QeQ8vJyBgYG7IKT02t0AbY+KKSfhE+ePElTU9OiH81cTjrt2LFjRCIRAKLRKG63G5fLRTKZJBwO4/P5+Nd//Vd7Da65uXnWz8mH0HdIV5Q+f/48TqeTW2+9lWg0SnV1Nbt27SIej+P1ejl79izRaJSOjg727t3LH/zBH7Bly5a8m4ExLQvDSOF3uZjQBMa0uBHJ+45NkI6KlBJkZo+uCQzTJJpI4lEJ2oVDtuaaw+Gwn2JN07Sf3rJOa2xsDI/Hwz333GPXK/N4PCSTSUzTzKtIsukcPXp0RumOLVu25Nii+aO+vp6Ojg58Ph8rV64kEAjQ1tZmp2tk89ouJOv8FrtjuxxLliyho6PDTkTP9tk0TXuWYmJigsrKSjvXK1/XF0dHR9m3b5+9PTQ0xC233EIgEMDj8TA+Ps7bb79NMBjEMAz7gecXv/gF9fX13HfffXkVDJZNxB4LpQWNs2iZ8P7paCIjqUU6FzcUjdNrTdBau/ApLfl5x8wTslGAQ0NDDA4Okkql7Hwft9uNlJJYLEZtba1d5ylbBqO5uRm/38/w8DCNjY257so1Mzo6ajs1SD/lFsK026UoLS3lrrvuoru7m8bGRpYvX47b7WbZsmXs27fPzt178MEHZ5zX2NiYFzf5S42kxsfH7ZytbEDUa6+9xle+8hWEEDgcDh544AFqa2u58847aWho4O67715g628cF1YnCIfDHDp0yC4xlY0KtSwLr9dLIpHAMAy6uro4duwYTU1NCypI0NfXRyhpXFcV6izv9QzY05FZsk5tZptEE+n+xy3JcDSOO2XxzkCAYr//uq8fShr09fVd0znKsc0zY2NjuFwufD4foVCIcDhsh/Nn19RSqRQOh4O2tjYikQgbN260p+70HCQ33giyU1NXastHrraiwenTpxkbG7PTOqSURCIRfvSjHxGPx3G5XJfUEsyHabkjR47YOXtOp5OtW7fa4f9ZBZ2ysjJb4FvTNDZt2pRjq+dGNunasiw6Ojro6+tDSkl5eTmJRAKXy8Wtt97K6OgoExMTWJZl68GOjY0xNDSUd0o7hmkhM0nol60PKdJTl0KI9MyTTN+/Uilj4YzNMG+OTQixBPgOUEvasT8tpfx7IUQF8H2gBegGPi6lnBDp/+6/Bx4DosBvSykPZj5rJ/CXmY/+spRyV6Z9M/BtwAs8D/xHKaW81DXmq6+XY2BggGQySSKRsIWNs/8cY2NjWJbF6OgoJ06coKKiAk3T6OjooKWlhaVLl+atMkVNTQ2aps1Ixr5QVklKiZQyL0Ys10I2/zBbeDJ7Q5gePOT1evNuvWU6kUjEdmqQnlLt6OigvLycrq4uli5dyqlTp1i/fj01NTUsXbrUjg7OZ1paWhgcHKS9vZ3u7m4SiQQVFRXs37+fsbExHA4HnZ2dbN68maNHjzI6Omqr7WiaRklJyYLa29TUhBkKckdt+XV/Rm9pEbFoGEwwEViXcG5SgpAWEvB4XFjJOGUlfu5srKa86PpHbPuGJ665UOt8fssM4D9LKQ8KIYqB94QQe4DfBl6RUn5FCPEXwF8Afw7sANoyrzuBrwN3ZpzUF4AtpB3ke0KIZzOO6uvA7wHvknZsjwK7M5852zUWFMuyiMfj1NXVsXz5clsvsLS0lEgkgmVZmKbJxMQEwWAQ0zRpa2uz/2FWrlyZtzd9r9fLsmXLePXVVxkZGcHn8yGlJBAIUFVVRVdXF2fOnME0TZqbm1m3bl3erDNdbiSVLVM0NDREUVERmqbx1ltvMTk5yac//Wk+/OEP097ejmVZVFRUcMcdd+Slg8uup13Y1tXVBUBRURHLli2jubnZVs+xLMvO18xXnE4nH/jAB3C5XPT29mKaJvF4nEgkYq8pZn+2tbVhmiZer9fWz1y+fHmuu3DNrGluZHhiikkrgjAtpGVhSol1wXEa2G1Jw0AXgngqhdux8N/veXNsUspBYDDzPiSEOAU0Ak8AD2QO2wW8RtrpPAF8R6bHunuFEGVCiPrMsXuklOMAGef4qBDiNaBESrk30/4d4KOkHdulrnFNXO2U06WQUtLd3U0oFGJkZIRQKEQwGCQSiRCNRkkmkwB2bbYTJ07Q19eH2+3m+PHjHDp0iPr6+jkvNudiWiuVStHd3U1DQwMjIyNEIhEGBgYIh8MAHDhwgGAwaN/wysrKWLJkyYLaOB9kS5kUFxdTW1vLyMiInb/2oQ99iDNnztjHjo+P09nZyS233JJDi6+P0tJSysrKbKUVgKVLl87on8/nw+/321Gh9fX1thhBefn1jyByTVdXF8Fg0O77vn376O3tJRqNIoRgcHCQ8fFxDMOgpKTEno4tKirKy4eY1tpqzlYNYY6YhOIxLKHjyASPpAwDSTqYxJw2kEsZFn63ht/tJpyI4/MsbB2+BZkXEEK0ALeRHlnVZpwewBDpqUpIO73p0QV9mbbLtffN0s5lrnGhXZ8BPgPMGoLc0dHBoWMnsXwVV+zjpYhFDCLBBBOhGOHJEIl4AmnFZhyTna5LpVIEp8I43QZT8RT0jTIUE7jc1+/YtOj4dZ87F6ampmxVley8fNapjY6O8sorrxCNRvF6vaxZs4aWlpaCcGzRTEKqpmmsXbuW6upqhoeHMU2TgwcP0t3dTXNzsz0llf2d5CN33XUXPT09xGIxysrKCIfDM8oS+Xw+HnnkEYqKiigqKiIQCPCrX/0KgLKyMu6+++68m5rs6Ojg1KlTACxfvpyf//znSCnx+/3EYjG7Nl1ZWRmWZTE2NkZlZSVSSsLhcF7OwLidTjQhiCWTpEyJaZnomkaRx4PpdJA0jPRo3LTsYJJsmH9deellZbjmi3n/VgkhioAfAf9JSjk1fbopsx42r4Jil7uGlPJp4GmALVu2zHqM5asgvubD1319AZjdx7Gs/VhxAxlPILEujpXNHJ0yDCy3hstZRKJuPeaarcTn8M/gOfmL6z53LpSUlOBwOPD5fAghsCyLSCTC8PAwkUiE4uJinE4niUSCo0eP2jfJ+vr6vK2yPDU1xcjICGfPnqWiooLGxkbWrFljT0Vmo+Ta29tpbW1lcHCQRCJBZWUlLS0tuTb/mnE6naxYsYKpqSnefPNNOxE9W3PQ5XKxadMmTp48ycGDBxkYGLA1FicnJ+nt7WXZsmU57sW10dHRwdmzZwmFQhQXF7N06VJSqRTj4+P2CK6ystIOFoG0UENFRQVVVVWEw+G8U/1PGgaDk5OkMoISUkLKtAhGokhmRkZCJnFbCISERDKZk7I18/r4IIRwknZq35VS/jjTPJyZYiTzM5uS3w9Mf2RvyrRdrr1plvbLXWPBkZaFEZkkOtJHKjSONOJgmXDRDDXp8TxgpeKYyTix8cHLRyEtYhwOB62trbhcLlatWkU8Huett95ieHiY0dFRIpGIrZ05MTFBX18fR48e5Ze//KU96sknssUn3W431dXVTE1NoWkad9xxh53LpOs6q1atAuCNN95gfHycYDDIsWPHGBoayqX5100ymWT37t0cOHCAM2fO2CVbsnJyBw8epL+/n6mpKQKBwIwUkHxT1ZFScu7cOdrb2zl58iTPPfccx48fJxqNUlZWZj/INTU1UVZWRllZGStXrmT79u08+OCDLFu2zM5TzSeC4SixRIoLXZh1UUuabJuuCULxRE4U/ufNsWWiHL8JnJJS/u20Xc8COzPvdwI/m9b+KZHmLiCYmU58EXhYCFEuhCgHHgZezOybEkLclbnWpy74rNmuseDEAueRQHIqgJVKznJEZgQrtPQ3QloI0qG1ZjxCLHB+lnMWN8lkktdee42zZ88ipaS1tRUhhC07pGkaY2Nj1NTUUFtbO2OUlkwmZ9z88oVgMEgikSAYDBIIBIjFYkxOTtryaVncbjfhcJiamhpcLhfd3d2MjY3lreTS8ePHGR4eTk+jB4N0dHQwMjLC2NgYwWDQHrX4fD5isZi9LYTIu/zMSCRi55aePHmSjo4Oe608GAzi9/tZsmQJq1atsvPYGhoaCAaDOJ1OPB5PXkY5GzItj2VaEtOyZnVmF2JJSdwwmAhFiOTgAWY+pyLvAf49cEwIcTjT9t+ArwA/EEL8DtADfDyz73nSof4dpMP9Pw0gpRwXQvwVsD9z3JeygSTAH/B+uP/uzIvLXGPBSYUnMaMhNN3B7DPNGWEa+f4IzkqlsMwURiKKZczmDBc3XV1d9tqRaZqcOXOGaDRq159zuVyUlJRQW1tLcXHxRcVU83GUml1jefPNN+2ggcnJSbq6uigrK8MwDIQQGIbBypUrZySrT05O5q3yfSAQoLa21k5d6ezstOXExsfHGR0dJZlMsn//fkKhEG63m61bt3LHHXcseOj7XPF6vXZk7+joqJ2X2N3dTUtLCx6Ph3g8zrlz5xgaGqKmpoZkMkkkEkFKyb333pt3a4oAuhA4NA1LyqtyalmklARjUSbDMUoWWFJvPqMi32RWLWgAHprleAn84SU+61vAt2ZpPwCsm6V9bLZr5ALN6SIZGk87KGu2RMVZfkVmklQ0hBGZRPfk3w0vO8XU19fH0NBQWl4nFCISidhTMXfeeSe///u/j9/v54033rAdocPhuKSW4GJG13UCgYA9ekkkEmzYsIGxsTE0TaOmpoYPfehDTExM8Ktf/QrLsmxNyebmZpYuXZrrLlwXpaWldmmWsbExSkpKKC8vJxAI2Im67733HqFQCJfLRV1dHYODg1RUXH9AVq7QdZ2pqSn6+vqIRqOkUilCoRBFRUVEIhGmpqZwu92cOXOGyclJuru7KSoqYsOGDSxfvjzv1tayBGNR4qkU0pxl+eQKuHQHsVlnquaX/Ht8yDNcJVVERs5jxi+1bnSJZ6BUAuH0kAwO46tqmDf7LmSuKQ4AsViM8+fP29FxQggSiYTt1FwuFwcPHuRP/uRP7KnJcDhsR5ft3r37ch9/VSx0isPY2Jh9Y4vH4/T19fHGG2/YDsuyLPbv38/w8DCBQACn08mmTZuoqKhg69ateRktB7B27Vr279/PyMiIrajT3d1tR/pmnVhlZaU9WsnVtOuN+G7v27ePYDBILBaziwFnoyFN07SFzbNMTEwwMjLCuXPnOHjw4A0JjFro7/bwRBAkCE0Da3bnJpgliERoeN0uqkoWviabcmzzTHxiGCMWBnmNIa9CIDMjt4Wko6ODs8cP0lx0/SG6AvDFwkxFQulKBW4nRiyK3+XEU1FEPJEk0HUCJnpAQGWxjyJfeh3KnOQSU7ZXz/nwwicAx+NxotEopaWlDA0NYWWkhbJ118bGxnjllVeAdD5XaWkp9913H5WVlQtu640kGo3aqRq6rnPw4EHa29uJRqM4nU6am5vp7++36xACOYsA7ejo4MSxU5T5rn+dKxxMYJnpkjzT7+TZhPWsmk5WUUdDA5dGcCxE56l+iovmNv06GV34h4KkYeFxOSEy+0P4bE4NwLAsfC4nlTmYZleObZ5JhSfATHLpP/+lEGBauPylVz70BtNcZPKXW+aWXxVJmOztlpkSFgkCIclUPEooYdE7kaTc52BdA5R5dVx6nA+suHFf/i8fWPgnxKzKTLbAaEVFBatXr8Y0Tfr6+ujr67OTc8+dO8fdd9/N8PAwZWVlea3EEQwGOXHiBAMDA+i6jmVZtnRWtl+/8Ru/wYsvvsiZM2coLS1l+fLlOatoUOar4cFVn7zu81eUn+Pfgk+TiHdjChPDfH+aTaABEiF1hJBoaDgdLrzOIqqKmllRtZk1LZvnZP8vTz9zzefMVQQ5pOlELTl7hhKXvqsJTaM3GOH1nkG8cwj5DyWvXWtSObZ5xjQtzFnkh64G4XDib2i7wRYtDH63zroGL11jSSwpuafVz9GBOCOhFAPBFCOhFPu6Le5e5qfE68jr0i2GYXDmzBl8Ph+maTI1NYXT6WRkZISuri7GxsYIhUIcP34cwzBIJpMcOnSI9vZ2SkpKuP/++7nzzjvzLrBASklPTw/vvvuuXVBV0zRbckoIgcfjoampiXg8jt/vp7S0lI6ODiorK/Muhw1gSe1y7l67jZff+ymh6CSWZWJlZmMkFprmwO30IKUFUuB2+nA53Th0B6FIkEhsCr934YJmVqxYMefPKK4dJxAMkTRMQqGrn0ESQuDx+ylvaKK2dlaNjKvmWvuRX/9J+YiZQBMapqZfInhkFoSOo6gMV0kl+hxUR3JNbbGT2uL0KMW0JKeG4kzGTBKGxUjYYDRq4ndr3L+iOG+dGqTX15LJJF6vlyVLlhCJROjs7GRwcJBly5YxPj5OKBQikUjY6zBOp5OOjg5Wr17N0aNHKS8vZ82aNbnuyjURj8c5ceKEXTw1W7FiYGDAFv5tbGzkqaee4o033iASiSCEoLm5GU3T8tKxAaxq2chkZIITnfsYmRwkkYigaTqWNNEAt9ODy+EmnoqRMuPE4w4mw2NMTAUILbBjuxFrcfv27WNsbIxjx46haRqxWIxUKoWmaTPWFbNV06WUOBwOKioqaGtr4/Of/zx33nnnDejN1aMc2zwjLQt0HeFwIg0yi68SsjdyefFirHC6cBVX4KmoR5oGwpGfShwAobhJ11iSpCmJGZZd1qLEreF16RS7NfT89WkAdomhYDBoK2wYhsHU1BTDw8O2IsX0oqJOp5NAIMDq1atJpVIzNBcXghsRSCGlZP/+/Zw9e9aulG5ZFv39/ei6TmlpKX/6p3/K4OAgU1NTdgmXjo4Oenp6eOmll+ZcWX2hAylC0SBn+44zPjVE0kzidroRIv1/rukONDSE0EikYsTiEXutzTRTDE/02eos+cTBgweZmJjAyEhnZRPRs9Pv06fSNU2zq8Y3NTVRV1c357/x9aAc22Xo6+tDiwbnJEvlHDkLsSkwkhnFkQxyemH1mehCokXH0PqP4vYk0PXr/zNp0TH6+ha+HhKAYUre641iZCbnNSEo8+pMxixqihz4XBp1JU4k0DeZJGlIaosd+N0Lu+Z0I27yo6OjvP3224yMjGCapj0tB9gVo7MBFEIIotEo4XCYYDBIbW0t+/fv55lnrn39ZDrXcpPv6Ojg9OHD1F350MuSHB4mFYthpFKY2Yg508RyOBgNBAiOjpJIpbAyDl2mUjgNAwIBApEIpXO46eVCq6Vz4BTnh84SmBzCskxMy8DlcKX/xsJBwoiRiMUxzWyfIZlKEY2HqalowpkDpfu5MD4+jsPhsOXvUqkUlmXh8/koKipiZGQEKSVOp9N2cKZp4vF4KC0tpaWl5aJyVQuBcmzziGka6A4nmq4hUiAR7/syOVNlTWRuglJKkBJpWiRiUeLhEP7S/FRCH48axFIW4xEDU0K5T2dJhRuf28GZ4TjjUROnBlMJk8FgilKvg67xJFuW+Cj1Lpxz6+jo4NCJQ1B2/Z8RmYoQSoYwpIFpmVhmemoOkXZkppF5qJEgNIGma8RSMQYmBrBK0zqa3dHu6zdg8tpPqQN+55KppldGSsm/CQ3pctNnmoQzOoK6lOiGgSFlWjcw+xPwahp3+HzchWC900XpHK7/zWsKxroxTIXHicbDJJIx4skIlmWSMtI3e6fDgWVZGGYSS5oINKQEyzIRQqehailFCzgNeaOorKykqqqKoqIiEokEjkw/i4qKGB8ft/NWhRD2iM0wDAKBAD6fj+rq6gW3WTm2y9DU1MRwwnHdIsjSMkmmXsUZSmCO9kMqgTRTaQeXcWBIC6SJFBqgIXSBXloLLjcJt49Q9Tr0Fbdddx88J39BU9PCPzEBuB2C9kCCWDL9JD8aNnh4VQkbGx249HQY8fmJFGcDMZrKXHidKVbWeOibTFK60MmsZWA9cO0JqFmix6Ik2hMIr4AYaSE9C9BATtPgFkKkHZtXw1PnwdvmRawSyCXZCNLrQ3stN3lwJQ4Hbk2j2uXGSMQJGwYGoElJCkgAOmmn5hQCv65T4XKzvKiI0jws4VJeXI2UJ0mZCRLJBIaZQspMnTlNRxPvRz9nK5ZJLHTNwaqlt+HzLHzE7lyoqKiw5d+y+qfZEdzk5CSmaeJyuWaM6LLpDi6Xi66uLlKp1IKX61GObR4Rmo67rBaHrxjN5QYhsKQLmYyDkXp/alJooLsRSISe1oy0JDh0Jw73ws9PzxXDlPRMJOkZS9A/mWAsYuDUNGpLnAgBoYRJuc9BKG5iWulw6WhK4tQloxGDZZULL5o6F8ykiZWw0F06CSuRznESgIYdFCOlROgC4RBIK/2Pb5kWqUgKoeXnIqMQgmafj2afl/awSbGuEzEMOw8xW3hSJzMN7XLRWlTE3RXl1OdA8b2vr49gNHRdIfNZDMNgMj5MOD5JIvV+SSaAWDLMbGk9hpkibk6x5+j3aKy7tkrQFzIZHUH2xa584A3kjjvuIJFI4PP5SCQSJJNJ4vE4paWlTE5O2tOT2eAoSJdjOn36tF2zb6FHbfkpd5BHuMtqwDQQuhOkxIpH08nadtBIZuRmGWhOF1JqmPEwVjKG5vTirsovoViAowMxusYSHBmIcm40SdJIi6JaEuIpi4moyYnBGEf6Y0zGLByaILuspglYUp5fT/JWyiI1lcJT5Uk7L02k/7O0TMJu9gneSjs3KSVYYEZNYsMxklP5pweapd7jwSEEJU4nxU4nbk3Dw/sz7o7Me00IdKHh0jRGkkk6M/qJ+UbKSFJRXonP408nX1/E7H0KhiaZmspNbcS5EovFKCkpYfny5el1UilJJpN2lY7sSO3CwJhEIsHAwEBOIp7ViG2eCXYeJj42hBELYsYjYP/xp/8DSDASmEYKNA0TNyKpIRxOrEQUvPkzfZEwLMajBklD0hFIEE1ZRJMWPpdGKBHnSL+LKr/OaCjF0YEo4zGLYreG2+Gjwufg0dXFFC1w8Mhc0T06RsLASBpoTg3dqYMbrLiFlZqmUCHBjKfX3fQiHVeZC82pETofomxVWd6lPERNk9PhEDUeL2dCYXqjUZKWhUVaPUYCbtKRcpoQSCQbiotxCo2heJwSh4Mq98KNzpuamhCJsTklaJ85f4Tz3T/HMgXWRY5ZoGnClhObjrQk1UXL5nRtSCdoNzYtrFpNtjpFLBajqKiIVCplR/1mH05m++6apkllZSX9/f1UVVUtqM3Ksc0jlmEw1X0C04hjRiOk4/0vewZYFjIJBpCYHCYxNZEe9eUJDk2gCUH3WJyUCR6HxlTcpFgTVPgdJAyLNzvj9IwnCCXANCQpXdI5luCeVj/hhKRqgf14X18fBK9/nUpIgd6ro01q6DEdM5lJ2E1JLqrEmLnnpQIptKiGt8iLjEr0uTrzSeiTfVc87EYynkwymUrRHppiKBYllUnlYPr0HKBZFn5NRwDvTQa5t8qJR9eJmCYLe7ubO/0jXYQi48RikczoZfpeCehoCKwZwnDpm77LmZ9pO8eOHaOsrMyu2JBMpmcYsg48GzRyIbquU11drYJHCg0zEUVKCyN+NU5tGpaRdm7hCZALW5iwr6+PSEifkyzVZMhBR79BJCGJJ1MkTINoWCPhLKb9TJLJiEEkbmJZEmkJosLBuKHx7aNQ06uxpHZunq0npOPvW7ibvJQSp8uJy+siHomnI8MsidAE0ph9akpaklQshdvtpqRi4SPl+vr6CDG3yMKBWJRjQ0MEo1GShpH+pFmmFy0gjiRgmkxGIwyF3RR5PCwtLsIzh+sPAuEF/DunVUYsDMPEsFKzTqValokmHJmlhnRKj4bA6fSwpGb5gtl6Izlx4gTRaJSioiLcbrc9YpuOYRhommZrpOq6TkVFBffffz/19fULbrNybPOIw1eMEJdWxL6sfqRlgdBw+srmybr5o7TIQ1WZn5Kkm3jKYGA0SCyRYng8iGFBOvFBZNTRIRpL4XZqRBJJgpE4NSkDt3PhvppNTU0EROC6oyKllLhOuzACBvpRHT2op9fXwhKZlMjEBX9jATjA0i1cK10UP16M5b3+iExIjzabGucWmHCtjIRChKc7tcuQHc05MgUoi4XAlWdRkZqmU1laSyQ+hWkZl4hiTeeuOTQHhpmuOi10B2XFlYxM9JNIxnC78qt8TVFREWNjY4yOjtqJ+NOdejaPrbi4mFgshmEYVFdX8+u//ut87GMfU2tshYYQAn9jG6G+05c44pLyoWguN86iUhzFC5vD1tTURNwYnLMI8nBIcHzQYH93BBFJMmoZJFIWpR6BZWmkhGTckliWxOUQlPsEy3xxHrnFxeraSVbXXX/U3JcPFOFpWribvBACZ5mT+Mk4ZsoELb3ulgqn0tORsyAtiXAK4iNxJk5NULWxakGjI5uampgcHb3uPDYpJV+OROm0rPdTM69wjmWaVDqdrJKStULwiATHHG5630RStoB/ZwCH7kBoGpaVXUW8GNNKoWsONKFjSQtpmUSjUwyO9jA80U9z7dz1GxeS22+/nZ/+9KfEYjFbbCCbjJ2V1nK73Xi9XsrLyxFCcPfdd/OpT32KJUuW5MRm5djmGXd5HcLlQRrpp7erQgg0lxd/02qcHv+82jdflHl1dCHoDxqEExamlZYRG5oyqfBKHLpOmVcjaUn8Tg1TCsIJg1OD8bwL9wewkhbuCrcdIWlEDeys5OzyQ3ZQJjJJ2k4NM24yeXqSsrYynMX5M4IJGwa1bjemlHZYf/bb7SQdPDJ9DKqT/jUkpaQnFmNVcTGOHNSgm4yOzCnc/+TZoxhW4oo5h+Y0XVhLSkKxSc70HUbzWDRPtFz39SejIzSysMEjzc3NbN26lX379pFIJIjFYui6jqZp9vqaw+FIj8gdDm677TZ+67d+i40bNy6ondNRjm2e8ZRW4/L4ScSjM0WQhTarTiRoCJeXkmXr8Vc1YKWSaHm46HxqKM5U3EQTEDMswgkrHRknIW4KlhTrhJOCaMLCkgJdg5ghcTrIyzBwAN2p4/Q70ZwaDr8DzaMR6Y1gpSyspJW+szvTx2UTuM24SXIyiZEw8sqxOTSN1iI/dR4PgXicVCYhW5LumiDtzBykA6EE4NQ0rIwjrMqBuPeNULrvG/Uhhq/9+2lJC6GBv9xJ4/Lrd0yNVN6QflwrVVVVbN26ldOnTxMIBDBNk5KSErq7u3E6nTQ1NRGJRCgrK+PWW29lZGSEoaGhnMhpgXJs847ERJstyfpSN29dw11ahbuyPh36n4zlpWMbj5r4XBpNpU5ODmZudlLicmg4NKjwOxDCRAAuh4bPqdFQ6mRVjRc9FwnLk3NT7/AlfCQDSSLDEQzDQEqJjo5TOokn4vZwxuvyYqSMtMRWCsyEiWVYyDclWtkcRjCTwDWmPA4xh+ARXWPc78dVWoouJYlkMi34zYyBKSlmjtwAhK7zgmVyao6SWENcmwrajRBL/tu//Vv+5V/+hdOnT5NIJC75EJbN94J0uoPX66Wuro7//J//M48//vic7VhoampqqK2tZWhoCI/Hg8/n4wMf+AD/8i//Qjwex+l0UldXR0NDAydPnrQrym/bto3W1tYFt1c5tiugRcfnJIKcHBvFmhyapWTNJf6pTQNjcpDk6TcobWzBH+udNZT2atGi43CNUrfnw3OLigQ4P5wiEkvicJQSIYkpUqAJEqYklhKcnnDicXspqfCQTJqEgK64zkSvl5pQET8fvP7Ry/mwzsprOP5GPAFblkWn1cmoHMXlcuH1ejl9+jTuYjfD0WG7lIcDh60V6XA47LWJJl8TyxrnUMal8dr6cSP6XAaUjo1x/Phx+vr67PI1WUklAJfLZZczEULgLSmhtKoKV1MTxStXzqnIatkN6se1UF5ebo9OBgYGSCQSaZk0IWw5KafTiRDCfsDJSlCVlJTwgQ98YEHtvVE0NTXhcrnYtGkTQggaGxtZt24db775JoODg9x6662Ew2GcTieRSIRQKITf7+fkyZM0NjbauXALhXJsl+FG/NOMl7toP2zMeIK7EkJKrESUNUtraWxsmKMFdQt+wwsGg5AcJpwcZnxsHOEpweMwiEQiGf08gfSUUtnUxNKlS+1oq9LSUmpqavDMUW5p5TX240Y8yb/zzjvs27ePwcFBJiYmqKqqwuv1Mjg4yOTkpK2dp+s6ZWVleDweXC4XRUVF1NXV8fGPf5x/9+/+3ZztuFpuVKmXeDzOz372M77//e9z4MABwuEwyWSSVCqFrus0NjYSDoeJRCIAFBcXs3XrVp544gm2bt264Im7c2XlypV4vV4aGhpwOByMj4/j9XppbGzk7NmzRCIR3G63LS+laRoul4vq6mq2b99OaWlprrtwXVRWVrJu3Tqmpqbw+Xw4nU5CoRBLliyhrKyMBx54gImJCQ4ePEgwGKS0tJSSkhKklESjUeXYFhM34p//mWeeYc+ePYyNjV11LSZd1/H7/WzcuJG//Mu/XNBw2RvR5xdeeIHBwUHeeustuyZZJBJhbGyMQCCArus0NDTQ0tLCHXfcwdKlS+1zb731Vpqbm+dsw0ISiUQYHR2lvLyc119/nVAoxODgIOPj48RiMbtkjcfjQdd1Nm/eTGdnJ5qm4fF4aG1tzUkS643A4/Fw66230t3dzdjYGAMDA+i6zvj4OC6Xi0gkQjQaJZVK5+xVVlbS2dlJV1cXjz32WK7Nv2ZWr17NLbfcwuTkJMXFxSxdupSSkhJqamoYHx+nuLgYwzBIpVI4HI4ZJV3mMvOSa8rKyqitrcXr9RIIBDh16hQ1NTUcPnyYkpIShBDU1NRQUlJiT9GeO3eOdevWUVZWtuD2Ksc2j5imSSAQoLy8nGAweNWOLZVKEY1G6ezspLOzk+XL8yuxU0rJ6Ogopmna6gTZHBdd1+2Xy+VicHDQVg83DIPe3t68c2zZiLDsyMyyLPtBxuPx2E/t5eXleL1eQqGQPXqvq6ujqqqKtra2HPfi+lm1ahUNDQ088MAD9Pb24vV6+Yd/+Afa29vx+/1IKe1R+ODgIKWlpYRCIRyO/Lv9lJeXs3btWsLhMAMDA0D6b1hfX09JSQm6rtPS0kJPTw/nzp0jmUymyxaZJidOnCAQCOTtQ8zWrVvp7Oykr6/P7qNhGASDQbxeL4lEgnXr1tltAOvWrctJHtu8PUIIIb4lhBgRQhyf1lYhhNgjhGjP/CzPtAshxFNCiA4hxFEhxKZp5+zMHN8uhNg5rX2zEOJY5pynROa3d6lr5Aqfz0dTU9NVV5HNfgmcTiepVIru7u55tG5+aG1ttdcUIJ3gWVFRQWVlJT6fD4/Hw9KlS6mvr0fXdSYmJjh06BBHjhzhxIkTdn2nfMHtdrNs2TJCoRAulwtN0ygvL8fpdLJmzRpKS0spLS2lrq6OZcuWEQ6Hqa6uZsWKFVRUVFBXV2f/rvKVkpIS7rzzTp588klaWloIBAL2aCX7oJJKpVV0ioqK6O7uJhAI5NLk60IIwWOPPUZTUxN+v5+6ujpKS0tpbGzE6/VSUVHBhg0bqKmpsdfatGm1FvPx/zmL0+mkra2N5ubmGeuo2XVETdOora2lsbGRNWvWsHz5clyu3AS+zefY+NvAoxe0/QXwipSyDXglsw2wA2jLvD4DfB3STgr4AnAncAfwhWmO6uvA700779ErXGPB0XWd22+/3V5TudQTqsfjwe122/8I2QrLHR0dOZGjmSu33HILjz/+OOvXr+f+++9n3bp19tRqtp/19fW0trayZcsW+vv77ZLzpaWlnD17NtdduGbWrl3Ltm3bWLVqFZWVlfj9frxeL9XV1fbotKamhtHRUSoqKqiurkbTNOLxOOXl5Vc9ml/smKbJr371K0KhEJCWWorH4/Y0bFFREaWlpVRXV9PZ2Zlja6+PkpIS/sN/+A888sgjLFu2jFWrVtHY2EhLSwsOh4PKykpCoRBFRUV2ME22vbJyYXPQbjSapl30oO7N1E685ZZbZhxbUlJCeXluxhXzNhcgpfyVEKLlguYngAcy73cBrwF/nmn/jkw/AuwVQpQJIeozx+6RUo4DCCH2AI8KIV4DSqSUezPt3wE+Cuy+zDVywtq1a2lsbMTj8eB0OrEsa4b6t6ZpVFVVkUqlbLXs7KgtHo9TXFycK9PnxPLlyykrK+Ptt9/G7/dTXl5OTU0N7e3tpFIpHn74Ye68805M0ySRSNh91XWdcHhuqie5IBQKkUwmKS0tpb6+3lZfyIZ6W5aFz+ejsrKScDiM1+u1H3QmJyfzLogiS3d3Nx0dHUA6YKepqYmxsTE7abe5uZnz58+zfv16O5giu7ZYVJQ/VSsuxO/384lPfIJgMEgymaSyspKf/vSnFBcXc++99xIOh/nxj3+MYRgkEglKSkpYv3593k2zz8b69espLS3F5/Pxzjvv4PV6aW5uZsOGDTQ1NdHf34/H46GlpSVnFSsWepK7Vko5mHk/BNRm3jcCvdOO68u0Xa69b5b2y13jIoQQnyE9Qpy3L9yxY8fo7OwkFothmqY9NZFdU/J4PDQ3N6PrOkePHrVHbNlpjlhsYYsK3iiklBw8eBBIr0FlgwgcDgcOh4PGxka7BEYqlSIWi9lP9blK6rxepJS8++67xGIxVqxYYU9DVVVV4XK5qKqqIpFI0NraSn9/vz1lWVdXR1tbG62trXlXsgZgfHycY8eO2dvHjh2jpKSE2tpa/H4/sViM0tJSNm3axIc//GF+/OMf09fXh8vlwrKsvB+9ABdFOZqmSTgcZsmSJdx6663U1tYipWTNmjVs3rw5L9cVIZ3O0tXVxfj4OBUVFSxbtoyWlhZefvllpqamGBoaYmBggJaWFjZs2JBrc3MXPCKllEKIeZWYuNI1pJRPA08DbNmyZV5seeONN4jFYmk9wWmir9kcpmyIeywWw+l0EovFcDgcGIaBy+WisTH/Co0CRKNRe62spKQE0zQ5f/48pmnidDpZunQplmXx1ltvEQgEePfddxFC8KEPfYimBdb/myuhUIhYLMbExIQd9blmzRoeeughACoqKhgcHGR4eJhoNIrL5SIWixGLxYhEItTWXvLZa1Fz/vx5jh49SiQSob6+Hikl8Xic9evXU1FRgWEYPPbYYzQ0NDA4OEhrayv19fXU19ezYsWKnCpTzAeTk5OMjY3R3d3N2bNn8fv9rFq1Cp/Px5o1a2b8/+cbx48fp6enB9M06e3tJRqNsn79egzDYGJiwi5l09HRQWlpKQ0Nc01TmhsL7diGhRD1UsrBzFTjSKa9H5iultmUaevn/WnFbPtrmfamWY6/3DUWnHg8TlFREV6vl+LiYju3JzslYxiGndQ6MTEBYIcGl5WVzQiDzze8Xq+dz9Pe3o4Qwh6hNTU14fV6GRgYoKOjg71799rTs2fPnqW9vZ21a9fmuAdXj8/nIxKJ0N7eDqQdXW9vL36/n9WrV7NkyRKEEHg8Hvx+P0VFRZimSTQatXOC8g3DMHj11Vc5evQoUkrefvttli1bhs/nY3R0FIfDQVlZGZs2baK2tpaWlhbC4TB+v9+eXs9X6TRI9//MmTN2iP+ZM2d455130HWdM2fOsHLlSgKBgC0M7HA4WLnyWmQDFhe9vb0MDAzQ39+PlJLe3l7Wrl17UaCXlJLjx48zPDxMRUUFzc3NN4W6/7PATuArmZ8/m9b+R0KIZ0gHigQzjulF4H9OCxh5GPivUspxIcSUEOIu4F3gU8A/XOEaC47L5aK+vp57772XYDCIlJJgMEgikUDXdZLJJOFwmOPHj1NbW4vT6cQwDDu5MTuKy0c0TWPTpk12Llt5eTmtra3s37+fsbExfvnLX3L+/Hneeecd2+EDBAIB28nnC9nAAIfDwdTUFBMTEzQ0NNDR0UEwGMSyLJqbm3nsscfo6+ujp6cHwF6byLcoUIDh4WEmJyftCsnj4+NA+sZWUlLC+Pg4gUCAffv2EYvFSCQSJJNJLMuiuLgYTdNYtmwOSis55ujRo/T3p5+ln3vuOd566y1GR0cBePXVV3E6ndx9992sW7eOUChEdXW1HWSRj5imSV9fH8lkEikloVCInp6ei8QUenp6KCkpYWpqijNnzhCJRFizZs2C2ztvjk0I8T3So60qIUQf6ejGrwA/EEL8DtADfDxz+PPAY0AHEAU+DZBxYH8F7M8c96VsIAnwB6QjL72kg0Z2Z9ovdY0FR9M0Nm7caD+tP/PMMxiGYed3ZV9jY2MkEgkSiYSdzGoYhh1IUVFRkasuzImqqiruvfdegBkhz+Pj44TDYcrKypBSEolEKCkpQdM0SkpK8nLtpaWlhWg0SldXlx0x5nK5mJqaoqenB6fTidPppKKigt7e9LJxtqRHPka+Zm9olZWVRCIRO5ggFosxNDTE6Ogouq7z+uuvU1dXh6ZprFq1ikQiQU1NjR0pm68MDqaX8ePxOIcPH2ZwcNCW0BocHOTUqVM8/vjjlJeX5ywy8EayZMkSfvGLXxAMBhFC0NbWxuTkpL2G7PV6MU0Tt9tNOBy2A4rOnz/PihUrFjzsfz6jIn/zErsemuVYCfzhJT7nW8C3Zmk/AKybpX1stmvkirq6OsLhMP/7f/9vADs4JF1kU9qlHqbXNXK5XJSVlaWrWUcieevYIL2+VF5eztmzZxkbG2N8fNy+8Wuaxp133snx48fxer14PB5uu+22i8KG84GWlhYGBwcJBAKMjY1RXV2Nz+fjyJEjdnXhaDTKihUrWLduHWNjYwghaGlpycu/b2VlJbfeeiuHDx8GsPPxhoeHbVFcy7IIhUL2dJxlWVRXV1NRUZEXTu2pp56yb9AX0t/fTzKZJJFIcObMGTs53zRNBgcHOX36NH/7t397xVHaihUrbpi82Y3gUn0OhUIcO3YM0zTRdZ3Ozk7OnDljp3U8++yzAJw7d25GfqKmaRw9enTGd3wh+pyfITp5RCwW44033iAej9sRjm63m2g0aiewZlMBUqmUXX7d7/ej6zqJRCLHPZgbQgiWLFnCmTNnKCkpsacpsv8gDQ0N3HLLLdTU1NiLzos9QvByN7xYLMbY2NiMaM9s+sLTTz+N2+2+qoX1xXbDm42PfexjbNiwgSNHjtiSYQcOHKC3t5dgMGgHxmTltLJJ6Pk4Ir+QyspKRkZGcDqddjpHMBhE0zSqq6vtiNhCoqKiwv4ue71eu+/TKSkpmeHYshJjC41ybPNMOBzG4/Hg9XrtSEe3201xcTF9fX32Ins2cXViYsKWo1m6dOmiF0293E0+SzYaELBrOX3zm99ECGHndl1O5T0fbvJZvF4vTU1NtspG9sk+SyHd7LJTUitWrKCjo4OhoSGeeOIJzp07x9DQEBUVFbS1tdHQ0IDX68XlcrF8+fK8GaFe6TtnWRaRSISenh5eeOEFent77bXlRx55hJqamgWy9MZxqT4nEgleffVV20kJIdi6detFf0vTNHn++ecZHx+nqKgIl8vF7bffvuDRr8qxzTN+v5+RkRFcLheJRALLsigqKmL9+vV2mYvVq1ezYsUKWltb+eY3v2nf5Kcna+cz0wNgsjf2pqamvA2MuRYnOzQ0xJtvvsm3v/1t/uiP/ogHH3ywoJwbvO/gsnqXZWVlfP3rX2d0dJSysjI+9KEP5XXgxKXQNI3i4mLWrVtHc3MzY2NjlJaW5o3jvhbcbjdbt26lo6MD0zQvOYWu6zoPPvgg7e3tJBIJmpubc5LSoRzbPHP69Glqampobm5mamoKwzBYt24dg4OD1NbWYhgGa9as4ZFHHiEej9PW1mZPP4bDYQ4ePMjDDz+c415cmqu5ySeTSfbt28fExASapnHLLbfkpArwQtLf38/Q0BB+v98u5fH222/zyCOP5Nq06+JyI3PLskgmk7YkXG9vL1NTU+i6zv/6X/+L559//qpGL/k0Mr+Q7DR7IVNaWsrmzZuveFxRURG33XbbAlh0aZRjm2fGxsYoKiqivr4et9tNMplk5cqVrFy5kp/97Gc4HA5qa2t57bXX6OrqYmJiwl5Yzybw5jsul4t7772XSCSCy+XK25Ha1dLd3W0rcgSDQX70ox/hcDj4wQ9+wPbt29m6dWtelzCZTjweZ3h42M5DLCsrI5lM2tqX4XA4b9VzFPmLcmzzTEVFBQMDA5SWltrJnDU1NbZG5Llz5zh06JCdtDoyMmJrrBUXF7N69eoc9+DGkQ+RcDeCvr731d6eeeYZIpGIHQ373//7f+ev/uqv8q6S8qVGUm+99Zadwwbph5if/OQnDAwM2Ckrq1at4m/+5m8WylSFQjm2+WbdunWkUimEEHi9XkpKSvD5fIyMjHDs2DFGRkZwu91UV1fbTm/VqlVUVVWxfPlyWltbc90FxTUyvVrwyZMnMU3T1gjs6uqit7fX1tzLdy4cjSWTSdrb29E0DV3XcTqddkqAQrFQKMc2z7jdbu66664ZbUePHuWXv/wl0WgUy7IYGRkhmUzOUMG/4447WLduXcEFGtwMrFy50g751zRtxtRr9gEnn+WkptPQ0MC5c+fs7draWh577DGef/55UqkUTqczb9cVFfmLcmw5YHJy0n7S9fv9uN1ugsEgVVVV3HfffSxduhSXy3XVxUkVi4vS0lK2bdvG+Pg4t912m53AK4Sgvr7eTlIuBFavXo3L5WJ0dJTS0lK7WsHu3WkhIE3T2Llz5xU+RaG4sSjHlgOWLl1qJzgCtLW1UVJSwiOPPGJPYxVK4cmbFYfDQU1NDX/913/N7/7u71JUVEQymeRv/uZv2Lp1a0GkcUB6BLpixYoZUa5VVVXs2LGDZ599lh07dhREQrYivyiM0Kw8Y+nSpTz44INUVVVRUVHB+vXr2bx5sx0pl5VaUuQ/K1eupL6+HofDwYoVK7j//vsvm4xeKDz++OP4fD4+8pGP5NoUxU2IcmwLQCgU4s033+T5559n3759JBIJHnzwQZqamigqKmL16tXU1dURj8dpbW3lvvvuU0+5BURW+/KWW24pmLW16WQVOKb37ec//znRaNTWEFQoFhI1FbkAvPfee7ZY6PDwMMePH2fz5s0YhmFLDUFaV62+vn7Ry2gprp7R0VHefvttUqkUv/jFL1i5ciUrVqzgtttuK4jAoPHxcQ4cOGBrRW7ZsgXTNNm9ezdSSnbv3s3OnTvVg5piQVGO7QYymzpDtnL0dHRdp7m5mb6+PuLxOE8//bS977nnnrvsDS+f1RluRnbt2oVlWYTDYTRN49VXX6WkpIRTp05x66235tq8OXP06FFbKScej3Ps2DH2799vj94sy2LXrl18/vOfz6WZipsMNRU5z2RzeaaTDRCprKycUagvKxqqKBz27Nlja4QahsGhQ4eAdGRsIZBVe5++vWfPHlsEOpVK8dJLL+XCNMVNjBqx3UAuNZIKBoMcPnyYqakpqqur2bhxo+3QDMMgEAjg8XgKoiChYibbt2/nueeeQ9M0NE2zNfQKZWqutraWoaGhGdvbt2+fkce2mLVOFYWJKMTF7Othy5Yt8sCBA7k2Q1FgjI6O8slPftKujP5nf/ZntLW1sX79eluNJJ9JpVKcOnWKiYkJKioqWL16NZOTk3zyk58kmUzidrt55plnCsaRKxYds+bN5P9/lkKxiJme0/Xkk0/y8Y9/PNcm3VCcTicbNmyY0aby2BS5Rjk2hWKe2blzJ93d3TeVAsfN2GfF4kFNRWZQU5EKhUKRd8w6FamiIhUKhUJRUCjHplAoFIqComAdmxDiUSHEGSFEhxDiL3Jtj0KhUCgWhoJ0bEIIHfgasANYA/ymEGJNbq1SKBQKxUJQkI4NuAPokFJ2SimTwDPAEzm2SaFQKBQLQKE6tkagd9p2X6ZtBkKIzwghDgghDgQCgQUzTqFQKBTzx02dxyalfBp4GkAIERBC9OTAjCpgNAfXzSWqzzcHqs83B7ns8wtSykcvbCxUx9YPLJm23ZRpuyRSyup5tegSCCEOSCm35OLauUL1+eZA9fnmYDH2uVCnIvcDbUKIZUIIF/BJQFU8VCgUipuAghyxSSkNIcQfAS8COvAtKeWJHJulUCgUigWgIB0bgJTyeeD5XNtxFTx95UMKDtXnmwPV55uDRddnpRWpUCgUioKiUNfYFAqFQnGTohybQqFQKAoK5dgWACHEt4QQI0KI45fYL4QQT2V0LY8KITYttI03GiHEEiHEL4UQJ4UQJ4QQ/3GWYwqq30IIjxBinxDiSKbPX5zlGLcQ4vuZPr8rhGjJgak3HCGELoQ4JIT4xSz7Cq7PQohuIcQxIcRhIcRF9a4K7bsNIIQoE0L8UAhxWghxSghx9wX7F02flWNbGL4NXJREOI0dQFvm9Rng6wtg03xjAP9ZSrkGuAv4w1n0Ogut3wngg1LKW4GNwKNCiLsuOOZ3gAkp5Qrgq8DfLKyJ88Z/BE5dYl+h9vlBKeXGS+RwFdp3G+DvSSdErwJu5eK/96Lps3JsC4CU8lfA+GUOeQL4jkyzFygTQtQvjHXzg5RyUEp5MPM+RPqf4EJZs4Lqd6Yf4cymM/O6MDrrCWBX5v0PgYeEELMWS8wXhBBNwIeAf77EIQXX56ugoL7bQohS4APANwGklEkp5eQFhy2aPivHtji4Km3LfCUz9XQb8O4Fuwqu35kpucPACLBHSnnJPkspDSAIVC6okTeevwP+C2BdYn8h9lkCLwkh3hNCfGaW/YX23V4GBID/k5ly/mchhP+CYxZNn5VjU8wrQogi4EfAf5JSTuXanvlGSmlKKTeSlnG7QwixLscmzStCiA8DI1LK93JtywJzr5RyE+nptz8UQnwg1wbNMw5gE/B1KeVtQARYtHUulWNbHFyztmU+IIRwknZq35VS/niWQwqy3wCZaZpfcvHaqt1nIYQDKAXGFtS4G8s9wEeEEN2ky0N9UAjx/7vgmELrM1LK/szPEeAnpEtlTafQvtt9QN+0GYgfknZ001k0fVaObXHwLPCpTFTRXUBQSjmYa6PmQmYN5ZvAKSnl317isILqtxCiWghRlnnvBbYDpy847FlgZ+b9k8CrMo9VEqSU/1VK2SSlbCGtyfqqlPK3LjisoPoshPALIYqz74GHgQsjngvquy2lHAJ6hRC3ZJoeAk5ecNii6XPBSmotJoQQ3wMeAKqEEH3AF0gHFiCl/N+kpb8eAzqAKPDp3Fh6Q7kH+PfAscyaE8B/A5qhYPtdD+wS6QruGvADKeUvhBBfAg5IKZ8l7ez/RQjRQTqg6JO5M3f+KPA+1wI/ycS/OIB/lVK+IIT4D1Cw322AzwHfFWlh+U7g04u1z0pSS6FQKBQFhZqKVCgUCkVBoRybQqFQKAoK5dgUCoVCUVAox6ZQKBSKgkI5NoVCoVAUFMqxKRTzhBDCzKi/HxFCHBRCbL0Bn7lRCPHYtO3fFkIEMtc5LIT4jhDiI0KIS6pCCCHWTzt+XAjRlXn/8lzty3z+KiHEO0KIhBDiT2/EZyoU14LKY1Mo5o9YRl4LIcQjwP8N3D/Hz9wIbCGdM5Tl+1LKP7rguGcv9QFSymOZz0EI8W3gF1LKH87RrumMA38MfPQGfqZCcdWoEZtCsTCUABMAQoh6IcSvMqOk40KI+zLtYSHE/1eka7m9LIS4QwjxmhCiMzMKcwFfAj6ROfcTs10oM4r7x8z7b2dqZL2d+ZwnL3HOciHEwWnbbdltka499v+IdP2xfUKIFZn2aiHEj4QQ+zOveyAtMyWl3A+kbtQvT6G4FpRjUyjmD2/GAZ0mXdLlrzLt/w54MTOauxU4nGn3k5abWguEgC+TluX6NeBLUsok8P8hPULbKKX8fua8rKM7LISYTe2hHrgX+DDwldkMlVKeA4JCiI2Zpk8D/2faIUEp5XrgH0mr+UO6PtdXpZS3A7/OpcvWKBQLipqKVCjmj+lTkXcD38mo/e8HvpURif6plPJw5vgk8ELm/TEgIaVMCSGOAS2Xuc6MqUghxG9fsP+nUkoLOCmEqL3M5/wzaZmkzwOfYKaw7/em/fxq5v02YI14v7RaiRCiaFpNOoUiJ6gRm0KxAEgp3wGqgOpM4dkPkFY+/7YQ4lOZw1LTxIEt0hW5yTiluTyEJqa9v1yBzx+RLsPyYeA9KeV0BX45y3sNuCszetwopWxUTk2xGFCOTaFYAIQQqwAdGBNCLAWGpZT/RHqUdGH5j8sRAornwUSklHHgReDrzJyGhPQILvvzncz7l0gL4wLpiM35sEuhuFbUVKRCMX94p1U2EMBOKaUphHgA+DMhRAoIA5+a/fRZ+SXwF5nP/b9vnKk23yW9pvfSBe3lQoijpEd/v5lp+2Pga5l2B/Ar4D8IIeqAA6QDZiwhxH8C1twMhWYViwOl7q9QKGwyeWelUsr/97S2bmCLlHI0Z4YpFNeAGrEpFAoAhBA/AZYDH8y1LQrFXFAjNoVCoVAUFCp4RKFQKBQFhXJsCoVCoSgolGNTKBQKRUGhHJtCoVAoCgrl2BQKhUJRUPz/AcW5CLwJACDAAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# now let's plot the house mean sale price based on the quality of the \n", - "# various attributes\n", - "\n", - "for var in qual_vars:\n", - " # make boxplot with Catplot\n", - " sns.catplot(x=var, y='SalePrice', data=data, kind=\"box\", height=4, aspect=1.5)\n", - " # add data points to boxplot with stripplot\n", - " sns.stripplot(x=var, y='SalePrice', data=data, jitter=0.1, alpha=0.3, color='k')\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For most attributes, the increase in the house price with the value of the variable, is quite clear." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "30" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# capture the remaining categorical variables\n", - "# (those that we did not re-map)\n", - "\n", - "cat_others = [\n", - " var for var in cat_vars if var not in qual_vars\n", - "]\n", - "\n", - "len(cat_others)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Rare labels:\n", - "\n", - "Let's go ahead and investigate now if there are labels that are present only in a small number of houses:" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSZoning\n", - "C (all) 0.006849\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Street\n", - "Grvl 0.00411\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Series([], Name: SalePrice, dtype: float64)\n", - "\n", - "LotShape\n", - "IR3 0.006849\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Series([], Name: SalePrice, dtype: float64)\n", - "\n", - "Utilities\n", - "NoSeWa 0.000685\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "LotConfig\n", - "FR3 0.00274\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "LandSlope\n", - "Sev 0.008904\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Neighborhood\n", - "Blueste 0.001370\n", - "NPkVill 0.006164\n", - "Veenker 0.007534\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Condition1\n", - "PosA 0.005479\n", - "RRAe 0.007534\n", - "RRNe 0.001370\n", - "RRNn 0.003425\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Condition2\n", - "Artery 0.001370\n", - "Feedr 0.004110\n", - "PosA 0.000685\n", - "PosN 0.001370\n", - "RRAe 0.000685\n", - "RRAn 0.000685\n", - "RRNn 0.001370\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Series([], Name: SalePrice, dtype: float64)\n", - "\n", - "HouseStyle\n", - "1.5Unf 0.009589\n", - "2.5Fin 0.005479\n", - "2.5Unf 0.007534\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "RoofStyle\n", - "Flat 0.008904\n", - "Gambrel 0.007534\n", - "Mansard 0.004795\n", - "Shed 0.001370\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "RoofMatl\n", - "ClyTile 0.000685\n", - "Membran 0.000685\n", - "Metal 0.000685\n", - "Roll 0.000685\n", - "Tar&Grv 0.007534\n", - "WdShake 0.003425\n", - "WdShngl 0.004110\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Exterior1st\n", - "AsphShn 0.000685\n", - "BrkComm 0.001370\n", - "CBlock 0.000685\n", - "ImStucc 0.000685\n", - "Stone 0.001370\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Exterior2nd\n", - "AsphShn 0.002055\n", - "Brk Cmn 0.004795\n", - "CBlock 0.000685\n", - "ImStucc 0.006849\n", - "Other 0.000685\n", - "Stone 0.003425\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Series([], Name: SalePrice, dtype: float64)\n", - "\n", - "Foundation\n", - "Stone 0.004110\n", - "Wood 0.002055\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Heating\n", - "Floor 0.000685\n", - "Grav 0.004795\n", - "OthW 0.001370\n", - "Wall 0.002740\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Series([], Name: SalePrice, dtype: float64)\n", - "\n", - "Electrical\n", - "FuseP 0.002055\n", - "Mix 0.000685\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Functional\n", - "Maj1 0.009589\n", - "Maj2 0.003425\n", - "Sev 0.000685\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "GarageType\n", - "2Types 0.004110\n", - "CarPort 0.006164\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "Series([], Name: SalePrice, dtype: float64)\n", - "\n", - "PoolQC\n", - "Ex 0.001370\n", - "Fa 0.001370\n", - "Gd 0.002055\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "MiscFeature\n", - "Gar2 0.001370\n", - "Othr 0.001370\n", - "TenC 0.000685\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "SaleType\n", - "CWD 0.002740\n", - "Con 0.001370\n", - "ConLD 0.006164\n", - "ConLI 0.003425\n", - "ConLw 0.003425\n", - "Oth 0.002055\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "SaleCondition\n", - "AdjLand 0.002740\n", - "Alloca 0.008219\n", - "Name: SalePrice, dtype: float64\n", - "\n", - "MSSubClass\n", - "40 0.002740\n", - "45 0.008219\n", - "180 0.006849\n", - "Name: SalePrice, dtype: float64\n", - "\n" - ] - } - ], - "source": [ - "def analyse_rare_labels(df, var, rare_perc):\n", - " df = df.copy()\n", - "\n", - " # determine the % of observations per category\n", - " tmp = df.groupby(var)['SalePrice'].count() / len(df)\n", - "\n", - " # return categories that are rare\n", - " return tmp[tmp < rare_perc]\n", - "\n", - "# print categories that are present in less than\n", - "# 1 % of the observations\n", - "\n", - "for var in cat_others:\n", - " print(analyse_rare_labels(data, var, 0.01))\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some of the categorical variables show multiple labels that are present in less than 1% of the houses. \n", - "\n", - "Labels that are under-represented in the dataset tend to cause over-fitting of machine learning models. \n", - "\n", - "That is why we want to remove them.\n", - "\n", - "Finally, we want to explore the relationship between the categories of the different variables and the house sale price:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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KpTh37lzhBrgeQBW5IpHO8fr5eKHySFdfkpbaAI0RP001+WSWyZUzwqVNiU2KLplM8swzz5BMJgE4fvw49913nxZRFPFcHs1OKKdVHfIxnMjRGMkntYDPqK1S/Yy50l+aCleOadoDAwNcunRpQtvf/u3fUldXx7lz5wCmfd5noS3EFGRZelKZHBeHM/gM2uqCBPxGLufoG8mQyORorQ1SHZp9Aro6abXXBfD7DMyIBPNDkn7f9ZazlOkoscmCisfj5Q5BZEaSmRwvnR4l6Y0JnrqU4o51NbzZE6c/lgGgqz/FbWsiNFbP7k9pXdjP5pYqTkRTOOdY1RDi5vYwPiWzolBiq3Dl6IUkEgmeffbZwlBkTU1NYShy7OHVRx99dMHjEpmNnsE0o6ksA/EszkEkaBy9eCWpATjnODuQnnViA1jfXMXaFSFyLv+ogBSPEpsUXTgc5r3vfW9h8khHR4fur8mSk8453u5NksjkuDiUIZXN0T+SBYPOFUHM8sloPivP+H2G5j0Wn/7aSElUVVWxcePGcochMmcBM7I5RyyZI57OEvD7CPmN0XSOoUSOhogfM2PtiiszflOZHEcuJojGstSFfdzYFqb2OlP2nXN09SfpvpwinnasbwpxQ5uGI4tBiU1EZBJVQWNbW5h3epNkHdSGfPj8sKG+iuYaP001AVbWBiY8a3asN0nfSH6ocjCe5dD5OHdtqJ30/OcH05zsT/JOf5J4KseRC3H6RjLcs7GWgIYm52XGic3M1gFbnHNPmVkECDjnhksXmlSKkZERent7qa2d/H9wWfzOnTtHbNi/YCWRFoNcznE+OkgiGeby8ChmxtvJagI+H6tbGggGru2JdffmyOQmPrP5VLQWv+/amZP9gzH6B7IMjV55YO2NoSD7L1ZRVx0u/g+0CJwe9lPjzYwupRklNjP7NeDTQBOwCVgD/CXw/tKFJpWgt7eXl156qfDMTl9fH62trWWOSmR6Pp+xqrmeWDxFfU0Vmawj4PdRXxOeNKkBhIJ+MskriSro90+a1ACqgn5y427QGRAI+Mjl5nHTToCZ99g+A9wOvAjgnHvHzFaWLCqpGMePH5/wIOrIyAgrVqwobKdSKXp6evD7/axatQq/X7fSF6OOjg6SmZ5lXgTZAAfEva9rJdI5Dl9IcXk0Q22Vn5vaw9SHJ/83c85x6Dw8/U4anGNVQ5CVdUnuWh8gHEyX7Kcopz96tZ6qBXiGdaaJLemcS43NAjKzAPn/wiJTyuUm1gUan+Sufizg+PHj3Hvvvfiu8wlXZLELB33ctrYa51xh1uT1mBnv7qhmc2uY7oH8igAdDUHCQf3+z9dM/wWfMbPfAyJm9iDwbeB7pQtLKsWmTZsmbNfW1ham/p85c6aQ1ACGhoYmLNwoslRNl9TGqw752LoyzNaVYRU9LpKZJrbPA33AIeDXge8D/0upgpLK0d7ezr333svWrVvZsWPHhPtrV/fmrtcmstTkco54Sr/L5TLTocgI8HXn3F8DmJnfaxstVWBSORobG2lsbAQmfpLt7Ozk1KlTpNP5+wk1NTW0t7eXI0SRork4nObIhQSZnKMm5OOWjmoi09STPBlN0j2Qxu+DTc1VtNVrNYz5mGmP7cfkE9mYCPBU8cOR5aS6upp7770XgGg0ipkxNLScJyfIUpfNuUJSA4ilcnT1XxluH4xnOXM5xXDiyqrYF4fTHO9PkszkGE3leLMnzqh6e/My08QWds6NjG14r6tLE5IsJ93d3QA0NzczMjLCSy+9pOFIWbJSGVdIamNiXpI6FU3y8pkYb/cmePF0jJ7B/EjFwGh2wv4OuDyaQeZupoktZmY7xjbM7DauN99VKlomk+Hs2bOcO3euKAmov79/wnYqlWJ4WM/9y9IUCfmouWrYsaUmgHOOk5dSE9pPRPM9ubrwtX+G68OaRDIfM73H9pvAt83sPPmHOdqBXylVULI4JZNJnnvuucLSM/X19fOent/Y2Dhh7bZAIEBNTc28YxUphkQ6R38sQyToo7lmZn8ub+mopqs/yUgyS0ttgI3NIZyDq5+7HnvyZVV9kKFEjnODafwGG5urqFNim5cZ/Zdyzr1sZjcA27ymY865ynyCUK7r7NmzE9ZTGxoaoqenZ16Lhm7dupVYLMbFixcJh8O8+93vnnIlgN7eXo4cOUIqlaKzs5Nt27Zdd1+R+RiMZ3nl7GihOkh7fZB3rYpMc1S+17Z99bX7NYb9HDwTI51zrIj4uXdTHZCfUHVDW5itrVWYze5RAZnclInNzH7ROfe0mf23V7211cxwzv1jCWOTRaYU0/ODwSC33347uVxu2p5fMpnk5ZdfLlzz7bffJhKJ0NnZOa8YRCZz+lJqQsmrC0NpNjZXzWnF7EzWMZzM0lTjJ552REI+/FedRlX9i2e6Htt7gaeBj0zyngOU2JaRNWvWcOLEicL0/EgkMuvp+dlslmg0yo9+9CMaGhrYvn071dXVMxrOvHz58jWJtL+/X4lNSiI3yUJrk7XNxGAiSybnWFEdYKygXDSWpaNx7vHJ9U2Z2JxzXzAzH/AD59y3FigmWaSqq6u577776O7uxufzsXbtWoLB2T1vE41GGRkZIZlM0tvby8GDB7nvvvtmdGx9fT3eSEGhbez5OJFiW9MYIhrLFGoHrqgOXHdttenUhHyFSpOFtiqVziqVaf9lnXM54D8tQCyyBFRXV7N161Y2b95MVVXVrI9PJBITtgcHBws9wJlce/v27QSD+dWL16xZw/r162cdg8h48VSOcwMpBuMTp9231AbY2VnDuqYQ21aGuaVj+vtr1xMO+tiyMozPu3/WVB1g3YrQvOKW65vprMinzOw/At8EYmONzrlL1z9E5FqhUIhM5sozOjU1Ndft9Q0NDZFMJmlubi4MVa5bt47Ozk5yuZxWApB56xvJ8Ma50UJPakNzFZtarnxga4j4aYgU5/esc0WIjoYgmZyjKqDeWinNNLGNTe3/zLg2B2wsbjhS6Zqbm8lm85+Ma2pquPXWWyfd74033uD06dNA/l7ePffcQySS/8RsZkpqUhQno8kJw4OnLqVY3xTCX6KJHH6fcX4wzZnLKfw+Y0NTSOWzSmCm0/03lDoQWR6CwSAdHR186EMfuu60/uHh4UJSA4jH43R1dbF9+/aFClOWiewkD5flHEz2sWlgNMNoOkdTdWDOS8tEYxmO9V4Zjn+zJ05NlW/O9+5kclP+1zGzO8zsdTMbMbPnzezGhQpMKttUz6qlUqkZtYnM19qr7nO11wcJ+vO9tYHRDOcHUyTSOY5eTHDw7ChvXUjwrydjDMyx5NWl0QwjySzRWIZUxuGAS1eV1JL5m+5jx18A/xFoBv4U+L9memIz+7qZ9ZrZm+Paft/MzpnZa97Xh8e997tm1mVmx8zsg+Pad3ttXWb2+XHtG8zsRa/9m2YW8tqrvO0u7/31M41ZFocVK1ZcU31kzZo1ZYpGKtmaxhC3dFTTuSLEjW1hbmoPA3BsXCJ7pmuYYxevFCbIOcepS3P7oHVxKENXX5Kzl1McvZhgJJmlXrMji266oUifc+6A9/rbZva7szj3N4A/B/Zd1f5nzrn/c3yDmd0EfAK4GVhNfrLKVu/tvwAeBLqBl83sSefcW8B/8c71hJn9JfAp4Mve98vOuc1m9glvP5X/WkJ8Ph933303J06cIJFIsGbNGlauXFnusJa9MyN+/ujV+nKHUXKZbI5zfRmc9+cxm80xHE/SWHtlVmQ4FKC9d+K/xcXRfIJqq568aEEmm6O7N81wHBLJDBgcjlXzVqapRD/J4nNmxM+WBbjOdImt8aqqIxO2p6o84px7dha9pY8CTzjnksBJM+sCbvfe63LOnQAwsyeAj5rZEeAXgf/e22cv8PvkE9tHvdcA/wD8uZmZc3N8slLKIhwOc9NNN5U7DPFs3ry53CEsGF86TYCzhe0gkI3FCI4bRWheuZKq2toJx6W6ugCoWjf5v5UvnSbIWZqg8CxmdXU1VatWFfcHWMS2sDC/S9MltmeYWHVk/PZcK4981sweAg4Cv+2cuwx0AC+M26fbawPG/Ybl2+8gPzQ64JzLTLJ/x9gxzrmMmQ16+08sIw+Y2aeBTwOqXiEyhYcffrjcISyoF154gb6+PgAGBgaor68nl8vR2trKjTfeSHNz8zXHPPLIIwA8+uijMzovwG233cbq1auLHL1MV3nkV4t8vS8DXyKfFL8E/AnwH4p8jRlzzn0F+ArAzp071aMTEQB27drFmTNn6O7u5vTp0/j9furq6hgcHCQcDs/7vLFYjPb2dlpaWooYtYyZ0V1LM2szs6+Z2Q+87ZvM7FOzvZhz7qJzLutVM/lrrgw3ngPWjtt1jdd2vfYo+WHRwFXtE87lvd/g7S8ico3R0VG6u7sZGSmspYzf76epqYmjR49y8eJFjhw5wsmTJ8nlcly8eHHO1/L7/WzYsIF3vetdSmolNNPpON8Afkh+YgfA2+TXaJsVMxs/mPxLwNiMySeBT3gzGjeQH4p9CXgZ2OLNgAyRn2DypHe/7CfAx73j9wDfHXeuPd7rjwNP6/6aiEzm/PnzPP300/z85z/nJz/5CadOnSq819XVNeGxlL6+PpLJJNXV1SWJZXh4mGPHjnHmzBmtIj9PM6080uKc+9bYrEjv3tWUD1+Y2d8D9wMtZtYNfAG438xuIT8UeQr4de98h83sW8BbQAb4jHMu653ns+STqh/4unPusHeJ3wGeMLM/AH4OfM1r/xrwN94ElEvkk6GIyDWOHTs2oaj20aNHWbduHWZGOp2mqamJy5cvc/nyZQDa2tpoa2srehzRaJQXXnihkNDOnTvHXXfdVfTrLBczTWwxM2vGK05tZncCg1Md4Jz75CTNX5ukbWz/PwT+cJL27wPfn6T9BFeGMse3J4D/bqrYRESACXVLIb+sknMOM2P9+vX09fWxZcsWEokETU1N3HvvvSWJ49SpUxN6af39/QwODtLQ0FCS61W6mSa2/5n8EN8mM/sZ0MqVYUARkSVp3bp1HDt2rLDd2dlZKLjd3t7O3Xffzfnz56murmbdunUli2OyVbNnskahTG6mtSJfNbP3AtsAA44552a21ojIFEZGRhgcHKSpqYlIJEIsFmNgYIAVK1aU7F6GyJitW7dSU1NDNBqlsbGRtWvXTni/ubl50qn9xbZx40YuXLhQKBDe3t5OXV1dya9bqaZMbFc9nD3eVm/BR62gLXN26tQpDh06BOTXaUsmk1y4cIGWlhZaWlq49dZb6ejomOYsIvPT0dFR9t+zxsZG3ve+93HhwgUikUhJ7uMtJ9P12D4yxXtzfUBbBOccR48eBfL3Nd566y1Onz5NR0cHg4OD+P1+jh49WvY/OCILJRKJsGGDFlIphoV+QFsEyCe2WCzG8PAw6XSaTCYzYSXtaDRKa2trGSMUkaVqppNHMLN/Q75IceGxe+fcF0sRlFS+CxcucP78eXp7e0mlUiQSCTZu3Egul8M5Rzqdpra2tjBDbUw6nWZ4eJiGhgYtNioik5pRYvOq51cD7wO+Sn5G5EsljGtJevzxx+nyCqFWMucc8XgcMyusaj1TY/8+n/3sZ0mlUsTjcTKZDNlslsbGRuLxOAMDAzQ0NPDMM89gZrS1tVFdXU0sFqOvrw/nHD6fj/b29nmVN1rMNm/evOzqM4oUy0x7bHc7595tZm845/43M/sT4AelDGwp6urq4rU3j5CtrtxlKHK5LEOX+sll88//BEJV1DU2TzpdeTK+VP5h2FP9UfKV1QrvkM2FSWSyJP3VxIZSxM/3k8tlCJ+5SLimjmw2A+OOOdE3QkNz5Q1X+kcvlTsEkSVtpoltbJW9UTNbTb6ix/JZa2EWstVNxG/48PQ7LlGJyxdJVJ0vbCcB2jcSrJndg6Su/xzJwd7CdlV9K6nWNaQuXyR56TyZkUGSwXyJT6tvJhepJ5MaJRC+snSIzx8gtH77vH6exShy9Jp6BCIyCzNNbP9sZo3A/w684rV9tSQRyaLmsplr23LZca9zZOLDmD8wIQldLdy8Gn8oTCYxgr+qhlB9/lmhUO0Kkpcv4FVUw3w+/OEa8BmB0MTn2oK1K4rxI4lIhZnuObZdwFnn3Je87VrgEHAU+LPShyeLTaiuidRgHy5fXQ2fP0CwOr+ScC6dYuTc2+Sy+dmNodoVVLetn/Q8ZkaovrmQ0Mb4giFq12wjHq0FTuGP1OLz+cmMDhGsbSKbHCUYqSVY30JVY+UNQ4rI/E1Xs+WvgBSAmd0H/LHXNoi3jpksL/6qCLVrthKqb6aqYSW1HVsxf/7zUXKwt5DUAFIjl8kmR2d/jVCY2lWbaLrxbiJNqzF/AF8ogstl8AVDOOeoamzFTCWHRORa0w1F+p1zY3eyfwX4inPuO8B3zOy1kkYmi5a/qprq1mtXHHfZaxd8yGUzXD0p3+VyJC71kIkP46+KEG5ajS8QnOQ6ESKta8mmkxOGO3PZNNlkfMqhThFZvqb7yOsft5jn+4Gnx70342fgpPJlErF8chq38p0vECIQqb1m30Q0P3Ekm4qTGr7E6MWTU57bH6yasG0YvqvaRETGTJec/h54xsz6yc+MfA7AzDYzzbI1snzEek6QHvV+HVyOYM0KfKEwVQ0tkw4XpmMTf3UyiRgumykMaV6takU72eQomeQoZj7CzR34rrOvSDklEgn6+/tpbp75IzBSfNOV1PpDM/sx+an9Pxq3ErUP0NOjQiY+ciWpAfh8+KuqCTe1X/cYfyhMLn7lXpzPHwTf9auI+AJBatdsI5dOYv4ANsW+IuWQy+Xo6ekhHo/z/PPP09jYyN13363qOGUy7d1359wLzrl/cs7FxrW97Zx7tbShyVIwfrLIGJdN47IZ0rFBsqnkNe+HWzrwBUIAmM9PpLVzRp9ufcEqJTVZlC5cuEA8Hi9sDwwM0N3dXcaIljeN58i8BKvr8fkDhUokhmHBMENn3ipM+Ig0r6aq8coyHOmRAXKZDBYIEWnpIFhTX5bYRYolmbz2A9xkbbIwlNhkXsznp7ZjK8nBPlwuS6iuOf+A9bhZjIloD6H6FjAfI2eOMHz2CI78M2sulcC/7mb8ocqs+SjLw6pVq/D5fORy+ZJvfr9fSy6VkRKbzJsvWEWkZU1hO94/sTqJw+FyWbLJGInLFwoTJ3PpFOnRITKjg0pssmikUikGBgaor6+fcZHtcDjM6tWrGRoaorOzk/Xr11NTo8dRykWJTYouVNdEPHqusB2M1OELhMiMDl8z89Hlspq6L4tGb28vBw8eJJvNYmazWsU9FArR0tLCe97znhJHKdNRYpOiyaVTZFMJQvXNmD9AJjaILxQplL4K1DQQrG3MT91PxPLL3rSuJVA9uwLKIqXy1ltvkfUKDTjnOHz4sIYUlyAlNimK5GAfif5zOBzm81OzahPV7ROXuff5A9R2bCNUu4JsOpmvFTmLQsbZVAKXSeOP1KiclpTE1RM+UqkUuVwOn0+/b0uJEpvMm8vlSETPFwoju1yWxKUealdvvmZff6iKSOvaWV8j3t9NcrAPyFc0qV29WUOYUnRr167l+PHjhe2Ojg4ltSVIia2Izp07h390sKLX08qk0ziXIxAMFZ49y+WyJPouTNjP5w8QGXq7KNfMZjLEoxeZkMYuvk6kvrEo519s/KNRzp27dnkgKb0bb7yRSCRCNBqlsbGRjRs3ljskmQMlNpmxkYFLpJL5h1D9gSB1K1rw+Xz4fH6CoTDpVKKwb1Wk+nqnmbVcLotzLv+snBl+f4Bc7tqCyyLzZWZs2LCBDRs2TL+zLFpKbEXU0dHBhWSgIlfQzsRHGAm+M6Et17SK8Ip86Sx/Lkv6Ug/pkYH8GmtNq4hPdqI5yGXSDL/2YzKJfPEbf1U1Deu3E1uxslDBpJJEjn6fjo626XcUkUkpscmM5DKpwuvM6DCZ+DAunSJY04g/FCabSpAavoTLZUlevoj5g1Q1tBTl2qnhS95MSz/ZxCiZ2ACjfWdIDfUTaV17zWKlIrK86a6ozEiwuh6fL0BmdJjkYB/ZVIKcyxE734Vz3uQRb3jQ4UhEz+FcrijXdtk05g8Qqm/B/H4sEMLlMjgc8SJeR0QqgxKbzIj5A9R0bMX8QQKRWsJNq/JV+rNpsvEY7qpiyM7lcLniJJxg7QqM/EQVl81i3goCkJ+BSZGuIyKVQUORMmP+UBXVK9cWpt2P8QVDBGubyF7uKbSNFUcuhkC4hppVm0gN9UNLjlwuU6jyH6yuv+46biKyPOkvgsxKVWMbmfgI2VQcw6ha0Y4vWEWgpp5QJkUumwIH4ebVRb2uP1xDyIyq5g4ysUEy8SH8VdVUNa4s6nVEZOlTYpNZ8QWC1K29gWwynl/00x9g9MJJUrGBfFHjkcuEGlrJxkcIN6+asFzNXGXiI8QunMDlshhGZGUnNas2FeGnEZFKpHtsMif+qgi+QJBMfJhUbACA9MhlMolYfsYkjkS0B5ed/4PGiUtXTUzp14QREbk+JTaZF5e5MmlkLIkVvuMKC5DO6py5HJn4SOHYXGbixJRcLqMJIyJyXRqKlHkJVNdj5suX2QrXkk0n8Yfz61D5Q5FZr7OWig0w+M6rZFNxAuEa6tZvJ1hdz0hPvn5fIFJLqHaFJoyIyHXpr4PMiy8QzK+gPdBLsKaR6lUbcNkMvmAVoYZWMokYvkBwxhVCBt95hdTIAOBV8z/5ev75NQfZ1ChZjPD67SX8iURkqVNik3nzV0Wobls3oS2bShI730Uuk8KwGU0kyaVTZOIjE9oSA70EI/UEahsIkF+3LRMbxK/ZkCJyHUpsReYfvVTR1f1namTwEsHElWqRuW6jqrWNQCpf7zEXrr/mGOdyRIbOkPJqQgL4/QFCbohcNovP58N8PsID7xCuqS39D1Em/tFLgGpFisyVElsRbd587fpjy9X581kSiYnrpa1Z08KZM6MAbN44+R/uwZZquru7SSQSVFVVsWrVKk6ePEk6nQWyNDU1sXnzevx+f6l/hDJq0+9SmQwNDfHaa68xODhIc3Mzt956K5FIpNxhySyZc67cMSwKO3fudAcPHix3GBXj1KlTHDp0qLBdV1fH/fffzyOPPALAo48+et1jU6kUsViMhoYGXnjhBS5evEg0GgWgvb2dD3/4w1r8UXj88cfp6uoq6jm7u7tJpa4U/K6urqa9vX1Gx47Fstg+lGzevJmHH3643GGUik3WWLK/Dmb2dTPrNbM3x7U1mdkBM3vH+77Cazcze8zMuszsDTPbMe6YPd7+75jZnnHtt5nZIe+Yx8xb9fJ615CFtX79et7znvfQ2trK+vXrufPOO6c9Jh6PMzQ0RCgUYsWKFfh8PuLxOIFAgLa2Ntra2nDOkc1qLTYpvlwuNyGpASSTyRkfH4lE1LtbJEo5FPkN4M+BfePaPg/82Dn3x2b2eW/7d4APAVu8rzuALwN3mFkT8AVgJ+CAV8zsSefcZW+fXwNeBL4P7AZ+MMU1ZIF1dnbS2dk5o30PHz7MiRMnAGhoaOCuu+4iGAyyevXqCZ/KW1tbCQaDJYlXlpZS9EKeffZZBgcHC9vt7e3s2rWr6NeR0ipZj8059yxw6armjwJ7vdd7gY+Na9/n8l4AGs1sFfBB4IBz7pKXzA4Au7336p1zL7j8WOq+q8412TVkkRoaGiokNYDBwcHC9g033MBNN91ES0sL69atY9OmTeqxScns2LGDpqYm/H4/bW1tbN+uR0uWooWePNLmnBsrAX+BK1O/OoCz4/br9tqmau+epH2qa1zDzD4NfBqYcc9Cim90dPS6bWbGpk2bqK+v55VXXuH06dMEg0Fuu+02WltbFzpUqXC1tbXcc8895Q5D5qlsd+C9nlZJZ65Mdw3n3Fecczudczv1R3LmLl++zKuvvsqrr77KwMDAvM/X0tJCKDTxAe7VqyeuDvDmm2+STudLa6XTad58801ERCaz0D22i2a2yjnX4w0n9nrt54C14/Zb47WdA+6/qv1fvPY1k+w/1TWkCGKxGP/6r/9KzqvVeOHCBe6//36qq6vnfM5AIMDdd9/NO++8QyqVorOzk7a2iR3tq3t1k/XyRERg4XtsTwJjMxv3AN8d1/6QNzvyTmDQG078IfABM1vhzW78APBD770hM7vTmw350FXnmuwaUgTnz58vJDWAbDZLT0/PFEfMTF1dHTt27ODOO++8prcG1/bgJttHpJyi0Sif+9znCo+mSPmUcrr/3wPPA9vMrNvMPgX8MfCgmb0DPOBtQ35W4wmgC/hr4DcAnHOXgC8BL3tfX/Ta8Pb5qnfMcfIzIpniGlIE4fC1RY0nayu2d7/73WzZsoXm5ma2bNnCu9/97pJfUyQWixGPx6ffEdi7dy+HDh1i37590+8sJVWyoUjn3Cev89b7J9nXAZ+5znm+Dnx9kvaDwLsmaY9Odg0pjo6ODrq7u+nv7wfy0+9XrVpV8uv6/X5uuOGGkl9HBPLPtL300kv09fUBsHbtWm655Zbr7h+NRtm/fz/OOfbv389DDz1Ec3PzAkUrV1NJLZkVn8/HXXfdVXjWp6GhocwRiRTf2bNnC0ltbLujo+O6M3H37t1bGKLPZrPs27eP3/qt31qQWOVaqkskc9LQ0EBDQwPOOYaGhgozFkUqwVSPoEzmqaeeIpPJL4ybyWQ4cOBAyWKT6SmxyZzFYjF+8pOf8Mwzz3DgwAHOnj075f69vb309vYSjUZnfN9CpBza29vxqvQB+aHwlSuvv1TSAw88QCCQHwALBAI8+OCDJY9Rrk+JTebsyJEjxGL5JWay2Sxvvvlm4VPr1fr6+njxxRcZGRlhcHCQn/3sZxNmV4osJitWrGDXrl20trbS3t7OnXfeOWUdyD179hQKc/v9fh566KGFClUmoXtsMmejo6Nks1lSqRRVVVVkMhmSyWThk+t4V/fm4vE40WhU1UNk0RorvD0Tzc3N7N69m+9973vs3r1bE0fKTIlN5qyqqorXXnuNbDZLMBhkx44d1NTUTLrvZI8EVFVVTbKnyNK0Z88eTp06pd7aIqDEJnM2PDzMqlWrGBgYIBwOT/k828aNG7lw4UJhu7Ozk/r6a1fRFlmqmpubeeyxx8odhqDEJnPknCMej7N69epCFZDr3V+DfI/t/vvv54knnsDv9/Oe97xnoUIVkWVGk0dkTszsmpWFp1tp2OfzEYlEril4LCJSTOqxyZzdcsstHD16lIGBAZqbm9m2bVu5QxIRUWKTuQsGg1qIUUQWHQ1FiohIRVFiExGRiqLEJiIiFUWJTUREKooSm4iIVBQlNhERqShKbCIiUlGU2GRJyWazWstNRKakB7Rlyeju7ubQoUNkMhkaGxvZtWvXlIWXRWR5Uo9NloR0Os0bb7xRKLQ8MDDAsWPHyhyViCxGSmyyJMTjcbLZ7IS2kZGRMkUjIouZEpssCXV1dVRXV09oW7lyZZmiEZHFTIlNlgQz44477qC9vZ26ujq2bt3K5s2byx2WiCxCmjwis5bL5UilUgs+caO2tpZdu3Yt6DVFZOlRYpNZ6enp4Y033iCVSmlmoogsShqKlBnLZrO8/vrrpFIpID8z8ciRI2WOSkRkIvXYZMYSiQTpdHpC2/Dw8ILGkM1mOX36NCMjI6xatYrW1tYFvb6ILH5KbDJj1dXV1NTUEIvFCm0LPTPx5Zdfpq+vD4DTp0+zY8cOOjo6FjQGEVncNBQpM2Zm3H777bS1tVFTU8OmTZvYunXrgl1/dHS0kNTGnDp1asGuLyJLg3psMiu1tbXcfvvtZbm23+/HzHDOFdqCwWBZYhGRxUs9Nlkyqqqq2LBhQ2Hb7/ezZcuWMkYkIouRemyypNx88810dHQQi8VoaWmhqqqq3CGJyCKjxCbzMjw8zNmzZwkEAqxbt25BEk1jYyONjY0lv46ILE1KbDJnQ0NDPPfcc+RyOQDOnj3L/fffj9/vL3NkIrKc2fgb8cvZzp073cGDB8sdRtE9/vjjdHV1leTc0WiUwcHBCW1jMyavZyyWxVbncfPmzTz88MPlDkNEZscma1SPTebM7NrfqcnaxotEIqUKR0QEUI+toFJ7bKWUSCR47rnnSCQSADQ3N3PXXXdNm9xERIpk0j82mu4vcxYOh3nf+97Hjh07uP3222eU1KLRKJ/73OeIRqMLFKWILDdKbDIvgUCAjo4O2traZtRT27t3L4cOHWLfvn1zut6lS5c4fPgwp0+fLkxaEREZT4lNFkw0GmX//v0459i/f/+se209PT387Gc/48SJE7zxxhu8/PLLJYpURJaysiQ2MztlZofM7DUzO+i1NZnZATN7x/u+wms3M3vMzLrM7A0z2zHuPHu8/d8xsz3j2m/zzt/lHaubPovA3r17C72sbDY7617b1XUhe3t7GR0dLVZ4IlIhytlje59z7hbn3E5v+/PAj51zW4Afe9sAHwK2eF+fBr4M+UQIfAG4A7gd+MJYMvT2+bVxx+0u/Y8j03nqqafIZDIAZDIZDhw4MKvjfb6Jv65mdk2biMhi+qvwUWCv93ov8LFx7ftc3gtAo5mtAj4IHHDOXXLOXQYOALu99+qdcy+4/JTPfePOJWX0wAMPEAjknzAJBAI8+OCDszp+y5YtExJZZ2enVu8WkWuU6zk2B/zIzBzwV865rwBtzrke7/0LQJv3ugM4O+7Ybq9tqvbuSdqvYWafJt8LpLOzcz4/j8zAnj172L9/P5AvYPzQQw/N6vimpibe//7309vbS01NDc3NzaUIU0SWuHL12H7BObeD/DDjZ8zsvvFvej2tkj9g55z7inNup3Nup1ZiLr3m5mZ2796NmbF79+45JaZwOExnZ6eSmohcV1kSm3PunPe9F/gn8vfILnrDiHjfe73dzwFrxx2+xmubqn3NJO2yCOzZs4ft27fPurcmIjJTC57YzKzGzOrGXgMfAN4EngTGZjbuAb7rvX4SeMibHXknMOgNWf4Q+ICZrfAmjXwA+KH33pCZ3enNhnxo3LmkzJqbm3nsscfU4xKRkinHPbY24J+8GfgB4O+cc/vN7GXgW2b2KeA08Mve/t8HPgx0AaPArwI45y6Z2ZeAsYeZvuicu+S9/g3gG0AE+IH3JSIiy4BqRXpUK1JEZMlRrUgRkVJRHdTFQ4lNRGScuY5izbcOqhSPEpuICNDf38/TTz/NP//zP/PSSy+RTqdnfOx866BKcSmxiciyl8vleOWVV4jFYgBcvHiRI0eOzPj4+dZBleJSYhORZW90dJRUKjWhbWBgYMbHz7cOqhSXEpuILHvV1dXX1B2dzbOW862DKsWlxCYiy57P52PXrl00NjYSDAZZu3Yt27Ztm/Hxe/bsKRTonksdVCmuchVBFhFZVBobG7n33nvndOxYHdTvfe97c66DKsWjxCYiUgR79uzh1KlT6q0tAqo84lHlERGRJUeVR0REpPIpsYmISEVRYhMRkYqixCYiIhVFiU1ERCqKEpuIiFQUTff3mFkf+ZW7pfRagP5yByFSAvrdXlj9zrndVzcqscmCM7ODzrmd5Y5DpNj0u704aChSREQqihKbiIhUFCU2KYevlDsAkRLR7/YioHtsIiJSUdRjExGRiqLEJiIiFUWJTYrOzNrM7O/M7ISZvWJmz5vZL83i+PvN7J9LGaPITJlZ1sxeM7M3zezbZlZd7phkakpsUlRmZsD/CzzrnNvonLsN+ASw5qr9tMitLBVx59wtzrl3ASngfyx3QDI1JTYptl8EUs65vxxrcM6dds49bmb/3syeNLOngR+b2RNm9m/G9jOzb5jZx8sRtMgMPQdsNrOPmNmLZvZzM3vKG6XwmdkpM2sc29nM3vHeazWz75jZy97XPeX7ESqfEpsU283Aq1O8vwP4uHPuvcA3gV8GMLMQ8H7g/yt5hCJz4I0yfAg4BPwUuNM5dyvwBPCfnHM54LvAL3n73wGcds5dBB4F/sw5twv4t8BXy/AjLBsaDpKSMrO/AH6B/BDOXwAHnHOXvLd/ADxqZlXAbvLDl/H8aKbIohExs9e8188BXwO2Ad80s1VACDjpvf9N4H8F/h/yQ/Df9NofAG4a97tdb2a1zrmR0oe//CixSbEdJv+JFADn3GfMrAU46DXFxr2XMLN/AT4I/Ar5T74ii03cOXfL+AYzexz4U+fck2Z2P/D73lvPkx+qbAU+BvyB1+4j38NLLEC8y56GIqXYngbCZvY/jWubahbZN4FfBe4F9pcyMJEiagDOea/3jDW6fMWLfwL+FDjinIt6b/0IeHhsPzO7ZWHCXJ6U2KSovP+xPwa818xOmtlLwF7gd65zyI+A9wJPOedSCxOlyLz9PvBtM3uFa5ep+SbwP3BlGBLgc8BOM3vDzN5CMytLSiW1RESkoqjHJiIiFUWJTUREKooSm4iIVBQlNhERqShKbCIiUlGU2ESWKDP7mJk5M7vB215vZm96r7VCgixbSmwiS9cnydcs/GS5AxFZTJTYRJYgM6slX4PzU+RrEk61b42Zfd3MXvKq0X/Ua392fAUMM/upmb2nlHGLLAQlNpGl6aPAfufc20DUzG6bYt//DDztnLsdeB/wf5hZDflivv8ewMy2AmHn3OulDVuk9JTYRJamT3KlaPQTTD0c+QHg816F+n8BwkAn8G3gvzGzIPAfgG+UKFaRBaXq/iJLjJk1kV/QdbuZOcAPOPLLAk16CPBvnXPHJjnXAfK9v18Gpur1iSwZ6rGJLD0fB/7GObfOObfeObeW/Hpga6+z/w+Bh81bDMzMbh333leBx4CXnXOXSxm0yEJRYhNZej5JfmmU8b4D/O519v8SEATeMLPD3jYAzrlXgCHyC2OKVARV9xdZxsxsNfn7bjc453JlDkekKNRjE1mmzOwh4EXgPyupSSVRj01ERCqKemwiIlJRlNhERKSiKLGJiEhFUWITEZGKosQmIiIV5f8H2IJ8qbH4XWYAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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y7rnn4o4CZowxJkvsUmSWnHPOOank1tXVRUtLC9XV1XR3d9PT00MymWTOnDmEw2H6+vqor6/nrbfeynHUxhiTfyyxZUlVVRVXX301F198MTNnzqS2tpajR53BT/r7+4lGoxw5cuSUY9ra2nIRqjHG5DVLbFlUWFhITU0NZWVlwKmJy+fzvSORVVRUTGh8xhgzFVhiy7L+/n6Ki4vp7+9n1qxZAIRCIUKhEOeddx7BYBARobq6moULF+Y4WmOMyT8Zdx4RkQXAElV9QkSKgICqdnsX2uTT1tbG1q1bSSaTxGIxbr75Zv7t3/4t1Xnk61//OsuXLyeZTJ7S2cQYY0z2ZNRiE5HPAb8FfuoW1QAPeRTTpJRIJHjqqad46623iEQilJaW8uqrrxIKhVI9H5955hlExJKaMcZ4KNNLkV8ELge6AFS1HpjlVVCT0ZYtW3jjjTdobm5m7969dHV18fLLL5M+ssvGjRtzGKExxkwNmV6K7FfV6EDLQ0QCeDzdzGQSDofp6OigsrKSXbt2oaoUFxczb948urvfvlo7e/aQ08IZY4zJokwT2zMi8nWgSESuwxl8+A/ehTW5+Hw+YrEY27Zt4+DBg8TjcRKJBCdPnjxlv4Hu/8YYY7yT6aXIO4FWYDfweZyR+P/Oq6Amm7KyMo4cOcKbb76JqqaG04pEIqfslz5upDHGGG9k+p+2CPilqv4MQET8bllk2KOmkKKiIqqqqkgkEhQWFiIidHd3U1hYmNonHA7T2NjItGnT7Bk2Y4zxSKYtts04iWxAEfBE9sOZvAZmzi4sLMTv9xMKhVIPagP09fURDofZvXs3f/rTnzh48GAOozXGmPyVaWIrVNXUUPXucrE3IU1OxcXFVFdX4/f7KS4u5lOf+hQLFixIbe/u7iYQCLBnzx4OHDjAnj17chitMcbkr0wvRZ4UkeWq+gqAiFwC9HoX1uQSDoc5fPgw733ve1m6dCnBYJBFixbR0tJCMpmku7ubcDhMLBbj0KFDzJw5k2g0yg033JDr0I0xJu9kmti+AvxGRJpxZiydA3zMq6Amm4F513w+X+reWU9PD6pKX18f8Xg89ZD2iRMnKC8vZ968efT29lJUVHTa8xpjjBm9jBKbqm4TkfOAc92iN1Q15l1Yk8vMmTMJBALE4/FU2Zw5cwBIJpMAFBQUkEgk6OvrI5lMMnv2bAoKCnISrzHG5LNhE5uIXK2qT4rIzYM2nSMiqOp/ehjbpBEKhVi1ahX79+8nHo8zf/58Zs+ezdy5czl06BAnT56kv7+fZDJJNBqloKCA3t5ekskkPp+NQ22MMdk0Uovt/cCTwFA3gxSwxOaaNm0aK1euPKVs3rx5qbEje3t7KSsrY8GCBcyZM4fy8nI6OjpSMwAYY4zJjmETm6p+U0R8wAZVfXCCYsobr7zyCj09PYgIBQUFRCIRenp6UvfkioutY6kxxmTbiNfBVDUJ/I+xViAifhHZISJ/dNcXishWEWkQkf8QkZBbXuCuN7jbz0o7x9fc8jdE5Pq08jVuWYOI3JlWPmQdE6Wvr4/nnnuOo0eP0tbWRiQSIR6Po6rEYjGKi4s555xzKC0tnciwjDFmSsj0Bs8TIvJXIlIrIlUDrwyP/TLwWtr694Efqepi4ARwq1t+K3DCLf+Rux8icgHwceBCYA3wL26y9AP/DKwFLgBucfcdro4JsXXrVp588slUr8hYLIaqUlhYyIoVK/jLv/xLzj333JFPZIwxZtQyTWwfw5m65k/Ay+5r+0gHiUgN8GfAz911Aa7GmdsNYD1wk7t8o7uOu/0ad/8bgQdUtV9VDwINwHvcV4OqvqmqUeAB4MYR6vBcIpHgwIEDxGIxIpEIqoqq4vP5CAaDLFu2LNX13xhjTPZl2t1/4RjP/084lzEHxpaaDnSq6kC/+Cag2l2uBg679cVFJOzuXw1sSTtn+jGHB5WvHKEOz/n9fhKJBK+++irhcJhEIgGAqhKNRtm8eTM9PT285z3vobp6wsIyxpgpY9gWm4isFJFXRaRHRF4UkfMzPbGIfAhoUdWXxx2lR0TkNhHZLiLbW1tbs3LOtrY2urq6iEQiJJNJVJVEIpF6HT58mCNHjrB///6s1GeMMeZUI12K/Gfgr3BaQf+I0wLL1OXAh0WkEecy4dXAj4FKd6JSgBrgiLt8BKiF1ESmFUB7evmgY05X3j5MHadQ1XtVdYWqrpg5c+YovrXT27dvH/F4nBUrVlBQUIDf7ycQCODz+fD7/cTjcVpaWk55mNuYySQej9Pe3k40Gs11KMYMaaTE5lPVTe79rd8AGf/3V9WvqWqNqp6F0/njSVX9BPAU8BfubnXAw+7yI+467vYnVVXd8o+7vSYXAkuAl4BtwBK3B2TIreMR95jT1eEZVWXbtm3s2LGDPXv28NJLLxGLxVLbwBlyS0SorKxk4cKxXt01Jnfa29t54okneOGFF9i0aRNHjgz5mdGYnBrpHlvloFFHTlkf48gjfwM8ICLfBXYAv3DLfwH8WkQagA6cRIWq7hWRB4F9QBz4oqomAETkS8BGwI8zX9zeEerwzO7du9m4cSMHDx6kra0tdW8tFAqlhtvy+/0sXbqUm2++mbPOOsvrkIwZk3Xr1tHQ0DDktiNHjqRaam1tbfh8PpYvXz5sh6jFixdzxx13eBKrMUMZKbE9w6mjjqSvZzzyiKo+DTztLr+J06Nx8D59wEdOc/z3gO8NUf4ozmzeg8uHrMMr0WiUbdu20d/fz8mTJ1Nd/AOBQCqxqSrJZJLGxkbq6+upra3F7/dPVIjGZMXABzYgleBU1Xr6mjPKSCOPfHaiApnMwuEwpaWlNDY28sYbb9DZ2UlZWRkiQjQaTXUg8fv9dHd384c//IFZs2Zx0UUX5Tp0Y95huNbVvn37OHDgAAD33nsvxcXF/OQnP5mo0IzJSEbPsYnIbBH5hYhscNcvEJEJfej5TFZZWcnu3bvp6OggEokQjUaJRCIkEgl8Ph/9/f2pXpE9PT10dXWd9lKPMWey888/nwsuuIBZs2ZRUVFBtjpdGZNNmT6g/Suce1nz3PX9OHO0GSASieD3+/H7/VRVVTFt2jTKyspSlyB9Ph+JRIL+/n6OHTtGc3MzjY2NqU4lxkwWIsKiRYtYuXIlVVVVNjuFOSNl+q6c4Q6CnATnAWogMfwhU0d/fz81NTWcddZZzJ8/nzlz5lBQUJAa/Li0tJSCggJUFb/fnxrZv7GxMdehG2NM3sl0Bu2TIjIdp8MIIrIKCHsW1SQzY8YMFixYQCwWIxaLkUgkKC0tpaGhIdUqKyoqQlVZsWIF06dPB6ClpcW6/RtjTJZlmtj+P5znyRaJyPM4z7P9xfCHTB0+n48rrriCefPmsWzZMnbu3ElTUxNbtmwhHo+nLkkCdHR0EAqFmDNnDnPnzs1x5MYYk38yHSvyFRF5P3AuIMAbqhrzNLJJpri4mLlz5xIOhzl69Ch+v59QKEQwGCQej1NRUUEwGGTGjBkUFBRw2WWX2ViRxhjjgWET26CHs9OdIyJjfUA7Lx08eJA9e/YQjUZpaGigsrKSZDIJOC06n89HUVERl1xyCeXl5Vx55ZU5jtgYY/LTSC22G4bZlvED2lPBwLM9oVCIxYsXp4bUEhGCwSDhcBgRYe/evZbUjDHGQ/aAdpa1t7cTi8WYNm0ahYWF+P1++vr6Us+xHTt2jJaWFk6cOMG0adNyHa4xxuSdTDuPICJ/hjOLdeFAmap+24ugJqPFixfzxBNP0NjYyPHjx4nFYvT391NYWJjqLenz+YjFYrz00kt84AMfsMRmjDEeyHTkkX/FmUX7dpzOIx8BFngY16QzMKjx0aNHU3OyxeNxIpEI3d3d9Pb2cvLkSQ4ePIjf77cpP4wxxiOZttguU9V3i8guVf2WiPwQ2OBlYJNB+ijokUiEp59+mkgkQn9/P6FQKDXnmoiQTCZJJpPs2rWLnp4eIpEIFRUVqXPZCOjGGJMdmSa2XvdrRETm4UwrYw9hpdm/fz+dnZ309/cTjUbx+/0UFRWlLkEOjBsZCATo7e21Fpsxxngk08T2RxGpBP438LJb9nNPIppE0ltYn/nMZ1IdR8LhMIlEgpKSktSlyIHekUuWLOEDH/gA119/Pddff30OozdT0XBzrY1WfX09MPxsAJmyKxYmm0Z6ju1S4LCqfsddLwV2A68DP/I+vMmjsLCQUChET08PqkphYSFXXnkljz76KH6/P9VTsrq6GhGxobRMTjQ0NLB392tUFs8a97mSUWcOtiMH2sd1ns5Iy7hjMSbdSC22nwLXAojIlcBdOB1IlgH3YsNqpaxdu5adO3dSVVVFb28vyWSSl19+mZMnT6YuQwK8/vrrzJkzh66urhxHbKaqyuJZfOC8j+c6jJSnXn/As3NHIhF27txJR0cHVVVVLFu2jOLiYs/qM2eGkRKbX1U73OWPAfeq6u+A34nITk8jm2TWrFnDK6+8wtatW+np6aGvr4+2trZUxxG/309PTw9z584lmUxy8OBBFi9eTGVlZa5DN+aMt27dOjZsGL6/WiQSecdUUF1dXcRib4/+FwwGKS8vH7E+EckoAa5du9YuoZ6BRkxsIhJwp6m5BrhtFMdOKcFgkNbWVlpaWjh27FhqctH+/n5UNbX+1ltvUVZWlnq2zRjjncF/Y/Y3NzWMlJzuB54RkTacnpHPAojIYmzamlPs3LmT5uZm2traiMVi9PX1pZLagHg8TldXF83NzfT29qamrzHGDO+OO+4YU8vohRdeoL397XuA06dP57LLLstmaOYMNOwD2qr6PeCrODNoX6Fv/5f24dxrM67W1lYikQjJZJJEIoGqIiLv2C8ejzN9+nSCwaDNPmyMx5YtW8b06dMREaZPn86yZctyHZKZACNeTlTVLUOU7fcmnMnr/PPPp6CggHg8TjQaTY3sP1goFKKiooLS0tIJjtCYqae4uNhaaFOQNRmyZO7cudTW1qY6g/j9/ne0yHw+HyUlJcycOZPLL788B1EaY0z+8yyxiUihiLwkIq+KyF4R+ZZbvlBEtopIg4j8h4iE3PICd73B3X5W2rm+5pa/ISLXp5WvccsaROTOtPIh6/BSQ0MDiUQCcJJa+lxsAIFAgKKiIs455xy++tWvMnv2bK9DMsaYKcnLFls/cLWqXoTz3NsaEVkFfB/4kaouBk4At7r73wqccMt/5O6HiFwAfBxnZoE1wL+IiF9E/MA/A2uBC4Bb3H0Zpg7PvPbaaxw/fpxoNEowGCQQCBAIBFIzaZeWljJz5kxWrVpFa2vrO7olG2OMyQ7PEps6etzVoPtS4Grgt275euAmd/lGdx13+zXi9L64EXhAVftV9SDQALzHfTWo6puqGgUeAG50jzldHZ45ceIEb775ZqoDic/no6ysDCA10sjq1aspKiqis7OTnp6eEc5ojDFmLDy9x+a2rHYCLcAm4ADQ6T4XB9AEVLvL1cBhAHd7GJieXj7omNOVTx+mjsHx3SYi20Vke2tr65i/z1gsxp49e5gzZw6BgNMfx+/3A05nkWAweMrYkZ2dnQSDwTHXZ4wx5vQ8TWyqmlDVZUANTgvrPC/rGy1VvVdVV6jqipkzZ475PCdOnKCgoIBYLEZBQQEA0WiU0tJSQqEQyWSS48eP8+ijj7Jz5046Ozt55pln6O3tHeHMxhhjRmtCRg9R1U4ReQp4L1CZNppJDXDE3e0IUAs0iUgAqADa08oHpB8zVHn7MHV4ory8HFUlHo/j9/uZNWsWnZ2dtLW1pQZF9vl8xONxgsEgL7zwAl1dXdTU1HDhhRd6GZoxxkw5XvaKnOlOdYOIFAHXAa8BT/H24Ml1wMPu8iPuOu72J90Hwh8BPu72mlwILAFeArYBS9wekCGcDiaPuMecrg5PFBYWUlNTQ19fH319fcRiMZLJZGoYLVUlEonQ1dVFU1MThw8f5rnnnsva9CHGGGPe5mWLbS6w3u296AMeVNU/isg+4AER+S6wA/iFu/8vgF+LSAPORKYfB1DVvSLyILAPiANfVNUEgIh8CdgI+IFfqupe91x/c5o6PNHX10dVVRUXXnghyWSSQ4cO0d3dneryr6qoKslkkkgkQm9vb2qKG2MmUlNTE+FIt6cj6o9WZ6QFbbLL8iZ7PEtsqroLuHiI8jdx7rcNLu8DPnKac30P+N4Q5Y8Cj2Zah5eam5vp6uqitbWVcDh82pFHotEoR44cYcaMGSxYsGAiQzTGmCnBRujPAr/fz759+9i/fz8dHR2nTWoDBu67DXQ0MWai1NTUIP3tZ9x8bNU1NiC4yR5LbFkgIpw4cYJIJJLq7j9ccht4FKC7u3tC4jPGmKnExorMgoERRvr6+oYdAHlALBYjHA4zb968CYrQGGOmDktsWRCLxVi4cCF+v5/+/v4R908mk0SjURtWyxhjPGCJLQtOnjxJMpkkmUxmlKwKCwsB2Ldvn9ehGWPMlGOJLQuKiopobGzMaCSRgdH+Bx7oNsYYk12W2LIgHA6TSCQyugwJTmeTuXPnsnDhQo8jM8aYqccSWxZEo1FEJNXbcTgDSe1d73oXZ5999gREZ4wxU4sltiwoLS2ls7Mzo0uLBQUFnHvuuQQCAYqLiycgOmOMmVossWVBNBolHo/jTAVH6utQ+vv7qa+vp6Kigvb29okK0RhjpgxLbFlQXFzMnDlzUpcih+sZmUwmaWtr46mnnrLu/sYY4wFLbFlQUlJCVVUVfr9/xGSlqnR3d3P48GEbBNkYYzxgiS0LBjqORKPRjPZXVaLRKJ2dnd4GZowxU5AltiyIRCLU19ePOJTWgIFOJtOmTfMyLGOMmZJsEOQsCIfDtLe3Z/wcGzgPddulSJMLnZGWrMzH1tN3AoDSwvF9QOuMtFCNje5vsscSWxbE43Ha29tH1RlERDhy5AiXXnqph5EZc6rFixdn7Vz19R0AVC8aX1KqZnpW4zLGElsWvPnmm6M+pq+vL6MHuo3JpjvuuCPr51q3bl3WzmlMNtg9tiyora0d9dxqJSUlnHPOOR5FZIwxU5cltiwQEUKh0Kj2B5g7d65XIRljzJRliS0LSkpKKCgoyHh/n8/HzJkzOXDggIdRGWPM1GT32Iawbt06GhoaMt4/EonQ19eX8f6JRILdu3fzne98h5qamoyPW7x4cVbvkRhjTD6yxDaEhoYGduzeR7K4KqP947EYfYnRDY91si9K/dETHO/P7Ffgi3SM6vzGGDNVeZbYRKQWuA+YDShwr6r+WESqgP8AzgIagY+q6glxbjz9GPggEAE+o6qvuOeqA/7OPfV3VXW9W34J8CugCHgU+LKq6unqGE38yeIq+i74UGb7xmPEd+0E2t1vdWQxfxH9C6+gr3pJRvsX7vtjRvsZY8xU5+U9tjjwVVW9AFgFfFFELgDuBDar6hJgs7sOsBZY4r5uA+4BcJPUN4GVwHuAb4rIwBOh9wCfSztujVt+ujo8Ee/tAV8AhhnV/x3HnDxBrHd0PSmNMcaMzLPEpqpHB1pcqtoNvAZUAzcC693d1gM3ucs3AvepYwtQKSJzgeuBTara4ba6NgFr3G3lqrpFnSej7xt0rqHq8IS/oBhfQTGMZrT+ZIJYT9i7oIwxZoqakF6RInIWcDGwFZitqkfdTcdwLlWCk/QOpx3W5JYNV940RDnD1DE4rttEZLuIbG9tbR3Dd+bwhwoIlU8n08uQTuU+fMHgmOs0xhgzNM8Tm4iUAr8DvqKqXenb3JaWp5OSDVeHqt6rqitUdcXMmTPHXEcs0kUiEmY0P07xBwkWlY65TmOMMUPztFekiARxktq/q+p/usXHRWSuqh51Lye2uOVHgNq0w2vcsiPAVYPKn3bLa4bYf7g6PBHv7aG/owXIbHR/APwh4nYp0kxCfX19xGKxXIdhJplIJMLOnTvp6OigqqqKZcuWUVxc7EldXvaKFOAXwGuq+o9pmx4B6oC73K8Pp5V/SUQewOkoEnYT00bgH9I6jKwGvqaqHSLSJSKrcC5xfhq4e4Q6vOEL0t/TPqpDtLcLKSjyKCBjvLF3714OHjyIqtLc3Mzs2UNe5TeT2Lp169iwYcOI+0UikVEN/N7V1XXKB6JgMEh5eTngjMY0UpJbu3Ztxs/xetliuxz4FLBbRHa6ZV/HSTYPisitwCHgo+62R3G6+jfgdPf/LICbwL4DbHP3+7aqDjzU9QXe7u6/wX0xTB2eSEYjkBhFa805Cl/Qm08rxozH6QYo6O/vp7m5ObXe3NxMS0vLiP9sbGABA7yjle9lq9+zxKaqzwGn6/9+zRD7K/DF05zrl8AvhyjfDiwdorx9qDq8EiwuRXw+NDG645KxiDcBGeOBgQlyB4RCIYLWASrv3HHHHZ58EHnhhRdob3/7ytb06dO57LLLsl4P2MgjWREoLB1TD5iTzQeYfu57sh6PMeNxun9qsViMzZs3n/JJe+XKlcyaNWuiQjOT2LJly95xj80rltiyINp9An8gSCLWO6rjeo83ehOQMR4IBoNcdtll1NfXE4vFqK2ttaRmMlZcXOxZC20wS2zZ4PODP/NpawaI3y7jGEdzczOHDh0iGAyyZMkSKioqch3SkMrLy7nkkktyHYYxw7LElgWBohJ8wQCju8UmVCxa7lFEZjJpaWnh5ZdfTq23tbVxzTXX2P0rY8bIElsWaDyG3x9kVH18QoWUL3xHvxeTZzKZAmnv3r1EIhFmzJiRKnvwwQcpLT31AX7rXWhMZiyxDaGpqQlfJJzxiPr+/n6CiX4yn5ENJB4l8PpGCiumjbwz4Iu009QUH3lHM+nEYjGi0egpZdZaM2bsLLFlgT8YpLC4hO5RTIyjyQQJG70h72XSwvrSl77EsWPHqKurQ0RYuHAhF1544QREZ0x+ssQ2hJqaGo73BzKej02TSXr3Hxh1PZ2ltRRecF1G+xbu+yM1NXNGXYc58/l8PubNm8dVV11FMBiksLAw1yEZM6lZYssC1SQnm4e/jzKU/rCnQ1iaSaasrCzXIRiTFyZk2pp819dxDO3rGfVx8dFcuzTGGJMRS2xZcPLYwTEd5ysoyXIkZrLr7+8nkRjl2GzGmFPYpcgsGOuEcr6A9XwzjmQyydatW2lpacHv93Peeedx9tln5zosYyYlS2xZUDRj3piO09OOEW2mmnA4TEuLc881kUiwb98+5syZ49l8VWZymci5zPKBJbYsCBSM7Q3W33k8y5GYiZTJw9eZqK+vJxwOc++9955S/tBDD436n5c9xH1myWRus0zmNRtuLrN02Z7XbLKyxJYFiVj/mI6TpN1LmcwaGhp4fedOxvMQRjKZpD8WI5BI0PPmm6lynwh9J04Q9WV+G/zYOOIwZ7aJnMssH1hiywKRsfXBKV1wQZYjmTpUlZaWFuLxOLNnzyYQyM1beQ5w6xgvKbf19/NEaws18QSFfh/zi4ooDQQJ+XzUFhVR7vOP6ny/GPPdXuOVbM1tNpFzmeUDS2yn4Yt0ZDyklkTGNmFoReRIxnX4Ih0wrrZB/lBVXnjhBTo6nInUi4qKuOKKKyb8weampia6GXtCqT/RQc/A5J2JBNt7ezm/ugq/z8cWYLTdko4CPU1NY4rFnNkmci6zfGCJbQiLFy8e1f4nTpxgT0EB/f2juyRZW1nE+YsyTVZzRh1XtvT39xOLxd4xKO9ESr+fFYlEOH781PuT69ev5+TJk4AzcsxwzpT7UNFBM1LHEgmSqoyunWamgomcyywfWGIbwmj/6fX29vLcc8/R2Ng4quPq6ur45Cc/OapjJtr+/fvZv38/qkplZSUrV64kFBr93HPZlEwm31GmqvT2jm6i1/Gqqamhs61tzJcinyss4o2etx/sn1EQ4kb/2P8kf4FSOUJSN2YqsMSWBYlEItVaGI2entGPVjJemfTka3IvZ82ePTu1PKCiooKqqqrU+kS1ftLriMfjPP3006lE5vf7ed/73sff/u3fAs73OBlcWlWFX4RjfX1UhkJcWlmZ65CMyQuW2LKgp6dnyFbESHLd8jmdgYQRj79zmpwzoTdWIBDgfe97H4cOHSKRSFBbW5vTy6RjVeDz8d7p03MdhjF5xxJbFsyaNWvUHRd8Ph8rVqzIahzZeq5qQEFBAX6//5QhnkpKTh0GrKGhYcQWmxetuoKCAs4555ysntMYkx8ssWVBW1vbqFtfoVCIEyeyOwhyQ0MD+/e8wvzS8T0fF4o5jy9E33qZyniCcE8viaRSWhgi0NZGX1vm53qrZ+SuENl80BlGf490KGdKBxNjzOh5lthE5JfAh4AWVV3qllUB/wGcBTQCH1XVEyIiwI+BDwIR4DOq+op7TB3wd+5pv6uq693yS4BfAUXAo8CXVVVPV4dX36cby6h7RBYUFNDZ2ZnVOJqamhhhAIOMzC5++7JqKOBnZuXYL/Op8o77dIM1NDSwY+8OqBxzNQ437B1HdozvPJ3jjMMYk1Netth+BfwEuC+t7E5gs6reJSJ3uut/A6wFlrivlcA9wEo3SX0TWIHzUM/LIvKIm6juAT4HbMVJbGuADcPU4ZmysrJRPyDs9/tZvny5RxFNQpWQvGr09ym94HvaJr0wZjLz7C9YVf8EdAwqvhFY7y6vB25KK79PHVuAShGZC1wPbFLVDjeZbQLWuNvKVXWLOoOs3TfoXEPV4ZlEInFKT8GRiAhz585l3ryxDZ58OjU1Ncg4xlU+2RflSFuY3Ye7ONA6tmHCBhMZ+bkyY4zJpom+xzZbVY+6y8eA2e5yNXA4bb8mt2y48qYhyoer4x1E5DbgNoD58+eP9ntJPw+VlZUEg8GMeg0ODGDa0tLC3Llzx1zvYON5gDsWi9HV1ISvAKLNzUSjEJ9x/rh7G56TQVxNTU0QPoNaSp3QpJmN4HGM8Q9lNTBQ0nj7Rx5j/FdzjckHOes84t4P83Rwu5HqUNV7gXsBVqxYMeZYiouLU+MVZpLYEokER48ezfpzbJl0djhdR42h7hH29vaOmNimcieLbI0E0+p2eqlcsmRc56kkezEZM5lNdGI7LiJzVfWoezmxxS0/AtSm7Vfjlh0BrhpU/rRbXjPE/sPV4alwOJzxs2yBQIBAIMC+fftYMs5/ZtmS3qtzYHmixl6sqamhVVrPqHtsNdUjXz4da0IPh8Ps37+faDTK/Pnz+cEPfgBMngfLjTnTTXRiewSoA+5yvz6cVv4lEXkAp/NI2E1MG4F/EJFp7n6rga+paoeIdInIKpzOI58G7h6hDs/EYjHq6+uJRqMZ7Z9IJAiFQhM+BBQM/8/40KFDvP7668TjcebPn8/SpUuR8dy0M+8Qi8V48cUXUy37jo4OTp48SUlJCfF4HL/fbz9zY8bJy+7+9+O0tmaISBNO78a7gAdF5FbgEPBRd/dHcbr6N+B09/8sgJvAvgNsc/f7tqoOdEj5Am9399/gvhimDs/09fXR1tY24mSBA+LxOEePHj3jekUuWLCABQsWoKoT/8+1Mwv32Aau7I53EJJO3r5jm2Xt7e3vuFzd09NDT08PGzZsIBgMcuGFF1JbW3uaMxhjRuJZYlPVW06z6Zoh9lXgi6c5zy+BXw5Rvh1YOkR5+1B1eKm1tZVgMJjx/iJCUVERXV1dHkY1dhOd1LJ1X2jgAe0l1eO8vFudvZgG39OMRqMcOXLklH2OHTtGMpk8ZQbt2tradzxCMpXvZxozGjbySBZMnz6dmTNn0tHRkdF9tqKiImbPnp31B7Qnq2z9sx44z5l8ryoUClFRUUE4HAac+5jFxcXvuIwdjUZzNnmqMZOd/eVkQUVFBR/60Ic4evQo3d3dqCrJZJLCwsLUPywRIZFI4Pf7KS8vZ/ny5Vxwgc2gPV4dHR0cOXKEwsLC1M/3THK6pB2NRonFYpSUlNDY2Mju3btT2wKBANdee+2orgIYY95miS1LPve5z3Hw4EGef/55+vr6CAQCBINBOjo6iMfjlJeX4/f7CQQCrFq1iq985StZf0B7qmltbWXr1q2pe5vHjh2jutqjm2NZFgqFUr1PFyxYQF9fH01NTRQUFHD++edbUjNmHCyxZUE8Hqe+vp5PfOITLFq0iPr6eurr64lEIogIIkJBQQGLFi1i2bJlrF69mqVL33F70AxjqOfvjh8/TiQSSa03NzcTDodzMtvAeIgI5513Huedd16uQzEmL1hiy4Le3l4SiQTRaBQRobOzk87OTk6ePEksFiMYDDJt2jTmz59PWVkZCxYsyHXIecHnO7UXZSgUoqioKEfRGGPOFJbYsqC0tJTi4mJUlWg0SiKRoKenh76+PpLJJLFYjN7eXpYuXcrcuXPZu3cvc+bMYdasWbkOfdIYqoXV3d3N888/n+o+X1NTw8UXXzzRoRljzjCW2LJARFi5ciWvvfYay5cvp6mpibKyMvr6+lL7hMNhfvOb33D55ZdTWVlJUVERN910U+6CzgNlZWVcc801tLS0UFRUNKqBqI0x+csSW5aUlpZy6aWXcskll9DZ2UlrayuRSIR4PA44YzG2t7fz0ksvcdFFF9HZ2UkkEqG4uDjHkU9uwWBw0nQYMcZMDEtsWebz+bjhhhtSiWvv3r0kEgmmTZtGQUEBIkI4HKaqqiqV9IwxxmSPJTYPzJ8/n09+8pNUVlZy77330traSnV1NcXFxcyYMYOqqiqmTZtGeXl5rkM1xpi8Y4kty2KxGI2NjUQiES677DI2btxIKBRi1apV+Hw+ent7uf7661m1alWuQzXGmLxkiS3LXnzxRcLhMM3NzTQ1NdHb28uMGTM4++yzaW9v5/LLL+e6666zB3CNMcYjltiy5NixY2zdupWXX36ZioqK1DiQvb29RKNRXnnlFWpra3n99dcpKytj9erVuQ3YGGPylGQ61Uq+W7FihW7fvn1UxwyMhpFIJDh8+DCxWIz29nZUlb6+PoqKigiHw3R3d1NWVkZFRQUABQUFLFu27JRR9M+00TCMMWYSGHIqknFOgGXAua+mqgQCAYqKihARAoEAIkJZWRnBYJCCgoLU/gPDbBljjMk+a7G5xtJiGxCPx9m0aVOq+/7Jkyepqalh3rx59Pb2snfvXhobG+nu7qakpIRrr72WFStWZDN8Y4yZioZsIdg9tiwIBAJceuml7N27l97eXi688EIuvPDC1FiGZ599Nq+99hrd3d3Mnj2bc845J8cRG2NM/rIWm2s8LTZjjDE5YffYjDHG5D9LbMYYY/KKJTZjjDF5xRKbMcaYvJK3iU1E1ojIGyLSICJ35joeY4wxEyMvE5uI+IF/BtYCFwC3iMgFuY3KGGPMRMjLxAa8B2hQ1TdVNQo8ANyY45iMMcZMgHxNbNXA4bT1JrfsFCJym4hsF5Htra2tExacMcYY70zpkUdU9V7gXgARaRWRQx5VNQNo8+jcXpmMMYPFPdEmY9yTMWawuIfymKquGVyYr4ntCFCbtl7jlp2Wqs70KhgR2a6qk2pwyMkYM1jcE20yxj0ZYwaLezTy9VLkNmCJiCwUkRDwceCRHMdkjDFmAuRli01V4yLyJWAj4Ad+qap7cxyWMcaYCZCXiQ1AVR8FHs11HK57cx3AGEzGmMHinmiTMe7JGDNY3Bmz0f2NMcbklXy9x2aMMWaKssRmjDEmr1hiGyURURH5Ydr6X4nI3+cwpBGJyE1u3OedZnuliHxhouMaiYgkRGSniOwRkT+ISKVbfpaI9Lrb9onIfSISdLdd5X6vN6Sd548iclUO4h54nZWFc/ZkIbRM6kn/mf9GRIpHefxZ7s//9rSyn4jIZ7Ie7Kn1jivutPN8RUT6RKQi2zEOUdeo39/u9oD73O1dXsc4WeO2xDZ6/cDNIjJjLAeLSC467NwCPOd+PYUbTyUwqsQmDq/fP72qukxVlwIdwBfTth1Q1WXAu3CeU/xo2rYm4G89jm04A3EPvBq9qMSj91L6zzwK/D9jOEcL8GX3UZuJko24wfkb2QbcnLXITm+s7+/rgP3AR0RkyBmkPXbGx22JbfTiOL18/t/BG9xPLE+KyC4R2Swi893yX4nIv4rIVuB/u+v3iMgWEXnTbWX8UkReE5FfZTNYESkFrgBuxXmeb6BV86yIPALsA+4CFrmftH7g7vPXIrLN/V6+lfb9vSEi9wF7gP8pIv+UVtfnRORH2Yw/zYsMMSyaqiaAlwZtexUIi8h1g/cXkUYR+ZaIvCIiu0/Xis02EblERJ4RkZdFZKOIzHXLF4nIY275swPxiPMM5otujN9NO8/g352XngUWi0iViDzkvhe2iMi73VjeL2+3SneISJl7XCuwGajzOL6sxi0ii4BS4O8Y4kOgx0bz/r4F+DHwFvDegcIcvbezEfdq973+ijit7dJxR6Wq9hrFC+gByoFGoAL4K+Dv3W1/AOrc5b8EHnKXfwX8EfCnrT8ACM7gzF04n3B8wMvAsizG+wngF+7yC8AlwFXASWChW34WsCftmNU4yVvcmP4IXOnulwRWufuVAgeAYNr535XNn7X71Q/8BlgzOF6gEHgKeLe7flVavM+4ZX8ErnKXG4Hb3eUvAD/34D2SAHa6r98DQfdnM9Pd/jGcZyvBSQBL3OWVwJPu8iPAp93lL6b9LE753Xnx/na/BoCHgf8O3A180y2/GtiZ9n6/PO29EBj43QBnA2+4v7ufAJ/x6m8yG3G7y38L/E/3PX8ImD1BMY/m/V0INANFwG3A3Wnn8/y9ne24cYbb+hNQ4q7/DfCN8cZoLbYxUNUu4D7gjkGb3gv8X3f51zgtpQG/UedTzIA/qPOb3A0cV9XdqpoE9uK8QbLlFpwkivt14JPoS6p68DTHrHZfO4BXgPOAJe62Q6q6BUBVe4AngQ+5nw6Dqro7i7EXichO4BgwG9iUtm2Ru+04cFRVd6UfqKp/AhCR9N/BgP90v75Mdn/WA9IvRf45cC6wFNjkxvx3QI37yfQy4Ddu+U+Bue45Lgfud5d/Pej8w/3uxmvgZ74d55P1L3Dex78GUNUngekiUg48D/yjiNwBVKpqfOAkqvomsBX4bx7F6UXctwAPuH+HvwM+MkExj+b9/SHgKVXtdWO8SZxpugZ4/d7OdtyrcKYWe949rg5YMN4A8/YB7QnwTzj/9P9PhvufHLTe735Npi0PrGfl9yIiVTifVN8lIorzCUuB/xoinlMOBf6Xqv500PnOGuK4nwNfB14n859FpnpVdZk4HQE24rRc1rnbDrjbZuD8UXxYVQcPm/Y9nCQSH1Q+8PNOMDF/AwLsVdX3nlLo/JPtVOeexFBO95DpcL+78eodHM/pboeo6l0i8l/AB3F+B9cDfWm7/APwW+AZb0I9xXjjDuJ8eNvkHhcCDuK0Nj2NeZTv71uAK0Sk0d1vOs7f+EBymYj3djbjFmCTqmb10q+12MZIVTuAB3HuXQ14Afc+Fs4lwGcnOq5B/gL4taouUNWzVLUW54/1fYP26wbK0tY3An85cK1bRKpFZNZQFajqVpwBp/8bb7cwskpVIzit46/KoA4TqtoG3Al8bYjjHgemAe/2Iq5ReAOYKSLvBRCRoIhc6Lb8D4rIR9xyEZGL3GOe59T3Ui49OxCDOL1L21S1S0QWuVcavo/T4eKUezqq+jrOfcAbyI3RxH0Lzi2Fs9zXPGCeiIy79TCSTN/f7geh9wHzB+LESSoTfT9wILZsxL0FuFxEFgOISImInDPe2Cyxjc8Pca4RD7gd+KyI7AI+BXw5J1G97RacezzpfsegPwRVbcf5dLVHRH7gJoT/C7woIrtxPnWXcXoPAs+r6onshX4qVd0B7GLoP+KHgGIRGZywwWm11Q5RPmHUmez2L4Dvi8irOPfeLnM3fwK41S3fy9sT4n4Z+KL783/HzfkJ9vfAJe77+i7e7hTyFfc9swuIARuGOPZ7OL3jcuHvyTzuj/POv5Xf8/aHC09l8v7G6bD2pKqmX+F5GLhBRAo8D3II440bp3/BZ4D73d/Hiwz6gDQWNqSWGTcR+SPwI1XdnOtYjDHGWmxmzMR5sHs/zjV3S2rGmDOCtdiMMcbkFWuxGWOMySuW2IwxxuQVS2zGGGPyiiU2Y3JEROaIyAMickCcsSIfHe8zPO5Ykn90lz8sIne6yzeJyAVp+31bRK4dYx2fcMdf3C0iL6Q9e2fMGcFGHjEmB8QZ3uL3wHpVHRic+iKcIYr2Z6MOd8SHgdFYbsIZM3Ofu+0b4zj1QeD9qnpCRNbijCu6chznMyarrMVmTG58AIip6r8OFKjqq8BzIvID9wHi3SLyMUi1xJ4Wkd+KyOsi8u9uckRE1rhlr5A23YqIfEacudAuAz4M/ECcEe0XiTPDxF+4+10jzij3u8WZZaLALR9ytHhVfSHtYfwt5O4BbGOGZInNmNxYijNQ7WA3A8uAi4BrcZLRwMDIFwNfwRk09mycoYgKgZ/hjOJwCTBn8AlV9QWclttfuwMzHxjY5h7/K+BjqvounKs4/z3t8DZVXQ7cgzOTxWC3MvSII8bkjCU2Y84sVwD3q2pCVY/jDCB8qbvtJVVtckef34kzevt5wEFVrXdni/i3UdZ3rnv8wOXP9ThT/gw47WjxIvIBnMT2N6Os0xhPWWIzJjf24rSwRiN9rL2JmplgyNHixZm08+fAje5Yo8acMSyxGZMbTwIFInLbQIGbLDqBj4mIX0Rm4rSeXhrmPK8DZ4kz+zOcfqT3wTM4DHjDPX6xu/4pRphmRpyZ4f8T+FRaS8+YM4YlNmNywL1s+OfAtW53/73A/8KZVWEX8CpO8vsfqnpsmPP04cxI/F9u55GW0+z6APDXbieRRYOO/yzOZKe7ceYD/NfTnGPAN3Dm0/oXtzPK9pG/Y2Mmjo0VaYwxJq9Yi80YY0xescRmjDEmr1hiM8YYk1cssRljjMkrltiMMcbkFUtsxhhj8oolNmOMMXnl/wdWR+8XolEC1wAAAABJRU5ErkJggg==\n", 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\n", 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mE3cCd4hIZaZo5A5gZ+Y2v4hcn6mG/OR5zzXeOfLO7/fzxhtv0N3dzYkTJ/jZz35GMpmcURWGej82TdNmsnxORc4FXhWRg8Ae4Bml1C+BbwJbROQUcHvma4BngdNAC/B/gD8GUEoNAn8H7M18fD1zjMx9vpd5zLvAjszxic6Rd93d3WTblO3evRul1IyrMNT7sWmaNhUKdSlG94rMmKpeke3t7Rw4cACAr371q8RiMUpLS3E6nXi9Xn75y1++53NomqbNBN/61rd46qmnuOmmm/i93/s96uvrKS8vn8pT6F6R02HBggVkC1HWrFmD1+vVFYaapr3v9Pf38+yzzzIyMsKOHTs4cOAAr776KkNDQ3k/t05sU8wwDK6//no2b97MV77yFcrLyxGRGVVhePLkSbZu3UpLS0uhQ9E0bYbatm0b8XicVCqFUooXXngB0zRpa2vL+7l1YsuTsrIyFi1aZG00WuwVhtlKTr/fz9/93d/R19fHH/7hH/LKK6/Q399f6PA0TZthdu3aRTKZBCCZTLJ//34A7Pb8bwOqNxrNs5mw0ejx48c5deoUAOfOnePw4cMkEgkikQhHjx4lGAxy++2343A4ChyppmkzxZYtW3j22WeJRqOYpsnatWtxuVwsXrw47+fWI7Y8K/YKw1Qqxbvvvmt9/fjjjxMMBsd8nUwmGR4eLkB0mqbNVE1NTYgIpaWlVFRU8IUvfIFbbrkFr9eb93PrxJZnM6HzyGi9vb3YbLYxX2d/OTVN03JVU1NjXYr58Ic/zDXXXDNtsz46sU0h0zTp6elhcHDQOlbse5vZbDYaGxutr+fMmUNZWRlOpxOAuro6rr32Wtxud6FC1DRthipUs4ec17GJyEJguVLqeRHxAHalVCCv0U2j97qOLRqN8tprrxEOhwGYN28eixcv5v777ycej+N0OnniiSeKckpSKUVXVxd+v5/h4WH+6q/+yjr+gx/8gOXLlxc4Qk3TtHFd/jo2EfkD4KfA/84cqgeempKwZonW1lYrqUG6A8l3v/tdEokE8XickZERHn300QJGODERYf78+VxxxRVcf/31LFq0CIDFixfrpKZp2oyT61TknwCbAD+AUuoUMCdfQc1E8Xj8gmO7d+8mFAoRCAQIh8M8/fTTnDhxogDRTc5nP/tZDMPg85//fKFD0TRNm7RcE1tMKWW9couInTxvNzPTNDQ0WHuaAXg8Hurr64lGo9ax0tJSTp48WYjwJuWVV15BKcXLL7/M8PAwR48e5fTp09aaFE3TtGKW6zq2l0XkbwCPiGwh3Xz4/+YvrJmnsrKSTZs20dbWhtPpZPHixfz93/+9tYYjlUoRiUTYv38/K1euZMWKFYUOeVz9/f3s2LEDpRRPP/008+bNsyoiOzs7+eAHP1jgCDVN0y4u1xHbF4E+4DDwIOlO/F/OV1AzVWVlJddeey1XXnkl0WgUpRQiQiQSIRwO43a7mT9/PidOnCAUChU63HFt27YN0zQBCIfD7N6927pteHh4Wvq8aZqmvRe5JjYP8AOl1EeUUv8F+EHmmDaBzs5O+vv7cTqduFwuHA4H8XicuXPnAowpNCkmzz33HKFQiFgsNqYNTtboNW6apmnFKNfEtpuxicwDPD/14cweo5dROJ1O7Ha7tTbM5XJRVVVVqNAmlEwmqa+vJxgMEgwGicfjXHPNNdbtdXV1lJWVFTBCTdO0S8v1GptbKWX1WVJKBUUk/31RZphAIMCxY8fYv38/pmkyZ84czp49i9vtxm63c9111zF//nxWrFhRlCOfc+fOWZuiKqWw2WxUV1ezZs0a3G63tR2PpmlaMcs1sYVEZJ1Sah+AiKwHIvkLa+YxTZM333yTs2fP0tHRAWDtxeb1evF4PDQ2NrJ+/foCRzox0zRpbm62rv8ZhsGePXtoaGgocGSapmm5yzWx/RnwExE5R3ql9zzgY/kKaiYaHh4mGo0Si8WsY8eOHcPtdlvLAH71q18VKrycVFRU4HQ6rcSmlKKioqKwQWmapk1STtfYlFJ7gSuAPwL+ELhSKfV2PgObabxeLyIyJhHYbDZExLq2NnqdW7Hp6urizTffJBwOY7PZcDqdeL3eGdO8GWZew2lN0/LjoolNRG7N/PvbwL3AiszHvZljWobb7eaqq66ioqKCRYsWkUqlWLJkCYBVPn/77bcXMsQJDQ4O0tzcTCwWY+HChZimicPhwDAMNm/eXOjwcja64bRpmkQierZc096PLjUVuRl4gXRSO58Cfj7lEc1gS5YsoaGhgX379jFnzhwaGxt59NFHCQaDVFRU8NGPfrTQIY6ru7sbSF9Tq62t5cyZM4gIJSUllJeXFzi63IxeWP7UU0/R0NCA2+2mrKyMD3zgA9OyB5SmacXhoiM2pdRXRcQAdiilPn3ex/8zTTHOKA6Hw7pGtWfPHiC9mWcqlWL79u2FDG1CPp/P+vzEiRO43W7Ky8txuVxFf10wa9u2bSilUErh9/v55S9/CYDf7+fo0aMFjk7TtOl0yWtsSikT+KvLPYGI2ERkv4j8IvP1YhF5S0RaROQJEXFmjrsyX7dkbl806jm+lDl+QkR+Y9TxOzPHWkTki6OOj3uO6ZBIJPD7/bS2ttLc3Ew8HiccDuP3+4s2sTU0NFgLx9euXWstTwiFQixatIi33nprzK7axWjXrl0kEglM0ySRSIxZWB4IzJrdlTRNy0GuC7SfF5G/EJEGEanKfuT42M8Dx0Z9/d+Af1ZKLQOGgM9kjn8GGMoc/+fM/RCRq4D7gauBO4HvZJKlDfgXYCtwFfDxzH0vdo68Ukrx+uuvk0qlCIfDeDweIpEILpcL0zSpq6srylZahmGwceNGbrvtNv7sz/4Mn89HOBwmGo2ydu1aent72bt3b6HDvKgtW7bgcDiw2Wy4XC7Wrl1r3ZZN2pqmvT/kmtg+Rnrrml8Bb2c+Lrkrp4jUA3cD38t8LcCtpPd2A9gG3Jf5/MOZr8ncflvm/h8GHldKxZRSZ4AWYGPmo0UpdTqz88DjwIcvcY68Ghoawu/343Q6ueqqq0gmk9aLLaQXQBdrxZ7f7ycSibBjxw5rDzmAt956i56eHvbs2cPRo0etQphi09TUZFWdVlRU8PGPf5ySkhKWLl3KFVdcUeDoNE2bTjmtY1NKLb7M5/+fpKcxSzNfVwPDSqns/icdwILM5wuA9sz5kiIykrn/AuDNUc85+jHt5x2/7hLnyCuHwzHm60AggGH8+r1Df39/URZj7N+/n8OHDzM8PMy//uu/4nK5rA4kO3fuZGBgAK/XSyQSYdOmTdx+++1Ft3ShpqaGrVu3sn37du65556irUDVNC3/LlXuf52IHBSRoIi8ISJX5vrEInIP0FvM691E5AERaRaR5r6+vvf8fKWlpSxY8Osc2tDQQGlpKSKCiLBixYqiS2zDw8O8/PLLnDp1iv379xMIBKydCYLBIOFwmOHhYRwOB6lUipMnT9Lf31/osMfV1NTE6tWraWpqKnQomqYV0KWmIv8F+AvSo6B/Ij0Cy9Um4DdFpJX0NOGtwLeBisxGpQD1QGfm806gAayNTMuBgdHHz3vMRMcHLnKOMZRSjymlNiilNkxVH8R169axadMm1q9fzyOPPEJZWRmVlZVUVlbyP/7H/5iSc0yloaEha3o0kUgA6d3AU6mUNY1qGAZdXV3EYjFSqdSYBs+apmnF5lKJzVBK7cpc3/oJkPOrv1LqS0qpeqXUItLFHy8opX4XeBH4L5m7NQFPZz7fnvmazO0vqPQr6Hbg/kzV5GJgObAH2Assz1RAOjPn2J55zETnmBYVFRUMDQ3R2dlpbf9SUlLCsmXLpjOMnFRXV1tTqNkNRW02m1VdGIlEePfdd2lra+PQoUM0NDQUbTPk0Qu0NU17/7rUNbaK8zqMjPlaKXU5C7T/GnhcRL4B7Ae+nzn+feDfRKQFGCSdqFBKvSMiTwJHgSTwJ0qpFICIfBbYCdhI7xf3ziXOkTfhcJjTp09bo53u7m78fj/BYBARweFwMDAwQHV1db5DmZSysjJuvvlmDh48SGVlJaWlpbjdbqtrh81mo6amBqfTSWNjI3V1dUVzfS0YDHLgwAGGh4cxDINf/OIXKKXYsWMHTU1NRfd/rWna9LhUYnuZsV1HRn+dc+cRpdRLwEuZz0+Trmg8/z5R4CMTPP7vgb8f5/izpHfzPv/4uOfIl1QqxWuvvUY0GgXg5MmTzJs3z1okrJQikUjwv//3/+Zv/uZvpiusnK1cuZL+/n6CwaDV19I0TUQEwzDG7MFWLEkN0kUvw8PDAPz0pz8lEAjg9XoxTZNt27bx0EMPFTZATdMK4qKJTSn16ekKZCbr6+uzkhpgNQ8+cOAAkE4GNpuNXbt2FVViM02TvXv3smPHDqs/ZDAYxOv1YrfbsdlsxONxBgYGKCkpIR6P4/EUx8bpSikrqQEcOHCAeDyO1+slkUjw3HPP6cSmae9TOZX7i8hc4B+A+UqprZmF0DcopfI+xTcTuFyuMV/X1dWRTCatEY/X68UwjKIa7QC0trbS0tLC0NAQR48eZXBwkEAggGma+Hw+4vE4hmHg8XhYsGABy5cvL5ouHiJCZWUlQ0NDAKxZs4ZDhw4B6WUXd9xxRyHD0zStgHJdoP0j0tey5me+Pkl6jzYNqKysHFPmX1ZWxr333stHPvIRKioqrMRXbGurAoEAsViM5uZmWlpaGBkZIRaLEY1GiUQiiAgul4vFixfj9XqJxWJWgUkxWLt2LVVVVRiGwUc/+lErNsMwdMm/pr2P5brRaI1S6kkR+RJYC6hTeYxrxlm3bh3Lli0jHo9TXV2NiPBHf/RH7Nq1y7rPgw8+WMAIL1RbW8vhw4eJRqOkUimrU4qIoJTC4/FgmiY2m41UKkVtbW1RdfHw+Xxs2rTJ+vqdd95h+/btbN26VReOaNr7WK6JLSQi1aQLRhCR64GRvEU1Q5WVlREIBHjttdcYHh7Gbk//9yqlim4aEsDj8TB//nxqampIJpO4XC4CgQAej4fKykoSiQTBYJDa2lqWLVvG/fffbxWXFKOmpiZaW1v1aE3T3udyTWwPkV5PtlREXiO9nu2/XPwh70/79u3D7/cD8OSTTzI0NIRSCsMw+PrXv863vvUtK+EVmsvlYuXKlSSTSZqbmwkGg7hcLioqKqioqGBgYACn08myZctYsmRJ0RSOTKSmpoZHH3200GFomlZgufaK3Ccim4GVgAAnlFKJvEY2AzzyyCO0tLRYX5umydmzZ4F0F4+Wlhai0ShlZWUopdi9ezef+tSnqKq6cGOEZcuW8ad/+qfTFjukqzeXLl2KiLBw4ULC4TCGYRAMBkmlUogICxYsYNGiRZimyenTp1m9evW0xqhpmjZZF01s5y3OHm1F5jqM3kF7FMMwcDqd+P1+RkZGrPVrsVgMp9OJz+ez2lYVi6uvvpqFCxcSiUSorq7mJz/5iRV3tl9kVjKZvMgzaZqmFYdLjdjuvchtOS/Qnq3GG2EFAgH+7d/+je7ubgB++MMfEgqFME2TO+64g2XLlrF582YWLlxYNA2RS0pKKCkpAdJl88888wymaRKJRLjmmmsArFGdps1k/f39PPzww3zta1/TBUazmF6gPcVKS0u59tpraWhI92d++umn6ejoYNGiRQSDQXp7e2lubqa9vZ2bbrqpaJIbpItcenp6rGIXj8eDYRgsWbKE+fPnU1lZWegQNe09Gd1P9KGHHqK3t5fOzk7cbjdLliy5YE2qNjPlXMUgIneT3sXanT2mlPp6PoKa6ebOncsbb7zByMiI1abqmmuuwe/3Mzg4yPz58zFNk7a2NlatWlXocC2pVMpa5AzpUdq7777L1VdfXcCoNG1q9Pf3s2PHDquf6NatW8dcI+/u7ubmm28uygpmbXJyWqAtIv9Kehftz5EuHvkIoOelJtDW1kZ5eTkDAwOEw2EikQhvvfUWhw4d4uDBg3R1deH3+y/YmLTQ7HY7N910E5AevSWTSW688cYCR6VpU2Pbtm3WlkumafLYY4+NuT0YDFqdbLSZLdfOIzcqpT4JDCmlHgZuAFbkL6yZKxKJEAqF6O3tZXBwkHA4TDKZpK+vj56eHnp6eojH47S1tdHY2FjocC/w4IMP4nQ6CYfDRKNRFixYwNtvF+1esZqWs127dlnFW4lEgj179lxwn2Jep6nlLtfEFsn8GxaR+aS3j6nLT0gzm8vlwuVy0d7ebjXpTSQSjIyMkEqlCAQCvPvuu1x55ZWYplnYYMfx1FNPkUqlME3TmrJ58803GRwcLHRomvaebNmyxZolcTgc3Hvvvbjd1pUVFi5caBVRaTNbrontFyJSAfx34G3gDPAf+QpqJjMMg9WrV+N2u1FKWYuxbTYbDocDp9NJW1sbyWQSr9db4GgvtGvXLgKBgLWv3OnTp+np6SEcDhc6NE17T5qamqzrZ4Zh8Ad/8AfceuutXHfddWzevLmo12j29/fzuc99ztrtXru4iyY2EfmAiMxTSv2dUmoYKAEOAz8B/nka4puRkskkHo+HxsZGq+rRbrfj8Xjwer243W6uueYaDCPX9xX5NTAwwP79+zl8+DCBQIBkMkkqlSIWi1lNkWtqagodpqa9JzU1NWzduhURsfqJ2mw25syZM2bPwWI0uppzcHCQc+fOFd2a2GJyqarI/w3cDiAiHwK+SbqAZA3wGLqt1gVM0+SnP/0psViMYDBIIBDAZrPhdDpJJBKUlJRw++23s3LlyoLEd363lGg0SldXl/V5b28vyWQSpZQ1HfnGG2/w8Y9/nPnz52Oz2cY8XyE6pmja5ZqJ/URHV3M++eSTzJ07l7KyMpxOJ5s2bdLTp+O41JDBppTKXlz5GPCYUupnSqn/CizLb2gzU09PD4ODg/h8PioqKojH49hsNioqKigpKaGyspJPfOITRVNSHAwGrSTc29sLYHUdye7LFg6H6enpob29nVSqeDd10NM12qVk+4nOpMXZ2WrOVCpFJBLhhRdeACAej495k6r92qVGbDYRsSulksBtwAOTeOz7UiKRwOPx0NHRwalTp4jH41aSKC0ttRoMF8r5o6sdO3bw2muv0dbWRn9/P5WVlYRCIWKxGA6HA7fbTSAQoKysjFtuuYUbb7yR6667rkDRT6ynp4evfe1rvPXWWzzyyCM8/PDDhQ5J06ZEtprTNE2SyST79+/nvvvuA3Sbu4lcasT2H8DLIvI06crIVwBEZBl625pxZftEtra2curUKZLJJMlkkoGBAYaGhli8eHHRzI2bpkk8Hsfv93PmzBmGhobo7+9HRKwdwAcHBxkcHOTkyZMcP36cEydOFN0fU39/P88//zyvvPIK0WiUn//85/T09BQ6LE2bEtlqTrvdjsvlYu3atdZtxbhkqBhcNLEppf4e+HPSO2jfpLKrG9OP+1x+Q5t5st1EysrKqK6utqog7Xa7VRUZDoc5deoUfX19hQ4XwIrNMAxrY9FYLIZhGNhsNqLRKCKC3W4nHo/T29t7wXW2Quvs7GTHjh1EIhGCwSB+v58vf/nLhQ5L06ZEtppTRKisrOTBBx9k0aJF3HjjjcyZM6fQ4RWlS04nKqXeHOfYyfyEM/ONLrqw2WzW5263G8Mw6OjooKWlhYGBATZu3MjcuXMLFqthGCxevBjAmipVShEIBKxrgA6HA8Mw8Pl8lJSU0NjYWDTXB7PcbjfNzc3WSFgpxeuvv87AwMCMupaiaePJVnNu376du+++m40bNxY6pKJXHPXms4RhGCxatAjDMKwSebvdjt1up6SkhNLSUgYGBmhububcuXO0tbUVOmSuuuoqNm/ezLJly6irqxvTBNbtduNwOJg3bx4f+tCHuOWWW1i3bl0Box1fNjlnZZNxIBAoUESaNrWamppYvXr1jKrmLKS8JTYRcYvIHhE5KCLviMjDmeOLReQtEWkRkSdExJk57sp83ZK5fdGo5/pS5vgJEfmNUcfvzBxrEZEvjjo+7jnyrbOzk1dffZXe3l5M02T9+vVUVlbidrvxer1WUQZAR0cH0Wh0OsK6pLvvvpubb76ZmpoalixZMmbqtKSkhOrqaq677jrWrVvHsmXFVwzrdDr5rd/6LbxeLz6fD7fbzdq1a6mtrS10aJo2JWZiNWch5XPEFgNuVUpdS3rd250icj3w34B/VkotA4aAz2Tu/xnSvSiXkV78/d8AROQq4H7SOwvcCXxHRGwiYgP+BdgKXAV8PHNfLnKOvAmFQjz11FN0dHRYL64iQnV1NVVVVTQ0NFgjiZ6eHgYGBsbdSbsQHA4Ht99+O8uWLWPhwoV4PB48Hg8OhwOfz0d9fT01NTVF3Vbrz//8z6moqLCua37pS1/C5/MVOixN0wogb4lNpWW3X3ZkPhRwK/DTzPFtwH2Zzz+c+ZrM7bdJ+mLOh4HHlVIxpdQZoAXYmPloUUqdVkrFgceBD2ceM9E58qa3t5dQKASkpyQbGxutBLFo0SKWL19OY2MjtbW1eL1eKioqCnp9bbT29nb27duHz+dDKYVSCsMwrM4ooVDI2nKnWNXU1HDvvfdSUVHBRz7yEa688spCh6Rp72uJRIL9+/fz3HPP8eabb1qvj9Mhr9fYMiOrA0AvsAt4FxjOrIsD6AAWZD5fALQDZG4fAapHHz/vMRMdr77IOc6P7wERaRaR5vdapVhSUoLX66W7u9tqflxfX09dXR0igmmaVuVhKpVCRIqmV+SZM2esFlrZGN1uN42NjcybNw+v18vJkycLuv4uFw8++CDXXnstDz74YKFD0bT3vXfeeYeOjg5isRh9fX00NzdP27nzushaKZUC1mQaKP8ncEU+zzdZSqnHSLcGY8OGDeoSd7+oaDTKsWPHOH36NH6/nzlz5rBq1Sq6urqw2WwopSgrK7OKSyoqKopmPZhhGAwODhIKhSgpKaGqqopgMEhFRQU9PT0cP36czs5ObrjhBmt3bU3T3t/Ob893vra2Nms9abbX7JNPPjnucqGpbs03Ld1DlFLDIvIi6X3cKkZ1M6kHOjN36wQagA4RsQPlwMCo41mjHzPe8YGLnCNvXn75ZWu37FQqRU9PDy0tLYRCIUTE6rPo8XiYP38+1dXVVuf/QhocHGRkZIQ33niD3t5e7HY7IyMj2O12Ojs7CQaDuFwuIpEIL774IldffTUNDQ2XfuJpdPr0abq7u/npT3/K/v372bZtGw899FChw9K09zWXy0U8Hre+zq6XnQ55e2UVkVogkUlqHmAL6aKOF0k3T34caAKezjxke+brNzK3v6CUUiKyHfh/ReSfgPnAcmAP6Z28l4vIYtKJ637gdzKPmegcedPb28vAwADBYJBEIkE0GuXJJ5+kv78fl8tFf38/Ho+H2tpaamtrWbhwYcGnIiORCLt27eLo0aMMDg7S1dVlLfgMBAIYhsHw8DBlZWVUVFRw4MAB2tvbiyqxnT59mnfeeQe/38/u3btRSvHss8/S1NSkK8g0LY8uNcKKRCJ84hOfIBaL8YUvfIE1a9ZMW8FcPtNnHfCiiBwC9gK7lFK/AP4aeEhEWkhfD/t+5v7fB6ozxx8CvgiglHoHeBI4CvwS+BOlVCozGvsssBM4BjyZuS8XOUfeXHPNNVb5vt1ut4ot/H4/fX19vP7664TDYVatWsWmTZuKYu+nnp4e2traCIVCDA8P43K58Pv9DA0NEQ6H8fv91o7ggUCASCTCyEhxdVLL7kywe/duUqmU1Sh227ZtjIyM4Pf7CxyhVkxOnjzJ1q1bdfPgaZCdnVq4cCG33nrrtFaB523EppQ6BKwd5/hp0hWN5x+PAh+Z4Ln+Hvj7cY4/Czyb6zny6bbbbmP37t3s3r2b4eFhkskksVjMKhQZHBxk7ty5hEIhKisrpzO0Cfl8PiKRCAcPHqSjo4NwOEw8HicWi1nz4PF4nHA4THl5OXPnzi267v5er5fBwUH2798/5vrfE088wfLlywGYN28eGzZs0NcGNb7xjW8QCoX4y7/8Sz796U/jcDjYsGGDXvOYR4XYd7LwF3lmiUQiYe06LSJEo1Frw04RIR6P4/F4CIfDJBIJnM5pWTN+UbW1tfh8Pvr6+ggEAoTDYZLJpNWayjRNRASbzYbL5bI6jBeTlStXMjQ0REVFBX19fXi9XhKJxJg1bN3d3fT09DBv3rwCRqoV2smTJ2ltbSUcDtPW1sb8+fOpqqri1KlTfOxjHyua5Tfae6dbak0BpRS7du2ivb0dl8tlde4YfXsoFOKXv/wlTz31FNu2bePYsWMFjPjXrrvuOux2O6Zp4na7sdvtKKWIx+MEg0Frk9RAIMDQ0JC1lKFYeL1ebrnlFqvS1O12Y5rmBXFGIpHCBKgVjW984xsAhMNhTNPkV7/6FZDeHeLMmTOFDE2bYjqxTYHh4WFrNDM4OMjAwIB1vS3bGT+RSNDR0UFPTw9nzpxh79691saehZTdANXlcllTBg6HAxHB7XYD6c1Iw+EwhmFw6NChaV1omQsR4a677rLeTLhcLtavX2/dbrPZ9GhNo7W1Ffj11Fj2erFhGEUxg6JNHZ3YpoDT6bT2SwqHw4TDYbI7/JimiWmaeL1eDMOgu7ubtrY2AoFAUYx+YrEYK1aswDAMIpGINXVaWVlpbWMD6anW7DWqYtzrrKmpyVo+4fF4+Ou//mvmz5/PggULuPHGG/F4PAWOUCu0RYsWAelmCk6nk/LyciDdRLsYe6Bql09fY5sCPp+Puro6q0TeZrNZm3VmW1RlO80bhsGJEye48cYbrUWLhRQIBEgkErhcLlKpFIlEAsMwiMfjmKaJw+EA0pWeyWQSr9drvSAUk5qaGm699VZ27tzJrbfeahWOaFrWl7/8ZX7/938fp9PJ3Llz+fKXv8zy5ctZuHCh9XuuzQ46sU2RbMPj7Ignm9SyGwRmC0uyDZLdbndRNEHOTp/29PRYOw9k/81uW5NNdtFolGuuuWbGrA/r6uqip6cHn8/H4sWLi2JBvFY4K1asYNGiRbS2trJ06VK2bt1a6JC0PNFTkVNkZGSEefPmMWfOHHw+nzUFmWWaJj6fj5qaGtauXVsUU2NKKQYHBzl9+jSRSOSCmOPxOH6/H9M0cTqd3HbbbVx77bUFjHhi/f39vPjiiwC88MILHDhwgObmZtrb2zl+/Pi09qnTiteXv/xlfD4fX/nKVwodipZHOrFNkezGlvF4HIfDYU1tmKZJMpm0RgvJZJKWlpaiGK1lu6JUV1dbW+pAOuZYLGY1bk6lUjidTgYHB4tuHRukdx/4h3/4BwYGBojFYiQSCb73ve+NuU9fX5+ujNRYsWIFO3bs0NfUZjmd2KaAUopDhw5ZRSMjIyNWUshORWanJ5PJJB6PxyouKaRsH7cbbriBBQsWWFWQkP6esmvuRIREIkEymaS9vb2o1rKZpsnrr7/Oiy++aC1RiMVi7N27d8z9DMPQU5Ea/f39fO5zn2NgYKDQoWh5pBPbFBgeHqa/v59FixZRX1+P1+vFbrdf0OmiurqahoYGGhoaOHfuXIGi/bU5c+Zgt9uZO3cuV199NbW1tbjdbmvkFo/HiUajRKNRwuGwdayYRj7ZpRWjt9SJxWIsWrRoTCJbvny5LhDQ2LZtG4cOHeKxxx7T7dZmMf0Wdgp4vV5M0+Tw4cOcOnXKmsbLjtSyi5z9fj/JZJJ4PE5paWmhw8bhcNDb20tzczORSMRazzN6uYKIoJTC6/XS2dmJ0+ksePNmSI8ou7q6rA1eRy+dEBE6OzutveTmz59fFP/fWmH19/ezY8cOAoEATzzxBI2NjSxatMhqUqDNHnrENgVcLhexWIwjR45YyQuw9mFLJpMMDg5y9uxZ9uzZw8mTJ8ds51AoZ8+e5bXXXuPcuXMMDQ0xNDRENBodM9VoGIY1LenxeLj22muLoufi/v37efvtt2lvb2doaIiFCxcCWEsV6uvr6ejooKWlpSivC2rTb9u2bcRiMWKxGEopXnjhBQYHB2lrayt0aNoU029TxnGpDfTOF4vF2LNnD8Fg0OoPme1Ekh35KKWIRqP09fVx5MgRvvOd7/Dqq69OanpsqjfjC4VCdHV1WdfPRseblUqlrMTm9XqLYuQTjUbp7Pz1Fnv19fX4/X7Ky8sxTZNAIGB1/TdNk7a2tqLf/VvLv127dllvKJPJJPv37+e+++4rqql1bWroxDaOlpYW9h8+iunNrXLRNE16h8OE4ikS8QSpRHLCUUIqlWLYH+RszyDmmV6McXaTHY8RHsw5/lw1Njbi8/kYHBxkcHDQ6jwynng8TkdHB2+88Qa33377lMdyOWKxmFXNOTg4SFlZmfVuvL+/37qfvramAWzZsoVf/OIXhEIhbDYba9euRUSoq6srdGjaFNOJbQKmt4roVffkfH+3bzGDr/2cZDSGaShIJSa8b8pwoOqvJb7qrtyf/+gvcr5vrkpKSnjggQf4xje+gd/vt7qLjMc0Tc6dO8fLL79c8MSWTCat1mQOh4OlS5fS2NhIe3u7VdySXU7hdrutVkra+1tTUxM7duygrKyMVCrF/fffz7XXXlsUS2+0qaWvsU0RV2klvrkLMRxuSE6c1ABMsWPYXaTi0WmKbmJLlixh4cKFY7arOf8a2uiF20eOHLEaPBfKkSNHmDt3LsuXL2fOnDkMDg7S29trLbPweDw4HA6WLFnCLbfcUhSL4bXCq6mpYevWrTgcDj72sY+xZcsWa8d4bXbRiW2KxIZ6iPZ3kIyFQV28WMGM+knFwiTDhS83fvnllzlw4AChUIh4PG61AgPGVIrZ7XYcDgfV1dUFb4IcCASsRs2pVIre3l76+vpIJpNEIhGUUgwNDVlbCGlaVlNTE6tXr6apqanQoWh5pP/qp0h0uI9kJJSZghTg4guwI71tGKs2T0tsEzFNk507d3Lu3DlE5IJqyNENnZ1OJ3PmzLHWuk230QU9fX19BINBIF3CbRgGHo9nzGavDoeDz3/+8xcsPIepL8LRZo6amhoeffTRQodR1CZbPHcxp06dApiSv7fJ/N3qxDZFUrEQZiKGiHGJlAagsJdW4PCVTUNkE+vp6UEpZbWhGk0pNWazVMMwqKyspK6uruDTN9XV1dYu5aWlpdjtdnw+H7FYjHg8jsvloqGhwSoqmT9/fkHj1bTJOHfuHIODg1RWVrJgwYJpP39LSwtHjhyhpKTkPT9X9nUluxfe5cq+kc2VTmxTQJkpxGYnlYyjzCRjR2vnjd7EwObwULF4zfQGOY5AIIDL5SIajV5Q5q+UIpFIWCM2u91OLBbjyiuvLMg6toneqY2MjLB3714ikQhf+cpXMAyD+fPn89BDDwHpLYVuvfXW6Qz1faO/v5+HH36Yr33tazNmx4did+LECU6ePAnAmTNn8Pv9XHnlldMeR0lJCevWrZv2805k3759k7q/TmxTIBmLEA+OgDK5cApybFLD5sBRUoHhKXz3jmwXlGzSOl92DVu2XL6np4dTp06xZcuW6Q71AqZpsnfvXrq6urDZbFRUVLBmzRoOHjzI/fffb92voaGhgFHObtn2VNu2bbPeSGiTN3rq7+zZs2MuCYgIixYtoqOjA0iv2bwYPc2ephPbFDBsdqK9bYBc/NKaYcfmcOIqq0HFCl8Rabfbqa+vx+l0EgqFLrhdKWU1c3a73djtdg4fPkwgECjoQm2/38/u3bvZt28fhmHQ2NhId3c3g4ODrFu3jltvvRW/309tbS2NjY0Fi3O2GRkZ4eDBg/j9fhwOB8888wxKKXbs2EFTU5MetU2B82dDsn1b9SLyyclbYhORBuDHwFzSL/ePKaW+LSJVwBPAIqAV+KhSakjSP9FvA3cBYeBTSql9medqAr6ceepvKKW2ZY6vB34EeIBngc8rpdRE58jX95qKhRGbDTMRy4zazmPYQQwMm4GZSpKIBImHR6Y8jsle9E0kEhw7duyi89fZriOxWMwqqf/sZz87qV20p/pd5NGjR63ekKZp8tprrzF//nyGh4cxTZOKigqWL19OKpUiGAzi8/mKog3YTPf2229bb4CeeOIJAoEAHo+HVCqlR23vwei/jba2Ng4ePGh9vXr1ahYuXGjd55FHHpn2+GaifI7YksCfK6X2iUgp8LaI7AI+BexWSn1TRL4IfBH4a2ArsDzzcR3wXeC6TJL6KrCBdIJ8W0S2ZxLVd4E/AN4indjuBHZknnO8c+SF2OyZgdoEL55iAwHTVGCzIYYQ7WtHrdiIGFO34qKlpYWTR/bRWJJbb0TTNJGRfhxicvGVd+lpSZRJ37k2kud8RIecOZ2jLZhbZ5XJCIfDlJeXMzQ0RHd3N729vTidTlwuF5C+UJ1MJjlw4ADJZBKfz8fGjRun5GL4+1UsFhszqj9w4ACJRAKPx0MymeQXv/gFW7dupaKioug6eYzeDxHSI/5gMEhtbW3RdaVpbGyksrKSoaEhKioqKCsrbIHZZCilrDe/hmFQU1NTsFZ2eUtsSqkuoCvzeUBEjgELgA8DN2futg14iXTS+TDwY5WuYnhTRCpEpC5z311KqUGATHK8U0ReAsqUUm9mjv8YuI90YpvoHHlhc/sw4xEYd1QgiGGgUglAIQKJcJDYcC+pWBi7Z2pfbBtLUnx5Q24VRL2BBD/yj3DUkSJ8Xk9mp5FO0zEz+12ADUWtJ8VfrA1RXZJbE+dvNE99Mqmrq6OzsxO3243b7cbn8+F0Oq1RmYhw6NAhq4tKKBTi6NGjbNy4ccpjeb9wuVz4fD4ruV199dW88847QLp358qVK63ZgiVLlnD11VcXLNas4eFh9u3bRygUory8nPXr19PW1sapU6eIx+N4vV5uvPHGousjWlpaWhQ9WSdrZGSE4eFhQqEQfr+f9vZ2li5dSn19vTWlOl2m5RqbiCwC1pIeWc3NJD2AbtJTlZBOeu2jHtaROXax4x3jHOci5zg/rgeAB4D3di0mlcTuK4O+znFuVKhkPL1oW9LX4Mx4hERwJJPsCscmil0nAgyfN33vgPTlwlHXCxWQVOAwhDJPYS/NXnHFFbS1tTFnzhwWLVpENBplZGSERCKBy+WisbHRaoKcNdlyYe1C69ev59ChQ/j9fkpKSqzti86//tPa2sqVV1457S9m50/Fd3R0jFnG4nQ6aW9vJxgMUlJSgmEYzJs3b9y/fV2EMXnZXrOjt5AaHBzE6/VSVlaG3W6ftt+JvL9CiUgJ8DPgz5RS/tHXOjLXw/K6lfTFzqGUegx4DGDDhg2XH4fNgRmLjn99DX7diUQpVCqJmUxg83hJxSI4Siov+7Tv1aneKF0jCZLnha0Ah0B8nP8Ruw0ctsJerxIR1q1bN2brn+7ubt566y1EhL6+PhKJhLU4tK6ujqVLlxYq3FmjvLycD37wgwA8+uij1ouUiFijN2DaE9p4TNO0klq2ACoWizE8PGy9ANtsNux2uy4wOk9HRweBQGDSJfbhcJiBgYExU9aBQIDTp0/jdrsREcrKyi6rxV0gELAqQ3OR18QmIg7SSe3flVI/zxzuEZE6pVRXZqqxN3O8Exhdm12fOdbJr6cVs8dfyhyvH+f+FztHXqSiQVKxMJfqNgKAMlGpBLHhPgxHbtep8uXdgTgpU3FBOpb04NI87wZDgcsmDIeTVHgLO2qrra3lqquu4t1330UphcvlwjRNurq6+M53voPdbmfhwoXWtaFCLHSdzbZs2cIzzzxDMpmkpKRkzFqrZcuWFSS5nT/Cev7559m7dy/hcBiXy8WmTZv4whe+QDgctpoM1NXVcffddzN37lyuueYa3YLtPfB4PJSVlVkjeI/HY+3AAelrcIFAwEpy+ZTPqkgBvg8cU0r906ibtgNNwDcz/z496vhnReRx0sUjI5nEtBP4BxHJDm3uAL6klBoUEb+IXE96ivOTwKOXOEdOOjo6MMIjOXfUt8ViGNFAZqrxUslNQSqOCg2hDmzHXZNbFw8jPEBHx/id9y9XfYXzguQFYCoIJy5M0ymg0msjnDCpmNJILs/SpUtZunQpw8PDPPfcc9Y7TcMwcLlcVFZWsn79eiC9mHgylZzaxWU75UN6Efx//a//1apILZZu+Q6HA7vdztDQEPF43NqE1m634/V6CQaDBINB/H4/8XgcEeHaa68tdNgFV19fTzKZvOwF2uFwmL6+PlKplNUEYnQiW7JkyaSLdvbt23fJNXyj5fNt1SbgE8CtInIg83EX6WSzRUROAbdnvoZ0VeNpoAX4P8AfA2SKRv4O2Jv5+Hq2kCRzn+9lHvMu6cIRLnKOvLA7nbh83pzfhShADCFe4O7+DZUuqn0XVi2Ot8zcus2EqgKP1s7n8/k4ffo0oVDIWnsXCoVoa2uzCkh0ReTUSSQSOBwO7rzzTkSErVu3smjRIpYsWVI0SQ3SlZzV1dX4fD5GRkZ46aWXGBgYIB6PW9PYPp/PGl2O3sNPu3xer5eFCxeyZMkS6urqrGKSRCKB2+2elkrUfFZFvsqE9e/cNs79FfAnEzzXD4AfjHO8GbhmnOMD450jV/X19fTE7JPaj80b8zD86k8hnsNCSlOBuxzbtfcSrc2tM4b76C+or5+Xczy5qPLamF/m4Jw/lcskKgoIJRRuR+GvobS2tnLu3Dm8Xi8rVqwY0w8yWxjgdDpJpVIsW7aMuXPHrR/SJqm9vZ1Dhw5hmiaLFy/miiuuKNpO+bW1tRw5coTm5mZ6enqIx+PWlkvBYJBEIsHg4CAHDhygpKSEm266qcARzy5KKUKhEKZpEo1GSaVS07Y3YnG99Z6hlJki0tsKZm7rx0Bh95ThzTGp5Uu5x0bMNHObQc1ImYqUqbAZhSsgOXv2LIcPHwZgYGCAAwcOMDw8jMfjIRwOM3fuXGpra7nvvvu46aabcDoLey1zpploob9pmrS1tVl9Rfv7+3G73Tz88MMXfb6pqDC8nI7zqVSKt99+m+PHj5NMJscUlBw9ehSbzUZnZyddXV1UVFTQ3NzME088Malz6OrJiUUiEeLxOGVlZdZ6vGxFar7pxDYFkuEAkd6zkMr9GlgyGiY63Iu7onCd8lOmwucwcqp5gfS8dTCaIpIwKXFN/cLrXI0u5e/t7eXEiRMopUilUogILpeLzZs3s2zZMl0MMIVSqRSRSMSqegsEAtaox2azUVJSMmZHiKnU0tLCkYMHKXXm/vOMxRN0t7eTjMdIJFOkzBSmqbAZBqlkglQyiUql6O/pQhJxiEaIDw9e+okzAvGpveY924x3aWa6OgDpv/opYKYSpOLxicv9x3uMShFoO1bQxAaC12XDMC6sgByPzQCfyyAYK2xi83g8mKbJyZMnaW9v59ixY6RSKWv6MRAI0NnZidPppLW1lZtuuqkoStBniolGIIFAgO985zvWmsBnn30Wp9PJRz7yEQzDoKSkhM2bN+ft/7rUaWfj3NyWx4SjMZrfPYMkYoipEGViqPS1bbsh2BFMATFTSCqFioSpn1vNtTk+P8Cenrx16ZsVPB7PmEX9NpuNysrpWd6kE9sUMBwuSObWiSNLxSPpxxVQtz+O12HgNLhgLdt4nDaoLXXgdRR2HduKFSs4ceKEtVDYZrMRDAatXcBbW1txuVyEQiG8Xi+1tbUF2fpjtunr62PZsmV0dnYyPDxMMBgkFouxf/9+a51gX19fUVzP7Bn2MxQMEU0kSKkUpkp3/bEbBoZhI5lK4XTYcdhtOGy29O1APJnEWQSjfKUUZ8+eZWBggPLycmvfxHPnzhVlK7CJLFiwgFAoRCqVyuuI/nyF/wnOAiqVArsj3RNS5XKdTRC7A++chXmP7WL6gilcNkHlmKdiSbhyrrvgnUc8Hg9r165leHiYeDzOuXPn6OzsJBgMYpom/f39jIyMcPLkSTZu3EhZWRn19fUzsk1RMSkrK8PpdLJ48WJOnDiB0+nEMAxSqRRnzpxhzZo10/bCdSkppQhE0lsx2W02a0bCEAOH3cDITIlVlfhwORyUetykcr5Gnn/Hjx+3rimeO3eOU6dO4XA4ePvtt3E4HNx4440zoo+kiBSkIlkntiniKq8lERiC1KX+OAzE7aVi8WpszsKO2Mo9NjpHEohKXz+71KAtpaDCXRxTegcPHuTFF1/E7/dz5swZq6w/lUoxNDSE0+nE7/dTVVXFmjVrOHfuHCtXrixw1DNbTU0Ny5Yt4/Tp08RiMUpLS62y+UQiQXl5ed62runo6CAQT+Y8/ReKJQmbEIwnSZqKpGliKpOkaZACHE4nSimGonHKHC7i8RS2JCQGAjnHFIgnL9kN43KKXiDd5T+VeS1JpVK0tLRQVlbGY489BsCPfvSjy97JPpeCl2AwOOnOI+MJh8MAVvu1y6V30J4iRngw5wXaTtPEGR2CHHo/ioBDpbAPtVJy9tWcr0cY4UFgasv9K9xCKJYimrx0UstqHYqTSKkpbas12T/+UCjE4cOHicViDA4OMjIygs1mI5lMWtVv2QW5hw4dIhQK8frrr0/qHa6udhvflVdeyfLly4lEIjz99K/7Hlx11VWsXLmSVCpVFAU7HpeLubU1ROMxQuEICUliYKCAlGniQOH1uLHZ7ShlUuIrpdTnm/I4WlpaOHHkGA2lk/vbTfSHSSTTryeJZBKbMrClhFhv+gVenAnCkclPR7YHui95n2XLlk36eSeSbWs3FWX+k4mr8L+BRWiyP9hUKsW7b8ex223WyGEihiH4vG7mlnu5an45vpz/mOZN6S8cwKm+GJHkOC21JmAT6BhKEo6blHumbsqppaWF/e/sJ9d2JuFAmF5/L2bKJJqKEk/FwUz3KDRNk5RK9wFUKALhAO1D7ciIIIEck/Hw5X4n7w8jIyM4HA68Xi+xWIxUKsXIyAh79+7F4XBwww03THmXl/r6elKBkZyLRwD85V72pOIc7+hKL8VRkFImdptBqdNBQ2U5NkOoKSulzOvBaVMsmWTxSC7dMBpK5/HnGz+d8/MCDASHONJ5AlOZCEIilcQ+apr3mgUrqS2d/Oj4W3t+eMn7TOUbukLtI6cT2zgm+4M9fvw4e/bsIRwOXzKx2Ww2SktLueWWW/jkJz/JmjVr3kOkY3V0dBAK2HLeKqa1K0ZHIPfez0kFb3Ym+Pbh0pxHmmcDNny5NC+tAPPm3FKsI+bA/rqdRDCBec7EZk/vcWcmTJxeJ8lYEkHAAY5aB64PuEhdm8LIcWG58VJxTLcWq1AohGEYlJaW4vP5SCaT7Nmzh9LSUq688kqqq6v5wAc+UOgw8bldeN0uqkp8jEQiKKUwRLDb7CyoqiCeTJBSCkfYjohQ6nEXTfFIdUklNyxdjz8aoNSVbjjQOdRNLBmjtrSaKl9FoUMsaoX/Cc4C8+bNs1rGjCYi1mLWLNM0icVidHZ2Fvzir8fjynkNW1Z6VKQoZPW8zWWjak0Vfc192Bw2nGVObA4bZsokFU+hhlV6PVuNi5K6EuJDk6tY1SYWiUSIxWIMDAwQiUQwTZMXXngBu92Ow+Hg6NGjeL3eokhsoWiMvhE/XcMjmKZJMpnCsNnwuYRgNIbP5UKZJpFY+vejxOPGbhRH8QuA0+6gpqSKZCrFYGiIMk8JVb7c+yW+n+nENgW8Xi8ul+uC0Vq2YixLRLDb7cydO5f6+vop74hRX19PNNmV80aj7YMx9h9K0j6J1/0lZSZ/uyGEw55bZvtGcwnuSTQvzYUyFSMnRwicDqT3aTcgpVLEhmPYnXYMm0EyksSMmcSGYngXeJECb7VTLC63mAHShQA9PT0Eg0GGh4cZHh4mFouRSCSw2+3W4tvvfOc7NDc3T+q5p/qaZiAS4Wj7OYZD6Q2ADcPAbk9XRRo2g6FAGKXS5f0KRXnSw5rFjRgF7Kgznkg8yr6zR9LT7UB1SRWr668ocFTFTye2KRCLxaxS6FgsZo3SRic1p9OJw+FgwYIFbNq0iZUrVxa823xfMMlA6NL3G80Qck5q+RLuC9O7p5ekP0nKTKGSmf/vaIoo0fTXAgYG7ko3ht1AiuwF63ypVIojR47Q3d1NSUkJq1atysuIvqWlhXcOH6PCO/mKuq7eTmKxCKFwCKVMwqEo8XiUWCyOzWbH6XDicDqJBVN0vjuQ8/MOh3PbVWoyVZFDI346e/sZjsSIJlPEkimUUthsgj+WwDQVI7EYLocDw2YjYkZpCcU5E8190fV0dB7pGOq2khrAQHCQ4bCfCm/xl/oXkk5sUyAWi7Fw4UIOHDgw7vQjpK+tZRcLz507l1tuuaXg66q6/HGik/zbjKZMBoIJqksKt0A08G4AM2aSSqVIRVKYCRNlqnRp56jLdDEVY+TMCN7691ZqPNWGh4fp7u7G6/VSX1+PYRgcP36ctrY2IL3r8J49e7jtttvy0oKowjuHW664P+f7J5Jx3jndTHdsAEfKjhMhHAuRjKZw2HwkxMTAhtPwUFsyn9++8UEqS2tyfv4Xjz9+yftMtnDK3dNDrz9ACQbxZBKXCMlkErvdjsvlQimFUgqx2bDZ7TjcbsrrFkx6ucJUF3SNppQiEA2SNE3so+b+i2m9XbHSiW0KVFRUcMUVV/Dqq6/S2dk57n3Ky8vZuHEjV199NVdcccW0dbm+mHK3bbKX2BgMmXQMFzaxGS7DSmKpeAomWmWRgMRIguGjw1SvrsZd5Z7OMIELp/6y03lZHo+HZDJJf38/Pp9vzCaNP/vZz8Z0mCjEEoREMsGR03s5euZtugfaicYjRONhYvFIemG/GcRms+N2ePG4fKxZcSNe99QvyJ3s993Z2ckjjzzCnj17aGhoIJFIWN083G43CxcupLOzExGhsrKSuro6Nm/ezO///u8XRfu1cDzCwfZjDAaHONPfQV3FHKp85XgcHiq9el/BS9GJbQrY7XZWr15NTU0NQ0NDBIPBC0ZtsViMZDJJZ2cn8+bNIxqNFrwtTlWJA4cB8dxbXDIcTmIv8PV1V6ULu89OPBRP7356ESqpSIaT+Fv8uDdOf2I7n9/vH/N1JBIhGo1azYSzvzexWCwvo7WOjg5GwoGcRkkAoXCQ3oEe2s6dJpVKEk8kiETDoMBmMzIl9CnsqTBucXD03OsMpdpxTmJ3+OFwL6ojh+2eJqGuro5bbrmFRCJBMBiktbUV0zQxTZNgMIjD4aCiooJgMEhlZSWLFi3C6XQyMDBAbW3tlMWRrlQO5FRmP1rf8ACRaIRYPEYgHCJ5NkFDXT1zKmt4qaeZRDKOy+GadKeX9kA3vo5JXn+YgXRimyK33norjz76KGVlZdYL1WjRaJQzZ85gGAaDg4NFsfFlKJYkNckhW9IEr3Nq39F2dHTASO5l9i6/C2fYSTwZJzHhcC3DhERfguSBJEY4x7iHoUPlsEQhB+ePNPbu3Ut399hFss8++yx+v581a9bg9/uttlWbNm1i8eLFUxLH5RIRDJF0VWEqSTIRRyA9WhMBSVeget1e3G4PNsOOw174PoaGYXDjjTcyODjIzp07CYVCjIyMjLk9W8RVWlrK4sWLqaqqwuUqbDegrGQySSKZxB9Kd0Kx2WxEo1FGAn6CkUxiEqitqMHjKvwbtmKjE9sUKSkp4Z577mF4eJjBwcELElt2GiQUCuFyuTh48OCUrmHLagvmvo6tpT2S8z5sWbEk/NPbTuZW5XaOtqCNFZM7xSWJCO4SN/FInHgkfslrDolYYuItb6fZ0qVL6evrswqLFixYgMPhwOFwsHLlSiKRCB6Px6qgnWr19fWMDB3L+f5ulweX043b5SaZTP8/2m12TKWAdFKrrKimqroWl8PJvDnzL2ukmctC58kqKyvj4x//OKdPn6azs5N4PG51pclOQS5atIiSkhKWLVvGwoULp7xgp76+nnBqaFILtIPREIc7j3Osq4VgWTqJeRxultYuZCgyMmYNW5m7lPWLVuX83N/a80O89dPTYb+QdGKbIiLC7bffzoEDBzh79iyJRIJUKmVNLcXjcVwuF7FYjEAgwM6dO1m5ciUej2fKYpjshewadzfGkbOYl1hUPppht+OYdyXuUTtWX8yKHOKqr6+nT/pyXqBtC9tQLyrMhImRNEgFU+my/wmIU2B57gvAjZcM6hfkZ71QVVUVmzZtoqOjg9raWkpLSxkZGSEajXL8+HECgQBOp5O1a9eO2RV8qlxOscOCpdV4ym20tbXh9/tJpVIkk0ni8Tgej4dlKxZTUlLCkiVLLisZL6A6b0UYIsKSJUs4c+YMx48fRymFw+HA4/Hg8XhYuHAhd9xxBzU1NXmbRWkPdOc8FRmKhBgYGUIpxUhwhGgsitdTQtJI8UrPfrw2N65R07x2m52XenNfWtEe6GYl+U1sqVSKvr6+gl5q0YltCl1zzTVcddVV7N+/n3A4bLUbyorH44yMjNDa2sqpU6fw+/1Tmtgme4H97bffprm5mTNnzuT8mNLSUr70pS+xfv36yYY3ZZLxJKFzIeLD8fTmorZMJarJhQvOBcQuuCqLY4rp1KlTnDx5EtM0OXfunNXvcmBggGuuuYYlS5Zgs9nw+Xx56ZR/ucUnQ0NDPPfcc+zdu5fDhw8zNDRER0cHc+fO5aGHHsLhcHDnnXcW/LrxeNauXUtvby82m43W1lbcbjcbN26ksbGRVatW5bWQa7IJe7AjhMuVTrCuOSX4/X7Kysro6urC5XYzr77eaiwMUFlZibeiIufnX0llXis5o9Eor776qlUE1d3dzbx5U9vjNhc6sU2xhoYGPvCBD4xZxApYlVZKKUKhEEeOHJm23WQnku2Cn+2xmAu73V7w64PDR4aJ++OolMKMmBcdrQE4S5xUXVM15XFMdrFzIpEY0w0+GAySTCYZGBggEAjw1FNPUVlZab3Z+c///M9Jx5SvysnKykp++7d/m7lz51JeXk5HRwfxeByHw0Fvb+9lj9amw8qVK0kkEqxYsYLjx4/j8XhYvXo1hmHkvTp5sj+L3bt3j0lchmFwyy238Bd/8RfY7Xb+5//8n7S2tjIyMsKcOXNYsGDBVIf8nrS2tlpJDdLFUaO/ni7F+Zs4g4VCIcLhMDabDYfDgc1mwzRNHA6Hdd3N7XZTW1vL4cOHue222woWq9PpJBwOTyrBlpeX56cV2HDuxSOJdxKoIUUqkrpkVSSAz+7D9sokRj/DQA6vFy0tLRw/cCDnPReiiQTBUVWRwWiU4VAI5XSi4nH6z57FHBjAabdT5vEwPKrYIReX7tv+3jgcDlasWEEgEGDZsmUcPHgQ0zRRSrF8+XK6urooKysr+Buf8xmGwerVq1m9ejXPPPMMgUCApUuXUl9fX/C2dudbunQphw8ftr5etGgRXq/XetNgGAZLliwpVHiXdH5tAZDzm+appBPbFBoaGqK9vZ1jx44xNDREJBJJT5WJ4PF4rCmm0tJSqqqqCv4O1+PxEI1Gx0yXXsqiRYumfGphslMjnoCHQ8OHSEaTmJioCVbjiQgOhwM3bpaULMm908uC3GLq6OiY1DpAp92OzTBIZf7QRQS7zYaYJk67nbBpEkkksNtsxFMpwrEY3klU6alMTPk0Z84c3O50FV5NTQ3JZJJbb72VY8eOWb9HV111lbWjdrEJBoNEo1GUUtb3UUyyxSz9/f2Ul5dTV1cHpJNDOBymq6uLuXPnFsVau/E0NDRw9uxZq7bAZrNN6eWWXOnENoX27dtHMBi0LrJD+hfSMAzq6uoYGkq368muoamYxNx4PoTDYQzDyHkq0mazUVJSMuVTqJOdrhkaGuKBBx7gV7/6FSMjI8Tj8THrBj0eT6Z9UvqNxPLly7nvvvv4rd/6rSmNe7IMEWpLS/FHIoRjMZxGeifnVGbUY7fZqPB68WR6iAai0UkltqlyqSnWZDKJ3+9neHgYt9vN3/7t3xKLxazbRYTGxkbrxbdY9rY7deoUg4ODAJw+fZqRkRFuvPHGAkd1oZqaGmpqft25JR6P09HRQSqVorm5mfLycm666aaiTG4VFRXcdNNNtLW14XQ6mT9/PoZhEAwGOXHiBLFYjPr6ehobG/MaR94Sm4j8ALgH6FVKXZM5VgU8ASwCWoGPKqWGJP1K+W3gLiAMfEoptS/zmCbgy5mn/YZSalvm+HrgR4AHeBb4vFJKTXSOfH2fWZFIhHA4zODgoPVCq1S6HDq7xUddXR0ej4fGxkauuOKKS25xk2/JZJK6ujoCgcCYef2JODO7DhdaS0sLH/vYx6itreXf//3frerT7Oi4pKSERCKB2+3G6/VimuYFa8emQn19PcP9/XxmMmsJ7A7abQnaJUFPyuR4IoHbZqPE7qA3FmOjYcOXeb4ShNWTeO7vo6jIQ9n8+ex2O1VVVdbvzPlvirK/+9PtUgm5o6ODc+fOAVg7UTc0NIw7c1LoZJxMJjl+/Dj9/f2MjIyQSCSsRDYyMkJ3d3deqmanwug37Xa7HaUUr7/+uvXmZ2BgAMMw8rLEIyufI7YfAf8L+PGoY18EdiulvikiX8x8/dfAVmB55uM64LvAdZkk9VVgA+mZlrdFZHsmUX0X+APgLdKJ7U5gx0XOkVcul4ve3l76+/utsn4RIZVKb3rZ0NCAzWbj+uuvZ9GiRZMq2MiXG264gYULF9LW1pZTYnO5XKxbt876ngrF7/fjcrm44YYb2LVrl7VGKfsmwjAMvF4vDofDaj49d+7cgsV7vnPRKAAppah0OAgmk9R4nFQ5HWM2fV3gKcxUWa4v6P39/Tz88MN86lOfshIGpLt+bNiwIV/hXTa73T5mR43s70sxOnLkCO3t7QCcO3eO4eFhqqp+XQA1mcsH+ZJL8dSpU6eIx+O0traOOe71esf8TU71G4m8JTal1K9EZNF5hz8M3Jz5fBvwEumk82Hgxyr9Nu9NEakQkbrMfXcppQYBRGQXcKeIvASUKaXezBz/MXAf6cQ20Tnyanh42BqZVVVVEYvF0rs5p1J4PB7KyspwOBwcP36c2tpaysrKCt4v0ul0snnzZs6cOUM0Gr1ocrPZbFRVVbFmzZqCJjWA2tpaa61g9s2BzWazRsjZd4slJSVWkmtoaChgxGOJkG5JJUJPLIaBMJhIMNflYn1FBTHTpMLhwFekVYZZ27Zt49ChQ7z88svcf//99Pb2UlZWxsKFCwsSz6VeGP1+Pzt37mTbtm38zu/8DjfeeGPBYh1tvARx9uxZ63c7lUrR09NDJBLhsccew2azsWDBgnH/Dgs90jyfx+PB4XBcMILP97KQ6f7LmauU6sp83g1kU/YCoH3U/Toyxy52vGOc4xc7xwVE5AHgAeA9z/n29fVRU1PDBz7wASKRCHa7nUQiQSwWw2az0dXVxYoVK0gkEnR2dnL77bdPaU+6y9HT00NlZSUVFRXWljvZKrfRDMPA4XBQW1vLqlW5dznIl6uuuopgMMjBgwcpLS2lu7vbitswDObPT3e/KC0txeVysXr1amvZxVTrJj0FOBkBt5vhcJgRM0XY6SRhs3HOYafWYafLYR81ipjc83YDFZN6RO7OnTvH0aNHrWskdXV17NixA6UUzz77LE1NTXnppDNVBgYGOHv2LG+88QZ9fX20t7cXRVKbiNPpJJoZ2dtsNmpraykpKaGyspLS0tKCv7mEyV0bP3XqFCdOnEApRXl5Odddd11e25cV7C1h5npYXifiL3UOpdRjwGMAGzZseE+xlJWVoZSymqoqpYjFYgwPD+NwOAgGgxw/fpwFCxZQVVVV8OtrgLUkobe3l3A4POH0hojg8/lwOp14vYXfAsZutzN//nyWL1+OYRjWCHlkZAS3283dd9/NG2+8gWEY1NTUYLPZiMenfhfty1noqpTCEQ7jiURQ585RYpr4/X5sNhtl8+dT3th42S9aFZcZ06XEYjHefvtt4vE4TqeTtrY2Hn/8cau0O5FIsG3bNh566KEpP/dUGB4e5o033mBkZITdu3djmiY7d+7kM5/5zKS3qcmH8RKE3+9n7969hMNh6xLA6IKSmWb58uU0NjYSj8enZbuu6U5sPSJSp5Tqykw1ZncY7ARGzxXVZ4518utpxezxlzLH68e5/8XOkVd1dXXU1tYSjUYZGhqydtRWShEIBDh8+DBVVVUMDg5SU1NT8Ao9SJduDw0N4ff7x73YLyLYbDZKS0upqamhvLyc3t7eorheVVFRwcjICGVlZcTjcQYHB62EsHfvXpxOpzXlGwqFiqKLR/Yi+uDgIJ2dnZw8eRKn08kLL7xAWVkZDz/8MOvWrZvyON+r48eP8/bbb5NKpSgpKWH58uW8+OKL1vUq0zR56qmnuOWWW6iqqqKxsbHgzQdG6+zsRCnF7t27gXS8sVisqJNxWVkZt956K5FIBLfbXbTXAifD5XJNW5Pp6f7f2g40ZT5vAp4edfyTknY9MJKZTtwJ3CEilSJSCdwB7Mzc5heR6zMVlZ8877nGO0febdiwgfXr11NVVYXP58M0TatnZDwet1bgd3V1FcWIzTAMotEo/f394y6szFa3eb1eq/Kwr6+vAJFeqKKiglWrVllVjyUlJVYPwGyVZ3Zqcvny5UWRjAcGBhgcHMQ0Tbq6uvD5fOmWSJlCl2Kcyksmk7S2tlpvHtra2mhtbbX+P5VS1hujjo4ODh06xNGjRwsc9VjZF9MDBw5Yf3epVIrnnnuukGFdkojg9XpnRVKbbnn7HxOR/wDeAFaKSIeIfAb4JrBFRE4Bt2e+hnRV42mgBfg/wB8DZIpG/g7Ym/n4eraQJHOf72Ue8y7pwhEuco68czgcrF+/HrfbTTgcttawZddTud3uomhJNdovf/lLRGTCd9imadLX10d3dzenT59m//790xzh+KLRKHv27KGqqgrTNBkYGCCVShEOhzlz5gx9fX3WnneVlZVFURqdLQYYXQ7v9XopLS0t2nfl4XDYang8MjLC4OAg3d3d1v/3yMgIw8PDtLW10dWVvrSd3Qm8WGS79q9Zs8aqjPR6vdxxxx2FDk3Lk3xWRX58gpsu6CGVqYb8kwme5wfAD8Y53gxcM87xgfHOMV0GBgbYvHmzVdwwMDCAzWbDMAxrVHT11VcXvHAkKx6P43a7UUqRTCbHLEHIbp1is9mw2WwEg0GOHDlCIBCYlnnyi2lubqavrw+lFHPnzsXv91ubuYZCIebMmUMsFqOsrIz169cXxYitpqaG0tJSAoEAtbW19Pf3A+nCo9LSUk6fPl107ZJKS0sJhUJ4PB5rb7gFCxbw0ksvkUwmreuyyWSSN998k2XLluW1ye7lcDgcfOhDH6Kuro4HHnjAesPZ1NR06QdrM1LxvUWcwbIVjx6Ph3vvvZctW7ZQWVmJz+ejoqKC+fPnc9VVV3HfffcVzTWIO+64A5/PZ635crlc2O126+vRCU8pRWVlpfWCXEixWMyKTUQoKyvDMAzsdjter5eSkhIWLlzIzTffXBSjNUhP/W7atImrr76aLVu2cPfdd2MYBmVlZbjdbt55552i+L8dTURYu3Yt5eXluN1u5s+fP6bNE6QTRzweJxQKMTw8TCQSGbOpZzEQEVasWMG9996LiLB169aiKBzR8qO4F8rMMNkFn6Zp4nK52LBhA6+88gqxWIx77rmH2tpaGhsbx72eVSgPPfQQqVSKZ555hng8Tl1dHe+++641AsqWHAP4fD5WrVqVe8/FPLriiit49tlnrRGny+Wy4lq3bh0rV660toEppIstYh0eHmZoaIiBgQEg3Q3jySefpLJy/P2yCrVG6YorrrAqZwGr7HxoaIhEImEtrVi8eLE1Fd/T01MUvyfna2pqorW1VY/WZjmd2KaQ3W5n+fLlnDhxAki/ACxatIhQKMTGjRut+43uflBoXq+X+++/n/nz59PR0UF3dzcdHR34fD5qamro7+/H4XBQWlrKXXfdxdVXXz2mA0IhmKbJiRMnWLVqFS6Xi6GhIa6//noef/xxUqkUc+bMYc2aNaxataqormeeL1vUMPr3oRgb8zocDjZv3kxXVxciQl1dHf/4j/84JtZUKsWGDRus78nn8xUq3Iuqqanh0UcfLXQYWp7pxDbFVqxYYV3zqampYceOHWNGaNXV1UVxvWe006dPj9kdORAIICIsWLCAsrIybDYb8+bNY/Xq1UVRudfb20soFKK6upobbriBRCJBV1cXy5YtQynF5s2bufrqq61rQoV0qRFWS0sLLS0tKKVYunQpK1asmKbIJsdut4/p3vIbv/EbPP3009ZoeePGjVZSW7BgQdFM/2rvTzqx5UF5ebk1DZPthHH99ddjGEZRzusPDAxw+PBhjh07RigUYmRkhJKSEtxuN6FQCNM0qa6uxuPxFGy0OXpKLxKJjGlqHI1GrY7zAN///vfx+XzMmTNn3OcqprZDxVhskYumpiaeffZZEokETqeTv/mbv7FGacU46tTeX3TxyDQQEWpra6murmZwcLBo1rFliQjt7e34/X6rvDsSidDe3m5V8sH4mwgWgtvtHvPi6Xa7raSbTbzTtRD0/aqmpoa77roLEeHuu++murr6gp+LphWKHrFNo3379tHZmW6Q4nK52LRpU8GvRWT3LWtoaLDWIWWrDZVSDAwMUFVVxdDQEOfOnStYSfr5IyylFL29vSQSCebOnUtfX5/Vy7ChoYFrrrmmKNeFzSa6EEMrVjqxTRO/328lNUiXq58+fbrgTYVFhMWLF1NSUmJtt6OUsqYfbTabtb/SvHnzaG9vL3ilYTbu0dcq58+fr6/rTDNdiKEVK53Ypsl403jFMrW3YcMGNm7cSEtLi9Uw2O124/P58Hg8eDwe5s2bR1lZmZ7i0zSt6OnENk2qqqooKSkhGAwC6RFHvrdHz5XNZmPLli0MDg4yNDTEM888g2EYbN68mVAohIhw5ZVX4nA4WLlyZaHD1TRNuyid2KaQ3++nra0NwzBYtGjRmC1eRIRNmzZx5swZa0+rQq8HG23x4sWsXbuW9vZ2a7+n7GLya665hurqampra/O+QeB7kd3R+Wtf+1pRVp9qmjY9dGKbIsFgkFdffdXqndfe3s4tt9wy5j5Op7NoRzxut5s77riDtrY2XnvtNUSEVCqF2+2mtbWVvr6+oulvOZEf/OAHvPbaa3zxi1/k937v91i9evWEXTw0TZu9dGJ7D0avrRocHLygP97jjz9urbe61LqpYlhb5fF4iMfj9Pf309/fj8/n48orr2Tp0qWEQiE6OjqKYtHzePr7+/n5z39OLBZj79693HzzzcRiMW6//XZdHalp7zM6sU2R8TayNAwDj8dTgGgmdrHehZFIxCrpTyaTvPzyy9bWMNn+gBUVFWMeUwwJGWDbtm3WLtlKKV544QXuu+8+QqFQwXci0DRteunE9h6MfkFPJBK8+uqrVnFIVVUVN9xww4waLSQSCRKJBA6HA5vNhohgmibJZBLDMIq67+KuXbusHROSyST79+/nox/9aMHXCWqaNv10Ypsi2Uax/f39VuusYtmaZrSLja5CoRDPP/88+/btsxJaTU0N1157LTfeeOOYYphis2XLFn7xi19gmiamaXL99dezYcOGGfXGQtO0qSHZnXzf7zZs2KCam5sLHUbB9fX1sWfPHk6fPk11dTXXXnstV111VVEm6dH6+/u5//77icfjuFwuHn/8cV0ZqWmz37gvTHrEpo1RW1vL3XffXegwJq2mpoatW7eyfft2vYmkpr3P6cSmzRq6d6GmaaCnIi16KlLTNG3GGXcqUl9Z1zRN02YVndg0TdO0WWXWJjYRuVNETohIi4h8sdDxaJqmadNjViY2EbEB/wJsBa4CPi4iVxU2Kk3TNG06zMrEBmwEWpRSp5VSceBx4MMFjknTNE2bBrM1sS0A2kd93ZE5NoaIPCAizSLS3NfXN23BaZqmafnzvl7HppR6DHgMQET6RORsnk5VA/Tn6bnzZSbGDDru6TYT456JMYOOezy/VErdef7B2ZrYOoGGUV/XZ45NSCmVt83GRKRZKbUhX8+fDzMxZtBxT7eZGPdMjBl03JMxW6ci9wLLRWSxiDiB+4HtBY5J0zRNmwazcsSmlEqKyGeBnYAN+IFS6p0Ch6VpmqZNg1mZ2ACUUs8CzxY6jozHCh3AZZiJMYOOe7rNxLhnYsyg486Z7hWpaZqmzSqz9Rqbpmma9j6lE5umaZo2q+jElgMRaRCRF0XkqIi8IyKfzxy/XkTeEpEDInJMRL6WOX6ziNxY0KDTcfxARHpF5MioY0Udc9Z4sZ93+80iMpL5Pg6IyFdG3ZYadfyAiCwSkdeLMd5MbEfOu+/XROQvcjjnf4jIIRH5wnuI+28zv9OHMnFdJyIvZfqsZmP9L5f7/IVwke8pp5JzEfmUiPyvPMQ17uvIefcp1t+Ty449c9u0/k3O2uKRKZYE/lwptU9ESoG3RWQXsA34qFLqoKT7U67M3P9mIAjk/IMTEbtSKjnFcf8I+F/Aj0cdK/aYs37EhbGf7xWl1D3jHI8opdacdyzfSftHXH68kyYi84APKKWWvYfnuAG4B1inlIqJSA3gzNz8u0qpvG5QmI/fn0t8T4U27uuIUuroefcrqt+TjPca+7T+TeoRWw6UUl1KqX2ZzwPAMdItuuYAXZnjKaXUURFZBPwh8IXMO5MPZt6dvJB517RbRBoBRORHIvKvIvIW8N9F5JSI1GZuMyS9M8FlLxxXSv0KGDzvcFHHfInYL5uIBDP/3px59/5TETkuIv8uIuNuVjgZUx1vVibW/yYie0TkpIh8MHPTc8CC7M/rMp++DuhXSsUAlFL9SqlzE8RRJSJPZX4f3hSR1Zmf97g//8zHz0Rkb+ZjU+Y+XxORfxOR14B/u8y4p+p7elNErh71dc6justxkdeR9yzPvyd5iT2ff5M6sU1SJgmsBd4C/hk4ISL/KSIPiohbKdUK/Cvwz0qpNUqpV4BHgW1KqdXAvwOPjHrKeuBGpdRDwP8f+N3M8duBg0qpqW5iORNjnsgNInJQRHaMfoECPKOmPP5znMetBf6M9M4PS4BN0xArTBzvpdiVUhtJx/zVzLHfBN4d9fO6HM8BDZkXwu+IyOZRt/37qP/DauBhYH/m9+FvgB8rpUwm/vl/m/Tv0weA/x/wvVHPfRVwu1Lq45cZ9+V+T+d7AvgogIjUAXX5HqVmnfc6cr5i+z0Z4zJjn9a/SZ3YJkFESoCfAX+mlPIrpb4ObCD9x/Q7wC8neOgNwP+b+fzfgJtG3fYTpVQq8/kPgE9mPv9/gB9OYfgAzMSYJ7APWKiUupZ0En5q1G2RzB/yGqXUb43z2D1KqY7MC/MBYFG+g2XieCdabzP6+M8z/77NFMaqlAoC64EHgD7gCRH5VObm3x31fzhA+uf/b5nHvQBUi0gZE//8bwf+l4gcIN31pyzz9wOwXSkVmarvYxLf0/meBLLXDz8K/DQfMZ3v/NeR824uut+T0S4zdpjmv0md2HIkIg7SP9B/V0plf4FQSr2rlPoucBtwbebd7WSERj1XO9AjIreS3npnx3uP/EIzMebzZd5YBDOfPws4JH09JRexUZ+nmIZrzReJdwCoPO/uVYxtGpuNd8pjzUxHv6SU+irwWdKjq8k8fqKfvwFcP+rFbEH2+2fU708+5Po9KaU6gQERWQ18jPQILq8meh0ZFVNR/p68x9hzMaV/kzqx5SAz3/t94JhS6p9GHb971FzwctI/kGEgAJSOeorXSferhPS0zcWmBL5Henpn9KhoyszEmMcjIvOy34eIbCT9uzwwHee+HBPFm3kh6MokBkSkCrgTeHUaYlopIstHHVoDTLTDxStkphxF5GbS17Gy79jH+/k/B3xu1LnWTFXcFzPJ7wnSyeyvgHKl1KE8xzbu68h59ym635P3Gvt0xHc+XRWZm03AJ4DDmakVSF9n+ATwzyISJl019LtKqZSI/F/gpyLyYdJ/3J8Dfigif0l6euTTFznXdtLTOe95Sk9E/oN0tWONiHSQnnffUswxXyJ2B4BS6l9JTyH9kYgkgQhwv1KFa6PzHuP9JPAvIpJ9wXhYKfXuNIRdAjwqIhWkfxdaSE/hjTcl9zXgByJyCAgDTaNuG+/n/6ekv6dDpF9nfkW6QCnfLvY9PSMiicz93lBKfSRz/NvA301DbBO9jjRCUf+eTEXs00q31Coykq7K+mel1GVXME23mRizNnX0z18rNnrEVkRE5IvAH/HrKrOiNxNj1qaO/vlrxUiP2DRN07RZRRePaJqmabOKTmyapmnarKITm6Zpmjar6MSmaQUgmT55o77OS0f5Cc59j4jsz7Q+OioiD2aO3yciV+Xw+Lz2VNS090pXRWra+0ime8RjwEalVIeIuPh1+6L7gF8A53ds17QZRY/YNK3IyMV3Vvgvo+6X7Y5eJyK/yjSYPSKZLu4icoeIvCEi+0TkJ5k+f6Wk39AOACilYkqpE5Lei+83gf+ReZ6lIrJv1LmWj/561PHxzqFpBaUTm6YVxuhu5weAr4+67WI7K4znd4CdKr3f1bXAgUyPvi+T7qK/DmgGHlJKDZLuFHJW0htQ/q6IGEqp1zPH/zLT2/FdYGRUK6xPc15nmYnOcVn/G5o2hfRUpKYVxpiNFyXdgT573eoG4Lczn/8b8N8v8Vx7Sbe7cgBPKaUOSHq7lquA1zLt+5zAGwBKqd8XkVWkO/D/Bek2a58a53m/B3xaRB4i3SR443m3Xz/ROTStkHRi07SZI0lmlkVEDDI7QyulfiUiHwLuBn6U6SU4BOyaaM8zpdRh0n3//g04w/iJ7Weke16+ALyd2b5mNLnYOTStUPRUpKYVn4l2VmglvdcYpK+HOQBEZCHQo5T6P6RHWeuAN4FNIrIscx+fiKwQkZJMd/6sNfy6+/2YHR6UUlFgJ/Bdxm9wPe45Lus71rQppBObphWfz5GeAjxEuqP65zPH/w+wWUQOkp6uzO5rdjNwUET2k54y/LZK72L9KeA/Ms/zBnAF6VHWX4nIicy1vYf59WjtceAvM0sBlmaO/Ttgkt6GZoyLnEPTCkr3itQ0bUIi8hek9yr7r4WORdNypa+xaZo2LhH5T2ApcGuhY9G0ydAjNk3TNG1W0dfYNE3TtFlFJzZN0zRtVtGJTdM0TZtVdGLTNE3TZhWd2DRN07RZ5f8Dz3JEk0WBnpAAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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n/EWnvH2S47nIOTRNm8UqqeKeeOIJtm7dSm1tLSdOnGB4eJhYLMbKlStpbGzkrrvumumqarPYlLXYRKRKRKort4H7gIPAE0DlMmwb8Lhz+wngc87syJuBhNOd+Axwn4jUOpNG7gOecR5LisjNzmzIz53zWpOdQ9O0WW7btm1s2LCBbdu2ceTIEY4ePcrw8DAdHR2TbrqraeeaspRaIrIM+Cfnrhf4O6XU10WkHvghsAjoAj6plBp1gtN/pzyzMQv8hlKqskTgN4E/cl7r60qp/+GUbwa+B4SAp4FHlFLqQue4WH11Si3tYoaHh/mTP/kTvvrVr+oxnQt49NFH6ejo+MCv09NTHjpvb2/nzJkz503nX7x4MYZx+dfkK1as4Etf+tIHrpc2K02aUmvKuiKVUqeAjZOUjwD3TFKugC9e4LW+C3x3kvJdwPrLPYemvV/bt29n3759/NVf/RWf/OQnqa+vp62tbaarNS/lcjn3tsfjmRDYPB7PhL0KNW0yOru/pl1CZdFwJpPhpz/9Kddccw3RaJR0Os2qVatmunqzxpVqFVVe59FHH2V0dJS33nqLUqmEx+Nh06ZNtLa2XpHzaPOXTqk1h+mcetNj+/bt2LbtLgp+/vnnAejs7JzZil0F6urquPfee7ntttu49957dVDTLosObHPY+Jx6xWKRd955h2effZY333yTbDY709WbN8YvGjZN053A4PXqDo/p4PF4qKurc9e3adql6MA2R52bU++VV17h7NmzFAoFBgcH2b1790xXcd6499578fv9hMNhvF4vmzZtQkRYvXr1TFdN07RJ6MA2R52bU+8f/uEfJjw+Njamk8NeIZVFw8FgkLq6On7nd36Hu+++mwULFlz6yZqmTTsd2Oaoc3Pq7d+/f8LjkUjkvNRD2vtTWTQsInzsYx9jw4YNVFVVzXS1NE27AB3Y5qhzc+o99NBD1NTUAOWgtmnTppms3rwzftGw9v4ppRgeHmZwcFDvp6ZNmcse/RaRxcBKpdQvRCQEeJVSqamrmnYx27Zt4+mnnwbKOfUefvhh6uvrMU1TT2qYAg0NDXzjG9+Y6WrMabZts3PnTncWb3V1NbfddpueFKJdcZfVYhOR3wJ+DHzLKWoH/nmK6qRdhvHdY1u3bnWzYeigps1WAwMDE5ampFIpuru7L/IM6O/v58iRI/T390919bR55HK7Ir8I3AYkAZRSJ4CmqaqUdnnuuOMOREQnhNXmhGKxeF5ZZZx4MseOHePtt9+mo6ODt99+m+PHj09l9bR55HIDW0Ep5f5VioiXKd5uRrswpRTd3d189atfJZ1O8xd/8RczXSVNu6TW1tYJ3Y4ej+eiM0vP3Rlb75StXa7L7bd6SUT+CAiJyL3AvwZ+OnXV0i5m//79PPXUUxw6dAilFKlUin379rFx47upOTOZDKdOncI0TRYtWqQT92ozzu/3c8cdd9DZ2Ylt2yxevJhIJHLB489NdKxn+WqX63JbbH8ADAEHgC8ATwF/PFWV0i7MNE06Ozv5u7/7O3cdW6FQ4Pd///cnHPPaa6/R2dlJT08Pb7zxBmNjYzNV5XlBpy+7Mqqqqli3bh3XXnst0Wj0oseem4dT5+XULtfltthCwHeVUv8fgIh4nDKdt2maGYaBbdskEokJ5WfPnnVvDw4OUigU3PtKKXp6eqitrZ22es51lmXR39+PUoqWlpYJ6cu+/OUvz3T1rgpLly6ltraWsbEx6urq3OUsmnYplxvYngM+AqSd+yHgWeDWqaiUdmGGYbB+/foJ23kEAgECgYB7zPjbFyvTJmeaJq+88grpdNq9/+STT6KU4qmnnmLbtm26a3eaxGIxYrHYhLJSqUSpVCIcDs9MpbRZ73K7IoNKqUpQw7mt/6pmyMqVK/nlX/5lotEoNTU1RKNRtm7d6j5eX18/IQt6JBJhyZIlM1DTuens2bOMjIy4iaR/+tOfurdLpRLbt2+fyepdNdLpNLt27eKVV17h5MmTAJw4cYJnn32W5557jtdee+2isyq1q9flttgyInK9Umo3gIjcAOQu8RxtCv3RH/0R43f8/p3f+Z0Jj2/evJlEIkGpVKK+vl5vzvgeHDx4kH379gHli4Ldu3e7s/mUUjzzzDO6O3KKKaXYuXOnu+loPB4nn89PmBk5OjrKyZMnueaaa2aqmtosdbmB7XeBH4nIWcpbcbcAvzZVldIuraGhgVtuuYU33niDW2+9ddKuMT0m8d6Njo6Sz+fx+XyUSiXS6TThcHjCDL3m5uYZrOHVoVgsTthJGybf/y6TyUxTjbS55LICm1LqbRG5Bqjs03FMKaX7AGaZgYEBTp06hYiwYsUKGhoaZrpKc04ul8Pn87Fu3TqGh4eBciqo8RldBgYGZqp6Vw2v1+tOlKpoa2tjYGBgQvdjS0vLTFRPm+UuGthE5MNKqedF5FfOeWiViKCU+scprNtVra+vj2PHjmFZFkuWLGH58uUTHh8eHuaNN94A4PXXX+f06dPuujYof/neeeedeibke9TY2OgGsba2NgA++tGP8txzz6GUQkT4pV/6pZms4lXB4/Gwbt06Dh8+jGVZRKNR1qxZw7Jlyzh+/DiFQoH29na9dZA2qUu12O4Cngc+NsljCtCBbQpkMhneeecdN0gdPnyYqqqqCVenjz766ITn/Pmf/zn3338/AGfOnGFgYIDBwUFuuOEGrrvuuvMWu2qT8/v93HrrrXR0dGCaJosXL+aWW27h5Zdfplgs4vP5dIb/abJkyRIWLFhAsVh0twkKBoPceOONM1wzbba7aGBTSn1FRAzgaaXUD6epTle9kZERN6hVDA8PTwhsL7300oTH33nnHe6//36SyaSbMDYYDNLb20tjYyMLFy6c+orPEzU1Ndxwww0TyrZu3coTTzzBRz/6UT3Vfxr5fD6d/V97zy55Ga+UsoHfe78nEBGPiOwRkSed+0tF5E0R6RCRfxARv1MecO53OI8vGfcaf+iUHxORXxpXfr9T1iEifzCufNJzzBXnrts5deoUP/vZz/j+97/vTnseH/hKpRK5XI6enh53gL2hocGdPJJK6d2FPii9H5umzR2X2z/1CxH5NyKyUETqKj+X+dzfAY6Mu/9fgL9USq0AxoDPO+WfB8ac8r90jkNE1gKfAtYB9wN/4wRLD/DXwFZgLfBp59iLnWNOiEajrF27Fq/XS19fH4cOHSKVSnHixAl++MMfMjY25k4MsW2bVCpFVVUV7e3tLF68mIULF7Js2TJ3ir+exffBVfZj0601TZv9Ljew/RrlrWteBt5xfnZd9BmAiLQD/wL4jnNfgA9T3tsNYDvwcef2Q859nMfvcY5/CHhMKVVQSp0GOoCbnJ8OpdQpZ+eBx4CHLnGOOWP58uX80i/9ErW1tTQ3N7tBKpvNcvjwYYaHhymVSpimiVLKnRpdU1PDsmXL3BREmzZt0l/G2qxhWRbd3d3s37+feDwOlHschoaGyOfzM1s5bd643On+S9/n6/8V5W7Maud+PRBXSpnO/R6gMq1pAdDtnM8UkYRz/AJg57jXHP+c7nPKt1ziHHNKsVikWCySTCaprq5GRMjlchw/ftxNxlsZfxi/19XixYtZuXLljNRZ0y7Etm1eeOEF3n77bbLZLF6vl9tvv51iseheoNXX1zM0NEQoFJrp6mpz2EVbbCKyRUT2iUhaRN4QkTWX+8Ii8gAwqJR65wPXcoqIyMMisktEdg0NDc10dSZIpVK88MIL+Hw+LMvi7NmzGIZBU1MTDQ0N7izHUqlEKBRyt/RoaWlh2bJlM1n1eUln9//gBgYGOHHihJuezDRNnnrqKZLJJFAeS96xYwepVIqhoSF3PFnT3qtLdUX+NfBvKLeC/h/KLbDLdRvwoIh0Uu4m/DDw34CYs1EpQDvQ69zuBRaCu5FpDTAyvvyc51yofOQi55hAKfVtpdRmpdTmxsbG9/DWplY8HufnP/85p06dwjAM7rnnHu644w5+/dd/ndtvv51kMjlh4WogEKCuro7777+fG2+8Ue9bdYUVCgX+5m/+hn379uk8kR/Q+F0noBzcCoUClmUxOjqKZVnuxKiurq6ZqKI2D1wqsBlKqR3O+NaPgMv+9ldK/aFSql0ptYTy5I/nlVL/G/AC8AnnsG3A487tJ5z7OI8/r8p/4U8An3JmTS4FVgJvAW8DK50ZkH7nHE84z7nQOWa9ZDLJa6+9Rl9fH/39/Rw+fBgoT17wer2cOXOGo0ePkk6n3XVVlUCmp0VfeT09PfzkJz/hJz/5CaOjozz++OO61fY+NTc3T1h24vF4WLVqFdFoFBHBMAxqa2vd3ohz/57HxsZ49dVXeeaZZ9i/f/+EiztNG+9SY2yxc7KOTLj/PjOP/D7wmIh8DdgD/K1T/rfA/xSRDmCUcqBCKXVIRH4IHAZM4ItKKQtARH4beAbwUN4v7tAlzjHr9fT0YNs2dXV17N+/n3Q6TSaT4a677mJwcJCqqirWrFlDOBzGsix3B+JAIIBt23R2djI6OkptbS1Lly7VC7Pfp2Qyidfr5eDBg/ziF79wWxKJRELvyfY+GYbBgw8+SHt7O93d3SxYsIB169YxOjrK0NAQVVVVpNNpXnjhBQBWr17tPte2bd5++223xdfV1YXf79cJkLVJXSqwvcTErCPj71925hGl1IvAi87tU5RnNJ57TB741Qs8/+vA1ycpf4rybt7nlk96jrnA7y8vuRsdHaW+vp5AIEBLSwuGYbizxqqrqzEMA8Mw3G6bXC7HoUOH3HVsfX19ZDIZNmzYMCPvYy5RSlEoFAgGgxSLRXbu3EkikXDHNvfs2eN+zpZl6ez+H4DP52PLli1s2bLFLQuFQiQSCUSEuro6amtriUQiNDU1ucekUikKhQLFYpGuri7S6TRDQ0MsW7bM/T+jaRWXyjzyG9NVEa1s0aJFdHd3k06n8fv9bp7IUqlEe3s7IyMj9PT0kM1mCQQCE1pk3d3dE16rp6dHB7ZLGBwcZO/evRQKBaLRKNFo1N2d3OPxkM1mqaqqcmedBgIBnVz6Ckmn06TTabq6uhgcHHTLc7mcm6SgEtBqamrwer0cOXLETTiQzWbZv38/mzdvnonqa7PYZU33F5Fm4D8CbUqprc5C6FuUUnOmi2+u8Pv93HXXXYgIZ8+epbq6vFIiFAqxbt06jh075n7JlkolEokEsVgMwzAIBALujDPQu2Zfim3bblCDcvfjmTNnJmR+WbFiBZlMxk3tFAwGOXv27AzVeG7L5/NuYMrlcuTzeQzDYN++fSxfvtztVs/n8yilOHDggNsDEQqFuOaaa3j77beBcnaeBQsWMNtmM2uzw+UOwHyP8lhWm3P/OOU92rQpYBgGt912G6tWrcIwDGpqati8eTPFYhHDMNzglcvlSCaTZLNZmpubWbdunduCMwyDtWvXXuw0V71isXjeLL1gMDjhfigUcncpD4VCesPWD+Ctt96ip6eHkZERXnvtNfcCwe/309v77sRlr9dLqVSasP9aLpcjk8lw5513snnzZlatWoXX6yUajU7329DmgMvdaLRBKfVDEflDcBdQW1NYr6uez+c7r4vFtm1OnTrFsWPHSKfTQLm7LJ/P09/fT0tLCx/5yEdIJBLU1NToFtslBINBotGou44KYM2aNcRiMbq7uwkEAqxcuZJ7772XZ555xj3m3nvvnYnqzmm5XM7t4q1MxBkdHaW9vZ0lS5bQ19cHlC8kGhsbsazzv14KhQLXXXcdu3fvJpPJUF1drbvatUldbmDLiEg95QkjiMjNQGLKaqVNKpfLTVicXZHNZlFK8fLLL7Nx48YJg+7axd14440cPHjQ/ZJds2YNHo+H9vZ295gvfOELbmCzbZsHH3yQ4eFhPdb2HgQCAXdX8kAgQDQadZephEIhHnroIVpaWggEAjzxxBMopQiHwxO61tvb24nFYnz4wx+mWCzqSSPaBV1uYPsy5fVky0XkNcrr2T5x8adok3n00Ufp6Oh4X8+1LIszZ86QTqcJBAKICLZtu4Huv/7X/4rX631fW9SsWLGCL33pS++rXnNZPp8nmUy6+QoXL17sjmtWjI6OAuXPv7LOsK2tjZaWFr032GUyDIMNGzawb98+TNPkuuuuo7GxEdu2aWlpcTd1rRARbrvtNk6ePEk+n6e9vX1CMm8d1LSLudxckbtF5C5gNSDAMaVU6RJP064wj8fjTmxobm52Z4eJiDsuAeUvYJ195PLs3bvXTSCdTqc5cOAAt95664Rjvva1rwHlIGjbNo899hhf/vKX6e/vd7t9tUtra2ujubmZXC5HVVXVJccrg8Eg69atm6baafPJRQPbOYuzx1slIu93gfZV7Uq0ijKZDGfOnOHhhx9meHiYbDbLAw88QDgc5tprr+WTn/wkUP6itm1bD7BfgG3bZDKZCWXjx9sqKpMYKmvZxk9NN03zvOO1C/N4PO7sx/fKtm0SiQShUOi8ST6aNt6lWmwfu8hjl71AW7uyKtlHYrEYw8PDrFmzBtM0SSQSFAoFlFK888477oB8Y2MjN910k85Ccg7DMKivr5+QImuy8cklS5bQ2dlJIBCgUCi4x1RXV1NXd7nbEmofRCaT4Y033iCXyyEirFmzhuXLl890tbRZSi/QnsNaW1s5e/YsH/rQh9yWR29vL6+99hpjY2PucUNDQ/T09LBo0aKZquqstWbNGnbs2MHY2BjXXHMN11577XnH/PEf/zH/6l/9K3w+HzU1Nfze7/0eK1euZPHixXr6/xTJZDLs3r2bqqoqli1bxrFjx9wuY6UUR48eZeHChXqsTZvU5U4eQUT+BeVdrN0+AKXUf5iKSmmXppTC5/OxatWqCTPHIpEIb731FosXL57wn77ypaC9y7Zt3nnnHUKhEKFQiFQqRTKZpL6+Htu2sSwLn883oVXm9Xq56aab9OatUyiVSjE8PMypU6fo6upCRKipqaG2ttZNjGzbNvl8Xgc2bVKXm3nk/wXCwN2Ud8P+BOUM+9oMGBoaYu/evZw5cwa/38+KFSvI5XLE43EGBgYwDIM9e/awevVqYrEYIkJra+tMV3vWGRoamhDwlVJ0d3dTLBbZv38/xWKR+vp6duzYMeF53/rWt/ijP/qj6a7uvFQoFIjH48RiMXfdZWWN5pEjRzh58iS5XI7W1laam5tZv349UO4G1mPH2oVcbovtVqXUBhHZr5T6ExH5C+DpqayYNjnbttmzZw/Dw8OMjY1hmiY9PT3EYjGSyaTbNZbP59m7dy9btmzhwx/+sP4SmMRkC9g9Hg979+51J4WMjIzws5/9bMJkhWeeeYZf/dVfxe/3097ermegvgeZTIazZ8+6y1Uq288YhsENN9zgJvxWSnHq1Cl3TC0UCpFOp2lsbKSqqkrvEK9d1OUGtsplbVZE2ihvK6ObAFPozJkznDhxAqUUy5cvZ+nSpUA5DVQ6nebYsWNks1nS6TSHDx/mxhtvxDRNFi5cyMDAgJsKKhQK4fVedo/zVaWSb7CSzikcDtPc3DwhlROUc3JWAptpmqRSKY4ePQqU/51uv/12PdZ2GRKJBK+99pqbVeTEiROsWLHCXY95+PBhWlpaqK2tdZdWANTW1rrps7Zs2aI/a+2SLvcb70kRiQF/BrzjlH1nSmqkEY/H2bdvn3v/4MGDRKNR6uvrCQaD2LaNbdsUi0Usy6JYLDI8PExNTc2E6eqVBa2pVGpCYl/tXddffz3Lli2jWCy6mUSCwaC7RRCUM17E43FKpRL5fH7CmFs8HmdkZERnIbkMnZ2dE1JlxeNxUqmU25tQydvp9/tZuHAhH/rQhzhw4AC2bRMOh7nuuut0UNMuy6XWsd0IdCul/tS5HwEOAEeBv5z66l2dxk8/LxQK7tYe0WiUkydPTlgHZJqmm3evubmZG2+8kVOnTmHbNmfPnsW2bRobL3vj86vSuUF/y5YtHDp0iGw2S0tLC+l0muHhYSzLwjTN8xIn652cL8+5QamhocEtGx4eBuDw4cNu1+QDDzxAe3s7Q0ND1NfXs3HjxmmvszY3XarF9i3gIwAicifwn4FHgOuAb6PTak2JSiaLsbExOjo6UEphGAYdHR2EQiGgnMWhsjFmdXU1sVgMv9/Ppk2byGazPPHEExiGwdDQEKtXr+bmm2+eybc0p0SjUW655Rb3fqFQcJdTKKXw+/1ulpfBwUHefPNNwuEwGzdu1C23i1i6dCm9vb3u+OX1119Pa2srr7/+Ort376ampobTp09z+vRpli9fTjAY1H+32vtyqcDmUUqNOrd/Dfi2UuonwE9EZO+U1uwq1tDQwOrVq9m+fTudnZ34/X4ymQwjIyN86EMfoqGhgba2NiKRCHV1ddx66614PB53LOLYsWMT1qy9/PLL+gviMliWxfHjxxkZGSEWi7F69WpEhP7+fvcYESGXy7F69Wp6enrc2abZbJbdu3fzkY98RC+Ev4Dq6mruvvtu+vr68Pv97mf31FNPEYvFUErR29vL0aNHyWQynD592h1bhvKSlcq+g5p2MZcMbCLiVUqZwD3Aw+/hudoHEIvFyGQyjI2N0dXVhWVZhEIhSqUSt912G8eOHWNoaAjDMCiVSlRVVdHU1ER1dfWE8SHAbV1oF5ZMJjl69CgDAwNAubWczWa57rrr8Hg8E1Jneb1e1q1bR1dXF11dXZimSUNDgzvpIRwOz9TbmPWCweCEYDV+zG1wcJDjx49TKBRIJBJ85zvf4TOf+QwrV65k37599Pf3IyIsWbLEnfavaZO5VHD6e+AlERmmPDPyFQARWYHetmZKdXZ2cujQITo6OkgkEiiliMVinDlzhp6eHneLD8uyePHFF3nggQc4ePAgJ06cYGhoiMHBQVasWIHf7z9vXzftXcVikZ07d5JIJNi9ezeNjY3u7ggDAwN4vV7q6+sZHR3FNE0Mw6ClpQXLsujp6XF3cB4bG+O6665zu4q1y+PxeNi0aRO/+MUvGBkZcRfF5/N5Ojo6eOaZZ9izZw/BYJBAIIBSitOnT9PW1qbTmWkXdKmUWl8XkecoT+1/VlWywJZ33n5kqit3tcrn87zxxhsMDg6ilHInJxiGQSwWo7Ozk+rqajKZjHvFWygUOHXqFENDQ7S2troz+D7xiU/orVUu4uTJk26OzXg8Tm9vL+FwmPr6esLhMIZh4PP5iMVimKaJ3+/HMAxGRkZoampyF8aHQiGampr0rL334dZbb6WxsZF0Ok06naa/v59kMolt23i9Xvbu3UupVGLLli3ulkLpdFoHNu2CLtmdqJTaOUnZ8ampjgbltVHV1dWEw2FKpZI7cSEUCrlr1SpT/vP5PJFIBKUUqVSKfD7vZhppbm7WMyIvIZfLYZomhw4dwuPxkMlk3K1rKuOSInJeNvlgMIjH42HZsmVu2VydOPJB9gj8IJRSJBIJUqkUhmEwNjbG8PAwqVQK0zQ5cOAAx44dI5PJUCgUePLJJ1myZAnhcJiFCxdO+/rMq3XPwrlIj5PNQrZtU19fz/XXX09XVxdNTU3k83nWr19POBwmHA6TTCbdjPM333wzdXV1GIYxYZPMaDSqt1W5hNbWVnfzy1AoxMqVK1m9ejWrV692LwqqqqrIZDIopcjn8wQCAUzTdLP+QzlH5/ggN5d0dHSw59AeiE3fOW3bZqBrgORQEssudz96A15CkRCxphj5XJ6UlaI4WsQslf+GU4UUqY4Ui9cuJj4Qn77KAkzz6bQPZsoCm4gEgZeBgHOeHyulviIiS4HHgHrKi70/q5QqikgA+D5wAzAC/JpSqtN5rT8EPg9YwJeUUs845fcD/w3wAN9RSv1np3zSc0zVe73SFi5cyOnTp/nQhz7EW2+9RT6f52Mf+xixWIyXXnqJZDJJLBYjFApRX1/P4sWLiUQifPrTn2bXrl3kcjk8Hg/9/f0cPHiQwcFBNm/erBPGTqK1tZVNmzYxPDyMz+ejra3tvP2+KhcHyWQS0zQplUq89tprbNmyxV3cXcnJOWfFwP7Q9K3HS51OkU1kKZVKWFmLoiriC/jwLPAQbgkTtsLYRZvi8SKSE8QjGBj46n2UNpbwL5jev2XjRT3TdS6Zyn+tAvBhpdRGyuve7heRm4H/AvylUmoFMEY5YOH8HnPK/9I5DhFZC3yK8s4C9wN/IyIeEfEAfw1sBdYCn3aO5SLnmBOqqqq46667ME2TlStXcvfdd2PbNrW1tSxfvpxAIMCZM2fcNWyFQoG1a9dy66238sUvfpFPfvKTtLS0sHr1ajweDyMjI5w4cWKm39astXHjRu644w6WL19OKBQiEomwePFi9/HW1lZM03QDXG1tLQBdXV1UVVVRW1s7t4PaNFNKUUwXQcDKWdiWjV20UbbCtmx8ER/Vi6pp3NxIVWsV4il/tobPINAQQFnqEmfQrnZT1mJzJpqknbs+50cBHwY+45RvB74KfBN4yLkN8GPgv0v52+Ih4DGlVAE4LSIdwE3OcR1KqVMAIvIY8JCIHLnIOeaMXC5HJBJxs4yUSiX27t1LMBikvr6epUuXcvz4cXfyyOjoKLFYDI/HQ01NjbvIuyKVSk37e5grRISbb77ZnZXX0NAwYS3awMAApmmSzWbd7Wxs256Q/Pjs2bNuWrNFixbpQHcBpXSJTG+GUqpEKV1CKYWVtxARDK+Bx++heum73elNtzaVA1/JJlgfxFflI1Cr17FpFzelY2xOq+odYAXl1tVJIO6siwPoARY4txcA3QBKKVNEEpS7EhcA4yewjH9O9znlW5znXOgc59bvYZy1ebNtE07TNN38hKlUilAoREtLi/vFWtmDrfLlOj4tVGULkPGpnybbGVqb6EJ7rN1000388z//MyKCUorGxkYGBwe56667gPIY1ZEjR9zj4/G4Tv80CaUUmZ4MdsnGX+0nsjBCKV3CG/TirfYSiAYQQ7CKFh6/h2KySLo7Tbg5TCldwlftI1gfnNp+Jm1emNLAppSygOucBMr/BFwzled7r5RS36acGozNmzfPmv6NUqnE4cOHOXbsGK+//joiQkNDA9dddx2FQoFCoUAul3PX/Kxdu3bC1GfDMLj55ps5cuQIuVyOBQsWTFgUq703Bw8exLZtd1ZqOp0mmUzyxBNPUFVVhc/nm9BC6+7uZv369Xo7m3MoU2GX3h3HC9YFia2KgQ3FdBHDZ+Cv9bufZa4/BzaIIXjDXrK9WbAhP5THX+MnsihygTNpV7tpmRWplIqLyAvALUBsXDaTdqDXOawXWAj0iIgXqKE8iaRSXjH+OZOVj1zkHHNCd3c36XSaQqGAUsr9efXVV1m2bBmRSIQtW7bQ3d1NU1MTjY2NvPPOOxSLRRYtWkRbWxuDg4OMjo5i2zbV1dW6a+wDGB4edltrHo+H0dFROjo62LVrl7vG8KGHHnJnpHq9Xv15T8LwGXiCHqz8u9lGwq1h0mfSGF4D27TJns0iXiEQDWDmy50uylKUkiWskoVCIQjFRBEza+IN64nd2vmmrFEvIo1OSw0RCQH3AkeAF3g3efI24HHn9hPOfZzHn3fG6Z4APiUiAWe240rKu3e/DawUkaUi4qc8weQJ5zkXOsecUFlcffr0aTebfG9vr5td3uv1uhkvisUi/+t//S9eeeUVTpw4wWuvvcbOnTt54YUXeP3119m5cyff+973OH369Ay/q9lLKUUymbxg6jERIRKJuBtgQnkHhkpQq6yDq1i1apXOF3kBkUURfNW+8kSQugC+ah/esBfxCWbWJNuXJXE0QfxonNxgjlRXikxPhuxAFo/fg/DuBYOeRHJlDA8P88gjj0zYVWSum8rLnVZguzPOZgA/VEo9KSKHgcdE5GvAHuBvneP/FvifzuSQUcqBCqXUIRH5IXAYMIEvOl2ciMhvA89Qnu7/XaVU5dvl9y9wjjmhoaGB48ePE41GGRgYQEQolUqEw2G3y7EyaeTYsWNubj0RYd26dezZs2fC1P5MJsPBgwd1d+QkUqkUb775prtEYuPGjcRiMbq7u/F4PO7Yq9/vx+v1ut3AhULBXRJQX1/PkiVL2LRpEzU1NRPWEmoTeQIeqpeUPx9lK4beGqKYKGLlLfJDeTwBDygoJorkx/L4I37sgg1eyA3mMPzlgOgNePFW6dba+xGPx/F4PO7f6fbt29m/fz/bt2/ny1/+8gzX7sqYylmR+4FNk5Sf4t1ZjePL88CvXuC1vg58fZLyp4CnLvccc8HY2Bhvvvkmfr8fn8/HqlWrSCQSJJNJ2traqK+vJ5vNUldXRy6XI5vNUiwWicfjFAoFisUiwWCQbDbLggULqKuro66uTo/3XMCRI0fIZDLE43GUUuzcuRMRoaenh5GREXdqP5Qz+BcKBSKRCKVSeUZffX09Ho+HxsZGGhoazstQol1YKVXC8BuIISi73PqyTRuraJEbymEXbMQQ7KKNr8qHr8YHCrwBL9VLqxFDd/e+F6ZpsnPnTsbGxoDy1leLFy/m6aefRinF008/zbZt2y44iWou0Zc8s0xHRweWZdHW1kZnZydDQ0PU1tayYsUKqqqqePvtt6mqquL06dN0dXVRLBbdtETj8xYahkEymWTFihUsXryYlStXzvRbm5VSqRSHDx92Z5mOjY0RjUbp7e1105OdPXuWurq6CWOen/rUpzh+/Lh7AWEYBi+//DJ33XWX3lblckl53C3cGqaYKIICq2hh5SxUSSGGUBwrltexWeVuzGB9EG/Ei+HTXb3v1ZkzZ9ygBuUlKk8++aTbvW7b9rxptenANstUuhi7u7uprq7m6NGjZLNZTNNkaGjI3c05GAySSCTI5/N0dna6kxUqLbmFCxdiGAbr1q3j2muvvWoD26XyIPb09NDT0wOUP/tUKuVOFKkwTdO9aCgWi1iWxde+9jWCwSANDQ2cOHGCF198EYAf/OAH560hPJfOOVjmi/jwBD2Qh2B9EH+tHytnUUwW8QTKk0xKuRKqpPCEPG7Xo6/KN8M1n5vO3c4K4Pnnn3f/1kulEs8++6wObNqVt2TJEs6cOUMmk3EnihSLRU6dOoVlWSQS5d2CEokE2WwWj8eDz+ejpqYGj8dDIBDA4/G4XY/19fW0t7fP5Fua1aLRqJsLslAoEAgESCaTKKUIBoNYlkWpVKKvr89dF2hZFkNDQ4RCIQYHB2lpaaGlpeW8af/axYkhRJdHKSaKKFvhCXooJooURgoYhkFuKIcRNAg1lLcC8ga9hJpD+Gt0arj3o62tjVOnTrmBzOv18tGPfpRnn32WUqmEz+fjvvvum+FaXhk6sM0yLS0t3HHHHQwNDXHq1ClqamrI5XIMDQ2Ry+Xw+8vrfCqBzzAMN9v/8uXLSafTxONxEokETU1NJJPJmX5LM+pSLaNSqcSLL77IgQMHOH36NLlcjlAoxPDwMLFYjHQ6jVKKn/70pyilME2TaDSKx+Nx916r7MTw4Q9/mF/7tV/TOypcBmUr8iP58pT9kJdiqjx9vzBSoJgqIiL4qnwEm5xsI3UBqhZUzXS157RYLMaWLVvo6urC4/GwfPlybrzxRnbs2AGU179u27btEq8yN+iO6lmotbWVD3/4w4TDYXdRsMfjcb90w+EwsVgMr9eLx+NhaGgIpRSLFi3iwQcfpKmpiXXr1rFmzRpM0+SVV15xp6ZrE/l8Pm688UZ6e3s5ffo0pVKJQCBATU0NLS0tNDU1sXz5cgzDwLZtd8p/ZWlAPp8nkUiQyWTc9YR6R4VLy/RmyPXnKCVLjB4YZXTfKInjCbID5bHOUHOIyJIINStqiC6P6qB2hTQ2NrJ582Y2bdpENBqloaGBrVu3IiJs3bp1XkwcAd1im7XWrFnDTTfdRHt7O48/Xl6G19jYiGEYRCIRlixZQjabJZVK4ff7iUajjIyMkEgkKBaLdHV10dzczKpVq+jr6+PEiROsXr16ht/V7HT06FE3YPX19dHd3U0wGKS2tpb+/n4KhQLV1dVYlkVTUxOFQgGPx0M+n8e2bYrFIiMjI/T39+Pz+RgbG9Ottguwiha5wRyJEwm8YS92ySZxLEEpV8Ib9uIJeMpjnJZCRBBv+XYpU9Jja1Nk27ZtdHZ2zpvWGugW26y2YcMGOjo6GB4epq+vD8uyCIVCZDIZgsEggUCAaDTK8uXLWb9+PcPDw2QyGfdLeHBwECh3b1YWdGvnq2xu6fP5sG2beDzutoarq6vdjS9bWlq4++67ueOOO2hvb0dE3OOVUm7OyHA4PNNvaVZStiJ1OkVxrIhdsMmczZDpzmDmTeyijZkyKcaL2CUbb8iLUopsX5ZUZ4rUqRSpztSEST3aldHQ0MA3vvGNedNaA91im5XOnj3L3r172bt3L2fPnqWqqop0Os3Q0BDFYtHNbOH3+90vX7/fTz6fp76+nmAwiN/vx7IsamtrWbBgwSVn6l2tKllDjh49SjKZxOv1uq2weDxOV1cXtm2745rRaJRcLodt2+7O5SKCaZqcPXuWtrY2UqkUVVW66+xcZtbEKpSn8xsBA3vMLo+nGYI/6i9/zobgqynPljR8Bmbm3W7dYrJIqjOFIHiCHkJNIXdLG00bTwe2WWZgYICXXnqJI0eOsG/fPk6ePEkoFHLXRlX2ZGtra+PEiRNks1mi0Sj9/f2EQiFKpRKJRILGxkai0ShjY2MUCgXdDXkBb775JoODg252l8okHNM06enpIZ/PuwuvLcuip6eHoaEhhoaG3IuJQqHAggULqK+vZ2hoiO7ublpaWmb6rc064hVyAzmsgoWZNyllSiDgCXnKW9aIh1BriIYNDYRbwuQGc25gs0s2ueEcvrQPf7WfUrqEVbDcLCaaNp4ObLPM2bNnGR0dpVAoYFkWxWLRbR1Eo1GWLl1Ka2srCxcudCeX1NbWEgwGqaurY3R0FK/XS2trK0uWLGHBggW0tLTo3bMnoZSis7MTwzCorq52g1VdXR233347b7zxBrZt4/WW/5tUdlMolUp4PB43438+n8fj8RCPxzFNk9dff50NGzbohdrnsIs2nqAHM29ipkw8AQ/+mJ9ivLwIO9wWLmfvH85TjBcJ1AXAgMJoobwMYKyAKikMj0ExUcTusrFKFtWLqsupuDTNoQPbLBMKhfD7/W4uwnA4jFIKr9dLKpXi9OnT9PX1ceuttxIMBkmlUhQK5XU/xWKRXC7nTjCpZKVftmzZTL+tWUlEiMVidHV1MTY2RrFYxOfzsXDhQnenBNM0OXbsGMPDw9i2TVdXF/39/e6syLq6OvfCoa2tjerqave4VatWzfA7vDw9PT2QAOPFqR1yl5wQjAfxml4kLeUuyJKf6kA1VsnCPmmjUNAPGFAUZ4f4vgIBCeAperCTNvFDcQAMj4E5apLbnyNaH53SuhOHHtUztefQrhgd2GaZZcuW0d3dzZEjRxgaGiIWi7F06VKOHTtGJpNxM/xnMhkikQimaWLbNiMjI25mjFAoRDKZxDAMwuGw7oa8iGAwyMmTJ+nu7nZbyAcPHiSZTBKNRimVSpw9e9bN2rB79253bE0pRUNDAw0NDW7g27hxI+3t7RSLxRl+Z7OPP+h3Ewd4/V5sy8bn95U/S1thWRaWaWGVLMI1zgQcA4LhoPuc1EiKYr6IL+DD4/WQy+TweHVrTZtIB7ZZxu/3E4lEuOmmm2htbWXPnj3U1ta6WUZs26ZUKnHmzBlCoZA7USSVSmHbNslkEtM0CQQC1NXVEYlE8Pn0NOkL2bVrl7tkIhAIYBgGXV1drF27lr6+Pl577TWy2Sy2bWPbtrvFTSRS3uSyr68PwB1rU0rR3t4+pzI4tLe3MyRD2B+a+rWOkVKEwlgBT85DtidLPpdHWQpfzIen5MGMm1hYmI0mgboAZswksy+DXbDxRX34WnyEk+WgZ1s2ps9E1gj2NVNbd+NFg/YFOoPPXKED22W6VM7BK8U0Tbq7uxkbGyMej5NOpxER0uk0pVIJEXFTO4XDYfx+P48//jg9PT34/X6UUuTzeUKhEE1NTTQ1NfH2228Ti8WmvO4wt/IgxuNxent78fl8FAoF0uk0hmEQjUZJJpP09PS4i7ErqbIqLbVsNusuDzh9+jRVVVU0NDS4GWFyuZyeiToJw2cQagphdVv4o36IQjFeJDeQw1/nx/AaKEvhj/nxVnlJHE/gj/qx8hZ2ySbYGMQb8pLqSmGXbAy/gbdaf41pE+m/iMvU0dHBngOHscN1U3oe27YZ7DlLfGQQZdmUiuXFwFU1dWQGBygWsiirvJOwaVl4PF4SmTylQr68yNi2wTAoKYPC4CiJksFAVhGuPj8B6pVmZEen/BxXUjweZ8WKFTz//PPYto1pmu5FRHd3NyMjIwSDQdLptJu5xev1YhgGIuLm5iwUCu5+eZU9ruLxuJ4ZeRFW7t1dtBWKXH+ObH8560i4OUwxVSQ3mCPbly0HtIYg4ZYwwcYgyWQSf40fw1Pem62UKMGCmXon2mykA9t7YIfryK99YMrPk0s/QX40jZISKhCAQIhSTTt+Xw1qdAAzn8Uu5bGUwsKAoonhr8ICrHwaZZawPH5MS7BVmMi1D5IPT/206ODhJ6f8HFdSXV0d9fX11NTUMDo6SrFYRClFf38/2WzWnS3Z2NhIIpHAtm2ampoIBALE43Hq6urw+Xyk02mKxSKmabrdwPNpsetU8Ea8WIVycMt0ZyilSyhTuTtp5wZy5Ywj6fIknWx/FsNnEF0WJdQUwh/zg+DuqF1ZT6hpoAPbrFNIDJGLD1BKj2KVCmCZWKFqPKFqrGwKf6wJa7ALw+PFNsv/6bFNlG1gFwuoUnnSgioWKCVHCTctwbZ07sLJVDL727ZNNBp1x8kAd4ZkXV2d27VrWZab7aWhoQERob6+3t0+qLq6mubmZjfFmXZh4ZYwKMj0ZMgP5bFKVjmw2UJuJIc37KWULeENesvT/JXCzJgYfoNgfdBt3QEE6gM6qGkT6MA2y2T6T2MmR1GWBWYJ2zJR2SS54V58wSrsQgYRA4VCDA9GIIyVz6Asu/wcKpsGWggezHwGMXTmtAvxer3cfvvtPPvss+74WSVVlmmaxONxIpGI2/UYCoXIZrNUVVVx5513AtDV1UUikeDGG29k/fr1eDweBgYGWLRo0Qy/u9lLDKFqQRVmxsRb43VnRCqrvE6tlC5hl8qZSTweD8HGIIbXINOXIbokiifoKQe+kLc8Vqdp4+jANstY+SxK2eVg5PGAVURZJYrJYexSHo8vSLBhIcXkMFY+jT8UwfIHsAtZTGVjlwDbBMvEtrzYloV3Groh56rq6moaGhpoaWmhr6+PYrG8ZYqIUFVV5e5zV2kRVPJvWpbFK6+8Qnt7Ox6Ph/r6endhfWXhvHa+YrI8dlZMFPFFfdhFm1BDCDNjIkb5cw/UBogsimCXbOKH4+ABT9BT3og0W+6+9FX78FXr2b7a5PSl/CwTbGgHBVapgF0qgG2DbYFZxMqnsZWFJxCkqm0FgVgj3kgN/lgz4g9hBMKgbECBshEUhsdLduDMTL+tWWvFihWUSiXa2tqoq6sjHA5TKBTI58uTcSrpzCqL5jOZDMVikVKpxNGjRzl79iyhUIgTJ05w7Ngxdu3a5QZLbaJSukTqdIrkiSTZvizJE0mKyWJ5FmTEjyfoIdwapmZ1Df5qP8G6INVLq8sTR5rD+Kp8GAEDq2DpZMjaRekW2yzjC1dT1bKMUi5JMVECnPExpVC2M3aWGiXY0I6vKgaGF8PrBbuRbN9JDJ8f2yyVW3yGBzwe8sPdVLUsmcF3NXuFw2E2btxIKpWitraWXC5HNpvFNE0SiQSnTp3CMAxKpZK70Whlh3KPx0MikeDVV191s/yvWbPG3QxWm6iYKGIVLGzLLl+8FSxK6RLeKi++qA8xhEAsQKA+QHGsPFbsj5XzQuaGcniCHgJ1ARLZBIbfILIogjekv8K08+m/ilnGLuaJLFhJKRPHzGWwrRJUrk5tCzE8KLNEKTVKqH4B2cEubMNDqK4VM5eilBrFsEwUCsPwYuczmMXCzL6pWSwWixGPx+no6CCdTk9IT5bNZt3Zjj6fD8MwMJzxykorrhIIs9ksfr+fp556imQyyebNm3V+znMYPgPxCHahPHambIWZMzG8Bt5w+avIzttUtVfhj/rLgTBvEV0WpZAokO3JUhgrULOivD4w25clukxP0tHON2VdkSKyUEReEJHDInJIRH7HKa8TkR0icsL5XeuUi4g8KiIdIrJfRK4f91rbnONPiMi2ceU3iMgB5zmPinOZfKFzzAWG10e69wSF1AiqVKA8m1nAkHIm9HAEwx9EgFTvcfIjvRTTY2QGTiMeH4YviOEPgA0YBiIGHp+f/Gj/zL6xWaq7u5szZ84QDAbxeDw0NDS42wFBeV2hx+Nxc0MGg0Gqqqqorq6mrq7OXRBf6b7MZDKcOnWKd955Zybf1qwUqA/gDXsppoqYWROlFHbBJtufdRMfW4Xy7Eh/1E9kYQRPyEMxWSxn8y9ZFEYKxI/FyQ/ny7sDaNokpnKMzQT+L6XUWuBm4Isishb4A+A5pdRK4DnnPsBWYKXz8zDwTSgHKeArwBbgJuAr4wLVN4HfGve8+53yC51j1rOKOZRtYhdyKGWXx9hQIF48oQieQBgMD4XUKMXkKEoEpWyUWcTjD1O35jYiC9cQbltKzYrrqV1zM95wNcWk3mj0XKZp8tJLL3Hy5ElM0yQcDlMsFt0xtEpLzev1UiqVKJVKBAIBmpqasG2buro6dzubUqlEPp93jzlzRo9rnktZqpw+K1LudiwlSpg5E7tkYxUsDK8BUk6VVeENe7FyVnlXbVthlSzMrEkpXcJMmXqsTZvUlHVFKqX6gD7ndkpEjlDOD/AQ8CHnsO3Ai8DvO+XfV+W/1J0iEhORVufYHUqpUQAR2QHcLyIvAlGl1E6n/PvAx4GnL3KOWc82S4jHRymbcLogK2M15en9xbEBDG8AbyiCskqIgHLyGAYjUerWbgFlkxvqhvHjPKLnCZ0rkUgwOjpKPp8vtx5sm0QiAZQz/xuGQS6XIxqNYhiGuwFpX18fpVKJ6upqxsbGyOVyFItFt9WXSCS45pprZvjdzT7FeLGyGgXbtEFwF2kbPgMjYCCGlAMc5QAXag2RPpOm0F/ANm0MX/kxX9SHt8qLlbf0OJt2nmn5ixCRJcAm4E2g2Ql6UN6gotm5vQDoHve0HqfsYuU9k5RzkXOcW6+HKbcOZ82aI391LXapCJY1LrDZoCzMQgGvz4NSHoqZOHapgLIsxLbxeP3kB88wvOcXhJqXYJcKGF4fhr+cGT1Y1zqj72s2qq6uplAo0N7eztmzZ0mn0wBuzk3ATaVVWdc2NjbmTv/fs2cPuVyOUqlUTmemFH6/n2uuuYa2trYZe1+zVWVCjSfgKS/AzpTwBDzlXbMjPvwxP4GaAL5qH5neDIWxArnBHPnhPIWRAqVsqTxzsimM4TUQr2D49QWbdr4pD2wiEgF+AvyuUio5fraYUkqJyJT2JVzsHEqpbwPfBti8efOs6NMIxJqpal9B4tRerGK+HNeUOEHOAvFhl0rYxSxggAF2Lo0tBrayKJ3cQ3bkLLHlG/FFmgjUNOIJVuFxApz2Lr/fz5YtW/jFL36Bz+dzuxWz2SxKKXeySDAYdFtsld0VAHK5HACGYeDz+fB4PHi9XhoaGqYt6fQVE5/6/diCdpDicBFf3AdF8JV8ePCgLEXYCBOwAoQyIdQpRTFZxPbaFEeLlHIlDGXgs31YaYtCuoCnz0Pd0jq8Q9PUWotz1eSjVEpx/Phxzp49SzgcZs2aNXMuk86U/lWIiI9yUPuBUuofneIBEWlVSvU5XY2DTnkvsHDc09udsl7e7VaslL/olLdPcvzFzjEnhBva8dc0lLsj3fEGKY+3KYVtOVOmxS6XKxuUjZVJYSmbYnKU3HAPsRXXU3fNFkLB8Ey+nVntpptu4vDhw3R3d9PT00M8HseyymM6Pp+PUCjk5oK8ENu2KRQK7u94PM7IyMicSYK8YsWKaTuXvcBmbGzM3end6/XS2NiIz+dzu3/37NmDZVjU1tQyWBgkZ+QoFAoUi0U8IQ9VVVVEwhGuabiG2tppmhe2YHo/p5l08uRJjh8/DkA6nSaZTHLPPfe4M4LngikLbM4Mxb8Fjiil/p9xDz0BbAP+s/P78XHlvy0ij1GeKJJwAtMzwH8cN2HkPuAPlVKjIpIUkZspd3F+DvjGJc4x65m5NIXEENVLNpAdOoOyTDAMEAOxbRADw2tgF3Ngq3JLTjkBzipRHsRQmNkUiZN7AAjVthBdup5AbNIe2avawMAA7e3t/NM//ZO7px3gjrllMhng3S7Ji/H5fDQ0NBAMBtmzZw9bt26d0rpfKdO5zdChQ4c4ffo0Silqa2vZsmXLefsFPvzww/T19fGbv/mb7N69m5MnT5JIJDh9+rSbZHrZsmWsW7eOz372syxYcJU0pc5xpbbS6ukpj+i0t5fbCX19fe7GuhU//vGPCQQCl3yt2bJt1VS22G4DPgscEJG9TtkfUQ42PxSRzwNdwCedx54CPgp0AFngNwCcAPanwNvOcf+hMpEE+NfA94AQ5UkjTzvlFzrHrGfmUgDlrCNQXmStFIigPAEss1ieTmJ4oFTAHY1nXE+qUmBbmJkkmZ5jKLOINxLDX12PePRA+3jZbJYTJ06we/dustnshMcsqzyx4XJn3hUKBfr6+jh58qReoD2JyoL3irGxMTo7O1m8eLGbRFpECAaDNDY2Ul9fzx133MGmTZt44YUXMAyDgYEB/H4/yWSS/v5+jh07dtUGtiul0qVe4fP5JgQ2EcHrnVvfG1M5K/JV3p3Sd657JjleAV+8wGt9F/juJOW7gPWTlI9Mdo4PoqenByObmPKtWYxCHnN0iFxnB4ZZwrIqGfwBywQRbNu66GtUuiZRNqV4P0Uzg5kfwp/vxjOFgc3IjtDT88F3EpiuTV0ty+L06dMcOnSIkZGR8x6vZBp5L683PDzM66+/TigU4qmnnrqS1Z3UbLlCvhyV1u94J06c4Pjx49i2TTgcZtWqVRQKBSKRiJtkOpFI4PP5eOmll0gkEiilCAaDRCKR8y5GriZX6t+98jqPPvooUL5A27VrF6Ojo/h8PtavX++25uaKuRWGrwL+QJBSvkCpWMC2z/lSVTbqgtcK51PKxjZLlAp5SoXihEbdbNbR0cHxg7tZFLlEAP+A0rkCA2cGGejru/TBl8k0S0gpR773MPl896Wf8AGcSXum9PWvtMpYWmXyTT6f5/Tp04gIkUiE0dFRDhw44ObfrMxUbWpqorq6mtWrV5NIJBgZGaG9vR2v18vGjRtn+F3NP4FAgNtuu418Po/f759TY2sVOrBdpvb2dgYK3infaLSYjpMaSGIH+lH5nDMrcnw348WeLecdYGNg+6oo1rRTXPfAlHZFBg8/SXv7lZkwsShi8ceb01fktS6ke7TAH3fGnUXwH5wAfkNR4zP5wy0lFsSmtv5f2xWZ0te/0nw+H7feeisnTpygVCrR1dVFKlXueu/s7MS2bZYsWQJAf38/L7zwAtFolOPHj3PttdcSi8Xo6+vD6/Xi9/u58847Wbhw4UXOqH0QweDcnUmtA9sskx/uxfD4yuM6YgDlCSNu1v6LOv/xSjJkr8+vx9fOYRiCdYmYdv6lwoVVjmuo8hILza3W1HSJRqPccMMNlEolhoaGCAaD5PN5stksvb297u7k+XzezQKTzWZ54YUXWLBgARs2bGDTpk1AuWszm80SDutZv9pE+ptuljG8Pqxs0pnmL+/OePT4yj+lPJVtaS6HQjAMD75o/VRWe06qCXloj/nY3wOFC/R6vpfeW78BIZ9BfcSDz6Mnj0xmdHSU48ePu8siVq9eTU9PD52dnUSjUaqrq8nlcuRyOc6cOcPY2BhDQ0OsXLmSsbExisUia9eudV8vlUrpwHYByWSSffv2kUwmaWxsZOPGjZc1s/HUqVPuBKjFixdTVVVFKBSavqUVV4AObLOMJxDGskyUbSEolBhOy60c5MTnAzFQZtGZ7m+XU2ep8jT/iQSPP4jh9+Orirm7Q2tlVX6DsM/Aek/tsslVPlV/Od0hiZxFY/XcG5uYSoVCgZ07d7qzTS3LIp/P09rayooVK2htbXV3JzdN0906yLIsisUiNTU1nDx5kp6eHsLhMA0NDdTV1c3wu5q9du3a5U7YGRgY4NChQ1x/fTm3fGX7pXO/D4aHhzl06BBQnjH86quvsnr1aiKRCIsXL2bDhg3T+ybeJx3YZhnD56eUHgPlbKaoFCgTMMH2oAxBDC+Gx49NqRzckHF5Ict7XSEG+EMY/hChunYMn98JgrqLrOLUcIGTowWqAwbZosWVGGnLmorOsRL9yRKNeofnCbq7uzlx4gT5fJ5YLEZrayttbW0sXryYSCTirhWsZLlYtWqVm+nF6/VSU1NDNpt1uzAbGhrcXRjmiumc8XtuIm6Px8OCBQsYGhoil8vh8Xioq6ujz5k89aUvfYmxsTHi8TgA8XicQqHAyy+/TFVVFVCea3DuusOp8EFn++rANssUUmMYXh8SCEE2ycT1aRYoL8osojy+cpDyehClUHaJcootf3lZgLKhVMBUFpmBTmquuQkx5taXwFQbyZgMp02yJVW+LvgAjTbFu+vl8yXFQNrk2itV0Xni+PHjDA8Po5Siq6uL48ePc8cdd9Da2sqSJUvo6emhWCwSCoUIBoPEYjGuvfZajh49itfr5cCBA9TW1rJx40Y3xdng4OCcyfAC5Rm/R/fuZTpqnI/HMa13+9jDzq4TA4kERcvCaxjEg0FCdXUYhkF8717yxSJpZ0JPNpulaFn4QyGUs45tbGQE/xSvabsSG2zpwDbLiFL4qmrIDvdeYBytXCaGF+wSiAfxlPPtCYLh9WPZthMELZTyYJcK5Ia6iS05b8nfVa0l6qNkQq5olxu+H5CtwLIVPkNRE9QXEeNlMhksy6Ktrc1dN1hfX09nZycHDhxg48aNhEIh7r77bnbs2IFlWSxcuJDR0VHq6+vd8bZEIsHx48fd3RMsa2qXhEyFFuDz72HZzvuViVTTkUmTMS1q/T6Wh6t4aWiIMyVnGZFtEcrl+JhlU1256PUHOBOy6MvnSQWC5CyTem+5hRbxetngnfrW2t9egXVJOrDNMqGmhZTeTuDxBbC8fjDPyVFoK/B4y8NqgLKtcjekGKhSCcs0y0ENAMHweBHDwEyPnnuqq1510EM0WN7A9Uos8au02nweg3Utc3eq9FQIBAJ4PB7a29uJx+NUV1dTU1PjLozP5XKICKdPnwbK3WbXXXcdUB4ryufzRKNRDh06xPDwMKlUiqamJpqbdZq4C6nyetlYE5tQdu5lQNG28Z2zTm1ROMzCUAgRIV4qMVIsEDA8tFzGxJPZQge298DIjk555hFl21SrPFDCEnXeHyIosErYynLXX6lzg597qIVdyGLZJtWFkanPmpIdhWnpZLky+hIlTEtdsYXrlU2G1jSHyJuK8Nz5HphyXq+X9evXc/DgQaqqqrAsi2AwSFdXF6FQiEQiQW9vL8ViEcuyJoydVSY4iAhKKZLJJKOjo9x8881zLtXTTFsYCpIzTZKmic8Q2oMhApMswK585jGfj9g0jKldafqv4jJNa2bvxFkGDJP+fIaCs1XKufkKpZI/0pnpeKF8hoIiEg6yYcVCFi6c6qDTMqcyoA+kSqSK6opMGhnvbLJIcO59F0y5RYsW0drayubNm3n++ec5ePCguxZt9+7dNDY20tjYSF9fH8FgkJ07d9LS0sLy5csZGBigu7sb27ZZuXIlixcv5tixYyxevHjOTSCZSUurIhRtRc6y8BrCyqrIvJwprQPbZZqufHyJRIInn3ySPXv2sGPHDgYGBkgmk+6VbOWP0DCMyxpf8Pl81NXVsWnTJv79v//3U139K6Knp4dMyjPlmTXODpv0ZxTmFYxseQtOxoXff9FDc93U1r8r5aGqp+fSB84iPp+PpqYmGhsbue6667juuut4/fXXSaVSrFy5klgsRk9PD9XV1fT399Pd3c2GDRu4++67SSaT1NfXu3vdmaZJPp93Z+xpF1ewLHpzOTwiNAX8LA1X4ZmD6bIuhw5ss0wikSAajXLnnXdiWRZ/93d/5+71Be9mmh+/jcqFWmuGYeD3+8lkMhw9enTqKz/HVIcCmFcyqjlMyyZbuPD+bVr5b7OyWLitrY1UKkVNTQ0nTpwgHo9jmiaPP/44jY2NnD59mk984hNs2rTJ3ScMyjug66B2+Y6kUmSdi+G0CX7Dw6J5urhdB7ZZpr6+HhHBNE0OHz48aUZ0uPRWKpWgVvkCaW1tnYrqTon29nbyZt+U54o83Jfj7X0GJ0eu3Mw6A6jymPyfN1isb5v6XJHBOZZ1vWLlypXs2VPeL7C9vZ10Oo1pmoyMjBAMBt1UWqOjo7S2tnL48GHuueceRIT+/n4ikYg7M3Iu6enpIcWVmfn3XpiWRZ81Mam6v1igORya1npcjj4g/QF7IuZnO3QOq6qq4oYbbkApxcjIyIXHzsYNqFcyCFR2IfZ6ve64QzgcZvny5dx3333T9h7mCo9HaKq+suMzNtAU8bC+bX5eCV8p7e3ttLe3Mzo6SqlUIhKJuLMl6+vr3R6JUqlEIpHgxRdf5Omnn2bJkiXceeedXH/99TqV1ntgGAbGuLE0WykKpslIOk22UJjBmk0N3WKbhVpbW7n77rv5y7/8SzewVSaIGIbhphwqFAoYhkEwGEQphVIKv9+PiBAOh/F4PCxZsoQHH3yQe+65otvTzQttUR+mpa5AQq2JCqaNZdl4PPq68UKGhobo6emhrq6O/fv3k8/n3RyQpmnS3NxMe3s7SilOnTqFZVns3r2bZDLJZz/72Tk7YaS9vZ348PC0rGObQIShqginMhkspRgoFoj5/AQKRSgUWaoUrcHzW29p06Qrm6Vo29T7/e4ygKn0tyhiH7AnQv/Pm6Xi8ThtbW1A+Wqr0hKLRCK0tLTQ3NyMz+cjGo1y7bXXEo1GCYVC7pWu3++nqamJj370o6xYsUJPi55Ef7KE1yNX/D9BT7zIW12TdyFrZZX1a6VSyd2tOZVKsXr1agKBAC0tLTz44IMEAgF8Ph8LFixAROjt7XVTQGnvTWMgwA21tayLRmnyByZM8x/In99qs5XiSCpJolQiZ1n05HL0jdtZezbT33azkG3bHDt2jEQiwcqVK+np6aFQKODz+aiurqZYLOL3+wmHw0SjUZqamqirq2Pv3r1YloVt22SzWUSEoaGhCRNNtHcVLUUyf+Wn++dKMJT+4DuJz2eVTPE+n49AIODumu31eqmvryccDtPS0sLChQsnXJRFIhGKRT0x5/3yilDl9WIYMmGbx/GLtJVSxEsl0qZJ0bIntNDGSiXaQrNvXO5cOrDNQvv27aO3t5dcLkcgEKCurg7btqmrq6NQKJBIJCgUCuTzeZRSnD59mpaWFvx+P4VCgVwuh23bbrdksVgkmUy6yWW1smTeIpG78imZPMDKJp155GKam5tZtWoVp06dYs2aNRQKBaqrq2lsbARgcHCQ48ePu70QHo+Hmpoali1b5vZkaO+PV4SFoTBnslkAPCIsdIKVpRQHkwkypoWpbHpyORaFwu74XHiOdAHrwDbLlEolent7aWpqYvXq1XR3d5PP56murqalpYWBgQFSqRSmaWIYBkopIpEI1dXV1NbWkkwm8Xg8KKUIhULuFfHg4OCcCmxn0lO/ju1Ed47hnLri89Msw+AnPY0wxUvMzqQ9rJraU7wnVyJzvWVZ7mt8+9vfBspd8ZFI+W/hxIkT7Nix4z295gfNFD8ftYdC1Pn95CyLGq8Xr9NiGy4UyJjliz2vGMR8PjKWSbXXR43PR/scaK2BDmyzjoiUc7TF47S2tpJOp6murqa5uRm/3082m6VUKpFMJikUCliWRXNzM8uWLSOXy5HJZEilUhSLRVpaWjh9+jTXXnut+8UwF0xX9hJP9jC+wCD5YuqKvm51rIHA4s1TPsi+imnOiDNN/H7/hPs+n4/6+vmzUW4/0zvdP1MoUCiV8Hk8RILBd/8uPQZ4DMqjnYp6IKlsEk7dlFLklSLo9RILh4iGQux0jp1K/UDsA76GDmyzjNfrZfHixbz99tvYts2KFStYuXIla9euJRgM8oMf/IADBw5gmia2bWOaJqdPn+bTn/40mzZt4h/+4R84e/asu5FgoVBgZGRkTiWLna6r6/379/O5z32OQ4cOYZpXZkysurqatWvX8nu/93ssXLjwirzmXHGl/t0OHDhAZ2cnUL7Qu+GGG+bUOsyLme4LkXg8TnFsDAFMoFRVRVNT04Rjhk6cACC2ciUR08Tu6SmPs8XjqGKRcH09lseDisWmZRftGB/8c9KBbRZqb29n1apV5HI5ampq3MH1G264ga1bt7Jjxw7C4TA9PT1YlkWhUODAgQMsW7aMX//1X+cf//Ef8Xq9VFVV0dDQwJIlS8jlcnrdzzlWrFjBli1b6OjouGKBzefzsWjRInp6eq66wHalXHvttTQ1NbkZ/OdSF/qlTHeX6PPPPz8hyYOIcP/990+YkFOp06OPPgpAOp3m5MmTvPrqqzQ1NREMlseLw+HwnFk2NGXT/UXkuyIyKCIHx5XVicgOETnh/K51ykVEHhWRDhHZLyLXj3vONuf4EyKybVz5DSJywHnOo+K0ry90jrkkm82SyWTw+/1u2qG6ujoAlixZwsKFC6mtrcXr9eL3+2lsbCx3G+Tz3HXXXXz2s59l48aNtLS0UFdXR7FYnJZdb+can893xQJaRS6XI5vNupMgtPenubmZFStWzKugNhPO/X/v8XgwLpEfMhKJcO2117J48eIJ3cKBObRtzVSuY/secP85ZX8APKeUWgk859wH2AqsdH4eBr4J5SAFfAXYAtwEfGVcoPom8Fvjnnf/Jc4xJ5w4cYJ33nmHUCjEmTNn6Ovro729nZUrVwLl1ty2bdtYtmwZgUCAqqoqNmzYAJSXCViWxa233kogEMA0TUZHR4nH4wwPD8/k25qVent76erqumKvV8n+Mjw8fN44kabNhGuuuWZCIDv3/mRM0+Sdd95hYGCAvXv3MjQ0hMfjmVMpzKasK1Ip9bKILDmn+CHgQ87t7cCLwO875d9X5TQbO0UkJiKtzrE7lFKjACKyA7hfRF4EokqpnU7594GPA09f5BxzwqlTpwCoqalhw4YN+Hw+Nm3ahG3bHD58mP7+fmpra/nkJz/J4cOHsSyLmpoaPB4PixcvpqmpiWKxSHt7OzU1NQSDQYLBIKdOnZo34xRXSiKRYGhoCCiPbb7X1lulO6cyQzUUChGNRgkGg+RyuSteX017rxobG/nIRz7C6Ogo0Wj0spJGnzx5kv7+fpqamqipqSGXy3HHHXdQXV09DTW+MqZ7jK1ZKVVJG9APVGY0LAC6xx3X45RdrLxnkvKLneM8IvIw5RYiixYteq/vZUqcO5Oucv/48eOcPHmSkZERXn/9dZRS1NfXUywWaWxs5Pbbb+faa6+dkKWksr3HZK+rQSgUIhaLud0zlbRlXq8XwzAolUqT5uo0DAOv18uCBQuor6/n2LFjFJx8ez6fj1AodN4AvabNlPeaBD2ZTAK4u4rEYjF3ydFcMWMptZzW2ZTOG73UOZRS31ZKbVZKbZ4tYyIrV65kbGzMDWQtLeXNQQcHB0mlUhw5coREIuHuIlwoFMhms3R0dPDyyy/T0dGBYRgTArWIzMtp4R9Ua2srt912GwsXLiQQCLhjlgDFYvGCCairqqqora3F5/ORy+Xw+XxEIhECgQA1NTXcdttt0zJ7TNOmQmWroH379nHgwAGOHz8+58Y6p7vFNiAirUqpPqercdAp7wXGTyFrd8p6ebdbsVL+olPePsnxFzvHnFBdXe3mgIxEIvT19bF27Vqqq6tJJBJ4vV53rVvliiqfz7Nv3z78fj99fX089dRTrF69mmg0yoIFC2hpaZlT69imS3V1NZ/+9Kc5duwYg4ODlEolLMu6ZJdkLpfDsiyi0SiZTIZgMIjX66WtrY177rmH9evXT9M70K5mV2JBPJTH9eHd2ZG5XI6Ojg5yuRyGYVBdXc3+/fupqam55GvNlsXw091iewKozGzcBjw+rvxzzuzIm4GE0534DHCfiNQ6k0buA55xHkuKyM3ObMjPnfNak51jTjh79qyb6DgSiVAqlRgaGmLNmjU0NzdjGAZtbW20trZi2zbFYpGuri5OnTpFV1cXBw8e5NSpU7zwwgs899xzHD58WE/zv4hTp06xbt06dzzycvJqmqZJNpslnU7j8Xjwer14vV6UUvT398+pNYOaFgqFCI3LKGKaJpFIhMbGRurr6/H7/Vd89vBUm7IWm4j8PeXWVoOI9FCe3fifgR+KyOeBLuCTzuFPAR8FOoAs8BsASqlREflT4G3nuP9QmUgC/GvKMy9DlCeNPO2UX+gcc0JokpQ1oVCIYDDIL//yL7NgwQIGBgawbZt33nmHbDaL3+/n7Nmz5HI5hoeHyefz7sSTY8eOsXbtWt0VOYnBwUH27NnDyMgIjY2NjI6OXnID13OfXxnHjEajFItFDMNg3759bNy4UY9ralNqqlpGhUKB559/fkIwu/XWW+dU9pepnBX56Qs8dN4KP2cs7IsXeJ3vAt+dpHwXcF6fj1JqZLJzzBVLliyhv7+feDwOwOLFi93xGhFhy5YtFItFRIQf/ehH9PX1UVdXR09PD01NTe5OxP39/VRVVRGLxRgbG5vBdzR7DQwMUFVVRXd3t9tSq0yFHt9yC4fD5PP581pzlmWRSCSoqanBsiyCwSANDQ10d3czNDSkJ5Boc1IgEOCWW25xExcsWbJkTgU10JlHZh2fz8cdd9xBIpHA5/NN2o1YmeBQVVVFVVUVy5cvxzAMstksTU1N7Nixg9HRUYLBIPF4fNJWoFYeY1uwYAGHDh0iEonQ0NCAZVnuxJFKgulQKEQikSCVejenZGUGJeDm8KykMautrSXrZE7XtLkoFouxefPmma7G+6YD2zS7UgO+gJv5/6mnnmJoaIiqqir279/P2NgYXq+X0dFROjo66OzsvOQsvdky6DudFi1axLFjxwgGg9TX17tT+Cs7KEA5A0Zl/CwQCJDP5908nbZtU19fT0tLi7tOqK2tjYULF+pxNk2bQTqwzWGVQd/29nYaGhpIJBJks1m8Xu+E9DeeObKH0nSrLKrOZDIopdy1aPfddx8LFy7Esizi8ThHjx4llUrR3NxMMBjk7bffdj/TpqYmTNOkoaGBLVu2sGnTJlavXq1byZo2g3Rgm2ZT3SqybZs33niD0dHyHJuamhpuvfXWCUlPtbJMJsOZM2dobm5mZGSEQqGAUsrNU3jw4EFuvPFGYrEY3d3dlEol7rrrLtavX08ymeTIkSMMDAzQ3NzMTTfdxI033khdXZ1ew6ZpM0x/280zhmFw6623Mjo6im3bNDQ06Nl5F+DxePB4PG6r1+v1cvbsWd588013cXx/f7+bsqxYLJLNZjl27BgnT57E6/VSU1PD0qVLWbJkCR6Ph3Q6PdNvS9OuejqwzUMiMudmMc2EYDDIhg0b6OnpYWBgwF2kXZmMs3fvXgYGBvB6vRQKBdra2hAR+vv73YXcxWKRTCbjTo3WMyE1bebNWEotTZsN1q1bx+c//3laWlrIZDIMDw/z+uuv09HRQW1tLQMDA27qskwmw8jIiJsdJhQKUVdXRygUYtmyZaxbt4729vZLn1TTtCmlW2zaVS8cDpNKpSiVSu4424kTJ/jIRz5CMBikubnZzdE5NDREOBymra0NKC8Z+PKXv8zGjRtn+F1omlahA5t21TNNk0Qi4Y5HJpNJvF4vkUjE3ajRtm0CgQDr1q1jeHiYeDzO+vXr+eQnP8mqVatm+B1omjaeDmzaVS8SibBs2TL6+vpobm6mubmZdevW8fGPf5yBgQF6e3vdhfKVhNSmafLQQw9dctNGTdOmnw5smgY8/PDD2LbN2bNnaW1tZcOGDaxdu5YNGzaQyWT42c9+Rjabdfekam9v57XXXiOXy3H99dfT0NAww+9A07QKHdg0jfIMyUceeYSBgQGUUjQ1Nblr/yr7r2UyGU6ePMny5ct55ZVX3BycO3fu5Ld+67f0DuWaNkvofhRNc3g8HqLRKCdPnuTnP/85r776KplMBtu26e7uZmRkhJGREZ599lkOHjzoPq9UKvHmm2/OYM01TRtPt9i0ee295ubs7e2lWCy694PBIHV1dXR0dGDbNs888wzZbNZNsVWxZ88enn/++cs6x9WYl1PTppMObJo2zvigBpDP5ydsJArlYDf+OI/Ho5Mea9osIu9lY8X5bPPmzWrXrl0zXQ1thr3++uuMjIy49xsbG7n55pt5+umneeeddygWi8RiMW677TY8Hg/ZbJZrr71W54fUtJkxab5A3WLTtHE2bdrEvn37GBsbo66uzl14/eEPf5jW1lZ3vds111yjd03QtFlKt9gcusWmaZo250zaYtOzIjVN07R5RQc2TdM0bV7RgU3TNE2bV3Rg0zRN0+aVeRvYROR+ETkmIh0i8gczXR9N0zRteszLwCYiHuCvga3AWuDTIrJ2ZmulaZqmTYd5GdiAm4AOpdQppVQReAx4aIbrpGmapk2D+RrYFgDd4+73OGUTiMjDIrJLRHYNDQ1NW+U0TdO0qXNVZx5RSn0b+DaAiAyJSNcMV+n9aACGZ7oSVwn9WU8f/VlPr7n6ef9cKXX/uYXzNbD1AgvH3W93yi5IKdU4pTWaIiKySym1eabrcTXQn/X00Z/19Jpvn/d87Yp8G1gpIktFxA98CnhihuukaZqmTYN52WJTSpki8tvAM4AH+K5S6tAMV0vTNE2bBvMysAEopZ4CnprpekyDb890Ba4i+rOePvqznl7z6vPW2f01TdO0eWW+jrFpmqZpVykd2DRN07R5RQe2GSAilojsFZF9IrJbRG69wHFLROTgJOUfEpGE8xp7ReQXU1/r+UdElIj8xbj7/0ZEvjqDVZq3ROTfisghEdnv/M1uEZHfFZHwTNdtrvqg3yPOYzeJyMtOXt09IvKd+fBvMm8nj8xyOaXUdQAi8kvAfwLuGn+AiFzq3+YVpdQDU1O9q0YB+BUR+U9Kqbm4OHVOEJFbgAeA65VSBRFpAPzAPwD/C8jOZP3msA/0PSIizcCPgE8ppd5wyj4BVDPH/010i23mRYExcFtir4jIE8Dh8QeJyDLniurGyV7EufJ6wznmdRFZ7ZR7ROTPReSgc7X8iFN+g4i8JCLviMgzItI6tW9zVjIpzwb7P899wLnKfd75zJ4TkUVO+fdE5FHnMz7lfBFUnvN/i8jbznP+ZPrexqzXCgwrpQoAzkXEJ4A24AUReQFARD4tIgecv9X/UnmyiKRF5OtOy2Sn84WMiDSKyE+cz/xtEblt+t/arPF+vke+CGyvBDUApdSPlVIDIvJVEdnuvE6XiPyKiPyZ8+/zcxHxOa/XKSJ/4rQYD4jINdP3li9CKaV/pvkHsIC9wFEgAdzglH8IyABLnftLgIPAamAPsHHccQnnNfYC/5byH7bXefwjwE+c2/8H8ONxj9UBPuB1oNEp+zXKa/1m/LOZ5n+HtPO5dQI1wL8Bvuo89lNgm3P7N4F/dm5/j/JVrkF554gOp/w+ykFSnMeeBO6c6fc4G36AiPN3ehz4G+Aup7wTaHButwFngEbKPUnPAx93HlPAx5zbfwb8sXP774DbnduLgCMz/V6n+XP9oN8j/wg8dIHX/irwqvNdsZFyC26r89g/jfu36QQecW7/a+A7M/25KKV0V+QMGd+FcAvwfRFZ7zz2llLq9LhjG4HHgV9RSo2/+prQFSkiC4HtIrKS8heBz3noI8D/q5QyAZRSo8651gM7RATKi9j7rvB7nBOUUkkR+T7wJSA37qFbgF9xbv9Pyl+oFf+slLKBw5XWA+XAdh/lLw4of5mvBF6eqrrPFUqptIjcANwB3A38g5y/R+KNwItKqSEAEfkBcCfwz0CR8oUCwDvAvc7tjwBrnb9hgKiIRJRS6al6L7PMlfgeuZinlVIlETlA+Tvi5075AcrBsuIfnd/v8O7/mRmlA9sMU0q94Yw5VHJVZs45JEH5SvZ2zulWOMefAi8opX5ZRJYAL17kWAEOKaVueV+Vnn/+CtgN/I/LPL4w7raM+/2flFLfuoL1mjeUUhblv8kXnS/Kbe/h6SXlNAkot1Iq31sGcLNSKn/FKjpHvc/vkUPADZQD3mQqXce2iIz/N7CZGDsq/x8sZklM0WNsM8zpk/YAIxc4pAj8MvA5EfnMRV6qhncTPf/LceU7gC9UBpFFpA44BjQ6V3mIiE9E1r3vNzHHKaVGgR8Cnx9X/DrlHKMA/xvwyiVe5hngN0UkAiAiC0Sk6UrXdS4SkdVOT0LFdUAXkKI8UQHgLeAuEWmQ8kbBnwZeusRLPws8Mu48112pOs817/N75L8D20Rky7jX+ZVxvRBz1qyIrlehkIjsdW4L5bEca1yXygRKqYyIPEC56zANJCc57M8od0X+MfCzceXfAVYB+0WkBPx/Sqn/7kx6eFREaij/HfwV5Su4q9VfAL897v4jwP8Qkf8bGAJ+42JPVko9KyJrgDecf8c08OvA4NRUd06JAN8QkRjlCTsdwMOUg9fPReSsUupup3vyBcr/J36mlLpQS6LiS8Bfi8h+yn/DLwP/+xS9h9noA32PKKWeEJFPAX/uXITZlD/Dn0/6AnOITqmlaZqmzSu6K1LTNE2bV3Rg0zRN0+YVHdg0TdO0eUUHNk3TNG1e0YFN0zRNm1d0YNO0aSTlHQX+17j7XhEZEpEnL/a8C7zWC1JOfju+7HdF5Jvv4TX+WsoZ4g+LSE7e3THiE5d+tqbNTnodm6ZNrwywXkRCSqkc5fRQvZd4zoX8PeVF5M+MK/sU8Hvv4TW+5Kx9WgI8WUnRpGlzmW6xadr0ewr4F87tT1MOUMBFd2lYJyJvOa2p/U4mjx8D/0JE/M4xSygnE37FyfD+ooj8WESOisgPxFm562Rk/y8ishv41XMrJyLfF5GPj7v/AxF5SET+pYg87rzuCRH5yrhjfn1c/b7lZA/RtBmhA5umTb/HgE+JSBDYALw57rGjwB1KqU3Avwf+o1P+vwP/zWlRbQZ6nFRgbwFbnWM+BfxwXE6/TcDvUt6FYBkwfluXEaXU9Uqpxyap39/ipGVzMtPcyrvZbG4C/n9OvX9VRDY7GVd+DbjNqZ9FOQ2Zps0I3RWpadNMKbXfaV19mnLrbbwaJt+l4Q3g34pIO/CPSqkTTnmlO/Jx5/f4fJdvKaV6AJzUS0sob0UC5U0+L1S/l0Tkb0SkkXIQ+4lSynQafDuUUiPOa/4j5aS6JuVkum87x4TQqcS0GaRbbJo2M54A/pxx3ZCOyi4N64GPAUEApdTfAQ9S3lrnKRH5sHP848A9InI9EFZKvTPutcbvQnBu5vVzs7+f6/uUc13+BvDdceXn5uBTlPMUbldKXef8rFZKffUSr69pU0YHNk2bGd8F/kQpdeCc8kl3aRCRZcAppdSjlIPZBijvdUY5cfB3OT9IfhDfo9yNyTn7d90rInUiEgI+DrwGPAd8orKbgfP44itYF017T3Rg07QZoJTqcYLUuf4M+E8isoeJLaxPAgedLsX1lFtUFX9PeZfjKxbYlFIDwBHO36PuLeAnwH7KXZS7nMD3x8CzTqb9HUDrlaqLpr1XOru/pmnnEZEw5Z2Sr1dKJZyyfwlsVkr99sWeq2kzTbfYNE2bQEQ+Qrm19o1KUNO0uUS32DRN07R5RbfYNE3TtHlFBzZN0zRtXtGBTdM0TZtXdGDTNE3T5hUd2DRN07R55f8PPwBoewYPig0AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for var in cat_others:\n", - " # make boxplot with Catplot\n", - " sns.catplot(x=var, y='SalePrice', data=data, kind=\"box\", height=4, aspect=1.5)\n", - " # add data points to boxplot with stripplot\n", - " sns.stripplot(x=var, y='SalePrice', data=data, jitter=0.1, alpha=0.3, color='k')\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Clearly, the categories give information on the SalePrice, as different categories show different median sale prices." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Disclaimer:**\n", - "\n", - "There is certainly more that can be done to understand the nature of this data and the relationship of these variables with the target, SalePrice. And also about the distribution of the variables themselves.\n", - "\n", - "However, we hope that through this notebook we gave you a flavour of what data analysis looks like." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Additional Resources\n", - "\n", - "- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n", - "- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n", - "- [Predict house price with Feature-engine](https://www.kaggle.com/solegalli/predict-house-price-with-feature-engine) - Kaggle kernel\n", - "- [Comprehensive data exploration with Python](https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python) - Kaggle kernel\n", - "- [How I made top 0.3% on a Kaggle competition](https://www.kaggle.com/lavanyashukla01/how-i-made-top-0-3-on-a-kaggle-competition) - Kaggle kernel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "644.467px", - "left": "0px", - "right": "1324px", - "top": "110.533px", - "width": "266px" - }, - "toc_section_display": "block", - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Pipeline - Data Analysis\n", + "\n", + "In the following notebooks, we will go through the implementation of each of the steps in the Machine Learning Pipeline. \n", + "\n", + "We will discuss:\n", + "\n", + "1. **Data Analysis**\n", + "2. Feature Engineering\n", + "3. Feature Selection\n", + "4. Model Training\n", + "5. Obtaining Predictions / Scoring\n", + "\n", + "\n", + "We will use the house price dataset available on [Kaggle.com](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data). See below for more details.\n", + "\n", + "===================================================================================================\n", + "\n", + "## Predicting Sale Price of Houses\n", + "\n", + "The aim of the project is to build a machine learning model to predict the sale price of homes based on different explanatory variables describing aspects of residential houses.\n", + "\n", + "\n", + "### Why is this important? \n", + "\n", + "Predicting house prices is useful to identify fruitful investments or to determine whether the price advertised for a house is over or under-estimated.\n", + "\n", + "\n", + "### What is the objective of the machine learning model?\n", + "\n", + "We aim to minimise the difference between the real price and the price estimated by our model. We will evaluate model performance with the:\n", + "\n", + "1. mean squared error (mse)\n", + "2. root squared of the mean squared error (rmse)\n", + "3. r-squared (r2).\n", + "\n", + "\n", + "### How do I download the dataset?\n", + "\n", + "**Instructions also in the lecture \"Download Dataset\" in section 1 of the course**\n", + "\n", + "- Visit the [Kaggle Website](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data).\n", + "\n", + "- Remember to **log in**.\n", + "\n", + "- Scroll down to the bottom of the page, and click on the link **'train.csv'**, and then click the 'download' blue button towards the right of the screen, to download the dataset.\n", + "\n", + "- The download the file called **'test.csv'** and save it in the directory with the notebooks.\n", + "\n", + "\n", + "\n", + "**Note the following:**\n", + "\n", + "- You need to be logged in to Kaggle in order to download the datasets.\n", + "- You need to accept the terms and conditions of the competition to download the dataset\n", + "- If you save the file to the directory with the jupyter notebook, then you can run the code as it is written here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Analysis\n", + "\n", + "Let's go ahead and load the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# to handle datasets\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# for plotting\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# for the yeo-johnson transformation\n", + "import scipy.stats as stats\n", + "\n", + "# to display all the columns of the dataframe in the notebook\n", + "pd.pandas.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n" + ] + }, + { + "data": { + "text/html": [ + "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
\n", + "
" + ], + "text/plain": [ + " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 1 60 RL 65.0 8450 Pave NaN Reg \n", + "1 2 20 RL 80.0 9600 Pave NaN Reg \n", + "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", + "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", + "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", + "\n", + " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", + "0 Lvl AllPub Inside Gtl CollgCr Norm \n", + "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", + "2 Lvl AllPub Inside Gtl CollgCr Norm \n", + "3 Lvl AllPub Corner Gtl Crawfor Norm \n", + "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", + "\n", + " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", + "0 Norm 1Fam 2Story 7 5 2003 \n", + "1 Norm 1Fam 1Story 6 8 1976 \n", + "2 Norm 1Fam 2Story 7 5 2001 \n", + "3 Norm 1Fam 2Story 7 5 1915 \n", + "4 Norm 1Fam 2Story 8 5 2000 \n", + "\n", + " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", + "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", + "1 1976 Gable CompShg MetalSd MetalSd None \n", + "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", + "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", + "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", + "\n", + " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", + "0 196.0 Gd TA PConc Gd TA No \n", + "1 0.0 TA TA CBlock Gd TA Gd \n", + "2 162.0 Gd TA PConc Gd TA Mn \n", + "3 0.0 TA TA BrkTil TA Gd No \n", + "4 350.0 Gd TA PConc Gd TA Av \n", + "\n", + " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", + "0 GLQ 706 Unf 0 150 856 \n", + "1 ALQ 978 Unf 0 284 1262 \n", + "2 GLQ 486 Unf 0 434 920 \n", + "3 ALQ 216 Unf 0 540 756 \n", + "4 GLQ 655 Unf 0 490 1145 \n", + "\n", + " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", + "0 GasA Ex Y SBrkr 856 854 0 \n", + "1 GasA Ex Y SBrkr 1262 0 0 \n", + "2 GasA Ex Y SBrkr 920 866 0 \n", + "3 GasA Gd Y SBrkr 961 756 0 \n", + "4 GasA Ex Y SBrkr 1145 1053 0 \n", + "\n", + " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", + "0 1710 1 0 2 1 3 \n", + "1 1262 0 1 2 0 3 \n", + "2 1786 1 0 2 1 3 \n", + "3 1717 1 0 1 0 3 \n", + "4 2198 1 0 2 1 4 \n", + "\n", + " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", + "0 1 Gd 8 Typ 0 NaN \n", + "1 1 TA 6 Typ 1 TA \n", + "2 1 Gd 6 Typ 1 TA \n", + "3 1 Gd 7 Typ 1 Gd \n", + "4 1 Gd 9 Typ 1 TA \n", + "\n", + " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", + "0 Attchd 2003.0 RFn 2 548 TA \n", + "1 Attchd 1976.0 RFn 2 460 TA \n", + "2 Attchd 2001.0 RFn 2 608 TA \n", + "3 Detchd 1998.0 Unf 3 642 TA \n", + "4 Attchd 2000.0 RFn 3 836 TA \n", + "\n", + " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", + "0 TA Y 0 61 0 0 \n", + "1 TA Y 298 0 0 0 \n", + "2 TA Y 0 42 0 0 \n", + "3 TA Y 0 35 272 0 \n", + "4 TA Y 192 84 0 0 \n", + "\n", + " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", + "0 0 0 NaN NaN NaN 0 2 2008 \n", + "1 0 0 NaN NaN NaN 0 5 2007 \n", + "2 0 0 NaN NaN NaN 0 9 2008 \n", + "3 0 0 NaN NaN NaN 0 2 2006 \n", + "4 0 0 NaN NaN NaN 0 12 2008 \n", + "\n", + " SaleType SaleCondition SalePrice \n", + "0 WD Normal 208500 \n", + "1 WD Normal 181500 \n", + "2 WD Normal 223500 \n", + "3 WD Abnorml 140000 \n", + "4 WD Normal 250000 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load dataset\n", + "data = pd.read_csv('train.csv')\n", + "\n", + "# rows and columns of the data\n", + "print(data.shape)\n", + "\n", + "# visualise the dataset\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1460, 80)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop id, it is just a number given to identify each house\n", + "data.drop('Id', axis=1, inplace=True)\n", + "\n", + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The house price dataset contains 1460 rows, that is, houses, and 80 columns, i.e., variables. \n", + "\n", + "79 are predictive variables and 1 is the target variable: SalePrice\n", + "\n", + "## Analysis\n", + "\n", + "**We will analyse the following:**\n", + "\n", + "1. The target variable\n", + "2. Variable types (categorical and numerical)\n", + "3. Missing data\n", + "4. Numerical variables\n", + " - Discrete\n", + " - Continuous\n", + " - Distributions\n", + " - Transformations\n", + "\n", + "5. Categorical variables\n", + " - Cardinality\n", + " - Rare Labels\n", + " - Special mappings\n", + " \n", + "6. Additional Reading Resources\n", + "\n", + "## Target\n", + "\n", + "Let's begin by exploring the target distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# histogran to evaluate target distribution\n", + "\n", + "data['SalePrice'].hist(bins=50, density=True)\n", + "plt.ylabel('Number of houses')\n", + "plt.xlabel('Sale Price')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the target is continuous, and the distribution is skewed towards the right.\n", + "\n", + "We can improve the value spread with a mathematical transformation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mHsWHAAcDz0TEYXkGZmZmxciyRzB9ZFrJHezPBd6QZ1BmZlacLPcj+LVI3Am8JZ9wzMysaFm6hv60ZvYgoBf4ZW4RmVnb9Kz8BivmDe93n2ifVlpuWc4aqr0vwTCwi6R7yMzMSiDLMQLfl8DMrMSa3aryskbrSA4XXJlDPGZmVrBmewTP1Fk2DVgKvBxwITAzK4Fmt6q8dmRa0nTgfcDFwABwbaPnmZlZZ2l6jEDSkcD7gUXALcAJEfFUEYGZmVkxGl5HIOkaYCPwNDAvIj423iIgaYGkxyTtkLSyyXbnSQpJveNp38zMJq7ZBWUrgFcAHwF+KOnn6eNpST8fq2FJU4DrgbOB44CLJB1XZ7uRbqfvHEgCZmY2MQ0LQUQcFBGHRsT0iDis5jE94zhDJwE7ImJnelObAepff3AlcDW+SM3MrC0UEWNvdSANS+cDCyLiknR+MXByRCyv2eYE4NKIOE/SIPCBiNhUp61lwDKA7u7u+QMDAy2JcWhoiK6urpa01U5lyKNZDlv27Cs4mgPXfSg88Yt2RzEx9XKYN2tGe4KZgLL/XYxXf3//5oio2/2e5criXEg6CPgUsGSsbSNiFbAKoLe3N/r6+loSw+DgIK1qq53KkEezHEYPdzCZrZg3zLVb2vZn1RL1cti1qK89wUxA2f8uWmlcg86N0x7gmJr52emyEdOB44FBSbtIRjRd7QPGZmbFyrMQbATmSpoj6RDgQmD1yMqI2BcRMyOiJyJ6gPuBc+p1DZmZWX5yKwQRMQwsB9YBjwK3R8Q2SVdI8v2OzcwmiVw7MyNiLbB21LK6YxhFRF+esZiZWX15dg2ZmVkHcCEwM6s4FwIzs4pzITAzqzgXAjOzinMhMDOruM6+Ft5KpaeDhpKwRLP3bNdVCwuMxCbCewRmZhXnQmBmVnEuBGZmFedCYGZWcS4EZmYV50JgZlZxLgRmZhXnQmBmVnEuBGZmFecriy03ja469RWnncdXfZebC4FNmL8krB7/EOgc7hoyM6s47xFYZv7lb1ZO3iMwM6u4XAuBpAWSHpO0Q9LKOuvfL2m7pEck3SPp2DzjMTOz/eVWCCRNAa4HzgaOAy6SdNyozR4EeiPidcAdwCfyisfMzOrLc4/gJGBHROyMiOeBAeDc2g0iYn1EPJvO3g/MzjEeMzOrQxGRT8PS+cCCiLgknV8MnBwRyxtsfx3w44j46zrrlgHLALq7u+cPDAy0JMahoSG6urpa0lY7FZXHlj37cmu7+1B44he5NV+YMuSRdw7zZs3Ir/EaZfj7bmUO/f39myOit966SXHWkKQ/B3qB0+utj4hVwCqA3t7e6Ovra8nrDg4O0qq22qmoPJbkeNbQinnDXLtlUnwcJ6QMeeSdw65Ffbm1XasMf99F5ZDnJ3YPcEzN/Ox02W+RdCZwKXB6RDyXYzxmZlZHnscINgJzJc2RdAhwIbC6dgNJfwjcCJwTEXtzjMXMzBrIrRBExDCwHFgHPArcHhHbJF0h6Zx0s2uALuArkh6StLpBc2ZmlpNcOzMjYi2wdtSyy2qmz8zz9c3MbGy+stjMrOJcCMzMKs6FwMys4jr7hGcz6zitGsXW9zVoHe8RmJlVnAuBmVnFuRCYmVWcC4GZWcW5EJiZVZwLgZlZxbkQmJlVnAuBmVnFuRCYmVWcryw2s47U6AplX3E8ft4jMDOrOBcCM7OKc9dQyY1397lVA4KZtcvIZ3jFvGGW1Hye3WXUmPcIzMwqznsEHabeL/YV84bpKz4Us47ig8uNeY/AzKzivEcwSY23r959+2Z2oCpVCEZ/WY4+mFRP3ruN/gI3m5xa+bc53u+Rog9459o1JGmBpMck7ZC0ss76l0j6H+n670jqyTMeMzPbX257BJKmANcDZwG7gY2SVkfE9prNlgJPRcTvSboQuBp4W14xHYiyHmDynohZwn8L+e4RnATsiIidEfE8MACcO2qbc4Fb0uk7gD+SpBxjMjOzURQR+TQsnQ8siIhL0vnFwMkRsbxmm63pNrvT+e+n2zw5qq1lwLJ09jXAYy0Kcybw5JhbTX5lyKMMOUA58ihDDlCOPFqZw7ERcVS9FR1xsDgiVgGrWt2upE0R0dvqdotWhjzKkAOUI48y5ADlyKOoHPLsGtoDHFMzPztdVncbSVOBGcBPcozJzMxGybMQbATmSpoj6RDgQmD1qG1WA+9Ip88H7o28+qrMzKyu3LqGImJY0nJgHTAFuCkitkm6AtgUEauBLwBfkrQD+ClJsShSy7ub2qQMeZQhByhHHmXIAcqRRyE55Haw2MzMOoPHGjIzqzgXAjOziitlIZB0k6S96XUKI8uOlHS3pMfTf49o8NwXJD2UPkYf3C5UgzwukLRN0ouSGp5WNtbwHkWZYA67JG1J34tNxUTcMJZ6eVwj6buSHpH0NUmHN3juZH4vsuYw2d+LK9McHpJ0l6RXNHjuO9LvgMclvaPeNkWYYA6t/46KiNI9gNOAE4CtNcs+AaxMp1cCVzd47lC74x8jj9eSXFQ3CPQ2eN4U4PvAq4BDgIeB4zoph3S7XcDMdr8PTfJ4MzA1nb663meqA96LMXPokPfisJrp9wI31HnekcDO9N8j0ukjOimHdF3Lv6NKuUcQEfeRnIVUq3Y4i1uAPy4ypgNRL4+IeDQixrqyOsvwHoWYQA6TSoM87oqI4XT2fpJrZUab7O9FlhwmlQZ5/LxmdhpQ7yyYtwB3R8RPI+Ip4G5gQW6BNjGBHHJRykLQQHdE/Cid/jHQ3WC7l0raJOl+SX9cTGgtNwv4l5r53emyThPAXZI2p8OMTGbvBP53neWd9F40ygE64L2Q9DeS/gVYBFxWZ5NJ/15kyAFy+I6qUiH4tUj2rxpV22MjuaT77cDfSXp1cZHZKKdGxAnA2cC7JZ3W7oDqkXQpMAx8ud2xHKgMOUz69yIiLo2IY0hyWD7W9pNRxhxa/h1VpULwhKSjAdJ/99bbKCL2pP/uJOnD/sOiAmyhLMN7THo178Ve4Gsk3SyTiqQlwL8HFqU/MEab9O9Fhhw64r2o8WXgvDrLJ/17UaNRDrl8R1WpENQOZ/EO4H+N3kDSEZJekk7PBE4Bto/ergNkGd5jUpM0TdL0kWmSg5pbmz+rWJIWAH8JnBMRzzbYbFK/F1ly6JD3Ym7N7LnAd+tstg54c/p3fgRJHuuKiC+LLDnk9h3VjiPmeT+A24AfAb8i6QdcCrwcuAd4HPgWcGS6bS/w+XT6TcAWkjM7tgBLJ2Eef5JOPwc8AaxLt30FsLbmuW8FvkdyxsqlnZYDyVk2D6ePbe3MoUkeO0j6nB9KHzd04HsxZg4d8l58laQ4PQJ8HZiVbvvrv+90/p1pzjuAizsth7y+ozzEhJlZxVWpa8jMzOpwITAzqzgXAjOzinMhMDOrOBcCM7OKcyGwjiFpqMDXuiYdIfWaUcu7Ja2R9LCk7ZLWZmhrXHHXjPT5SDoK5e822G5to9FCzcbDp49ax5A0FBFdBb3WPpJrTV4YtfxGYHtEfDqdf11EPDJGW+OKW9IuklFZn5T0t0BXRLy3Zr1I/nZfzJ6RWWPeI7COJun16eBbI+PpH5EuP7FmbPdrasd9r3muRtalv8Dfli5fDXQBm0eW1Tia5AIgAEaKgKQuSfdIeiBtq+4Io5I+KGljGtt/zZDifcDvSepRck+DL5JcdHRMuucwM233L9I2H5b0pXTZUZK+mr7eRkmnZHg9q6J2XiHohx/jeVBnHHaSqzBPT6evAP4und4KvDGdvoqacd9rnnseyVDEU0hGo/1n4OhGr5UufwvwM2A9cCnwinT5VNLx5IGZJFeuqrYtkiENVgEi+RG2BjitzmvsIh37H7iO5D4BPcCLwBtGbwf8W5Irl0eeM3LV/K0kg8UBvBJ4tN3voR+T8+E9AutYkmYAh0fEhnTRLcBpab/59Ij4drr81gZNnArcFhEvRMQTwAbgxGavGRHrSIZc+Bzw+8CDko4i+XL/W0mPkAxhMov9hzp/c/p4EHggff5c6lsv6SHgMODj6bJ/ioj762x7BvCViHgyjXFknPszgevSdlYDh0kqpGvNOsvUdgdg1mnSL9pbgVslrSG529R04ChgfkT8Ku3nf+mopwr4eETcmOFl+ke+2AHS4vbMOEM9iGQP4pfjfJ5VjPcIrGNFxD7gKUn/Ll20GNgQET8DnpZ0crr8wgZN/B/gbZKmpL/qTwP+sdlrSjpD0svS6enAq0m6lGYAe9Mi0A8cW+fp64B3jvwqlzRL0u9kTLeZe4ELJL08bffIdPldwHtqYn99C17LSsh7BNZJXiZpd838p0iGFL8h/XLeCVycrlsKfE7SiyRdPvvqtPc14I0kIzkG8JcR8eMxYphP0t0yTPJD6vMRsVHSD4CvS9oCbKLOEMIRcZek1wLfTk78YQj4cxrcGyOriNgm6W+ADZJeIOl6WkJy39vr0+6qqSQHnv/jRF7Lysmnj1opSeqKiKF0eiXJQeD3tTkss0nJewRWVgslfZjkM/5PJL+QzawO7xGYmVWcDxabmVWcC4GZWcW5EJiZVZwLgZlZxbkQmJlV3P8HqRRZESps+HwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's transform the target using the logarithm\n", + "\n", + "np.log(data['SalePrice']).hist(bins=50, density=True)\n", + "plt.ylabel('Number of houses')\n", + "plt.xlabel('Log of Sale Price')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the distribution looks more Gaussian." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Variable Types\n", + "\n", + "Next, let's identify the categorical and numerical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "44" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's identify the categorical variables\n", + "# we will capture those of type *object*\n", + "\n", + "cat_vars = [var for var in data.columns if data[var].dtype == 'O']\n", + "\n", + "# MSSubClass is also categorical by definition, despite its numeric values\n", + "# (you can find the definitions of the variables in the data_description.txt\n", + "# file available on Kaggle, in the same website where you downloaded the data)\n", + "\n", + "# lets add MSSubClass to the list of categorical variables\n", + "cat_vars = cat_vars + ['MSSubClass']\n", + "\n", + "# number of categorical variables\n", + "len(cat_vars)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# cast all variables as categorical\n", + "data[cat_vars] = data[cat_vars].astype('O')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "35" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now let's identify the numerical variables\n", + "\n", + "num_vars = [\n", + " var for var in data.columns if var not in cat_vars and var != 'SalePrice'\n", + "]\n", + "\n", + "# number of numerical variables\n", + "len(num_vars)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Missing values\n", + "\n", + "Let's go ahead and find out which variables of the dataset contain missing values." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PoolQC 0.995205\n", + "MiscFeature 0.963014\n", + "Alley 0.937671\n", + "Fence 0.807534\n", + "FireplaceQu 0.472603\n", + "LotFrontage 0.177397\n", + "GarageType 0.055479\n", + "GarageYrBlt 0.055479\n", + "GarageFinish 0.055479\n", + "GarageQual 0.055479\n", + "GarageCond 0.055479\n", + "BsmtExposure 0.026027\n", + "BsmtFinType2 0.026027\n", + "BsmtFinType1 0.025342\n", + "BsmtCond 0.025342\n", + "BsmtQual 0.025342\n", + "MasVnrArea 0.005479\n", + "MasVnrType 0.005479\n", + "Electrical 0.000685\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make a list of the variables that contain missing values\n", + "vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 0]\n", + "\n", + "# determine percentage of missing values (expressed as decimals)\n", + "# and display the result ordered by % of missin data\n", + "\n", + "data[vars_with_na].isnull().mean().sort_values(ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our dataset contains a few variables with a big proportion of missing values (4 variables at the top). And some other variables with a small percentage of missing observations.\n", + "\n", + "This means that to train a machine learning model with this data set, we need to impute the missing data in these variables.\n", + "\n", + "We can also visualize the percentage of missing values in the variables as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot\n", + "\n", + "data[vars_with_na].isnull().mean().sort_values(\n", + " ascending=False).plot.bar(figsize=(10, 4))\n", + "plt.ylabel('Percentage of missing data')\n", + "plt.axhline(y=0.90, color='r', linestyle='-')\n", + "plt.axhline(y=0.80, color='g', linestyle='-')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of categorical variables with na: 16\n", + "Number of numerical variables with na: 3\n" + ] + } + ], + "source": [ + "# now we can determine which variables, from those with missing data,\n", + "# are numerical and which are categorical\n", + "\n", + "cat_na = [var for var in cat_vars if var in vars_with_na]\n", + "num_na = [var for var in num_vars if var in vars_with_na]\n", + "\n", + "print('Number of categorical variables with na: ', len(cat_na))\n", + "print('Number of numerical variables with na: ', len(num_na))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_na" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Alley',\n", + " 'MasVnrType',\n", + " 'BsmtQual',\n", + " 'BsmtCond',\n", + " 'BsmtExposure',\n", + " 'BsmtFinType1',\n", + " 'BsmtFinType2',\n", + " 'Electrical',\n", + " 'FireplaceQu',\n", + " 'GarageType',\n", + " 'GarageFinish',\n", + " 'GarageQual',\n", + " 'GarageCond',\n", + " 'PoolQC',\n", + " 'Fence',\n", + " 'MiscFeature']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_na" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Relationship between missing data and Sale Price\n", + "\n", + "Let's evaluate the price of the house in those observations where the information is missing. We will do this for each variable that shows missing data." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def analyse_na_value(df, var):\n", + "\n", + " # copy of the dataframe, so that we do not override the original data\n", + " # see the link for more details about pandas.copy()\n", + " # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.copy.html\n", + " df = df.copy()\n", + "\n", + " # let's make an interim variable that indicates 1 if the\n", + " # observation was missing or 0 otherwise\n", + " df[var] = np.where(df[var].isnull(), 1, 0)\n", + "\n", + " # let's compare the median SalePrice in the observations where data is missing\n", + " # vs the observations where data is available\n", + "\n", + " # determine the median price in the groups 1 and 0,\n", + " # and the standard deviation of the sale price,\n", + " # and we capture the results in a temporary dataset\n", + " tmp = df.groupby(var)['SalePrice'].agg(['mean', 'std'])\n", + "\n", + " # plot into a bar graph\n", + " tmp.plot(kind=\"barh\", y=\"mean\", legend=False,\n", + " xerr=\"std\", title=\"Sale Price\", color='green')\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's run the function on each variable with missing data\n", + "\n", + "for var in vars_with_na:\n", + " analyse_na_value(data, var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some variables, the average Sale Price in houses where the information is missing, differs from the average Sale Price in houses where information exists. This suggests that data being missing could be a good predictor of Sale Price." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Numerical variables\n", + "\n", + "Let's go ahead and find out what numerical variables we have in the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of numerical variables: 35\n" + ] + }, + { + "data": { + "text/html": [ + "
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LotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSold
065.084507520032003196.0706015085685685401710102131802003.025480610000022008
180.0960068197619760.0978028412621262001262012031611976.0246029800000052007
268.0112507520012002162.0486043492092086601786102131612001.026080420000092008
360.0955075191519700.0216054075696175601717101031711998.03642035272000022006
484.0142608520002000350.0655049011451145105302198102141912000.038361928400000122008
\n", + "
" + ], + "text/plain": [ + " LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd \\\n", + "0 65.0 8450 7 5 2003 2003 \n", + "1 80.0 9600 6 8 1976 1976 \n", + "2 68.0 11250 7 5 2001 2002 \n", + "3 60.0 9550 7 5 1915 1970 \n", + "4 84.0 14260 8 5 2000 2000 \n", + "\n", + " MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF \\\n", + "0 196.0 706 0 150 856 856 \n", + "1 0.0 978 0 284 1262 1262 \n", + "2 162.0 486 0 434 920 920 \n", + "3 0.0 216 0 540 756 961 \n", + "4 350.0 655 0 490 1145 1145 \n", + "\n", + " 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath \\\n", + "0 854 0 1710 1 0 2 \n", + "1 0 0 1262 0 1 2 \n", + "2 866 0 1786 1 0 2 \n", + "3 756 0 1717 1 0 1 \n", + "4 1053 0 2198 1 0 2 \n", + "\n", + " HalfBath BedroomAbvGr KitchenAbvGr TotRmsAbvGrd Fireplaces \\\n", + "0 1 3 1 8 0 \n", + "1 0 3 1 6 1 \n", + "2 1 3 1 6 1 \n", + "3 0 3 1 7 1 \n", + "4 1 4 1 9 1 \n", + "\n", + " GarageYrBlt GarageCars GarageArea WoodDeckSF OpenPorchSF \\\n", + "0 2003.0 2 548 0 61 \n", + "1 1976.0 2 460 298 0 \n", + "2 2001.0 2 608 0 42 \n", + "3 1998.0 3 642 0 35 \n", + "4 2000.0 3 836 192 84 \n", + "\n", + " EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold \n", + "0 0 0 0 0 0 2 2008 \n", + "1 0 0 0 0 0 5 2007 \n", + "2 0 0 0 0 0 9 2008 \n", + "3 272 0 0 0 0 2 2006 \n", + "4 0 0 0 0 0 12 2008 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Number of numerical variables: ', len(num_vars))\n", + "\n", + "# visualise the numerical variables\n", + "data[num_vars].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Temporal variables\n", + "\n", + "We have 4 year variables in the dataset:\n", + "\n", + "- YearBuilt: year in which the house was built\n", + "- YearRemodAdd: year in which the house was remodeled\n", + "- GarageYrBlt: year in which a garage was built\n", + "- YrSold: year in which the house was sold\n", + "\n", + "We generally don't use date variables in their raw format. Instead, we extract information from them. For example, we can capture the difference in years between the year the house was built and the year the house was sold." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'YrSold']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# list of variables that contain year information\n", + "\n", + "year_vars = [var for var in num_vars if 'Yr' in var or 'Year' in var]\n", + "\n", + "year_vars" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "YearBuilt [2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 1965 2005 1962 2006\n", + " 1960 1929 1970 1967 1958 1930 2002 1968 2007 1951 1957 1927 1920 1966\n", + " 1959 1994 1954 1953 1955 1983 1975 1997 1934 1963 1981 1964 1999 1972\n", + " 1921 1945 1982 1998 1956 1948 1910 1995 1991 2009 1950 1961 1977 1985\n", + " 1979 1885 1919 1990 1969 1935 1988 1971 1952 1936 1923 1924 1984 1926\n", + " 1940 1941 1987 1986 2008 1908 1892 1916 1932 1918 1912 1947 1925 1900\n", + " 1980 1989 1992 1949 1880 1928 1978 1922 1996 2010 1946 1913 1937 1942\n", + " 1938 1974 1893 1914 1906 1890 1898 1904 1882 1875 1911 1917 1872 1905]\n", + "\n", + "YearRemodAdd [2003 1976 2002 1970 2000 1995 2005 1973 1950 1965 2006 1962 2007 1960\n", + " 2001 1967 2004 2008 1997 1959 1990 1955 1983 1980 1966 1963 1987 1964\n", + " 1972 1996 1998 1989 1953 1956 1968 1981 1992 2009 1982 1961 1993 1999\n", + " 1985 1979 1977 1969 1958 1991 1971 1952 1975 2010 1984 1986 1994 1988\n", + " 1954 1957 1951 1978 1974]\n", + "\n", + "GarageYrBlt [2003. 1976. 2001. 1998. 2000. 1993. 2004. 1973. 1931. 1939. 1965. 2005.\n", + " 1962. 2006. 1960. 1991. 1970. 1967. 1958. 1930. 2002. 1968. 2007. 2008.\n", + " 1957. 1920. 1966. 1959. 1995. 1954. 1953. nan 1983. 1977. 1997. 1985.\n", + " 1963. 1981. 1964. 1999. 1935. 1990. 1945. 1987. 1989. 1915. 1956. 1948.\n", + " 1974. 2009. 1950. 1961. 1921. 1900. 1979. 1951. 1969. 1936. 1975. 1971.\n", + " 1923. 1984. 1926. 1955. 1986. 1988. 1916. 1932. 1972. 1918. 1980. 1924.\n", + " 1996. 1940. 1949. 1994. 1910. 1978. 1982. 1992. 1925. 1941. 2010. 1927.\n", + " 1947. 1937. 1942. 1938. 1952. 1928. 1922. 1934. 1906. 1914. 1946. 1908.\n", + " 1929. 1933.]\n", + "\n", + "YrSold [2008 2007 2006 2009 2010]\n", + "\n" + ] + } + ], + "source": [ + "# let's explore the values of these temporal variables\n", + "\n", + "for var in year_vars:\n", + " print(var, data[var].unique())\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the values are years.\n", + "\n", + "We can explore the evolution of the sale price with the years in which the house was sold:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Median House Price')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot median sale price vs year in which it was sold\n", + "\n", + "data.groupby('YrSold')['SalePrice'].median().plot()\n", + "plt.ylabel('Median House Price')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There has been a drop in the value of the houses. That is unusual, in real life, house prices typically go up as years go by.\n", + "\n", + "Let's explore a bit further. \n", + "\n", + "Let's plot the price of sale vs year in which it was built" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Median House Price')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot median sale price vs year in which it was built\n", + "\n", + "data.groupby('YearBuilt')['SalePrice'].median().plot()\n", + "plt.ylabel('Median House Price')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that newly built / younger houses tend to be more expensive.\n", + "\n", + "Could it be that lately older houses were sold? Let's have a look at that." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this, we will capture the elapsed years between the Year variables and the year in which the house was sold:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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bYWZzzCzXzArMrNjMDsQiOBFJXG8u2sKE6TnclNGTC/t3jnc4Uk2R3Bh+DLgGWAM0AW4hmBtARJLU2h0Hue/VxQzp0YafXRhxhXhJQBFVdHL3tUD9cH6ATOD86IYlIonqUH4R35s0nyYN6/P4tYNpWF+F4WqzSO7iHA4ng19oZr8HthJh8hCRusXd+dnUJazbmctzNw+nU6vG8Q5JjlMkX+Y3hO+7EzgEdCfoLSQiSea5met5c9EW7j33ZDL6tI93OFIDKptovj7wW3e/DsgDHohJVCKScBZs2Muvpy3n6307cNuZveMdjtSQCq8E3L0Y6BE2B4lIktpzqIA7Js+nY8vG/PlKFYarSyK5J7AOyDKzNwmagwBw9z9HLSoRSRjFJc5dUxayK7eAV28bRaumDeMdktSgSJLA5+GjHtAiuuGISKL5yz/X8Onqnfz2kv7079Yq3uFIDYtkxLDuA4gkqU9W7+SRD9dw6eCuXDOse7zDkSiIZFKZFOAnwKnAF/3B3H10FOMSkTjbvO8IP3xxASd3bMFvvt1fheHqqEi6iE4GVgI9CXoH5QBzohiTiMRZflExt0+eT1Gx88R1g2nSqH68Q5IoiSQJtHP3Z4BCd//E3W8CdBUgUof95u0VLNq4jz9eMYBeKgxXp0VyY/joxDFbzewiYAvQNnohiUg8vbFwM8/OWM8tZ/Tk/NNUGK6uiyQJ/JeZtQLuBf4CtATujmpUIhIXa7Yf5L5XlzA0rQ0/vUCF4ZJBJL2DpoWL+6nCJDJm1p1gApqOgANPufsjZnY68CTQnOD+wnXurtLUInGWm1/E9ybNo9kJ9XlMheGSRrm/ZTM71cy+Wer5w2Y2PnwMjmDfRcC97t4PGAHcYWb9gKeB+9y9P/Aa8OPj+xFE5Hi5O/e9upjsXYd49JpBdGypwnDJoqJU/ztgV6nn5wFvAx8B/1nZjt19q7vPD5cPAiuArsBJwKfh295HxehE4m7i9BymLd7Kj847mVG9VRgumVSUBDq7+/RSzw+4+6vu/hxQpU+JmaUBg4BZwDLgW+FLVxBUJS1rm3FmNtfM5u7cubMqhxORKpi/YS+/+fsKzj6lA9/7qgrDJZuKksC/lYhw9xGlnnaI9ABm1hx4FbgrbPu/CbjdzOaFxygoazt3f8rd0909PSUlJdLDiUgV7M7N547J8+nUqjF/ukKF4ZJRRTeGt5jZcHefVXqlmY0g6CZaKTNrSJAAJrv7VAB3XwmcG75+EnBRdQIXkeNztDDc7kMFTFVhuKRVURL4KTDFzCYA88N1Q4Abgasq27EFY8yfAVaUrjhqZh3cfYeZ1QP+g6CnkIjE2CMfruFfa3bxu0v7c1pXFYZLVuU2B7n7bGA4UB8YEz7qASPC1yqTQTAr2WgzWxg+LgSuMbPVBKUotgCZx/UTiEiVfbRqB49+uIbLh3TjqqEqDJfMKhwn4O47iKAnUDnbfgaU18D4SHX2KSLHb9Pew9w9ZSF9O7Xg1986TYXhkpxGg4gkkaOF4YqLnSevH6LCcBJR2QgRqSN+PW05izft58nrh5DWvlm8w5EEoCsBkSTx+oLNTJq5gXFf7cX5p3WKdziSICKZVCYd+AXQI3y/Ae7uA6Icm4jUkNXbD/KzqUsYltaWn5x3crzDkQQSSXPQZIL6PkuAkuiGIyI17cvCcA147NpBNFBhOCklkiSw093fjHokIlLj3J2fvrKY9bsPM/mW4XRQYTg5RiRJ4H4zexr4EMg/uvLoCGARSVyZWTm8vWQr913QlxG92sU7HElAkSSBsUBfoCFfNgc5oCQgksDmrd/Db/++gnP6deTWr/aKdziSoCJJAkPdXXeSRGqRXbn53DF5AV3bNOGPV5yuAWFSrkjuEE0PJ4MRkVqguMT54YsL2Hu4gCeuG0yrJioMJ+WL5EpgBLDQzLIJ7gmoi6hIAnv4/dVkrd3N7y8bwKldVBhOKhZJEjg/6lGISI3458rtPPbRWq5M78aVKgwnEai0Ocjd1wOtgYvDR+twnYgkkI17DnP3lEX069ySB791WrzDkVqi0iRgZj8kGDDWIXxMMrPvRzswEYlcXmFQGK7Enb9eP5jGDVUYTiITSXPQzcBwdz8EYGYPATOAv0QzMBGJ3IPTlrNk836eumEIPdqpMJxELpLeQQYUl3peTPnzBIhIjE2dv4nnZ23g1jN7ce6pKgwnVRPJlUAmMMvMXguff5tg2kgRibOV2w7w89eWMLxnW358robzSNVVmATCeYBnAh8DZ4Srx7r7gijHJSKVOJhXyG2T5tOicUP+osJwUk2VTS9ZYmaPu/sgvpxsXkTizN35ySuL2bDnMM/fMpwOLVQYTqonkj8dPjSzy0zjzkUSxjOfZfPO0m389PyTGa7CcHIcIkkCtwIvA/lmdsDMDprZgSjHJSLlmJuzh9+9s5LzTu3Id7+iwnByfMpNAmaWES6muHs9d2/k7i3dvYW7t4xRfCJSys6D+dzx/Hy6tWnCH1QYTmpARVcCj4b/To9FICJSsaLiEn7wwgL2HS7kieuG0LKxCsPJ8avoxnChmT0FdDOzR4990d1/EL2wRORYf35/NTPW7eYPlw+gXxddjEvNqCgJfAM4GzgPmFfVHZtZd+BZoCPBJDRPufsjZjYQeBJoDBQBt7v77KruXySZfLB8O098/DlXD+3OFekqDCc1p9wk4O67gBfNbIW7L6rGvouAe919vpm1AOaZ2fvA74EH3P0dM7swfH5WNfYvkhQ27D7MPS8t5NQuLfnVN0+NdzhSx1Q6YriaCQB33wpsDZcPmtkKoCvBVcHRa9lWwJbq7F8kGeQVFnP788GF+F+vG6LCcFLjIikbcdzMLA0YBMwC7gLeM7M/EtyYHlXONuOAcQCpqamxCFMk4Tzw1jKWbj7A099JJ7Vd03iHI3VQ1MeZm1lz4FXgLnc/ANwG3O3u3YG7KacOkbs/5e7p7p6ekpIS7TBFEs4r8zbxwuyN3HZWb87u1zHe4UgdFcl8Ah3N7Bkzeyd83s/Mbo5k52bWkCABTHb3qeHqG4Gjyy8Dw6oetkjdtmLrAX7x2hJG9mrHveecFO9wpA6L5EpgAvAe0CV8vpqgSadCYZmJZ4AV7v7nUi9tAc4Ml0cDayKMVSQpHMgr5LZJ82jVpCGPXqPCcBJdkdwTaO/uL5nZzwDcvcjMiivbCMgAbgCWmNnCcN3Pge8Cj5hZAyCPsN1fRMLCcC8vZuPeI7w4bgQpLU6Id0hSx0WSBA6ZWTuCXj2Y2Qhgf2UbuftnlD/5zJCIIxRJIk//K5t3l23jPy46haFpbeMdjiSBSJLAPcCbQG8zywJSgMujGpVIEpqdvYffvbuSC07rxM1n9Ix3OJIkIhknMN/MzgROJvjLfpW7F0Y9MpEksuNgHnc8P5/Utk35/eUDVBhOYqbSJGBm9YELgbTw/eeaGcfc7BWRaioqLuH7zy/gYF4hz908jBYqDCcxFElz0FsEN3CXACXRDUck+fzxH6uZlb2HP11xOn07qTCcxFYkSaCbuw+IeiQiSej95dt58pPPuWZYKpcN6RbvcCQJRdIB+R0zOzfqkYgkmfW7D3HPSws5rWtL7r+4X7zDkSQVyZXATOA1M6sHFBLcHHbNLiZSfXmFxdw2aT71zFQYTuIqkiTwZ2AksMTdPcrxiCSF+99YxvKtBxg/Jp3ubVUYTuInkuagjcBSJQCRmvHS3I1MmbuRO77Wm9F9VRhO4iuSK4F1wMdhAbn8oyvVRVSk6pZt2c8vX1/KqN7tuOeck+MdjkhESSA7fDQKHyJSDfuPFHL75Pm0bhoUhqtfTwPCJP4iGTH8QCwCEanL3J0fv7yIzXuPMOXWEbRvrsJwkhjKTQJm9pi732lmbxEWjyvN3b8Z1chE6pCnPl3HP5Zv55ff6MeQHioMJ4mjoiuB7wB3An+MUSwiddLMdbt56N2VXNS/MzdlpMU7HJF/U1ES+BzA3T+JUSwidc6OA3nc+fwC0to143eX9VdhOEk4FSWBFDO7p7wX1TtIpGJFxSXc+cICDuUXMfmW4SoMJwmpoiRQH2hO+RPDiEgF/vDeKmZn7+Hhq07n5E4t4h2OSJkqSgJb3f3BmEUiUoe8t2wbf/t0HdcNT+WSQSoMJ4mrohHDugIQqYacXYf40UuLGNCtFf+pwnCS4CpKAl+PWRQidUReYTG3TZ5PvXrG49cO5oQGKgwnia3c5iB33xPLQETqgl++vpQVWw+QOWaoCsNJrRBJATkRicCUORt4ed4mvj+6D1/r2yHe4YhERElApAYs3byfX76xjDP6tOeus0+KdzgiEVMSEDlO+w8HheHaNWvEI1cPVGE4qVUiqSJaLWbWHXgW6EhQe+gpd3/EzKYAR2votgb2ufvAaMUhEk0lJc69Ly9ky74jTLl1JO1UGE5qmaglAaAIuNfd55tZC2Cemb3v7lcdfYOZ/QnYH60A3F3D9CWqnvz0cz5YsYP7L+7HkB5t4h2OSJVFrTnI3be6+/xw+SCwAuh69HULvp2vBF6IVgzPfJbN2MzZfLp6J5oYTWrS7tx8Hv1wDX98bxUXDejMmFFp8Q5JpFqieSXwBTNLAwYBs0qt/gqw3d3XlLPNOGAcQGpqarWOe0LD+izZfIDvjJ9Nnw7NGTMqjUsHd6Vpo5j82FIHLd9ygMysbN5YtIWCohJG9+3AQ5cN0BWn1FoW7b+Qzaw58AnwG3efWmr9X4G17v6nyvaRnp7uc+fOrdbx84uKeXvxVjKzcliyeT8tGzfgmmGp3DCyB93aqB+3VK64xHl/+TYys3KYlb2HJg3rc+ngrowZlcaJHVUTSBKXmc1z9/QK3xPNJGBmDYFpwHulq46aWQNgMzDE3TdVtp/jSQJHuTvz1u8lc3oO7y7dhrtzbr9OjM1IY1jPtvpLTv6f/YcLmTJ3AxOnr2fzviN0bd2EG0f14Kr0VFo1VUVQSXyRJIFo9g4y4BlgRRllp88GVkaSAGowHtLT2pKe1pYt+47w3Mz1vDB7A+8u20a/zi0Zm5HGxad3oXFDDfNPdmt35DJhejavztvMkcJihvVsyy+/cQpnn9KRBvXVq1rqlqhdCZjZGcC/gCVASbj65+7+dzObAMx09ycj2VdNXAmU5UhBMa8v3ExmVjart+fSrlkjrhueynUjetCxZeMaP54krpIS55M1O8nMyuHT1TtpVL8e3xzYhTGj0jita6t4hydSLXFvDqop0UoCR7k70z/fTWZWNh+u3EF9My4a0JmxGT0Z2L111I4r8ZebX8Sr8zYxcXoO63YdokOLE7hhRA+uGZ6qyeCl1otrc1BtYmZk9GlPRp/2rN99iInT1/PS3I28sXALg1JbMzajJxec1omGagqoMzbsPszEGTm8NGcjB/OLOL17ax65eiAXnNaZRg30e5bkoSuBcuTmF/HK3I1MnLGe7F2H6Ngy/AtxWKpGhdZS7s6Mz3eTOT2HD1Zsp74ZF/TvzNiMNAanaqCX1D1qDqoBJSXOJ6t3Mj4rm3+t2UWjBvX49sAujM3oySmdW8YlJqmavMJiXl+wmQnTc1i57SBtmzXi2mGpXD+iB51a6d6P1F1KAjVszfaDTJiew9T5Qa+REb3aMjajJ2ef0lFFwxLQ1v1HeG5G0Ats7+FC+nZqwU0ZPfnmQPUCk+SgJBAl+w8X8uKcDTw7I+g/3q1NE24cmcaVQ7vTqon6j8eTuzN/w17GZ305HuTsUzoyNqMnI3ppPIgkFyWBKCsqLuH95dvJzMphds4emjaqz2WDuzEmI43eKc3jHV5SKSgq4e0lW8jMymHxpv20aNyAq4d25zsj0zTDlyQtJYEYWrp5PxOm5/Dmwi0UFJdw5kkpjMlI48wTU6inpqKo2Xkwn8mz1jN51gZ2HsynV0ozxo5K49LB3Wh2gjq/SXJTEoiDXbn5PD9rA8/NXP/Fl9KYUWlcpi+lGrV0837GZ2UzbdFWCopLOOvkFMaMSuOrSroiX1ASiKOCohLeWbqV8Vk5LNq4jxaNG3BVenduHKXmieoqKi7hH8u3k5mVzZycvTRtVJ/Lh3TjxlFqfhMpi5JAgpi/YS+ZWTm8s2QrxV/cqExjZK92ulEZgX2HC3hh9kaem5HDlv15dGvThDGj0rgiXTfiRSqiJJBgtu3P47mZOTw/68sui2Mz0vjWwK7qsliG1dsPkpmVw2sLNpFXWKIuuSJVpCSQoPIKi3lz4RbGZ2WzcttB2jRtyLXDg8FLnVs1iXd4cVVS4vxz5Q4mTM/hs7W7OKFBPb49sCtjMtI0OE+kipQEEpy7M3PdHjKzsnk/LGNw/mmdGJvRk8GprZOqqehgXiEvz93ExBk5rN99mE4tG3PDyKBMR9tmjeIdnkitpAJyCc7MGNm7HSN7t2PjnsNMnJ7DlLkbmbZ4K6d3a8XYjJ5c2L9uFzTL2XWICdNzeGXeJnLzixic2pofnXsy56tgn0hM6EogwRzKL+LV+ZuYkPVlaePrR/Tg2jpU2tjd+WztLiZk5fDPVTtoUM+4qH9Quvt0le4WqTFqDqrFSkqcT8NJTj4JJzm5+PQujM2ovZOcHCkoZuqCIMGt2fHlJD7Xj+hBB03iI1LjlATqiLU7cpk4PYdX52/icEExw9LaMjYjjXP61Y7pDjfvO8KzM3J4cfZG9h8p5NQuLRmb0ZNvDOisXlEiUaQkUMfsP1LIy3M3MmF6Dpv2BhOf3zCyB1cP7U7rpol189TdmZOzl8ysbN5btg2A804NbnoPTWuTVDe9ReJFSaCOKi5xPlgRjJyduW4PjRvW49LB3Rg7Ko0TO7aIa2z5RcW8tWgrmVnZLNtygJaNG3DNsFRuGNmDbm00UloklpQEksDyLQeYMD2b1xduoaCohK+c2J6xGWmcdVKHmNbQ2XEgj0kz1/P87A3syi3gxA7NGZORxiWDutK0kTqhicSDkkAS2Z2bzwuzg8J12w/kk9auKTeGpRWaR7Fw3aKN+8jMyubtJVspLHZG9+3A2Iw0zujTXk0+InGmJJCECotLeGfpNjKzslmwYR/NT2jAFendGDMqjR7tmtXYMd4NjzF/wz6aNarPFWFxvJ7ta+YYInL8lASS3MKjf6UvDgrXjT65A2MzepLRp3qF6/YcKgiuNmasZ9uBPHq0a8qNI9O4Ir0bLRqrkJtIolESEAC2H8hj8sxg4pXdhwo4qWNzxozqySWDutKkUeVdNFduO0DmZzm8vnAz+UUlZPRpx9hRPfla3w4q5CaSwOKaBMysO/As0BFw4Cl3fyR87fvAHUAx8La7/6SifSkJ1Iy8wmLeWhRMwbh86wFaN23I1UNT+c7IHnRp/e+F64pLnA9XBFNnzli3m8YN63HJoG6MzUjjpDj3QBKRyMQ7CXQGOrv7fDNrAcwDvk2QFH4BXOTu+WbWwd13VLQvJYGa5e7Mzt5DZlYO/1i+DTPj/FM7MSb8gn957kYmzshh454jdGnVmBtGpnH10O60USE3kVolrgXk3H0rsDVcPmhmK4CuwHeB37l7fvhahQlAap6ZMbxXO4b3CgrXTZq5nhdmb+DtJVtpUM8oKnGGprXhvvNP4bxTa8eoZBGpnpjcEzCzNOBT4LTw3zeA84E84EfuPqeMbcYB4wBSU1OHrF+/PupxJrPDBUVMnb+Zz3fmcumgbvTvVjvrE4nIlxKilLSZNQdeBe5y9wNm1gBoC4wAhgIvmVkvPyYbuftTwFMQNAdFO85k17RRA64f0SPeYYhIjEX1Ot/MGhIkgMnuPjVcvQmY6oHZQAnQPppxiIhI2aKWBCzoiP4MsMLd/1zqpdeBr4XvOQloBOyKVhwiIlK+aDYHZQA3AEvMbGG47ufAeGC8mS0FCoAbj20KEhGR2Ihm76DPgPJGEl0freOKiEjk1PdPRCSJKQmIiCQxJQERkSSmJCAiksRqRRVRM9sJVHfIcHsSswuq4qoaxVU1iqtqEjUuOL7Yerh7SkVvqBVJ4HiY2dzKhk3Hg+KqGsVVNYqrahI1Loh+bGoOEhFJYkoCIiJJLBmSwFPxDqAciqtqFFfVKK6qSdS4IMqx1fl7AiIiUr5kuBIQEZFyKAmIiCQzd0/oB9Ad+AhYDiwDfhiubwu8D6wJ/20TrjfgUWAtsBgYXGpfqcA/gBXh/tLKON4JwJRw+1llvSdOcY0BdgILw8ct0YyLoNz3wlKPPODb8T5fVYgrpucrfO334T5WhO+xMo5X5n4TIK5fAZtLna8LYxDXQ8DS8HFVOceLx//HSOKK1uerLzADyCeYdbH0vs4HVoUx33c85+vftqnsDfF+AJ358gugBbAa6Bd+sO8L198HPBQuXwi8E/6SRwCzSu3rY+CccLk50LSM490OPBkuXw1MSZC4xgCPxfJ8ldpnW2BPopyvCOOK6fkCRgFZQP3wMQM4q4zjlbnfBIjrVxzzpRPluC4i+PJrADQD5gAt4/35qkJc0fp8dSCYcfE3pX8f4e/uc6AXwRwsi4B+1T1f/7ZNZW9ItAfB/MTnEGTEzqVO9Kpw+W/ANaXevyp8vR/wWQT7fw8YGS43IBip9//+copDXBF96GoqrmP2MY5gdri4n68qxBXT8wWMBOYBTYCmwFzglDL2X+Z+EyCuXxFBEqjBuH4M/LLU+meAK+P9+apCXFH5fJX3+wh/j++Vev4z4Gc1cb5q1T2BcML6QQSXOR3dfWv40jagY7jcFdhYarNN4bqTgH1mNtXMFpjZH8ysfhmH+WJ7dy8C9gPtEiAugMvMbLGZvWJm3SuKqQbiKu1q4IVyDhPr8xVpXBDD8+XuMwgu+7eGj/fcfUUZhylvv/GOC+DO8HyNN7M20YyL4C/Z882sqZm1J2jmK+t3FOvPV6RxQXQ+X+WJ5P/Dv70v0vNVa5LAsRPWl37Ng7TnleyiAfAV4EcEl1u9CLJ5bYnrLYL2vQEEl6sToxzX0f10BvoT/IVx3GIYV0zPl5n1AU4BuhH8RxxtZl+paJsI9xuruP4K9AYGEiSLP0UzLnf/B/B3YDpBIp8BFFe0TSRiGFdc/j9GQ61IAuVMWL89/CI4+oWwI1y/mX/P3N3CdZuAhe6+LsyQrwODyzjcF9ubWQOgFbA73nG5+253zw+fPg0MKSumGozrqCuB19y9sJzDxfp8RRRXHM7XJcBMd89191yC9uaRZRyuvP3GNS533+7uxe5eAvwvMCzKceHuv3H3ge5+DkHb/OoyDhfzz1ckcUXx81Weyv4//L/3VXa+jkr4JFDBhPVvAjeGyzcStLUdXf8dC4wA9oeXXXOA1mZ2tKLeaII79scqvd/LgX+GmTqucR39wIS+SdDT4/+pwbiOuoaKm1xifb4iiisO52sDcKaZNQj/059ZzjHL229c4zrmfF1C0DMmanGZWX0zaxfucwAwgKCH3LFi+vmKNK4ofr7KMwc40cx6mlkjgqbQN8t4X0Tn699UdMMgER7AGQSXSosp1X2NoJ3rQ4IuVh8AbcP3G/A4wZ30JUB6qX2dE+5nCTABaBSufxD4ZrjcGHiZoIvVbKBXgsT13wRdzBYRtPH2jUFcaQR/WdQ75hjxPl+RxBXT80XQe+NvfNnN98+ljvF0qfeVud8EiOu5cLvFBF8knaMcV+MwnuXATGBgIny+qhBXtD5fnQhaBw4A+8LlluFrFxJclXwO/OJ4zlfph8pGiIgksYRvDhIRkehREhARSWJKAiIiSUxJQEQkiSkJiIgkMSUBSWphP/HPzOyCUuuuMLN3y3jvTWa2xIJSAUvN7FuV7HuCmV1exvqzzGxazfwEIsenQbwDEIknd3cz+x7wspl9RPB/4rcEZXuBLwb8dAd+QVARcr8FZQBSytqnSG2iJCBJz92XmtlbwE8Jygc/CxSb2SqCYl9DCEr0HgRyw21yjy6b2UDgSYIqnZ8DN7n73tLHMLPzgf8BDgOfRf2HEomQmoNEAg8A1wIXENR6BzgReMLdTyX44t4OZJtZppldXGrbZ4GfelBMbAlwf+kdm1ljgno8FxMklE7R/EFEqkJJQARw90MEMzI9518WBlvv7jPD14sJmoguJxi6/7CZ/crMWgGt3f2TcJuJwFeP2X1fINvd13gwRH9SlH8ckYgpCYh8qSR8HHWo9IsemO3u/01QwOuyWAYnEg1KAiIRMLMuZla6xPdAgiuF/cBe+7JG/w3AJ8dsvhJIM7Pe4fNrohqsSBXoxrBIZBoCfzSzLgST2+8Evhe+diPwpJk1BdYBY0tv6O55ZjYOeNvMDgP/IphvViTuVEVURCSJqTlIRCSJKQmIiCQxJQERkSSmJCAiksSUBEREkpiSgIhIElMSEBFJYv8HlZL5nvFmvV4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def analyse_year_vars(df, var):\n", + " \n", + " df = df.copy()\n", + " \n", + " # capture difference between a year variable and year\n", + " # in which the house was sold\n", + " df[var] = df['YrSold'] - df[var]\n", + " \n", + " df.groupby('YrSold')[var].median().plot()\n", + " plt.ylabel('Time from ' + var)\n", + " plt.show()\n", + " \n", + " \n", + "for var in year_vars:\n", + " if var !='YrSold':\n", + " analyse_year_vars(data, var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plots, we see that towards 2010, the houses sold had older garages, and had not been remodelled recently, that might explain why we see cheaper sales prices in recent years, at least in this dataset.\n", + "\n", + "We can now plot instead the time since last remodelled, or time since built, and sale price, to see if there is a relationship." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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Kl4ZhGEmkEi6qekLI6zeSzhOR6cB5wNfdtgAL8RYeA7gN6HOfL3DbuP1nueMvAO5U1TdU9QVgJ97aMmcAO1X1ebccwJ3ABQl15EacMT9uZtCsNPtRnmpBzBXZMIyiSHJFng/cDLwTLyPyH6jq03Hn1HADnr3GXxL5bcCgqh5y27sBv1ftAXYBqOohEdnvju8BNgSuGTxnV035/IQ6au9vKbAUYObMmRluK5k4I30z0uz3xCxm5mOuyIZhFEXSzOVrwBfwOuyv4AmLVIjIB4FXVHVT3a0rGFW9WVV7VbV32rT8MxinmRms2TzAmSvXc8LyB1Mvj5yGMPVbkKJdkYu6L8Mw2oOkOJcuVX3Efb5bRK7McO0zgfNF5FzgKOCtwFeBbhGZ5GYW0wG/1xkAZgC73UqXU4BXA+U+wXPCyl+NqaOpRBn9fYqMefHP94M1p1TKiHjBn0UHbobd1xWrt3D56i3mBm0YE4Qk4dJdE50/ajsuQl9VrwSuBBCR9wJfUNWPi8jdwIfxbCRLgO+4U9a67R+6/etVVUVkLfB3IvIV4HjgROAJvLCNE0XkBDzh8THg37pzHouoo6kMDlWZc813Izv1omNexmuVyzg36CKDRg3DaB2ShMs/Mjo6P7hdb4T+F4E7ReRaYDNwiyu/BfhbEdkJ7MUTFqjqdhG5C/gJcAi4TFWHAUTks8A6oIS3mNn2hDqazmAgHmZgcIhl92wFvI61U3N+JbW/nZNm2tIFhpGOpAj9T+VRiap+D/ie+/w8nqdX7TGvAx+JOP9PgT8NKX8Ib8nl2vLQOvIkjcE8jOqwcs392+mb29OxOb+i7itIOwrQZqbuMYx2J5UrsogcKyK3iMjDbvsUEck9dqSdaGQJY9+NuVkxL80myZkA2lOAxqkxDcMYTdogym/gqZ+Od9v/hLfGy4Tlga0vN3yNZsW8NJvgfYFnHAvSrgK0U9WYhlEEaRcLe7uq3uV7i7k4lOGkkzqZwZjcYkl0B7zIxsvoXjTB++oUO0WnqjHHi075XRjhpBUur4nI23BOPyKyANhfWKs6nA+edtx4N6GpdIoAXbbopFE2F2jfWdh4Y/arzietcPnPeK7C7xSRx4FpeK6+RgRdAkdO6mIoZLXKezft5rFn9hQyYrPRYHHUxg7Z91s/tvR255N2meOnROT38JY5FmyZY7or5UjVmB8oePnqLaH7h6qHR9QraUZsaQWGjQaLp1NmYeON2a86n1iDvohc6L+A8/GEy28CHwpZ+nhCcfX5p1Luis88nJY4jyNfYAwMDqHEJ8U0b6axWBqa1qRZS08Y40fSzCVseWOfCb3Mcd/cHu7uf4nHn9s7Zp8vAI4+osRrB9P5PUSN2KIExtVrt4+ZzcSNBieiusxmcq2L2a86n6YEUXYiV63ZFipYfIaqw1TKaT29o0dsUQJjcKg6opbzI/+nRKjqplTKE7KTNb1+62L2q84nrUEfETkPb8Guo/wyVf1SEY1qB+7YuCvxmDBjPry5lr1P3IgtTbQ7eJH/1eHDVMqlMaNBEUI72WvuHzv76aQ/dzvo9SfijNLH7FedTdoI/f+NtxrlH+L1jR8B3lFgu1qeNItxRaGQOnBy2aKTKJfS2XZeOzgcGpQ5GLGw2b4D1VS2nHal1fX6WexphtFupJ25/I6q/paI/EhVrxGR/wU8XGTDWp2SSN0Cpqe7wuPLF6Y/IUM1YcGLaU8PUxm188i61fX6prYzOpm0RgFfj3BARI7Hy048sSIBa1jwG1PrPnfW29KPnFet20H1cHYhFhwVZyGoMmr3kXWrp9dpB7WdYdRL2pnLAyLSDfwPwF9Z8uuFtKhN+Omr9XcAP3x+L2euXJ9qNpClo5k6+c20MmGj4jQEVUadMLJuZb2+pZMxOplY4SIi/wrYpapfdttvAbYBzwDXF9+81qWedPs+h5XUQZRpDfqlLmHFh04d2a5n9FurMoqqN+u9t7NqrUhaXW03HthvpXNIUov9FXAQQER+F1jpyvYDNxfbtIlDXKBjWoO+/yD9oMGsirSSyBiVUUnC640qD6PdVWtF0upqu6w0GrBqv5XOIkktVlJVP5jjYuBmVb0XuFdEthTasglG7EwjhaSoHlauXrudNw4dzqwOq5RLoZ1alMNCFkeGTlCtFUkrq+2ykEfAqv1WOoukmUtJRHwBdBawPrAvdYyMkUyUnj2LQX9wqBopWGrnGv523Gi5J6JNUeVhmNF6YpBH6iH7rXQWScLlDuAfReQ7eB5jPwAQkXeRkHJfRI4SkSdEZKuIbBeRa1z5CSKyUUR2ishqETnClR/ptne6/bMC17rSle8QkUWB8sWubKeILA+Uh9bRykStbJnXH0sEKuWuEfXL9RfP4YaL5wBwxeotoWqMZYtOGpM/rdwlmWwC4xFrYvnEmk8egqHV45KMbMQKF7d2/efxVqJ8j+qIPqQLL6AyjjeAhap6GjAHWOzWgfkz4HpVfRewD/CXS74U2OfKr3fHISKnAB/Dyw6wGPhLESmJSAn4GnAOcApwiTuWmDpyI4PZIRWPPbMntDztH6tSLo3yFqvlsHoZAz6+YOZIjE0q/XbUlCclzV7K2fT240MegqFTl/2eqCTGuajqBlX9tqq+Fij7J1V9KuE8VdVfuc2yeymwELjHld8G9LnPF7ht3P6zRERc+Z2q+oaqvgDsBM5wr52q+ryqHgTuBC5w50TVkRsNBOiHEjXCi1qPvlLuYurk8ihD8IoPnZq4dr2ftiaNGmPVuh1Uh0ffaHVYM6k6ijBax81MLDP0+JCHYOg0B4eJTqF2Eze72AS8C2+W8RwwqKqH3CG7Af+X0wPsgpFllPcDb3PlGwKXDZ6zq6Z8vjsnqo7a9i0FlgLMnDkz073FredSD90Rs46oBH9+2eCBKq/8cmjU2jFHlISDw/HG+DRqjLx04HkarZMMx+2mt+8U19u8ElF2ioODUbBwUdVhYI4LwPw2cHKR9WVFVW/GuVT39vZmmoscPJQ9QDG+LV6m5Ts27mJYlZIIl8yfwbV9s8f84Wo72Nr8mFGCxefMlevpnlxmX0jOsaAao5WC/PxOOKw9QY+iVmpzEp22JIAJBiNI+pzwDaCqg8BjwG8D3QEPtOmAr9MYAGYAuP1TgFeD5TXnRJW/GlNHbhyIyHhcL4NDVb654aWRmcWwKt/c8BJXrdk25th6o+99BgaH+NXrh8bEz9SqMVpFB54mlY0/M2mVNqfBVHhGJ1PYzEVEpgFVVR0UkQrwATxD+2PAh/FsJEuA77hT1rrtH7r961VVRWQt8Hci8hXgeOBE4Ak80/KJInICnvD4GPBv3TlRdbQd39zwEg9sfRkRGDxQTR2xD/CJBTNHZkK1VA8r3ZUyRx85KVKNUfSaG0kqobjZSi3+zKSd1glpNxWeYWShSLXYccBtzu7SBdylqg+IyE+AO0XkWmAzcIs7/hbgb0VkJ7AXT1igqttF5C7gJ3gJMy9z6jZE5LPAOqAE3Kqq2921vhhRR1sStO0MDA6NWQ8mimv7ZnNt32xOWP5g6PH7h6psWXH2mPJgpz+lUh7xjHvtjUNcc/92rli9peFOO0klVLs/jtqZSVb1TDPsHmF1tJMKzzCyIpq321Ob0tvbq/39/amPn7X8wQJb0zhnvvMYPtI7M3bkH5b6f83mAZbdvTVV4GZUZH+q9q1cH9ouv01R+8OOz1PIQWP3laWOi+b1cO+mgULrNoyiEZFNqtpbW25R9h2IL1iW3bN1jCuxT5Qd4uq121NnBGgkNUeSSihJsKTthJNmJc1IORJVx2PP7OG6C2e3hQovDZ3i+WbkgwmXDsIfxa9at2OUa3LUcWF//Kzu1fXaB5JUQnGLsaWdraTxxmqG3SOujiI9rJrZ2Xea55vROCZcOoiBwSGuWL0l0R4TZjsB6vJSSmMfCOvkktLNxyXHTLuKZ9SM4fLVW1i1bkfT7B7jYVtJ09nnKXws6aRRS1NckY3mkUahNThUZd+B6kh6lGV3b2XZPVszr9OSxsU3Kh0LEBuN3V0JDyqNKg8jbvbht+N9J08r3HU5zD1aXBvS5D6rJ1dakptz3mlyOtnzzXLV1YfNXIzUNpZu5znmu0SnGenGdXKPL18YeX5U7rYsOd2S3LabZfcIukfXevslqY/qVTcldfZ5zzQ61fPN1H31Y8KljUnrktwIjXpjxXVycWqZwZDsAXHlYYSp3sLa0YzIcr+OMC+4uE69XiGQ1NnnPdMI+66Ds7N2Ne6buq9+TC3WZvirQPpp87OsrZIV3y24kT9R1Mh1SqUcq5bJI8tuMBFiFFE53Yoia6derxBIylSQd3r7sO86ODtbdvfWtlQndbK6r2hMuLQZvz7lKG64eM5Ipx+1Dgxkzo4/inIp27otUUR1ciLE2gTSpnFJ0of3ze3h8eULI201cWFeeeja/WvMWv4g77zyociZZtbOPlge1s6kDMNFpMmJ+679lVLbDVtjpn5MLdZmDAx6GZCvuX87Kz50auQ6MD3dFd538rTI9C9JVId1lFdVvbOXqHQsV0S4SvsjwjRpXGoDPv0RcvB8n/0RLtZR5Xno2muvEfUc4jr19508jW9ueCm0PE07o9paZJqcKHf2ODf3Vo2RaSV1X6t+R1GYcGlT9h2oxtoTBgaHuHfTQF2CpfY6jRowwzq5qMwBCqP+tHF1hgV8+iPk2vOmRCyRMCViRpOHrj1NgtEkm1bU4MEvb6Sd453FOJg7LouTQ6P1Zemc++b20P/i3lGDtKLbGUY7OhaYWqyNSeq4GsmcXHudvDP1Ri2CBundYrOMkLN6n+Wha8/q2l1PO1rRJhC1ImqwvDbTde0QaKg6zOfvys9OU6/r9ZrNA7GDtGZlsW7HDNomXIxUZO2s0thC4oztef9xsnqfRenUu0RSd3ilFH7TSZ1cks6/FW0CKz506pjlHMolYcWHTh3ZTjOrG1bNbYnqejvnNO1shiBvxUFEEiZcjFRk6azSjhJ9A3BUF5z0x0k7Qj5z5fpYQ3qYIIyaWWXp8NKqJMM6Ob9NvsooSNBG02iAZhH0ze1h1YdPG+VMsOrDp41S36TtFPMaZKTtnGt/C1mWeyiSVhxEJGHCpYOpHT3Wi9+ZpfWeyjpKrHeWkDRCTlpkrFIu8b6Tp8VmEAibfaTt8LK4iQc7uTCVkd+KWq+v2hlgmO2iCAGT1kvvhZXnhbqzZ+kU8xidp/W6q/0tJP2DmrUQXTstgudjwqVDEcglwtLvzIDUOuusU/hli06i3DX2bzysyhWrtzArpgOLGyHHqTT8+3rsmT2xBvHDEbOPNB1e1KwijGAnF9buuEfpd+Q93ZVQ20Xeevk8UsfE2dxqyWN0nqZzjvrea59ZlKAvkiTX8lbEvMU6FCV9WpcoSiIjSSLPXLk+MQmk/0OvKxVIRK8bHIVfsXoLl6/eMsrDKs7jKUoACG8mv0xyiW4krUmYu+/7Tp7G6id3jVoKoTamKE1OtOD1k87LWy+fhydd1HcTtr5N3Og8rQdYGtfrqO9J8Trz8XYBHm/vvqyYcDEiGVYdcQvO0uElxWbUsmrdjsh1Z4LURnz79UWRRjAkHZOUvTmJ2g5hzeYBVj+xa/RBNbeeJidasCP3O9isAZr1kpcQC+sse99xTGp34azuuUmdc9T3HraonpGMqcXqZFKIGqcT8f+wSWlSgq6jUbEZ39zwUqh6q56RdfWwF+QZZ/uJU4WkNZjXqiO6K2WOKndxRULdtfj1Xb56S2hsTlB1lUZl5H9naexKeevlizQuJ9lqguTtnhumni135ZOpYiJiwqVOjsjJWN5K3BCRq2yoOowqiR2e70kVN+oO08830inF6fuj9NRAaoN5EMWL6A8uV5A2ViLpewkK2DQ50fzvLI1dKW9VSqsYlwtRA0YZWIzMFCZcRGSGiDwmIj8Rke0i8jlXfoyIPCIiz7r3qa5cRORGEdkpIj8SkdMD11rijn9WRJYEyueJyDZ3zo0inmtPVB15cqB6OO9LjislEfrm9kT+MfcPVRM7PPAEUVJ8R+3oMotxN831gp5Mvj3ohZXnjVqlM43BfM3mAeZ+6btcvnpLbLBfHrEStQLWH8HfcPGc2I48ya5UhI6+VYzLec+gwtSz1WHN3SFiolCkzeUQ8HlVfUpEfg3YJCKPAL8PPKqqK0VkObAc+CJwDnCie80HbgLmi8gxwAqgF++/vUlE1qrqPnfMp4GNwEPAYuBhd82wOowIhlVZs3kg1gbh66znXPPd2DxRw6pUyqXEVPdBY+zkI0ojbrRxSxxHMVCjJqrVw/e/uHeMsTjqOr7jQNolDZJGykn740b9SYbo8VpHpRWMy43aw2qJmlnmkWlhIlLYzEVVX1bVp9znfwaeBnqAC4Db3GG3AX3u8wXA7eqxAegWkeOARcAjqrrXCZRHgMVu31tVdYOqKnB7zbXC6jBiuMKN0MM0A8GgvDjBAm+OZONmObUp9187ODzSkdeTD63kYmI+f9fWUD38HRt3pU6HozXvSSR15HH7Gx31t4qKajxIM4PKktk6asadJtOCMZameIuJyCxgLt4M41hVfdnt+jlwrPvcAwTdaHa7srjy3SHlxNRR266lwFKAmTNnZr2tjiOqU6310hKJTlUf1rHVzgCiUu43gm/viRJMjSbwjCNu2QOIHmGnESppsx63Qrbc8cjaGzeDyupNNh6/nU6mcOEiIm8B7gUuV9VfSmAUoKoqIoU+ubg6VPVm4GaA3t5e+wXVEKYWSoqdCRrM/T918IySCBfN6+FbIa7KjRInrOpRtaXlga0vc23f7Mj99QiAYMbgWmpdkVtBRdWKWXujvMmilpLoiXFFNrJTqHARkTKeYPmWqt7nin8hIsep6stOtfWKKx8AZgROn+7KBoD31pR/z5VPDzk+rg4jA1m74qmTyyNL+UZ19MOq3LtpgO7JZfZlWLK4EYRiR5+DQ9VRo/buyWVUPSeIoCCpdx2YMMY7YWHtLOW1Nw613HLAWYNR87bhTHQKEy7Oc+sW4GlV/Upg11pgCbDSvX8nUP5ZEbkTz6C/3wmHdcB/D3h8nQ1cqap7ReSXIrIAT932SeDPE+owCsTvZJM6vqHqMEPV4dQG80ZpRh3BTikoNP3F3S5fvWVk9pS0hks93mXNJGyRtiiaaQwPzvZKIonPPWwGCK2hYuwEipy5nAn8O2CbiGxxZX+C1+HfJSKXAi8CH3X7HgLOBXYCB4BPATgh8mXgSXfcl1R1r/v8GeAbQAXPS+xhVx5Vh1Egql4nWyl3pXLV9uNLOkEfmcZ+5M+eklRGjXiXNYOwRdqiaJYxPO2qn7XUftetoGLsFAoTLqr6f4gOQTor5HgFLou41q3ArSHl/cC7Q8pfDavDKJ6h6nDkAlxh+F1ApwiZtATjY2pHynHpX/xZT/+Le/n8XVsZVqUkwiXzZ8TaffIkyVswSLOM4Wlme2HUrnxq5IflFjNyp57+ZCIJFh9fZRbc/vzdW7nkjBmsfmJX5Ozg7v6XePy5vSPbw6ojudyy5OZqBs0yhqexQUXFXqXNVWdkw4SLYbQQw4c1NOmnz8DgUOSs5lsbXhoVKJq3x5Zv00hLUeq7MJfnpGSfPS7r8h0bd4XOpqqHlavXbg/9nrK6WI+HS3YrYrnFjKYQtl6LkS/KWNuP73rb6KqUafKjBfFdzvPuVKPWkpn1tugZkr8o3L2bBmLVdGHqvqxr1+Sx1k2nYMLFaA4SvSyxUTwDg0P859Vb6u7ksto0fJfzvDvVqNiVDc/vCz2+JBK5KFwj9UXN4KKOv+b+7akzBXQKJlyMURQ1wagOa6rMykZxHAauvO9HdZ0bZ9OI8ggrYhXMqHYkRdenscmEDX6y5huLqmffgeqEm82YcDFGEedhWhLhEwtm1m2k3T9U5aJ5PZaraRwZqh6uax2aqJ9FT3eF5647N9ItNK9gz6R2xP2m0qxHVC4JKz50aurrRpWnjT8qQvC2GmbQN1IT9EqqhymVcqLe2yieqCWja0nKFODbMuI6/TyCPdNkLBhWjXRnH6oOc+SkrjHeYv7xcd9B3IwozHAfFuUfxXhnWSgaEy4dSCvGjBSRrNKon2Ay0iiPsjg7S5fAoeHh2MFGuZR+Fcc4D6u09p643/zgUJUbLp6T2YsrKt9Yt8vqXeuZd92Fs7loXs+IV1pJhCMmCUMhQcXjmWWhGZharANpNcHip0JvVi4xIxtRKpq4kfVhhcQkDCl/iH46maBNYtndW0dUd3mkkKlVY+177Q2uuGsLs5Y/yDuvfIir1mwLPS9qSYOwgZLvmfetDS+NzHiGVTl0WMd4S9Yut92Jhn4TLnVimVLT468AabQuA27xtiCNjqyrhzWVG3RYOhk/7gTySSETXIJb8VaS9TVevro3TMBErRkzGDNQGpNJfFipHtaR+whbbtsXqles9gRePXaxrAKqaMFmwqVOGl2at9XJ694q5a6RUanR2gRnC5C8Tk1aoryj/M4tKp2MX56Hja4kkqhau2Pjrtj9QeoRvP4Krb46LkzdV6uuTOrw642raUY8jgmXOgmOaDqNqZPLHDkpn5/GUPVw6iSHxvgSnC0APPbMntyuXat6yxKUGRcfVTunKZdkjAoq7ZILvpE+OJq/as22UJVdXNBmHMHvIU328KQZf9Y4nEbPy4IJlwbom9vD48sX8okFnbWK5evVw5mSExqdQ/C55+3NFLxelqDMKLnQXSlz/cVzRqmsVn34NFZ95LSRQV8W55YuGaum+uaGl0JVdv83kNstK/73kGb2k/QMovYXdV4WTLjkQF4jvC7x1EiCN1rrrmSLaBfJR51lHl0G5O/NFLxemk7M//3vjxjoRJX7g76e7kom55YjJ3WlF3gJ++OsRFPcfaVRrSc9g6j9RZ2XBRMuOZCXtPcGSML1F89h8387my0rzs40K1KlY1V1RnMIqqCiPKXq5X0nT0sMhvQpdwkfPO242GOPKnfF2g3S/i/94ODXU6xBlIae7grXXzwnUp3n+yfUqtZrBVKaxJ9Rz6io87JgcS4N4Pvm52lRqF3nI4shvKe7MrLYUZrAM8MIUhulHrUyo7+OTFa+/dTAqKzNUZREOOOEqYnHvnHo8JiMEsHVJZMyJfv8+pSj6H3HMTz2zJ7U/7ejjyhxWEfP8ivlEtddOHvke7sisJxCkKCnWXBxsnqyKde7emYzVt0UtWhpAHp7e7W/vz/18Vk675LAcMavOWsgZFi0cXDZ16ICK7srZY4+cpJ5g3UAN1w8J1Uq+SKede3vs5HfqwAvrDyPq9ZsS51Rwq8vTb3lkrDqw6cB8Z3zmSvXh35XUyeXmXzEpI5JyS8im1S1t7bcZi51EmeQrE0nMeea72Y2kKf5Y4X9IWojrmtHRXl3DPuHqmxZcTan/NeHUy1tbLQuSYJl2d1bC/P8q71qI7X4doMstlANvMcJGBE4+ohJXLF6S6JgCEsFUy4Jv3r90EhAcZo1d9p1fZjCbC4icquIvCIiPw6UHSMij4jIs+59qisXEblRRHaKyI9E5PTAOUvc8c+KyJJA+TwR2ebOuVHE02RG1ZE3UfpcAR5fvnDUw48yPDaK4qkQav8IYS6FQSNnFpJ07L5x8sgOjvkxwgMdW5VZb6tEzhrSoHgz8trffrlLmNQlDA5VU8WGhAVgHn3EpDHfY5wLcDuvD1OkQf8bwOKasuXAo6p6IvCo2wY4BzjRvZYCN4EnKIAVwHzgDGBFQFjcBHw6cN7ihDpyJYu3RZE5hKJ031HCL8yQF+XZ0tNdScxi7O+Ki1gu2UJhbYHfYYVFbhfpmh4VU1X7q0n7K3r8ub0Nz9D3D1XHCIa3HDWJ6nCyYAh+f6vW7WDZopN4YeV5PL58YeRAM+r/2ox4lKIoTLio6veBWmfwC4Db3OfbgL5A+e3qsQHoFpHjgEXAI6q6V1X3AY8Ai92+t6rqBvWMRrfXXCusjlzJ4m1RZDR/1tTfYaOpjy+YGXovaVbv23egypkr10emNO/prvC/AnEHeVHqEiaXzdkxC0md85X3beOqNdtCR8pFcvDQ4dDf38fd8g7+7/T6i+ekFjCNcrxzjnl8+cIRwRA1gAoKhqSZRlYX4GbEoxRFs/+dx6rqy+7zz4Fj3eceIJh7YbcriyvfHVIeV8cYRGSpiPSLSP+ePdliVaJyDoXpQtNE84dNw8e0t2a7Ui5xyfwZlEuj9yRlo/X/NNdfPAfw1l4/qtxFd6U86l7Srt43MDjEr14/NKYdwVQXjy9fyE9XnscnFsxMzBWVJIh8gXXhvOmJbWs1KuVSQ2vi1IvfOfvPIIyh6jB3bNwVOlIucvKpEPpfurZv9qjO3fcAq5fuSpmpk73f+NFHlCIFVbkr/P8TNYAKlifNNLK6ADcjHqUoxs2gr6oqIoUqcZPqUNWbgZvB8xbLev2gwTztsWFeZpVyiavP91xAo7y7KuUSF83r4bFn9owy7AGsfqImJ1KKO6ltx74DVSrlEtcHPIaiXCnDqB7WEc+xOMPjtX1epxGlE+/prvD48oWJ+4FCVANFLlfQE3hmeaZWScK3A/pc2zc70osqapZ6WL3Z4nCM3UXwOtqs2a9LIqn/S1nWS6nljUOHR8577WD0+X7CzavXbkfEU/ke313hjYg6g19Z0kwjqwtw2P3mHY9SFM0WLr8QkeNU9WWn2nrFlQ8AMwLHTXdlA8B7a8q/58qnhxwfV0dLkPTjyurzfubK9aHpKYKxMmnXyAjGCACp4wR8fM+xNCT9acL2C94sac4130WEQlL4X+/W/GhEZy/A5CNKHDg4TPfkMqpvOnX0v7g3VaxHnije7yT4/EsimWJVJpe7qA4rSa3e/N+855/FO/GS+TMSj/Gp/f9MqZRHCYC4+rJ+50E7U9x1B4eqI99vVBuCM42sg1IoNh6lKAqNcxGRWcADqvput70KeFVVV4rIcuAYVf1jETkP+CxwLp7x/kZVPcMZ9DcBvvfYU8A8Vd0rIk8AfwRsBB4C/lxVH4qqI6mtWeNcWoUTlj8YOdKuXXkvGOQVdZ4fIwDh7qflLuEtR00K7diDs4o0JAnQZsTp1PJTd+9RM6e4uJ5yl7DqI6eNGiCECcjx8rkKPv9Zyx/M/fphM9crVm+JXZr4kvkzuLZvdm5tKOK+0uJrF2oHD7XBlZ1GVJxLka7IdwA/BE4Skd0icimwEviAiDwLvN9tgyccngd2An8NfAZAVfcCXwaedK8vuTLcMV935zwHPOzKo+roSKJ0r2EpxoO639S63BCXnfN+67hcUkfUGkxr/3z15ojKgyjd+NXnnzpiP7qhNmliQLBA+Owwz/vI6iQSfP5523zKXcJrBw+NMWRPiciP19Nd4bnrzs1VsPjXDSOPNWGSGKoO89gze1LbYjudwtRiqnpJxK6zQo5V4LKI69wK3BpS3g+8O6T81bA6OpUo9VKUCsDX/abR5a5at2OM62V1WEf+QM2aqo+HZ0xQHTEwODQirP3O2VdtxN1zEe3u6a6M+s6zqvAGEp7/kZO6Mrsd93RXOHDw0JjZ7FB1mKPKY9euL9JmEHVfYTOKIvjZ4FAmtVcnYxH6bU6UTjaq0/FnJml0uXHGyaL+QGGqsiy2n6DjQ6OxDv79ha2VHtwfRVS7w5w1jip3pbIhhakdwzrT1w8Nh6aq90fwUc8/7HppiGr74IHqiB2rGQORuN917zuOGSkP2sL8z4ND1YbVlu3gxdUsLLeYo11tLlFEeaVlmaKn8djKk6g2J406w/KqQXb9e9h9xUV619aX5X7CvP6SOnUReOG680Lrqe1ML4/x9PPtSlFkMcYndcZF/VZ88k6N0kiapE63rURhucUmGHl4mTTbDTLKg61WDVfrIRR1Xz0RM4fuSnmUWypE31ecaitpFpPmGQQ7xymVcuwMJmocGDaLjOog09ha0mbWThIsRbvMrtk8wLJ7to6obgcGh1h2z1YgeVYZhX/vadPHRA1sDBMuHU2jqqtmu0HmrYaLEo7BmKKk+0rj3hp0364lrt21nffgkBdrNDUiViSNYIjzsMva2Tfi9hvV2eY507jm/u2hNsFr7t/e8G80jb3MBEo8JlyMWJppnEwTI5CFtDFFcaQJ2qvXcB81UztyUn1G8FphFczwW29HGPf8s6pNa9uXxX4VRtQML4/4pzTCs0h1XydgyZmMlqGI1fGS3J3TnJ+Uuqde4RcllMKSJqbR5Ue5PvsdYd6DhKzPq52SMMblA2yXCPnxxmYuRsvQqtHISal76u1o4mZq9cwYm53kMOvzyrt93ZVyqNt0d0RsTRbCXNGHVU0VlgETLkZL0coxAnkLv7wdJvJWK6Yhy/PKu31Xn39qaAYJ36bWKK38W2wHTLgYRgby7HBaXVjlTd7ta9ZMt11XghxvLM7F0WlxLkZr0OyOqdU7wlZvXy15xIt1OlFxLiZcHCZcjLyxjqn9aXYgcTvS9MSVhjHRaSfvKCOcdl4Jcrwx4WIYBWEdU/vTzitBjjcmXAyjIKxjan+KiL2aKJhwMYyCsI6p/QkG0U709VmyYq7IhlEQrRoUamTD4l3qw4SLYRSIdUzGRMXUYoZhGEbudKxwEZHFIrJDRHaKyPLxbo9hGMZEoiOFi4iUgK8B5wCnAJeIyCnj2yrDMIyJQ0cKF+AMYKeqPq+qB4E7gQvGuU2GYRgThk4VLj3ArsD2bldmGIZhNIEJ7S0mIkuBpW7zVyJSb16OtwP/L59WtSx2j52B3WP702r3946wwk4VLgPAjMD2dFc2ClW9Gbi50cpEpD8scVsnYffYGdg9tj/tcn+dqhZ7EjhRRE4QkSOAjwFrx7lNhmEYE4aOnLmo6iER+SywDigBt6rq9nFulmEYxoShI4ULgKo+BDzUpOoaVq21AXaPnYHdY/vTFvdni4UZhmEYudOpNhfDMAxjHDHhYhiGYeSOCZcG6bQcZiIyQ0QeE5GfiMh2EfmcKz9GRB4RkWfd+9TxbmujiEhJRDaLyANu+wQR2eie5Wrnadi2iEi3iNwjIs+IyNMi8tud9hxF5Ar3O/2xiNwhIke1+3MUkVtF5BUR+XGgLPS5iceN7l5/JCKnj1/LR2PCpQE6NIfZIeDzqnoKsAC4zN3TcuBRVT0ReNRttzufA54ObP8ZcL2qvgvYB1w6Lq3Kj68Cf6+qJwOn4d1rxzxHEekB/gjoVdV343mGfoz2f47fABbXlEU9t3OAE91rKXBTk9qYiAmXxui4HGaq+rKqPuU+/zNeh9SDd1+3ucNuA/rGpYE5ISLTgfOAr7ttARYC97hD2voeRWQK8LvALQCqelBVB+mw54jn8VoRkUnAZOBl2vw5qur3gb01xVHP7QLgdvXYAHSLyHFNaWgCJlwao6NzmInILGAusBE4VlVfdrt+Dhw7Xu3KiRuAPwYOu+23AYOqeshtt/uzPAHYA/yNU/19XUSOpoOeo6oOAP8TeAlPqOwHNtFZz9En6rm1bB9kwsUIRUTeAtwLXK6qvwzuU89/vW192EXkg8ArqrppvNtSIJOA04GbVHUu8Bo1KrAOeI5T8UbuJwDHA0czVp3UcbTLczPh0hipcpi1GyJSxhMs31LV+1zxL/zptnt/ZbzalwNnAueLyE/xVJkL8ewT3U69Au3/LHcDu1V1o9u+B0/YdNJzfD/wgqruUdUqcB/es+2k5+gT9dxatg8y4dIYHZfDzNkebgGeVtWvBHatBZa4z0uA7zS7bXmhqleq6nRVnYX3zNar6seBx4APu8Pa/R5/DuwSkZNc0VnAT+ig54inDlsgIpPd79a/x455jgGintta4JPOa2wBsD+gPhtXLEK/QUTkXDz9vZ/D7E/Ht0WNISLvAX4AbONNe8Sf4Nld7gJmAi8CH1XVWqNj2yEi7wW+oKofFJHfwJvJHANsBj6hqm+MY/MaQkTm4DksHAE8D3wKb0DZMc9RRK4BLsbzctwM/Hs8m0PbPkcRuQN4L15q/V8AK4A1hDw3J1T/Ak8deAD4lKr2j0Ozx2DCxTAMw8gdU4sZhmEYuWPCxTAMw8gdEy6GYRhG7phwMQzDMHLHhIthGIaROyZcDCMjInKsiPydiDwvIptE5Ici8m/GoR2nisg/iUglUPagiFwScux7RWS/iGxx2XP/QUT+hdv3+yLyF+5zXwckXzVaABMuhpEBF1ewBvi+qv6Gqs7DC8ScnvL83JYWV9XteFHp/8Vduw8oq+odEXX+QFXnqOpv4QUAXxZy2T68DN+G0RAmXAwjGwuBg6r6v/0CVX1RVf9cRGaJyA9E5Cn3+h0YmTX8QETW4kWQIyJr3Kxnu4gs9a8lIpe62cgTIvLXgRnFNBG5V0SedK8z3SlfAj7iAiZX4gSGiFwtIn8rIo8Dfxu8AScgfw0vHX2w/HeA84FVbobzzvy+NmOikdsoyjAmCKcCT0XsewX4gKq+LiInAncAvW7f6cC7VfUFt/0HLsK6AjwpIvcCRwL/1R37z8B6YKs7/qt4a5T8HxGZCawD/qWqHhCRLwDfB76iqs8G2nMK8B5VHXKZCP61iGzBywD9Gl7mhRFU9f86AfiAqt6DYTSACRfDaAAR+RrwHuAgXiLFv3CziGHgNwOHPhEQLAB/FLDTzMBb7OnXgX/007GIyN2Ba7wfOMWbdADwVhF5i6r+SlXvF5FB4C9rmrdWVYcC2z9Q1Q+6a38R+B/Af6zvzg0jHhMuhpGN7cBF/oaqXiYibwf6gSvwckGdhqdyfj1w3mv+BzeLeD/w227m8T3gqIR6u4AFqvp6xP7DvJkLbkydIazFy3xtGIVgNhfDyMZ64CgR+U+BssnufQrwsqoeBv4dXjLTMKYA+5xgORlvOWnwjOy/JyJTnRH+osA53wX+0N9ws6NGeA/wXEj5P+PZYwyjIUy4GEYG3EJNfXhC4AUReQJv2dkv4qmllojIVuBkomcOfw9MEpGn8YzwG9y1B4D/DjwBPA78FG91RXBrxTs34p9QnzrrXztD/VY84ff5kGPuBJaJt3qlGfSNurGsyIbRQvh2FDdz+TbeMg7fHu92GUZWbOZiGK3F1c6j68fAC3gxNYbRdtjMxTA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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def analyse_year_vars(df, var):\n", + " \n", + " df = df.copy()\n", + " \n", + " # capture difference between a year variable and year\n", + " # in which the house was sold\n", + " df[var] = df['YrSold'] - df[var]\n", + " \n", + " plt.scatter(df[var], df['SalePrice'])\n", + " plt.ylabel('SalePrice')\n", + " plt.xlabel(var)\n", + " plt.show()\n", + " \n", + " \n", + "for var in year_vars:\n", + " if var !='YrSold':\n", + " analyse_year_vars(data, var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that there is a tendency to a decrease in price, with older houses. In other words, the longer the time between the house was built or remodeled and sale date, the lower the sale Price. \n", + "\n", + "Which makes sense, cause this means that the house will have an older look, and potentially needs repairs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discrete variables\n", + "\n", + "Let's go ahead and find which variables are discrete, i.e., show a finite number of values" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of discrete variables: 13\n" + ] + } + ], + "source": [ + "# let's male a list of discrete variables\n", + "discrete_vars = [var for var in num_vars if len(\n", + " data[var].unique()) < 20 and var not in year_vars]\n", + "\n", + "\n", + "print('Number of discrete variables: ', len(discrete_vars))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OverallQualOverallCondBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageCarsPoolAreaMoSold
07510213180202
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" + ], + "text/plain": [ + " OverallQual OverallCond BsmtFullBath BsmtHalfBath FullBath HalfBath \\\n", + "0 7 5 1 0 2 1 \n", + "1 6 8 0 1 2 0 \n", + "2 7 5 1 0 2 1 \n", + "3 7 5 1 0 1 0 \n", + "4 8 5 1 0 2 1 \n", + "\n", + " BedroomAbvGr KitchenAbvGr TotRmsAbvGrd Fireplaces GarageCars PoolArea \\\n", + "0 3 1 8 0 2 0 \n", + "1 3 1 6 1 2 0 \n", + "2 3 1 6 1 2 0 \n", + "3 3 1 7 1 3 0 \n", + "4 4 1 9 1 3 0 \n", + "\n", + " MoSold \n", + "0 2 \n", + "1 5 \n", + "2 9 \n", + "3 2 \n", + "4 12 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's visualise the discrete variables\n", + "\n", + "data[discrete_vars].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These discrete variables tend to be qualifications (Qual) or grading scales (Cond), or refer to the number of rooms, or units (FullBath, GarageCars), or indicate the area of the room (KitchenAbvGr).\n", + "\n", + "We expect higher prices, with bigger numbers.\n", + "\n", + "Let's go ahead and analyse their contribution to the house price.\n", + "\n", + "MoSold is the month in which the house was sold." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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iVYX3wEPAW8BTQGFk427gx/b7p4BP2KMj3wPM2N2JzwAPiUidPWjkIeAZ+7NZEXmPPRryExeda6FrKPKjIoeHhwEYHh6+7Fy2cDjM66+/ztjYGENDQ/zyl7/U53BKqSWtmF2RLcCrInIUeB34mTHm58BXgAdF5AzwgL0N8DTQC/QA3wT+AMAYEwH+Ajhgv/7cLsPe51v2MWeBvXb5pa6hyI+KLKy3ZlkWe/bsueS+Q0NDF2xnMhnGx7UBrJRaujRXpG0l5Yp85JFHiMfjznYoFOLnP//5gvv+8pe/5NChQ4RCIRobG3G5XNxzzz3U19cvVnWVUupSNFekynv/+99/wfa999674H69vb2MjY0Ri8Xo6+vj3LlzrF69WoOaUmpJ02Vr1CUVBoxs27aNWCzmvFdKqaVMW2xlYmZmht7eXiKRyBX3feWVVzDGEIvFmJqa4sc//jFTU1Pv2M/jeft7T0VFBaFQCDtrmVJKLVka2MpAf38/L7/8MsePH+df//VfOXv27GX3v+uuu0gkEiSTSSzLYt26dRw4cMAZUFKwadMmXK63/xfp6urC7XYX5R6UUupG0a7IMtDd3X3B9pkzZ1i/fv0lW1dnz569YMj+yMgIqVSK2dlZamtrnfKWlhbuv/9+Jicnqa6upqampij1V0qpG0kDWxm4eGTrxS2viw0MDODxeMhms0B+XpvH46GysvId+waDQTo7O99RrpRSS5V2RZaB9evXv2P7cs/C1q5dSzAYdPJCtrW1sW3bNiYmJpibmytqXZVSqtiueh6biKwBuowxz4lIEPAYY8rmr+Byn8c2MTFBOBymtraW1tbWy+77+uuv8/nPfx7It/a+9KUvkU6nnRbcli1b6OrqKnqdlVLqOl37PDYR+X3gn4Cv20UdwL/ckGqpG6KpqYktW7ZcMahBflRkQTab5Tvf+Q5jY2NOl2Z3d7emzVJKLVtX2xX5h8A9wCyAMeYM0FysSqnievbZZwFIJpPMzs6yf/9+zp49S19fH5B/RpfL5UpYQ6WUunZXG9hSxph0YUNEPBR5uRlVPC0t+eXpkskkAHV1dWSzWSYnJ8nlcjQ3NxMIBIB8EuTjx4/T19enwU4ptSxc7ajIX4jI/wkEReRB8smHf1K8aqliGhkZcd7H43Hi8TiJRALLsti6dSvr16+nt7eX7u5uBgYGaGlpQUQYGxvj7rvvLmHNlVLqyq62xfYFYAI4BnyafCb+Py1WpVRxFTKKuFwucrkcLpeLqqoq1q5dS2NjIydPnuT48eMcO3aM/v5+zp8/D8D4+DixWKyUVVdKqSu62hZbEPi2MeabACLitsvilz1KLTmzs7OEw2E8Hg9ut5uKigqy2SxbtmyhurqaWCzGwMAAgJN1JBwOs3btWkREM48opZa8q22xPU8+kBUEgedufHVUMQ0MDPCLX/yCWCzGzMwMlmXhcrnw+XxUV1fjdrtpbm7G7/cD+fltIuIMJlmzZo3z7E0ppZaqq22xBYwx0cKGMSYqIqEi1UkVgTGGZ555hv7+ftxuN5lMhkwmQ1VVFaFQiJaWFjZu3EggEGDbtm0cPnwYt9uNy+WipqaGdDpNc7MOhFVKLX1X22KLicjthQ0RuQNIFKdKqhheeuklXnzxRU6fPs3o6KjzrMzr9ZLL5bjrrrucddba2tp44IEHqKys5NZbb2Xjxo0Eg0HefPPNd6TvUkqppeZqW2z/DvhHERkmP9O7FfhosSqlbqxsNsvrr79ObW0tk5OTGGNIp9N4vV5EZMFg5ff78fv9F+SdTCaTZLNZJxWXUkotRVcV2IwxB0RkC7DZLjptjNHUFMtEOp3G5/NRX1/vLFXjcrmu+LystbWVc+fOOdsNDQ0a1JRSS95lA5uI3G+MeUFEfvuijzbZ3/R/WMS6qesQDoc5ceIEyWSSjo4ObrrpJp5//nmi0SgVFRW4XC6i0Si1tbW0t7eTTCbxer0XjHq86aabcLvdTExMUFNTw5YtW0p4R0opdXWu1GL7deAF4MMLfGYADWxLUKHrsZDUuKenh40bN3L77bdTXV3NxMQEo6OjWJZFKpXCsiz27duH1+vllltuob29HcgP99+6dStbt24t5e0opdS7ctnAZoz5ooi4gL3GmCcXqU7qOk1PTztBrWBubo7t27dTUVHBE0884Sxrk06nOXHiBACZTIajR4/S0tLiTOJWSqnl5oqjIo0xFvB/XOsFRMQtIm+IyE/t7XUi8isR6RGRfxARn13ut7d77M/XzjvHn9jlp0Xk4Xnlj9hlPSLyhXnlC15jpaiqqnImVwPkcjnGxsY4f/48w8PD5HI5RIRQKEQymWRqaornn3+eM2fOkE6nicd13r1Savm62uH+z4nI50WkU0TqC6+rPPaPgZPztv8z8FfGmI3AFPApu/xTwJRd/lf2fojITcDHgG3AI8B/t4OlG/hbYBdwE/Bxe9/LXWNF8Pv9bN++HZ8vH89TqRS5XI5MJsOqVauoq6ujtrYWEWFubg4RYWRkhDfffJPJyUmqqqpKfAdKKXXtrjawfZT80jUvA4fs1xVX5RSRDuA3gG/Z2wLcT35tN4A9wG/Z7x+zt7E//6C9/2PAE8aYlDHmHNAD3GW/eowxvfbKA08Aj13hGitGR0cHDz30EI8++igtLS243W7i8Tjj4+NEo1GMMSSTSadl5/P5nNyRg4ODJJNJent7GRoaumDIv1peJicn+cxnPkM4HC51VZRaNFc73H/dNZ7//0u+G7PQBGgApo0xhQdAg0C7/b4dGLCvlxWRGXv/dmD/vHPOP2bgovK7r3CNFaWQ27G6upozZ87Q398PwMzMDIFAAJfLhcvlwhhDZ2cnR48eddZmy2Qy3HHHHcTjcdasWcPDDz98hauppWjPnj28+eab7Nmzh8997nPkcjnOnz/P3NwcLS0tV7UwrVLLzWVbbCJyt4gcFZGoiLwmIlc9PE5EPgSMG2MOXXcti0REHheRgyJycGJiotTVKZqbbrqJmZkZIJ9pJBAIkEgkCIVCBINBpyvS4/HQ2trK9PQ03d3d/OIXv2BgYIBXX32Vo0ePlvgu1Ls1OTnJ3r17Mcawd+9ewuEwhw4d4vjx4/T393PgwAHny45S5eRKXZF/C3yefCvoL8m3wK7WPcBvikgf+W7C+4G/BmrthUoBOoAh+/0Q0AnOQqY1QHh++UXHXKo8fJlrXMAY8w1jzE5jzM6mpqZ3cWtLXzwe56233uKVV15hYmKCm266idtuu40dO3Y4Ix7dbjc1NTU0Nzeza9cubr31Vuezubk5ZyFSgLNnz5bkPtS127Nnj5NVxrIsvvWtbzE2NnbBPoUliZQqJ1cKbC5jzD77+dY/Alf9198Y8yfGmA5jzFrygz9eMMb8v4AXgX9j77Yb+LH9/il7G/vzF0z+X+VTwMfsUZPrgC7gdeAA0GWPgPTZ13jKPuZS11gRstksL774Ij/5yU949tln+eY3v8nk5CRer5ehoSGi0SiJRIKJiQnn/dq1a53J2fMz/QNUVlZSWVlZyltS12Dfvn1kMvkEQZlMhueff/6C0bKAZpJRZelKga1WRH678Fpg+1r8e+BzItJDviX4d3b53wENdvnnyC9uijHmOPAkcAL4OfCHxpic/Qztj4BnyI+6fNLe93LXWBHGxsY4ffo0c3NzQD6z/+zsLMFgkFwuh9/vJ5fLkUgkMMZgjCEcDvPxj3+czZs3c8stt/DhD3/YmQqQyWTI5XI6DWCZefDBB53A5fV6eeSRR9i4caPzudvtZtOmTaWqnlJFI5fL1i4if3+ZY40x5v9946tUGjt37jQHD15xoOeSFo1GOXToEC+88AJvvfWW0+qqq6ujvb2d9evXk0ql+MIXvkAsFiOXyxEMBvF6vXzlK1/h13/91+np6eHgwYMkEgncbjcnTpygqqqKe+65h0AgwP3336+Tt5eJyclJPvaxj5FOp/H7/TzxxBM0NDQwOzvL3NwcjY2Nztp7Si1TslDhlTKP/F5x6qJutFQqxRNPPMHRo0edrCORSASXy0VDQwMVFRU0NDQwPDyMMQbLskgmkxhjCAQCNDQ0cPLkScbHx5mYmCCZTJJIJGhoaADyGUpEhImJCdra2kp5q+oqNTY2smvXLp566il27drl/C6rq6uprq4uce2UKp6rmscmIi0i8ncistfevklEVtSk56Xu1Vdf5fz580xPT3Py5EkikQjGGHw+H7W1tfj9fgYHB4nH407gq6ysdLqqNmzYwPj4OIAzQTuRSBAOhxkeHmZgYIBUKqXf8JeZ3bt3c+utt7J79+4r76xUmbjaCdrfIf8sa5W93U1+jTa1BCSTSaLRKF6vF8uymJqaYmJiApfLxfDwMBMTE4TDYYwxhEIhQqEQlZWVznu/308ikXACWmdnJ3V1dfh8PtLpNPX19UQiESKRiLMYqVoeGhsb+Zu/+RuntabUSnC1D0sajTFPisifgDOBOlfEeql3wefz4ff7Wb9+Pf39/Xi9XkKhEDU1NUxNTdHX14fb7SYWi7F582Y8Ho8zWq6gqamJ2tpaDh06RDweZ8eOHWzZsoVsNsvc3Bxut5uKigri8TihUKhEd6qUUld2tYEtJiIN5JeqQUTeA8wUrVbqXXG5XNx88838y7/8C8FgkOrqarZu3crs7CyWZREMBoF8xhGv10swGMTlcpFMJhERvF6v0xq7//77SaVSBAIBDh48yMjICKFQyNlPuyKVUkvd1Qa2z5GfT7ZBRP6V/Hy2f3P5Q9RiisfjdHR00Nzc7AwSqa+vp6Ojg7Vr15JIJAgGg9x77738p//0nwgEAgQCAZLJJLFYjCNHjiAi3Hbbbc56bJs2beLAgQMMDw/j9Xp58MEHL1iIVCmllqKrzRV5WER+HdhMfnjlaWNM5gqHqUU0NTWF2+0mGAxy2223sXbtWlavXs3AwACJRILm5mbe+973vqMbcf7cNGMMp0+fdgLb2NgYHR0dNDQ04PP5CIfD2hWplFryLhvYLjMJe5OIYIzRFbSXiLq6OmdUo8vlorm5mba2NmZnZ3G5XPh8vndk6V9oDmM0GmVmZoaamhpngncgECAWi+H1epmbm9PAppRa0q7UYvvwZT4zgAa2JWLjxo0kEgkGBwcJBoPcfPPNHD58GLfbTW1tLQA9PT1s2LDB6YIUEfx+vxPwzp07h8/n4+WXX6a2tpb29na6u7vZv38/iUSCuro6Nm/eTEtLSwnvVCmlLk8naJcJl8vF9u3b2b59+zs+syyL/v5+RkZGMMYQjUad7CGhUMgZ0h8Oh51h4dPT0zQ1NXHu3DnC4TAul4tsNsvx48dZv349FRUVi3p/Sil1ta46N5KI/Ab5VawDhTJjzJ8Xo1Lqxti4cSNvvvkmvb29HD58mJqaGp5++mlGR0dpbm4mnU6TTqed1FuRSOSC44eHh/H7/axdu9YpK2Ql0cCmlFqqriqwicj/DwgBHyC/Gva/IZ9hXy1Rk5OTnDt3jlgsxpkzZ6iqqmJ6epp4PE4kEiEWi+Hz+YD8gqTPPfccHR0djI+PO5O329vb6e/vZ2pqyjlvRUUFdXV1pbotpZS6ossmQXZ2EnnTGHPrvJ+VwF5jzPuLX8XFsZSSIH/1q1+lp6fnmo83xjAwMEAul8MYw9DQEFNTU06G/lQqhYjg8Xjw+/243W6qqqoQESfdVldXFxs3bmR0dJRIJEIymcTr9bJx40ZnXtyVbNy4kc9+9rPXfB9KKXUF7z4J8jwJ+2dcRFYBEUAz4S5RhSVp5ubmyOVyeL1eUqkUxhhyuRyWZTnPzIwx+P1+pqamsCyLiooKfD4f586do7Ozk5aWFmpqapyJ3hev56WUUkvN1Qa2n4pILfBfgEN22beKUiN1za2cSCSCZVnU1tbyl3/5l85w/XQ6TTweJ5PJcOLECWKxmLMidjab5Z577iEcDmNZFoFAgNbWVkSEj370ozQ1NVFVVcXq1as1qCmlloUrzWO7ExgwxvyFvV0JHANOAX9V/Oqpq2GM4eWXX+bo0aOMj4/j9XqpqqoiFAoRi8WcNFtNTU3EYjEikQhDQ0N4PB68Xi87duygp6eH8+fPO3PbPB4PfX19xGIxIP/MbufOnaW8TaWUuipXarF9HXgAQETuBb4CfAbYAXwDTau1JIyOjvLcc89x5swZRkZGyGazNDY2smbNGifP49zcHFNTU9TX1zM2NkZtbS2JRILHHnsMl8tFVVWVs6J2XV0da9euvSCT/8jIiC5bo5RaFq4U2NzGmMIY8I8C3zDG/DPwzyJypKg1U1dtYmKCoaEhJ+mxy+UiFotx7NgxfD4fN910E5WVlQQCAaqqqggEAoyOjtLe3o7X62VycpK5uTne//73Ew6HSSQSTE5OMj4+Tm1tLUNDQ2QyGdasWcO2bds0X6RSakm7YmATEY8xJgt8EHj8XRyrFoFlWUxMTDA4OOi0qioqKjDGkMlk8Pl89PX14fP5uOWWW7Asi/Pnz5NIJJiYmODs2bO4XC5GRkY4deoUAGvXrsUYQ3d3N1VVVeRyOdatW8e5c+cwxiw4CVwtTZOTk/zZn/0ZX/rSl3RNNrViXCk4/QD4hYhMkh8Z+QqAiGxEl61ZEoaGhhgdHaWyspJYLEYymcTn8zE1NYXP52NoaMhZn2316tUMDQ05LbBMJkMqlSISiTA2NsbMzAwiwszMDJ2dnWSzWdxuN21tbUQiEbq7u/H5fBrYlpE9e/Zw5MgR/v7v/57Pf/7zpa6OUoviSim1/qOIPE9+aP+z5u1Jby7yz9pUiRVGOPr9fjZt2uSspTY7O4vf72dubo5sNktbWxvZbJZIJMKGDRuYmpoimUwSDAaprKxkZmaGVCrF9PQ0xhjOnTuHx+PBGIPP50NE6O3t1W/9y8jo6ChPPPEE8Xic73//+9x///3cfvvtpa6WUkV3xfHbxpj9xpgfGWNi88q6jTGHi1s1dTWCwSCnT59mamqKWCxGKBRi7dq1bNy4EbfbTSAQcJadicViZLNZzp8/77TsCitjNzY20traSiCQz5iWTqcxxhCLxZiengagpqaGUCjkTOJWS9tf/dVfkU6ngXyX9X//7/+dRCJxhaOUWv70OdkyNz4+zpYtW+ju7gbA5/PR0dFBfX092WyWo0eP4vf78Xq9nD59mvPnzxOLxQiHw9TU1HD77bczNDRERUUF586do6GhAbfbTSgUwufzOfPadu7cyZo1a/B6vTqfbZl4+eWXnS8h2WyWw4cPE41GrzpzjFLLVdECm4gEgJcBv32dfzLGfFFE1gFPAA3kJ3v/rjEmLSJ+4LvAHUAY+Kgxps8+158AnwJywGeNMc/Y5Y8Afw24gW8ZY75ily94jWLdayml02luueUWGhsbmZ2dJZVKsXXrVk6cOMHExATNzc10dHRw+vRpZzSjZVlYlkUmk+H06dPcddddGGOorq5mdHSU8fFx/H4/zc3N7Ny5k7m5OWeC9qZNmzSwLRMf/OAH+Zd/+ReMMYgIO3fupK6ujomJCWKxGM3Nzbq2nipLxWyxpYD7jTFREfECr4rIXuBzwF8ZY56wkyt/Cvia/XPKGLNRRD4G/GfgoyJyE/Ax8isLrAKeE5FN9jX+FngQGAQOiMhTxpgT9rELXaPsdHZ2EolE6OjocJ6NhcNhUqkUHo8HESEQCLBmzRr6+vqA/MKhLpeLXC5HOBzm9OnTtLW18fDDDzM0NERvby+WZXH77bdTW1vLmjVrqK2tpaamhpqamtLesLpqn/jEJ3jmmWdIpVK4XC4+/elPc+LECc6fPw/klzq6++67aWxsLHFNlbqxihbY7IEmUXvTa78McD/wP9vle4AvkQ86j9nvAf4J+L9FROzyJ4wxKeCciPQAd9n79RhjegFE5AngMRE5eZlrlJ3Vq1fj9XoZHR3F5XI5a6/Nzc0xMDCAiDA6OkptbS2hUIjGxkZisRijo6O43W4SiQRnz54lnU6zfv16Vq9ezerVq+no6KCxsZGpqSlEhFAopEFtmfnJT35CKBQiGAw6Kzhs27bN+dyyLHp6ejSwqbJT1D4lEXHbE7nHgX3AWWDanhcH+ZZWu/2+HRgAsD+fId+V6JRfdMylyhsuc42L6/e4iBwUkYMTExPXcael1dbWxtatW2lsbHRWzO7r62NkZIRz587R3d2NMYa7776btWvXsn37dpqbm/H7/axatcoZaNLf3w/gLETa29vL0aNHOXr0KK+++iojIyMlvlP1buzbt89JkWaM4bnnnnO2CwqrpytVToo6eMQYkwN22AmUfwRsKeb13i1jzDfIpwZj586dV16/Z4kaGxvjwIEDRCIRpqamqKioIJPJkE6nyeVyZDIZxsbGeP/738+GDRsQEd566y3C4TCZTIampiY6OjoYGxvjV7/6FR6Ph2w2y/PPP09lZSXT09O43W7Gx8f55Cc/6azjppa2Bx98kKeffppMJoPX62XXrl20trYyOjrq7LNu3boS1lCp4liUUZHGmGkReRF4L1A7L5tJBzBk7zYEdAKDIuIBasgPIimUF8w/ZqHy8GWuUZZOnjzJ66+/TiQSwePxOHPRampqyGazWJZFOp3G4/GQTqfxer3U1tYyNzfnDAQZHR3ltddeI5lMUllZycGDB525bnV1ddTX1zM0NMTJkyd1gvYysXv3bvbu3Qvkn6ft3r2buro6BgcHicVitLS0XJAPVKlyUcxRkU1Axg5qQfKDPP4z8CL55MlPALuBH9uHPGVvv2Z//oIxxojIU8D/EJG/JD94pIv86t0CdNkjIIfIDzD5n+1jLnWNsvT6669z+vRpotEo58+fJx6P43K5qK2tpb6+nkAgQFtbG6FQiA0bNnD48GH6+/uJx+OcPXuWkZERNmzYQDKZZHx8nGQyyezsLG63m1wuRyqVIhaL0djY6MxpU0tfY2MjH/jAB3jmmWf4wAc+4EyuX716dYlrplRxFbPF1gbsERE3+Wd5TxpjfioiJ4AnROTLwBvA39n7/x3w/9iDQyLkAxXGmOMi8iRwAsgCf2h3cSIifwQ8Q364/7eNMcftc/37S1yj7BQCUGFE5Pj4uLOumsvlIhgM0traSldXF3V1dWzevJkf/vCHRKNR5/nK5OQks7OzDA4OkslkcLlcNDQ00NbWRnNzMwBNTU20t7dr5pElJJ1OMzc3R01NDR7Pwv+UZ2dnL/ip1EpQzFGRbwK3LVDey9ujGueXJ4HfucS5/iPwHxcofxp4+mqvUY6MMXR1dXHgwAGmp6dJpVJYloWIYIxxhnSvXr2a/v5+stksmUwGYwzGGOLxOKOjoySTSae7MhQKkclkuOeee4hGo5w+fZpcLsfIyAj3339/qW9ZAcPDw7zxxhtYloXX6+XOO+98x5eOyclJXnvtNQB++ctfEg6HnX1GR0cZGxtzBpP4/X7WrVvnZJ5RajnTmbbLXE1NDXNzc8TjcServ8vlIpFIMDMzQzQa5eTJk+zfv5+zZ89y9OhRqqurSafTzjf+wjpsfr8ft9tNTU0Nd9xxByMjI5w5c4ZIJML4+Di9vb38j//xP94xsk4tLmMMb731ltPizmQyHD9+/B37ffWrX11wu7+/nwMHDnDq1Cl++MMf8txzz9HT08Mrr7yi6dJUWdDAtsxls1mGhobw+/1UVFRQW1vrDAjx+Xyk02kGBgbYv38/r732GpZl0draSnV1NV6vF4/HQ1VVFR6Ph0wmg4gwNTXFkSNHOH78OP39/czMzDAxMeEsZDowMHCFWqliMsY4OSALksnkO/Z76aWXLth+8cUXAZwJ2pOTkxhjnATYhWesSi13mitymRseHiadTrN69WqSyaTzR8/j8TjPXQYHB8lms/h8Pl566SXa2trw+/0Eg0E2bdrEqVOnSCQSiAixWIyqqiosy6K7u5tsNksgECCXyzkjLLXFVloul4vW1tYL5hW2ty84VXNBXq8XwEmxJiLO+8JnSi1nGtiWuUwmQ0dHB+l0ms2bNzM9PU0mk2FmZoZYLIYxBsuymJ2dpb+/n+7ubhobGwmHw1RWVuLxePD7/QQCASoqKpxuycnJSXK5nDMyMpFIUFlZyZYtW2hrayv1ba94O3bscOYYNjY2sn79+nfs09nZeUHrurMzPztm06ZNRCIRmpubmZycpKGhAY/HQ1NTk2YhUWVB9Nt33s6dO83BgwdLXY13LZFI8NJLLxGLxejp6WFgYIDu7m76+/sZHx93glsoFMLlciEirFmzhqGhIVKpFKtWrQIgl8vhcrkIh8PE43Eg3+UVDAZZvXo1LS0tfPSjH+V3fud3qKysLOUtq6vU3d3Nv/23/9bZ/va3v83GjRuB/IjKyclJQqEQyWQSj8ejQU0tR7JQobbYlrlgMMi9997LuXPnOHLkCH19fSQSCdLpNKlUChFBRJyuxpqaGqampkgkEiSTSWZmZpzJ3D6fD7/f7zy/SafTzgCF2267jd/93d+95LBytfRs2rTJabV1dnY6QQ3yz18LX2qUKjc6eKQMuFwu3nrrLfbv38+ZM2cYHh4mm81ijHGemRQSGft8PjKZDMlkEsuyiMfjGGPI5XLU1tZSXV1NZWWlkzYrm80yOTnJ1NQUc3NzpbxNdQ2++MUvUlFRwZ/92Z+VuipKLRr9+r3MGWN46aWX2Lt3L6Ojo3i9XieoFZat8Xq9WJZFMBikubmZmZkZIB/sstksqVSKmpoaYrEYyWSSqakpZwCK1+ulqqqKsbEx+vv7qaurK/Edq3dj06ZNTlotpVYKbbEtc6dPn+b111+nv7+fXC5HPB4nFApRW1tLVVUVXq/XGcXY3NzMhg0bMMY42UpyuRyQHy4eiUSclFmFz7LZLLFYjHQ6TXV1daluUymlrpq22Ja5oaEhjDFUVlYSCASIx+Pkcjna2tqoqakhkUgQiUQwxlBfX080GiUajeJyuZwJ2ZlMhmw2SzweJ5VKkU6nyS+Fh5PFJBQKcf78eTo7O/U52xKUzWZxu93O702plUz/Qi1zoVCI6upqfD4f2WyW+vp6urq6nNbX7OwsxhhExJnPlkgknAwTwWCQbDbrZCuZP0/N7Xbj8/loaGjgjjvuIBKJMDg4yNq1a0t4x2q+TCbDoUOHmJiYwO/3c8stt+h0DLXiaWBb5rZs2cLTTz9NX18f8XicbDbL4cOHiUajJBIJ3G432WyWYDCIZVlYloXP5yOZTDqtNLfb7Sx1U+imhHxrLZFI0NfXx969e4lEIrS2tmpgu0G++tWv0tPTc13nCIfDFyQ4FhFWr17tZJ8ZHBwEoKOjY8HjC19irqelt3HjRj772c9e8/FK3Wga2Ja5aDTK3NwcuVyOWCzmTMbO5XJUVlYiIuRyOSoqKoB8lvdCgIP8kH632+0EM5fL5cx3y2az5HI50uk0Z8+eJRQKce+995bsXtU7pVKpC7aNMWQyGfx+P5Cf57iQQlLr0dFRjDE0NDTQ0dGhi8iqsqCBbZk7e/Ysb731FqOjo063o9/vx+PxOJlDUqmU8wew0N1YmN8G+e4syGd4L2T+z+Vy5HI5PB6P88dyYmKCvr6+Ut1q2bkRrZzu7m5Onz7tbPv9fh544AGnxVa4xsUJkd966y2efvppZwqH2+3mvvvu47777rvuOilVahrYlrmpqSkGBgaIRqNkMhknIHm9XmfB0cLSJoWh/yLiZBeBfDeUZVnkcjksy3L+KBbKQqGQM7nbGEMikSAYDJbqltU8GzduJJPJMDIyQigU4qabbnJ+f5czNTXF2NiYM7CopqaGsbGxRaixUsWngW2ZK3Q1uVwu5xlZoQUmIk5XYzqdxu/3O+u0XfxspZATstAFWSgrzIsLBAKsWbOGzs5OTYK8hLhcLrZt28a2bdve1XGhUIipqSnnC04mk6GqqqoYVVRq0ek8tmXuPe95D26323lmVuhitCzrHd2KyWTSSXJceBWG/Xu9XoLBoNNqK3xe6LasqqpyFjINhUIlvmt1verq6ti2bRtVVVWEQiE2b95Ma2trqaul1A2hLbZlrqGhgfb2dgYHBxERp+VWmHhdGOpfmJBdCHrw9nIlwWCQyspK4vG400IrHJ/L5chkMkSjUWZnZ3UttjJRXV3Npk2b2LRpk1OmSZBVudAW2zJ35MgR7r77bm677TaCwaATuAott8IIx0KC48J6bfO7I71eLzU1Nc4SNgWFFls6nSYajTIzM0MikbhgeLla2gqLhx46dIhIJOKUNzY2sm7dOqcruq2t7ZJTApRabrTFtsy5XC5mZ2edRUZdLpczItLn8zktOBEhk8lcMNQf8n/4crkcNTU1dHZ2cu7cOfx+/wVL1xhjiEajDAwMEAgE3jHEXC1NsVjMWYx0eHiYnp4etm7dSltbG01NTdx8881s2rQJy7IIBAIlrq1SN44GtmtwIybW3iiRSISjR49y7tw50uk02WzWGSCSSqWclbRTqRS5XG7BgR+ZTIbz588TDAZxuVz4fD5isdgF+xhjmJ2d5fvf/z7nzp1bMmm1dHLwpc1fYXtiYoJz584xPT3NqlWr6OrqYsuWLTpvTZWlpfHXaZnp6enhjWMnsEL1pa4K6VSKcCxF1jLkchYGMIUWmbjIWRZWOosRgxGBS4xoTKVSpNJpvL4AHo8Pl9uLlctcsE8maxGOZXijdwyXPaG7lFzxyJV3WsHmT8kYHh4GcLqae3t72bRp01VNDVBquSlaYBORTuC7QAtggG8YY/5aROqBfwDWAn3AR4wxU5Lv7P9r4FEgDnzSGHPYPtdu4E/tU3/ZGLPHLr8D+A4QBJ4G/tgYYy51jRt5f1aonuRNH7qRp7wmxlikJ6LkwlOYTA6ydjehy43L68fKZTFWDiyT/y2IG0zuEmcTMlmLnMsCfxCSFlj2vi4PBGvItW4lfctvLsatXVHgxE9LXYUlrbq62sk8A1BTU+MsO6RTNlQ5K+bXtSzwvxljbgLeA/yhiNwEfAF43hjTBTxvbwPsArrs1+PA1wDsIPVF4G7gLuCLIlJYFOxrwO/PO+4Ru/xS1yg7xlikZscx2XS+ReZ8AFYulw9Mzt8wKx/URFjwV28syGUw2RwmkwW3h/zK6wLiwu314g5W5wOlWtJ6e3t56aWXnGek9913H5s3b3ZaaHV1dfziF7/gpz/9KQcOHHDmPipVDooW2IwxI4UWlzFmDjgJtAOPAXvs3fYAv2W/fwz4rsnbD9SKSBvwMLDPGBOxW137gEfsz6qNMftN/uvndy8610LXKDupyCi5VAJxecDKvv2BHaSwTD6QGcgHKRe4/fm3C7IwmTgml4ZMGruZh9iDUDKzYcRV+m5IdWm5XI5Tp0452263G5fLxT333MOmTZvYuXOns3yRMYbR0VFOnjxZwhqrYiiMZl6JFuUZm4isBW4DfgW0GGMKT7VHyXdVQj7ozZ8kNWiXXa58cIFyLnONi+v1OPnWIatXr363t7UkZGIzuEM1JCeHIZe98ENjAWL/NG+/rNy8YHcJ84MkFiaXI5eYu0xAVEtFYUL+fOl0mvr6emdNvotHtk5N3dCeelVi3d3dnDlzBsuyqKur46677lpRA4WKHthEpBL4Z+DfGWNm5y+PYT8PK2pn/+WuYYz5BvANgJ07dy7Lhw7eyhoys5OYbPqiT8zbP02+1eWwrqHbyeUClwuXV4eFL3U+n4+WlpYLcj92dnY670OhEIFAgGQyCeRHxY6Pj/PCCy9QX1/Ptm3b8Hq9i17vcrUYo6jnL0+UyWSc7YKamhrq669vsNtyGoFc1CFRIuIlH9S+b4z5oV08ZncjYv8ct8uHgM55h3fYZZcr71ig/HLXKDvGCJm5yGUGhDh7glzHr9vK4nJ5Ec/K+da3nN1xxx1s3bqViooKmpqa2LBhg/OZy+Vi586d1NTUkMlkGB4exuVyEYvFGBgY4OjRoyWsuboWiUTCyRtbyPU630p7hlrMUZEC/B1w0hjzl/M+egrYDXzF/vnjeeV/JCJPkB8oMmOMGRGRZ4D/NG/AyEPAnxhjIiIyKyLvId/F+Qngb65wjbITG+7GylzlhGljXXmfSxIMhsR4H7lcFrdbZ4qUWjabJRwOU1FR4ayjV5ho7Xa72bhxI83NzQseW1dXR1tbGzMzM4yPjxOJRNiyZQvBYFCz/N9gi9HKmb88US6X47nnniOdfrsX5/bbb6e9vf1Sh5edYv51ugf4XeCYiByxy/5P8sHmSRH5FHAe+Ij92dPkh/r3kB/u/3sAdgD7C+CAvd+fG2MKE5j+gLeH+++1X1zmGmUnM2/5mcsTLv9Q7Qpcbtz+SkwmRXJykIqWtdd+LnXd+vr6+MlPfkI2myUWi1FXV8fq1avp7Oxk+/btTqqsXC7H7OwsBw8epLW11UmblU6n6e7uBvLz3eLxOIODg3R1dVFdXV2y+1LXz+128973vpfu7m5SqRQdHR0rKqhBEQObMeZVLj3U4IML7G+AP7zEub4NfHuB8oPAzQuUhxe6Rjny+IPgcudHQF7WdT5CzOXIxCK4vB59zlZis7Oz/OhHPyISidDf38/Zs2dpa2tj586dxONxqqurWbt2LS6Xi8HBQSKRCC+++CKtra3cc889tLW1OenVANauXcuJEycYGxtj3bp13HrrrSW+Q3W9qqur2blzZ6mrUTLan7TMuXxBFmWooslhZdPkElHc/pWxyOhSSp02XyQSobe3l7m5OUZHR8nlcnR3d3Po0CEsy6Kqqor29nYaGho4duwY2WyW733ve7jdbr72ta/R0NCAy+Uik8ngdrtJJpNMT09TWVnJ8ePH+ed//ufrHmhwIy2nQQtqadDAtsylY1MXDc0vAhFw+3H7Q7gDFaSmxvBV1BT3mktAT08P3W8dZnXl0pqQnosncUdnmJ2YQrIZTCZHNiNMJ+byqzokZzk/O8a4z8tMZA4AKzELWAT9PrJTITxuF36vh4aaCqan5giJ4IvNkYnB5AQEmmtxSenndvRHiz9ncql+gXk3zpw5AyzO87xiuZFfYDSwLXe59FV2RV4HA2CB24PbF8Rfs3LW7VpdmeNPdy6tSa45y/CvvRb7TgvnJsHjdjOXzDEZy9FZ66MmmCWdzeJ2JahqNCQyFpmcYS6Voy1gUeNO4/e4WVXt4bGbhcmYYSqeZXgmTixtUel38bFbXVT4Sz8R/8sHK4t+jZ6eHt44/gbUFv1SxWOPC3tj6I3S1uNaTd/Y02lgW+aC9e24fEGsTLKIVzGQzSC5DJWdm/FVLZ1uqpXI7RLev6GSRNbippYsA9MZ3hiIUe13kbEMWSs/7bDS76YmKAzPpJlN5PAKZHIW4CKZyWIZNxOxLCGvix/1TDMZzRL0CresCtIzmWJ7+wpaKb0WrPuuZ9Swuh6ul27szDNN7b3MVXZuzXcLSpG/XbvceELVhJpXk0slinstdUUiwvvWVbKlJUiV380Dm6u5qS1Apd+FZQzr633c3hGkLugm5HNTX+llTYMfn0uYTuSYTRrSWRibzTA2l2EumSObs0hkLEZmMozOrqx5T6q8aIvtGgwODuKKzyyJ7PKSiFPlE9JuN1a2iM+CrCzpyDDevl/hmT6DP1j6b/OueJjBwSI/X1xCZhI5+iIpsha013hprfaytTVAOJ7/b9BQ6eHYcIJ42qK+0sPwXI5k1qLK72JDox8RGJxKM53M0VzpJegVphMWY3MZsjlDwOvG64ZExpDMLMtEPEoBGtiWPZfHSzI6i7VAtoEbzcpmCI8PUdfcVvRrLQWDg4PE5tyL8pznSnKWxfDEDJZ5O9VVc52foN/LyCSkszky2RxTUfB53JzozX/JEfEzNRejYtqNz+shk/UgAtNzLoZzPrKThshcnFTaQyqTw+dxU10ZID1ey7ElcN/n59xUXJQe6kYbHByEmRvfHabehWkYNDfu96yB7Rp0dHQwlvIsifXYEuFhMkcOg0xd91S1KxIXc2mIb/wgnmDp/+gFTvyUjo7WUldjUSRTWayLfr/xVJqg30tTbQVT0QRzsRx+j5vCYEZjWUxFk+Qsi5lckupQABHw2Fljook02WyOptoQE9Mx3C4XAZ+HjqYaaqtK3yJX6lppYFvmEuFhxOPP54G8rpRZV8HlAcsil04uicBWbB0dHSSzI0tiVORMIseB/gufbXY1Waypz0e7ZMbi9fMZDg8kmEvlmEtZTMWyeH05aoMu1tT7cbsyeFwu2mtdhGNZMEIWw/ZVBssKMp3I4fcIu25y4/NcbUab4vrywUoCHR1X3vE6dHR0MCETOnikhFwvuehov3G/Zw1sy5y43PkRkYsy58jgDVTirdCUS4utJugm4BFOjSXxe1x0NQdor/FhWYaB6TRvDiWYTmZZ1+BlZFY4ORojmbVIpC0QaLcMPne+qy2RsZhL5kjnDJU+N8lMjoDXTX2Fh5taA/g82iWnljcNbMuclUnlkyAvwqrWLrcXf30rLs3wv+j6I2mSWcOqGh+WMYRjGY4OJ5iIZsjlDG8MJhibS1Mb9FAXdFMXclHp9zKbyBFNWcTTFp11ftwCx4YTDEyncYtw86oAfeEMD20N0lzpoTakfxLU8qf/Fy9zqemxfMJbU/xRbC5fAOvixUzVohi2h9+LwImRJKOzWba1BZiJZ0lkC4vIQjiWZTqRxVjQWOEh4HURSlncuirAtrYg43MZfnnOyi85awznw2kG3RlcLtjSEqCj1sfGRj8uV+mzjiyq6WU+eKTQW75cnxBM8/Yy0TeABrZlLpdO2svWFD+wZeOziMeLsXKIq/RZKVYSnzsfaM6F05wLp0hnDUPTKdI5iMRy1AXd+D0uJqJZ6kMe3G5DMpMf6t9a5eU3b6llMpbjYH+cTM4ins635NKWwRio8bup8LqxTP5aaxv8Jb7jxbNx48ZSV+G6FVJqdbV3lbgm16j9xv4eNLAtcy63d5GerwHGIpeMXt+CpctMf3RpDPdPZQL0j07RNxplNp5FROieMWRzOTwuNy4XJNMWddWVRFNeKoNehucsxF9JTWWIsWOCMYaj5+NMznmZjeZIZy0m4ln8Xg8j8Sy/Gk3S0RSgusJNe1Pp7xny//03Ffkayzm/YsH89diUBrZlr7JjM+Fjv1i064m4nbW+yt1S+iYfAKq8fQRTPRBIkE6nSWazGDHUNDTi8Xhwx+P4q6pobG7G5/Ph8XiYmppiPBrFXd2G3++nZlUFVMxQm80yNjZGJpPB6/UiLhdJYCLtw13fQrRyFY2Npc8Juoml9XsoB7FYDK/Xi89Xvs/KNbAtc55gJbJYq1kbC/GtnLXYltI3eWMMP/vZz3jjjTc4ceIEuVyOgYEBWlpa2LBhA6FQiEwmw5o1a5zFRNevX88XvvAFqqqqePzxxwGIRqOcPn2abDZLd3c34+PjBINBEokEIkJXVxePPvoobreb++67j6qqqlLetrqBMpkMv/rVr5iamnJ+15s3by51tYpCA9s1csUjSyKlVmJilIBbyFzvCtlXRZDxM7iP/DNeX+mfwbjiEWBlTNAWESeIJZNJYrEY1dXVGGMIBvPr49XX13PvvfeSTqdpbGyksvKd3YmFddqmp6fp7OzkyJEj1NfX57OsxGJ4PB76+vpYs2YNyWRSA9sNsBjL4lzNsjVTU1NMT09fUNbe3n7VLbfltC6eBrZrsJS6RoaDFnMj50jFvKTT6aJey+v1UF8VZEtbDbW1tUW91tVpXVK/i2LbsWMHAPF4HMuySCQStLa2Mjc3RzKZZMeOHWzbts3Z37Is3G43udzbU0E6OjpYvXo1IyMjuFwu7rvvPn7+858zNDSE3++nrq6OcDhMIBCgoaFhsW9RXaPCl5vLyWTemdg6m82WZZekBrZrsJS+tRw4cIDvfve7PPvss/T09GBZxcue4Ha7ufnmm/lv/+2/0dzcXLTrqIXFYjHGx8epqKigu7ub0dFRDh8+jDGGzs5O2tvbOXjwILfffjsulwuXy0VraytjY2NEIhE6Ojro6OjA4/HQ2dlJNpvl2LFjdHZ2Mjk5STQaxePx0NTURGdnJy7XyhkkVExL5e/F8PAwhw4dcra9Xi8PPPAAHk/5hQH9P3eZCwQC3HPPPbS2ti7Y9XSjrxWNRskuQsJl9U7nzp3Dsiz6+/uJRCJEIhFCoRC5XA6Xy0U6nWZkZISRkRHnGGMM2WyW+vp64vE4r732Gsae8xiJRMhkMng8Hnw+H5WVldTW1rJmzRr94lKGVq1axfbt26mvr6e1tZVf+7VfK8ugBhrYlr3Cs5CZmZkLupyKxRhDKpUq+nXUpSWTSZLJ/MKymUyGbDaLZVnEYjEA5yfA7OzsBcfOzs4SDocBnC9CIsKGDRvw+/0EAgHq6+u5+eabF+NW1CJbvXo199xzD3feeSfV1eWbGq88w/UK4na72bNnDz09PSQSxV0ANJlM0tLSQktLS1Gvoxa2fv16RkZGqKurY2pqinQ6TUNDA8YY3G43VVVViAitrW8PqFloaobbnZ9cHwqF2Lp1K6dPn6aqqooPfOAD3H777Vf1vEappUwD2zL3zW9+k9OnTzvPVIr5jK26uppf+7VfIxTSJU1Koaamhvvuu4+uri6Gh4cZGhoiGo2yc+dOAoEATU1NrF+//oJv4jU1NRe04Jqamqirq3O2N27cyJo1a8hkMvp7VWWjaIFNRL4NfAgYN8bcbJfVA/8ArAX6gI8YY6Yk/7Xyr4FHgTjwSWPMYfuY3cCf2qf9sjFmj11+B/AdIAg8DfyxMcZc6hrFus9S6+npIZfLOd/CL0dEnOcr75bX6+Xmm2/mscceu6bj1Y0RCoXo6uqiq+vqUid5vV46OjrYvn07fr9/wWdnXq8Xr9e7wNFKLU/FfMb2HeCRi8q+ADxvjOkCnre3AXYBXfbrceBr4ATCLwJ3A3cBXxSRwtfNrwG/P++4R65wjbJ011134fP5nAEdl8sK8m6DWuFcXq+XUChEY2PjNQdGdWPlcjlOnTrF/v376e7uvmxL3e12s3r1alpaWlZM1hi1shUtsBljXgYiFxU/Buyx3+8Bfmte+XdN3n6gVkTagIeBfcaYiN3q2gc8Yn9WbYzZb/J/ab970bkWukZZeuSRR7jrrruorq4mGAxSW1vrPGu5lEsN43a5XLjdbjweD4FAgEAgQGVlJW1tbWzYsAER4dVXXy3Wrah34ejRo5w5c4aJiQlOnz7NT3/6U5599lleeOEFhoaGSl09pUpqsZ+xtRhjCmORR4HCKIR2YGDefoN22eXKBxcov9w13kFEHiffQmT16tXv9l6WhGQyydatW+nt7cXn85FI5PMIVlZW4vf7SafTRKPRC77Rz291Fbong8EgwWCQiooKjDFUVFRcMPpORBgbG2NycnLR71FdyBjD8PAw6XSagYEBRkZGmJub46GHHsLtdvPGG29QW1tLRUVFqauqVEmUbLi/3dIqar/Wla5hjPmGMWanMWZnU1NTMatSNOfOnSMcDuN2u53gVZjXlEqlyGazTiArtOJEBLfbjcvlQkScbWMMlmXR2dlJZWUlyWSSXC5HLBZjZGSEdDpNe/sNXDRJXRMRIRAI0NPTQzgcZmpqimg0Sn9/P5APfJHIxZ0lSq0ci91iGxORNmPMiN2dOG6XDwGd8/brsMuGgPsuKn/JLu9YYP/LXaMsiQjRaJR4PM7U1BSpVAqv10s0ml95MBAIOK2yQoDz+/1UVFSQSqXIZDLOZ8lkknQ6TU1NDclkkkQiQTabJRgMYoyhvb29bJOmlsL15BCcm5vj+PHjAKTT6fwyNt3dFL6gFXIAXk0Oweu1nHIIqpVhsVtsTwG77fe7gR/PK/+E5L0HmLG7E58BHhKROnvQyEPAM/ZnsyLyHntE5ScuOtdC1yhL9fX1zMzMMDEx4bTQ4vG40wrLZrMXtNRcLheBQD5Df+F5WjabJZFIkEqlSKfTDA8PEw6HMcY4gTMajTI3N1fKW1XzVFZW0traSl1dHe3t7VRXV+PxeHC5XDQ0NDj5/wpdzEqtJMUc7v8D8q2tRhEZJD+68SvAkyLyKeA88BF796fJD/XvIT/c//cAjDEREfkL4IC9358bYwp9LH/A28P999ovLnONsuRyuYhGo07Ly7KsC1poxhgnI0kh2DU2NjI7O0sqlcLv9xOPxzHG4PF4MMYQjUYvmBNnWRbGGM6dO8cPfvADvvzlL5fylsvG9bZyJiYmeOONN0ilUlRUVHDnnXdqNn6lKGJgM8Z8/BIffXCBfQ3wh5c4z7eBby9QfhB4R94fY0x4oWuUq/HxcVKpFB6PB4/Hg2VZWJaFy+XC5/Nd0AXpdrvx+/PLzbhcLmexwcIcuMLztkImC5fLRSaTcY5LpVK88MILpNPpsswIvtw0NTXxwAMPkEqltFWm1DyaeWSZGxsbo76+noqKCqdlVhi2HwwGqayspKamhpGRETKZDNXV1bS2tjI0NORsx2IxJ/gVgmJVVRWxWIxsNovH48Hr9ZJMJvF6vUxPT2uS3CXC5XJpUFPqIhrYlrmGhgY2bNiAy+Vysr9XVFQQCoWcVtaGDRvwer00NTXR1NSE1+t18ko2NDRQW1tLOp3GsizS6bTTRRkIBJysJiJCVVUVt9xyS1knT1VKLX8a2Ja51tZW3ve+97F27VpuvvlmwuEwXV1dBINBUqkU/f39BAIBtm3bRi6XY9OmTXR0dHDq1CkSiQTNzc20tbWxfft2WltbOXXqFD/72c8YHR0lnU7T1NTkDDrZunUrH/3oR53BJ0optRRpYFvmtm7diojQ0NBAVVUVFRUVFwwh9/v9VFVVsXr1aqanp1m3bh2NjY1UVVWRTCZZtWoVFRUVjI6OcssttxCJRJx5bP39/RhjaGpqorm5mTVr1nDnnXeW8G6VUurKNLAtc263m1WrVjE1NUUkEsHv99Pe3s6rr77K3NwcmUyG5uZmamtr6ejoIJFIEA6H6e3txbIsqqqqnNZYLpejqqoKYwxjY2PMzMzQ0tJCU1MTHR0dbNu2TQeNKKWWPF1odJnL5XK8/vrrzuTsvr4+ent7WbduHfX19QwODrJ//35nrtr58+cJh8OICOl0mkgkgtvtJpFI0NXVRTabZXp62pnEHYvFSCaT+Hw+7rzzzkvmmVRKqaVCW2zL3NzcHOl0+oKy/v5+amtrGR4eJpvNMjAwwPHjxwkEAmQyGXp6eshkMliWxdjYGOvXr+fOO+90BopUVlZiWRZNTU1Eo1GqqqrYvn07w8PDrFu3TtftUkotafr1e5mrrKx8x1psra2tTExMALBq1Sra2toYHh6mpqaG2dlZJ/u7z+dj3bp1dHZ2Mjw8zOHDh4nH4/j9fu644w66urpobGxk/fr1Tu7Ic+fOLfo9KqXUu6GBbZnzeDzcfvvtzkjFlpYWHn30UWeV5MrKSidANTQ0OCssF5ajufvuu2lsbHSG/9fX19PZ2emsulxXV8emTZuc62UymUW+Q6WUene0K7IMtLa20tLSgmVZTuvtYx/7GK+88gqWZREOh2lra8Pr9dLc3ExHRwc9PT1UVFRQXV1NS0sLAwNvrw5UCHa1tbWcPXv2glyTy3V5H6XUyqGBrUwU8kAWVFdX8/DDDxMOhwkGgxw5coSZmRncbje33norBw8exOv1cssttzA+Ps7w8DANDQ3OxOydO3fi9/vZuHEjfX19pFIpOjo6qK+vL+FdKqXUlWlgK2Mej4eWlvw6q+973/sYGxsjHA5z7NgxJicnSaVS/M3f/A133303lZWVzMzM8J73vIcNGzY4OSX9fj+bN292Mv0rpdRSp4FthXC5XLS1tTEwMEBPTw/ZbJZUKsWpU6eYmpqira2NUCiEz+e7ILPI6Ogox44dI5VKsWrVKrZv3/6OwSpKKbWUaGBbgq5nAcorGRwcZHBwkHA4TDqd5tChQ1RWVlJbWwvA66+/Tmdnfs3XXC7HwMCAkyAZoLa21hmYciW6AKVSqhR0VOQKU1tb6yxZ4/f7naVrALxe7wWtsXQ6fUFQA0ilUotaX6WUerfk4j9cK9XOnTvNwYMHS12NRXH27FleeeUVstkssViM6upqfD4fVVVV3Hzzzaxfvx6AbDbLvn37yGazzrFbtmyhq6urVFVXSqn5Fnzwr12RK9CGDRvYsGEDANFolFOnThGPx2lra2PdunXOfh6PhzvvvJPjx4+TSCRob293jlNKqaVKW2y2ldRiU0qpMrFgi02fsSmllCorGtiUUkqVFQ1sSimlyooGNqWUUmWlbAObiDwiIqdFpEdEvlDq+iillFocZRnYRMQN/C2wC7gJ+LiI3FTaWimllFoMZRnYgLuAHmNMrzEmDTwBPFbiOimllFoE5RrY2oGBeduDdtkFRORxETkoIgcLK04rpZRa3lZ05hFjzDeAbwCIyISInC9xlRZbIzBZ6kqootPf88qwEn/PPzfGPHJxYbkGtiGgc952h112ScaYpqLWaAkSkYPGmJ2lrocqLv09rwz6e35buXZFHgC6RGSdiPiAjwFPlbhOSimlFkFZttiMMVkR+SPgGcANfNsYc7zE1VJKKbUIyjKwARhjngaeLnU9lrhvlLoCalHo73ll0N+zTbP7K6WUKivl+oxNKaXUCqWBTSmlVFnRwLYCaR7NlUFEvi0i4yLyVqnroopDRDpF5EUROSEix0Xkj0tdp6VAn7GtMHYezW7gQfIZWQ4AHzfGnChpxdQNJyL3AlHgu8aYm0tdH3XjiUgb0GaMOSwiVcAh4LdW+r9nbbGtPJpHc4UwxrwMREpdD1U8xpgRY8xh+/0ccJIF0geuNBrYVp6ryqOplFpeRGQtcBvwqxJXpeQ0sCml1DInIpXAPwP/zhgzW+r6lJoGtpXnXefRVEotXSLiJR/Uvm+M+WGp67MUaGBbeTSPplJlQkQE+DvgpDHmL0tdn6VCA9sKY4zJAoU8mieBJzWPZnkSkR8ArwGbRWRQRD5V6jqpG+4e4HeB+0XkiP16tNSVKjUd7q+UUqqsaItNKaVUWdHAppRSqqxoYFNKKVVWNLAppZQqKxrYlFJKlRUNbEoViYjk7OHXR0XksIj82g045475w7lF5JMi8n9ftM9LIrLzCudx9hGR3xGRk3aW+PtEZMau95si8pyINL/LOn1JRD5/bXeo1PXTwKZU8SSMMTuMMduBPwH+rxtwzh3AjZ6n9Cng940xH7C3X7HrfSv5Cf1/WII6KXXNNLAptTiqgSnILzUiIi/braK3ROT9dnlURP6rva7WcyJyl92y6hWR37Qzxfw58FH72I9e6aIi8jUROWif888W+Pz/A7wP+DsR+a8XfSZA1bx63yUir4nIGyLySxHZfJk63TSv7p+91v9oSl0LnaCtVJGISA44BgSANuB+Y8whEfnfgIAx5j/a6+OFjDFzImKAR40xe0XkR0AF8BvATcAeY8wOEfkksNMY80f2NT4J/FcuzPe5EbjPGHNQROqNMRH7Os8DnzXGvCkiLwGft/eZ//4+4MfAOaABiAF3GWNmRaQaiBtjsiLyAPC/GmP+pwXq9CXgIeAD5APjaaDVGJO5of+BlboET6kroFQZSxhjdgCIyHuB74rIzeS7975tJ6/9F2PMEXv/NPBz+/0xIGWMyYjIMWDtZa7zD4WgYl/rpXmffUREHif/b72NfJB88wr1fsUY8yH7XP8e+C/A/wLUAHtEpAswgPcy5/iZMSYFpERkHGghv0SSUkWnXZFKLQJjzGtAI9BkLwB6L/lW1ndE5BP2bhnzdheKBaTsYy2u4UuoiKwDPg980H5e9jPyrcd34ym7rgB/Abxor8b94SucKzXvfQ79Eq0WkQY2pRaBiGwB3EBYRNYAY8aYbwLfAm5/F6eaI9+9dzWqyXclzohIC7DrXVyn4H3AWft9DW93eX7yGuukVNHptyiliicoIkfs9wLsNsbk7OdY/7uIZIAo8ImFD1/Qi8AX7PNedpSlMeaoiLwBnCK/avq/XuU13m+fX4AZ4N/a5f+FfFfkn5Jv/b3rOim1GHTwiFJKqbKiXZFKKaXKigY2pZRSZUUDm1JKqbKigU0ppVRZ0cCmlFKqrGhgU0opVVY0sCmllCor/39vwZzGz2PLEwAAAABJRU5ErkJggg==\n", 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mJdTX17Ny5coS1mjh6H5sSqklY8+ePeRyOXK5HMlkkn379vHoo48CcO7cOVpbW0tbwTJ3++23s2HDBnK5HDU1NaWuzoLRwKaUWjJeeOEFe0F2Pp/nnXfe4dFHHyWVSnHixAn6+/uprq6mo6OD9evXl7i25SkQCJS6CgtOA5tSasm48847efnll3E6nfh8PtavX08sFuPdd98llUrh9Xqpq6sjmUxSVVVFW1vb5Z9U3XB0jE0ptWScOXPGvh0IBMhms9TW1rJy5Uq8Xi8A4+PjxONxRkdHS1VNtcRpi00ptWScP39+xvHw8DDLli1jcnJyRrkxhtra2sWsmioj2mJTSi0Zy5cvJ51OU1j9Ax0dHXR0dBAIBFi5ciVOp5Oamho2bdpUsTP61LWbd4tNRFYB640xvxQRP+Ayxkxe7nFKKTUfx44dY8eOHRw7dgyHw0FNTQ1/9md/RkNDA/feey8DAwN4PB7a2trweDylrq5awuYV2ETkjynskVYPrKWQl/Hv0EXQSqkrMD4+zuDgIIFAgPb2dhyOQqdRPB6nu7ubd999FyjMiEwkErzyyits27aNmpqaip6erq6v+XZFfh74EBAFMMacBpYtVKWUUpVnYGCAX//613R2dnLkyBHeeust+77i5qLvvPOOXWaM4fnnn1/0eqryN9/AljLGpIsHIuJigbebUUpVlq6urhnHg4ODJBIJAGpra6mpqSEUCtn3e71empsv3koxl8vR1dXFu+++Szgcvuh+dbHJyUnS6fTlT6wQ8x1je0VEvgb4ReQBCsmHf7Zw1VJKVZpit2ORiGAlSQfg7rvvJp1O4/V68Xg8eDweBgcHL3qeAwcOcPz4cWKxGDU1NfzWb/2WZiS5hGQyyRtvvEE0GsXhcLBp0ybWrl1b6motuPm22L4CjADHgD+hkIn/3y9UpZRSlWfdunUzgtvKlSvx+Xz2scfjoaOjg2AwaE8OuXAD4KmpKQ4ePEhvby/j4+OcO3eOV155ZXFeQBk6ffo00WgUKIxbnjhxgmQyWeJaLbz5ttj8wJPGmL8HEBGnVRZfqIoppSpLY2MjH/nIRxgeHiYQCFwUtICLtlG58FhELup+1IXalxaPz/yITqVSvP/++7S2ttLU1DSjxVxJ5ttie5FCICvyA7+8/tVRamkaHR3lC1/4AmNjY6WuSlnz+/00NjbS0NBwVY+vqqqa0e3ocDjo6Oi4TrWrPC0tLfbtqakpTp48SXd3N2+88caMiTqVZr4tNp8xZqp4YIyZEpGqBaqTUiWXTCZJpVLU1NQgIvYeYXv27OHP//zPS129shSJRDh48CCJRAK3280dd9zBsmUzJ1ffe++9vPzyy/bxhz/84Rn3iwi//du/zSuvvEIqlaK+vp6dO3cuRvWviyeeeILOzs5FvWY0GmVqaopoNIrT6eTAgQP2fe3t7XR3d5NOp3nsscfI5/PU1NQQDAYXtY4XWrduHV/84hev+vHzDWwxEbnDGPM2gIhsAxJXfVWllrDTp0/z/vvvY4whGAyyfv16e4+wvXv3snv37qtucdzIjh8/bs+CzGQyHD16lPvvv39Gd1gxH+SljqHwYfy7v/u7RCIR6urqymqxdmdnJycPH6bl8qdeV1VAfHKSxAUzI8fHxjAiRCMR8tZEnTFgWU0NXrd7kWtZcPF0oSs338D2JeCfRKQfEKAF+PR1uL5SS0oymbSDGhS6b/7Lf/kv9nE+n9dW21WKxWIzjhOJBPl8HqfTaZe9+uqrM8751a9+xde+9rWLnsvn882YeFJOWoDPsfhjWxNeH++lM/ZxrdvNZpebwWSSswaYVqfl6Qwd7tJ8YfjedVhJNq8xNmPMQWAT8P8C/p/ATcaYt+Z+lFLlJ5FI2EGs6Ne//rW9gDiTyfCLX/yiFFUre9PHe6Aw49HhcNDZ2cmvf/1r3nnnHXbt2jXjnAcffHAxq1jRQh4Pt9bW0ub3syYQYFN1NQC+aV8sivyzlJWTOVtsIvJRY8w+EfmdC+7aICIYY360gHVTatGFQiGqqqpmzCZ74IEHeP3118lkMrjdbv2wvUqbN2/G5XIxOjqK3+9n8+bNnDlzhhMnTgCFdFvF/dXS6TSJRIKGhgZOnjzJxo0bK3YG32Kqdrmods382A+53bT4fAxaywDqPR6aZukCLieX64rcBewDfnuW+wyggU1VFBHh7rvv5tSpUyQSCdra2rj77rvZv38/UJiFt3v37hLXsjw5nU42bNjA5OQkg4ODjIyMMDk5SVXVB/PQnnvuOdLpNFNTU/aYZn19PX6/n1WrVpWw9pVtTSBAu9+PMQZvmbfW4DKBzRjzdRFxAHuNMU8vUp2UKqmqqiq2bt06o+zhhx/m2Wef5eGHH9aJI9egq6uL4eFhoJAaa2BggLa2Nnu87N1338VvfcBCYcIJFJZbaGBbWB5H5exidtlXYozJA//uai8gIk4ReUdEfm4drxaRN0SkU0T+t4h4rHKvddxp3d8x7Tm+apW/LyIfm1b+kFXWKSJfmVY+6zWUulq7d+/mtttu09baNTDGcOrUKbq6uuz1gNMz/DudTvx+P+5ps/GMMRhjGBsbY9++fbz++uscO3aMffv2sW/fPs6dO1eS16KWtvmG6F+KyL8VkRUiUl/8medj/ww4Me34PwLfNsasA8aBz1nlnwPGrfJvW+chIjcDnwE2Aw8B/90Klk7gvwEPAzcDv2+dO9c1lFIlcvToUYaHhxkZGeHMmTP09/cTCAT45Cc/yX333ccDDzxAe3s7TqeTQCCAiFBbW4uIEI/HicVinDt3jmeeeYZoNEosFuPo0aOaCFldZL6B7dMUtq75FfCW9XPocg8SkXbgt4D/YR0L8FHgn61T9gCPWrcfsY6x7r/fOv8R4IfGmJQxpgvoBO60fjqNMWetnQd+CDxymWsodVWmL9C+kDGGiYmJGyIH39XK5/P09vbS2NjIypUrqaqqIplMsmbNGgYHB4nH4/T09DAxMUE+n8fhcFBbW0swGKSlpcVeEhCNRsnlckxN2fkiGBkZKdXLUkvUvNaxGWNWX+Xz/xcK3ZjV1nEDMGGMyVrHvUCbdbsNOG9dLysiEev8NuCDpfIzH3P+gvKdl7mGUldsdHT0kgu0E4kE+/fvJxaLISJs2rSJdevWlbjGS4+I4HQ6yefztLS00NLSQl9fH6dOnWJwcJC+vj5uvvlm+vr6yOVydvdkJpMhn8/T3d2Ny+XC7/cjIvj9H2T4m77VjVJwmRabiOwUkSMiMiUi+0Xkpvk+sYh8AhheyuvdRORxETkkIofK8Vuf5i9cHHv27LlogXbRqVOn7IXHxhhOnjypLbdZiAgbN260j2OxmD0bcmBggFwux+DgIMlkkmg0SjKZJJlMMjg4SG9vL6lUiv7+fgYHB9m1axd+vx+n08m6detm3bNN3dgu12L7b8C/pdAF+UkKLbCPzfWAaT4EfFJEPg74gBrgb4CQiLisFlU70Ged3wesAHqtjUxrKWR3KZYXTX/MbOVjc1xjBmPMd4HvAmzfvr3sNk6d3j32J3/yJ6TTaUKhkK73uc5eeOGFixZoFzOPFFNEFRljSCaTZZsVYyGtXr2axsZGIpEIAC+//DJnz55lZGSEYDBIPp8nk8nYXyKK72U4HGbjxo1kMhmcTie33Xab3WK+cI83peDyY2wOY8wL1vjWPwEX7zNxCcaYrxpj2o0xHRQmf+wzxvwB8BLwKeu03cBPrdvPWsdY9+8zhb/wZ4HPWLMmVwPrgTeBg8B6awakx7rGs9ZjLnWNijG9e+yf/umf+MlPfsKvf/1rOzmsun4eeOABe6behQu0ly9fPuPcQCBAbW3totavXGSzWc6cOcO7777L22+/zenTpxkdHbXH35qamnC5XLhcLrLZLMlkEmMMJ06cIBqN4na7cTgcuFwuHA6HBjV1SZf7ywiJyO8Uf2Y5vhpfBv5cRDopjId9zyr/HtBglf85hc1NMca8CzwNvAf8C/B5Y0zOao39G+B5CrMun7bOnesaFaPYPZbNZonH4+zbtw8obAF/9uzZEteusuzevdtuBV+4QHvlypVs2bKFpqYmVq5cyd13360t5ks4efIk58+fJ51O2xtgZrNZGhsbueeee1i3bh0tLS1UV1eTz+dxuVxUVVVRU1NDT08PAMFgcMb4mrq8nDGcnppif3iMtyfGGb8gEXIlulxX5CvMzDoy/XjemUeMMS8DL1u3z1KY0XjhOUng9y7x+L8A/mKW8uco7OZ9Yfms16gkxe6xfD5PNpvlnXfe4dFHHwXQMZ7rrLGxcc4F2itXrmTlypUlql35KG4Imk6nOX78OGfPnmXNmjW0trYyPj5OKpUikUjg9/vtmZEul4utW7cSjUYREaampvjlL3/JmjVruPnmmy9zRQXQl0gwYvXiJHN5Tk1Nsa2uDlcFfwG7XOaRP1qsiqgr88ADD/Dcc89hjMHj8XD77bfb9xXz7anrZ/fu3XR3d+sC7WsQCoWYnJyks7OTWCxGNpslHA4zMjKCy+Wy98Bzu904nU5cVk5Dp9NJdXU1XV1dxGIxampq7HHMFStWzFjQrS42mc3OOM4ZQzybpaaC37d5dVKLSLOIfE9E9lrHN4uILnouoWL3mIhQV1fHZz/7WVpaWtixY8dFmzcqtRTcdNNN1NfXEw6HiUajNDY2Mjg4yMDAgD0LMpVKYYyxp/a73W5uuukmIpEIw8PDxGIxTpw4wY9//GOeeeYZfvazn+k6tsu4MOmxU4Qq13x3LCtP8x19/T6FsaziSPkpCnu0qRIpdo+JCJ/4xCfYtWsXO3bsuGhrEHV9zLZAOxqNcuTIEQ4fPmzP9FOX5vV6+dCHPsSaNWvYvHkzuVyORCJBNpvF4/HQ399PPp+/aHxt9erV9kSRfD7PiRMn6O/vZ2JigiNHjnDo0GVzRdzQ2vx+mrxeHCL4nU42BoMV3Q0J899otNEY87SIfBXsBdS5BayXmgftHlscsy3Qrqqq4rXXXiNrdfP09fVx3333EQgESlzbpW/t2rWMjIxQX19PLpfD6/XS3t7OxMQEUJh5Ov19dDgctLa2ks/nOXv2LA6Hg2XLluFwOMjn8/bEEjU7pwjrg0HWl7oii2i+gS0mIg0UJowgIncB+hW1xBobG/nbv/3bUlej4s22QPvRRx+1g1qxvL+/n/Xrb6SPj6uzefNmEokEVVVVjI2NEY/H8fl85HI5/H6/PZ2/qJjRZXBwkObmZtasWUN1dbV9vy7QVheab2D7cwrrydaKyGsU1rN9au6HKFUZZlug/elPf/qi87xlvjnjtXriiSfo7Oyc17nxeJxIJEIkEsHlchEOh+2p/Ol0GofDQSAQwOVy8ZnPfIaenh4ymQwejwcRweVy2a294eFhe7nLbNatW8cXv/jF6/UyVRmYb67It0VkF7AREOB9Y0xmQWum1BJRnIE6fQft1tZWGhsb7SnsdXV1Ohv1ClRVVVFVVUVrayvxeBwRweFw0NzcTF9fn/1Form5mfPnzzM1NYXb7SaTyVBbW2vvdF4MfkpNN+dfxByLsDeICMYY3UFbVbzdu3ezd+9e4IMF2g6Hg7vvvpvx8XGMMdTXz3cXp8p1ta2iaDTKvn377Az+X/3qVxERmpub+eQnP8nrr7/O8PAwIkJ7ezsbN27kIx/5iCabVpd0ua86vz3HffNeoK1UOZtrgXZdXV0Ja1Z+RkZGGBgYIBAIsGrVKt555x1725qJiQkymQzBYJDJyUkeeughstkswWCQsbExcrkckUiEhoYGbR2rOekCbaXmQWegXru+vj7efvttAHK5HC+++CLxeJyamhqampowxpBKpWhqaqKpqYmqqiomJydZv349Pp+PSCTC5s2b+djHPqZptRbBZCbDUCqFQ4RWnw+/1aIuB/PunBaR36Kwi7WdttwY842FqJRSS43OQL12586ds293dXVx5swZAoEAY2NjpNNpvF4v6Wl5DIsptFpaWrjjjjtwOp186EMf0iUV11kmn8d9QULpkVSKF0eGSeXyBFxO2vx+doTqcJVJ4ul5BTYR+TugCvgIhd2wP0Uhw75SSs1LMfXVxMQE/f39VFdXY43V8+6779pbLsViMeLxOD/5yU9YuXIlxhjq6urYtWsXDofDnsSjrk0sm+XU1BSJXA6f08HGYDUBayLOwfEwsWxhqXIkk8VBknAgw7Iymfk73/B7jzHms8C4MeY/AHcDGxauWkqpSrNmzRref/99Tp48SW9vL/F4nIaGBs6fP09PTw8ul4tVq1YxNjbG2NiYHejOnj1Ld3c3b775Ji+88AK/+MUv5r2sQF3amViMRK4QvJK5PJ2xKQBSuRzZC3anjOVyuMsoW8l8uyKLuynGRWQ5EAZaF6ZKSqlKFIvFWLNmDdFolIaGBt566y1isRhDQ0MEg0FSqRSHDx8mnU6Tz+cZHBzkxIkTAJw9e5aenh7uueceO61Wa2urdkteg1huZnLkYgvN7XDQ7PUQyWRI5/MANHk8hMqolTzfFtvPRSQE/H+Bt4Au4AcLVSmllppTp07x8MMPa0vhGqTTadxuNw0NDQQCAZqbm8lkMjQ0NJDL5RgeHmZoaAgRsbOSjI2N2UmRu7q67LRbAFNTU6V7MRXgwkBVPHaIsLG6hpuqg6zw+7m1poaPLFtWVvsMzhnYRGSHiLQYY75pjJkAgsAx4J+Aby9C/ZRaFMlkktOnT/P+++8Tj8cvuv9b3/oWsViMb3zj4vlSxcS9am6tra32WrVMJsPQ0BA+n4+pqSl6e3sZHBykv78fl8tFdXU1oVCI6upqGhoacDgcRCIRJicnAewAqS5vPJ3m1OQkvYm43QIDWBsI0uj14HE4aPB4WBcM2veF3G6219Xz0WXLuLO+Hm+ZTBopulxX5P8P+E0AEfkw8JfAF4CtwHfRtFqqAmQyGV599VV7g9auri527dplTyk/deoU3d3dAHR3d9PZ2WkvDj516hSdnZ0YY1i9erVufjmHQCDAhz70Ibq7u8lkMqxatQoR4dy5c1RVVdlb1XR1dZFOp7ntttvIZDJ2dheHw0EikaCpqYlNmzZpxpF5eC8a5Z3IBMlcHpcIawMBttXV4Xc68TgcbAhWz/n4ct0F4HJ/GU5jTNi6/Wngu8aYZ4BnROTwgtZMqUVS3A+sKJPJ0Nvbayc0/ta3vjXj/G984xs89dRTjI2N8f7779vlZ86cob6+XrcOmkNtbS1btmyhtrYWESEcDhOJROxNR4vps7LZLCJCfX29vZt2TU0Ng4ODNDQ0EAqFSv1SlrxoJkNvIkEyV2ilZY1hJJ1iMJlktTU2mc7n6YnHiedyhNxu2v1+HGUazKa7bGATEZcxJgvcDzx+BY9Vqiw4ZulmcU5bjFpsrV14XBzvSafTDAwMkEqlqK6u1sA2D42NjeTzeYaGhohGo5w4cYLBwUFisRiZTAZjDAcOHKCmpoZsNmtnHGloaODYsWOcO3cOt9vN1q1bqa2tLfXLWZJyxlxUljfWFi2W9ycn7R22p7JZ8sbQUQETci4XnH4AvCIioxRmRr4KICLr0G1rVIVobW3lzJkzRKNRoJCgt7293b6/vb2d3t5eexxt5cqVAPYYz/vvv08iUZg43NfXR09Pj32OmmlwcJCDBw8yPDzM6dOnGRwcJBqNMjk5STgcJp/Pk8vlMMYwMjJCJpMhEAiQz+eZmJhg2bJl/PSnP2VgYIC6ujpCoRBf/epX6ejoKPVLm5fe3l4mge9xcdC53ozbxaDHzWgqSdb6263zuBnxenBjyOXz9Gdn5rJ3pVO0BqoWvG5zGQCmenuv6Tkul1LrL0TkRQpT+39hjP0VwEFhrE2psud0Orn33nsZHBzEGENzc/OM8Zv29nZOnjxJKpUCYNOmTQCEQiHWrl3LkSNH8Hq9tLS0UFNTQ19fnwa2Wbzyyis888wzHDt2jFgsxvLly4lEIoyNjeFwOAgGg8TjcXufO5/Px7Jly6ivr2dsbIza2loymQxnz56192ObmJjgJz/5CV/60pdK+MqWJhFhWW0tXrebSDyO3+2moaYGt9Ub4RDB6XCQmzahxFVGabPmctnuRGPMgVnKTi1MdZQqDYfDwfLly2e977XXXrODGsCbb77JyMgITU1NrF27lq1bt2Kmdfv4fL7ZnuaGlk6n+elPf8r58+eZmJgglUrR09NDKBQilUrZ29gUZ6S6XC6ampoIBoOEQiFqamrsVnFxI9J0Oo3H4yEWi5XypV2R9vZ2JkZH+RyLM46VMHA8kyHjdEHesCyR/GD2owhjgQCdUzFyxuBxOLipKkBgkep2Kd/DEJrWY3I1dJxMqcuYvlM2gDGGaDRKU1MTXq+XjRs38v7772OMwe/3s2GDJuW5UC6XY2RkBBEhk8mQSqVwuVx0dHSQz+dJpVKEQiG8Xi8jIyOEQiHuuusuurq6GBsbw+12c9dddxEMBjl48CATExOICH6/n/vuu6/UL2/J6k8myOQ/+NI1nEqx3O+nymqZNXi81NZ5SOVyVDmdZbVWbS4LFthExAf8CvBa1/lnY8zXRWQ18EOggcJi739ljEmLiBd4CtgGjAGfNsZ0W8/1VeBzQA74ojHmeav8IeBvACfwP4wxf2mVz3qNhXqtqrI98MAD/PjHP7aPb7/9dpYtW2Yfr1+/nvb2dhKJBKFQaNbJKDc6v99PW1sbr7/+OoFAgEQigcPhoL6+nttuu42RkRFGRkYYHR3l8OHDZDIZenp6uP3228nlcoyPj3P06FHGxsbI5/PU1NRQU1PDtm3buPXWW0v98pas6UENIJ7L0huP015VZQc3l7UjeSVZyP+BKeCjxpgtFNa9PSQidwH/Efi2MWYdME4hYGH9HrfKv22dh4jcDHyGws4CDwH/XUScIuIE/hvwMHAz8PvWucxxDaWu2Je+9CVqampwuVy43W6+/OUv22M8RX6/n/r6eg1qc/jEJz5BR0cHy5cvZ8uWLaxcuZLa2lrWrl1LXV0dv/Ebv0FDQwMulwuv14vH47HH1Yr7uKVSKYwxrFq1iptvvpmNGzfO2BFAzTQ9afFwKsVAMsVIKsXhiQnG0qk5HlneFux/oSko5rxxWz8G+Cjwz1b5HuBR6/Yj1jHW/fdLoV38CPBDY0zKGNMFdAJ3Wj+dxpizVmvsh8Aj1mMudQ2lrlhjYyOf+MQnqK2t5bHHHmPz5s2lrlJZam1t5dFHH2Xnzp00Njbi9/sZGxujv7+ftrY2hoeHaWlpscfVstksr776Kt3d3WSzWVwuF1VVVXi9XiKRCOl0Gp/PpxlILiGVzzOcSpHK5YhmMuSMYWWV3+5u7E0kLvMM5WtBv15aLavDwDDwAnAGmLDWxQH0AsWtcNuA8wDW/REKXYl2+QWPuVR5wxzXuLB+j4vIIRE5NDIycg2vVFW63/u93yMQCPDYY4+Vuipla8WKFdTW1pJMJvF4POTzebxeL4ODgwDs3LmTdevW0dzcTDKZJJ1Ok06nyWazuN1uNm/ejNvtxhhDPB5ncnKSnTt3Vsy40PV2ZmqKcDqN1+kk4HKRMXnc8sFHfn7hVxyUzIIGNmNMzhizFWin0MLatJDXu1LGmO8aY7YbY7Y3NTWVujpqiTHG2N1cP/vZz4jH4zz77LMlrlX58ng83HfffdTX1+N2u6mpqeHUqVMcOHCAY8eOMT4+jsPhIJ/PMzU1Zc9EHRgYsGdSFgNacfLJz372M83TeQmRaWvUnCIzghpAawXP3l2UEUNjzISIvERhH7fQtGwm7UCfdVofsALoFREXUEthEkmxvGj6Y2YrH5vjGkrNy+joKO+8846dauvnP/85xhj27t3L7t27tfvrKhhjGB0dJZVK4fF48Hg8DA4OkkwmefPNN+nv7+djH/sYoVAIl8tFJBKxs7w0NTXh8Xjwer324vlYLMbY2BgDAwO0tc3aKXNDCzhdTE2b0bsuEGC5308smyVUZtvQXKkFa7GJSJO11Q0i4gceAE4AL/FB8uTdwE+t289ax1j377MWhD8LfEZEvNZsx/UUdu8+CKwXkdUi4qEwweRZ6zGXuoZSl2WMmRHUfvzjH9tbpOTzefbs2TPXw9UlHD58mDfeeIPJyUmMMQwPD+P3+0mlUqRSKc6cOcORI0dIJBIkEglOnDjB2NgY4XCYkZER4vE4IyMjJBIJotEo8XicxsZGMpnM5S9+A1oXCNgzH/1OJ+uCQZq8XjoCgYoOarCwXZGtwEsicpRCEHrBGPNz4MvAn4tIJ4XxsO9Z538PaLDK/xz4CoAx5l3gaeA94F+Az1tdnFng3wDPUwiYT1vnMsc1lLqsVCo1Iyny4cOH7W6xTCbDL37xi1JVrWwlEgl6e3sREUKhEIODg5w9e5azZ88yMTHB2NgY2WyW4eFhuru77UXXxYXv0WiUo0ePMjQ0xNmzZ8nlclRXV5PP52lt1T2PZ1PlcrE1FOLO+npuD4UIVNiU/rks2Cs1xhwFbp+l/CyF8bYLy5PA713iuf4C+ItZyp8DnpvvNZSaD5/PR3V1tb3310033cTx48cxxiAiPPjggyWuYfmZnpklnU6TSCSIxWJEIhEymQxTU1NEIhF8Ph9DQ0NkMhk7q78xhlwuRzqdJpVKMTU1hdfr5ZZbbrEXdatLK9etZ66FLrpRahY7duxg2bJlnDt3jtWrV5NOp5mYmCCfz/PJT36y1NUrO1VVVbS0tBAOhzl9+jTJZJJEIoHL5bIn6UQiEbq6ukgkEuRyOXsDV2MMDocDr9fL1NQU0WiUnp4ejhw5wnvvvVfql6aWIA1sSs0iEAiwZs0aVq1axcmTJ4nH40QiEcLhMD/4wQ9KXb2ytG3bNqqrq/F4PHbLq5j/UUTI5XIkEgkymQyZTAaxMmJUV1cjIiQSCZLJJPl8nmw2S19fn92FqdR0GtiUuoRUKkU2m+XNN9+0JzjEYjF+9rOflbpqZevIkSMcPnyY3t5eu1XmdrtxOp3k83l7Kn8ul8Pj8RAKhQgEAjQ2NuK2JjwU0z8ZY8hkMjP2zlMKNLApdUnFhcLJZNL+EM7lcjMmlqj5O336NG+88QZDQ0OkUimCwSBVVVUEg0FEBBGxxzGLyZKNMbhcLlatWkVHRwd+v99etxaJRBgcHLQXeCtVdONMk6kgg4ODvP/+++RyOTo6OlizZk2pq1SRpu/QPDk5aW+XUl9fX+qqlaX9+/cTiUQwxhCLxRARAoEAy5YtwxhDMpkkHo/bgSubzdoTeGKxGDU1NTQ2NhKLxQgGg9TW1uL1ejl48KCOe6oZNLCVmXg8zqFDh+xZZu+++649MK+uXjqdticutLW1UcxE09zcjM/nm9F60DGdq1PM95hIJGbsu5bNZvF4PMTj8Rlr0orvuYiQzWZxOp2sWLGCcDhsd08Gg0Fdx6YuooFtkT3xxBN0dnZe9eMnJycZHR0FsH+vXr2axsbGa6rXunXr+OIXv3hNz1HO9u/fTzQaBeD8+fPs2LGDlpYW6uvrue+++9i3bx9QaMU99NBDpaxq2Vq9ejVNTU0cPXqUXC6H2+1mamrKzvWYy+XI5XIXPa6qqop8Po/H42HZsmVEIhF7DK6hoYHt27cv9ktRS5wGtjLj8Xjs28U8hrqO59pMTEzYQa2ou7ubSCTC2NgYDoeDUCgEgNPp1K1prlIgEGBsbAwRwev1Ul1dzcTEBOFwGKfTedH2M8Uxt6mpKdLpNPF4nK6uLlasWEFDQwMtLS38wR/8AatWrSrRK6osQ8kko+k0XoeDdr8fXxlPytHAtsiuR6uos7OTU6dO8Z3vfIdgMMiTTz6pGc6vgXuW9ELnzp2juOPDr371K0SEYDAIwCuvvMLXvvY1AJLJJOPj49TW1lJVVbV4lS4z+Xye/fv343K5EBHS6TThcNhuoU1fkF0cY3O73fj9fvuLhMPhIJvNMjExwW233UZVVZV2Q14nw6kkZ6xsLwCRTIY7QqGy/VzRwFaG1q1bx5o1a3juuedwOBxl+8e3VAQCATo6OuyEux6PZ8YHZk1NDaOjowQCAUTE7vYdHBzkjTfesP8NbrvtNlauXFmKl3BNrrV7fD5yuRwvv/wy0WjUXjpRDGTARRn6i3/TIkIsFsPlctlptiYnJ/n5z39OTU0NnZ2d19wNP1+V3F0/kprZWk7l80xls1SXaU5JDWxlyuFwaJfYdXTrrbeycuVKEokEDQ0N/OxnP6Ovr4/a2lrC4fCMLxD9/f1MTU3xwx/+kLGxMbxeL+vWrePEiRNlGdg6Ozt55913ILSw1wknwkSiEXLk7ECWJ1/YfvhCUrhvKjEF+ULgE4fgcruIp+JMpaYIJAI4ljk4nzo/yxNcZxMLf4lSG0omERHq3G7cDgce7YpUqvzV1tZSW1trZ5gfHBykt7eXdDpNXV2dfV4ul+Po0aP2uFwqleLs2bNs2bLFnsVXdkKQv29h9zWrDlUT/mmYbD4LbgoBLQ+Idbv42wHiFnL5HCZjwGmdB+SyORxOB7gg5UvRm+2l/SPtOD0L+yHseLlyv0QmcjnG02mmclnSeUMkk+bDjU14y/iLc/nWXKkFkMlkOH/+vN0VWewyu3AcLhKJMH1z2rGxMSKRCEeOHLloIooq8NR6MDkDaQqBKk8hiDmlENSc1g+Qz+QLQc0AWT4IgtZPLpEjMZogNZIi1hub7XJqnkZSKRwirKkK0FFVRUfVB9vdlCsNbEpdIJPJ2FujFNddRaPRGRnqGxsbaWtrY/Xq1VRVVZHL5ZicnOTkyZO89tpr9jY3qsDkDZH3Irir3DhcDsQl4KTQ+hIKx3kgxwcB7KInKdyfz1rJkfOG1GQKk5+tL1PNl2vaeKbf6cTtcOAsx16HabQrUqlp3G43dXV1dsb58+fPY4yxM2DU1NQgImzZsgWn04nf7ycajeL3++nt7aW3t5fVq1czNDRUluNtCyWXyiEuQRwCbnDgKIy1kf8goM0nPhns1pvD7cDpduJv8S9o3SvdMq+XwVSSZK7wbSLoclFvLStK5XL0JBIkcznqPR6W+3xl0dWugU2paVKpFE1NTdTU1BAOh2lvb8fpdOJ0Oslms2QyGTweDx6PhzvuuINEImFvROp0OhER+vv78fl8pX4pS4rT68QVdJHL5Mgn83brV4xgMB+Mr82HgCvgwtfgo2FrA56g5/KPUZfkcjjYWhtiPJNGKEweKQavY9EIXfE4iWwOv8vJjlAdHYFAiWt8eRrYlLJks1leffVVEokELS0t9Pf3U1VVhdvttsfYstks+XyeSCSCiPDqq6/aC7mj0Sitra3U19fPGH9TIA4hn84jbsHpd5LP5Mmlc4WgNt/WWpEBIwZvyEvD1oaFqvINxSFCg2dmood4LseZWIypbHGtYZaj0YgGNqXKydDQkL0/WCgU4u6772Z8fNweQ0skEkxOTuJwOPjhD39ITU0NwWCQ1tZWe7+wtWvXct9995VFd81iyyVyuPwuTMYUJpEUs2fN1Vqb7b58oSyTyDB+fJyG2xsKY3XquvKIEM/OTHGWzi/szNnrRQObUpbp+3olEgkGBgZoamqyy4vBKp/P09vbSyAQYNWqVaxcuZLq6mpisRj33nsvHR0dpaj+kuet95I5kiEzlSGXzM0+QWQ6B4XAdnH6SPKpPOmJNMmJJOnxNL5G7fq93lwOBx2BKrpicQzgcQgryyS7jgY2pSzLli0jHo9z+vRpurq6WL58OStWrLB3c75QTU2Nnem/rq6O1atXs3bt2sWudtlw+pw4PU7SJn1xUBMQr2DSZuZ9l2jJ5RI5UqMpYj0xGm7R7siFsqOunqDLxWQmS63bxcbqmlJXaV40sCllOXfuHGNjY/T09NDT00MqleL222+3U2y53W4ymYyds3DNmjXceeedDA4O4vP5dBbkZWTjWcQt5FOzZxsxOavQwQfBbY4eXZM3pCfShZmWakEEXS521NWTzufxlNGCbQ1sSlk6Ozs5f/48ExMTJJNJzp07x8svv0wqlaKqqsoOaIlEgvr6epxOJ8FgkJtvvrnUVS8LDo+D1Ghq9mA1fSF20VxdldbaN4fbgcNdPh+45aqcghosYGATkRXAU0AzhT/X7xpj/kZE6oH/DXQA3cBjxphxKfT1/A3wcSAO/KEx5m3ruXYD/9566m8ZY/ZY5duA7wN+4Dngz4wx5lLXWKjXqpauK0nw29PTw5kzZ+wkvZlMhpGRETKZjJ1l3uv14vF4eOWVV/jVr37Fnj17rnhH7UpOpjsXb8iLOAWHy0E+O0vUusKZkflcYdmAu6Y8E/UuZYlcjlw+j9PhwF+GWUgWMgxngf+3MeZm4C7g8yJyM/AV4EVjzHrgResY4GFgvfXzOPAdACtIfR3YCdwJfF1Eion7vgP88bTHFXeAvNQ1lLrI+Pg43d3dpNPpQkYLY+zWmNvtxuv1kkwmiUQipNNpkskkw8PDTExMkEgkdOuUeXLXuHH5r+K79GwtPAGXz4W/2U98IH7NdbvR5IxhMJnkXDxObNqO8HljeC8a5cXhYZ7u6+P/PzjA4YmJspkNWbRgLTZjzAAwYN2eFJETQBvwCHCfddoe4GXgy1b5U6awcvOAiIREpNU69wVjTBhARF4AHhKRl4EaY8wBq/wp4FFg7xzXKHvxeByHw6ELgOfpci2jwcFBnnvuOc6fP08ul2PLli309PTYOyd4PB62bt3K3/3d3wGF9//RRx8lFArR19fHzTffzE033cSKFSvYunXrQr+cspaJZvA1+EhNXGG6sektuWnT/911blxVLrKx7GyPWrIGge9dUfP0+huejJKyvpBNJuLU1dTQ6nYTSyUZTSUZTyULNcxkeDuVpD7hom6R1q8Ncu0bTSzKGJuIdAC3A28AzVbQg8JraLZutwHT95/otcrmKu+dpZw5rnFhvR6n0Dpc8gP/uVyOQ4cOMTw8jIjorsHXycDAAN3d3RhjGBkZobe3l2AwyMqVK/F6vaRSKaqrq3G5XCSTSZYvX8769esZGhqitrYWl6vwX+j8+fO0t7cv2t5g5SiXyCEIDp+DXCx3ZV2PRcXH5CAxmMBX5yskUS4T69atK3UVSKfTuPv6KHbgRvv7iYkQ2rqVfDiMb2QE3/gHIze+6mr8DQ2EWloWpX4hrv19WvDAJiJB4BngS8aY6PRp09Z42IJ+dZnrGsaY7wLfBdi+ffuc9ViMzRjnEolECIfDM8ri8Tgej2dJjdeU2/iR2+3GGEMsFmN8fNzO5O9yuWhvb2dwcJBsNkt9fT09PT3s2rWLQCBAe3s7Z8+eZWRkhMnJSeLxOOfPn+fWW29l8+bNVzzuVkq9vb0QWfitWXwTPnKDOUzUIA75YBbkVcrH8qTOphCv4Ohd4MkNE9Brei972uUshf8bIyMj/OhHPwIKyby///3vEwgEeOKJJ5iYmOBXv/oVhw8ftpe53HbbbWzfvr2s1mcuaGATETeFoPaPxpgfWcVDItJqjBmwuhqHrfI+YMW0h7dbZX180K1YLH/ZKm+f5fy5rnHVOjs7eefYe+SrSvOBFZ+MkIzP3J4j6HXixc1bZwZLUqcLOeLhy5+0xGzcuJEVK1bw2muvMTY2ZmcTgUKW/9tvv521a9fy9NNPc9NNN7F161Z7W5vx8XFisRiJRIJ4PE53dzfd3d0cPnyYxx9/HK/Xe5mr31gCtQF8QR+R0cg1B7WidDxNOp2+/IkKKORCfeedd+wvckNDQ+TzeQKBAFNTU4RCIe666y6qq6vp6emhsbGRTZs2lVVQg4WdFSnA94ATxpi/nnbXs8Bu4C+t3z+dVv5vROSHFCaKRKzA9Dzwf02bMPIg8FVjTFhEoiJyF4Uuzs8Cf3uZa1yTfFU9yZs/cT2e6sqvnZgi3X/aPhZxkF15E3nX0kkA63vv56WuwhVzu91s27aNgYEBstksNTU11NXV0dTUxJYtW9iyZQuTk5M4nU7S6TRVVVVUVVVx8uRJXC4XDoeDXC5nb1MTi8UYHh7myJEj3HnnnSV+dfPT3t7OiIws+EajAG5xF75+zpJN5Gpk8hnym/Lkdyxs3R0vO2hva7/8iUtcX18fqVSKdevWEYlESKVSGGMYHh7mpZdeoqmpiR07dvCRj3yk1FW9JgvZYvsQ8K+AYyJy2Cr7GoVg87SIfA44Bzxm3fcchan+nRSm+/8RgBXAvgkctM77RnEiCfCnfDDdf6/1wxzXKFsuf5Cq5g7SkVHE4cAbasaxhIJaOctkMtxxxx0EAgHee+89+vv72blzJ/fccw8A7733HmNjY2QyGf7X//pfjIyMkEgkaGhowO/3U11dbW9r47G2+8iX2SyyxZDP5En0JjDZ6zj64CiM3anLS6VSDA0NEY1GqampIRQKEY1G7XWaUOim7Onpoa2tzf5bLkcLOSvy11w6b8D9s5xvgM9f4rmeBJ6cpfwQcMss5WOzXaPceYJ1eIJ1lz9RzVs4HKarq4szZ84QDoepr69ndHSU9957j7a2NhobGzl37hw+n4+hoSHGx8ft1ll/fz9ut5tNmzaRTqcZHR2lsbHRTsWlZhKXkAqnCguBrhdHIWCquU1MTPD666+TSqXo6uqipqaG1atXXzTDenJykn379rFs2TJqa2vZtm0bgTLI5n8hzTyibljxeJz9+/fj9XqZnJykq6uL4eFhnE4nZ8+e5dVXX2X16tWMjY1x6tQppqamyOfzZDIZAoEAPp+PfD7P6tWrWb9+PWNjY1RVVREKhWhra7t8BW4w+VyefO76BiFBFnY1boU4ffq0vSP8Lbfcwvj4OJs2baK1tZUf/OAHGGMwxnD27FnWrFkDFCasHT9+nJ07d5a49ldOA5u6YRUHzovjbMeOHSORSNj7rXk8HoaGhgiHw4yMjADgcrnsWZQiQjAYJJlM0tzcTC6XIxQK4fV6mZiYIBQKlfYFLjGp0dT1384nj+aKnIdiFp1EIkEoFKKxsZHW1laCwSAtLS1EIhHq6+tZtWoV1dXV9uNGR0d56623mJiYoKGhgc2bN9t7Ey5lGtjUDas4rpDJZJicnGRgYMBeUlEMbqOjoySTSXvMLJvNYoyxJ41kMhnef/99XnnlFbLZLGvXriWRSPDRj360ZK9rqYr1xq7vmjMHOFwOsvHyWqBdClNTU3R1dQGFpATbtm2zZ//6fD58Ph+/8Ru/gTGGiYkJ+3EjIyP23348Hiefz3PHHXcsev2vlAY2dcNqampiamqKl156ib179xKNRsnlcuRyOaqqqkgmk2QyGXK5DyYnGGMQEZxOJ/l8nqmpKd555x1yuRzBYJBTp07h9/vLr8U2sfDr2KRHcE+5cXqc5NLXYcJHHshAcCy44HVngg/SPywR811bm8vl6OnpsdPBOZ1O3n33Xfbt2wcUuimhsMYuk8kwNjZGOp3G5/PZG+sWORyOOZNDLJV1rBrY1A1raGiIrq4uDhw4QDgcJpvN2rkii9vTQCGYFTkcDowx5HI5u9VWvB2LxXA4HEQiESKRSKle1hVbrGwYw+5hOk0nvflexsbG7L3srpbD4aAmUMO9W+5d+BRzbUsja8jVKHb/ejwee6bj9GDl9/vt2263m5ZpGUZSqdSMf6dymSmpgU3dsAYHBzly5AhjY2MzuhtFxA5sxZ/ifcXWm4jYSZPhg+n9xV0B2tvLZ83TYn3DTiaTHDhwgBdffJE9e/YQiUSIRqNX9BwigsPhsCfp/OZv/iZf+cpX7AkPN5Ir+Xc7ceKE3bpzOBzs2LGDZcuWXfZx4XCYt99+m0QiQTAYZPv27TPG4JYqDWwVwhhDLlnITOLyB0tcm/JQVVVFPB4nkUjMKDfGkEoVJjoUJ4tcqDiLbLpsNovH42HTpk00NOiuzhfK5/NUV1czODhIMBgkl8sRj8evqOVWfN9TqRSTk5McP358Rlexmt1NN91Ec3Mzk5OTNDU12ePLl1NfX8/9999POp0uq0w6GtgqgMnnifWfJpsqbN/h8gUILF+HiM6Dnsvq1avt6fsXLqguThC50nRNXq+XW265xR6LUx84ePAgp0+f5sSJE/T29tpdv5fi8Xgu+f7n83m8Xi+JRIJXXnmF9evXL1S1K0Z9ff1V5TAVkbIKaqCBrSJkpsbtoAaQTcbITE3gqS6fRLylMDw8zLJlyy754VocO5utdXYpiUSCiYkJDWoXSCQSnDlzhmeeeYYzZ84QixV6Fy58791uN7lczv4wLQa3Ylk+n0dEqKqqYvny5QQCAc6dO1eKl6SWMA1sS1g6OkYmFsHp8eEOhMhlkjg9fpxe/4zz8rmLN7o0ufKdAr1YOyn09fWxb9++i7oip7vS1Fjj4+P86Ec/slNsLbSlMgvtcrxeL8eOHaOrq4tYLDbjy0JxlmlxUk6R0+nE6XTa3cHJZNIe7yyuJQyFQtx6662lellqidLAtkSlJoZIjPUDkAwPkI1H8dYXZiv56lvx1X0wc8kdqCM1PoQxxckPDtyB0KLX+Xrp7Ozk1PG3WRlc2LGTwbMDxCYj826NzVciPEiy++DlT7xGPVPOBb/G9eJwOKivryedTtspyaa3aqd/gSi2yooTeIpjavl83p444vF4qKqqYsOGDdx1112L/nrU0qaBbZ56e3txxCOLlsE+NTaMxxpUj09FIJvDFT1X+I9+XvA2tcz4YPBmMqQShe4dX1UA5+nzsz7vQnLEx+jtvT4txZXBHP9++9R1ea5L+U+RKQ5e56AmwMZQhq/cPolrgTfA/Nah8pok9OEPf5if/OQn9PX1zfplwul02uOaxW5HKGyMmclk7FZeJpOhtraWu+++m3vvvZdM5uIeC3Vj08C2RDkcTnLFbLHGIPLBN9zif/Dpgc3lduNyh0pQ0/JV7XVwnVMX4hDwuR0MTWZoC5XHmp/FctNNN11yHZQxxp5VCtiTeorZXYqBrvgTj8d56623ALj99tsX7TWo8qCBbZ7a29sZSrkWbT82ScbIDJzB5HMQiyBAJlALFLL8p5s7FqUeV8L33s9pb1+c7eOvhzWNC7Oo1+2A/ILuC19+jDG8/fbbNDc34/V67YXt04mIXVZcPzi9NVbsliyeNzAwwOnTpxkcHGTTpk2L+nrU0qaBbYly+QLUrNpMNhnD6faRS8fJxKM4PX48NbpG6nqo9TvxuiBxHefZeBwQjudZFiyf8a/FUExP1tjYiMPhuGjWqIjgdrtnjLUVuysdDgdOp9Ne71bc4NXj8dg7PSs1nQa2JUwcTtxVNQA43J6ynhByJXp7e4lNOhd8DGkobHA4XXCNqZ2mS+bhRFj4i0MBPO6F/e91btJJoLd3Qa9xvfh8PqqqquwZjcUJIsXg5XQ6ZxwXA1wx9ZPb7UZE7B9jDC6Xi5qaGt37Tl1EA5u6YdVV+/F4XMRS1y+wGQOxVIZ4Kr3gga2ciAgbN25kaGiIpqYmcrmcPTvS5XLZY2oul2tGd6Tb7bZ/e71eQqEQyWSSbDbL6tWr+c3f/E1uueWivYbVDU7/56klp729nWR2YMFnRaazeUbOOXj+BFyvhQUeB6ysMfxW6wQf2biwOzt/61AQXxnlpPT5fNTX19v7fxVbbR6Ph2Qyicfjwefz2VsD+f1+nE6nva1KQ0MDPp/PbrXdeeedfPzjH7+qbBqqsmnOJXXDiiRyLA/58F3H4bA8UO1xUuPXMbYL1dfXU1NTQ39/P8lkklwuRz6fJ5VKYYyxN7AsjqktW7aM2tpa2trauOWWW/B4PMTjccbGxjDGsGrVKjuDiVLTaYtN3bD8HgfprCF3HZebuR3gcAgbmhd4G5UyNDw8zPbt2zl58iRTU1O4XC57Kr/L5cLlctmbX3q9XrZu3UoymaSurs7O51lMTr1q1Srq6uro7OzUMTZ1EQ1sFSyfSZFNxXF5AzjcuqbqQrk8hGO56zo1P+CBuiqHdoXMIh6P28mPGxsb6e/vR0QIBoMEg0Gi0Sh+vx8Roba2lqamJlatWoXH46Grq4vly5czPDxMJpNhamqKkydPsmPHjlK/LLUEaWCrUOnJMInhHgwGQfA3r8ITrCt1teatZ2rhZ0WORWKcjfvImSvbE2wukaRwYsLDt4/VXrfnvJSeKScbFvwq14/f76e7u9ue+RgMBonFYng8HrxeL4FAgJqaGrLZrD39f3R0lDVr1tDS0sLp06cZHx9nYmKCuro6JiYm7BaeUtNpYLsCjnh40VJqzYckCx/Ixldz0X3JkSHc+Q+mROT73sLX2Lyg9XHEw8C1L9BerJ2K3cPDxHsm8foK+7JdF04XoZYOfB0L35LYQHnt6uxyuexWlzGG6upqwuEwLpcLj8dDY2MjHo+HoaEhwuEw4XCYiYkJ+vr6yOfzjIyMkMvlCAQC9vH03Z+VKlqwwCYiTwKfAIaNMbdYZfXA/wY6gG7gMWPMuBRWa/4N8HEgDvyhMeZt6zG7gX9vPe23jDF7rPJtwPcBP/Ac8GfGGHOpa1zr61mKHyCnTxcyyK9fe3Ew6XYmZ+TjczgcrFq10FlBWq7L+7RY2er379/Pf/7P/5n333+fU6dOXfHeaxfyeDw0NTXx4Q9/mL/+67/WrWsu0NDQwKZNmxgaGqKqqorx8XGmpqYIBoNkMhlSqZQ98zGXy9HT00M+nyeZLPwtx+NxJicnaWtro7q62t6oVKkLLWSL7fvAfwWemlb2FeBFY8xfishXrOMvAw8D662fncB3gJ1WkPo6sB0wwFsi8qwVqL4D/DHwBoXA9hCwd45rXJOluDVIsU5PPPHERfcdP36crq4u+3jt2rXcfPPNi1a3cuD3+6murqajo4N4PM7Zs2ev6PHTFxQXBYNBamtry27H4cXgcDj4nd/5Herr63nllVeoq6vD5XLZ427xeBy3243P5yOZTNLU1MTIyIg9C9Ln81FTU0MmkyEYDLJu3Tp7JqVS0y1YYDPG/EpEOi4ofgS4z7q9B3iZQtB5BHjKFD4lDohISERarXNfMMaEAUTkBeAhEXkZqDHGHLDKnwIepRDYLnWNG8rmzZuprq5mfHyc+vp6nTk2ixUrVrBs2TJGR0ftD87iomFgzu1spi8khg8y02cyGXvMSM00MTHB8ePHyWaz3H///UxNTfHSSy8Rj8dJp9NMTU3hdDrJ5/NUV1fbmUoymQzpdJqGhgacTiehUIi2tjY2bNjAqlWrSv2y1BK02GNszcaYAev2IFAc9GkDpu+z0muVzVXeO0v5XNe4iIg8DjwOsHLlyit9LUtacUq0/se/tIaGBu6//37efvttRGTGeE0ul5uR3qmY4snpdNoLh9PptL2YePqOz8FgkNHRURobG0vyupYiYwwHDx6kr6+Ps2fPYoyhtrbW3l9tamqKhoYGGhsbicViBAIB+/3r7u4mHo/jcrnYtm0bK1eu5N5776WtrY1QKFTaF6aWpJLNSrZaZwuaA/1y1zDGfNcYs90Ys72pqWkhq6KWqHQ6TU9PD8PDw0xNTZHJZMhkMrjdblyuwve+6S234k7PxRae1+vF7Xbb42urV6+mra2N4eHhUr2kJSkej5NMJunp6SGVSjExMWF3MaZSKWKxGOFw2O6OXLduHffeey/RaJSmpiZWrFhBOp1mdHSUpqYmli9frkFNXdJiB7Yhq4sR63fxf38fML2vrN0qm6u8fZbyua6h1AzGGP7hH/6BSCRi7/1VzIaRzWbt1tj0wDZ9n7B8Po/L5bLTPi1fvpxPfepTdtZ59QG/34/b7SYcDtPd3c3w8DCdnZ3k83lWrFhBS0sLjY2NrFmzhkwmQzabxev1smzZMpxOJxMTE6xfv56VK1fi8Xg4ffp0qV+SWsIWO7A9C+y2bu8Gfjqt/LNScBcQsboTnwceFJE6EakDHgSet+6Lishd1ozKz17wXLNdQ6kZzp8/z+jo6IyM81AIeMVuxgv3DCuWFVt2TqeTxsZGNmzYQHt7O+Pj46xYsYL2MsrhuBgcDgfbt2/H6SykGgsEAtTW1pLL5Whra6O5uZnGxkZcLhdut5uRkRHeeOMNksmknW0knU7b3ZPZ67gjg6o8Cznd/wcUJnE0ikgvhdmNfwk8LSKfA84Bj1mnP0dhqn8nhen+fwRgjAmLyDeBg9Z53yhOJAH+lA+m+++1fpjjGkrNkEgkEBG7hTAfImLvJ1ZM8VRsrRW7y1auXKlT/WfR2NjIQw89RGdnJ4lEAqfTSTgcpq2tjWw2SyqVYvPmzZw6dYpkMkk8HieVSpHL5aiqqsIYYyc81rFjNZeFnBX5+5e46/5ZzjXA5y/xPE8CT85Sfgi4aL8KY8zYbNdQ6kLFmXbLly8nFovN2OTyUlwuF4FAgFQqZc+EjMVinD59momJCTo6Ovj1r3/NJz/5yUV4BeVn7dq1JJNJ+3jnzp1UV1dz88034/P5aGxs5IUXXgCws/8Hg0FWrlzJmjVrqK6uZvv27eiYuJqLZh5RNyyfz8eKFStwOp0MDAwQjUYv2ujyQsUWQ7FF5vF4ZnRbplIpwuEwQ0NDNDcvbKaXcrRmzRp71mhtbS3Lly+/qHXr9/uJx+Pccsst9r/HnXfeic/n06Cm5kUDm7phrVmzho6ODkZGRmhvb+fMmTNAYYzN4/GQzWbJ5/O43W7S6TROp5OdO3cyNTVFf38/tbW19kSSYjYMv99PfX09k5OTGtguYdmyZSxbtuyS91dXV5PJZGhtbSWbzeL3+6mqqqKjo0ODmpoXDWzqhuV0OvmDP/gDotEodXV1eDweBgcHiUQiOBwOO72T2+1mbGyMlpYWvva1r+Hz+XjmmWd48803GRkZoaOjg/b2dqqrqwmFQjgcjjk/uNXcRISGhgYefvhhoNBlXEyKDJBMJgmHw9TW1hIIBEpZVbVEaWCrENFoFBHRaeZXqLGxkQ996ENEo1GSySSnT5/m/Pnz+Hw+QqEQTqeTtWvXMjg4SDAYpL6+np6eHtasWUMulyObzTI0NISI0NjYyG233cYtt9xCTc3FianV1fF4PthyaXBwkLfeesvuKt6yZUvFJVdQ104DW5kzxrB//35GR0cBaGlpYfv27Torb56KrYODBw+SyWRYtWoVgUCARCJBNpvF5XKRzWaJRCJks1lOnjzJwMAA6XQah8OBx+MhFApRX19Pa2srGzZsoLW1tdQvq2KdOHFixvjniRMnWLFihf69qxl0P8QyNzU1ZQc1KHyjHRwcLGGNysvAwACHDh3C4/HQ0NBAS0sLoVAIv99v534Mh8NEIhFOnz5NV1cXPp/PTr+VSCRIp9P2mrajR48SDocvc1WVzWYZGBi44vcqk8lc9Dxz5fRUNyYNbGVutvVXiUSiBDUpT2+88QanTp3i9OnTdkqtXbt2sWrVKjtTfzKZZHBwkHw+zw9/+EOWLVvGpz/9ae677z6WL1+Oz+ejoaGBqqoqgBlfNNTFYrEY+/bt49ChQ7z22mu88847837shd2O7e3tOBz6MaZm0q7IMhcIBHA4HDOS9La0LPS+a+XjiSeeoLOz85L3Hz16lP7+fvvY4XAwNTXFmTNnGBsbI5FI2IuE8/k84+Pj/MM//APPPfcc6XSa7u5uu9Xh8/mora2lubnZDnKzWbdu3ZLcBulaXe69LhodHWVycnJGWVtbmz2WVkyXdan3aHJykmQyidfrpbq6+rLdkJX6fqtL08BW5jweD3fddRddXV2ICGvWrJnzQ1XNJCL2WjQoBCcofGGYnJwkl8sRjUYJBAKEQiFCoRDhcJjm5mYmJiZwu90EAgFisRjJZJLW1lZ9/y9jtjWC08sutyt2dXW1TpJSc9LAVgEaGhpoaGgodTWWpMt9U3/ppZd46aWXyOVyOBwO1q5dyyOPPILT6eTZZ59lcnKSb3/723g8Hurq6hARUqkUTzzxBAcPHrTHM4sfzPfddx+1tbUL/rqWovm2iop5IItjY9XV1ezatUsngKjrRgObuqHdeeedTE1NMT4+TiAQoL29nbq6OgB++7d/m/Pnz7N37157JqTH42H16tX09fUxOTnJ0NAQTU1NOBwO6uvrb9igdiWampq4++676evrw+fz0dHRoUFNXVca2JaofD5PIpGgqqpK/9MvoEAgwMc+9jGGhobweDwzFlbX1tYSDAbZtWsXTz5ZSFfqcDjYsGEDf//3f4/L5aKxsZF0Om1vgKnmR3sZ1ELSwLYEjY2N8dZbb9mZ43fs2KGbKi4gj8fDihUrZr1vYGCAn/zkJ/ZxMpnkf/7P/8ljjz1GLpfj+PHj1NXV4ff7yWQybNy4cZFqrZS6FA1si2w+M8fOnz8/Yxq/x+Ohra3tovMuN3vsSujMsdnlcrkZa61yuZy9nCIejzMyMoLf7yeXy3Hq1Cnq6uo0nZZSJaYLQJYYY8xFa9MuXJRa5Pf7LzuDTF2b1tbWGeukXC6X3T2cTCYREVavXm2fMz4+XqqqKqUs2mJbZPNpFb355psMDQ3Zx21tbdxxxx0LWS11CR6Ph40bN9Ld3Q0Uxt2qq6u5/fbbCYfDZDKZGa1pHTdSqvRE09EUbN++3Rw6dKjU1QAKLbT33nuPiYkJGhoa2LRpEy6XfgcplVOnTvGv//W/to+ffPJJ1q1bB8C5c+c4ffo0xhjWrl3LmjVrSlVNpW5Es86s00/LJcjtdrNly5ZSV0NZiomNBwYGWL58uR3UAFatWsWqVatKWDul1IV0jE2pefjmN79JIBDgW9/6VqmropS6DO2KtCylrkillFLzMmtXpLbYlFJKVRQNbEoppSpKxQY2EXlIRN4XkU4R+Uqp66OUUmpxVGRgExEn8N+Ah4Gbgd8XkZtLWyullFKLoSIDG3An0GmMOWuMSQM/BB4pcZ2UUkotgkoNbG3A+WnHvVbZDCLyuIgcEpFDIyMji1Y5pZRSC+eGXqBtjPku8F0AERkRkXMlrtLVaARGS12JG4S+14tH3+vFVa7v978YYx66sLBSA1sfMH0fknar7JKMMU0LWqMFIiKHjDHbS12PG4G+14tH3+vFVWnvd6V2RR4E1ovIahHxAJ8Bni1xnZRSSi2CimyxGWOyIvJvgOcBJ/CkMebdEldLKaXUIqjIwAZgjHkOeK7U9VgE3y11BW4g+l4vHn2vF1dFvd+aK1IppVRFqdQxNqWUUjcoDWxKKaUqiga2MqW5MBePiDwpIsMicrzUdal0IrJCRF4SkfdE5F0R+bNS16lSiYhPRN4UkSPWe/0fSl2n60XH2MqQlQvzFPAAhawqB4HfN8a8V9KKVSgR+TAwBTxljLml1PWpZCLSCrQaY94WkWrgLeBR/du+/kREgIAxZkpE3MCvgT8zxhwocdWumbbYypPmwlxExphfAeFS1+NGYIwZMMa8bd2eBE4wSzo8de1MwZR16LZ+KqKlo4GtPM0rF6ZS5UxEOoDbgTdKXJWKJSJOETkMDAMvGGMq4r3WwKaUWnJEJAg8A3zJGBMtdX0qlTEmZ4zZSiHt4J0iUhFd7RrYytMV58JUqlxY4z3PAP9ojPlRqetzIzDGTAAvARclFC5HGtjKk+bCVBXJmtDwPeCEMeavS12fSiYiTSISsm77KUxGO1nSSl0nGtjKkDEmCxRzYZ4AntZcmAtHRH4A7Ac2ikiviHyu1HWqYB8C/hXwURE5bP18vNSVqlCtwEsicpTCl+UXjDE/L3Gdrgud7q+UUqqiaItNKaVURdHAppRSqqJoYFNKKVVRNLAppZSqKBrYlFJKVRQNbEotMSKSmzbV/bCVWupS5/6hiPxX6/b/KSL/1rr9fRHpsh5/UkS+Po/r/qGILJ923C0ijdfhJSm1qFylroBS6iIJK83Rtfo/jDH/LCI+4D0RecoY0zXH+X8IHAf6r8O1lSoZbbEpVQamt55EZLuIvHwFD/dZv2PW4/8/InJQRI6LyHel4FPAduAfrVae33rMF0TkbRE5JiKbrtfrUWohaWBTaunxT+uG/PE1PM9fWZnbe4EfGmOGrfL/aozZYe0t5wc+YYz5Z+AQ8AfGmK3GmIR17qgx5g7gO8C/vYa6KLVoNLAptfQkrOCy1Rjz/7iG5/k/rC7NFuB+EbnHKv+IiLwhIseAjwKb53iOYhLit4COa6iLUotGA5tS5SHLB/9ffXOdeCFrM8mXgd+wxtv+O/ApY8ytwN9f5vlS1u8cOiavyoQGNqXKQzewzbr9u1fyQBFxATuBM3wQxEatPc8+Ne3USaD62qqpVOlpYFOqPPwH4G9E5BCF1tN8FMfYjgLHgB9Z+279PYXZj89TyOpe9H3g7y6YPKJU2dHs/koppSqKttiUUkpVFA1sSimlKooGNqWUUhVFA5tSSqmKooFNKaVURdHAppRSqqJoYFNKKVVR/m/Fh3uWlnzw6gAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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BELDd+QH4+kXOoWmadp7R0dFx97u7uzFNUw9NzlBT9q+mlDoFrLvA9n7g3gtsV8DvX+RYTwFPXWD7LmDNRM+haZo2VjabJR6Pk0wmSSQSRCIRAAKBAB6Pp8it066WLqmladqsZNs2b775JoODg4gIx48fZ3R0FMMwWLNmDc5okzYD6X62pmmzUn9/vzsE6ff7Wbt2LZWVlWzevBmfz1fk1mnXQgc2TdNmJaUUvb29pFIpgsEgHo+HSCTCnj17iMfjVFVVceONN+L3+4vdVO0K6cCmadqsk8lk2LdvH4ODgwwPD5NMJvF6vQwNDZFMJgHo6OhAKcXGjRsvczRtutHX2DRNm3VaWlpIp9MsX76cWCxGOBxmxYoVblAr6OvrK1ILtWuhA5umabPO2KVqAoEAoVAIv99PWVnZuP10ia2ZSQc2TdNKXiqVorOzk3Q6DUBTU9O4BBGv18vcuXO56aabiEajQD6orV27tijt1a7NhK+xicgCYJlS6iURCQFepZQuha1p2rTW1tbG3r17UUohItx8883U19dz11130dbWxpw5c4hGo3i9XioqKrjnnnv05OwZbkI9NhH5HeBfgCecTU3Aj6eoTZqmaZPm8OHD7tCjUoojR44A+V6caZoYhoFhjP8o1EFtZpvov97vky9g/C6AUuqEU7Ff0zRtWju3oHE2m6Wzs5Ndu3YB+QSRc5NGtJltotfYMkopd2lZEfGSX2tN0zRtWps3b964+/Pnz+fUqVNYlkUymUQpRTKZJJVKjdsvm83S3NzMkSNH9AKkM8xEe2y/EJH/HQiJyP3kiw//dOqapWmaNjluvPFGysvLGRwcpKqqinnz5vH888+zd+9eRkZGOHPmDBUVFaTTaUKhEJAvt/XGG2+QSCQAOHXqFLfffrvOkpwhJtpjexToBQ4AXyBfif8rU9UoTdMmR19fH4888gj9/f3FbkrRiAgLFy7kpptuYv78+YgI6XSaRCJBe3s72WyW0dFR3nvvPUzTBKC3t9cNapAPdC0tLcV6CdoVmmhgCwFPKaU+qZT6BPlK+6Gpa5amaRNl2zYdHR20traedz1p27Zt7N+/n23bthWpddOPUgqfz0ddXR01NTVEIhECgQCZTIbe3l6AC1b219X+Z46JBraXGR/IQsBLk98cTdOuRGHIbPfu3ezdu5dXX32VTCYD5Htr27dvRynF9u3bZ3WvrSCdTpPJZKirqyMSiRCLxfD5fASDQSA/WRugqqqK6upq93mBQIBFixYVpc3alZvoNbagUspdiU8pNSoi4Slqk6ZpE9TV1cXQ0JB7P51O09LSwrJly9i2bZub5m7bNtu2bePLX/5ysZpaVEopfvazn7F7925CoRC33HILmzZt4o033iAcDhOJRGhoaGDOnDlAfvjy1ltvpaenh1wuR11dna74P4NMtMeWEJENhTsicjOQusT+mqZdB7ZtX3Tbjh073KHJXC7Hiy++eF3bNp28+eabPPPMMxw5coQ9e/bw4x//mNraWh555BGWLVtGU1MTN99887jniAh1dXXnVSnRpr+JBrY/BH4gIq+LyBvA94EvTlmrNE2bkLlz57qZfJCfWFxIb7///vvdD2Sfz8cDDzxQlDZOBzt37iSbzc9YUkrR3d3NmTNnEBFCoZA7BKmVhgkNRSql3hORlcAKZ9MxpVTuUs/RNG3qeb1e7rzzTlpaWrAsi3nz5hEO568SbN26le3btwNgGAZbt24tZlOLKhaLISLjih/X1NS4t5VSHD9+nO7ubsrLy1mxYoV73U2beS4Z2ETkQ0qpV0TkV895aLnzR/KjKWybpmkT4Pf7Wbp06Xnbq6ur2bJlC88++yxbtmyhqqqqCK2bHm6//XaOHTtGT08PADfffDPLli1zHx8cHOTYsWMAxONxOjo6KC8vJ5lM0tDQwA033HBe2S1t+rpcj+0u4BXgoxd4TAE6sGnaNLZ161bOnDkzq3trLS0tnDx5khUrVrjX0tatWzcuUI0tqWXbNjt37mTNmjX4/X5OnTqF3+8fFwi16e2SgU0p9VURMYDtSqlnrlObNE2bJNXV1XzjG98odjOKJpPJcODAAWzbpqamhpqaGioqKohEIuP2G1v0uFBma+y23t5eHdhmkMv2rZVSNvDHV3sCEfGIyB4R+Zlzf5GIvCsizSLyfRHxO9sDzv1m5/GFY47xp872YyLy4THbH3S2NYvIo2O2X/AcmqbNLolE4rzM0ZGREUzTZN++fbz44ot0dnYSjUbda5OmaTI4OMirr77K4cOHSaVSupTWDDPRQeOXROR/E5F5IjKn8DPB5/4BcGTM/b8G/k4ptRQYBD7nbP8cMOhs/ztnP0RkFfBpYDXwIPDfnGDpAf4R2AKsAj7j7Hupc2iaNovEYrHzMh5ra2s5evQoLS0tZDIZ0uk0g4OD3HPPPdxwww3E43FEhJ6eHg4fPqx7azPQRAPbvye/dM1rwG7nZ9flniQiTcBHgH9y7gvwIfJruwFsAz7u3P6Ycx/n8Xud/T8GfE8plVFKnQaayS+hcwvQrJQ65aw88D3gY5c5h6bNKsePH2fLli00NzcXuylFYRgGt956K7W1tZSVlbFy5UoWLVpEX18ftm0zOjqKbduYpkk6nebs2bMcOnSI/v5+dw5bdXX1eaXKtOltoun+V1tL5v8hP4xZ5tyvAuJKKdO53wY0OrcbgVbnfKaIDDn7NwLvjDnm2Oe0nrN902XOoWklr7e3l87OTsLhMI899hiJRILHHnuMp59+GsgPtXV2dmLbNg0NDSU/+bi8vJxNmzaN2+bxeNi3bx+5XI7e3l5isRgDAwP86Ec/Yvfu3SQSCcLhMKtXr+bmm28eN1dQm/4u2WMTkU0isk9ERkXkbRG5YaIHFpFfAXqUUruvuZVTREQ+LyK7RGRXofipps1kHR0dvPPOO5w9e5aXXnqJAwcOAHDmzBmam5uxLIvXX3+dvXv3sn///nG1JWebQrAqrKD97rvvYpomgUAAr9dLMpkkk8mwZMkSneo/w1zuX+sfgf+NfC/ov5LvgU3UB4GHReQM+WHCDwF/D8SchUoBmoB253Y7MA/chUwrgP6x2895zsW291/iHOMopZ5USm1USm0cO1lT02aqsUurfP/738c0TXcplscee4zOzk5GR92yr6TTaVpbW887TqmzLIuVK1eydOlSDMNgZGSE7u5uRIT6+noWLlzI0qVL+eAHP0h5eXmxm6tdocsFNkMptcO5vvUDYMKf/kqpP1VKNSmlFpJP/nhFKfUbwM+BTzi7bQV+4tx+1rmP8/grKl8m4Fng007W5CJgGbATeA9Y5mRA+p1zPOs852Ln0LSSNjZFvTAZudDbOHPmzLjKGwUX2lbqGhoa6O3tZfv27Zw6dYpjx47x/PPPMzAwgGVZhEIh6uvrmTdvHvX19cVurnaFLneNLXZO1ZFx96+y8sifAN8Tka8Be4BvOdu/BfwPEWkGBsgHKpRSh0TkGeAwYAK/r5SyAETki8ALgIf8enGHLnMOTStpy5Yto7e3F9M0qa2tZXh42A1sCxcupL6+nuPHj7sTkv1+v1tbcrYYHByko6ODd999l1OnTmFZFn6/n8HBQW677TbKy8uJRqNEIhGqqqpIJBK6vNYMI5f6tiYi//0Sz1VKqf9l8ptUHBs3blS7dl020VPTpr1CQsTp06d59NFHyScKw1NPPcXSpUvJZrO0tbVh2zZNTU2z5kNbKcXRo0f56U9/ilKK06dPc+DAAY4fP47P52PJkiVs2rSJ5cuX09zcTHd3N8FgkI0bN/KJT3yCysrKYr8E7XxyoY2Xqzzy21PTFk3Tpkomk+H48eO0t7cTj8eJRqP4fD73g9nv97N48eIit/L6sm2b3bt3c/ToUUZGRoD84qFjM0LLysqorq7m8OHD7NmzB4/HQyQSYc+ePaxcuZI77rijWM3XrtCEUn1EpE5EviUi2537q0RET3rWtGno4MGDjIyM8PLLLwMwOjqKYRhs27btMs8sTZlMhldffZVXX32VY8eO0d/fTzweJx6Ps2rVKmpqaqitreUjH/kI0WjUnZTd09NDR0cHHR0dDA4OFvtlaFdgojms3yZ/LavBuX+c/BptmqZNM8PDwwDs3bsX27axbRvLsmbtQqOnTp0ikUgQiUQQESzLorOzk56eHnw+H01NTWzevJlPfvKTBINBysrK8Pl8jIyMMDQ0RDKZZGhoSE/SnkEmGtiqnSLINuQnUAPWlLVK07SrVltbC8D69evxeDx4vd5ZvdBoYZ5efX09FRUVZLNZRITly5dTXl5OPB5nZGSETCZDJBJhyZIlNDQ0UF5ezty5c7nllluoqKigvf2Cs4a0aWhClUeAhIhUkV+qBhG5FRiaslZpmnbV1qxZg2EYfPSjH2Xv3r0Eg0Fs254RS9c8/vjjk17+K5VK0dXV5d7v6+sjmUxy/PhxvF4v3d3dDA8P88gjj5BOp0mlUgwODpJOpwmFQuzZs4d9+/ZRVVU1qXPali5dype+9KVJO572SxMNbF8mP59siYi8SX4+2ycu/RRN04rB6/WyevVquru7yWazZLPZ85Zpma6am5s5uncvcyf5uKFslkQ2iyGCP5Gge3AQy7axbBvx+fCaJkMnTqCUoiwYJChCwuPBl0iQTCTwGgbm4CDxSapA0nX5XbRrMNFake+LyF3ACvLplceUUnrAWdOmqTNnzvDd734XwzCwLItUKsWTTz7Jn/7pnxa7aZc1F/jchbO4r54/AP4AQ7kch9IZ2iNRBrJZ+rNZooaH9X4/XgREaDQ8eDxeIqEwadtGgGq/H+8kltX6FrNvUvz1dMnAds7k7LGWi8jVTtDWNG2KDQ8Ps3fvXiwrfym8kDwyEwLbVLKcebuNoRBVfj+BZBKFcsNMbyaDrRSGM/dvWTRKzTnL3mjT3+V6bB+9xGMK0IFN06ah2tpa1q9fz65du7AsC6/Xy5YtW4rdrKKL+XwEPQZJ06ItlaI1lSRrK04lkqwpLyfm87lBDaAjndKBbQbSE7Q1rQQ1Njbyn/7Tf+I//sf/iNfrJRqN8rnP5aee5nI5NwV+7ty5NDQ0XOZopcMQ4cbyCt7u76c9nSZnK0Ty23szGQQoK/FlfGaDiSaPICIfIb+KtVt/Ryn12FQ0StO0a7do0SI3i2/ssis7d+5kYGAAgPb2dkzTZP78+UVpYzEkLIv+XBaAwVwOU9mEvF5M2yZj2wQ8Hqr9fkSExqBeh20mmmjlkf+X/Craj5BPHvkksGAK26Vp2jWwLIs///M/Z2BggMHBQVKpFE888QSJRMINagVjl7qZDXozGcq9PvwiJEyTEdOkN50maVlkbJujw8McHRlhKJfjRGKUU4nErFwBYSabaJrPZqXUZ4FBpdRfALcBy6euWZqmXYvTp0/zxhtvYFkW6XSagYEBfvzjH9PZ2Xneh3RgFl1DSlsW7akknek0AY9Btd8HCiIeD2nT4mwySUsqxc7BQQ4OD2Pbiq50mp5ZuhjrTDXRwJZyfidFpIH88jF6kSJNm6aGh4fJZDIkk0nS6TRDQ0P09vZy5MgRRkdH3eDm8/lYvnz2fEc9MjKCiGDaNu2pFEOmRXXAT9TrY9QyMQRMpTAE2lJJUk5W6YizWKs2M0z0GtvPRCQG/Bdgt7Ptn6akRZqmXbOamho3eBVW0LYsiwMHDlBZWcnGjRupqKigqqpq3OKkpSxtWaQsC58YVPn9+AwDvyHE/H6SpuXOnCvzeUk7AQ0nQ7JcJ5TMKJebx/YBoFUp9ZfO/ShwADgK/N3UN0/TtIkYGBhwsxx9Ph/z5s3DMAyc+abYtk08Hmfv3r1uIsmv//qvz5qgBuAzDHK2TUc6TWsqCUpR6fMRMAxiIR/zg0GGLQsbsDw2PkNImiY1AT+1s2i4thRc7q/6CeA+ABG5E/g6+QSS9cCT6LJamlZ0r7zyCm+++SbZbJY5c+bw2c9+lqqqKiorK0mn0xiGQSaTwTAMkskkqVSKgwcP8uMf/5j77ruPxsbGYr+E6yJn2/TnspwYHWU4l6MvmyHm92MYBl4R7qzJF49uS6fI2TaVPj9Bj4eEc+1tQThc5FegTdTlrrF5lFKFFKp/DzyplPqhUurPgKVT2zRN0y5neHiYV199ldHRUSDfc3vppZdIp9MMDw+jlMLj8RAOh/F6vZimSUVFBV1dXZw6dYrXXnutyK9g6mUsi45UioPDwwxls9T6/czx+6j0+8k69SKjHi8jZo7WVJKYz0+VE9QKutLpIr4C7UpdrsfmERGvs0zNvcDnr+C5mqZNsf3793Pw4EFGR0fdRJB4PM7OnTvJZDIopRARPB4PSim8Xi89PT00NDRgWRbHjh2js7OT+vrSzAVLmib7hoY4lUhwdGSYznSaoOFBASnbIiwGlT4/Hek0I6ZJxOvhbDLfY/tAZSWKfBmuMp/+uJtJLvev9V3gFyLSRz4z8nUAEVmKXrZG04oqlUrR2dnJyMiIu9zK0aNH+dCHPsTQ0BA+n89de8zn8xEKhZg3bx62bQPQ09PD+vXr6ejoKNnA1pXJcDaZ4ODIMCO5HCnLImlZGEDKtkkbBnYyQco0iXp9CJBVNqJgIJuhMRgi4PWyKBImZ9v4JrEQsjZ1LldS669E5GXyqf0vql9OgDHIX2vTNK1Ikskkg4OD3HjjjZw+fZpsNsv8+fNZvHgxra2tGIZBJBIhmUxi2zYiQn19PYZhkMvlCAaDVFZWEi7ha0eWsulIZzCdYO73ePCJgAJbLFAKj8r3yoZzWRTCsJlfuGTUCYIfrKomaHjoTKeZX8LvVSm5bP9aKfXOBbYdn5rmaJo2UZWVlZSVlRGJRFizZg2QL6NVUVFBLBZDRNyfQCBAOp2mo6ODyspKfD4fSil8Ph+LFy8u8iuZOjX+AAGPgd8wnFR/cVZLBpQipxTDpolPBJP8lADb+f6etEwSpkFzYpTqQICMExy16U8PHGvaDGUYBg899BCZTIbu7m6qqqpYtWoVDQ0N5HI5YrEYmUwG0zRJp9PYtk17ezv9/f3ce++9VFRU8PDDD+P3+4v9UqZMzO9nU+UcjnlH6Ein6M1kyFgWI5ZF1rbxGwaGQG0wRLnXQ1sqRU8mQ04pLKVI2RZp22Y4l+PGiopivxxtgqYssIlIEHgNCDjn+Rel1FdFZBHwPaCK/GTv31JKZUUkADwN3Az0A/9eKXXGOdafAp8DLOBLSqkXnO0PAn8PeIB/Ukp93dl+wXNM1WvVtKn0+OOP09zcfMl9stksR48e5YUXXqC/vx8RIZvN/8lbloVSCqUU77zzDiLC7t27aWho4Ic//CG1tbUXnM+2dOlSvvSlL03Ja7qebqyooMLno3l0lIxtk7Vt3u7vwyuCRwx8AgvCYRaHw1T4fIwODpK1bQIihLxeoh4PS6NRqkr4C0CpmcoroRngQ0qpdeTnvT0oIrcCfw38nVJqKTBIPmDh/B50tv+dsx8isgr4NPmVBR4E/puIeETEA/wjsAVYBXzG2ZdLnEPTSpLX62VgYIC+vj5GR0cZGRlxr6+Fw2FyuRzZbBallFuJJJFIkMlkGBwcLHLrp1bAMFgWjbI0GqU2EKDM46HM68NvGFT4vFQFAvgN4dY5c9gyt557qqpZGY2yoqyM9RUVLIpEWF5WVuyXoV2BKeuxOYkmo85dn/OjgA8Bv+5s3wb8Z+CbwMec2wD/AvyDiIiz/XtKqQxwWkSagVuc/ZqVUqcAROR7wMdE5MglzqFpM85Eek3Hjh3jxRdfpLOzExHBMAwqKyv53ve+Ry6XY9GiRfj9frLZLNlslqqqKpYtW8aDDz5IZWUlt99++3V4JcVVFwjQm8lQ5vPREAqSU4rBXJahbJaox8POeJxbKivZVF3FskwZSctCAWvKy/GOWXxUm/6mNHfV6VntBXqAHcBJIO7MiwNoAwplDxqBVgDn8SHyQ4nu9nOec7HtVZc4x7nt+7yI7BKRXb29vdfwSjWteLq6ujhw4AA9PT0kEglaW/P/LYLBIDU1NVRWVvK5z32OxYsXE4vF3CzIhQsXAlBXV1espl83Hc61sxVlZZT7fFR4fYQ9Bn4xUAhtqTT74nHeHuinLhCk0u9nYTjMzZUxGkJ6TbaZZkqTR5RSFrDeKaD8r8DKqTzflVJKPUm+NBgbN27UCy5pM9LZs2cJBALU1NTQ0tJCf38/gUCA5cuXU11dTUVFBTU1NZSXlzM6OoqIsGHDBhobG6mqqmLp0ulTRKitrY0R4FtM3n/Hk93dDKdSoBQZ06S6rIwhM8cokPMY5FAIikHL5MjQEC/kckQCAQI+H7XeMJ5JbEtBJzDa1jbpx9XyrktWpFIqLiI/J7+OW2xMNZMmoN3ZrR2YB7SJiBeoIJ9EUtheMPY5F9ref4lzaFrJ8TmV51OpFI2NjZSVlVFWVkZHRwcDAwPYtk00GuXGG29k5cqV9PX18d5779HW1kZjYyMej4dNmzYV+VVMjZFUiv6REZLZLDnLwrbyVfwDPl8+3d80sWwbn9dL1jQxbRtPOk0ykyESDBL0+aiMRIr9MrQrNJVZkTVAzglqIeB+8kkdPydfPPl7wFbgJ85TnnXuv+08/opSSonIs8A/i8h/BRqAZcBO8jNRljkZkO3kE0x+3XnOxc6haSVn6dKl7jCkx+PJF/X1ekkkEgAMDQ2Ry+Woq6tjYGCAnTt30tfXh8/no7m5me3bt7Nq1SrKpkGCRFNTE/G+Pj7H5FzT2p/N0ZlO4zNNspbNkJnDB6yIlnE0myXgzGWrEqHM4yFlWQQyGQwx8OZyxLJZ7q2toykUwjuJVUe+hSLW1DRpx9PGm8oeWz2wzcleNIBnlFI/E5HDwPdE5GvAHuBbzv7fAv6HkxwyQD5QoZQ6JCLPAIfJL3D6+84QJyLyReAF8un+TymlDjnH+pOLnEPTSk55eTn33nsv2WyW/v5+jh07xsDAAOFwGMuy8Hg8zJs3j7fffptXXnmF/fv3IyKk02l3Mnculyv2y5gSfsNwBxJtJ1b6DA89zlSIuYEAFjCcy2HnTMJeDwO5HEnTJG3bdPr8DOZM7qmpYUMshqGTSGaEqcyK3A/cdIHtp/hlVuPY7Wngkxc51l8Bf3WB7c8Bz030HJpWqnw+H/Pnz2fPnj28+uqrtLS0uNfUVq5cSSKR4LXXXqO9PT8qPzo6immapFIp5s+fTzQaLfIrmBplXi/ryss5MjJCTyZDuc9HUIScnc94zCpFyOMBBSY2fvFiK8WIZRExDFK2xelEggWhEIsiYar8el22mUBXHtG0Ga6wvtoLL7zA66+/ztmzZxkZGXGD19GjR3n22Wc5ceIEyWQSj8eDbdtks1kCgQC1tbW0t7ezaNGiYr+USVcXDLI8WsbZVIoapUhaNjkUtqVIWRYoMBAyyqbGl19V21aKgAiWUqAUoDidTDIFOSTaFNGlqrUZq6+vj0ceeYT+/v5iN6VoLMvijTfe4N/+7d/YsWMHx44dY3h4GADbtsnlcgwNDXH27FkSiQTJZJJkMolpmpSXl7NkyRKCwSADAwOXOdPM5BXhhvJy1kTLmBcKU+b1kDBNfIbBvGAIBLK2RczrJZ4z6UqnUeQv4CNC2PBgiBDzefHP4sr+M+3/2uz9l9JmnKNHj/Liiy/y85//nK6uLrZt28b+/fvZtm1bsZtWNH19fRw6dIg333yT7u7u86qLFBw/fhylFNlsFsMw3B5bZ2cn2WyWWCxWnBdwHfgMg0HTxOfxoBC8IgQNgzkBP1GvlzKvDwvBcspt1QQCNIbCzPH5CXs93FRRwZqKivyQ5SyUy+X4+te/zuuvv85/+S//xV32aDrTgU2bEVpbWzlx4gSZTIbR0VFeeeUVfvazn6GU4rnnnpsx3yQnW0dHB9///vc5ffo0oVAIwzDc34UFRgFGRkawLItAIIBhGDQ0NFBZWUlhJarGxgvWMCgJpm0zNxjMBzSPgVLQn8vSn8mQsWxsZWOgUAJp2ybk9VIXDFLh99EUDiNiMMfvn9SsyJnklVdeYceOHeRyOV544QXefPPNYjfpsmbnv5Q245wbuF566SVSqRSQ/0a5bds2d37W7t27S77+IUB7ezvf+MY36OnpIR6Pc/LkSUzTxOv1UlVV5f54vV5yuRyhUAifz0cul3OHKf1+PxUVFQSDwWK/nCkT8niY4/exIBym2h8g7PWQtW36czkgf90t6vUR8/up8PkIiEHSslgQCrEqWsayaDR/PW4WsiyL73//++59pRT/83/+zyK2aGJ0YNNmhHOHyvbu3ev2RpRS/Nu//RvvvPMOXV1ddHR08Pbbb7uBr1Tt3r2boaEhysvL3er8mUwGwzAwDIORkRGGhobclP/e3l76+/tJp9MkEgmUUkQikQtW9i8lhgjLo1H6s1lGTZOgCFU+Px7J92qHzRxpy6LK56c+GMRnCDV+P0uiZUR9Pjwi5OzZmTliGAb79+93h7ZN02Tv3r3FbdQE6MCmzQgLFixg/vz5GIaB3+9n0aJFbmADiEaj7rAa5L9pdnV1FaOp1004HCaTybiBC8Dj8RAKhRgdHcW2bUzTRClFf38/yWSSVCqFUorR0VE3a3LZsmXuEjelKGfbHB4ZoTWVZMjMMWJZJC0TW9kEDYNqf4D54TAWivpAkA0VMbyGcGRkmI50ipxtUxeYnWn+IsKv/MqvuNVtfD4fDz/8cJFbdXml/VVNKxkiwrp161i7di2mafLYY4+RSCTw+XwEg8ELDj2GSrx47a233orf7ycajZLNZgmHw5imSS6Xc4O8bdtYzjBaYY22wsX/eDxOS0sL8Xjc/eAqRd2ZDF2pNEHDQ9ayGTZNvICt8kvaRL1e2tMpMpZN2OPlRGIUWzmZkQospVjgFI6ejb74xS/y+uuvk0qlCIfD/O7v/m6xm3RZusemzSiFRTIXL15MLpdzeyEPPPAANTU17n719fUlX7U+EAjwW7/1W9x9992sWrWKG264gZqaGkKhEKFQiEgkQjQaRUTcVbINw3CHlTweD6lUiubmZqSEK2rkbBvTtunN5lfPTpgm7ek0/dksPZkMpxIJejMZ0rbFQCbDoeER+rJZfIaHhlCIoMdDegZkAk6V6upqHnroIXw+Hw899BBVVVXFbtJl6cCmzSi5XI5zlxgqXFfatGkTtbW15HI54vE4HR0dRWrl9eHz+Zg3bx7BYJCmpiYaGhp48MEH+cAHPsDSpUuprq52h24bGhrcHl2Bx+OhqqqKTCZTxFcx9WylGDBzdKdStGbSpCwrv76aQMaySZkmpp2vQBI3c4w619xytkV/Noshgm+WZkQWbN26lbVr17J169ZiN2VC9FCkNqN4PB78fj8HDx4ct+0Xv/gFv/Ebv0FPTw8+n49UKsWePXuYM2dOyQ5JxuNxmpubeeWVV8jlclRWVtLb24vf76e+vp6uri4sy8I0Tc6ePUsul8Pn82FZFrZtu8OPGzZsKPIrGa+LyVu2xlaKzmyGIZ+PHhFGbBsRQZzKIqJsDI+HFIrBbBavx0MgEKAvFKRPhGNmjkWVMQ4VxiUnSRcQm7SjTb3q6mq+8Y1vFLsZE6YDmzajGIbBmjVrqKyspLu7G8MwCIfD1NXVnXedTSnF4OBgyQa2H/3oR/zgBz/g7Nmz9PX1ISKEQiHS6TSRSMQdpjUMg0QigWVZKKUIBoOICGVlZdxyyy188IMfLPZLcU322nCWZdFy4ADD2SyBWAzD6bH6fD4My3JXG7csC9MwwOslXFaGikbziTjRKKqujkBV1aT+HcWY/Neq/ZIObNqM09jYiGVZ7vIsAN3d3VRWVrqrR0P+elxlZWWxmjmlbNtmx44ddHV10dvb6y5Rk0gkMAyDTCZDNBp1E0UKH96Q/7AXEUZHR0kmk+5zp4MvfelLk37MRx99lNOnT6OUoqWlhb6+Purq6rBtm40bN/Lqq6/S19dHU1MTHo8Hn89HLBYjGo1y991309DQgNfr5f777y/5qRGlQv8raTPO0NAQ8+fPp6+vj2w2Sy6X48EHH2T+/PmMjo7S0tKCz+fjhhtuKNnemmEYDA0N0d3dPe4amW3b2LbtBrdCyv/YdH7LsvD7/SilOHv2LM8//zxLly4lUKIp7YsXL2Z4eJhMJsOCBQsoKytj0aJFKKXIZDJudm00GqW3txfbtkmn09xwww3uMUzTZHh4mDlz5hTxlWgTpQObNuMUll4pKMzLEhFWr17N6tWri9Sy62vBggW8/fbbbk9sLMNJdhg7t69AKeXWk4zH427h5LFZpaXknnvuoaOjIz8s2dLC3LlzGR4edodlvV4vgUCAgYEBBgYGyOVy7kTkiooKysvLicVilJeXF/ulaBM0u1N9tBnHsizOnDnDO++8QyqVcofa3nvvvSK37PqbO3eue71srMIiooXqIhdSmO+WSCTo6+sr6Q/tZcuW8cgjj/DhD3+Yj3/84+Mm8+dyOTweD4lEwk0q8Xq97krkhXJlGzZs0MOQM4gObNq0VlhipeDw4cNks1kWLVrkLpTp9/u57777itjK4ggGg0QiEbd3dq6LBTXIBz/TNPH7/axbt454PD5FrSy+1tZWjh07RigUYs2aNTQ2NuLxeAgGg9TU1GBZFuFwmIqKCnw+H2VlZfh8Pmpqaqiurmbjxo0lPyey1OivINq0lMlkeO+99xgcHMTj8bBq1SoWLlxIT08PHo+HOXPmEHaqQUQikZIuCXUxc+fOJZ1Ouz2NQiC7VEArKJTeKisrY8mSJSU7Qbujo4O9e/diWRanTp1ieHgYn8/Hhg0byGQyxONxqqurMU2Tm266iWw2SzweJxKJUFFRQU1NDYsXLy72y9CukA5s2rR04sQJN33fsiwOHTpEfX095eXlJJNJDh065A4XiQivvfZakVt8/RXm6xXqQV4py7LIZDKkUqmSvb7W2dkJ5ANc4e+poaGB0dFRbrnlFjKZDO+//z5KKWKxGCtWrCAajVJdXU1FRQWf/OQnZ0SlDW08Hdi0aWl0dHTcfdu2SSQSrF69mmQy6c4/ikQibo9ltnnttdeuOqgVFiTt7e2lra2tZN+/Qq9+7JSGWCxGbW0t/f39nDx5ku7ubmzbZunSpTzwwAPMnz+fbDZLfX19yWbVljod2LRpqa6u7rzSWaOjowQCAe666y5+7dd+jZdeesn9QJ7u19gef/xxmpubJ+14Sil+9rOfMTIyctXHyOVyDAwM8Mwzz9Dd3e3Wk5wMS5cunZI5aVdqyZIlbnLMyMiIG6zi8Thnz54lk8kQDoexbRuPx8PmzZuL3WRtEujANg0dP36cP/iDP+Ab3/jGrK1OsGjRIizLoqOjg5GRERKJBPv27QNg3bp1fOYzn+Hll1929//Upz5VrKZOSHNzM3sO7ZnUOkojqZGr6q2NlTNz9CX72Ne+D69/kj4O4pNzmMng9/u54447uPHGG3n77bdpbW3FsizWrl3L8ePH3f0MwzhvlGA2O3HiBCdPnkREWLZs2Yy7zjhlgU1E5gFPA3Xki6w9qZT6exGZA3wfWAicAT6llBqU/FfvvwceApLAf1BKve8cayvwFefQX1NKbXO23wx8GwgBzwF/oJRSFzvHVL3Wyfa1r32NRCLBY489xtNPP+1OtJ1twyJLly5l6dKlvPzyy25av23bHD16lAMHDozb99lnn+XLX/5yMZo5cTGw7568KvH+Vj8MA+Zld70o27JRcxXGAwY2k9M249Xpl2zd3t6OaZrU19cD4PV6qa6upq+vD8hniS5fvtytNTqdK9Zcbe+/ra1tQovvFhJoxorFYtfUow+FQjQ1NV3x86625z+VPTYT+COl1PsiUgbsFpEdwH8AXlZKfV1EHgUeBf4E2AIsc342Ad8ENjlB6qvARvIBcreIPOsEqm8CvwO8Sz6wPQhsd455oXNMe8ePH+fMmTMAnDlzhn/7t39zJ9SGQiEMwyCZTFJbW8v69esndfhoujJNk8HBQc6cOYNpmlRVVfH888+P2+eFF16Y/oFtkoXqQ3iPejETJheNSV4uG/hSvaW90rhSyv0/VdDR0cHWrVt5+eWXeeuttygvLyeVSvHuu+8CUFFRQWVlJbZts2DBgvNWcC+m5uZmDh04Qixce0XPS6TTmPal/xhM06S3r5dUMoHH6yUUdD5zRtMQvvovLHY2TXum/4qeE0/2XPX5piywKaU6gU7n9oiIHAEagY8Bdzu7bQNeJR90PgY8rfJjK++ISExE6p19dyilBgCc4PigiLwKlCul3nG2Pw18nHxgu9g5pr2vfe1r7u1cLsff/M3f8OUvfxmlFG+99RZNTU3U1NTQ3d3NkSNHWLduXRFbe300NDTw1ltvub02r9frrh5dMBvnGQWrg5ff6XIjlQLmqIlt2Rie6dfTmiyFSdherxefz4fH4yEWi/Frv/ZrvPLKK8TjcXK5HJD/f/fKK6+wbNkyysvLaWtr484776SsrKzIryKvra3tqp4XDV6+F9rb303EX46dfysQ20skVEZVeQ1+//UvuXa1r/W6/CWLyELgJvI9qzon6EF+9YbCJ1Ij0DrmaW3Otkttb7vAdi5xjnPb9XkR2SUiu85NVCiWsd8sLcuipyf/raVQE3HsUMKFVo0uNYlEgvb2doaHh0kkEjQ2NjJv3jy6urrG7dfd3V2kFhaPlbTwRi/z3fT8alvn8ZX5sFIT2HGGyuVyDA0NceDAAfbu3UtnZycrVqxAKcXevXtpaWmhra2NlpYWIL8cUGG5H8gPf59bxq0U5euK5pc2CgXDGIZg2zblZbGiBLVrMeXJIyISBX4I/KFSanhsWrFzPWzyFjm6gEudQyn1JPAkwMaNG6e0HRO1cOFCN7j5fD63J+L3+/H7/ePG/mdDQdYf/ehHvPfeexw9epRYLIbP56OhoYHNmzeze/dud7+77rqriK0sDittoWx18WHIifCAx+fBE/RMWrumm+bmZsLhMGvWrCGRSFBeXk5lZSUdHR3uahDBYJDu7m5isZjbq6uoqHCPMZ2G/JuampBMP/es/PSkH/tE60HS2aR7vyI6h/l1xUlg+/nR79HYdHVzCKe0xyYiPvJB7TtKqR85m7udIUac34WB1HZg3pinNznbLrW96QLbL3WOae8rX/mKe9vj8fDXf/3X1NTUUFVVxSc+8QnmzZuHx+OhoaFhXPXxUnTo0CG2b9/Ovn37GB4e5uTJk7S2tmIYxqwI6peiLIUn6MFOX2PChwW5ZA47N3lJLdNNYQ5bOBympqaGQCBAMpmko6OD48eP09/fj2marFy5klgsxoYNG9i0aZM71F1eXs78+fOL+RKum3l1i4kEoxhiUBaOUV+1oNhNuipTmRUpwLeAI0qp/zrmoWeBrcDXnd8/GbP9iyLyPfLJI0NKqU4ReQH4P0Wk0FV5APhTpdSAiAyLyK3khzg/C3zjMueY9pYvX+722hYuXMgHPvCBYjdpSl0qw6uQcjw2DVspxbe//W36+vqwLItEIoFpmvzLv/wLbW1tEy5UO13mWV01yb8XVu4ahxAVYOcTSMrmT49rSJOtsJp4QSAQoKysjDNnzhCPxzFNk5GREbLZLHfddRenT58mm826a/5t3rx51hRADvrDLG5cVexmXLOp7LF9EPgt4EMistf5eYh8sLlfRE4A9zn3IZ/VeApoBv4/4PcAnKSRvwTec34eKySSOPv8k/Ock+QTR7jEOaYV27bp7Oyks7PTTYyAfK8tEonw53/+50VsXfF5vd5xRX49Ho9boLayspLR0VGy2az7IVQonzQbiCH4Y/5J6WnlRnPXPB9uOmtqamLt2rXMmTOH+vp6brvtNoaGhggGgyxevBiv14vX66W+vp5cLudeCigrK8O2bffamzZzTGVW5BvAxer03HuB/RXw+xc51lPAUxfYvgtYc4Ht/Rc6x3RimiZvvPGGWzkiGo1yxx134PV6Wb58Odu3b7/MEUrDpXpNfX19PPHEExw+fJjOzk5qa2v5rd/6Le677z6Gh4e5/fbb3ffv/vvvp6amhl//9V935+AsWbKkpOv8+SK+i/8PuwIikj9WCVuwYAELFvxyWM3jyV9TrK6udv9GFixYQDKZv740MjJCMpl0a5NqM8vs6F8XyaWG2UZGRtzJoQXbtm1z04uBq5rQCCUwzEY+zffNN9+ko6OD0dFRbr75ZjZs2EAgEMDv9+Pz5T+Ic7kcjY2N9Pf3U1VVxfbt290qCb29vdx9991EIpFivhTASVsemtzJy4nmBIY1CROrMxDcF5y8dP84tKmrS9O+HkzTZGhoiPr6erq7u91C0n19fVRUVNDV1eX20kSEVatm/tDcbKMDW5GMHXosKAwHTaQ6QCnL5XLs3buXo0ePMjw8TDqd5vDhwyilWLZsGX6/H8uyqKurY2hoiAULFhAMBt0hy/7+fjf7rauriyVLlhT5FU0+pRSp4dSk9Nhsy8bKWW4vptTkcjkOHz5MX18ffr+foaEht+hBYTg7FAoxODjIe++9RzgcprKyEsuyqK2tZWBg4PIn0aYVHdim0KV6TZlMhldffdVdR8zn83H33XcTDAbd5z3++OPXpZ3TTSqVcpNCkskkIyMj+P1+9u/fz8GDB2ltbaW3t5fu7m4qKyuJRCJUV1dTW1tLMpnk5MmTAHR1dbF06dJpEdiamprold5JK6llmzZWh5WfpXkNJbUAJCAkliXwLpmcjwPjVYOmxqsbbZgKBw8edEdBDh8+TDKZZNWqVZw5c4bDhw8zOjpKOp2mt7eXOXPmkM1mWbZsmft8yyrdOX5XK5NN0dF3lnQ2STRUQUP1Ajye6RNOpk9LZplAIMCdd95JS0sLSinmz59PMDiBShKzQFlZGXPmzMG2bQYHB0kkEiQSCSKRCLFYjFOnTnHq1Cl6enoIhUKkUinS6TTBYJDnn38ej8dDTU0NK1asKNnCtmIIePJp/9fKtuz88UpUocgB5INUoZfW19eHz+dzR0q6urqoqalhyZIlDA0Nuc9ZtGjRdW/zdNfSfdKd7xYf7QeBebXF/wJZoANbEYVCIVasWHHedqWUW3Gjrq5u1qQaF4gIt912G8ePHycajboTZguLYvr9frq7u8nlchiGQUtLC6dOneLgwYOMjIwQi8VoamqivLy8ZN87MQRv0JufoH2NFKqkA1t5ebl7PbumpoZEIuEOu9bV1bkrsBuGweLFi1m1apVb7aampmZaLsIaT/bw86Pfu27nG03nqxxFg5XYtkV37/gMZMPwUDdQP6nnjCd7aOTqkr9K83/9DFZYqiWbzfL+++8TDoe588473WSJ2SIajbJ69WqGh4f54Q9/yODgIMlkklgsRiqVIplMYpompmnS3NyMz+ejt7cXy7JobW3FNE0qKip46KGHiv1Spkx2JHv5WpATYHiNks6KvPHGG9m1axcjIyPU1dWxadMmkskkfr+fdDrNu+++i2VZfOYzn3EzJ682cet6KMZSVidO5K8zNi6pyvdwgxm35BjkJ7/X1U1uBnIjVVf9WnVgm2Y6Ozvd624AyWSS1tbWGbce0mTo7e3lO9/5Dq2trWSzWQKBgLuwaCgUcpMAstmsO8nW4/Hg8XgYHBzEMAwaGxsvc5aZSdmK7FD28jtOgOEzCMyZWbUAr0Q0GuXuu+8mk8ng9/vdv6G1a9fS19fHT37yE/x+P0opTp06RWNjI4HA9H0/ipHxfO51/8HBQfbs2UMikWDOnDls2LBhWi2rpQPbNHOhC9WDg4MMDw9TXl5ehBYVRyKR4NSpU3R3d7uBqzAZ27ZtN/iLCB6PB9u2yeVy5HI5/H4/8XiceDyOUoqx9UlLhRiSHz40uLZakYCv3FfSQ5EFFwpW1dXVlJWV0d7e7q7x19zczF133TWtg1uxVVZW8qEPfQjLmp7ZtKW7TsUMEI/H6enpGZf639DQ4P6hWJbF8ePHOX36NL/4xS/YuXNnSVeIGKunp4fXX3+dXC6HbdvYto1Silwuh4i4c9NyuRyWZZHJZFBKYRgGXq8X0zQZGBgoyaBW4I14rzkjEsDj9ZR0rchzZTIZurq63InXhQzcsY9f7XIps810DGqge2xF8/7777tLYYRCIW6//XaCwaBbvX50dJRQKEQul2P//v2Ew2ESiQTz589n7ty5RW791DJNk5/85Cek02kgH7wKAd22bXdFcdM0ERH3i4GIYFkW6XQaj8fjBsXC/LZSI5MwiU18guExJiUJZTrr7Oykvb2dZDLJwMAAHo8HEWHt2rXFbpo2BXRgK4J4PD5ufadUKuWO7Q8MDGCaJj6fj7NnzzI0NITX6yWRSNDc3MymTZuK2PLro6enh9bWVtLpNOl0elwv1TAM0um0Wx9SRMYFN8j3dFOpFMePH+eVV17hvvvuK8bLOF98ciuP2O3X3ssSJYQyIXxvT2LySJxfrow4DbS3t/P+++8D+Tlttm1z4403Avl5beFwGMMwME0Tr9dLMBic1skj2uXpwFYEhetDw8PDbgHkgYEBd2LxoUOHCAaD1NfXc/bsWebNm4ff78c0zXHrsZWqWCxGR0cHhw8fHpdIA7iZkAVKqXH3C3K5HD09Pbz33nvccsstRb8+ORWZbPHqOINd17jYrA3rV6xnWeOyy+87UY3Fydy7mLHDioXrsIlEgmg0Si6Xc5etGRwcpL6+nrvuumtarb+mXTkd2Ipgzpw59Pf3s3PnTkzTpLu7m3g8zpo1a1i7dq173ai6utodjotEIni9Xvbt20dTU9O0qKZRcKmamFdr796917xCeHd3N9/97nc5ceIE0Wh0klp2dbU4pyKT7Y//+I85ceKEu27Y1YhGoyxcuLCkq9yMLXxQU1NDR0eHO30mEAiQSCQwDIOamho3wBUW+NVmJh3YimDPnj2k02kymQzNzc2EQiGUUpw8eRLLsrBtGxFh/vz5WJaF3+8nk8nQ2NjI8PAwhw8fxuv1jqtWXkzNzc0cP/g+86OTV3rIHu27/E6XIVhkBjsx2/eTDkzOUFvL6PS5WF6Y2nAtDMPg6NGj7jBcKVq6dCk9PT2k02mamppYvnw5lZWVVFRUXLC3X5jvps1cpfmXPI0lk0m6urooKysjEokQCAQwTZNIJIJpmrS1tRGPx90hx/nz5zNv3jzeffddOjo6iEajVFVV0dXVNW0CG8D8qMVXNk5e+aquUxlaOiF9DbHSJ3BHk8Wf3prG57n6Xs1YX9s1eT2/a7Vo0aJrrmOYzWYJh8MlG9QgP9px7733MjAwQCgUGrfaQ2GJowIRmZaVRrQrU7p/zdNUIUOvvLycBQsWcPLkSQYHBwmHw4yOjhIMBpk3bx6maXL06FEqKirYt28fp06dorOzE4/HQ319Pb/6q79a5FcytcqDxmQU1SDsM/B5SjPlPx6PEwqFrmkoUinFww8/PImtmp4Mw6C6unrcNqUUkUiEmpoahoaGiMViLFmyhIqKiiK1UpssOrBdZ8FgkPnz59PS0sLy5csxDINdu3a5a0KJiFtRw+/3MzIyQjwe59ChQyQSCfx+P3PmzKG9vZ2RkRHKysqK/ZJoa2sjMeKZ1N7MgXgYi2GupWaUaQtv9wQntV1nRzxEpskcp8KK4SJy1fMb/X4/8+bNm8xmzQgDAwPs2bOHZDLJ0NAQtbW13HHHHcVuVtENDw/T3NxMNptl3rx5M7Zyjw5slzEViRGQH5IsVKUfGhoinU7T3d1NR0cHyWQSy7L4yU9+glLKTfsvXE/JZDJ0d3ezZ8+eSV9Ec7osUjqnLIh1rZXrBcLB0q2BmMlkiEQiJJPJ87JHJ8IwDAKBADt37izpmpoXsnfvXneCdjabpb+/v8gtKj7TNHnrrbfI5XJAvqSd1+udkdcbdWC7jObmZvYcOIwdnjNpx7RMk9H4AKPDg6RGh8mkk85k2/w3b68BhsegfSCBx+slPprEUpDLWXgMD31DI3grcmQ7hjCMkUlrl5G8ugUVm5qaSJudk3qN7f8aGOItgexVxjavQMQn/P5NilsXTV67vrYrSnCazHHyer2sXLkSwzDo7u7GsiyUUhdcxPZCCsPeIyMj7rJAs4FpmiQSiXHbruaLwUxzuS/piURi3BI/AP/8z/9MTU0NJ06cAK4+u/d6f2HWgW0C7PAc0qt+ZdKOl+w6TdrbRco6TWI0g+0VbKXAzBGI1WB5A5i2TbpyPqHqRkK+OVjJUdKJQbByeKsaCd3+78kGwpPWJoDg4Z9N6vGulm0rmvuy+LyQzV3dMQwPzKv0cqAzw6aF0ZIsrXXTTTcxMDCA1+sll8uRTqdJpVJuQomIkMvl8Hq9bj1NwzDIZrOICIFAgEAgwJo1a9ze32zg9XqJxWLjEkemUwHfYrnQCiKFpKKZ9v7owFYEtplFKYvM8AB2Lg2GB38wgplJY+WyGN4Avkg5nmAYELyBKKmBTsQ2EY8fX6iMdH8H4frFiJReuSjDECqCnnzSR+7qumxhn1BX5mcwaZK1FAFv6QW2Bx54gFQqxYsvvsi8efNobW11E0l8Ph+2bY+rzpLL5fB4PHi9XpRSKKWoqqqivr5+Vkz8H+vmm2/m4MGDDA0NEY1GmTMnPyIzOjrK6dOnsSyLBQsWlNT7MpEe06FDhzh9+jRKKWKxGLfeeuuMXDJLB7Yi8EYqSPS2YGdT2DkTMDFtBbaF+AIYXh8ef5Ds6CDJrtNgGHj8IWwgEKsDEbKjcfyJYXzRWJFfzdT4+NoYBzqSjKRzXE1CezanONOfYeOCMKalCJTgX3osFsMwDDKZDMPDwyilCAQCZLNZlFL4fD73diExqRDofD4fgUCA06dPs3//fh544IGS7NVeTDgcZv369Xi9Xn76058C+eHIN954w73G1N7ezh133FH0qjXX0+rVq1myZAnZbHZGv+4S/O8+/fkravAYPkI18xGPl2xyGGXm8ATzQ0FWJomybTA8qGwaK5fBNnMY/iBmesTppQm2dZXjdFOgZXRysyKzZgh/JEVgeIBk5spL2Gdtg+6k8FKLn/7g5KVvt4x6WD5pR7s2haK+FRUVWJZFIpFwVz4oLOeTSCTcYAa/nG5iGAbRaJSamhqOHDnCyZMnWbZsEstqTWPpdJpdu3YxODjoVh6JRCJ0dXW5QQ3yBbfb29tn9Af81QgGg+OqtcxEUxbYROQp4FeAHqXUGmfbHOD7wELgDPAppdSg5P/X/T3wEJAE/oNS6n3nOVuBrziH/ZpSapuz/Wbg20AIeA74A6WUutg5pup1Xg0RQfwBAhXViM+LN95LLp0gXN1ENjGElUmRSw6BAsMfRDxerMQwZiaJ3y4HpcgMdlKx+MZivxRgauoCDnV3k/HFMXxJvFb6ghUiLkZEMLxeQmUxKuffQHDhyklr13KmTx3EQCCA1+t116izLMsNXEopt3RboZKNx+PBMAxExC30W11djcfjIZVKFfnVXD9Hjhxxy7VlMhn6+voIhUIXXH9Nr8k2M01lj+3bwD8AT4/Z9ijwslLq6yLyqHP/T4AtwDLnZxPwTWCTE6S+CmwkP6Fpt4g86wSqbwK/A7xLPrA9CGy/xDmmjexQH1YmSS4Rx8okEV+AUDSGmcuQGx0EZWP4gpipBLaZwROIYPgDiOFDKbCyaTLD/WSG+ghVNRT75UxJttNPf/pTzp49S19f33mFjy9FRNy5fqtWreKv/uqv+MAHPjDp7ZsOYrEYqVSKjo4O0uk0ZWVleDweotGou/CqYRhusPP5fFRWVroJAYX6o0uXLmXRokXFfCnX1fDw8Lj7hS8FAB0dHfT29lJbW8uKFStm5Ry/UjBlgU0p9ZqILDxn88eAu53b24BXyQedjwFPq/ws03dEJCYi9c6+O5RSAwAisgN4UEReBcqVUu84258GPk4+sF3sHFelra0NIzk0qRmD6b5uopaFaeawTJNsKomkPIwM9mFlUng8PjAMVCYNYuAJjeIBJ6vNAyKQGsDamyRYP3n/8YxkP21tk7By5STwer0MDQ1hGAYej8dNhgDca0Tn9jJCoRCxWAz4ZW9mz549bNy4sSSvH+3bt4+GhgZuu+02fD4fuVyOiooKhoaG3B5JoZ5kYRmWsrIyAoEAIkJdXR0PPvggmzdvnhYT/a+X2traccGtEOh37dpFQ0MD5eXl2LbN0qVLZ2TihHb9r7HVKaU6ndtdQGHmXyPQOma/Nmfbpba3XWD7pc5xHhH5PPB5yNdkvN68Xh8ejyefDWlZ+PxBsukUVi6L5XwOe30+guEoppklMTRIocBEIBzGvsY6gdNZYY6WaZrE4/FxvQ3TNEkmk25SROGDurKyklQq5d4fHh7mnXfe4Vd/9VfPK6c009m2zdDQEGVlZZSXl7Ny5UpOnTpFIBBwe2JdXV3Yto3P56O8vNxNKClUv2lsbOS2226bdWuPrVixAtu26erqIhqNUldX5y5eC7grQfT19c3YyhuzXdGSR5zrYVO6bO/lzqGUehJ4EmDjxo0X3K+pqYnujHdS57EZw/2ke1vy18ri3ViBDLnkIJlcN1lGEGwMjxdvuBxvbC7+hWuwe1ow1ElsK4eIgenxYC+5nfSS9ZPWruDhn9HUND1W566oqGDBggXuBf7CmlmWZRGJRIjFYgwPD2MYhrv6QTqddqtwlJWVMTQ0RCKRYGRkpOQCm2EYVFRU0NfXRyqVYmhoiLlz57rzjQYHB0mn0+56f5lMhmQy6V5rU0oRCoWu6NplqTAMg/nz5xMOh6moqLjo2muzLWmklFzvSVDdzhAjzu/CNPd2YOyYWpOz7VLbmy6w/VLnmDb85VWUNa1A2TYKg/RAB6n+LtLxbuxsCiubRtkKyzTJjQ4x1LybZF8rnnAF/rI5eEJRArG6kk31h/yCkK2trYyMjDAyMkIqlcK2bWzbdjMBw+GwW2as0EsTEbLZLIlEgmQyiWmaJXudZMOGDXR3d9PS0kIkEqGpqYlAIEBVVRVer5eysjL8fj9lZWVUVVVRVVWFbdt4PB4GBgaIx+OzMjmio6ODX/ziFxw8eJA333yTwcFB/H4/q1atwuv1IiI0NjZOq9UztCtzvQPbs8BW5/ZW4Cdjtn9W8m4FhpzhxBeAB0SkUkQqgQeAF5zHhkXkViej8rPnHOtC55h2xOPBti2yIwOYiSGsdBKUQqEwM0myQz2k413kRgdRpkkuMYjhCxCa00AgVksgVlvslzAllFI8//zzdHV1MTg4yMDAAMlkktHRUTdgdXR0EAqFKC8vd9Pb0+k0IoLX63UTJpYsWXLNa5ZNV9FolEgkwrJly2hqasLv9zM8POz2XJPJJIFAAJ/PRywWY3R01P3gDoVCVFZWzopSUuc6fvz4uKLRQ0ND7jy/hQsXcscdd7Bhwwb3b0ibeaYy3f+75JM4qkWkjXx249eBZ0Tkc8BZ4FPO7s+RT/VvJp/u/9sASqkBEflL4D1nv8cKiSTA7/HLdP/tzg+XOMdVM5IDk15uysxlGe3qINl+FnJZ7HQSbAuloLBgiw2gbBQW2UwaZdvYQ934Q2GaFq8gevKVSW1TvlZk8Ycih4eH6e/vZ3h42J1grJQik8ng8/ncOVvDw8OYpkl1dTWBQIBQKOQu5RKNRlm0aFHJDUGeq6qqipMnT7pZkIU0/+PHj7vX2oLBIIFAwL3O5vF4KC8vp7q6elauPXZuLU3btuno6ODQoUMAnD59mjvuuGNWJdSUmqnMivzMRR669wL7KuD3L3Kcp4CnLrB9F7DmAtv7L3SOqzVVc5bi8Tg7W47ixcTrMzCUH9M03f90tm3jd7K1xMpiYOPxeagojxIIBKgJCWsX101ytt/caTFHqzDHqtC7CAQC7vyrsT+FYbVAIMDChQvp6Oigrq6Oqqoq/H6/myRRihmRBatXr2Z0dJS+vj6Gh4fd625lZWUopdz3rbGxkfXr17N//35M06SsrIzbbrutZOpDXskqHIUvTgU9PT1YlsWTTz7pbvvOd75DVVXVZY81XVbD0MbTlUcuYyr+aEdGRnj11Vepra1l9+7dtLa2EolESKVS+P1+2tra8Pl81NXVkUgkyOVyJBIJFixY4F43ueeee3j00UdLcuXjQCDAgw8+yGuvveZWgqivr3evnw0NDbnTAbxeL4FAgGAwSH19vbuOXWHRyPvuu6/Ir2ZqLVy4kEgk4i4x8swzz9DS0kJra6sb+D0eD2VlZSxdupSlS5cyNDREJBLhQx/6ULGbXxTl5eXudBG/349lWe70iIJS/jI0G5Tep+IMMDKSX2qmqamJM2fOuENs69ev55ZbbuGpp57CMAzWrVuHx+MhmUzS0tLifsgHg0G3zl2pqqys5N/9u3/H3r17gXwdvxtuuIHW1lb279/P4OCgW7U+FotRW1vrfhkwDIP169dTX19f8kORADU1NdTU1HD8+HEaGho4ceKEWyrKMAzC4TCrV68mGo3S2tqK3+9n06ZNJTVH61q+gCql3CQSyC++escddxAOT+7qGdr1U7qfjNNYVVUVHo+HRYsWkcvleOutt2hqamLTpk1UVFSwcuVK/H4/f/EXf0EsFuOdd97hhz/8IUePHiUSifDZz36WD3/4w8V+GVOqp6eHhoYG6uvr6erqorW1lbq6OizLciuwt7S04Pf7WbBggTvUNjo6SjKZ5IYbbmD58ulS1fH6iMfj1NTU8NBDD3HgwAFaWlo4deoUlZWV+Hw+TNN0F408cuQINTU1JTMUeS1EhM2bN9PV1YVpmsydO/eiUwC0mUEHtiIIBAJs2rSJY8eOEYvF2LBhA+l0Gtu2mTNnDrFYDI/HQ2Njo5sReM8993DrrbdSXl7Ohg0bSurb9oWUlZXR09ODiDA0NOQW962vr6e7u5u1a9fS1tbGwMAA3d3dhEIhjh07xqpVq3jooYdm5cTaqqoquru7CYfDfOADH6C+vp7+/n48Hg8NDQ10dXW5+5qmSWtrKytXTl4dzZnMMAwaGopfnk6bHDqwFUkikSCdTqOUYunSpdTX15PL5YhEInz/+9939xsZGaGtrY2Ojg4gHxTnz59flEop19OyZcvo6enh/fff5+TJk1RXV2PbtpvO3tvbS39/P9lslt27dxMKhWhqaqK2tnZWBjWARYsWkUqlaGtrIxgMsnnzZo4dOwbkh73HBjagZKdBaJoObEUwODjI3r17GR4exrZtBgcHueGGGy44kTgSidDZ2enez2Qy5y1rP5NdKputq6uL0dFRLMvi/fff5+WXXyabzZJOp8dVIgF4//336e7u5vXXX+dHP/rRZc9bitlshmGwZs0a1qw5L1mYuro6d9XodDpNPB5n7ty5xONxt76mppUKHdiKoK+vz10Da2BgAMuyuPfee1mwYMF514UMw2D58uW0t7ejlKK2tnbGr5U0UalUys3qq6mpob+/n+rqaizLore3161c7/F43MQa3Qu5MMMw+OAHP0hbWxuvvfYaNTU1dHV10dPTM+sW09RKnw5sRZDJZNi5cyc9PT0MDw+7y9AvWLCA5uZmLMtyP6BjsRhz586louKXi2WW0rWAS/Wa3nzzTQYGBtz7/f397tyiP/qjP3KHcguFkB955BGWLFnC5s2bp7zd19OVzNE614kTJ4Bfvs8jIyP09fWN2+e73/0uc+bMOe+5pdir1WYHHdiKoFBFoyAQCNDV1YVSCtu2x5X7MQyDzZs3c+LECdLpNE1NTbPmGtK6devYvXs3w8PDlJeXc/PNN7N3714sy3KXXwkEAmQyGTc7ct26dcVu9rRSKIpccKEere7laqVGB7YimDNnDtXV1YTDYcrLyxkeHiYajSIi1NbWnjc/LRwOz8oP7Gg0yl133XVeD7a9vZ0/+7M/42//9m8REcLhMH/zN3/DLbfcUuQWT43J7DUppXj33Xfp7e0F8pOVN2/eXPJZttrsogNbEdTX13Pvvffy1ltvuXOM7rzzTlatWsXChQv5zne+c8Hn2bZNd3c3lmUxd+7ckp6gPdbYHsXAwACJRIJ169bh9XqxLAuv11uyQW2yiQi33norg4ODWJZFVVWVrrKhlZzZ8ck4zRiGwac//WnWrl1Lb28vK1euPG+xx7HDkZAPav/6r//KkSNH3KVYfvM3f3NWLTty4sQJjh49CsCBAwcYGBigoqIC0zTHXX/TLq+ysrLYTdC0KaPXZSgSn8/HunXruO+++8YFNdu26e3t5cyZM7z44ou0teUXCj99+jT79u0jk8lgWRZnzpxh586dxWp+UZw9exbIT5f49re/zeDgoNvzeOKJJ4rcOk3Tpgs5t2cwW23cuFHt2rVrUo95NdlsQ0NDHDx4EPhl9uO8efPo7+/n9OnT4/atra1l8eLF5x2jVLPZfvGLXxCPx9m7dy9PPZVf8CESiRAIBIjFYvz85z8vcgs1TbvOLjiOrnts00whw29srbpMJkMsFjtv4cPZNrH2hhtuwLZtd1HIwqrZlmXp60Saprn0NbYpdDW9prNnz7J//373vmEY3HfffQQCAU6cOMEbb7xBNptlw4YNbNy4cVZ9oNfW1rJlyxYgf73twIEDQH5Yt9SXp9E0beJ0YJtm5s+fTyKRcJcXueGGG9wEkWXLlrFs2bIit7C4gsEgW7Zsoby8nD/6oz/C6/USiUT4whe+UOymaZo2TejANs2ICKtWrWLVqlXFbsq0FQwGuffee/nkJz/JCy+8wIc//GGdEalpmksHNm3G+sIXvkBXV5furWmaNo7OinRMRVakpmmaNqV0VqSmaZpW+nRg0zRN00pKyQY2EXlQRI6JSLOIPFrs9miapmnXR0kGNhHxAP8IbAFWAZ8REZ1mqGmaNguUZGADbgGalVKnlFJZ4HvAx4rcJk3TNO06KNXA1gi0jrnf5mwbR0Q+LyK7RGRXYX0qTdM0bWab1fPYlFJPAk8CiEiviJwtcpPGqgb6it2IGUC/T5en36PL0+/R5U3H9+h5pdSD524s1cDWDswbc7/J2XZRSqmaKW3RFRKRXUqpjcVux3Sn36fL0+/R5en36PJm0ntUqkOR7wHLRGSRiPiBTwPPFrlNmqZp2nVQkj02pZQpIl8EXgA8wFNKqUNFbpamaZp2HZRkYANQSj0HPFfsdlyDJ4vdgBlCv0+Xp9+jy9Pv0eXNmPdI14rUNE3TSkqpXmPTNE3TZikd2DRN07SSogPbNKTrXF6aiDwlIj0icrDYbZmuRGSeiPxcRA6LyCER+YNit2m6EZGgiOwUkX3Oe/QXxW7TdCUiHhHZIyI/K3ZbJkIHtmlG17mckG8D503K1MYxgT9SSq0CbgV+X/8dnScDfEgptQ5YDzwoIrcWt0nT1h8AR4rdiInSgW360XUuL0Mp9RowUOx2TGdKqU6l1PvO7RHyH0rnlZWbzVTeqHPX5/zobLpziEgT8BHgn4rdlonSgW36mVCdS02bKBFZCNwEvFvkpkw7zhDbXqAH2KGU0u/R+f4f4I8Bu8jtmDAd2DSthIlIFPgh8IdKqeFit2e6UUpZSqn15Mvu3SIia4rcpGlFRH4F6FFK7S52W66EDmzTzxXXudS0CxERH/mg9h2l1I+K3Z7pTCkVB36OvnZ7rg8CD4vIGfKXRT4kIv+zuE26PB3Yph9d51K7ZiIiwLeAI0qp/1rs9kxHIlIjIjHndgi4Hzha1EZNM0qpP1VKNSmlFpL/LHpFKfWbRW7WZenANs0opUygUOfyCPCMrnM5noh8F3gbWCEibSLyuWK3aRr6IPBb5L9h73V+Hip2o6aZeuDnIrKf/BfKHUqpGZHOrl2aLqmlaZqmlRTdY9M0TdNKig5smqZpWknRgU3TNE0rKTqwaZqmaSVFBzZN0zStpOjApmnXmYjUicg/i8gpEdktIm+LyL8rYnu2iMguZyWAPSLyt8Vqi6ZNBh3YNO06ciZO/xh4TSm1WCl1M/mJr00TfL53ktuzBvgH4DedlQA2As1X8PxJbY+mTQY9j03TriMRuRf4c6XUXRd4bCHwP4CIs+mLSqm3RORu4C+BQWClUmq5iPyYfOm1IPD3SqknnWN8DvgTIA7sAzJKqS+KSA3w/wLznWP/oVLqTRF5GnhVKfXUBdrzUeArgB/oB35DKdUtIv8ZWAIsBlqArwH/3dnPAH5NKXXiat8jTbtW+tuWpl1fq4H3L/JYD3C/UiotIsuA75LvQQFsANYopU479/8XpdSAUwrqPRH5IRAA/szZdwR4hXxwA/h74O+UUm+IyHzylW1uANYAFxt6fAO4VSmlROQ/kq/w/kfOY6uA25VSKRH5Bvng+h2nDJznit4RTZtkOrBpWhGJyD8CtwNZ4D7gH0RkPWABy8fsunNMUAP40pjrcvOAZcBc4BdKqQHn2D8Yc4z7gFX5kVAAyp3K/5fSBHxfROrJ98bGnv9ZpVTKuf028H8463b9SPfWtGLT19g07fo6RL5HBYBS6veBe4Ea4H8FuoF15Htq/jHPSxRuOEOT9wG3Oas/7yE/JHkpBvne13rnp9FZZPMQcPNFnvMN4B+UUjcCXzjnHG57lFL/DDwMpIDnRORDl2mLpk0pHdg07fp6BQiKyH8asy3s/K4AOpVSNvkCxhcb0qsABpVSSRFZCdzqbH8PuEtEKp2kjl8b85wXgUcKd5xeIcD/DfzvIrLc2W6IyO+OOU9hyaStF3tBIrIYOKWUehz4CbD2Yvtq2vWgA5umXUcqn631cfIB6LSI7AS2kU/4+G/AVhHZB6xkTK/oHM8DXhE5AnwdeMc5djvwfwI7gTeBM8CQ85wvARtFZL+IHAZ+13nOfuAPge86xztIPikE4D8DPxCR3UDfJV7Wp4CDzkrUa4CnJ/ZuaNrU0FmRmlZCRCSqlBp1emz/CjyllPrXYrdL064n3WPTtNLyn52e00HyyR4/LmprNK0IdI9N0zRNKym6x6ZpmqaVFB3YNE3TtJKiA5umaZpWUnRg0zRN00qKDmyapmlaSfn/Aa/N8Z5B2xKwAAAAAElFTkSuQmCC\n", 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for var in discrete_vars:\n", + " # make boxplot with Catplot\n", + " sns.catplot(x=var, y='SalePrice', data=data, kind=\"box\", height=4, aspect=1.5)\n", + " # add data points to boxplot with stripplot\n", + " sns.stripplot(x=var, y='SalePrice', data=data, jitter=0.1, alpha=0.3, color='k')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For most discrete numerical variables, we see an increase in the sale price, with the quality, or overall condition, or number of rooms, or surface.\n", + "\n", + "For some variables, we don't see this tendency. Most likely that variable is not a good predictor of sale price." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Continuous variables\n", + "\n", + "Let's go ahead and find the distribution of the continuous variables. We will consider continuous variables to all those that are not temporal or discrete." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of continuous variables: 18\n" + ] + } + ], + "source": [ + "# make list of continuous variables\n", + "cont_vars = [\n", + " var for var in num_vars if var not in discrete_vars+year_vars]\n", + "\n", + "print('Number of continuous variables: ', len(cont_vars))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LotFrontageLotAreaMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchMiscVal
065.08450196.07060150856856854017105480610000
180.096000.097802841262126200126246029800000
268.011250162.04860434920920866017866080420000
360.095500.0216054075696175601717642035272000
484.014260350.0655049011451145105302198836192840000
\n", + "
" + ], + "text/plain": [ + " LotFrontage LotArea MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF \\\n", + "0 65.0 8450 196.0 706 0 150 \n", + "1 80.0 9600 0.0 978 0 284 \n", + "2 68.0 11250 162.0 486 0 434 \n", + "3 60.0 9550 0.0 216 0 540 \n", + "4 84.0 14260 350.0 655 0 490 \n", + "\n", + " TotalBsmtSF 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea GarageArea \\\n", + "0 856 856 854 0 1710 548 \n", + "1 1262 1262 0 0 1262 460 \n", + "2 920 920 866 0 1786 608 \n", + "3 756 961 756 0 1717 642 \n", + "4 1145 1145 1053 0 2198 836 \n", + "\n", + " WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch MiscVal \n", + "0 0 61 0 0 0 0 \n", + "1 298 0 0 0 0 0 \n", + "2 0 42 0 0 0 0 \n", + "3 0 35 272 0 0 0 \n", + "4 192 84 0 0 0 0 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's visualise the continuous variables\n", + "\n", + "data[cont_vars].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# lets plot histograms for all continuous variables\n", + "\n", + "data[cont_vars].hist(bins=30, figsize=(15,15))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variables are not normally distributed. And there are a particular few that are extremely skewed like 3SsnPorch, ScreenPorch and MiscVal.\n", + "\n", + "Sometimes, transforming the variables to improve the value spread, improves the model performance. But it is unlikely that a transformation will help change the distribution of the super skewed variables dramatically.\n", + "\n", + "We can apply a Yeo-Johnson transformation to variables like LotFrontage, LotArea, BsmUnfSF, and a binary transformation to variables like 3SsnPorch, ScreenPorch and MiscVal.\n", + "\n", + "Let's go ahead and do that." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# first make a list with the super skewed variables\n", + "# for later\n", + "\n", + "skewed = [\n", + " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", + " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# capture the remaining continuous variables\n", + "\n", + "cont_vars = [\n", + " 'LotFrontage',\n", + " 'LotArea',\n", + " 'MasVnrArea',\n", + " 'BsmtFinSF1',\n", + " 'BsmtUnfSF',\n", + " 'TotalBsmtSF',\n", + " '1stFlrSF',\n", + " '2ndFlrSF',\n", + " 'GrLivArea',\n", + " 'GarageArea',\n", + " 'WoodDeckSF',\n", + " 'OpenPorchSF',\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Yeo-Johnson transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Let's go ahead and analyse the distributions of the variables\n", + "# after applying a yeo-johnson transformation\n", + "\n", + "# temporary copy of the data\n", + "tmp = data.copy()\n", + "\n", + "for var in cont_vars:\n", + "\n", + " # transform the variable - yeo-johsnon\n", + " tmp[var], param = stats.yeojohnson(data[var])\n", + "\n", + " \n", + "# plot the histograms of the transformed variables\n", + "tmp[cont_vars].hist(bins=30, figsize=(15,15))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For LotFrontage and MasVnrArea the transformation did not do an amazing job. \n", + "\n", + "For the others, the values seem to be spread more evenly in the range.\n", + "\n", + "Whether this helps improve the predictive power, remains to be seen. To determine if this is the case, we should train a model with the original values and one with the transformed values, and determine model performance, and feature importance. But that escapes the scope of this course.\n", + "\n", + "Here, we will do a quick visual exploration here instead:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's plot the original or transformed variables\n", + "# vs sale price, and see if there is a relationship\n", + "\n", + "for var in cont_vars:\n", + " \n", + " plt.figure(figsize=(12,4))\n", + " \n", + " # plot the original variable vs sale price \n", + " plt.subplot(1, 2, 1)\n", + " plt.scatter(data[var], np.log(data['SalePrice']))\n", + " plt.ylabel('Sale Price')\n", + " plt.xlabel('Original ' + var)\n", + "\n", + " # plot transformed variable vs sale price\n", + " plt.subplot(1, 2, 2)\n", + " plt.scatter(tmp[var], np.log(tmp['SalePrice']))\n", + " plt.ylabel('Sale Price')\n", + " plt.xlabel('Transformed ' + var)\n", + " \n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By eye, the transformations seems to improve the relationship only for LotArea.\n", + "\n", + "Let's try a different transformation now. Most variables contain the value 0, and thus we can't apply the logarithmic transformation, but we can certainly do that for the following variables:\n", + "\n", + " [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]\n", + " \n", + " So let's do that and see if that changes the variable distribution and its relationship with the target.\n", + " \n", + " ### Logarithmic transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mSdoJWABc36F1mZWB93krlY5Uy0TEsKRzgB+SNAu7NCLua3IxbTt8LZA/wyTRpn2+WVX5bvw5OqAjJ1TNzKxYvreMmVkFObmbmVVQqZK7pF0k3SnpHkn3STq/6JjykjRF0s8lfb/oWPKQtE7SGkl3S1pVdDz2JknTJV0j6UFJD0g6quiYmiXp4HTfqj1ekPQXRceVh6TPpPnqXknLJe1SdExQsjp3SQKmRcSQpLcCtwF/HhG3Fxxa0yR9FpgH/EpEfKDoeJolaR0wLyKqcHFJpUi6DPhJRFyStszZLSKeKzis3NJbNwySXPS1vuh4miFpJkmeOiQiXpZ0NbAyIpYVG1nJSu6RGEoH35o+yvPv0yBJBwDvBy4pOharFkl7AscA3wSIiFe7ObGnjgN+2W2JPWMqsKukqcBuwOMFxwOULLnDG9UZdwObgZsi4o6CQ8rjq8DngNcLjqMVAdwoaXV62byVwyzgKeBbabXfJZKmFR1UixYAy4sOIo+IGAS+DDwGPAE8HxE3FhtVonTJPSK2RcRckiv8Dpf0roJDaoqkDwCbI2J10bG06OiIOAw4AThb0jFFB2RAUko8DLg4In4b2AosLjak/NJqpZOA7xQdSx6S9iK5QdwsYH9gmqTTi40qUbrkXpMeav4YOL7gUJr1u8BJaZ31CuAPJH17vJkkDUl6Z6eDa1RaIiEiNgPXktz10Iq3EdiYOaK9hiTZd6sTgJ9FxKaiA8npPcCjEfFURLwGfA94d8ExASVL7pL2kTQ9fb0ryb2xHyw0qDFIWiDpDklbJW2WdAewATgwInpJDjdviYjT0+mXSfrSSMuKiN1rt4ttcN0LJYWkj7bho9Qve5qkPWqvgfcC97Z7Pda8iHgS2CDp4HTUcXT3rbRPpUurZFKPAUdK2i1tEHIc8EDBMQElS+7AfsCPJf2C5F4dN0VEKZsSSuon6XXnH4G3Az3An5KU3HcaYfopbQ7hLOAZ4Mxx4sxzi4ke4DZJ9wB3AjdExA9yLMc649PAFenvZC7wf4oNJ5+04PCHJKXdrpQeQV0D/AxYQ5JTS3EbglI1hewWaYuFx4EzI+K7o0yzDHgZOAj4fZJ6udNJDqm/OML0AcwG3gZcB8yMiG3pex8Czo+I/5EOHwQ8CvwRcBVwQFqiQ1If8G3gX4DPADeR/BF8DvgUMB24GfjTiHgmnec7wO8BuwL3AH82AfdFMbMOKlvJvVscBexMkoTH8jHgAmAPkraw40pLAluBP6hbzpWZ4TOBVekfywPAaXWLeTuwN8kfyyKSkt7JJH8y+wPPknQJV/OfJH8s+5KUQK5oJFYzKy8n93xmAFsiYrg2QtJPJT0n6eVMy5LrIuK/IuL1iPh/TSx/OUldJGnd94lsXy95Jm8m+yvZsWrmdeDciHglIl4mqS7664jYGBGvAOcBH6lV2UTEpRHxYua9Q9OjEzPrUk7u+TwNzMjWZ0fEuyNievpebbtuyLn8K4EPS9oZ+DBJa4L1AJJ+l6TZ1YrMtHMkzc3M/1Tdn8lBwLXpn89zJKX9bUBPel3BEkm/lPQCsC6dZ0bO2M2sBJzc8/lv4BWSevSx5DqhERH3A+tJmonVV8mcBQi4W9KTwB2Z8aOtdwNwQkRMzzx2SZs7fiz9HO8B9gR603mUJ3YzKwcn9xzSNvjnAxdJ+oikPSS9JS09j3e14JT0Bmm1xw4ta1JXAn9Ocqn5dyC5sRpwCkk9+tzM49PAx8ZoGfN/gQvSE7G1Jqe1P6Y9SP6onia5dLorW16Y2fac3HOKiH8APkvSCmVT+vg68Hngp2PMupikFU3tccso0y0nOQF6S+bmXSen81weEU/WHsClJFcujnbB1z+RdPl2o6QXgduBI9L3Lic5ShgkaS/ddTdpM7MduSmkmVkFueRuZlZBTu5mZhXk5G5mVkFO7mZmFZTnplJtN2PGjOjt7c09/9atW5k2rfv6K+jWuKGcsa9evXpLROxTdBxmZVCK5N7b28uqVfn7YB4YGKCvr699AU2Qbo0byhm7pG7tps2s7VwtY2ZWQU7uZmYV5ORuZlZBpahzt/boXXzDdsPrlry/oEjMrGguuZuZVZBL7l3CpXIza4ZL7mZmFeTkbmZWQU7uZmYV5Dr3LlVfB29mluWSu5lZBY2b3CVdKmmzpHsz486TNCjp7vRxYua9L0haK+khSe/rVOBmZja6Rkruyxi5b86vRMTc9LESQNIhwALgt9J5LpI0pV3BmplZY8ZN7hFxK/BMg8ubD6yIiFci4lFgLXB4C/GZmVkOrZxQPUfSmcAqoD8ingVmArdnptmYjtuBpEXAIoCenh4GBgZyBzI0NNTS/EVpJu7+OcNNL7+T26Rbt7nZZJE3uV8M/B0Q6fOFwB83s4CIWAosBZg3b160cm/wMt5bvBHNxL0wR+uYdac1tuw8unWbm00WuVrLRMSmiNgWEa8D3+DNqpdB4MDMpAek48zMbALlSu6S9ssMfgiotaS5HlggaWdJs4DZwJ2thWhmZs0at1pG0nKgD5ghaSNwLtAnaS5Jtcw64E8AIuI+SVcD9wPDwNkRsa0jkZuZ2ajGTe4RceoIo785xvQXABe0EpT5ClQza42vUDUzqyAndzOzCnJyNzOrICd3M7MKcnI3M6sgJ3czswpycjczqyAndzOzCnJyL0Dv4hvoXXwDawaf98VKZtYRTu5mZhXkDrJLwKV3M2s3l9zNzCrIJfcKG+mIYN2S9xcQiZlNNJfczcwqyMndzKyCnNzNzCrIyd3MrILGTe6SLpW0WdK9mXF7S7pJ0sPp817peEn6Z0lrJf1C0mGdDN7MzEbWSMl9GXB83bjFwM0RMRu4OR0GOIGkU+zZwCLg4vaEaWZmzRg3uUfErcAzdaPnA5elry8DTs6MvzwStwPTJe3XpljNzKxBedu590TEE+nrJ4Ge9PVMYENmuo3puCeoI2kRSemenp4eBgYGcoYCQ0NDLc0/0frnDAPQs+ubrydKu7ZTt21zs8mm5YuYIiIkRY75lgJLAebNmxd9fX25YxgYGKCV+SfawvTiov45w1y4ZmKvI1t3Wl9bltNt29xsssnbWmZTrbolfd6cjh8EDsxMd0A6zszMJlDe5H49cFb6+izgusz4M9NWM0cCz2eqb8zMbIKMWycgaTnQB8yQtBE4F1gCXC3pE8B64JR08pXAicBa4CXg4x2IudTq7+fie7mYWRHGTe4Rceoobx03wrQBnN1qUGZm1hpfoWpmVkFO7mZmFeTkbmZWQU7uZmYV5ORuZlZBTu5mZhXkPlQ7bKR+TIvkdvhmk4NL7mZmFeTkbmZWQU7uZmYV5ORuZlZBTu5mZhXk5G5mVkFO7mZmFeTkbmZWQU7uZmYV5ORuZlZBLd1+QNI64EVgGzAcEfMk7Q1cBfQC64BTIuLZ1sI0M7NmtOPeMsdGxJbM8GLg5ohYImlxOvz5NqzHOsD3mjGrpk5Uy8wHLktfXwac3IF1mJnZGJT0aZ1zZulR4FkggK9HxFJJz0XE9PR9Ac/WhuvmXQQsAujp6fmfK1asyB3H0NAQu+++e+7522nN4PMNT9uzK2x6uYPB5DBn5p4NTVembV5z7LHHro6IeUXHYVYGrVbLHB0Rg5L2BW6S9GD2zYgISSP+e0TEUmApwLx586Kvry93EAMDA7QyfzstbOIWv/1zhrlwTbnuurzutL6GpivTNjezHbVULRMRg+nzZuBa4HBgk6T9ANLnza0GaWZmzcmd3CVNk7RH7TXwXuBe4HrgrHSys4DrWg3SzMya00qdQA9wbVKtzlTgyoj4gaS7gKslfQJYD5zSepjlVbaello10udxCxqz7pM7uUfEI8ChI4x/GjiulaDMzKw1vkLVzKyCnNzNzCrIyd3MrIKc3M3MKsjJ3cysgpzczcwqyMndzKyCnNzNzCrIyd3MrIKc3M3MKsjJ3cysgsp1M3ErJXfFZ9Z9nNybULU7QJpZdblaxsysgpzczcwqyMndzKyCnNzNzCrIJ1RTPlnauN7FN9A/Z5iFY2wzt6gxK1bHSu6Sjpf0kKS1khZ3aj1mZrajjiR3SVOArwEnAIcAp0o6pBPrMjOzHXWqWuZwYG3aiTaSVgDzgfubXVAjF9CsGXx+zCqCkbjaoLNGquaq3+Z5qsL8vZk1RhHR/oVKHwGOj4hPpsNnAEdExDmZaRYBi9LBg4GHWljlDGBLC/MXpVvjhnLGflBE7FN0EGZlUNgJ1YhYCixtx7IkrYqIee1Y1kTq1rihu2M3mww6dUJ1EDgwM3xAOs7MzCZAp5L7XcBsSbMk7QQsAK7v0LrMzKxOR6plImJY0jnAD4EpwKURcV8n1pVqS/VOAbo1buju2M0qryMnVM3MrFi+/YCZWQU5uZuZVVBXJ3dJ0yVdI+lBSQ9IOqromBoh6WBJd2ceL0j6i6LjapSkz0i6T9K9kpZL2qXomMxse11d5y7pMuAnEXFJ2ipnt4h4ruCwmpLeqmGQ5CKv9UXHMx5JM4HbgEMi4mVJVwMrI2JZsZGZWVbX3hVS0p7AMcBCgIh4FXi1yJhyOg74ZTck9oypwK6SXgN2Ax4vOB4zq9PN1TKzgKeAb0n6uaRLJE0rOqgcFgDLiw6iURExCHwZeAx4Ang+Im4sNiozq9fNyX0qcBhwcUT8NrAV6KpbC6dVSScB3yk6lkZJ2ovkJnCzgP2BaZJOLzYqM6vXzcl9I7AxIu5Ih68hSfbd5ATgZxGxqehAmvAe4NGIeCoiXgO+B7y74JjMrE7XJveIeBLYIOngdNRx5LilcMFOpYuqZFKPAUdK2k2SSLb7AwXHZGZ1ur21zFzgEmAn4BHg4xHxbKFBNSg9P/AY8M6IeL7oeJoh6Xzgo8Aw8HPgkxHxSrFRmVlWVyd3MzMbWddWy5iZ2eic3M3MKsjJ3cysgpzczcwqyMndzKyCnNzNzCrIyd3MrIL+P242guskrV+wAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Let's go ahead and analyse the distributions of these variables\n", + "# after applying a logarithmic transformation\n", + "\n", + "tmp = data.copy()\n", + "\n", + "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n", + "\n", + " # transform the variable with logarithm\n", + " tmp[var] = np.log(data[var])\n", + " \n", + "tmp[[\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]].hist(bins=30)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The distribution of the variables are now more \"Gaussian\" looking.\n", + "\n", + "Let's go ahead and evaluate their relationship with the target." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's plot the original or transformed variables\n", + "# vs sale price, and see if there is a relationship\n", + "\n", + "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n", + " \n", + " plt.figure(figsize=(12,4))\n", + " \n", + " # plot the original variable vs sale price \n", + " plt.subplot(1, 2, 1)\n", + " plt.scatter(data[var], np.log(data['SalePrice']))\n", + " plt.ylabel('Sale Price')\n", + " plt.xlabel('Original ' + var)\n", + "\n", + " # plot transformed variable vs sale price\n", + " plt.subplot(1, 2, 2)\n", + " plt.scatter(tmp[var], np.log(tmp['SalePrice']))\n", + " plt.ylabel('Sale Price')\n", + " plt.xlabel('Transformed ' + var)\n", + " \n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The transformed variables have a better spread of the values, which may in turn, help make better predictions.\n", + "\n", + "## Skewed variables\n", + "\n", + "Let's transform them into binary variables and see how predictive they are:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for var in skewed:\n", + " \n", + " tmp = data.copy()\n", + " \n", + " # map the variable values into 0 and 1\n", + " tmp[var] = np.where(data[var]==0, 0, 1)\n", + " \n", + " # determine mean sale price in the mapped values\n", + " tmp = tmp.groupby(var)['SalePrice'].agg(['mean', 'std'])\n", + "\n", + " # plot into a bar graph\n", + " tmp.plot(kind=\"barh\", y=\"mean\", legend=False,\n", + " xerr=\"std\", title=\"Sale Price\", color='green')\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There seem to be a difference in Sale Price in the mapped values, but the confidence intervals overlap, so most likely this is not significant or predictive." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Categorical variables\n", + "\n", + "Let's go ahead and analyse the categorical variables present in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of categorical variables: 44\n" + ] + } + ], + "source": [ + "print('Number of categorical variables: ', len(cat_vars))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSZoningStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinType2HeatingHeatingQCCentralAirElectricalKitchenQualFunctionalFireplaceQuGarageTypeGarageFinishGarageQualGarageCondPavedDrivePoolQCFenceMiscFeatureSaleTypeSaleConditionMSSubClass
0RLPaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2StoryGableCompShgVinylSdVinylSdBrkFaceGdTAPConcGdTANoGLQUnfGasAExYSBrkrGdTypNaNAttchdRFnTATAYNaNNaNNaNWDNormal60
1RLPaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1StoryGableCompShgMetalSdMetalSdNoneTATACBlockGdTAGdALQUnfGasAExYSBrkrTATypTAAttchdRFnTATAYNaNNaNNaNWDNormal20
2RLPaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2StoryGableCompShgVinylSdVinylSdBrkFaceGdTAPConcGdTAMnGLQUnfGasAExYSBrkrGdTypTAAttchdRFnTATAYNaNNaNNaNWDNormal60
3RLPaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2StoryGableCompShgWd SdngWd ShngNoneTATABrkTilTAGdNoALQUnfGasAGdYSBrkrGdTypGdDetchdUnfTATAYNaNNaNNaNWDAbnorml70
4RLPaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2StoryGableCompShgVinylSdVinylSdBrkFaceGdTAPConcGdTAAvGLQUnfGasAExYSBrkrGdTypTAAttchdRFnTATAYNaNNaNNaNWDNormal60
\n", + "
" + ], + "text/plain": [ + " MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope \\\n", + "0 RL Pave NaN Reg Lvl AllPub Inside Gtl \n", + "1 RL Pave NaN Reg Lvl AllPub FR2 Gtl \n", + "2 RL Pave NaN IR1 Lvl AllPub Inside Gtl \n", + "3 RL Pave NaN IR1 Lvl AllPub Corner Gtl \n", + "4 RL Pave NaN IR1 Lvl AllPub FR2 Gtl \n", + "\n", + " Neighborhood Condition1 Condition2 BldgType HouseStyle RoofStyle RoofMatl \\\n", + "0 CollgCr Norm Norm 1Fam 2Story Gable CompShg \n", + "1 Veenker Feedr Norm 1Fam 1Story Gable CompShg \n", + "2 CollgCr Norm Norm 1Fam 2Story Gable CompShg \n", + "3 Crawfor Norm Norm 1Fam 2Story Gable CompShg \n", + "4 NoRidge Norm Norm 1Fam 2Story Gable CompShg \n", + "\n", + " Exterior1st Exterior2nd MasVnrType ExterQual ExterCond Foundation BsmtQual \\\n", + "0 VinylSd VinylSd BrkFace Gd TA PConc Gd \n", + "1 MetalSd MetalSd None TA TA CBlock Gd \n", + "2 VinylSd VinylSd BrkFace Gd TA PConc Gd \n", + "3 Wd Sdng Wd Shng None TA TA BrkTil TA \n", + "4 VinylSd VinylSd BrkFace Gd TA PConc Gd \n", + "\n", + " BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2 Heating HeatingQC \\\n", + "0 TA No GLQ Unf GasA Ex \n", + "1 TA Gd ALQ Unf GasA Ex \n", + "2 TA Mn GLQ Unf GasA Ex \n", + "3 Gd No ALQ Unf GasA Gd \n", + "4 TA Av GLQ Unf GasA Ex \n", + "\n", + " CentralAir Electrical KitchenQual Functional FireplaceQu GarageType \\\n", + "0 Y SBrkr Gd Typ NaN Attchd \n", + "1 Y SBrkr TA Typ TA Attchd \n", + "2 Y SBrkr Gd Typ TA Attchd \n", + "3 Y SBrkr Gd Typ Gd Detchd \n", + "4 Y SBrkr Gd Typ TA Attchd \n", + "\n", + " GarageFinish GarageQual GarageCond PavedDrive PoolQC Fence MiscFeature \\\n", + "0 RFn TA TA Y NaN NaN NaN \n", + "1 RFn TA TA Y NaN NaN NaN \n", + "2 RFn TA TA Y NaN NaN NaN \n", + "3 Unf TA TA Y NaN NaN NaN \n", + "4 RFn TA TA Y NaN NaN NaN \n", + "\n", + " SaleType SaleCondition MSSubClass \n", + "0 WD Normal 60 \n", + "1 WD Normal 20 \n", + "2 WD Normal 60 \n", + "3 WD Abnorml 70 \n", + "4 WD Normal 60 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's visualise the values of the categorical variables\n", + "data[cat_vars].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of labels: cardinality\n", + "\n", + "Let's evaluate how many different categories are present in each of the variables." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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d69Z3XjjKTue69Z3nt5q7z57vXXdv9QPsAnyp9HpP4DNtl5eWMVdlZ/669Z0XjrIL63Zrf+k7z9SyC+t26ztrf+k7T/7pEvbwO2CV0uuV03siIiIiIjNSl8rv5cDaZraGmT0MeBnw/fFsloiIiIjI+C3WtqC7P2BmBwI/BBYFvuzuN3TcnmNUdqFYt77zwlF2Ote9MJadznXrOy8cZadz3frOC0fZ6Vy3vnOm1gPeREREREQWNprhTURERER6Q5VfEREREekNVX5FREREpDdU+RURERGRKWdmi5jZ06d9O6ZrwJuZ7VT1e3c/taLswTVlP56x/i2Bq939fjPbA9gY+JS7/zqj7Bx3/1Tde7nMbGl3/3vG5wx4BfB4d3+Pma0KPMbdf9ZmvQ238eHAIcCq7v5qM1sbWNfdfzDV6x63nP1tZisCHwAe6+47mNn6wBbufmyD9WwFrO3ux5nZbGBpd/9VZtlFgRUpZWRx999klh12bt0LXOfud+cs439dl+tPKr9xTfkrK8quBazo7hcPvL8l8Ad3/2XVssdhxDX0XuAKd7+6puy0XAu6npMdz8dW607n8Q3u/oSc9VQs5+Hu/o8uy2ixzsb3m3Hcm9voej6XlrMacYz8yMyWBBZz9/syyi0B7AyszuRr9nty1puWsSRxTlXNglv+/HOAR7j7twfe3wW4193PyVjGue6+Xd17I8quCfzW3f9tZtsATwFOcPe/ZpS9yt03qvvciLJPdvfr2pSdtJxprPwel/77aODpwI/T622BS9x9x4qyh6X/rgtsykR+4RcAP3P3PTLWfy2wAfEH+wrwJeCl7v7MjLJXuvvGA+91+WP+xt1Xzfjc0cBDwLPcfT0zWw442903rSl3FDDyD+3ub8xY9zeBK4C93P1J6QZ4ibtvWFHmycAXgccBZwJvdfe/pN/9zN03q1nnaTXb/cK67R6x3Nr9bWZnAscB/8/dNzCzxYCr3P3Jmes4DNiEqBSsY2aPBU529y0zyr4BOAy4i/h7A7i7PyVz3acDWwDnpbe2If52awDvcfevVpTdEjgcWI24iFta9+MrynT6O6fP3cfE3/phwOLA/e6+TEWZ6xh+fBTbPHJ/dbn+pPLnVfza3f1ZFWV/ALx98AKe9uMH3P0FVetOnx323e8F5gLvc/c/1ZQ/kTg+T0tv7QhcS9y8T3b3D1eUbXMtKP9951P1dy4to/U52eV8HMO6vwe8IffhdaDs04l709LuvqqZbQAc4O6vqyjT+rwYWE7j+03p3jyUux8xFdvd9XxOy3g1sD+wvLuvmR7qPp9ZETyL9PAIPFi87+4fqyubyr8A+CjwMHdfw8w2JK7VI+9xZnYx8GJ3v2fg/RWA09x9i4qys4CHE/eIbYh9DLAMcFbOw5qZXU2cU6sDZwDfA57o7s/LKPtR4KfAqd6wEmpmFwJLEPW2r7v7vU3KF1rn+e3K3fcBMLOzgfXd/c70eiXiS1WVPSJ99gJg4+LJzMwOB07P3IQH3N3N7EXEtMzHmtm+VQXMbHfg5cAaZlae0GMZ4M81ZUc9ERuwdOY2P83dNzazqwDc/S9pgpE6czOXX2VNd98t7QPc/R+pZaDK0URF6lJgP+AiM3thatlaPGOdH227sWPY3yu4+7fM7O0wL6/1g3WFSl4CbARcmcr/3swekVl2DnGTrqzAVFgMWM/d74J5rVYnAE8DLgBGVn6BY4E3MXARr9H174y7z9s36bh6EbB5TbHaG1rF+lpff1L5bduum2j1na/lwt2vM7PVM5dxJvH3OTG9fhlxM/sDsf11FeiViWvn32FepeV0YGvibz+y8kuLa0Hx9zWz9wJ3Esdg0bK4Us22Frqck13Ox67rXg64wcx+BtxfvJn58P4J4DmkBh53v8bMtq4p0/q8GND4flNVuc0wbedz8npgM+CytMxfmNmjM8uu7O7PbbTRkx2e1n1+WvfVZrZGTZklBiu+qewfzWypmrIHAAcBjyWdE8nfgM/kbTIPpfPgJcBR7n5UcaxkOAA4GHjAzP7FxANO7UOwuz8jPZi8CrginVfHeUZLd9m0VX5LVikO1OQuoLYVNFkR+E/p9X/SeznuSxeyPYCtzWwR6m/UlxAX7hWA8hPdfUSrSZUPAB8BHhjyu9zY6/9adKM5gEXX3UPVRcDdj0+f39XdTy7/zsx2zVz3fyy6ZYp1rwn8u6bMI9z9rPT/j5rZFcBZZrYnw5/wB7f7J5nbNkzX/X2/mT2Kie+7OfFkn+s/6eGqKF93MSq7o+G6Bq1SVHyTu9N7fzaz/9aUvdfdz2y4vk5/50GpJeC7qUL2torP1YYoZehy/QHAzJ4ErA/MKm3bCRVFlq343ZKZq322T+59us5Sj5RFGFedRzP5/P0vUSn/p5nVnddtrgWFF7r7BqXXR5vZNcC7M8p2OSe7nI9d1/2uhuuaxN3vGHi2qKx0j+m8gJb3m/TZWcC+wBOZfF68alSZGXA+/9vd/1Ps69S6n3v9usS6dcf/193vHfg71617GTNbzN0n3ePMbHFqriMeIZqfMrM3uPtRrbY4jo/dgb2ZeNhu3NjRRnoweSfRsPdpYKP0AP4OzwxxmQmV33PN7IfASen1bsCPMsueAPzMzL6TXr8YOD6z7G5EK+6+7v4Hi3imj1QVSCfnr83s2cA/3f0hM1sHeAJQd9BfCXzX3a8Y/IWZ7Ze5zZ8GvgOsaGbvB3YB3plZFuDtwMkZ7w1zGHAWsIqZfR3YEnhlXSEze2TRLeHu55nZzsApwPK5G52e8j7I/BWMkV3xdN/fBxOtLWum7qXZxP7O9S0z+wKwbOpOexURGpDjNuB8i/CFeZUKz4+XO9+ia734u+6c3lsK+GtN2fPM7CPAqQPrHhnDCt3/zjY5Zm8RojvtX5llNweOAtYjQiYWpSZkoqTL9adoMd2GODbPAHYALiKuTaPMNbNXu/uk4yEdl/MdryMsamabeYq/NLNNie8Nwx/4Bn0duMyiSx7i5nViOkZurCnb6lqQ3G9mrwC+Qdzcd6fUGlqjyznZ5XzstO6OD/F3WIQ+eKrUzAFuyinY8byAbvebrwI3E63W7yFa+BfEdnc5n39iZu8AljSz7YHXMREWNGpbi1CNxYB9zOw24rrZKMSE6Bl4OXFerw28kWhsq3Iq8EUzO9Dd70/bszTwqfS7HF9OlchV3X1/axa/vw/wGuD97v6r1FJd1auImT3B3W+2EWMm6u4zaRlPSet+PnAO8AJ3v9IilOmnZH73GTHDW2o2L7pyLnD371R9fqDsU4GtSmWzmt3TRf5f7v5gqQJ7prvXtYyRWraeQXRnXQxcTrQsvKKizLrAn9z9j0N+t+JAS13Vup8AFDFIP3b32guKme0APA94KfDN0q+WIbqIamMy03IeRXRFG3DpsO8y8PmXA7e5+6UD768KvMvdX5253ouIG+4niJv0PsAi7j6ytWgc+zs9+a9LfN9bco6NgfLbA/+Xyv8wt1vGRsTN5XYppifgnYlKCcQxeopnnOw2PJbVvTqGtfPf2SZi9iAqb7cDX/SMAXpmNpfo9j+ZqDTvBazj7m+vK5vK70Scz9D8+nMdMXbgKo9Y0BWBr7n79hVlViQqFf9horK7CXGjf4m7/yFjvZsCXyZCeIzortwPuAF4vrt/K2MZm1A6Rtw9Ozyq6bWgVG514ua8JVFpuBg4yN1vzyzf+JxM58PKxDW+8fnYZd2pXON49lLZFYj99ey03rOBOZ4REtX1vEjLaHy/SeWucveNzOxad39Kqrhf6O51oUzTdj5b9P7uS+kYAb5Udd20GCA3Um5rtkXc/P9L6yat+33uPrIBIB2P7yPO+1+nbV6FCF17V+a50Th+f6B800F6x6RKduP7TGkZPyHi4L/t7v8c+N2eXjGmZdJnZ0jld0Ui3sWJAWvZI9Kt5aj4NhXYUtmie/ENwJLu/mEzuzr3gOkiPTFtRbpxZD4pbQBsSDyBlyuM9wHneRqcVLG+kXLW35WZXeHuTzWz6zwNMCnea7CMRqOlLbrtXsfEvr6QGPyQ1Ro5DukpHs/IBDKTNN3XY1jfXHffpLjRpveu8pYDUBuu+2fuvlm6nmxLnFM3ed6AkW2BJ6WXN7j7j6s+P2IZjwTwzEEfZraMu//NzIa2yLt75diF0nJ2YuLcuKjJA0NbXc7J8rWj5bpfTwyu+Wt6vRywu7t/ruFy5sWzu/vIkJ702UWJ0fO196QR5TufF23uN6lccV5cQPzN/kDc26t668a23W1ZxDQ/gfi+t7j7f2qKFOVaZz5I5Tduey9NFdC10stbByuENWWLfT1v/5rZNT45NGlU2caD9GqWt3iDB8pGle5hpj3swcxeSoQbnE88uRxlZm/xgfQdI8qWR8U/mMo7ceDVFvcYqLEv8LlUgb0mf7NtC6Irpxgkt2jF58sF1wHewsRoegAyn3jeDexKdCcbcJyZnezu76sq5+7XANeY2YlNWy+ZHNs836KBnO1u/Z2Tf6en8l+Y2YHA78gcJGil0dJA1mjp5ASiIlPEQ72c6NKpjJEutfIUx+K8X5EZ0G8RQ/pVUsiAmf2ReDK/oa5s+vxOwIeIuE5ruO5HEudU0RPzE+KCVlu5aruvLQadHkp0c0LEcb3H3S+yUjhFhX+km9bVZvZhIi4/K46+y74qttXMliW60K8A/k50veVYNK0PMkM8CjaQWslSrKDXp1Y6kRhYdAVDjk8gp3LyOeJmW3QtH2Bmz3b312eUnQ28mvlTQo2MBS1pdU4mV5rZpu5+ecZnh3m1u3+2eOEx+OvVQKPKb2pF/K7VxLOnzz5oZquZ2cNyK2EDWp8X0P5+kxyTHhDeRYSLLE1eXHen7e547Xs+8Hngl6ncGmZ2gOeNgTgF2MQijeExROaDE4ke1xwfM7PHAN8Gvunu1+cUshjQ93oi7ArievSFnJ6BpEv8/uHMP0iv9vpRlh4Gn0WcyzuSMWarXOkm/kYb0qbS7e7T+gNcAzy69Ho2cE1m2VuBR7Vc71VEOqhLifQcEHlQc8puTZzQb02vHw98usH3fS1x0Dy1+Mksewswq/R6SeLpNPc7r02cXDcScaW3Ed3VC+Jv3Oo7p/KbEhfPlYl0Q6cSLSc5ZS8juoKuKr13fUa5G3Pem6L9dQmwben1NkRXVG75W4lsD23WfQpwRDqmH09UhE+dqn2djou5xAVwmfTzrLQPdsu5FhAPVUumsocBHwfWmup9NWRZqwNPyfjcKkRM+k/Stn48/f8sIoXPfhnLOIsIYTqUyLl7CHBIRrmt0r+z6j5bsYybSb2G6fUiRGt37rH9ISIEa+fiJ7Ns63MybfMDRMXmWmKMxrUNvvN1A9+5yN+bU3an0s8uwJHATzPLnkD0Sr6LiDs+GDg4s2zr8yKV73S/6XB8Tcv5nI6RtUqv1wRuzix7Zfr3UCKtHeXrYOYyHkPE+l6cjrd31nz+mcTg6COAF6afI4j77RrAVzPWuX269txDjAO4Hdgmc3svHfyeuecUETL1aeA3RIPB3sBymWWvAB45sN6sulv5Z9pbfonYzXKYw5/IfzrtMip+DjHY6zvufkN6YhkWhzIfd7+ASBlVvL6NOGhzPODuRzfd2OT3xICvopVoCaIVNNdxTMTObkuKnc0pmLp1jwVO8oowiRG6fGd8orXm78Q2Ny3faLR0cqWZbe4pjtXMnkaDlHFm9jHgWHevGzw0zFLuPu9YdPfzrdno9Ls8MzZviDXdfefS6yMs8jlmabGv3whs6ZO723+cnu5/S6Rdq1tnEVf3T+Li30SXfVWMV/ixu9/r7reb2bJm9mJ3/25Fsc8SD8tfGVjWXkSrsRMt6FXaplb6FPHweQkxsU8btxIj6Iv9vkp6L8fD3f2tLdfb5Zx8Tst1Fs4CvmkxaA4iVdNZFZ8vK6edK+LZX5RZ9pfpZxGg0Qj5jucFdLjfpFbjYdtUO+nDNJ7P97l7+Ti+jehpyFFkPtiLhpkPCh6x/p+2iIc9lGgpr2pl/wiRPeWq0nvft0gAcA0xrqBuneeY2ZVMxO/P8cz4fVoM0jOzDxC9Cb8heo6OAOZ6ykiVqU1mjPnMhMrvWTb/6MwzMsu2HhXfsQI7H0uB3BkfPc3MXkccmOVtzom1u5c44M4h/tjbE9kuPp2WUbf9S7r7uWZm6QJzeKrU5nRH7UZUPC+3GJBwHJHwPOeg6/Kdu4ZNtB0t/VQifU0RP74qcIul0b1eP4r3JmIk7mLEvjrJ85Nx32Zm72Ji5OwexLGea67FQIbvMnl/54yC/aeZbeXuFwFYTHqRG0PWal8POw7c/U9m9mt3/3xdeTP7FUMufp4RX0i3fQVwmJfiXd39r6lL+7sVZZ4wWPFNZU9IN4ecSmnb1Er/NbNjgJWL68bANuRcAx8B3GSRX9OJHp25lnKfe3X34w/M7HnunnuNL+tyTja+OQ54K1HhfW16fQ71Dyix4pSDtg3vkDe343kB3e435Qwes4gu7dzBctN1Ps81szOAb6X170rc73bKWEbjzAdlZrYecY/dmWgA/CbRm1NlaR8ywN8j/OAuMhqKUtjBDpRm8bNSFpkabyAG6f2bqL/9EHhvTZn9gJ8TueFP84iRbnputsmMMZ+ZMuCtGDwBMSK09okllTts2Ps5F4wUe3Yo8+chrBrVPiptkxHdsytnrPdXQ972nBPbzPau+n3d05OZXULs528TM+D8DjjS3detW3dpGYsQF7KjiVa944hpoUdWZLt851T+GiIWa3D2nNq0UDZ5tPQixAk6x+tnwFqt6veeP4p3XeIitDvRnfXFcqvuiDLLEU/E884J4PDcFnebnDmhtMn1cZUpfup4olvJiMlbXukRN15XtvG+NrPLgP0Hl28RL3yMuz8tY72PKr2cRdy0lveKbCClsq33VSo/b1BO6b3KwVVm9gt3X3vI+4sQ3crz/W7IZ28k4m5/RYPUSulv9Gwi9GC+/ZPTAmNmz6z6vVek9rKIiV+KyHTx34kiWTGZrc9Jm0hJZcQxsgaxr59Yt96uzGxlIk65yKxxIXFe/Daj7HkMrwjmjLVofV6k8p3uNwPLWoLIsLFNxmen5XweUbbRMtoys58SFd5vufvvM8vcBDx98L6Q6ikXu/t6w0tO+myrWWPbshjEuT1xP9yO6G1/NpGfOSdFIzY5M0aRleO93nAw+kyp/LbO9tBhnWcTB9ubiSe2vYF7qrrkLGb0KVKKFIoL6uPcPWe2tdZSV/Dp7p6VaHxI+U2Jp+9liSe0RwIf9oEUVRXli/x6zyMOuK8TFbQ9fQozXVjDzA5jWmeXsIViGYsSDwr7EF3D3yL21/3u/rKxbOgUMbNlANz9b1O8nq2I4+g4Jqf92hvYo2iBbrHcBXLMmNmXidzJxWCo1xM36ldWlPkEEcN+kE/k51yKCEf6p7vPyVjv0Ipgg4eyDXIeaEaUfQORzq1p+NNYWMy6VW6wqM3uM2QZGwOvc/esHOvWYtrvUtlziMFP5Z6cV3hFOrxS2fIxPItoGXzA3Q/N2e4hy8s+L7rebwaWtRxwubuvVfvh4eUX+D0gh5l9y91faiOmZs7oIeyy7v2JwaNvZmKWtqcSD7bHuvsxGcsoMldd5ZnZHszsk+5+kJmdxvDvnDXwLD0Q7UhUhJ8BnOvuL88pOw7TXvm1+bM9PAOozPaQKhX7EQOgznT3S0q/e6dnjEa1ifRZ5XQql1c98ZjZL4Dthl1szewOd18lY72LE11nxWj684EveF5Ovq8Rg/ROAb7s7jfXlRkXi/CIvxJxv6e4+79LvzvV3XeqKNv6O6fyhxOzlDUOm7CI5f4UEdPkREzlmzzCXKrK7UdUWtuELRQVnB2JFvZjy91IZnbLsNb2rhcVMzvUI2vJUSPKj+ymNLM93P1rNmJaaM8IJeqwr1ckKo1FK9yNwGc9I99tKl8OEygmyHht1QW8VLZ1q1wqvxQxGOnZ6a1ziPycIyduSOfDB4mJIcr5OY8nZigaObLfxpeqrHXWBTN7H5GH9Uoi1/APvcGNxMxeSOla4HkJ9YtyHyOmZL2bqIje1Lb1tq6FfuCzNzNk2u+6HqRU9urBxoFh7+WylEYs43Otz4tUvvX9ZqAyuCgxkP097l47de50nc9typrZSu5+Z9uH0YrKc25Pzo5M9GBD5Pn+iLtXTs5RKn8Z8HTiwWTjdF042yvSypnZU939ilE9QFU9PxXLfASR43zk5ECj7oul9TbK9jATKr/XANsXrb1p5/+o5snjS8Q89j8D9gR+4u4Hp99d6ZOn/Ry1jEvdfXOLeONPE8H933b3NSvKvJ7IaTlfi4llThOYtn1xJmai2xN4sEELxDLEk9I+xIFQVMxGBuZbisUbJeegMbPHD1ZizGwNdx8W0jBYtut37hIqcinRKlfElL+MGI1b252eyjcOW0jl9iG6sOarBNmI9F1dLypm9gJ3P21Ud6VXdFNapPT5gg0PJXLPGKgyhn3dKnejTU6YXgwo+mjOcrq0ynVlk/Nz/tIzciOb2Q/cfUebiIuc1AuVc06k5VxC3NwHK3OnZJY3ottxH6Jy8i3iIe+XNeWOJLK3fD29tTsx4KV2AoN0r3gWcX/YyCJP8h7uvm9NUQYe6hYh4qof5e5ZA+HM7LLc43hI2XNJ1+n01u7APu6+3ehS88qWH3IWIVr2Pj3s4XlI2dbnRWkZje83qVy5MvgAMRAtt1t7Ws7n6bgWdK08j2H9ryBijTcm7s+7EFkmamd9NbM5HtMkV7438PuhjSuFqkaWUffFUtlGle6ZUPmd9PRtEfd2TdUT+UBr7WJErsUViJP00qqnltIydiQu/qsQT3vLAEe4e2VFMW3f5l5qbW5iWJdCXTfDkGU8iqhAHkSEMaxFXBCHVr7N7B4iM8ZJRDqqycMkMw6aYQ8VltkVNY7v3JYNj8nMTeLdOmzBzM4dvLkNe29E2cYXlYHP7jp48Rr23oiyW7r7xXXvjSjbZV+PNWF6rratcl1b6dMyhvWW3Euk7ZnS0K8uLY+lZWxAnBvPJWL3NgfO8YoueTO7FtjQU1d6OseuqmvhSp8tEvJfA2zkMb187vFVfqgrKlSneGacYKq0L0rDab9T2dWIe8wWxLFyCfFQeEdG2fJDzgNEjPd7vGU4UBtN7zepzLMp5Z5te79sqksre8eyXfILL0o80G1b99mBckN7+ApeM3i1qMsQ4zq2S9t8rufP4jesTnBVTavx0HFapW1uPcCzqYU128O82Nr0NLm/RWqVH5M5+YFPdLXdC2QfdOmC+1mgtoI9woNmtmbRQmLRVVyZDsrMdnL3Uy26/fYhLj4nAJu5+90WAeA3MpH8fdBjmAgyfzlwOvH0XjtpgsX0lk8EHjlws16GUtxdjcbfeWAbuoRNnGlmbwO+QVwodgPOKFpUfKCb2Mw+4O7vsMlhCx/wibCFD5nZyBYIi1moHg6sYBHnVjxoLAM8LmN7IeJdByu6rxzy3ihvJ6YGrXtvmKOYP9vAsPeGabSvBxzO/AnT16hboZltRIyKnnejJeLYbzWzxTJam/5kZnswuVUuJ0F80Tr00YzPjrIvUSEqWrq2IVpi1zCz93jNNJ1m9jjmz4BywegSkzTOumBmB7r7Z8xsDpHS6Y9ExoO3uPt/0830F0Q3bJVliRsuxLiDXH+1mPXwAuDrZnY3k7MKVLlx2AMheecEQNHqu0npPSdjkh8iLd2khyGLGOLayq+7154Dw3Q5L7rcb8xsFWKCh/uYiOHf2cz+SaR329PdR2bJmMbzuWvZDwMvyK04lnlMZvKQ5U3oU5adenPEeh8ys8+mymqTkJaiHrGGTe5VfgQT5/WodXbJXjI0rpq4xz7UuDHNOyakHscPEcRfJHt/ScbnvwY8d8j7+xE54KrKHkWEOQz9ydzej6ZttpzPD5Tdjshxdz6RXPp2mJjQYESZIoH28cDWo5abuf4liIrUPcCBGZ9/EdHV9af0b/HzaWKk6ZR854HyX0rf/Vnp5zhizvWcsr+q+Jlvgo/Svt6HyLc7bJmPrFjfHCZG4N9WWtc1dfubuNieBvyFmESl+DmPeCKv+647pOP7roHj+ivEQNKqslsQN507KCXTJyqluZPONNrXA2UbJ0xP5+CtwKuIWR2fkv5/dfo+OftstbSP7yHiSL9LhF7kfN9FiSlvG10DSuV/CKxYer1iem956icH+VA6j85Ix8xpwPcbrPs+YpT3v9L/7wP+VlOmODeOAFYb8ZnKCQbSMf7rdEwen46N3TK3eam0zxcjHhDfSOYkR8W21703FT9d1k1kOnhE+v87iZbnjWvKdDov6HC/SefSK4e8vxcRI37FVG13WkaX87lL2Ys7HiPfI+6Rx9KwPlJaxtJE+rMmZRrXZdJ+2oYY0/HM0s/GwGKZy5hFjPP4HDFu4MtEXHndegd/VifGiZ3RdJ9Pe9hDW23DD2wM6VtsIl3Pg0QO1OwujlR+CaCI2brFS4PHRnw+K445Y53PJ24+qxMn+ZfdPTdp+Rbunjtt66j1Z3/ngbILLGwidaluw0BoSMHzBxRlxYAPlFmNSMH0QSZPfXofURGsbPVI3dAbAu9hchqr+4DzvGJ0vkU81TZE5pNybt37iHyMv8j+Ii2Y2bHAucT33pmo2Czu7q+pKHMtkeT99oH3VydaMj7u7u+Yqm1O67qISBPUePpZM7vR3dcvvTZi1rD1M7oPbyFmk8s+j7oax3UoLWclIu4X4qEsa2Bjy3XtQGSneSmR3aewDLC+ZwwcS8tZEfgA8Fh338HM1ge2cPdjK8psQQwmOojI5FFe90tyrl9FKJFFVpT3EYPD3+0V8cddz4suf2cz+7m7rzPid78lKu5DQ3pmwvnclpl9iuhh/S4t8oWPqpdk1keeRPRELU/ct+4B9vK8nt2iLvMA8SDcqC7TlpmdTPxNX07cr15BDF6dk1l+o1R2V+IB+hTPGEw5aRnTXfntGCtTeYMYUWYW8SR9z8D7s4kZXhrlimuw3me5+49teJxf5UliZv9g+AxKuSNCTwCeRLQSfcMz5w1PZV9NjMj+Rbo5H0tUTn5NPOGPjHnr8p0HlnMlsKtPDpv4ds4F2iZmpjvR3f+a8fl/MzGL0WAF2L1mQJFFOrk7ihu6xaxdxf46PLfy3IWZLe6ZmTSGlF3NWw6yaLqvB8qWczdCtIC+r+p8HKw8DvxuaEaN0u8/Atzq7l8YeP8AYA13f9vwkvMt5wRgPeJhcl4XvOdlx/gcMVFD0fW+MzGr3VuAH3hFDKCZnUmcE3/P2c4Ry2iUdcHMHgCGDcqrvWab2XOI6+63B97fBbjX3c/J2N7G94ouD4QDyzmT6HH6f+6+gcVYk6u8emxK5wfK4h5nZh8kYsFPzHgwan1epM+0vt9YhxzW03U+W8fY2bSM44YXzc8NnOogDNZNMspdQhyX56XX2xChek9vspwG67vI3bdKFefyfmtcdys93C1OzPGweUWZdYjGu92JkKtvAm9299VafY8ZUPm9lZaxMmb2UaLp/VTP/CIWsxudNVjxspim9P/c/bXDS863nKY3jiPc/bA2J4mZ3UC0XgxVV1kxs4eYuDE3OljN7HpicMl/LWZVOYSooGxEzG71jIqyrb/zwHK2I248t6VtXo0YLX1eRtm1iBCG3YgYqeOomJmuzQPVQPkrgWe7+5/NbGsi/vUNxA14PXffpaJs54tKWs7aROvx+kzOh5qTHaPx5C+lso32dVeplf4FPpB6MLWgn1Zzk74C2GRw29JN+lp3f1LmNhw25G33vOwYRlR4i9RKFxMtGLX7y8xOATYgWsvLLU21N+pUvnHWhS7nhpldDLx4SKPDCsTfaouMZXS5V7R+IEzlL3f3TW1yPtSrPW8wVJcHyh8QD+PbE93K/yRay0e2Gnc5L9LnWt9vrEMO6+k6n0e1uha82dS7jaRrwGHAgUQ2j2Jg41E515C0jNY9o9ZhYHYXltL1mdkFwOuAPxDH9ch7VKrHXAjs62kaajO7Lee+NpR3iFMZxw8dYmWYiFv7L/A38uLWqmKObshc75HETedV6ecc4IOZZdfIeW/g91d13MeP71D26tL/TyTyHhavc+PWGn/nIZ9fgok4sCVafI9FgBcSN5LfEHGLy0/Bvr6m9P/PEq298+3LqfwBLiLirK8lHhQOJ0aI55Q9mxiIdRMRx/Vl4ENTsa8HypwDLFt6vRyRP7aqzIuJqTJfCTw5/ewD3EJUtKrKjoypzb0OpM/umvPeFPyN9x7206D8tcAipdeLUh9jfVWH7Z1btS2Zy+hyr1ibmNnyRuIh+jZq4tAHyp8PPIqJeNjNiRSbOWXXAY5J59aPi5/Msg8HdgLWTq9XIhppqsq0Pi/G8HdenIgj/SMx4O0Kohu+yOQyJds9rvO5VGY5qI+DBQ5N/w4dS5RR/uB07Vuj9N7jiZ6vN2Vu63eIXOOrp593At+pKTOLCJO4Jn3X5dPP6sDNNWWXr/rJ3Ob90nq3Tufi3cABGcfHN4hxKV8k7nG/an2sti3Y9Sed0DsRI9i/SbQ8FO/tNIXrvanN7wY+1/jGUfrssMEPIyvk6fef6fidr0j/1g4YGLa9xAV3FjGQ6okt9lfj75w+86yBY2XST4Pv8BSi5eGWdFF6GtGCffWQz75y4PXDG+6v60lB/0RM09bl39WU7XxRGfh7X9dkfw+Uvbb03uVTsa8Hyl2V896Qz2xAjEQvbrQnABtklLucVKEYeH9tKipqQz7fZTDTTkR2hHvJfHgfKP8wIpzpSUR8dJPj9NryMZWOsbrK7zuarGOg7M8ZMhiGqCz9ImM/dbpX0OGBMJXfmGiZvzf9+3Mi5jqn7DVEtprNiDy9TwWe2mDdGxAtgwfmHNulMuXz4qsNyn6GaIF8acu/9SLEIKSiApt9DZ2O85kIh3lC+v8SxMPJn4kK2bNryu6Y/m31MApcBaww5P3ZZD6EEJXITxP36ivTebJcTZk5TB6YXfzkDMz+FZMHc5d/ah8o0/HR6thK5Zci4n1PI3q0j6bmgXDYz3SmOntB6f//YCLWD6LLNzcetOlsQXeb2WZemnErLWdT4gk117I0SNdjHVKGufuBaRmNB10ki5jZO4B1bEiSaa+OT3w30YW9KDGa/Ia0Lc8kToCRunzn5JnEhegFQ35XeYyY2dnu/n82eWa6t/nEAKHLLNINTV6o+1dS+acTWSaWBlZNsYMHuPvrarb5JOAnZvZHoovywrS8tYgbZ5UrmMjpuSqR9cGIY+03xGC4HP9O3X2/MLMDiRbYrBSARC8KwJ1m9nxi8pflqwq03dcDHjKzVT11eaauTq/bWI8JZ/ZKZZbyipnVBrybSM32PiZPq/x2YoBSJZsYSPU4M/t06VfLEN2WOVqnR0pxfccTGR8MWMXM9vb8VGcfBK6ymFDAiGtoZZyzu38grfvTQ359L1HJ+N6I4qcCX7RIl1Z0hy9N3KjrrvXjuFcs6e7nmpl5dNsfno7Xd1cVKo5Jd78yXfPWJfbXLZ4fRvGAux+d+dnB9c8hZuIrvuPXzOwYrxlMWz4vmirdbw4lcps3Lf+QmX3aW4TIuPs1Znaau0/adqvPU97lfN4NeG/6/97E33c20WJ/PPCjirK7EPH5x6fzr2mIxOLu/sfBN939nhQHW8sjbv2NFrOkueeNA7iE+Nvu4u5HpdCPnYnryYk162uVfq9U/qG2x1Yqfz+xjSdapBPdFXgr0bPSaEHT9kNUqD7aoXzj8APi6ft24sn/BennCOKp5WmZ622crofxpAw7kxi1fE16vRil1r2Kcuumg+NOIr5o0k9G+cUYeJIknr4q06qM4zun5bQJFSm6J1uFfBCTgazC5NRblS23xXYRXaIvoZQqjbiQVqYoKn32i8DzSq93IPIa5277pkRld+W0v08hMqPklN2ReJh7EpFi7QpiBPaU7etU9rlEBf+rRCrDXwPPySy7BdGd/Zv0egPgcxnlnpTO36KV6XjgyZnr3IC4Uf6aya09Ow2eKxXL6NKNfwWw7sDxldW6XyqzEhGe8kLgMQ3KHUPk2n1D+jk/HWffBz45osxixPV6sDv8SBq2WrfcX5cQLU6nEi2oLyEqsHXlriz9/5SW6z6ciGtciebdw9cOXEeWIr+XsXW4RSp/JPBm4jrYdLu7pANt1ZvS9nxm8jX+FErd73XrHTg+GqfOqyqTuzyidf2qdC36dfruT6pbb/G3JB58f5/+Xu8lBpNXlS1ayTce9jPVx9a4fmbCgLefesZghxFlr6XFbEFm9mgix1wRBH898FlvMKuStUzXYx1ShnUZdJE+u4O7n9ly3Q8nuq9XdfdXp0FV63p9S3un75zKN55dzsxuI06uobwm04Sl6UwH9nXtIIJiu7oMGrCBWQ9HvZexnId7xpS5XXXd16XlrEA8OEDk/Z2vRWREucuIFpjvl/5W13vmoLX0+SatxuVyyxAz/j2YXi9KxKTnTFXcOj2SDZ9Nb773hpQbR9aFS4EtS995MaKHYyviYXzUiP1FiMF9f01v3eru/8xYX1XGmb3d/aqMZWxKxLEvS9zglwE+4u6X1pQrn//z/t+EdZue/TpgU09ZTyyyFV2ecy1IA8g+z/xTWF8xstD4trtxOlAbQ1q6YS3Eda3G6Xjejwjru4UISflV+t3N7v6EirLz7k3D7lMZ2/sgwydqMWCWu9e2/lqLbA/le5nFpF33uPvh6XVlnSL1POxvk6ehLrjnDY5ufWyNy0yY4e1qi1lCTmZyqqCsGyYtZgtKldzDzOxhRJqih5i4II9kZk9w95vNrDjAf5v+fayZPdar034d6u4fBl5uMUPK4DbljNK+32KqSU/L3Jz6rvSyS8zs40yEifyEiHvLWcZxxEW0eFD5HfE3G1n57fqdO4ZNPJJoxRyWrzenq/SOFPrgqftpDnHzrNMlxKTwezN7J9ECCpED8fcZ5YB42CAqCNkhG9Yt3U/XfV14kIizmwWsb2Z4Zje+u98R9aJJy6rVZl8NOBt4NlB0NS6Z3stJM7QM7bvx55rZl5h8jOTM+PRuYuDIoPOJGLrayi8RY7g0E9eepYhWmwctUgUO5e27w+cQvWwQvW4bEIOCNiJ6kUZmnCmt+3KIEePuvk+DdfuI/+cvoFs38XFE2NB3iPPrRcTxmqN1uAV02253f0SLYr8njuEXMhG6ABEL/6bMZbSZ3fIgYjDkbOATpYrv84gW1SorpzAgK/1/nrp7nLsvWrP8HEt5KfORu59vkWGjyqI2MWPedsD+pd9V1gvdvfjsDj6QijI9nOVYr0PZsZgJld9ZRLd4+Wkh9wbQOG6tkA7sLwC/TGXXMLMDalpGDyYOko8N+Z1TPdVlUXHqMiXhwUTX4poWqYNmEy1eub5MtHK/NL3ek7i47jSyxIQ13X23ohLr7v+wgdrGEF2/87pEpWpZJsf93UfEwVX5tTfIsTjEa4h4xMcRFf2zid6COi8jKheLEdM9trE7EZLynfT6gvRerk8CzyGOFTzi6LauLDHxN9qSSJFWtLzsSoQUVOm6rzGz/YhKzsrEjE6bE2kMa1sRaP+gAu32VdksL8XYufvfUy9JrYaVsEGvJY7H4uZ6ITFbUp0lfEgeUXf/Y8YNs/BhotHifCauux9I5aviIwHONbOdaZCekqjEFfG1OwInuPufgB+Z2YdzFtDhIWcDM/sb8T2XTP+HjJbM0rpbT8/u7h9P+3kr4h6zT05Ld3Kamb2OuI6Uexayc42n82p1Jk+hfUJGOSMeyNZw9/daTHu8kg+MtSnziFO+xsxOzNk3A+trHYOfWv/na931mPq7bvrvt5T+3/g+Z2nq94pty/lb3WZm72JiyvU9qBmPQ7exKYVLmH/a+2HvjbvsWEx72ENX1j784GZipGaRL25N4PSqLo70uUWIQWYXd9js1lIXY5tBF0O7M+q6OEqfu4R4QrzY3TdO++uknK6orqxF2ETbLspxsQ4hJmNYd6uQjfS5S4GtUotAceOuSz7eeV8X3btEuMOGqdX/A+5e+2BmES7xKaIF1ogHlTmpglRXtvW+Sp+9GHhD0etjZk8lsrOMDOUqekRGtbZXtRaZ2brufsuI321Zd10ys58TXcgPDLy/OHCjV0xCMPD5lYjxExDd8Fk9E9auO/xKYnbKvxChDs/yiYG3N7n7ehnr7Rwa01ZqoV+ciEGFaHR40N33yyy/MdG6/RBx/R3ZwzhQrlPXspl9FViTeBgtelI8p5fSzI5O2/ssd1/PYmDS2e6+aU1RzGxHIjRlNaLSnXOMbED72S3n66Ery+ytK5bVKNQs/Y0chg9yzml9T/v2CCYekC4Ejqj6zqnc5kQc+tk+MQh1HWIsT1Uv9mOIRqGvEQ84hWWAz1fVoQbKvpyJ3sLasuM27S2/ZrYykSOvGA1+IXHj+m1FmdbhByX3FRXf5DbiRKmUuu4+Q3S5ZTOz06juVn5hRdlRFYB1UtdwbrfyP81sK3e/KC13S+IGlOMw4CxiVPnXib/XK6sKdPnOA16TbnJ/TctdDvhYTWvjnpnLHmqw+yqpG9Fe1jrEJF2A3sz8LS45raDQrSV0OeJCVLQ4LJ3eq9JpXyf/cvd/mRlmtkQ6vytnoip4xAa/ovaDw3XZVxBdpieb2e+JC/ljiNHjVbr0iNyUKiWv9/lHdR9FfctJ66wLpett4Y7072PM7DE5192W3eGtM84MrLtVaMwYbDrwMPVji3jcWmb2bqL35RTi+DrOzE529/fVlc2pONXYhHhQatNC9rTUSHJV2pa/WIQZ5vgk0Rt5Xe66u7QaM9FDty7xAP799PoFwMiW6rK2PQvF38jMvkjk5j0jvd6B4eFJ5XXOInoo1wKuAw5p8t19SLy7u/88o+hziHv/ysTAxsJ9QN300+Wy5YeKnLJjNe2VX6Lb/UTiBIdosj+OmNFmlNbhB6WK5FwzO4NIt+Fp/ZdnbnObrrviINmJuEEWsXq7E4H2VYal+io0ial8LXC8mT2SuJD+mRihXsvdz0ktMJunsnO8fkBSl+9c9hQvTZebLqSVDx+epnC29tNnzyK6wspTz/6K6Abd1t0PqinfJcTkZGKgypdod4NuG7IBMQp3MJTo8KoCY9jXAL81s2WJwV/nmFnRyler44NKl32Fu1+eWqmLinptb4y7n5b++w8fMjinZpU3EA/7V5rZXgM3sLowJIgE+O8Dfm1mxf5dlbhxv6umbHG9nUVUjK5J63wKUTnNmaWtTXf4DyxS3z16oIV5LhPnV52uDzldPGhma/rk6dlzz+tXEHluiwFvRxItsbWVX4vwm4OJQcr7W4NBysn1xHX7zszPl/3XYvBnMT5lNtESnOMOIrNOm0r3c8ysUauxux+RtvECIlvBfen14cDpmev9JN3CpzZ393mhfO5+ptWH9BxPpKa8kMgItB4ZaRrHYAVirE9xHDmRueUiT/HSo3ikgjvezHZ291OmdjOrTXvYg7XsireW4Qc2fKrdeTwjDq9N112p7Fx336TuvalkMUIdd/9b3WdLZbYkJiq438z2IFqYPuUZ03Z2/c6plWSbohvHIk7qJ5434rnVlKjWckR7qXyXEJPKTBZTLXVNPS29vMzzQ4laTz87sJxnEoPoznL3/2R8/hiGP6g8iki6flCX7alZd1HBWM2bZ0EZlsWkcsR48ft0Yz2OuAG+L/VIZY82T9u9Znp5q7v/M7W4jxywVip7KpEi8br0+knETIa14w+sW3d44/1V+lzr0JiurNv07OcBLyn1ei1LNLrkjKj/JjFwbC93f1L6m1+Scw0qrXtDovWzHDNc22NnZq8gekA2Jo7RXYB3uXttbleLzBzvJXrLyuutDT9I16BGrcalsrcQDS3/Tq+XINLK1fZAWffwqR8S95fyANat3f05FWXmZQBK96ef5Z7/XdjwKd2XJyr/h7v7NzKWsQRxnV6dyb2b7xnTZtaaCS2/f0qVqZPS692JAXCVvGX4QU7lNmMZbQcyASxlZo9399sAzGwNoiKdxWLigSdSynaQe8CkFt/DSF3xZtYk28PRRKvnBsTN/lhi9p1nZpTt9J2JFqefmtnJxM1jF+D9mWXvalkZazWivaRLiEmrgSrWLWND2aLEk/xiRGjNOp6XdaHxvrbhAz6uS/8uzUT4RZWnMPlB5WhKDyo16+8a3tImC0rnCTLc/QKL+OKjgQtTZaOJi4bcKH9K3oCTdYuKb9qW682sNu42adwdbhNxgkumHp9ynGDl4EIz28ndT/UY0Heg18RBTgWPyTXWZnLvQM41BOJYvMHMziHO7e2BnxXHTc053WaQctnhDT47ibt/3WISke2Iv9eLG1wb3k9kT5lFzGLYRJdW4xOIfVsMNH4xE3Hatevt2LNQHuTs5A1yntfD5O4PNPvTtle0lA9K1/IfEVMQ1/kecWxfQeketyDNhMrvq4hYtU+k1xcT83nnaBN+AMxrAR422KR21HqbrruSNwHnW+RGLVoBDsjc5s8TF/ttiS7xXciMSUq6dMU/4O5uZi8iciIfa2b7Zq639XeGGF2cLqTbprd2cve6DASFuakF5Ls0y6XaZUQ7dAgxKX2uPJLYifROVcoxpEcQF9NGzOxDRIvNDUx0UxYX4zpt9nV5VrtBOd8Zuj2odA1vaVPB6JLSad6yU2vg7hazM11EpFmrLtyhIllyrc2fZu3azLJtusO7xAm+k4mwsHNZkKPJo1HH3P2rqbJ7bXp/TzN70N0rZ9JKvsNE1heITBG5/mNmSzKxr9cko6Jhkff1RHf/SYN1DS7jq+6+JzHF++B7dR7r7QciHgqckRp2GrUau/v7zews4qEZmmXW6Bo+9WdgjjXLN15kIgEmZSNpEm42Nu7+5wYPVyu7+3OndINqTHvYQxfWLfxg59LLWcSMP7/PaR3r0nWXyi/BRGqVm3NbASwlsS/9uzRwprvX5rlM5bt0xf+EGPC2D1ERvJuYaS5r4oW237lUflFgRSZ3kfwmo9ywMBfPfMhpNaJ9YBmNQ0zGwdon5J/U9dewbOt93UV6CHsnUTGY96BC9CYd7u5vqSjbNbyldRYUM1vcm6d0ep27z5fSzCKO9FB3f01N+b2JiuQmTH5Yug/4SsZDYTHQppy66wLgaB/I2zmibJfu8MZxgjaGSSrassgwsZ0PDExMD9AXeEZok5k92gcmX7KKjB8Dn9ueOC/WJypjWwKvdPfza8rNIVI2rkSMiTmpQSWwWMakcJR0/a49n9JnPwz8yN2bTVcbZc8mWo2vo/RQNaq1ckj5VveZrlKr8ZeITAtt8o1POzPbljiXc0JyjgGOKvcgLWjTXvlNF+1PEQOpnOh6e1PRRb4At2MRoiuwNjm9TcTdlS+sTeJ72uZOLOKKLiVaa/9MdPGslbnenwJvGeiK/6hnzLCXWoxeTlQCLzSzVYk43NrtTuVbfedU9g1EK+ZdxINO8ZBTOZtVV+mhZm0mh5hkTbpgAyEmNMv20DovaGkZjWcbSuXOBHYdvGEvCBYD5ual63H37zYo2zb11i3AZsXfJf3dfubu6+ZUltpWMFLZtYlc5esz+RjLnuXIYtKbrYm0SFkzd6Vy0zbgxGKAYNEdfm5ud7hFvOu7aXBOWaS03J2Y2ngwvRKemTasjapz0DJm40ufu4XSw4GZHQLsm1OJTJ9/FBODlLNnTUxlVyMqwS8jehVOIirCIzMCmNnbidb4JYkJXEjr/g/wRXevzcNfatT6N9G136RRq3X6ui73GesYPmXTmIqvKYu0lIMVx+WJHq293P3m+UvNt4wbiSwVvyL+zgvknl42E8IeTgQ+S7S8QpxoJzEx4Gak1MTeNvxg0NrEKPUcrUey2ojciUS8UZ0fpBvAh5noLv1S5jZDt2wPf6DU5ZiehnMrr12+M0T81LreYHCKdcilmsp3mXQBuoWYHE3kBS1a+PZM72XlBe3oH0S4x7lM7jasyj3baV+nZXyOuBgWsf+vMbPt3T236/BfxKj0WcBaZrZW5oNKp/AWb5cFpXAccbP9BBHSsw9RSRvJzH4AvM0jznYl4EqiBXdNi2lHP5m57nOtYSq+ETe9eTIrCV26w4+l+Tl1JxPXrUnXMGoyA43BksO6sc3sEeTHsm4DHGORBWRFIo60SW71WUTu2MVoPmvir4nsLR+yCJH5MvHwMXJWMnf/IPBBM/ugu7+9wXaWl9FlTM0ZZvZ/bVqNaXGfKekaPjWdqfia2nHgtQN/GjzOa+wwxu1pZSZUfh/u7l8tvf6amY3sphzwOVL4ATE69O9ERTpn5PB9TMQaOnFhfGvmej9NxGE92szeT+q6yyzbOHeixejXO9z9ven10kS3zs1MxErXcveriZOxeIK+n3jYqI3XK+0viAv34sDf3f2RGavuki8SYhBDzqC8siImuO3scnOYmHRh29Ra9YEG5dd093JozRFmdnVm2VZ5QQf+Rg+3FrNREal6vl/7qcm67muIc3i94hgxs+OJuONaXR5UPGLXz2CiQvGOUqtxVbjEYItekQ5qVTNbNbNFcUmPwVCWKhqHW8S2v7uizBqeUssRleVz3H2vVKG6mEi5lKNNRXLwptfGE8svUiNCbmaTxueUu29b9fspdizwbTN7Tfr7YmarE/eorCmK3f1OizjUtxP3urfl9spYt/j9IgRoB+IesR3RA3V4TlmgnEO/+Du/Myf8wIZnFvpkZvjBa4E3W8T6N2o1pt19ptB60G2xbpu+VHyNeEaGp5xlmNlWwNruflxqQFy6+9blm7bKr02M8j7TzN5GjBB04mStm1Kw0DqRdpenS+82krVN7sQvECl6sEhxdCTwBiINzTHUTHGcKruvJ4Lxv0e0aL0eOISo+H69bgPK+yu1uL+IqGTk6JIvEiJF0Plmdjr5gxh2AX7g7seb2d4e+QWbaD3pQtIl20OrvKAdW0yKZTTdT9B9X0PcLFdlIrfvKgzcQCt0fVBp02o8LMd4IbdF8d8W4Va/MLMDiYEydTeAcujLdsAXAdz9PjPLzaMK7SqS8930LFKI/anuwbbcHT7wUPYf0nfI0PqcMrPXA1/3yRPl7O5D4qfHxd0/amZ/By5IDRZGxFYf6e5H5yzDzH5EdCc/iTgnjjWzC9z9zRnFX0y0ZDYdX7E9ESryfOAy4t68f8OWve0sxtXsS6QcPI7oXchRzix0CNG7+VUyMgt1vAa2uc8UumYH6jRgbmFjkS5tEyIDynFEY9rXmJjsbMpNZ8vv4Cjv8uh/J5506zQOP7CIY/qrT8T4bUtcJG4nshjk5BXt0nW3AnCjmTXJnbioT6S52g04xiNe75TM1sSvEl1fPwVeDfw/Yr+/JLUGN5JudN9NB3BtDBftvnPZb9LPw8jvLix3wc4hP2VNofWkC0mXbA9vAc6zydkxOqfoy2ET021O4tVxqF33NcQsSzelY8SJlti5ZlYkja86Vlo/qLRtNR5Ti+IcIsPCG4meq22pP0busIhN/C3RInYWgMWo/sUbrLtxRdJiOtQjiWP5vcR1ZQVgEYsJN84aVXYc3eFEBeGEdE5BXNNyz6lXu/tnS9vzFzN7NROhRVPC3T8PfD61zONpAoUGPuMTse9/Ta2DufvvNuKYaDp49e1EOOIh3jI1nLu/3Mx2I1o97wde7vk5+cuZhT7jDTILdWw1bnOfKXTNDmTu3naWyoXRS4g0tVcCuPvvi3NkQZm2yq93n3oR2oUffIvY8fea2YZEjM4HiVbUz5EXV9ml6+7wzM+VLWpmi7n7A0Rrz/6l3+X8DR/vE8mwv0S0cq3qGaOzCzZ5iuVFiKe23PKH565nmJyusnFz9yIG/XCLZO+PJFU0MstfTcsQE++WF7Sr8sQjs4iZD4fl4h23qq7+Ol0eVFq1GluKc07/39VLM7WZ2QfcvXaqTne/PH3+Ic/PP74v8B6iJ2g3n5j5cHOiBSVXm4rkZ4jW20cCPwZ2cPdL0z47ibzzo3V3uMcUtvPOKXf/m5kdRF6atUXNzEphNYvSvILTiJkdPOS9ef+valE0sye4+83u/l0rTT7ikc/1nJr1FrH3jeP30++flZazppn9w93/bWbbEA+5J5SOuaptWJs4t04hZh7b02IA6T+qSwJwX+op2APYOvWO5D7YdWk1bn2faRs+VXKxmd0OfBM4JWcfL+T+kx5wivOxSd7/sZj2bA/QORNAo5HDVhpla2YfBR5y90PTCXa1VwzasDGMZE3LWZGJuOSf+UAqmyGf/39EUvw/El3DG6cDZy3geHev7Cqw+dPONM4EYJPTWD1AtJR/sW7bS+UbfeeBsucxvDVyZOucmd1NdNcZ0Vo+KfF21Q0g3RhvcPcnjPpMRdnKEBN3f1FF2Xl5QQfe3xPIzQs6dlYz41yXfT2wnNWIGLAfpZbMxZq2lFnz2eEud/dNUw/K09KN/gZ3f2JNuXnnUNvzy8y2IGI/py290WBF0isGzFkpLaKZ3eTu65V+d5VnpBEzsxOBZRnoDs/sxh+2vN+4+6oZn/sI0YPyhfTWAcQ4ikParDdz24o82+sS174ilv4FxDVwj4qyrY8vi3R2I3lmWFI6JzYh7s1nENezJ7r78zLK3gy8Pj3IGzEx0qvqzqtUtnVmIZvIxPRu4HepUpp7Ps4m8gQPTiKVNSjSOmQHSuU3IxpIXkyMo/iGu3+tstBCyszeTOyr7YnGx1cRuaWPWmDbMN2VXxuRCSDnhmlDQg2GvTfw+/KUgFcCb3f3H6bXuelnWnfdmdlLgY8wkZP0GUT6sW/XlNucyLt4tqfYKzNbh7hxVg6uMbMHiZZH0jqLyvsCSYbd9juXypcrXrOIkbQPuPuhFWU63QDM7HvAGzK7ywbLFSEm2xEZRIosAFfXlO2cF7QrmzyQq2jhf61XpPEbx83Wogt6fyJObs3UcvR5d9+uplzrB5VU/jtESMlBRKjDX4DF627wVpE/tkFFsHF6I0thIKN4fijRsGVXViTHUeFPn92NGPTVtDt82LLucPdVMj63CHF8PTu9dQ7wJU8DlKaSmV0APL94kLPo3j3d3beuKDOO42spIiSoGIS1KLBEZutruSL5lrScoxqsexkfyG1uMVPkyDRpI5aTFVNe+nzrfPQWOYK/CbyZ6BnZG7jH3WsHwtuI8KncivPAslYgspK8wt1HZtZYGKUGuxXd/WKL2PL/I+6PfyNi8n+5wDbG3af1hxjRaC3LXjnwelHgxpoynyJCHz5FpCJZPL2/EpGTL2e9+w5Z72GZZa8BHl16PZs4Oaf9b1Gz3SsTISZ3p59TiFlapuU7Ey0nOZ/bNee9IZ+5gBicci4TGRC+n1HuuoHj4m5gVua2Xlnxu2sX0N/5vNLPOcRgpHWncl+nz11NdENfNWxf1pT9HhHG0/W7P5OYde1hTf5WQ65DI/+OA5+7LP1b/s6V5wUx7fSVRFfq1mmb5/10/P531Pz+QeImdR/R+/O30uv/Zq5jbeASogX2AuDzRMafttv8mxZllicmcul0vDRY3y1EpbN4vQQRyjTVx9elRONI8Xpp4JIG230ZMfDteiLLCERe+aoyh5b+v+vA7z5QU3ZzooHkVCIe9HoiC9PdwHMzt/kxRCvzM9LrVYncszllr0j/Xlt67/LMstcRDTNXp9dPIGaezd3XyxCV7TOBnxMp5p66oI7RBfVDTPv+5CHvPxk4bUFuy0xIddY4E4B1Gzl8ENE1uxKwlU9MHPAYYiBYji4jWRfxyV3+f6Imt+cMcRwxCGLX9HqP9N72GWU7fWebyAxCKvdUoms7x9uZyL1Y9d6g3NR1g8rzrT9oZr/1/NjqceQF7cS7DeRqu68B/u3u/7EUE2mRZim3W2o54AaLwXLz9p3XtIIOthp7s+lci6lFjfmvQ7NGF5ukTXqjxxDn3O5E1/DpxMQDWWnhalTubx9PK9RpzN8dfjkD4yjKbHIKv0m/ImNK57SM84kHm8WIwdZ3m9kl7v6mZpvfygnAz1IvA0S3dl1vyMoWEydY6f+k14/LXO8sL/UiufvfzSx3CmuI1tPXAO9391+Z2RpE/GyVlxGDv2D+c/+5VE9H3Tmm3Ev56FML6h2eGULJxLX7TjN7PpFlI3e8Q9fsQNcQYxbe4+4/bVBuYbOiD5nVzd2vs0gDuMBMZ6qz04gL2iNomAnAO4wc9njMmBeTaJNnSPph5jK6jGQ9y8x+yEQy/yap3abTbHcvx/1+xWKwSY6u37k8c9UDRIt95ehfM9uBiJN+nE2efWeZtIxKDStCZV3mW++cF7QrazEzXdd9XazHzIoH2u2B1xEVpRytHlTSw8ktFnl5G4W3jKki2Di9kUcX9lnEObUEUQk+38yOcPfP1K1wHBXJjjbz1B2ersUfS/eCkXwMKfyAR3rENe9HDNo6zMxyBsp15u7vt8jVu1V6ax+vny64PEhqMH92bj7t+81sY09hcSl8LDfdIu5+I5GJpHj9K6JFsoqN+P+w14MW8zQ5hZm9x90vTeu92ay6qHXIRFLyvnT9OwQ4irh+HZRRDrpnB3p8Oh/+1y1b8bsFcf2ZZzpbfj86hmU0HjlsY5ghyVqMZC3FurzFJqZxhYgNrc2zOwP8yWJAVlGB3Z1owR2p63cuKiXeLjPI74m/6wuZXHm+D6ht7UkX06OIv+/DiBCG+2sqr50qRT6GvKBj0GZmuk77OnkrkWnlOmIw0hlkzl7Y4UEFWrYaj4PHTHCN0xulSu/ziXNwdSay3uSsc4GmEypYyo6RKqCTsmMAr6S6RXAcFkvX+5eS38M3TlcTvZuLwcS1bdSHPcXJD9lXWMz2luMg4GQz+z1xLXkM0TKbxdpNv+0j/j/s9aBymtLBSnpd2XG0Gv8g/fdeYFuA3AYe75gdCFjBzFoPtluIzDWzV7v7pB769FCaPT37OEz7gLcurMXIYSuN5E4tTU/w0gxJnjfgrfFI1lTpfvtgk7+ZPZmIhXpB3Xqnk8VI/KOALYgL0SXAG6su4F2/s00eYHOKT07Kn7vdi5dCW5qUm0vcKE4mBn3tBazTtKehLWufF7Treq/2NKK/6r0RZdvu666D1lo9qKSyzxz2fscKdd06h04DXVp3VSaSE4gJD84gRoNfP+qzM4mNabBch/XvSvQQXOTur7OYOOYjba4pLdb9BqI35S4ibrroBcq518y3b3L3V3pIeohSykQiBC0rbaKZXcTE9NsvIE2/7e4j0xLaxODq8sBq0utZ7j4yZVnHsvOuUdYyE8mI5dZmE+l6/UrLaD3YbmFikfXpO0SIalHZ3YS4br8kha0sENMe8zuiG+5eoiXpEHe/bVTZluEH45ghqXHXHTMo1qWN1A3ftDWs63cu93VVtTZUeY6ZvZdIc7QYeeEHEB+61cwWTV3Nx1nMJDjlld90gfgA8FhgBzNbH9jC3RdE6EOXmela7esu4QfJZxjyoJJTcCoruRXK3dZHEBWMXHsQ17o5wBtL3cELJHNLB126wztLracnl17fRmSNWRDmEINGK3vKysYUSvTTVEme94BkkeEo90Gj8fTbHXu+uoQSdWk1rlJ7bI7h+gXwKI+0bHPSNeknZnZ5y2XNWO5+F/B0i8nFiqw2p7v7jxf0tkx75ZeYi/63xGAqI25iaxLhCF8GthlVsE34AR1mSOrYdbdsxe8WaKxLE11aqej+nau60HJ9kuiyv65hTNU/LKbKvsbMPkx0WS6ogYlfIXoxiu7ZnxOtAgui8ttlFq1P0m5fQ8fwg7YPKl1ajdvyUuo3i9y62TPiufvCMDh2mC7d4a0V1+xR17Ga69e43MHEtLe5WocSWeTJfRwRP78RExW4ZYgZBXO1mX57uoxjAOowUzrotqTLYLuFjrsXGYWmzUyo/L7QJ+cQPSZ1Ybw1hSVUaTxymG4zJHUZyTpjYl0a6tJK1fU7V13Qclu57iDS8zS9we5JVHZfT9xsVmbBtRSt4O7fsshqgsesTlOaj9Qm4qvnm0WrwWLa7mton10Duj2otG41HpOFN+6smamqnNQpsmfkDhKbCrcRgxJPZ/Kg7pEzvKXz8BozO7FFKNFziMaYlUmZD5L7aBZbPYfJ028/i/wH4QWqS6vxiN5naDYItMv1C4YPtlsQmUh6a9pjfs3sp0RMUTHhwS7Awe6+eV2soY0pkXaDbZ0XOzQYR1QXVzSTYl3aaho7NRO+s5ltSly4f0LGjcdiPvmV3f2z6fVlxEQVTuSwzJqYo+M2n09UtM/xSDK/OfAhdx8anzqmdY4jvrrRvh4Xi3j0u4jj6k3EjeNod7+1smCUnevum9jkmR9bxwg2tSDiXWV62cRMb5N4xnS6ZrYjcU41Dtsys53d/ZSGmysLkJnNInrb1iLCN49199ywFulgJrT8voJI9/M5ooJxKbBHCkM4cFiBLuEH1m2GpNZddzMp1qWDRk9KM+Q7vx/4O9G6lJMr91Amj4hegsgrvDTRMzDllV+iB+P7RAaSi4lJQXaZ4nWOI7666b7GzC5y962GtL7U3uSHPKj8hIkHlZ8ykA1mhAUe3jLwXR/eskdDMnS83o9FTiW3widpGEpkZnt4TIu7upkdPGR7Kh9GZ8I+W9h0CJ86ngh5uBDYgcisMWcKN1WSaa/8poEHo0b9XzTi/S7hB1sQ3bMnETPYNBls0bnrbibEuixo0/ydH+sV08UO8TB3v6P0+iJ3/zPwZ4vpQqecu19pkYVgXeLYuqVF12fj1Y74fxNN9zWkVF/eLgXXOB5UFnh4S8vvKu10ud6PhZnNJo7VNmms2oQSFdepYfG5OcuZ9n22EGobPrW+p6mXzexY4GdTtoUyyXROctFlIEKXkcOtZ0jqOBp1ofQ/0Ep1hpn9n6fk6RmWK79w93Lvw+zxbdb8LHIhD7OOmeHup07h6scRX910X0OExbQNt2j9oDKmVmOZ+aZyRrxcXycGrO5IKY1VZtlDifOqSSjRGekz87U4pzCKOjNhny10Wg66Lc8I+oDVTOYh4zOdLb9dBiJ0CT/oNENS3/wPtFK9Fnizmf2buNDUVeYuGzFI7wCm/qm8Ku+xE3PeT4kxPdg13dfQLdyiy4PKTAhvkSk2Q673XdJYNQ4lImYYe667315+08z2Ad4J/GBoqWSG7LOFTdvwqS4zgkoH01b5dffT0r/FTDYP9+oUZWWdwg+swwxJsnBpUXl/E/BdM3s5kW4PolK0BPDiMW7afNx9n6lc/lRr+aDUJdyiy4PKtIe3yIIxA673XdJYtQklOhg428ye7+6/AEiZY14OZA2anQH7bGHTKnyqj73JM8VMyPawBZG/dGl3X9XMNgAOcPfXTdH6FsoZkqQdi0karnb3+y2mZ94Y+KTXJCM3s2cxkTLvhgU9MDHdJAdjBN+zILehqTb72qpndaob8PZo4LtEd/B8DyppwOWosre6+1ojfvdLd19zVFlZeMyE630KNbgQWIWJNFaHFw1ANWU/DPyoYSgRZrYd8AXigX0/YDPg+e7+l4yy077PFhYzITuQtDMTKr+XESPZv+8TacSub/G0m7u+h5hIQt1odLksfMzsWmAD4CnE5BFfAl7qU5g2rCsz+zyRX3NbYnt3AX7m7vtO64bVmK593eZBxcy+Dpw/otV4G3ffffxbKgvaTL3eW0xu8smMz91HDGBrEkpUlH0G0Vp7CXEe/itz22bkPpuJUjaelxW9SGZ2NZEPeWngOHffbho3TypMe7YHAHe/YyDQe8oS+vvCO0OStPOAu3t6Qv9Mir2b0ZVI4Onu/pSUe/YIM/sYcOZ0b1SGadnXqbLbtGV+2sJbZMGZwdf7g4k0ZpXahBKVBikbcTxvB9xtcZOtrbzO4H02Eyl8aiE1Eyq/d5jZ0wE3s8WJHHc31ZQRyXVfinfbA9jaYrrOymmsZ4Bibvp/mNljgT8DK03j9uRaaPa1u99N5KAutxovbHm3ZeGVNay/TSjR/8Ag5YXJtGUHkm5mwhPea4gg8ccRc4dvmF6LjMNuRJfhvh4zyq0MfGR6N6nWD8xsWSKX9RXAr4icmzPdQrev3f3H7n5U+lHFVxaU3HjDo4mH4A2IqW9/CXx1yrZKmrrMzF49+OYCyg4kHUx7zK/IgmJmKwB/8hl60FtMD3xHqjhiZnsRrag3EwNk/jyd29fETN/XIlPN5p+1cN6vgCXdvbbn1dL012b2buB3KZRIU2LPEF0G3cr0mrbKbzqZR3F3f+8C2xj5n2Mx3eSRRMjAe4nWkhWI3o693P2sady8oczsSuDZ7v5nM9sa+AbwBqI3ZD13n+opjltZGPe1yMIgTW5xFrAPsDVwN3CNp1nBZGaY7uxA0tx0Vn4PGfL2UsC+RFLwYVMzimQxs7nENNePBI4BdnD3S83sCcRsRRtN6wYOYWbXuPsG6f+fBe5x98PT66vdfcNp3LyRFsZ9LbIwMLPHEPl5L3f3C81sVSIbyQnTvGkiC7UZEfZgZo8gBrrtC3wL+FgakCLSSrmyaGY3uft6pd9dNRMrZGZ2PbChxzSXNwP7u/sFxe+mKv1fVwvjvhZZ2CiUSGR8pnXAm5ktb2bvA64lMk9s7O5vVcVXxuCh0v//OfC7mXrzOImY+vR7xDZfCGBmawH3TueG1VgY97XIjGVmm5vZ+WZ2qpltlB6MrwfuMrPnTvf2iSzspjPs4SPATkQ36Wfd/e/TsiHyP6lm5rBZ7j4jU3Cl+NmVgLPd/f703jrEDIhXVhaeJgvrvhaZqRRKJDK1prPy+xAxQvIBNIuMiIgIoFAikak2bZNcaBYZERGRoRRKJDKFZsSANxEREQkKJRKZWqr8ioiIiEhvKPRARERERHpDlV8RERER6Q1VfkVERESkN1T5FREREZHe+P8pn5Aw15jtbAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# we count unique categories with pandas unique() \n", + "# and then plot them in descending order\n", + "\n", + "data[cat_vars].nunique().sort_values(ascending=False).plot.bar(figsize=(12,5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All the categorical variables show low cardinality, this means that they have only few different labels. That is good as we won't need to tackle cardinality during our feature engineering lecture.\n", + "\n", + "## Quality variables\n", + "\n", + "There are a number of variables that refer to the quality of some aspect of the house, for example the garage, or the fence, or the kitchen. I will replace these categories by numbers increasing with the quality of the place or room.\n", + "\n", + "The mappings can be obtained from the Kaggle Website. One example:\n", + "\n", + "- Ex = Excellent\n", + "- Gd = Good\n", + "- TA = Average/Typical\n", + "- Fa =\tFair\n", + "- Po = Poor" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# re-map strings to numbers, which determine quality\n", + "\n", + "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", + "\n", + "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", + " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", + " 'GarageQual', 'GarageCond',\n", + " ]\n", + "\n", + "for var in qual_vars:\n", + " data[var] = data[var].map(qual_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4, 'Missing': 0, 'NA': 0}\n", + "\n", + "var = 'BsmtExposure'\n", + "\n", + "data[var] = data[var].map(exposure_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", + "\n", + "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n", + "\n", + "for var in finish_vars:\n", + " data[var] = data[var].map(finish_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", + "\n", + "var = 'GarageFinish'\n", + "\n", + "data[var] = data[var].map(garage_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n", + "\n", + "var = 'Fence'\n", + "\n", + "data[var] = data[var].map(fence_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# capture all quality variables\n", + "\n", + "qual_vars = qual_vars + finish_vars + ['BsmtExposure','GarageFinish','Fence']" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# now let's plot the house mean sale price based on the quality of the \n", + "# various attributes\n", + "\n", + "for var in qual_vars:\n", + " # make boxplot with Catplot\n", + " sns.catplot(x=var, y='SalePrice', data=data, kind=\"box\", height=4, aspect=1.5)\n", + " # add data points to boxplot with stripplot\n", + " sns.stripplot(x=var, y='SalePrice', data=data, jitter=0.1, alpha=0.3, color='k')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For most attributes, the increase in the house price with the value of the variable, is quite clear." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# capture the remaining categorical variables\n", + "# (those that we did not re-map)\n", + "\n", + "cat_others = [\n", + " var for var in cat_vars if var not in qual_vars\n", + "]\n", + "\n", + "len(cat_others)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rare labels:\n", + "\n", + "Let's go ahead and investigate now if there are labels that are present only in a small number of houses:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSZoning\n", + "C (all) 0.006849\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Street\n", + "Grvl 0.00411\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Series([], Name: SalePrice, dtype: float64)\n", + "\n", + "LotShape\n", + "IR3 0.006849\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Series([], Name: SalePrice, dtype: float64)\n", + "\n", + "Utilities\n", + "NoSeWa 0.000685\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "LotConfig\n", + "FR3 0.00274\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "LandSlope\n", + "Sev 0.008904\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Neighborhood\n", + "Blueste 0.001370\n", + "NPkVill 0.006164\n", + "Veenker 0.007534\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Condition1\n", + "PosA 0.005479\n", + "RRAe 0.007534\n", + "RRNe 0.001370\n", + "RRNn 0.003425\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Condition2\n", + "Artery 0.001370\n", + "Feedr 0.004110\n", + "PosA 0.000685\n", + "PosN 0.001370\n", + "RRAe 0.000685\n", + "RRAn 0.000685\n", + "RRNn 0.001370\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Series([], Name: SalePrice, dtype: float64)\n", + "\n", + "HouseStyle\n", + "1.5Unf 0.009589\n", + "2.5Fin 0.005479\n", + "2.5Unf 0.007534\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "RoofStyle\n", + "Flat 0.008904\n", + "Gambrel 0.007534\n", + "Mansard 0.004795\n", + "Shed 0.001370\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "RoofMatl\n", + "ClyTile 0.000685\n", + "Membran 0.000685\n", + "Metal 0.000685\n", + "Roll 0.000685\n", + "Tar&Grv 0.007534\n", + "WdShake 0.003425\n", + "WdShngl 0.004110\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Exterior1st\n", + "AsphShn 0.000685\n", + "BrkComm 0.001370\n", + "CBlock 0.000685\n", + "ImStucc 0.000685\n", + "Stone 0.001370\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Exterior2nd\n", + "AsphShn 0.002055\n", + "Brk Cmn 0.004795\n", + "CBlock 0.000685\n", + "ImStucc 0.006849\n", + "Other 0.000685\n", + "Stone 0.003425\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Series([], Name: SalePrice, dtype: float64)\n", + "\n", + "Foundation\n", + "Stone 0.004110\n", + "Wood 0.002055\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Heating\n", + "Floor 0.000685\n", + "Grav 0.004795\n", + "OthW 0.001370\n", + "Wall 0.002740\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Series([], Name: SalePrice, dtype: float64)\n", + "\n", + "Electrical\n", + "FuseP 0.002055\n", + "Mix 0.000685\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Functional\n", + "Maj1 0.009589\n", + "Maj2 0.003425\n", + "Sev 0.000685\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "GarageType\n", + "2Types 0.004110\n", + "CarPort 0.006164\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "Series([], Name: SalePrice, dtype: float64)\n", + "\n", + "PoolQC\n", + "Ex 0.001370\n", + "Fa 0.001370\n", + "Gd 0.002055\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "MiscFeature\n", + "Gar2 0.001370\n", + "Othr 0.001370\n", + "TenC 0.000685\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "SaleType\n", + "CWD 0.002740\n", + "Con 0.001370\n", + "ConLD 0.006164\n", + "ConLI 0.003425\n", + "ConLw 0.003425\n", + "Oth 0.002055\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "SaleCondition\n", + "AdjLand 0.002740\n", + "Alloca 0.008219\n", + "Name: SalePrice, dtype: float64\n", + "\n", + "MSSubClass\n", + "40 0.002740\n", + "45 0.008219\n", + "180 0.006849\n", + "Name: SalePrice, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "def analyse_rare_labels(df, var, rare_perc):\n", + " df = df.copy()\n", + "\n", + " # determine the % of observations per category\n", + " tmp = df.groupby(var)['SalePrice'].count() / len(df)\n", + "\n", + " # return categories that are rare\n", + " return tmp[tmp < rare_perc]\n", + "\n", + "# print categories that are present in less than\n", + "# 1 % of the observations\n", + "\n", + "for var in cat_others:\n", + " print(analyse_rare_labels(data, var, 0.01))\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the categorical variables show multiple labels that are present in less than 1% of the houses. \n", + "\n", + "Labels that are under-represented in the dataset tend to cause over-fitting of machine learning models. \n", + "\n", + "That is why we want to remove them.\n", + "\n", + "Finally, we want to explore the relationship between the categories of the different variables and the house sale price:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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wc+f9s8AjzujIncCo0534AvCgiFQ4g0YeBF5w9o2JyE5nNOQjM6412z3UInvnnXfo7OxkYGCAQ4cOcfbs2UseG4lE6OnpAaCnp0dHRSql8iKfXZF1wBsi8iHwLvD3xpg9wHeAT4vISeABZxvgOeAMcAr478DvARhjhoA/Bt5zXn/klOEc833nnNPA8075pe6hFtHY2Bjj4+PTyrq7uy9x9PRRkbZt66hIpVReaK5Ih+aKvHzJZJK9e/dOm4zd1NTETTfddNGxsViMX/mVXyEWi1FUVITH46G4uJjnnntuKauslCosmitSLS6/38+mTZvcASHBYJCNGzdedFwikeC1115j7dq1xGIxRkdHyWQy3H333UtdZaXUNUCXrVEfy/r162lsbCQWi1FeXu4Gual6e3tJJpPkegds275ovptSSi0WbbGpKzY5Ockvf/lLXnvtNTo6OkilUu6+gYEBzpw5QzQadeeutbW1uftFhNdff33J66yUKnzaYlNX7P3333czi+QGjdx8880cPnzYHR3Z1tbGLbfcQllZGa2trRw5cgSPx0MgEOC2225bpporpQqZttjUFUmn09PSZUG2lZZMJuns7HTLjDF0dHRw9913k06nKS0tpby8HMuyOH369BLXWil1LdDApq6I1+slFApNKwuHwxhjmDnS1rZtLMticHCQQCDgPoc7f/48Sim12DSwqSt28803u8GtvLyc5uZmhoeHqa+vn3ZcS0sLAGvXrp1WPnNbKaUWw4LnsYnIGqDVGPOSiAQBrzFmfL7zrhY6j+3KpdNpDh8+TFdXNid1IBBg3bp1ZDIZ6urqqKjIrjK0f/9+vvGNb7jn/cmf/Am33HLLstRZKVUQrnwem4h8FfgJ8BdOURPw/y1KtdRVb3Jy0g1qkJ23Fo/H2bRpkxvUAF577bVp583cvpShoSHa29s5f/78JVfmVkqpnIV2RX4NuBMYAzDGnARq81UpdXWZOsx/rrKZ66+9+OKL8167p6eHN998k1OnTnHw4EEOHDhw5RVVSl0TFhrYEsaYZG5DRLzkebkZtfINDAzwy1/+kuHhYYLB4LR9zc3NFx1fV1c35/ZsOjo6pm339vYSj8evoLZKqWvFQuexvSoi/wYIisinySYf/kX+qqVWqomJCc6cOcOxY8fYv38/fr8fn8/HzTffTGtrK4lEgsbGxmkLjOb09vbOuT0by5r+t5eIzJrdRCmlchYa2L4JfAU4DPwu2Uz8389XpdTKlEqleOONN9zkx2NjYzQ1ZZfEO3r0KJ/61KcoLS295Pler3daKq1cRpK5bNiwgaGhIffZ2po1awgEAh/zkyilCtlCA1sQ+IEx5r8DiIjHKZvMV8XUytPf308ymSSTyZDJZDDGMD4+TjAYJB6P4/F45jx/YmJi1u1EIsGFCxfw+/3U1dVNa5HV1NRw77330t/fTygUQlc6V0rNZ6GBbR/Zdc2iznYQeBG4Ix+VUitTrqXk8XhYu3YtbW1tbqtr8+bNFBcXX/Y1o9Eob7zxhjvYpKamhp07d047pri42J3zlslkiMVihEIh7ZJUSs1qoYGtyBiTC2oYY6Iicvn/iqmrWnV1NfX19fT19bF161Zqa2sJh8OsWbPmoryP4+PjHDt2jHg8TmNjIy0tLdTV1XHhwgX3mLq6Os6cOXNR8uTh4eFp0wRs22ZsbIyRkRHa29tJp9OEQiFuv/32i7KfKKXUQgPbhIjcbIw5ACAitwCx/FVLrVS33norIyMjpNNpqqqqZm012bbN22+/7T5PGxkZwev10t/fP+24/v7+WeelTS0bHh7m3XffJRaLcejQIdasWUNlZSUTExO0t7ezY8eORf6ESqmr3UID2x8APxaRHrIzveuBL+arUmplKy8vn3P/8PDwReut9fX1XZRD0hjDmjVr6O7udoNZWVkZlZWV7jFHjx4lmUySTqdJpVJ0dnZSUVGBiBCNRlFKqZkWFNiMMe+JyCbgOqfouDHm4hm46ppg2zYnTpxgYGCAcDjMpk2b8Pv97v7c86+pgexSXYYVFRV88pOfpLu7G7/fz+rVq6e1Aicns+OTAoEAxcXFTE5OYts2Ho/nopyUSikF8wQ2EbnPGPOyiPyTGbs2Ov9w/V0e66ZWqKNHj7rrrV24cIFoNModd3w0jqioqIjrr7+eY8eOMTQ05HZFlpSUTGtlrVq1CoDS0lI2bdo0671WrVrlTtJubW0lFotRWVlJXV0dra2tefqESqmr2Xwttk8BLwO/Oss+A2hguwb19fURi8U4efIk8XictrY21q1bR0NDg3vM+vXrqa2t5cUXX6S8vJx0Ou3OY8uNrpx6/KVs3rwZv9/P4OAg4XCYjRs34vP58vbZlFJXvzkDmzHmWyJiAc8bY55ZojqpFS4UCnHw4MFpqa0OHz5MfX39tG7E8fHxaV2Up0+fJhAIuIFtIXkfLcti48aNbNy4cRE/gVKqkM37jM0YY4vIvwSuKLA5k7n3A93GmM+JyDrgaaAKeB/4LWNMUkQCwFPALUAE+KIx5qxzjX9NNvNJBvi6MeYFp3wX8GeAB/i+MeY7Tvms97iS+quLbdmyhddffx34aE7b6dOnyWQy7iRtj8dDKBSaVmaMmfY+N9Jx1apVs6bgUkqpK7HQJMgvicg3RKRZRCpzrwWe+8+B9inb/wn4U2PMBmCYbMDC+TnslP+pcxwishn4ErAF2AX8vyLicQLmnwOfATYDX3aOneseahGEw2F27drFDTfcwPbt20mlUoyNjZFOpzlw4ABvvvkmo6OjDAwMuLkkRYT6+nqKiooAGBsbw+Px0NnZydtvv01fX98yfyqlVKFYaGD7Itmla14j2wJ6n2wrbE4i0gT8I5y8kpLtp7qP7NpuAE8Cn3feP+xs4+y/3zn+YeBpY0zCGNMBnAJuc16njDFnnNbY08DD89xDLZKtW7eyefNmwuEwgUCADRs2kE6nmZiYwLZtIpEIAwMDJBIJ7r//fm644QZSqRTGGHfo/tQpAWfOnGF4eJh0On3RvdLpNAMDA5rVXym1IAsd7r/uCq//X4F/CeQy41YBI8aY3L9eXUCj874ROO/cLy0io87xjcA7U6459ZzzM8pvn+ceapF4PB62bt0KZJ+dtbW1YYzB7/czPDzsDiwpKSlhbGyMyspKIpEIlmVRUlIC4Aa2sbExzp49SyQSwev1cvPNN7tL2gwNDfHuu++SSqVIp9OUl5dTVVVFU1PTtPluSimVM2eLTURuF5EPRSQqIm+LyPULvbCIfA7oN8a8/7FrmSci8qiI7BeR/QMDA8tdnavWunXraGxsxLIsNm3aRFFRET09PYyMjDA2NsZLL71EMpl9xGnbNqlUyu2ShOxiorW12XVr0+k0hw8fdvcdO3bMbem1t7fz6quv0tHRwVtvvYX+P1NKzWa+FtufA98g2wX5ENkW2K8s8Np3Ag+JyGeBIqCM7ECPchHxOi2qJqDbOb4baAa6nIVMw2QHkeTKc6aeM1t5ZI57TGOMeQJ4AmDHjh26cOoVsiyLm2++mW3btmFZlhuIvF4v0WiUVCrF4OAgkA1smUyGsrIy/H4/FRUV+P1+YrGYO/E6N7lbRNzux2g0SiyWzeKWyWSwLItz585ptn+l1EXme8ZmGWP2Os+3fgws+F8RY8y/NsY0GWPWkh388bIx5jeBfwC+4By2G/i58/5ZZxtn/8smm7riWeBLIhJwRju2Au8C7wGtIrJORPzOPZ51zrnUPVQe5YJSc3Ozm/U/FApRXV2NZVnEYjFGRkYYHByku7ub8fFxhoeHsW2bl19+mTfffJPDhw8zOjrqThvIrcSdG00ZDofdeWw6n00pNZv5WmzlM7KOTNu+wswj/wp4WkT+A/AB8JdO+V8CfyUip4AhsoEKY8xREXkGaAPSwNeMMRkAEfl94AWyw/1/YIw5Os891CIaGBjg/Pnz+P1+1q9fTzAYBOD222+nt7eXgYEBgsEgDQ0NlJWVkUqlsCwLESGRSDA5Oel2S+aykni9Xtra2ti3bx+f/OQnaW1txe/309/fj8/nc1fUzt1TKaVmmi+wvcr0rCNTtxececQY8wrwivP+DNkRjTOPiQP/9BLnfxv49izlz5FdzXtm+az3UItncHCQd975aExPb28v9913Hx6PB2MMVVVVHDlyBJ/PR3NzM6dOnSKZTGLbthugbNtmfHycdDpNZWUlyWTSXYH7/PnzHDt2jBtuuIE1a9awZs0abr31VoaGhojH49TW1i5oBW6l1LVnvswjv7NUFVFXh5GREWzb5vz589PKJyYmeOONN7Asi66uLtra2ohGoxQVFfGzn/2MTZs24fF4SKfTTE5O4vV63a7Eqqoqenp6CIfDQLbbMRwOMzQ0dNH9dSSkUmo+C/qTV0TqgP8IrDLGfMaZCP0JY4x28V0jjDG899577kKhw8PDlJWVuc++zpw5Q0NDAyUlJbz++uucOnWKiooK0uk0vb29RCIR/H6/OzoyEAgQCoWor69n27Zt3HXXXfziF78gnU7T0NCA1+vVIKaUuiILnaD9Q7LPslY52yfIrtGmrhEDAwPTVr+emanf4/FQUlKCMYbJyUkSiQTGGM6cOcPQ0BDDw8NEo1E8Hg+VlZWUl5fj8/lIpVLU19ezevVqvvzlL7N582YikQijo6PzrvumlFKzWehDimpjzDNOzsbcBOpMHuulVpiZC4f6fD62b99OfX09fr+fUCjExMQExhjq6upIJpNMTEyQTqepr69n7dq1HDp0CBGZNoctN38Nsmuz+Xw+mpqaADh48CAej8dd3kYppRZioYFtQkSqyA4YQUR2AqN5q5Vacerq6twWFuAO6889F7vxxht5//33icViNDU1sW3bNmKxGC+88AJVVVVUV1cTCAQIBoMUFRWRSqUQEWpqakilUvh8PkZHRxkfHwey67z19PRw+vRpPvvZz2p2f6XUgi00sP0LsvPJ1ovIm2Tns31h7lNUIfH7/dx1112cOXOGTCbDmjVr3KAGUF1dTUNDA8eOHWP16tX4/X6uu+46MpkM3d3d2LaNZVlUVFQQCAQYGxsjmUxy5MgRTp48yZ133ukucRONRuns7ASyk7+PHz9OOBx202wppdRcFpor8oCIfAq4DhDguDEmldeaqRWnpKSEG2+8cdZ9fX19HD9+nOHhYXw+n5vP8aabbuLcuXOMj4/z6quvuplJcoNIINvN2dHRwdatW1m/fj1vvvkmkO3uzC1GOjw8rIFNKbUgcwa2GZOzp9ropD3SFbQVkM33eOTIEWzbBrKDTTZv3kxdXR1r164Fsq2vTCbjdmdOlcvqv2nTJnfdtqqqKndCdkVFxdJ8EKXUVW++FtuvzrFvwRO0VeHLLVeTE41Gp6W8MsaQSCTcZ2jxeJzS0lIikQjj4+OsXr2aaDTKO++84+aNHBkZoa6ujpaWFm2tKaUWTCdoq0VRVlbG+vXr6ezsZHh4mMrKymmB7sKFC25Qg+wzu9HRUY4dO8a6des4d+4c7e3thEIhIDtYxbIs7rvvPvfZm1JKLcSCcxKJyD8iu4q1O1bbGPNH+aiUuvrU1tYyOjrKyZMn3a7EQ4cOEQ6HqaqqYnJyctrxsViMRCLhZiopKiqiv7+fjRs3ut2Ptm2TSCQ0sCmlLstCM4/8N6AYuJfsathfIJthX13DYrEYHo+H1157jVdffZUPP/yQoaEhmpubGR8fZ9++ffT09HDPPfewdu1adzmaXMBKpVKcPn2akpISysrKqKmpcbP6Q7YVmMsdqZRSCyXZVV7mOUjkkDHmxik/S4DnjTF357+KS2PHjh1m//79y12NRfe9732PU6dOLcq1MpkM0WjUzfeYSqWIxWJEIhFs26a/v59EIoHX68Xr9RIMBqmvr6eyspJwOExbW5s7aXt8fByv1+uuph0Oh9myZQsiwsTEBD6fj/Ly8iVLdLxhwwYee+yxJbmXUmrRyGyFC/1XI+b8nBSRVWSXlWlYjFqpq4Nt2/T09JBKpRgfHycWi1FVVUUymWRyctJdi01EiMVibsssFovR39/PxMQE0WiURCKBx+PB6/WSTqfdNFwNDQ3uvLiysrJl/rRKqavZQgPb/xSRcuD/Bt53yr6flxqpRbVYrZDu7m4OHDgAwPHjxxkdHWXVqlX4fD4++OADotEow8PDnDt3jlQqRSqVoqGhgebmZowx7Nq1i+985zvuKtg+n49wOMyjjz5KfX09N910E2vWrFmUuiqlrm3zzWO7FThvjPljZ7sEOAwcA/40/9VTK0Xu2Zdt25SXl7urXNfW1rJt2zZs26atrY2amhra29sZGxvDtm16e3vZsGEDp0+f5oYbbmDfvn34/X6Ki4u5/fbbERFaWlouCmqTk5MMDQ0xMTFBT08PNTU1VFRUkEwmWbVqFYFAYDm+BqXUVWC+FttfAA8AiMgnge8AjwHbgSfQtFrXjLq6OgYGBjh79iyWZVFaWkpLSwvl5eXcc889lJWVEYlE+IM/+APGxsZIp9OMjIywefNmqquryWQyrFu3Dp/PRyaTwe/3c/fdd1NbW0t/f7+bcguyk70PHDjAqVOneO+996isrGRsbIyGhgbuvPNOjh8/zt133+1ODVBKqanmC2weY0xutccvAk8YY34K/FREDua1ZmrFmJiY4NVXX6Wrq4tAIEBdXR2VlZXs3LkTYwxtbW2cOnWKiYkJMpkMNTU1xONxIPu8rKKigvHxcXw+H62trXR1dfEbv/EbpNNphoeHAYhEIoTDYY4fP87LL7+M1+vl8OHDpFIpuru78fl8dHZ2sm3bNkpKSjh79ixbtmxZzq9FKbVCzRvYRMRrjEkD9wOPXsa5qgAYY3jjjTfYt28fXV1diAi2bVNaWkpnZyddXV288847nDx5ktHRUSKRCMFgkIaGBjfTSGlpKaOj2cUgMpkMTU1NJBIJTpw4wfDwMOFwmLKyMuLxuJs3sq+vj66uLjweD6FQCK/Xi2VZ2LbN+Pg4H3zwAYODg6xevZp169Yt87eklFpJ5gtOPwJeFZFBsiMjXwcQkQ3osjXXhJGREc6dO0c8HieVSuH1ennttdc4ffo07e3tRCIRTp06RTQaxbIsRkZGGBkZIRaLUVlZSUlJCT09PTQ0NJBMJvH5fJSUlNDb28uJEycYHBykqKiIVatWEYlE8Hq9dHR0MDQ0hNfrJZPJ4PF4KCoqor6+nmAwyJEjR9i0aRNjY2McOXIEv99PY2Pjcn9VSqkVYr6UWt8WkX1kh/a/aD6a9GaRfdamClxxcTGnTp1icHCQWCzG+fPnSSQSiAjRaJSzZ88iIng8HqLRKDU1NQwPD7tdlplMhuPHj5NMJmlpaSEcDpPJZLAsC7/fT1lZGV6vl+PHj7vJkf1+P+Xl5Xg8Hnbu3EkymWTXrl2Ul5czODiIbdvTBo9cuHBBA5tSyjVvd6Ix5p1Zyk7kpzpqpRkfH6e2tpbOzk5s2yYejxMIBCgpKaGzs5NUKkUwGGR0dJRkMomIUF5eTm1tLWfPnuXGG290l6i5cOEC1dXVxGIx0uk0wWCQeDxOcXEx0WjUnf+WS5Dc0tLC+vXraWlp4ZZbbgFwW3ZTaXYSpdRU+pxMzSkWi7Fx40ZEhL/9278FsoNJzpw5466InVtz7cKFC26Q6erqIplM0tXVxR133EFTUxOhUIje3l4SiQQtLS2ICMXFxWQyGWzbprW1Fb/fTyKRIJlMUltby3XXXcf111/v1qekpITrr7+e48ePY9s2tbW1+oxNKTVN3gKbiBQBrwEB5z4/McZ8S0TWAU8DVWQne/+WMSYpIgHgKeAWIAJ80Rhz1rnWvwa+AmSArxtjXnDKdwF/BniA7xtjvuOUz3qPfH3WQlZbW0symeSDDz7AGOO21lKpFMXFxdxxxx2UlJSQTCa5+eab6erqorOzk5qaGoqLi/H7/aTTaTo7O1m1ahUnTpzAtm0efPBB/H4/1dXV1NTUEIlEqKyspLOzk4MHDzI4OMiqVas4f/48zc3N09Zj27BhA2vXriWdTlNUVDRH7ZVS16J8ttgSwH3GmKiI+IA3ROR54F8Af2qMedpJrvwV4HHn57AxZoOIfAn4T8AXRWQz8CWyKwusAl4SkY3OPf4c+DTQBbwnIs8aY9qcc2e7h7pMgUDAfQ5WVlZGWVkZxhgqKipobW2lqakJn8+H1+vl+uuvx7IsJicnqaiooKmpiXA4THd3N9dffz19fX10dnZSWlrKiRMnaGpq4s4772Tjxo0cOHCA7u5uNwflqlWrmJycpK2tjVAoxD333APgDmDJvZRSaiYrXxc2WVFn0+e8DHAf8BOn/Eng8877h51tnP33SzbdxcPA08aYhDGmAzgF3Oa8ThljzjitsaeBh51zLnUPdQWMMdx5553U1dVRVVVFdXU1a9eu5aabbqK8vJxgMMi2bdvw+/1s2rSJyspKmpqaaG1txePxsHHjRiorK+nv7wc+Wi27p6eHjo4O9u7dy/j4OA0NDfj9fioqKqiqqgKyz/hyz9/27NnD008/zYsvvnjRczallMrJ65+8IuIh2xW4gWzr6jQw4syLg2xLKzecrRE4D2CMSYvIKNmuxEZg6gCWqeecn1F+u3POpe4xs36P4szNW7169ZV9yGtAbW0tiUSC+++/n9OnT1NRUUFFRQXxeJyysjJ3OP5NN93Eyy+/zB133EF3dzdnzpzh5ptvdidrh0IhPB6Pm2EklxjZ7/cTj8eZnJxk+/btbiqunp4eamtrqa6u5uc//zlHjx4F4PTp0/T19fG5z33ODYBKKZWT18BmjMkA250Eyj8DNuXzfpfLGPME2dRg7NixY/71e65RW7dudSdKb9++nYmJCfbt2+emxtq8eTN+v58TJ06QSCQIh8OUl5eTSCQYHh7mxIkTRKNRamtrqampIRAIsH79evx+/7RFROPxOJs2beLcuXM888wzJBIJ+vr6qKysJDfTZGxsjM7OTs6dO0dpaSnNzc3ceuuty/XVKKVWoCV5SGGMGRGRfwA+AZRPyWbSBHQ7h3UDzUCXiHiBMNlBJLnynKnnzFYemeMe6gp4vV5uuOEGAIaGhnjzzTfdgR3JZJLz589TU1NDR0cH77//Pn6/n+3btzMyMsLJkycZGhoiEAhQVFTEmjVrMMbw4IMPMj4+zokTJ8hkMpw5c4aRkRE8Hg9Hjx6lrq6ORCJBMBjk4MGDrF27lu7ubjo7O5mcnGRiYsINfMPDw9MGlyilrm35HBVZA6ScoBYkO8jjPwH/QDZ58tPAbuDnzinPOttvO/tfNsYYEXkW+BsR+S9kB4+0kl29W4BWZwRkN9kBJr/hnHOpe6iPKZlMYts2DQ0NlJaWEo1GCYVChEIh2traiMVijI2NcezYMTo7O+nt7XW7HnMrbq9atYr6+nqKiooYGhrilVde4fz586xfv55z587x+uuvMzIyAmRXFSgqKqKsrIzjx48Tj8fd9dzOnj3L1q1b3XlySikF+W2xNQBPOs/ZLOAZY8z/FJE24GkR+Q/AB8BfOsf/JfBXInKK7EKmXwIwxhwVkWeANiANfM3p4kREfh94gexw/x8YY4461/pXl7iHukJ9fX10dHRw7NgxTp48STgcZv369TQ0NFBUVMQrr7zirtRdWlpKY2MjR44cIRaL4ff78fl8TExMEAwGicVi7N27l2QyydmzZ0kmkxQXF/P888/j9/u5cOEC4+PjVFZWunPcNm7cSG9vL/39/aTTaSKRCG1tbdx2223U1NQs87ejlFpJ8hbYjDGHgJtmKT9DdkTjzPI48E8vca1vA9+epfw54LmF3kNdmVOnTtHW1sbzzz/P0NAQpaWlDA0NMTw8zD333OMGmtHRUQKBAIFAgEQiQVFRERMTE/T29hIOh2lpaQGyQTKZTJJKpejv72dwcJBjx45x7tw5/H4/RUVFFBcXu3PmampqqKysJBQKMTEx4a71BtmuUaWUmkonAql5dXR0MDg46AaRvr4+JicnKSkpYXR0lJMnT7Ju3TqqqqoYGRmhuLiYWCxGcXGx2004ODhIXV0dlmUxMDDA5OQkfr8fEWFgYMBNs5XJZNxEyatWrWL16tXuagG2bTMwMEAymcTj8dDf388bb7zBDTfcwKZNK2pcklJqGWlgU/PyeDxuS2lycpJoNEpRURHGGC5cuOCuANDa2sqaNWsoLy9n/fr1HD16lJqaGiYmJjDGuNcJBAIMDAywZs0aampqiMViBINBUqkUmUyGVCpFOp3G4/HQ2NjIXXfdRSqVYmhoyG3pGWMYGRnh0KFDOqdNKTWNBjY1r40bN/Lqq68SCoWIx+PuummNjY1kMhkCgQDl5eWkUinq6+u59dZbCQQCeDwefD4fRUVF2LbN4OAgk5OTBAIB+vv78Xq9rFmzhsbGRt566y0syyKdTjMxMcGqVatoaWkhGAxy+vRpenp6iEajGGOwbZt0Oo3P58MY404FUEop0MCmFiAcDrNlyxYqKio4dOgQdXV1dHZ20tHR4Y5QjEQi1NfXs27dOsLhMIcPH6axsZHjx4+7Lbvy8nK3xZdIJKirq+Ott96isrLSDYKpVIpQKMTIyAiTk5PYts2hQ4fcZW5yxxljKCoqYuPGjaxZs2a5vyKl1AqigU3NKxKJ0NHRwdjYGPF4nP7+fkZHRzHGkE6nKS4udjPyT0xM8JOf/MTtOiwtLSUQCGBZltuN6PF4+PDDD/H5fHR2dtLQ0EAoFCKVSrnH5aYMDA8PU1JSQllZGSJCTU0N4+PjlJeXs2nTJj7xiU+watWq5f6KlFIrSN5yRarCMDw8zOHDh2loaHCfaQ0ODrrPwkQEr9dLOBzGtm2eeeYZIpEItm3T3d3NyMgIxhh3de1cN+bo6Ch9fX3U19fj9Xrx+XzEYjFEhGAwSGVlJclkkqGhIWKxmPuML5PJsH79ejZv3szdd9/NF77wBTwez3J/TUqpFURbbGpOAwMDANi27a5w3d/fj2VZ2LZNIpGgvLzcHfKfSqU4e/YswWAQEcG2bYaHhxkcHCQajZJOp7Esi3A4TCqV4tZbb6W9vZ1wOMy6desYHR1lYmICn89HTU0No6Oj9Pb2ulMAKisrqaqqIhwOY4yhv79fFxpVSk2jgU3NKRwOA9DZ2cnQ0BC2bWNZlrtydi64+f1+otEo4+PjiAiZTIampiYCgQAdHR14vV53JKXf76e4uBiAl19+meHhYYwx1NbWEo/HGR4edq8Xi8XczCWBQIBVq1aRTCZJJBJcuHCBN954g4qKCiorK5ftO1JKrSzaFanmVFdXR0tLC9FoFK/Xy9q1a7Ft283GX1lZyZYtW4hGo3R2djI8PMzExAS2bXP99dfziU98wl0M1O/3Y4whk8kwPDzsjqYE6O7u5uzZsyQSCQB3SD9kW4slJSWk02lisRiDg4MkEgm8Xi+nTp1yM54opRRoi00twJYtW/jc5z7HCy+8QEdHB5lMBsuy8Hq9pNNpRIRYLMbo6KibOisUCnHkyBFKSkrcKQE+n889pry83H0+V1RUxOjoqNsa9Hg81NfXs2rVKjo6OtznbrFYzM03uW7dOndlgFwwVEop0MCmFuiTn/wkhw8f5s0338Tn85FOp0mn09i2zdDQEKOjo8TjcaLRKCUlJe7yMsFg0M0kkpvDJiKcO3eOaDRKRUUFPT09VFRUkMlkgGxrraamhtbWVrc11tfXRzQapa6ujqamJoaHhwmHw25CZaWUytHAphZERLjhhht46aWXsCyLQCDA5OSkO7gkFou5A0xGRkbw+/0kEgl3KkBuWsDk5CQej4d0Ok00GiUazS6yvmrVKi5cuEAymcTv91NSUsLAwABVVVV0dHTQ09ODz+cjk8kwOjrKjTfeyObNm6mrq2Pt2rXL+M0opVYaDWxqQYwx7srVxhgmJycxxhAKhdyh/6FQyD02nU67S9yICMlkEhFx80H29/cjIpSXl+PxeIhEIqTT2UXPW1paiEQirF+/npKSEuLxOMYYN0dkbm6cx+Nhx44d+Hy+5fxqlFIrjA4eUQty/PhxNy9kbmBHKBRyn5WlUils23afpYmImyIrFwQTiQSZTMadGpAb9Tg+Po4xBq/Xi23bdHR0MDExwfHjx2lvb8eyLCzLIpPJEIvFGBgY4Pjx4/zd3/0dP/rRjxgcHFzur0cptYJoi03Na3BwkJdeeomTJ0+6S8kEAgFSqRRjY2Nu4LFtm0wm4y4OOj4+DmRbcLmsIrmRjrkWWO78kpIS9zqJRIIjR47g9XpJpVIEAgG8Xq/bArRtm66uLoqLi9m7dy/V1dU89NBDy/kVKaVWEG2xqXkdOHCADz/8kIMHD5JOp6mqqqK+vp5gMEgymZz2XKy4uNgNQoAbyNLptDvB2+PxEAwG8Xq9eDweMpkM0WjUbenlBqZ4PB4sy2J0dBSPx4Pf7ycQCLgtt9OnT3PmzBna29uX7btRSq082mJTczLG8NJLL7ktq9HRUcbHx6murqa0tNRNWOz1et1h97nnaplMhkwmg9frRUSmXTfXZQlQUlJCRUUFExMTbhaT3AhKwA2SudGYsVgMn89HPB7XJWuUUhfRwKbmlJtsPTIy4i4ImmuhBQIBotEolmUxMTHhJijOra+WSCRIJBIYY9w5a7nXpk2b3BGOHo+HpqYmhoaGSCQSVFdXu8/fRAQRobS0lGQy6U4xyCVTFhGqqqqW+2tSSq0g2hWp5hQMBqmrq6Onp4fJyUk340c0GmVkZISxsTHGxsawbRuv10tLS4ubaSSX/Dj3M7fETVlZGZ/61KfYuHEjxhh3QEhuOZqGhgY3CXIuMObukRsNWVRUhM/nIxAIUFFRsczfklJqJdEWm5pTbkj9448/7o58zGQyxONxgsEg6XSaQCBAIBAgGAxSXFyM3+9neHiYTCaDbdsAWJbljpSMx+O88sor7oASy7IYGBhw11/LpeUqKipyW4eA20L0er1u9pPi4mIaGhqW8ytSSq0woqsPZ+3YscPs37//Y13je9/7XkHmLWxvb+fIkSPucjSA2/rKDdPPZQ3JpdnKDfHPlQFuEMul47IsC5/P53YxJpPJadfLrQ6QOzY3rSCXxsvv91NWVsYDDzxQcN2RGzZs4LHHHlvuaii10slshdpiW0SnTp3i4JF2MsWFk2k+lUzQ393P+GQCLC92JgkCfn8RGWOTSadIphPZLkeEWCIb0Kb+wZTO2O7gkXQmOzKSeBKPz4uVSGFjk04k3WdxtjNR22RsPB4PqUwGk8rg8XkxYoHlwRjIYJH2BGjvGSE4ml7y7yZfPJNDy10Fpa5qeQtsItIMPAXUAQZ4whjzZyJSCfwtsBY4C/y6MWZYsv/y/RnwWWAS+G1jzAHnWruBf+dc+j8YY550ym8BfggEgeeAf26MMZe6R74+61SZ4kpimz67FLdaEonRQVLpEqykBZFeLF8Kjz+IFBWDnUHik5hkjHQmjeUPYuxxTHpmkBGs4jB2Ko6dESxLstlJ8OD1FZNJTIDHB5mUExQFxIPlEWzLws5kQMAYwfIGECvbOpTiMP4NO0m27oSywmmxBY89t9xVUOqqls/BI2ngfzfGbAZ2Al8Tkc3AN4F9xphWYJ+zDfAZoNV5PQo8DuAEqW8BtwO3Ad8SkdxogceBr045b5dTfql7qMvkKy4DO43HG8i2lOwMdjpJOjaOnYxjLAvbNtiJGOnYONgG7FlaT3YGO5WCTAo7ncZkbCyPF29pJf5wDeL1gWWBSPbPIATj9SEeH5bHi1jZVbLtRAw7lcQKBPGVVoBt4y3WhUaVUh/JW2AzxvTmWlzGmHGgHWgEHgaedA57Evi88/5h4CmT9Q5QLiINwK8Ae40xQ06ray+wy9lXZox5x2T7vZ6aca3Z7qEuk+Xz4w/XkUnGEWMjlgeTSmCATCxKZmIUOzEBGEinMOnUjCsIiJBJJbIBz9jZXnHJjpr0eHx4ikswxoaMDXbulcbYYNtpDAbxeBDLm33v9WJZHixfAH+oHDupy9YopT6yJM/YRGQtcBPwS6DOGNPr7Ooj21UJ2aB3fsppXU7ZXOVds5Qzxz1m1utRsq1DVq9efbkf65ph0km8xWXEhnvJJGJgMhjLizE2dtwJalggBiwPiJVtuZlMdp+dyQa0HNsGsRBLSIwOYHD2ifMf47T6UpPg9YMlGNvpfnRadHY6hZ1OkskksfxFS/uFKKVWtLzPYxOREuCnwB8YY8am7nNaWnkdljnXPYwxTxhjdhhjdtTU1OSzGlctk0kTH7lAYvgCdnwSY6cxqVQ2oE0bUWtng5dtQDzTAxnG2TbuJpaF5SvC8nrxBIJYlpdsUJtynJ3BZFJggwRL8QZC2WdxGMTjxU7E8RWHsbya3V8p9ZG8BjYR8ZENan9tjPk7p/iC042I87PfKe8Gmqec3uSUzVXeNEv5XPdQlyk1MYonUExiPJJtrWXSIIKx09jx2Cxn2OBxgtQlGUAQy0IsCzsRJ5NOzv5szoBJJ7DHh0hNjGCScTAgHg/eYEn2OZtSSk2Rt8DmjHL8S6DdGPNfpux6FtjtvN8N/HxK+SOStRMYdboTXwAeFJEKZ9DIg8ALzr4xEdnp3OuRGdea7R7qMhlgovd0NqBhZ1tpJuMEIaercSrLQuaaG2l5wJttdWVi48RHB0lPjGa7J2djp7NdmZmMe4x4vHgDJXiLyy7KQamUUvl8xnYn8FvAYRE56JT9G+A7wDMi8hWgE/h1Z99zZIf6nyI73P93AIwxQyLyx8B7znF/ZIzJTfT5PT4a7v+882KOe6jLJCKkJ0ex7XS2m3G+nmNn4MclGbJBCiGTjgE2xhnxOLdcd6ZFJp0iFY/iSyfwhsoX+EmUUteKvAU2Y8wbXLo/6v5ZjjfA1y5xrR8AP5ilfD+wdZbyyGz3UJfP8vhIxyawEzEW9jjUzj568/ohHb94t8nMcspCJlfnns/ZkEmSHBkEj5ex0wep2nq3PmdTSrk0CbKak/iLSE2OzxgoMo9MBuyZw/4XkW2DnSI5fIFI+9vEIt3zn6OUumZoYFNzspOxbGtILudXJeN0W+ZL9tqCIT05zkR34eXnVEpdOQ1sak5ieSmuW5vNCHI5rDz/ahnAGDz+QH7vo5S66mhgU3PyFhVT1nwdRZUN2flpcw7jn+oSoxznJWR/Lee7j40RC8sfJFA56/x7pdQ1SgObmpe/vJ7Qqla8JeUgXrJZRuYJPpcavj+nXFCz5752jjFkEnHszJUGUaVUIdJlaxZRd3c3nsnRgsrOboxhuPMU9sAFPHactDjpsfLyCM2QnRsHC2nxiZ3CjPeT+PA5gqnCGUDimYzQ3V04y/AotdS0xabmlEmlECOk4jEyqdQVtsQux+U9y8ukU4izDI5SSoG22BZVY2MjfQlvQa3HZqeSJDOvYSZSpGOngOSVXUg82ZhlzzKPzWUhgWJMOgGZ+aYLCMbyIqEKfFseIN6648rqtQIFjz1HY6M+N1TqSmmLTc3J8vkJlFUjdvry5rLNJJ75pwBY2RyUCwlqWBaWx0dR5SrCay6ao6+UuoZpYFPz8pdWEqhpwvIsJPXVJdjJ+acA2DakFrK2mgED3pJywhu2Y6eusBWplCpIGtjUvOxMCslk8AZLnCH/V3qh+QZEXMYqRs5acKMn37/8OXZKqYKmgU3NyxhDfHQgu2zNimFIJ+Ikx4aw09piU0p9RAePqHmloiOYTAZj7BkLiC6zdJx0PMrljqRUarlMTk5y8OBBhoaGqKysZPv27RQXFy93tQqOBjY1r3QsikknMZkpq1uvFJkMsf6zBMoql7smBe173/see/bsWe5qTDM5OXnVTfMYHR0llfpocJTP5yMcDuf1niKyooLnrl27eOyxx/J6D+2KVHMydobkWIREdAiTXEldkQ7LYrK/c7lrodSCpNPpObfV4tAWm5pTYqSfxPAFSDkraK8wnkAQ7YrMv8ceeyzvf2VfC9566y0ikYi7XVVVxR133LGMNSpMGtgWmWdyqKBSasUv9CKj5/F5DHNNrV4uVnKSsnhfQX3nnskhQCdoF6Lt27df9IxNLT4NbItow4YNy12FRTdc4eedkV4uxKLMsh72sistDrBlTT3NzYUUCOoK8ndJQXFxsbbQloBcbQ9f82XHjh1m//79y12NFenpp5/m29/+NidOnCCZXDlD6/1+P62trTzzzDNs3rx5uaujlFp6sz6H0MEjal6f//zn2bp1K36/H+syFxC1LAufz4ff70fmmEgtIhdd27Ksec+JxWJ0dxdOZn+l1MengU3N6/Dhw+4w5VwA8vv9eL3eeQOPbdsYYzDGzHmsMQZ7ysoBIuKeNxu/3099fT1NTU28+eabV/7hlFIFRwObmld7ezsDAwNu8DHG4PF45m2FTQ1Kl9vamyuo5YKez+ejsrISz8fJYamUKjga2NS8uru7uXDhwrRAk8lkEJFLTi6d2rILBAIEAgG83o8/VinXPWnbNiMjI5SXl3PjjTd+7OsqpQpH3gKbiPxARPpF5MiUskoR2SsiJ52fFU65iMh3ReSUiBwSkZunnLPbOf6kiOyeUn6LiBx2zvmuOE2HS91DXZlMJsP58+fdrkePx4OIUFZWBkAsFnPLc0En16KCj7IeVFdXz9q6m/lsTUTweDx4vd5Zuzpzx1uWRTwep62tjR07CmctNqXUx5fPFtsPgV0zyr4J7DPGtAL7nG2AzwCtzutR4HHIBingW8DtwG3At6YEqseBr045b9c891BXYGJiAo/HQ21trRuALMvCtm0ymQyJRIJMJuN2T1qW5Qa13HM1v99PUVERoVDIvUYuYOVadUVFRfh8PsrKyigvL6e6upqSkhJ8Ph+WZbnBM8eyLAKBAPF4nGeeeWaJvxWl1EqWt3lsxpjXRGTtjOKHgXuc908CrwD/yil/ymT/RXxHRMpFpME5dq8xZghARPYCu0TkFaDMGPOOU/4U8Hng+Tnuoa5ASUkJpaWl9Pf34/V6sW0bESGdTrvdi8lk0g1sxhg38OW6KsvLy2lpaaGsrIz333+fTCaD1+vFGON2U7a2thIMBunp6aG2tpaysjI6OzuJRqMMDg6SyWTweDzuvQKBAEVFRQSDQc6ePbvcX5NSagVZ6gnadcaYXud9Hx+lV2gEzk85rsspm6u8a5byue5xERF5lGwLkdWrV1/uZ7kmWJbF+vXrCQaDWJZFUVERgBtgAILBIKlUCtu23VYUgMfjIR6Pk06nqa2txRhDUVERtm1z2223EQgE6Orqoq6ujkAgQCQSIRgMMjg4SCKR4LbbbqO7uxvLspiYmCCVSlFSUoKIuEGturqadevWLdv3o5RaeZYt84gxxohIXmeHz3cPY8wTwBOQnaCdz7pczVavXk1RUZHbzZgLYJZlkUgk3O1EIoHX68Xn82HbNqlUimQyyYULF/jggw9Yt24d1dXVQDYDQ2NjI6WlpWQyGTo6OpicnCSZTOL1eolEIpw8eZJUKsXw8DCAe73a2loCgQBNTU3cdddd/ON//I+X8+tRSq0wSx3YLohIgzGm1+lq7HfKu4HmKcc1OWXdfNStmCt/xSlvmuX4ue6hrlDu+VgwGGRychIRoby8HBFhZGQEwB39mEgkgI8GgeS6Dbu6uli7di2WZTE6OkpnZyeDg4Ok02mSySTd3d1Eo1FKS0sJhUJuC83j8Uzr/jTGkEqlaGlp4YEHHuCf/bN/lvdlP5RSV5elHu7/LJAb2bgb+PmU8kec0ZE7gVGnO/EF4EERqXAGjTwIvODsGxORnc5oyEdmXGu2e6gr5PP5aGhoIBQKud2LAwMDRCIR4vE4xhhisRgej4dQKOS27nLP2pLJJB6Ph5qaGlKpFPF4nEQiwfj4OL29vfT29lJSUkIgECAajXLhwgWSySSJRMIddZlKpdzuTsuyiEaj9Pf3a1BTSl0kby02EfkR2dZWtYh0kR3d+B3gGRH5CtAJ/Lpz+HPAZ4FTwCTwOwDGmCER+WPgPee4P8oNJAF+j+zIyyDZQSPPO+WXuoe6QrmRieFwmIGBAQBs2yYej2PbNul0GsuyyGQyFBcXk0wm3W2AaDRKQ0MDAwMDxONxMpkM0WgU27bdUZder5fKykrGx8fd6QTRaBQRcTOeQLZlmBuRWVZWRjwed5/7KaUU5HdU5Jcvsev+WY41wNcucZ0fAD+YpXw/sHWW8shs91BXrrKykqqqKkTEzUASi8XcFFi5uWu5FptlWYyMjLitq2AwSG1tLZs3b+bQoUNMTEy4z+py10gkElRUVGBZFmVlZe4zulyGkUQi4bYYvV4va9as4cYbb8Tv9y/nV6OUWoF02Ro1L8uy3O7EiooKRkdH8Xg8ZDIZbNvG6/USDAYpKyujoaGBdDqNbdskEgkCgQDFxcUUFRVRXFxMMBiktLSU4uJiJicn3YBp2zZjY2PU1NSwbds2+vr63DRc5eXljI2N4ff73WkFt99+OzfddNNlJ2VWShU+DWxqTiMjIxw6dIidO3dy8OBBamtraW5u5ujRoySTSVKpFH6/323V5SZaDw4OugNMvF4vzc3NNDc34/f7CYfDNDY2MjIygjGG1tZWEokEDQ0NrF+/nlgsRjgcZsuWLYgI4+PjDAwMYNs2dXV1rF69mi996UvU19cv99ejlFqBNLCpOfX3ZweVVlVVce+991JZWUldXR0PPvggr732Gul0murqaq6//nrWrVtHe3s7x48f57Of/SyZTIZMJsOtt97K9ddfz8GDB8lkMqTTacbHxykvL6euro6qqir6+vooKyujpqaGsrIyqqqq2LhxIydPnuTgwYOMjY255507d44333yTTZs2sWXLlmX+hpRSK40GNjWn0tJS971lWVx33XUEAgE8Hg+PPvoozc3N1NXVubkjH3roIc6fP09nZye2bbN+/Xrq6+t5/fXXOXToEGNjY25X4tDQEFu3bmVkZISamhoaGhrc6+RGVp44cYLu7m7KysowxjA6Oko4HGZwcJAzZ85QV1fnzo1TSinQwKbmUV9fz5o1azh37hwAW7ZsYevWi8bscP78eU6ePIkxhvXr13PHHXfQ3t7O0aNHefvtt90h/oDbkmttbeXzn/885eXljI6O8uKLL/Lhhx8iIiQSCfbu3cuRI0ewLIuhoSFKSkrc++WerY2Pj2tgU0pNo4FNzUlEuPHGG7n++uuB7ND/eDxOJBKhvLycUCjEyMgIBw8edM85fPgwFy5c4MSJE0B2BYCOjg53dGMu52RLSwstLS14PB6OHTtGc3MzpaWlnDhxgjNnzjAyMsKFCxfw+XxMTk4SjUZpaWkhEAi4qwXU1tYux9eilFrBNLCpBfH5fAD09fXx6quvEo1GKSsr49Zbb3Xnq+VkMhmee+65aStinz17lvr6evx+P7FYjE2bNvHAAw/g8XhIp9NuBpNkMglkVxUYHx93uybLy8spKSnhoYceIhQK4fP52LBhA6FQaAk+vVLqaqKBTV2Wffv2cezYMQB38vSDDz447ZjBwUGCwSATExMADA0NUVFRQTgcJhAIUFZWht/vp6uri9raWrxeLyUlJUSjUcrLy7Esi9LSUjweD4ODgzQ3NxMKhbjuuuvYvn07jY2NF9VLKaVyNLCpBbNte9oSMcYYOjs7qays5LrrruPUqVMANDY2Ul1d7XYnFhUVUV9fTzqdpri4GACv10t3dzdbtmwhEAhw00038cEHHxCNRvnEJz5BaWkptm2zdu1aRITS0lLKysp0iL9Sal4a2NSCGWOorq6mq+ujFYOqq6uxLIuNGzeyYcMGAOLxOK+++iqtra3u2muhUIh3330XyHZrVlZWuvkkIdvVeO+997rrruXs2LGDrq4uvF4vTU1N0/YppdRsNLCpBfN4POzcuZMDBw4wNjZGKBTi3nvvdffnglRxcTF33303nZ2dANTV1dHf38/mzZvZu3cvwWAQj8fDunXr3Gd34+PjDA0NUV5ePi2xcTAYpLW1dQk/pVLqaqeBTV2WG2+8kcrKSjf91cxRifF4nA8//JBIJOJmD3n33Xfdof5+v5+ysjJ27NhBZWUlAF1dXXzwwQfuNbZs2UJLS8u06xpj3ETISik1Fw1s6rKICM3NzZfcf/jwYTdbydDQEPv27ZuWqNiyLIaHh9m/f797rdyKATknTpxwA1sikeDAgQMMDg5SUlLC9u3bqaioyMMnU0oVCskm1lc7duww+/fvX+5qLLrvfe977qCOpXD27Nlpw/wTiQR+v99tbeUykqxbt849JpVKuV2SkA1+a9asQUTo7+8nGo26+3J5Jxe79bZhwwYee+yxRb2mUirvZv2HQFOjq0U1c220XCb/nEAgMC1NF2Sfo808Jxe4cl2YOel0+qJ5c0opNZW22ByF2mJbarFYjIMHDzI4OEg4HOamm26itLSUwcFBMpkMfr+fN998k9zvnYjwqU99iomJCTebydR5agcPHuT8+fPudigU4r777lvyz6WUWpFmbbFpYHNoYFs6fX19nD59GhFh/fr11NXVXfLYVCrFoUOH6O/vp6ysjBtuuMHNRqKUuubNGth08IhacvX19QueaO3z+bjlllvyXCOlVCHRZ2xKKaUKigY2pZRSBUUDm1JKqYKigU0ppVRBKdjAJiK7ROS4iJwSkW8ud32UUkotjYIMbCLiAf4c+AywGfiyiGxe3loppZRaCgUZ2IDbgFPGmDPGmCTwNPDwMtdJKaXUEijUwNYInJ+y3eWUTSMij4rIfhHZPzMRr1JKqavTNT1B2xjzBPAEgIgMiEjnMlfpWlENDC53JZTKA/3dXlp7jDG7ZhYWamDrBqaurdLklF2SMaYmrzVSLhHZb4zZsdz1UGqx6e/2ylCoXZHvAa0isk5E/MCXgGeXuU5KKaWWQEG22IwxaRH5feAFwAP8wBhzdJmrpZRSagkUZGADMMY8Bzy33PVQs3piuSugVJ7o7/YKoMvWKKWUKiiF+oxNKaXUNUoDm1JKqYKigU19LCLyeRExIrLJ2V4rIkec9/eIyP903v+2M1fwoIi0ichX57mue65S+eT8/v7JlO1viMj/Nc8514nIK87vc7uIzPlsTUR+JiKfn7J9XET+3ZTtn4rIP7nyT6Gm0sCmPq4vA284P+fzt8aY7cA9wH8Ukbo81kuphUoA/0REqi/jnO8Cf2qM2W6MuR743jzHvwncASAiVcAE8Ikp+z8BvHUZ91dz0MCmrpiIlAB3AV8hO1dwQYwx/cBpYI2I/FBEvjDlmtEph5aJyN87f93+NxHR31eVD2myoxn/cOYOpwfiZRE5JCL7RGS1s6uBbKo+AIwxh53jPSLyn0XkPeec33UOeQsnsDk/fwHUSNY6IGaM6RORx500f0dF5N/n5+MWPv2HQn0cD5NNaXMCiIjILQs5SURagBbg1DyH3gY8RnaFhvWAdtWofPlz4DdFJDyj/HvAk8aYG4G/JttSA/hT4GUReV5E/lBEyp3yrwCjxphbgVuBrzqB631gq5Mw4g7gbeA4cL2znWut/Vsnc8mNwKdE5MY8fNaCp4FNfRxfJrtyAs7P+bojvygiB4EfAb9rjBma5/h3nRUaMs45d32cyip1KcaYMeAp4Oszdn0C+Bvn/V/h/A4aY/4H2aD0Y7Jd6++ISAB4EHjE+T3/JVAFtBpjEsBR4GZgp7PvbbJB7Q6yXZUAvy4iB4APgC1k/6hTl6lgJ2ir/BKRSuA+4AYRMWQzvBiyf/leyt8aY35/Rlka5w8sp6vRP2XfzEmWOulS5dN/BQ4A/2MhBxtjeoAfAD9wBkxtBQR4zBjzwiynvAl8Eig1xgyLyDvA7wM3AX/htOy+Adzq7P8hUPTxPtK1SVts6kp9AfgrY8waY8xaY0wz0MH05NMLcRbIdWE+BPim7LvNyfdpAV8kO0hFqbxwehCeIdudmPMWHz0//k3gdQAR2SUiPud9PdmWWTfZNH7/25R9G0UkNOVavwt86GwfItt6Ww0cAcrIDioZdQZWfSYPH/OaoIFNXakvAz+bUfZT4F9f5nX+O9lnCR+S7faZmLLvPeD/AdrJBs2Z91Nqsf0J2aVnch4DfkdEDgG/Bfxzp/xB4Ijze/sC8H8YY/qA7wNtwAGnFfcXfNQz9hbZZ8tvQzanLdAP7DfG2MaYD8l2QR4j2/2Z655Ul0lTaimllCoo2mJTSilVUDSwKaWUKiga2JRSShUUDWxKKaUKigY2pZRSBUUDm1Ir1NSVEqaU/V9O9vnfFpFVU8q/LyKbnfdncwl9ReStKdf6jSnH7xCR76JUAdLAptTV6bcBN7AZY/4XY0zbzIOMMbnEu2uB35hSvt8YMzN9lFIFQQObUlenHcBfO+uBBZ21wXbMPGjKagnfAe52jv/DGWvlhUTkByLyroh8ICIPO+VbnLKDTqb61iX7dEp9DBrYlLo67Qd+01kPLLaA478JvO4c/6cz9v1b4GVjzG3AvcB/dtJA/a/Anzlr6O1gyjItSq1kmgRZqZXrUmmBFjtd0IPAQyLyDWe7iGz+wreBfysiTcDfGWNOLvJ9lcoLDWxKrVwRoGJGWSXZvJmLSYBfM8Ycn1HeLiK/BP4R8JyI/K4x5uVFvrdSi067IpVaoYwxUaBXRO4Dd6mgXWRXORgHSi/jcnMd/wLwmIiIc5+bnJ8twBljzHeBn5Nd/FKpFU8Dm1Ir2yPA/+ksXPky8O+NMaeBHwL/LTd4ZAHXOQRkRORDEfnDGfv+mOxyQYdE5KizDfDrZDPYHyS71thTH/fDKLUUNLu/UkqpgqItNqWUUgVFA5tSSqmCooFNKaVUQdHAppRSqqBoYFNKKVVQNLAppZQqKBrYlFJKFZT/H4gxISwNd0woAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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JC/AT5/YW4CPO6Mh1wLDTnfgUsFFEqpxBIxuBp5zHRkRknTMa8iPnPNdEx7ii6PXYStu+ffvYsWMHzz//PNFolB/84Ae61aZpBZDPFlsD8JKI7AN2AD9TSv0c+DKwQUSOAe9x7gM8CbQBrcCjwO8CKKUGgD8Hdjo/X3LKcLb5J2ef48BWp/xCx7iiXI3rsV0t3XKWZdHV1cX27dtzZfF4XI9+1bQC0LkiHTpXZH5Fo1E6OjrYvHkzL774Ih/84Ad56KGHil2tvFFKsW3bNj772c+STCYBcLlcNDY28vOf/7zItdO0kqFzRWrFEYlEeOGFF9i1axc/+9nPGB4e5sknnyzpVpuIsHLlSm666SbcbjciQkVFhR79qmkFoANbER09epRNmzbR2tpa7Krk1enTpzFNM9ctZ5omiUSi5Lvlmpqa+NKXvkRFRQWVlZX4/X59PVXTCkAHtiKwLIszZ87wZ3/2Z4yOjvK5z32OV199leeee45jx44Vu3ozzjDsl9nevXtzKcUymcxVMSm9qamJ+++/H5fLpUe/alqB6IVGCyyTyfDSSy9x5MgRDh8+jGEYDA4Osn//fhobGzly5Agej4fm5uZiV3XGLFy4kNOnT7N69WreeOMNAILB4FXTLacXGtW0wtIttgLr6uoiEonwve99D4BUKkUqleLxxx/PbdPTU7Rpd3kRDAa5++67efDBBwmHw4TDYVwu11XzQX81jn7VtGLSga3Asl1x2eBlGAZKqXHBLBwOF6Vu+eT1elm9ejUf+MAHMAxDd8tpmpY3OrAVWGNjIx6Ph/r6esAeAl5RUcGcOXMAqKur45prrilmFfPqapuUPjQ0lEt8rWlaYUx6HpuILASWKqWeEZEA4FZKRfJauwIq5Dy2WCzGCy+8wP/8n/8Tn8+Hy+Xi0Ucfpbm5GZ/PV5A6aPllWRY7duwgm1y7urqadevW4XK5ilwzTSspE85jm9TgERH5BPYaadXANdh5Gf8RuGemane1GBwcpK+vjzVr1lBfX8/o6CihUIjly5cXu2raJD388MOXnKIRjUZz3ct9fX0AXHvttZSXl0/qGEuWLOHBBx+8vIpq2lVqsqMiP42dwPh1AKXUMRGpz1utSlR7ezt79+4FYGRkhP7+fnw+H4lEgv7+fn3NqYRkr6WCPUDo3DJN0/JnsoEtqZRKZZdZERE3eV5uphQdP348d3v79u1Eo1HcbjeJRIL/+3//L//rf/0v/H5/EWuoTcZkWlLxeJznn3+eTCbDI488AsDf/u3fUl1drbsjNS3PJjt45AUR+TMgICIbgH8Hfpq/apWW7u5u9u3bR2dnJ6ZpArBnzx6UUgwPDxONRtm6dSsvvPACiUSiyLXVZkIgEOAd73gH8+fPJxAIICK89tprPP3007S3t1/6CTRNm7bJBrbPAr3AAeBT2Jn4P5evSpWS9vZ2duzYwenTp0kmk7nMItm5XNnBO+FwmFQqpT/0Skg4HGb16tW43e7c/zmTybB//37S6XSRa6dppWuyXZEB4FtKqUcBRMTllMXyVbFScerUqdzturo6AoEAzc3NxGIxXC4XmUwGEWFgwF6JJ9vdq5WOc4OYZVlEo1EqKyuLUyFNK3GTbbFtxw5kWQHgmZmvTunxeDzj7peXlzMyMkI8HieZTBKPx7EsC6UUfr+f+fPnX+CZtNkmk8mQTqcJBoPjyv1+f0lOwte0K8VkW2x+pdRo9o5SalREghfbQbMtW7aM/v7+3LW12tpaent7qampoa+vD7/fj8vlYsmSJaxfv/68QKjNTocPH6atrQ2lFKlUioqKCsrLywkGgzQ0NLB7926UUixatIja2tpiV1fTSspkA1tURNYopXYDiMjNQDx/1SodVVVVvOc976Gvr4+ysjISiQS9vb2MjIyQSqVIJpN4PB46OjowTVMHthLQ398/bp7b6OgodXV1rF+/nmg0yvPPP49lWYA9sOhd73rXpOe3aZp2aZMNbL8P/LuIdGHP9J4D/Fq+KlVqvF4vjY2NgN0VWVZWRjgczi206fF4KC8v5+jRo9x4443FrKo2A0ZGRs4ry85l6+7uzgU1sFfaPnPmjA5smjaDJhXYlFI7ReRaIJse4y2llB7WNQWZTIbW1laGhoZoaGggkUjg8XjweDy4XC6GhoYYHR299BNpV7y6ujpEhLHp6gKBwLjfY517DU7TtMtz0cAmIu9WSj0rIh8656Flzhv3P/JYt5Kyd+9e2tvbcbvd9Pb2cuutt47rkrr++utziZC12S0UCrF27VqOHTuGZVnU1tbi9/s5fPgwZ86cYWBggGAwiN/vp76+Ptea1zRtZlyqxfYu4FngfRM8pgAd2CZhZGSEbdu2EYvF8Pv9LFmyhIGBAcLhMLFYDMuyqKysZNGiRcWuqjZD5syZk/ui8sMf/pCBgYHcdbds9pF3vvOdugtS0/LgooFNKfV5ETGArUqp7xeoTiXnwIEDublMiUSCtrY2Dh06RHl5ee6D7c0339Rz2EpYPD5+rJVpmhiGXjVK0/Lhku8spZQF/PF0DyAiLhHZIyJPOPcXicjrItIqIt8TEa9T7nPutzqPN495jj91yt8SkfeOKb/XKWsVkc+OKZ/wGMUyPDxMZWUl/f399PT0cPz4caLRaG50ZHaStla6zh3t6vF4dF5QTcuTyX5lfEZE/khE5otIdfZnkvv+HnB4zP2/Ar6ilFoCDAIfd8o/Dgw65V9xtkNEVgAfBlYC9wL/zwmWLuAfgE3ACuDXnW0vdoyiiMVi9Pb2EgwG6e3tRUSoqqoiGo2SSCQYGRlh1apVxayilmfV1dVUVFQAdlBbtWqVToasaXky2eH+2aH9nx5TpoDFF9tJROYBvwT8JfCQ2M2SdwO/4WyyGfgC8HXgfuc2wA+Arznb3w88rpRKAidEpBV7CR2AVqVUm3Osx4H7ReTwRY5RFF6vl+rqatra2giHw6TT6dwaXclkknQ6zdhFTrMTumtra3V3VYlwu93cddddJBIJvF6v/r9qWh5Ndrj/dEc1/B12N2b2CnkNMKSUyi5M1QE0ObebgHbneBkRGXa2bwJeG/OcY/dpP6f8tkscoyj8fj8igsfjyaXQikajpNNpLMvC7XaTyWQ4ePAgra2tRCIRwuEw5eXl3HnnnXrSdgnR3Y+aln8X/dooIreJyD4RGRWRV0Xkusk+sYj8J6BHKfXGZdcyT0TkkyKyS0R29fb25u042etrZWVluewj6XSaZDKJZVl4PB5M0+Rv//Zv2bVrF2+99RbHjh1jdHSU06dP561emqZppehSLbZ/AP4I+AXwfuwW2HsvtsMY7wDeLyL3AX4gDPw9UCkibqdFNQ/odLbvBOYDHc5CphVA/5jyrLH7TFTef5FjjKOUegR4BGDt2rV5Wzi1srKSlStXEolEqKmpobu7m/3792frQDKZxDAMotFobp/shO1sxgpN0zRtci7V0W8opbYppZJKqX8H6ib7xEqpP1VKzVNKNWMP/nhWKfWbwHPArzibtQA/cW5vce7jPP6sslM3bAE+7IyaXAQsBXYAO4GlzghIr3OMLc4+FzpGUTQ0NBAKhZgzZw7BYJAzZ85gmiapVIpYLJbL8l9WVobb/fZ3DaUU8+bNK2LNtZlkmiYHDx7k2WefZefOneO+yGiaNnMu1WKrPCfryLj708w88ifA4yLyF8Ae4JtO+TeB7zqDQwawAxVKqYMi8n3gEJABPq2UMgFE5DPAU4ALe724g5c4RlFUVFSwdu1a2traGBkZYe7cuYC99pphGHg8HtxuN5ZlsWzZMgYGBggEAtx33316Au8s0tnZyb59+3Jp05YvXz7ui8mRI0doa2sDIBqNEo1GWb9+fZFqq2ml61KB7QXGZx0Ze3/SmUeUUs8Dzzu323h7VOPYbRLAf77A/n+JPbLy3PInsVfzPrd8wmMUUzYTRTZn5OjoKOl0OhfY/H4/ixcvZtGiRdxyyy0sXLhQDxqZRY4ePcqOHTt46623AOjr6yMWi41bd62np2fcPpFIhHg8PmH+SE3Tpu9SmUc+VqiKlLpkMsm+fft49tlneeGFF3KLi6bTaaLRKIZhsHLlSm677TY9WXsWOnXq1Lis/n19fSxcuDA3rQPslR3GJrr2er34fL6C1lPTrgaTGu4vIg3A/wYalVKbnInQtyulitrFN1ucPn2an/3sZ7nRjv39/blBIYZhoJTCsizKy8tzQ/212cXtdlNWVpa7bxgGIkJlZWWubMWKFUSjUUZGRvD5fKxatUrPZ9O0PJjsBO1vA/8M/A/n/lHgexT52tVskB0w0NvbSyKRYHBwENM0c8HM7XbjcrmwLIvdu3eTSCRYtWoVCxYsKHbVtSm49tpriUajjI6O0t3dTXNzM9dddx3V1W8n6AkGg7zrXe/Sk7Q1Lc8mG9hqlVLfF5E/hdwEajOP9SoZqVSKTCaD1+slmUzm0ipluxuzvy3LynVLHT16VAe2WWbu3Lncc889DAwM5JJbX6hLWU/SLk19fX188Ytf5Atf+AI1NTXFrs5VbbJfGaMiUoM9YAQRWQcM561WJSQQCFBZWcmCBQsQEbxeL9dffz0VFRWEQiHKyspy3VbNzc0A41ZY1maPQCBAU1NTriv50KFDbNu2ja6uLhKJRJFrp+XD2Pfq5s2b2b9/P5s3by5ijTSYfIvtIez5ZNeIyMvY89l+5eK7aFm33norg4ODNDc34/P5qKmpwev1EgwGERHS6TQikkuKq9dlm/2OHz/O8ePHAXvgUHd3N6Zp6sTHJcKyLPbt20dnZyder5e5c+eydetWlFJs3bqVlpYW3WorosnmitwtIu8ClgMCvKWUSue1ZrPQww8/nFtMcizTNHOpsZRStLW1UV9fn/sWHwgEqKmp4Xvf+x5+v59t27Zd8BhLlizhwQcfzM8JaJclmUxy9uxZ/H4/56ZosyyL4eHhcdfctNmrra2Njo4OwP6//93f/R2ZjJ2e1rIsNm/ezEMPPVTMKl7VLhrYzpmcPdYyEZnuBO2rjmmaud8igtvtprGxkYGBATKZDG63m5UrV+p5a7PYyMgIL7/8cu7DLZFInHctLRQKFaNqWh4MDQ2Nu79nzx68Xi9er5d0Os3TTz+tA1sRXarF9r6LPDbpCdpXi4laUqZpcuLECb773e8Sj8eJRqOICGvWrOGb3/wmHo+He+65hy9+8YtFqLE2U9ra2nJBDew110KhEKOjo7hcLqqrq/F6i7rerTaDampqOHPmTO7+TTfdRFtbG6Zp4vF42LhxYxFrp+kJ2nn2+uuvs2/fPtLpNIODg8TjcWpra/H5fLhcLqLRKB//eFHXQdVmwLkDflwuF6tXryYcDvPUU08Ri8U4cOAANTU1NDY2FqmW2kxpbm4mHo/T3t6Oz+fjj/7oj3jggQcwTRPDMGhpabn0k2h5M9nBI4jIL2GvYp3rX1FKfSkflSoVw8PD9Pf3k0wmcblclJeXo5QiGAyyb9++3HymV155herqaiKRCMePH0cpxZIlS3I5JbUrX3NzM11dXdg5uKGqqoqqqioABgcHGRkZ4eTJk5w8eZJIJMLy5cuLWV3tMokIK1asYMWKFbmyTZs2sWXLFjZt2qQHjhTZZDOP/CMQBO4G/gl7ROSOPNarJGTnMWXXYwsEAkSjUeLxOH6/n5GREVwuF4899hjf+c53mDNnDvX19Xi9Xl544QV+7dd+jaVLlxb5LLTJqK6u5q677qKzsxO/38/8+W+vqBSJRMZte/LkSR3YSlBLSwsnT57UrbUrwGRbbHcopW4Ukf1KqS+KyP8FtuazYqUgHA4zZ84cAGKxGO3t7bzjHe/A5/Pxi1/8gmQyycjICIODgwQCAY4dO0Y4HObmm2+moaGB559/nubmZj2oZJYIh8MTpkNzBlrl7o9dmkgrHbW1tXz1q18tdjU0Jh/Y4s7vmIg0Yi8ro/vJJmHt2rUcPnyY3t5ebr75Znw+H9XV1YRCISzLIh6PYxgGmUyGeDye+3a/atUqli5dSiQS0UPEZ5H29nZOnDiBYRgsXbqUhoYGqqqq6O/vB+wgp1trmpZfk8088oSIVAJ/DbwBnAAey1elSomIMDo6Sl1dXS5l1rFjx7jxxhvx+Xy5XJGJRAKXy4VSCrfbzeDgILW1tSWXELmvr48HHngg90FfSvr7+9m7dy/Dw8MMDg7mFhMNh8M0NTWxatUq7r77br14rKbl2UUDm4jcIiJzlFJ/rpQaAkLAAeDfga8UoH4lIZVK0dXVxf79+2ltbUVE8Pl8lJeX43a7yWQymKaJ2+1mzpw5LFiwgDlz5nDrrbfO6m4ry7Lo6+sbd42plNMOjV1vbWhoiLa2tlz2Ea/Xy4IFC8atAKBpWn5c6lPzG8B7AETkLuDLwAPAauARdFqtSxoZGeGFF17g2WefRUSoqanhhhtuoKmpiXg8nhstGQqFEBEWL17MvHnz+OAHP0htbW2xqz9tiUSCV155hWg0CsCCBQtoamoq6bRD2dXODx8+zJ49e7Asi46ODnp6eqivry9y7TTt6nGpwOZSSg04t38NeEQp9UPghyKyN681KxHPPvssQ0NDhMNhYrEYp0+fpr29HY/HQyKRwOfz0dDQQHl5OSMjI6TTaTZu3Mhdd91V7Kpflra2tlxQA3tNuieeeCI338s0zZJLO9TU1ERnZyc/+tGPUEpRVVWFz+ejq6trVn9J0bTZ5pKBTUTcSqkMcA/wySnsq2F3SRmGgWEYdHd309nZiYhgWRaxWAy32013dzddXV2A3X33xhtvsHLlylm9dE0ikSCZTOLxeHLrjj3zzDO59GKZTKbk0g4lEgmUUrlMI9nWqGma4yZwJxIJTpw4QSqVYv78+XpwkKbNsEsFp8eAF0SkD3tk5IsAIrIEvWzNpCxatIiOjg5EhJGRkdyK2dmFRpPJJGfOnMktOppMJnniiScYHR3lfe97H7fccsusS8U0PDzMiRMn2LdvHx6Ph2uuuYb6+nre/e53j0vwPNtbpWMppXjttdcYHR2lpqYmNzIyO1E7e63Usixeeukl4nF7oHF7ezt33HGHDm6aNoMulVLrL0VkO/bQ/qfV25NxDOxrbdolrF27FqUUpmkyMjJCb28vvb29RCIR0uk0Sik8Hg+ZTCaXAf7kyZOcOHGCs2fPcuzYMVauXFns05iSAwcO4PV6WbZsGX19fSQSCe6880527CjdOf2RSITR0VEA1q1bR2VlJYlEgve+970MDAzktjt+/DgDAwMEAgHADojt7e06sGnaDLrkcH+l1GtKqR8ppaJjyo4qpXbnt2qloaysjPXr1/MHf/AHfPSjH6W6ujrXZWUYRm59LsMwsCwrN58tHo9z8ODBWTksPvsBX1lZyZIlS6ivr8fn8/Hiiy+O2+4Xv/hFMaqXF36/P9fl6nK5WLlyJffffz+33347Lpcr11LbtWsXBw4cyI2WBGZdi1zTrnSTncemXaZwOMzixYspLy+nvr6euro6XC4XhmHk5rJlA11ZWRkul4t0enYueXfuCMC6ujoMw2DDhg25Ljm3211SGdC9Xi/XXXcdhmGQSqUYHh7G5/Pl/oeRSITBwUFCoVBuwvbw8DCBQEAvLKtpMyxvgU1E/CKyQ0T2ichBEfmiU75IRF4XkVYR+Z6IeJ1yn3O/1Xm8ecxz/alT/paIvHdM+b1OWauIfHZM+YTHKKbh4WG+8Y1vcOrUqVwXpMvlQkQIBoP4/X6qqqpYunQpS5YsIRgM0tjYyMKFC4td9Sm78cYbaW5upry8nPnz57NmzRrAzqU3tlVTajn1Fi9ezDvf+U5EhHA4zPHjx3nxxRcxTZPR0VGGh4dRSrF06VKuu+46VqxYwbvf/e7z1m3TNO3y5HNkYxJ4t1JqVEQ8wEsishV4CPiKUupxJ7nyx4GvO78HlVJLROTDwF8BvyYiK4APY68s0Ag8IyLLnGP8A7AB6AB2isgWpdQhZ9+JjlEUAwMD/Mu//AsnTpzA6/WilMp9yJWVlbF06VJCoRCNjY14vV5CoRDhcJjly5fT1NRUrGpPm9vt5oYbbjivvLa2tuQzoHd3d4/L7TkyMkJbWxuWZXH06FHKysq49tprqaqqYuXKlblAr2nazMlbYHMGmow6dz3OjwLeDfyGU74Z+AJ20LnfuQ3wA+BrYqfHvx94XCmVBE6ISCtwq7Ndq1KqDUBEHgfuF5HDFzlGwR0/fpxDhw5x8OBBUqkUsVgs98HndrsJBoPMmTMHn89HIpFg8eLF1NbWsmbNGm655ZbcNbhScSVmQH/44YdpbW2dkecaGhpicHAwdz8ajdLb24vX6+W1114jHo/z8ssv09jYyNatM5tHfMmSJRMudqtpV5u8zkUTERd2bskl2K2r48CQMy8O7JZWtknSBLQDKKUyIjIM1Djlr4152rH7tJ9Tfpuzz4WOcW79PokzNy9fc8aOHTsG2MuauFwuMplMbh02y7IYHR3l1KlTrFq1igULFlBdXU1DQwPr1q3LS32K7UrMgN7a2sqeg3ug8vKfyzIthgeH356InjYxXAamZdIx2gFAIpVgsHfwYk8zdUMz+3SaNpvlNbAppUxgtZNA+UfAtfk83lQppR7BTg3G2rVr1SU2v5zj4HK56Onpoa+vD6UUIkImk8HlcjE4OIhhGJimSXd3N93d3Sxfvpzrr78+X1XSzlUJ1nrrkptNRnmmnNjZGCgIVYUYPTWKsuyXl7gE91I3lmdmjpVlPK+7NDUtqyDvBieB8nPA7UCliGQD6jyg07ndCcwHcB6vAPrHlp+zz4XK+y9yjIJbvHhxbiVtl8tFOBxGKUUsFiMej5NOpzFNk8OHDxOJROjo6GBgYIA333yTRCJRrGprlyHaGSU1mCI1lCLWFSPYGMTwGLhDbsLXhMnEM4yeHiV2JoaVntkAp2lafkdF1jktNUQkgD3I4zB2gMsmT24BfuLc3uLcx3n8Wec63Rbgw86oyUXAUuzVu3cCS50RkF7sASZbnH0udIyCW7ZsGStXrqSxsZHy8nLi8ThKqdyE7EwmQyaT4ejRozzzzDO0tbUxNDTEm2++SV9fX7GqrU2DmTSJnIoQ64rlFhZNDiUZPDSIlbbIjGaItEcYPDhI7EyMeG+cyInIuEVINU27fPlssc0FnhOR/dhBaJtS6gngT4CHnEEgNcA3ne2/CdQ45Q8BnwVQSh0Evg8cAn4OfFopZTrX0D4DPIUdML/vbMtFjlEUK1euZPXq1TQ3N+fmqmVTa6XTaYaHh4lEIrS3t9PX10dHRwfDw8O5ic6lpFTXY8vEM4y0jhA/GyfRnyDeY6fMSg2nUBk7cFkZi/43+kn0JYj3xol3x8kkMmRimYs9taZpU5TPUZH7gZsmKG/j7VGNY8sTwH++wHP9JfCXE5Q/CTw52WMUi8/n46677mJoaIgDBw7Q3d2de0wplVtFO5VK0d/fTzqdprq6mlQqVcRa58c3v/lNdu3axaOPPspnP/vZS+8wSyT7kyhL4fK7sJIW8e446ZE0lmlRvrAcM2kS741jpk3cztvOTJqYCRPDra+PadpM0u+oAjh16hTf//73OX36NBUVFfh8vnHD+LNdkul0mpGREfr7+2lra+OZZ54hFosVseYz6+jRozz22GOMjIzw+OOPs3t36WVlSw4lSY2mMJMmqdEU7qCbeG+c2NkYqSH7i8rY62reKi8uX2lN6dC0YtOBLc+6u7t57LHHeOWVV3juuedIJBLY0/PONzZXZCqV4oknnuDVV18tcI3z5+GHH84Ng7csi69//evjlnOZzXw1PhSKdCSNMhWesAd/tR+Xx2XfL/cQbAziq/IhLkFcgivgwlflK3bVNa3k6MCWZydPnuTkyZPEYvaAgsHBQUZGRkgmk7ltsoFORBAR3G43qVQKpRT79u0rVtVn3Ouvv55bj800Td544w1M0ySdTnPkyBF27txJe3v7JZ7lyuQOuKlYUoGv2oevyoe34u0sbp6QHeT81X6CDUGsjDPHLW7Su6OX4eN6BShNm0l6sdA8MwyDkZERDh48SDKZpK+vj2QymQteQC5XoIiQTqfJZDJEo1GCwSChUKiY1Z9Rd955J08//XTu/rp16/B4PLzyyiu5wSRnz54lnU6zePHiYlVz2twBN+FFYVxeF4m+BKlICitt4fK4iJyM4Al7MHwG3govZsIO8MpSRE9HCc4J4inzXOIImqZNhg5seTYwMEBrayvt7e1kMhlSqRSGYeDxeDBNM5cvMp1Ok06nc9lJwO6uu+WWW4p8BpcvlUrR1tZGKpXC6/UiIrhcLhobG4nH4+eNkGxvb5+VgQ0gODeIp9xDvDfOwIEBzLhJcjCJuATDa+Ap86B8KhfYAMQtZGIZHdg0bYborsg8SqfTvPzyy/T19ZFKpXKBLJt5REQwDCO3HpdpmliWhcfjoaysDNM0cwtSzmZPP/00zz77LM899xzJZBKv10sgEODFF1/E4/Gclw9ztme794Q8mHETMQQzaYIFqaFUbhqAJ+QB5zKry+fCG/bqoKZpM0gHtjyKxWLs2bOH3t5eTNPMtcQMw0BEsCwr9yGeSCRyk7YNwyAcDlNTU8OpU6eKeQqX7fjx4+zYsYMzZ85gGAbxeDyXUaWmpga32821116bu87o8Xi49torKvPatIhLcr8ziQzKUohhlxkeg9qbaylfWE5ofoiyeWW4g7rzRNNmin435ZHf76evry/XUsuOAHS5XLlsE4lEAsuycq04y7JIJBIMDAxQVVU165d26erqyp1rJBLJtVgBzpw5A9hpx+bOncvo6GguWfRslQ1gZU1lxLvjWKZFcjCJO+DGE/LYoyAVBGoClM0pK3Z1Na0k6cCWR5ZlEQ6H8Xq9udaaUopkMpkLdNlgl+2azI6KTKfTHDp06LzVqGebYDBIQ0MD3d3duVZZtnt17LSHQCAwq7tdM/EM0Y4oZsLEXebGV+3DW+nFyli4lrrwlHnwhrx2EuSgG8OjO0s0LV/0uyuPAoEAy5cvp6qqioqKCvx+f+7DfGx+QLfbnUuvlR0OX19fT1VVFQcOHChK3WfKNddcw+LFi7n++utZu3Yt1dXVuWuK73nPe4pcu5mTDWqpSIqRthHOvHAGZSoCdQHK5pbhCXow/AbeSi+hBaUz0lXTrkQ6sOXZL/3SL1FRUYHb7cbr9eLxeMZ1xwFkMplx91OpFF1dXfh8PoLBYDGqPWMqKyt55zvfycqVK/nCF76A3+/Hsizi8Tjr1q1jeHj2z+FSlj3KMTWaIjmQzI2ETPQkUCislEWiP4FKKzKxTG4em6Zp+aG7IvPs1KlTLFy4kIGBAQYHB0mn0+OCWLYFl22pAbkFSOvr61m7dm3B6zyTTpw4waFDhxgZGeHZZ59lcHCQeDyOZVn88z//My+++CIf+tCHuOGGG4pd1WkTw+5ejHfHnQJwB91kkhnSI2nSkXQubZaVsoh1xQhfEy5ijTWttOnAlkepVIoTJ05gmmbuwxzsUZHZ22MHlYxlGAbveMc7KC8vL2idZ1IqleLQoUNYlsXp06fZtm0bAMlkknQ6zfbt2wmHwzz22GOcPn2aUCjEihUrqKurK3LNp65sXhmJ/gRmysTlt6+pRTujKFORHk1Tsawit62ZNC/yTFqpUEpx5MgR2tvb8fl8s/a1PRvprsg8crvdnDlzhsOHDzMyMnJel2PWRIEtk8nw6quvEo/HC1HVvMiO+DRNk76+PtLp9LiEz6lUilOnTnHo0CEikQgjIyPs3LmTdDpd7KpPmcvnomZ1DRVLKvDX+MkkMnirvBgeAzGESFsk1wXpDrpJDadIR2ffeWqTd+rUKVpbW0kmk7P6tT0b6RbbRTz88MO0trZOe3/TNHnuuefo7e0dtwTNZBL/ZjIZtmzZwuDgIBUVFZfc/mKWLFnCgw8+eFnPMR3l5eV4PB727NnD8PAwqVQKn8+Xm5ieTSEWCoUwDPs7lmmaDA0Nzcpvti6vi/DSMOlI2u5y7LZXZvCG7RRaZsLEN9dHJpohHbE/4LyVXkLz9WCSK9FU3/8dHR0AzJs3D7AToJ+7Osd//Md/jLtuXqz3ZqnTge0iWltb2XPgEFawelr7ZzJpBiJR0ubUu55MS9HTP8T+E2cJlk+/1WbEBqa97+USEerr6wmHwyxevJhgMIhhGPj9/lyrrbm5eVz2kezk9NlKRPCGvbiCY+biiZ39Xwwh3hu3s/07mUZSQykytRncAf1WnO3O7V3x+XznBbbsiGAtv/S76RKsYDWJFf9pevuaJhw5SiYyMsU9BcSA8jriC+/AmLNoWscH8B96Ytr7zgS3283ChQtJJpO5hM6BQIDR0VGUUtTW1mKaJqdOneL6669n5cqV+HyFXcqlo6MDhsF4fuZ65qvMKjJ9GdLJNGbaJBaPEQgFMFwGmXQG02NiZkwEwXfMh7f6Mj/whqBDdcxI3TXbVFtS2e0ffvhhwO6Z2bt3L11dXXi9XlasWJFrzWn5pQNbHhkuF+LyIAjnX1m7CBEQQSmwMrN7FW2Px8O+ffvGjfoMBAKk02nC4TA33HADZWVlWJbF7bffPqsHy4zlcrmon19P96luYiMxMpkMiVgCr89LOpVmdGgUl9uF2+MmHovjL/Pj8el8kbNVNBolkUiM+1JmGAZr1qxh9erVueQLWmHowJZHVjqJp6wCcXtRmQww+flLYrhQqQSGe/Z2XWRHQy5fvjw3eMTj8ZBIJDBNk1gsRlmZnVYqe42tGObNm0ev9GKtn9n5ZWbSJBaJ4Qq7MAdNIr0RzLiJ2+fGXenGU+FB/EKyJklqZQpX/fRTiRnPG8xr0q2BYjh06BDHjx/nzJkzuN1u4vH4uCw6xXxtX610YMsjcXkw/GVY6TRTCWoohbjdWJYJrtn7L0omkyQSCUKhEKFQCJfLxejoKEAu4XNWfX19ybTWANKRNMNHh3MDSUbPjNoZ/0VIJ9N2qi23CzNlYiZNatfUFrvK2jREo1GOHz+eu5/JZGhtbZ3V8zJLwez91JwFxDAwYxGwpjrEVwGCy+cnEx/NR9XySinF/v37aW9vp62tjVAoxJw5c/B6vbkh/y6Xi5qaGhYtWkRlZSWNjY3FrvaMivfEEbfgLfcSOxPDSlkYHgO3342y7GwkSil72aJKL1ZKZyOZjbIrVVyqTCssHdjyyEwlSEdH7IEgamofXFYyhplJYyajeapd/pw+fZrTp08DsHDhQjo7O/H7/SSTydxiq0op4vE4oVCIpqamkrv+YGUsxBCCc4NYGYt4nx3oDJ9ht9aUi2BjEG+FF1+lD5WZ0lVY7QpRVVVFMBgcN/qxqampiDXSII8TtEVkvog8JyKHROSgiPyeU14tIttE5Jjzu8opFxF5WERaRWS/iKwZ81wtzvbHRKRlTPnNInLA2edhcT4dL3SMQhOXG0TBBJOyJ7E3VjpBov/MjNcr38bmf/R4PDQ3N3Pdddfh9XpzE7YTiQSJRILdu3eze/fuItY2P3xV9iACd9BNxbIKKldU4q/2Y3jsVbRrb6olvCiMv9pvt+wqZ++11KuZYRjccccdNDc3EwwGqaurK7neh9kony22DPCHSqndIlIOvCEi24CPAtuVUl8Wkc8CnwX+BNgELHV+bgO+DtwmItXA54G12H10b4jIFqXUoLPNJ4DXgSeBe4GtznNOdIyCMlxuXF4/uFyQmeJcNgFBsJKxS297hamtrR23QKqI5NaVE5HcZHW3283w8DBPPvkk27dvp6ysjNtuu43Vq1fjds/OzgQzYRI7EyM1kiLRl0A8gr/Kz9y75pLoS4CC8uZyDJdBot++76vy6Xlss1ggEOCGG26goaGh2FXRHHl7NymlzgBnnNsRETkMNAH3A+udzTYDz2MHnfuB7yg759RrIlIpInOdbbcppQYAnOB4r4g8D4SVUq855d8BPoAd2C50jIIzPL7ptdgsC0Rw+WffgIrGxkZisRgnT57E4/GwfPlyAoFALqVYNlemaZp0dHRw5MgR5s+fTyQS4ZVXXiEQCLBy5cpin8a0jJ4eJZPIEOuKYZkWbnET7YiSGk7hrfCCYSdCdofdlDXqhUZnu66uLo4cOUI6nc4tDqwVX0G+JopIM3ATdsuqwQl6AGeB7NecJqB9zG4dTtnFyjsmKOcixygopSz7GpuZmcbOFlgm7rIrOwvHZNMOWZZFKBTKXV9LJpP4/X5+/OMfk0gkOHbsGGDP/3rhhRfGXaeYLWmHsiMcrbSFZdrXVBMDCUQEM21ipkwysQyZaAZvuZfyxeW5rP/a7BOPx9m9e3cu/+vw8DAej56LeCXIe2ATkRDwQ+D3lVIjYwcJKKWUiOT1qvnFjiEinwQ+CbBgwYIZP7aVTpGJDtoTrqfRakunkmSiQzNer2IwDIM5c+YwMjKCZdkjAisqKnLr00WjUUzTzK04PhsZbgPDbZCJZuxUWckMKq0wvAaGzyA5kMRb5QVlDy5J9CUoa9KtttlqYGDgvKTmekTklSGvgU1EPNhB7V+VUv/hFHeLyFyl1Bmnq7HHKe8E5o/ZfZ5T1snb3YrZ8ued8nkTbH+xY4yjlHoEeARg7dq1Mx5gDY8XDGOag0dApeNYmWm09groYi0p0zSJRqO5JMcvv/wyv/3bv00kEmH+/Pn8/u//Pi6Xi6effprTp08jIixdupSHHnqI5cuXF/AsZoYYgq/Gx/DxYTKxDKloCjLgLnPjrfCSSqYgDa6AszabXnB0VquqqkJExgW3QqeDK7ShoSHa2tpQSrFo0SKqq6eXRzff8jkqUoBvAoeVUn875qEtQHZkYwvwkzHlH3FGR64Dhp3uxKeAjSJS5Yxu3Ag85Tw2IiLrnGN95JznmugYBResX2QHt+mwFGLMzmHwPT09bNu2jRdeeIHt27fT1dXFq6++mls9/IYbbmBgYADTNPF4PNx+++3cfvvt1NfXs2fPnmJXf9qUpXAFXIjbWUA2ZWKZFi6fC1+lD1+tvbpBOpomNZRi+NgwycFkkWutTUcwGGTVqlX4fD4Mw6C8vLykkgycKxaL8corr9DZ2Zl7P2cTLlxp8tliewfwX4ADIrLXKfsz4MvA90Xk48Ap4Fedx54E7gNagRjwMQCl1ICI/Dmw09nuS9mBJMDvAt8GAtiDRrY65Rc6xpR0dHRgxIannUg4nUpS50mSCJYTGx2a+hMoi1Ck47ISGRuxfjo6CtvqU0qxb98+0uk06XSa3t5e3nzzTXbs2IFSCp/Px4kTJ1i0aBGjo6N0dnYSj8eZP38+hmGQucJbqReSGk4R7Ywy3DpMaihlfykRsJIWVsoifE0Yb5U9GTsTzyCGYCZMoh1RXD4X7qAeGTnbzJ8/n/nz56OU4qmnnip2dS7bxa6ZDw8PMzAwfrWQv//7vycUCk0puXMhrpnnc1TkS8CFmhv3TLC9Aj59gef6FvCtCcp3AddPUN4/0TEKzeVyk0zEScSnN8laxMDlnX1dG5ZlkUgkOH78OL/4xS8wDINkMjkuhVZ/fz8vvvgi9fX1xONxDh06RFdXF/fccw8333xzEWs/PWbKZLR91E6hlbRID6dRStmTsj0GRsCgfHE53nIv8Z44Znz89I/0aFoHtllsogQDSilOnz5Nf38/lZWVNDc3z+q8kdmlpcZKpVJX5GLI+p10EfPmzaM76Z72sjXKshjp+rad83EaXOVVmIvuILFwxbT2B3vZmnnz5kx7/+lwuVwMDQ3x3e9+l76+PkzTpKKiggULFuSG+YdCISzL4ujRo9TU1FBRUUEmk2HZsmVcd911Ba0vAEOXt2xNOpZGhgVzwMQT9ZCyUmTSGSQjYII6pOw5fFYKwzLI9GXIpDIYLgOv34unxoPhv4wPvSHeHhOsXRGOHDmSa/10dnYyMjLC6tWri1upS7hYS8qyLHbu3ElPjz1koba2ln/7t39DRHJL9VwpdGDLIzMVx0zGmNqaNW+z0mnUdHcuIqUUx44dY2hoiOHhYUSETCZDKBTCNE0Mw2D+/PmMjo7S19fH4OAgZWVlVFZWFqUbcsmSJZf9HKlUis7OTnrMHgatQUhCUpIYhoHb7UYyQjgZJhAIEIvFcHvcWC67BVtRXsF111xmMG+amfPQpq6trY2zZ8/S29s7bh5bNq1cVkdHBzfeeOOsbbUZhsFtt93GyMhIblTzY489RjKZZNeuXWQyGRYsWHBFZF7RgS2PXF4/Ll/AzjxiTn0EnCgTKzX7hg8nk0kikQjRaJR0Op0bOebxeAiFQrkh/8PDw8Risdx1NZfLxe7du/nwhz9c0Df/TPX3t7W18dRTT/HTn/4U0zRJp9OUl5czODiIx+NhzZo1BINBIpEIVVVVXH+93Yvucrm4++67cwuxloK+vj6++MUv8oUvfCGXdabYJjvncirGXnfq6urC5XLlXk8dHR2k028nQM+OAL5cxZ7XOXaFe9M0OXPmDHv27KGvrw+v18sHPvABrrnmmqLVD3RgyysxXATnXsPI6SNkhieccXCJJ3AhxuybwOv3+8lkMpimvUxLdpHRvr4+zp49SyaTYe/evVRUVBAKhYjH4ySTSWKxGP39/XR3dzN37twin8XUzZs3j4ULF3LXXXdx+vRpRkdH7RGQTnA3TZPW1lZ6enqora3N5c80DGPWzt27kM2bN7N//342b97MQw89BNiBv729Ha/Xy/Llyws+VLy1tZWDBw5TGayfsefsG+hhNBYhmUyQTMfweX2cfOssHreHZBL6+gdJpuyWe33tHDqP91/W8YZiU/scyUcwH+vQoUP09fXx6KOP5sqeeOIJVq5cOaNfTqcazHVgyzNPIISnvIpMpM9OkzUVInYi5Vmmra2NZDJJeXk5yWQSl8uFUoqzZ88Sj8fJZDL09/cTi8UwTTPXPZlKpejv76etrW1WBrb+/n5M02R4eJiqqir8fj+GYdDR0UEoFKKjowOlFOXl5RiGwalTp1i2bBnLly8vqcDW19fH1q1bUUqxdetWWlpaSCQSHDx4MLfN0NAQ73nPewqeqaMyWM/d1354xp7vWMdB3jq1FwL2JQNBWNd8H+FQFdH4CK0dB0mk4ng9PsqDlSyZd3mp4p478viUtm9tbeXNffso9+bnc0Sl0yRjMZJjhv0nIyO0Whl8M/SajqSmfnli9n1qzjKZaIREX9fUgxqgLBNzlq3Hlk6nOXDgAJlMhqqqKpRSudWyE4lELgHyyMgI0WgUEUFEcq282Zr8GMh1JWYDWiAQIBwO09fXh2VZDA4OIiIsXryYW2+9lcrKSu65555xqy2Xgs2bN+cmLVuWxebNm1m/fv24bbJfbubMKezApplW5gvhdrnJmGkEoSpcRywVJUwVAyO9GIaLoN9+XcSTUaLxCGWBws51K/e6ubUhfzksD7ssOvvt7livx01tuJylc2vxuGemt2lH9+CU95m9nyKzgJVOEh/oQE1zTTUrlcCaZtaSYkmlUnR3d1NdXZ2buJrtissGtaxsF6VhGESjUVKpFNFolLq6umJU/bKVl5ezfPlyuru7Wbx4MZFIhLq6Oo4dO5ZbsieVSpFIJKioqMDv9zMyMoLf7y+p9ei2bduWu7aUTqd5+umned/73nfedqUwmTkcqqKxtpl0JoUYBi4xCHjtLyoyQVecUeBLCx0dHURSmWkFh8my3D6S3gCpdBo3LsyMMNo/MmPPH0ll6OjouPSGY+jAlkdmOkUqMjTlRUZzlIWnrHImq5R3ZWVlZDIZXnvtNYaGhnIX1ieaA5OVnd+WyWQ4cuTIuEUbZ5tly5axcOFCVq1axalTp3C5XOzatQsRYc2aNfT09BCLxWhvb2fBggXs2LGDhoYGbr311mJXfcZs2LCBJ598knQ6jcfjYePGjSxatIiBgQF6enpwuVwsX76csrLZnyczFAhTX9XI8a5DDEf6CfrLqQzVUB6spLaigZHRAUxnuk+4rIqAL1jkGs8cpRSmZeEyDOpqqnPX1K+EUZ86sOWRuNxYiel/SBteP8ZFAsKVKplMEo/HicfjuW5Ij8dz0VaJYRh4PB6SySR79uy54uf7XIzP5+OWW27hlltuAewL+NFoFJ/Px/z583NZVrLdrt3d3QwNDVFZWVnEWs+clpYWtm61kwAZhkFLSwtut5vbbrstd811Nnc5n6uqvJbyQAWhQAWCcKa/nVgyyoKGJSxbcCMj0SE8bg+hQEXB6zZv3jzMyPCMd0XGkik6BwYxLRO3uGiqqiLgy8914h3dg1PKbAI6sF2SERuYdkorM5PBm5p+k9wtFv7Tr+IfOTrt5zBiA0Bhr2NEo1Hq6+vp6uoiGo3mJmVfTPbDTkSm/CK+0s2bN4/Tp08TDAYpKyujvLz8vEET1jSuwV6pamtr2bRpE1u2bGHTpk3jhvuXYpLgRCqOiIEAPYOd9Ax0IoaLwUgv1zWvoTo8O7vWszKmxXAshmlZhIMB/B4PZ4eGc+/pjGlydmiYRQ1XznnqwHYRlzvh1TRNkj21DHRPrX84y2MISxoqLjNzyJwpncdMDA8+fPgwO3fuZHh4eNL7pNNpLMsiEAiwefNmfvrTn15WHYo912esqqoqPB4PmzZtory8nHA4zI4dO3LBrKKiouQWqGxpaeHkyZO0tLRceuMC6ejoYDgWmfLIwksxTZPe/rNkTJP2rpMkkwk8Hi8new5yuOt1Fi9cOmPHGor1oDoKl8LKUorTvX2knMQJg6NRFtTVkEqPH6mYusLyu+rAdhEz8cH4sY99jDfffHNaGTVEhE2bNvGJT3zisusxWa2trRx9czcLQtNLA6aUYuBUO6nE1AfM+NyCjxTRU3sIjkx/gdXTo8Xpvh0YGGD//v2Mjo5SX1/P6tWrc8P4Q6EQq1at4uTJk5w8eZLGxkY8Hg/BYJAFCxaU1OARsFttX/3qV4tdjbwzTRPLMqmsqKZvoBfTtBMNmGaGdDpFb383C5oW4nYXbzrHVAaPxDImpvX2gLVUKsVIJDJumyMj9qC2sYPBfF4vHVbvpI7hMoTgFEZM6uH+V5hkMklv7+T+2RMpVpb7BSGTz62d3jSDwViGL52O0ulRxKewGosAbkzmB5O8d+4I910//QvQf7Gr8Bk8lFK88cYbuYUmu7u7OXz4MKtWrcptc/jwYY4fP56739TUlMs+ouXfvHnzkGT/jM1j6x06Q/eAPTfR6/Fxa/NcnldbaO89jnIGjNVWzGVpzTqa5y6bkWM+d+RxmuZNPpPLVHudOjo6xiU1Frcbzzmjmb2BAGVlZbnMQh6Ph7KyskkPGgkEAlO+3DDV89CBLY/GptOZDp/Px8KFC2eoNoUR8Bi4DIilpnbNSAGxtGIgblITmn0DZuLx+HmrJw8Ojv+WfO6Q5a6uLlavXn1FjCLTpiadSeeCGkAqnSSWiNDcuIy+kbNYlonX42dO7XxS6eKlxZuJXqfXX389l/jY6/Xyzne+k2Dwyh7dqQNbHgWDQZYvX8727dun1fq67bbbrtgVai/E7zG4odHPzw9PfdBMxoJ4yqK2bPa9LAOBAIFAIPdtNzuf79lnn6Wvry83ry+ZfLsZ6/V6S64L8mJGRkbo6urKfWO/2BSQK13GTI1bOdsuS3PTsjtJZZKMRAcJBSooD1YQLPCE7Jl266230tvbSyqVoqGhoeDZYqZj9n2CzCKGYbB48WIaGho4derUlPe/3BZfsfhcQl2ZEElObXK5AMkMnOhLck3d7MrGISKsXbs2d41taGiI6upqotEokUgEpRQrVqxg586duRRiK1euvGoCW19fH6+99louGHR2dnLHHXcUvB5DsZ4ZGTyilKK3vxvTfPsLq8sDrxw3URkhmUrii/kJxsuoSFVyrP/1yz4m2PVvorBJpUWE+vqZy69ZCDqw5VkoFKKqqor29vYpD+k+dOjQed1Z+dbR0UE04rqs61QvH+ihcxqzHBSQwsOj+128Fp3+8U9FXJRNMVPBTKisrOSuu+4ik8nk5nFlxeNx6urq2LBhA0NDQ4TD4Vk19H2qo2Wz3a7Zaynd3d3nTbxvbGwc9zfI90jWmV7WZ05zJYODg2QyGcrKyjh27BipVILGJnvZFq/XS1PTzC6S10SNXp5oEnRgy7NQKIRSCp/PN+WVZmOxGK2trWzYsCFPtZt5qYxJLJmZ9ipyadMiXDb7EgIrpTh16hQ9PT25oDW22zHbfePxeGZtyrCpOPe1PlHLtNCt1XwGzUgkwgc+8AHcbjcf+chH8Pv9AGzcuHFWfYEpFTqw5Vl2gc2pttZEhPLy8vP68fNt3rx5JDJnpj0qMpWx6D+dpqfPInXpzccxgCqfxf+8Q7GgevrJn/9iVwh/gSd5Hzt2jLfeeguwWycejwe/308ikcDtdlNdXU08Hp+1CY+nGhSy22dXVh4aGuKVV17JTeqdO3cua9eundlKFkksFuOll14ilUqRTCY5ePAgN9xwAxUVFSW1asNsogNbnlmWRU9Pz3mZN7KLbwK43W4syxoX/Hw+H/PmzeP2228vaH0vl9dt4HMLl0g0MiEXEPa7iKdnV+JnOH/EYzqd5u6778ayLH784x9z9uxZnnnmGcLhMLfccssVP6psplVWVvLud7+bs2fPEggEZt01m3MppRgdHcXv99PZ2Ukmk6G8vDyXZScej7N+/fqr5hrqlUYHtjw7cuQIkXMmOAK55VqUUrnAZhhGLoloeXk5K1euZPny5UWo9fSZlsLvFnskyBQZLqjwCz2jJssbZr5u+eT3+4lG356U7na7c92P2WVrwB4ZePjwYW6++eai1LMYlFJ0dXUxPDxMWVlZrptutorFYrz22mtEo1FcLhcVFXYOSJfLRXV1NWvWrOGWW265YlYOvxrpwJZH2WzX2cB1rmxw83g8KKUQEQKBADU1NVRVVbFgwQK6urpm1cViQ+DMcBprGo0ur2HHw+H4lZWeZzKuu+46Xn/99dwSPStWrMAwDGKx2Hn/+4m+6JSyN998k5MnT/Lmm29y6NAh6urqWLt2Lb/8y788K68/HTlyJPclxjRNBgYGxrXAa2traWxsLFb1NHRgyyuXy0VjY+N511VEhFAoRCqVspeMr6/H5XIxPDxMIBAgk8lgGEauBVdop0enPyoykzE5OuwhPY2cvilctA67+dfDLvYmpj8q8vSoi5nJ8zB5VVVVvOc972FoaIhQKJRrlQSDQTwez7ipGw0NxW+OzkRO0Is5duwYAJ/5zGc4deoUiUQiN+VFRHjppZf44Q9/eNmjBouRE3RsyzzrlltuoaGhARHhzjvv1F2QRZa3wCYi3wL+E9CjlLreKasGvgc0AyeBX1VKDYr9Kvh74D4gBnxUKbXb2acF+JzztH+hlNrslN8MfBsIAE8Cv6eUUhc6Rr7O82Kyq0jX1tZy5syZXGaKbAoat9uN2+2mqqoKESGTyVBRUUFtbS2VlfZQ4rlz5xa0zpfbOsxkMgSPD0Pv5BMgZ3l8ATxllUjFPPzNa6Zdh2XM/NDuyXC73dTW1p5X3tDQwMDAAOXl5TQ0NFwR3cvPP/88/X195GtoQ7bNfXDPHjrPnCEaixF3BtIYhkEqHic+MsLIZaScS2Ff2yx0YJs7dy5DQ0O5+9nE1tlWmw5qxZfPFtu3ga8B3xlT9llgu1LqyyLyWef+nwCbgKXOz23A14HbnCD1eWAt9jSnN0RkixOovg58AngdO7DdC2y9yDEKLrs68jvf+U4ikQg9PT3E43F8Ph8bNmzgF7/4Bel0mrKyMmKxGCKCy+Wip6eHsrIy1qxZQyqVKuhAg5n4kPjSl77E3/zN3zA6OvmRjSKC1+ulsbGR//7f/zv333//ZdfjSuHxeGhoaGD9+vXFrso4XiDfX5v6YjGqXS5cLhcZpZBMhspgEA+wIBC4rKnGZ2aqklN0zTXXAHD27FnKysq49tpri1QT7ULyFtiUUr8QkeZziu8H1ju3NwPPYwed+4HvKHuY4GsiUikic51ttymlBgBEZBtwr4g8D4SVUq855d8BPoAd2C50jIIrLy/H5XLR0NDA+973Pk6cOEFfXx+1tbUsXLiQUChEOp2mqamJ/v5+lFIkEgkMw6C/v5+jR4/OyrRDd911F48//jjHjx8flwH8QrLXGSsrK3Ot1VKglCISiWCa5hX3f5w3bx5DfX18fDqjfBxJy+J0LEY0k6HC42FBMIjrnNbK6+kMptdH3OXilNtNTzJJs8/HklA5N4fKkcs4/jdRVBZh7T4RYcmSJbPq2vfVptDX2BqUUtkvWmeB7MWGJqB9zHYdTtnFyjsmKL/YMc4jIp8EPgmwYMGCqZ7LJXk8HtasWUMkEqG9vZ358+fzW7/1W1x//fWMjo7y7LPPEo1GCYfDdHV15QaQZDIZhoaGqK+v58yZM5SXz65cc83NzdTU1HDmzBmUUuOuGWavNblcLizLwuVy4ff7aWpqoq6ujsWLFxMOT3/JmivF6Ogor7/+OrFYjPb2dioqKjhy5AiGYbBgwYJZPzIQ4GgkQsTJgRozTRSwuKxs3DYht5vhdJqAy821oXKuLy9nWaic8CVWVNdmh76+Pr74xS/yhS984YoaBVq0wSPO9bC8Tli61DGUUo8AjwCsXbs2L3UJBAKEQiHuvvtuotEoHR0dzJs3j7NnzxIMBkmlUtTW1uL1evF6vbmBJj6fj3A4PCsSjp7rpZdeYt26dSSTyVyrLZ1Oj5tsng3i2ZbM0NAQlZWVGIbBsmWFHvox844cOZJLIZVOpzl27BhHjhzB5XJx6tQp1q9fX/T/7VnsVs90WJZFZ2Z8LlNXKkljmd1t3u+UVZQFGYhGSabTZEwTAVyREdyGQW04jOcyWrJngcpp763NhM2bN7N3716+9rWv8cd//MdXTAKCQge2bhGZq5Q643Q19jjlncD8MdvNc8o6ebtbMVv+vFM+b4LtL3aMoujo6MCyLBKJBEeOHKGrq4uXXnqJRCJBNBqloqKCiooK1q1bRzwe5+DBgxiGwZIlS6irq5vyukXFlkqliMfj+P1+brnlFkZHRxkcHCQej4+75mZZVm66A7zdHRkMBmlra+OGG24o1ilc0mRGFHZ2dua6YU+fPk0ikeCRRx7JBfJ/+7d/u2hL/ErPm6iUYrS9fVzigUAgQOUce7X3XmdUZO3SpdRiD4s/ffr0+CcJhai8jPRilRRnkFDWua/hq01fXx8/+clPGBwc5Ec/+hFLly7lzjvvzEvv11QVOrBtAVqALzu/fzKm/DMi8jj24JFhJzA9BfxvEalyttsI/KlSakBERkRkHfbgkY8AX73EMYoiO0/nzJkztLa2sn//fiorK6moqKCjo4PKykqGh4dZsGABc+fO5fbbb2dwcJAlS5Zw5513zrq1utxuN42NjYyMjNDZ2ZlreWYz2p+bgSU7xyubU/Ps2bO0trZe0YFtMrKtcSCXVmnsdbZifxjORNDs6elhz549uQFOt956ay5Yj02pZVkWe/fuZfv27YRCIebOnYvL5aK2tnbWZdYBO6gfOHCA9vZ2XC4Xy5YtY/HixcWuVsFt3ryZ0dFRlFIopdi+fTvV1dXMnz+/6K/vfA73fwy7tVUrIh3Yoxu/DHxfRD4OnAJ+1dn8Seyh/q3Yw/0/BuAEsD8HdjrbfSk7kAT4Xd4e7r/V+eEixyiKuro6nnjiCZ555hkOHTqUywEZiURwuVy43W5WrVqFz+fLrdkWDocREVKp1Ky7FmMYBjfeeCMAvb29VFZWkkwmSSaTE05StyyLdDpNLBYjk8nQ1tZW9C66S5lMUFBKcfz4cbq7u/H5fPT29ub+vxUVFbPyS8u56uvr2bBhA/F4nGAweMEPs/3799PZ2Uk6naarq4tUKsXixYtnPPP9TLtQyzwSidDX1zeurKmpKTdPbypfGooxD2+mbNu2LXfNPJPJsGfPHj74wQ/mrp0XUz5HRf76BR66Z4JtFfDpCzzPt4BvTVC+C7h+gvL+iY5RLEePHsUwDILBIMFgkIGBAYaHh3OTdEOhED6fD8uyOHjwIDfeeCNut5uhoSEOHTrEmjXTn89VLPPnz6e+vp6RkRFOnjxJrzNXyeVy5b7dgd2aNQwDy7KoqKjA4/FQW1s76xZXnci5I+dM0+Ts2bMYhkFDQ8OsC2pTndCdnaD94IMPcvLkSZRSmKZJNBrFsiyWLFnCtm3bxu0zWz7kx67aMLbsSrm+VCgbNmzg3//934lEIrjdbm666aZca7zYdOaRPItEIgwNDeVaaolEgkwmQyAQoKGhIdd1k0qlzrvmMptTL/l8vtx1woULF5JMJunu7s5dl/D7/YRCIcrKyojH4yxYsIBVq1axatWqWfehPxkul+uKb6HMpLEf8i6Xi0wmg8vlIhwO43a7Z8VI3wsF2TNnzrBr167cfRHh7rvvpuycEaGlrqWlhSeffDL3nv6d3/kdbrrppmJXC9CBLe/q6+tz19myCWDr6urYuHEje/bsya20nM0POXYdq9m+btfSpUtZunQp0Wg0l4aor68P0zQJhULMmTMn1wW7dOlS7rjjDlwuF4sWLSpyzbVzXU5LqqenhzfeeINMJoPb7ebmm2+e1dn9586dy3XXXcfJkydxu90sX778qgtqYOfEvO+++9iyZQv3338/t9xyS7GrlKMDW55dd911DA8Pc/ToUUKhEHV1dVx33XXMnTuX/fv3U1NTwzve8Q7AnjR76NAhRkdHaWhomPUZDSorK3n/+99Pb28vvb29XHvttezdu5d0Ok0gEGB0dBS3201DQwMjIyMcO3aMlpaWWR/QtfGy1+JGRkYoLy+/4q+hToaeoG1raWnh5MmTtLS0FLsq4+jAlmcul4t3vOMdNDQ08MYbb+Dz+XIjqc4VCoW49dZbi1DL/GlubuaBBx7g0UcfpaurizfeeAPTNHPXmbJTAxKJBLt27aK8vJzf/M3fJBSafhJk7cqTXWxVKy21tbV89atfvfSGBVZ6FzOuUEuWLGHjxo2sXLmSuro62tvbc91ypa6+vp4PfvCDhMNhkskkSiksy8oNhzcMg0QiQSqVoru7O7cStabNJn19fTzwwAP09/dfemMtr3RgK6CqqipisRi9vb0MDQ0RiURyIwZL3YoVK9iwYQONjY14vd7cGnSGYXDq1CkGBgaIxWK535o2m3R3d/PXf/3X7Nq1i82bNxe7Olc9GZvm6Gq2du1aNXak03RMZkj0qVOncvO5urq6ALjjjjsmPaHxShsSPZVh4J2dnRw9epTe3l78fj8+n49gMEhPTw9erxefz0dZWRnLli0bd53tSjtnTRurtbWV119/nb/+67/OLT3105/+9IrKnVjCJvzg1C22Aht74TybG7LYs/QLpbKykmAwSGVlJfX19cydO5fy8nIWLVpEU1NTLrO/vr6mzSZtbW1s3749dz8ajepWW5HpFptjJlpskzE0NMTOnTtJJBJ4PB5uvvnmq2oUYDQaZffu3XR2dlJWVoZlWWQymVzaqbKyMtavX1+Sc9m00vTMM8/wx3/8x7mJ2yLCvHnz+PnPf17kml0VJmwV6FGRBVZZWck999xDNBqlrKzsqvsAj8fjfP/73+d//I//QSgUIhQK0d/fT3t7Oz6fj0WLFl11fxNtdlu2bBmrV6/OzdULh8Ns3Lix2NW6qulPkCIwDIPy8vKr8gP8G9/4Bvv27eNb3/oWbrebtrY2IpEIK1eu5Lrrrpt1uTE1bcGCBXzuc58jFAoRDocpKyu74uZ1XW10i03Lu+zaa319fbn8gFu3bmXp0qW5QNbW1sZdd92F261fktrss3jxYj70oQ+xZcsWNm3apAeOFJn+FNHyJpVKsXv3bnp7eykrK2P79u25EaGJRIItW7bwq79qL74QjUY5e/bsrFt/TtOyrtQsHFejq68vTCuYQ4cO5ebpRaNRnnjiiXGraO/du3fc9lfL6FCtNGWzcOjWWvHpwKblzdDQ0Lj72YwjQC61WFY2KbKmadrl0l2RWt5UV1ePW3pn7dq1uXW6DMPgQx/6ENdffz0ul4vGxsYrYh0nTdNmP91i0/JmxYoVNDU14fF4qKqq4nOf+1wueBmGwac//WkWLVrEggUL9KARTdNmjP400fLG7XaftwL4hg0beOqpp9i4caO+FqFpWl7owKYV1Kc+9SnOnj3Lpz71qWJXRdO0EqVTajkKlVJL0zRNmzE6CbKmaZpW+nRg0zRN00pKyQY2EblXRN4SkVYR+Wyx66NpmqYVRkkGNhFxAf8AbAJWAL8uIiuKWytN0zStEEoysAG3Aq1KqTalVAp4HLi/yHXSNE3TCqBUA1sT0D7mfodTpmmappW4q3oem4h8Evikc3dURN4qQjVqgb4iHLeY9DlfHfQ5Xx2Kec4/V0rde25hqQa2TmD+mPvznLJxlFKPAI8UqlITEZFdSqm1xaxDoelzvjroc746XInnXKpdkTuBpSKySES8wIeBLUWuk6ZpmlYAJdliU0plROQzwFOAC/iWUupgkaulaZqmFUBJBjYApdSTwJPFrsckFLUrtEj0OV8d9DlfHa64c9a5IjVN07SSUqrX2DRN07SrlA5smqZpWknRgW2GiMgHRESJyLXO/dUict+Yx9eLyB2XeI5mEXlzEsea1Hb5ICKmiOwVkYMisk9E/lBELvo6cur7G5N47pMiUjtT2820Mee+T0R2X+r/6ezzT9l0btl6i0iliPzumG2K9v+8EBGZIyKPi8hxEXlDRJ4UkWVT2P+kiBwQkf0i8rSIzJnCvpN6vVyu6fw/C0lEPioijQU4znwReU5EDjnv698TkX9w/jaHRCTu3N4rIr+S7/rMBB3YZs6vAy85vwFWA/eNeXw9cEW9caYprpRarZRaCWzAzsf5+Uvs0wzk/YOqALLnvgr4U+D/XGoHpdR/VUodOqe4EvjdCTa/IoiIAD8CnldKXaOUuhn7fBsms++YLzp3K6VuBHYBfzbJY7sp3Otlyv/PAvsokPfABmSAP1RKrQDWAZ8G/kEptRr7M+y483darZT6QQHqc9l0YJsBIhIC7gQ+DnzYmTv3JeDXnG85fwL8DvAHzv13ikiDiPzI+ba4b8y3RZeIPOp8c3paRALOMW7Obov9wis6pVQPduaWzzgfaC4R+RsR2el8U88uk/1l4J3Ouf+Bs93/JyJvOts9MOZpH3C+PR8Y0/qtcf4WB0Xkn7jA4oIFFgYGIdcafyL7gIh8TUQ+6tx+XkTOnbz6ZeAa5+/xN2MfcL6l/4eI/FxEjonIX+f3NCZ0N5BWSv1jtkAptQ/YIyLbx/x/7nfq3Cz2ShrfAd5kfHIEgF8AS0TELyL/7Oy7R0Tudvb/qIhsEZFnge2c83opwPnC+P9n6ALnWSYiP3Peh2+KyK855SdF5P849d0lImtE5CmxW7u/kz2AiPz3Me+NLzplzSJy+Nz3vNMyWgv8q/O8gXyduFLqjFJqt3M7AhxmghSEIvIdEfnAmPv/KiL3O/+/nziv9WMi8vkx2/yWiOxwzuEbznvfJSLfdv6GB/LyP1ZK6Z/L/AF+E/imc/sV4Gbsb1tfG7PNF4A/GnP/e8DvO7ddQAX2N9UMsNop/z7wW87t/cBdzu2/Ad4s0rmOTlA2hP1t/pPA55wyH/Y39UXYrdUnxmz/34AfAG7nfrXz+yTwgHP7d4F/cm4/DPwv5/YvAQqoLcK5m8Be4AgwDNzslJ97fl8DPurcfh5YO+b8ap3/85tjts/dd143bc7rwQ+cAuYX+DwfBL4yQbkbCDu3a4FW7C8ZzYAFrBuz7cns/8j5e/wV8IfYc0oBrgVOO+f4Uex8rtnXwbi/ZxH+nxc6z18GHh2zf8WYc/1vzu2vYL9Xy4E6oNsp34g9LF6wGxRPAHdx8fd87rVTwP99s/N/CY+5n31tvgv4cfbcgRPO3+qjwBmgBghgf7lZC1wH/BTwOPv8P+Aj2J+P28Ycs3Kmz0O32GbGr2OvIIDz+9cvsm3Wu4GvAyilTKXUsFN+Qim117n9BtAsIpXY//xfOOXfnYlK58FG4CMishd4HfuFvnSC7d4DfEMplQFQSg2Meew/nN9vYL+pwP4A+Bdn25/hfLMugmzX1bXAvcB3RCQfrcftSqlhpVQCOAQszMMxpkOA/y0i+4FnsL/VZ7snTymlXjtn++ec10IYu5vvTt7+Px7BDtrZ63bbznkdFMKF/p8XOs8DwAYR+SsReeeY9yy8ndnoAPC6UiqilOoFks77d6PzswfYjR3Ys++N897zeTnbSxC75+mH2F+4R859XCn1AnZGpzrsz7gfZt/D2P+/fqVUHPs9fCdwD3YQ2+m8Du4BFmN/cVssIl8VkXuB8451uUp2gnahiEg1dpC6QUQUdutLAdPNdJIcc9vE/gZ0xRKRxdj17MH+QHhAKfXUOdusn8JTZs/f5Ap+fSqlXhV7AEsd9jfusV8S/Zf59Oe+Bgr9dzgITDRI4Dexz/dmpVRaRE7y9rlGJ9j+bqVULjnuJb4DTLR/wZzz/7yPCc5TKXVURNY4j/+FiGxXSn3JeYrs/8xi/P/Pwv7/CfB/lFLfGHtcEWnmCnjPi4gHO6j9q1LqPy6y6XeA38JOU/ixMeXnTohW2Oe8WSn1pxMcbxXwXuxLNL8K/Pb0a38+3WK7fL8CfFcptVAp1ayUmo/dRF+A3R2RFTnn/nbsLjmcPueKCx1AKTUEDInInU7Rb85g/afN+eb2j9hdrgo7hdl/c94kiMgyESnj/HPfBnxK7IEC2S8HF/MLnMEEIrIJqJrRE5kG5/qfC+jHbnmsEBGf8+38nkvsfu7f40rzLOATe/ULAETkRuyWY4/zYX83U29Jvojz2hV7hOUCYKIVNQr+9znn/1nBBOcp9gjFmFLqX7AvB6yZwiGeAn7baRUhIk0iUn+JfQryd3Baqd8EDiul/vYSm38b+H0ANX5Q1AYRqXauBX4AeBn7M+5XsufpPL7Q+QJhKKV+CHyOqf0dJ+WK/UY8i/w69vWDsX6I3b+8wmmC/x/svuYfOBeiHwB+D3hERD6O/S3tv2H3U1/Ix4BvOa3Cp2f0DKYm4JyTB7ul8l0g+2b4J+xulN3Om6UX+0W+HzDFHvjybeCr2F1Q+0UkDTyKfR3mQr4IPCYiB7GvYZ6e0TOavOy5g/1ttEUpZQLtIvJ97GsLJ7C7my5IKdUvIi+LPcR/K/Zq71cMpZQSkQ8Cfyf2wKcE9nWkLwAPi8gB7OunR6b41P8P+Lqzfwb7OmRygpbcuNeLUuor0z6Zi5vw/yki/wr8dILzvAH4GxGxgDTOF9PJUEo9LSLXAa865zuK3fIxL7Lbt4F/FJE4cLvTzZcP7wD+C3BgzN/jz5SdlnAcpVS3iBwGfnzOQzuwP/fmAf+ilNoFICKfA54We6RsGnvgWxz4Z3l79Ox5LbrLpVNqaZqmaZMiIkHs64hrstcYxR4BvFYp9Zli1m0s3RWpaZqmXZKIvAd7KsBXzxk4c8XRLTZN0zStpOgWm6ZpmlZSdGDTNE3TSooObJqmaVpJ0YFN0wpM7Dyh/yYibWJnzn/VGV5f6Hp8TN7O2p5y8vbtFZEvF7oumjaT9OARTSsgZ37fK9gZGf7RKVsIvF8p9dVJ7O8ek8ZoJut1EnvIdt+lttW0K51usWlaYb0bSKnxmfNPKaW+Knam9xfFziqfWx9M7NUDXhSRLdi5IxGRHzutvYPnZAj5uIgcFTuj+qMi8jWnvE5Efih2dvmdIvKOiSonIr8tIn835v4nROQrTt2OiJ3R/bCI/MCZ05RdeeIFpz5PicjcPPzdNG3SdItN0wpIRB4EFimlzluqwwkUllIqISJLgceUUmvFzrX5M+B6pdQJZ9tqpdSAk8JoJ3bmdR92a3ANdjqmZ4F9SqnPiMi/Af9PKfWSiCwAnlJKXTfm2CexM7IngH3AtU5KqVeATznPdwK4Uyn1soh8CzvI/j3wAnC/UqpX7KVc3quUmtHcf5o2FTqllqYVkYj8A3Ym9BT2qgdfE5HV2KmWxq5YvSMb1BwPjrkuNx87U/wc4IVslnwR+fcxz/Ee7BRv2f3DIhJSSo2OrY9SalTsddH+k5M6yaOUOiB2st52pdTLzqb/gr28zc+B64FtznO7uHhqOE3LOx3YNK2wDmKv6wWAUurTTlLYXcAfAN3AKuzLBIkx++Wy3zstuPdg5w+MicjzXHpFAQN7vbTEJbYDO+fnn2HnSPznMeUXyuB+UCl1+ySeV9MKQl9j07TCehbwi8jYBLpB53cFcEYpZWEnpXVd4DkqgEEnqF0LrHPKdwLvEpEqsVdO+OUx+zyNnXwbAKdVOCGl1OvYrcDfAB4b89ACEckGsN8AXsLOzl+XLRcRj4isvNBza1oh6MCmaQXkLO/zAewAdEJEdgCbgT/Bzn7f4mS1v5YLr1H2c8DtdBV+GXjNee5O4H9jZ1p/GTsjfzan34PAWhHZLyKHsNfBupjvAy8rpcYu6voW8GnnuFXA15VSKeylm/7Kqfde4I5J/Ck0LW/04BFNKyHZ62ZOi+1HwLeUUj+axvM8AXxFKbXdud8MPKGUun5GK6xpeaBbbJpWWr7grKmVXRvux1PZWUQqReQoEM8GNU2bbXSLTdM0TSspusWmaZqmlRQd2DRN07SSogObpmmaVlJ0YNM0TdNKig5smqZpWkn5/wHCFWJWuP22bQAAAABJRU5ErkJggg==\n", 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\n", 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EHdQu7f9eCQ2oBeKHRjjmR4AbI+JTwDrg2lK/FvjbiOgG+qgFFZn5UETcBDxM7TaDKzJzL0BEfJDam73bgOsy86EhxpAkVdyQj9TKzLsa1B47lEEy8wfAD8r049SuaOy/zk5qTzRptP2ngU83qN8G3Nag3nAMSVL1Dfc+NkmSJgSDTZJUKQabJKlSDDZJUqUYbJKkSjHYJEmVYrBJkirFYJMkVcqQN2irsSk7+pjx8K2tbmPEYuc2AHLGnBZ3MnJTdvQBr2h1G5LGGYNtBJYuHfTFBhPChg0vALDstyZyMLyiEp+FpNEVv3784+TW0dGRXV1drW5jzKxevRqAzs7OFnciSSMWjYp+xyZJqhSDTZJUKQabJKlSDDZJUqUYbJKkSjHYJEmVYrBJkirFYJMkVYrBJkmqFINNklQpBpskqVIMNklSpRhskqRKMdgkSZVisEmSKsVgkyRVisEmSaoUg02SVCkGmySpUpoWbBExIyLuiYifRMRDEfHxUj8+Iu6OiO6I+EZETCv16WW+uyx/dd2+Plrqj0bEhXX15aXWHRFX1tUbjiFJqr5mHrHtAs7LzNOBM4DlEXEO8Fng85m5FHgOuKysfxnwXKl/vqxHRJwEvBc4GVgO/HVEtEVEG/BF4CLgJOCSsi6DjCFJqrimBVvWvFhmjyg/CZwHfLPUrwcuLtMryjxl+fkREaV+Y2buysxfAN3A2eWnOzMfz8zdwI3AirLNQGNIkiquqd+xlSOr9cBmYC3wc+D5zNxTVukBFpXpRcBTAGX5VuCY+nq/bQaqHzPIGP37WxURXRHR1dvbexh/qSRpvGhqsGXm3sw8A2indoR1YjPHO1SZeU1mdmRmx4IFC1rdjiRpFIzJVZGZ+TzwfeCNwNyImFoWtQMby/RGYDFAWX40sKW+3m+bgepbBhlDklRxzbwqckFEzC3TM4G3AY9QC7g/KKutBG4p02vKPGX5nZmZpf7ectXk8cAy4B7gXmBZuQJyGrULTNaUbQYaQ5JUcVOHXmXEjgOuL1cvTgFuysxbI+Jh4MaI+BSwDri2rH8t8LcR0Q30UQsqMvOhiLgJeBjYA1yRmXsBIuKDwO1AG3BdZj5U9vWRAcaQJFVc04ItMx8AXt+g/ji179v613cC7x5gX58GPt2gfhtw23DHkCRVn08ekSRVSjNPRUqSmuBXv/oVW7ZsYd68eSxatIja7bvaz2CTpAnkscce49FHHwXgl7/8JVu3buXkk09ucVfji8EmSU3U2dlJd3f3qO3vySefZO/evQfmI4K2tjYigvb29lEbp7+lS5eyevXqpu1/NBlskjSB9D/tGBHs3LmzRd2MTwabJDXRaB/l9PT0sH79emq37MKpp57K5z73OaB2dCiDTZImlPb2dubNm8eWLVuYO3cuc+bMaXVL447BJkkTzKxZs5g1a1ar2xi3vI9NklQpBpskqVIMNklSpfgdm1RxTz75JD//+c+B2r1IixcvHmILaWIz2KQKe+655/jJT35yYH79+vUcddRRzJ07t3VNSU1msEktMtpPpGjkZz/7GS+88ALHHnvsgdrNN988qsE2kZ5IocnB79ikCtuzZw+7d+8+qDZt2rQWdSONDY/YpBYZi6Oc1atX09fXxyWXXALAa17zGl73utc1fVyplQy2SWLXrl1MmzbN11tMQvPnz2f58uVEBFOmeJJG1WewVdz27dvp6upi27ZtzJw5kzPPPJP58+e3ui2Nsba2tla3II0Zg20cGs2LCjZt2sRLL710YH7q1KksXryYDRs2AM09HeZFBZJawWCruP4XDuzZs4d9+/Yxc+bMFnU0cYzFVYvNNhb/gGk2/4GkQ2WwjUOj+R/xunXr6OnpOTA/b948zj333FHbf5V1d3fz2E/vZ8nsvUOvPE5Ne7n2ndrOX97b4k5G5skXPYWqQ2ewVdwpp5zClClT6O3t5eijj+aUU05pdUsTypLZe/mLjhdb3cak9amu2a1uQROQwVZxRxxxBKeffnqr25CkMWOwSZrU/C51fBjN71INNkmTWnd3N+seWgdzW93JYdhX+7Vu47rW9jFSz4/u7gw2SZoL+96yr9VdTFpTfjC6Dw4w2KQB9PT0sP2FNi9gaKEnXmhjVt1VvdJw+HwdSVKleMQmDaC9vZ2de572cv8W+lTXbGa0t7e6DU0wHrFJkiqlacEWEYsj4vsR8XBEPBQRf1rq8yNibURsKL/nlXpERGdEdEfEAxFxZt2+Vpb1N0TEyrr6WRHxYNmmM8qj6wcaQ5JUfc08YtsD/KfMPAk4B7giIk4CrgTuyMxlwB1lHuAiYFn5WQVcDbWQAq4C3gCcDVxVF1RXA5fXbbe81AcaQ5JUcU0Ltsx8OjPvL9MvAI8Ai4AVwPVlteuBi8v0CuCGrLkLmBsRxwEXAmszsy8znwPWAsvLsjmZeVdmJnBDv301GkOSVHFjcvFIRLwaeD1wN7AwM58uizYBC8v0IuCpus16Sm2wek+DOoOM0b+vVdSODlmyZMmh/lmaBJ58cWJf7v/Mjtq/XRceOTHv0XryxTZOaHUTmnCaHmwRMRv4B+DDmbmt/g3OmZkRkc0cf7AxMvMa4BqAjo6OpvahiWfp0qWtbuGw7S6PWprx6mUt7mRkTqAan4PGVlODLSKOoBZqf5eZ/1jKz0TEcZn5dDmduLnUNwKL6zZvL7WNwFv61X9Q6u0N1h9sDGnYJvJz9/bb/zd0dna2uBNp7DTzqsgArgUeyczP1S1aA+y/snElcEtd/dJydeQ5wNZyOvF24IKImFcuGrkAuL0s2xYR55SxLu23r0ZjSJIqrplHbG8C/i3wYESsL7WPAZ8BboqIy4AngPeUZbcBbwe6gR3A+wAysy8iPgnsf1PiJzKzr0x/APgaMBP4TvlhkDGkytm+fTvTp09n6lSftyBBE4MtM/8fEAMsPr/B+glcMcC+rgOua1DvAn7jzZmZuaXRGFKV7Nixg7vvvpsXX3yRqVOncuqpp9LuUzokH6kltcrhvgds8+bNbN++/cB8RLBkyRKmTPn1Nwxj8Z6u0XyPljQaDDZpgnr55ZcPms9M9u7de1CwzZw5c6zbklrOYJNa5HCPch577DEeffTRA/OzZ8/mrW996+G2pYrb9/I+9uzcw9QjpzKlrZqPCzbYpAlq2bLavWmbNm1i9uzZnHjiiS3uSOPdrud2sX3jdkiIKcHsJbM54qgjWt3WqDPYpAkqIjjhhBM44QSfzXE4enp6YOvov8V5vMlMdm7eSez79TV9Ox/ZyfRjp7ewq+J56MnRe6FstT9JSdIBue/gByzt2zsxH7U2FINN0qQ23m+RqN0JNYQXy88gIoJpM6YdVJs+cxwcrRWj+Tl4KlLSpDZen0X58ssv09vby67du5g2bRoLFixg2rRpDdfdf1vHskWDPxN033H72LZtG7t27WLGjBnMmTOH+uf3tsyi0f0cDDZJk1qz78Eb6f2KW7ZsYdeuXQDs3r2b3t5eFi1aNMRWg5syZQpz584d0bYT6X5Fg02SxqH9obbf7t27ycyGR1jer3iwGNb520mgo6Mju7q6Wt2GJAFw7733smnTpgPz8+bN49xzz21hR+NSw/OoHrFJ0jh02mmnkZls2bKFuXPnctppp7W6pQnDYJOkcWj69OmcffbZrW5jQvJyf0lSpRhskqRKMdgkSZVisEmSKsVgkyRVisEmSaoUg02SVCkGmySpUnykVhERvcATre5jjB0LPNvqJtR0fs6Tw2T8nJ/NzOX9iwbbJBYRXZnZ0eo+1Fx+zpODn/OveSpSklQpBpskqVIMtsntmlY3oDHh5zw5+DkXfscmSaoUj9gkSZVisEmSKsVgmyQiYm9ErK/7ubLVPWn0NficX93qnjS6ImJhRPx9RDweEfdFxI8j4l0N1vtBREzKy/99g/bk8VJmntHqJtR0fs4VFhEBfBu4PjP/qNReBbyzlX2NNx6xTWIRcXREPBoRry3zX4+Iy1vdl0ZPRMyOiDsi4v6IeDAiVrS6Jx2W84Ddmfml/YXMfCIz/2dEzIyIGyPikYj4FjCzdW22lkdsk8fMiFhfN/8/MvMbEfFB4GsR8QVgXmb+TWva0yip/5x/AbwbeFdmbouIY4G7ImJNejn0RHUycP8Ay/4E2JGZr4uI0wZZr/IMtsmj4SmqzFwbEe8GvgicPuZdabQd9DlHxBHAf4+INwP7gEXAQmBTa9rTaIqILwLnAruBjUAnQGY+EBEPtLK3VvJU5CQXEVOA1wE7gHktbkej74+BBcBZJfCeAWa0tCMdjoeAM/fPZOYVwPnUPmMVBpv+A/AI8EfAV8u/8FUdRwObM/PliHgr8KpWN6TDcicwIyL+pK52ZPn9f6n9d0xEnAKcNsa9jRs+eWSSiIi9wIN1pe8CX6V2hdXZmflCRHwOeCEzr2pBixoFEfFiZs6umz8W+CdgNtAFnANclJm/bE2HOlwRcRzweeANQC+wHfgSsIbaf9OnU/vH6iLgiszsalGrLWOwSZIqxVORkqRKMdgkSZVisEmSKsVgkyRVisEmSaoUg00ax+qe1v/TiLg5Io4ceqvf2Me/i4j/VTe/KiJ+Vn66IuItdcuOiIjPRMSG8nzJH0fERaPz10hjw2CTxreXMvOMzDyF2mOT/v3h7Cwi3gG8Hzg3M08EVgH/OyIWlVU+CRwHnJKZZwIXA0cdzpjSWDPYpInjR8DSiJgfEd+OiAci4q7ywFsGqvfzEeA/Z+azAJl5P7Wbeq8oR4OXAx/KzF1l+TOZedOY/HXSKDHYpAkgIqYCF1F7eszHgXWZeRrwMeCGstpA9XonA/f1q3UBJwFLgSczc9vo/wXS2PHp/tL4Vv8amh8B1wJ3A/8aIDPvjIhjImIOtae8N6pLk4rBJo1vv/G6odpLlEfsYeAsag/T3e8sakdt3cCSiJjjUZsmMk9FShPPj6i9joZyReOzJYgGqtf7S+CzEXFMWe8M4F3AlzNzB7Ujwi9ExLSyfEF5X580YXjEJk08/w2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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for var in cat_others:\n", + " # make boxplot with Catplot\n", + " sns.catplot(x=var, y='SalePrice', data=data, kind=\"box\", height=4, aspect=1.5)\n", + " # add data points to boxplot with stripplot\n", + " sns.stripplot(x=var, y='SalePrice', data=data, jitter=0.1, alpha=0.3, color='k')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, the categories give information on the SalePrice, as different categories show different median sale prices." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Disclaimer:**\n", + "\n", + "There is certainly more that can be done to understand the nature of this data and the relationship of these variables with the target, SalePrice. And also about the distribution of the variables themselves.\n", + "\n", + "However, we hope that through this notebook we gave you a flavour of what data analysis looks like." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Additional Resources\n", + "\n", + "- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n", + "- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n", + "- [Predict house price with Feature-engine](https://www.kaggle.com/solegalli/predict-house-price-with-feature-engine) - Kaggle kernel\n", + "- [Comprehensive data exploration with Python](https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python) - Kaggle kernel\n", + "- [How I made top 0.3% on a Kaggle competition](https://www.kaggle.com/lavanyashukla01/how-i-made-top-0-3-on-a-kaggle-competition) - Kaggle kernel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "644.467px", + "left": "0px", + "right": "1324px", + "top": "110.533px", + "width": "266px" + }, + "toc_section_display": "block", + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb b/section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb index f777a746d..247337f69 100644 --- a/section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb +++ b/section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb @@ -1,3140 +1,3140 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Machine Learning Pipeline - Feature Engineering\n", - "\n", - "In the following notebooks, we will go through the implementation of each one of the steps in the Machine Learning Pipeline. \n", - "\n", - "We will discuss:\n", - "\n", - "1. Data Analysis\n", - "2. **Feature Engineering**\n", - "3. Feature Selection\n", - "4. Model Training\n", - "5. Obtaining Predictions / Scoring\n", - "\n", - "\n", - "We will use the house price dataset available on [Kaggle.com](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data). See below for more details.\n", - "\n", - "===================================================================================================\n", - "\n", - "## Predicting Sale Price of Houses\n", - "\n", - "The aim of the project is to build a machine learning model to predict the sale price of homes based on different explanatory variables describing aspects of residential houses.\n", - "\n", - "\n", - "### Why is this important? \n", - "\n", - "Predicting house prices is useful to identify fruitful investments, or to determine whether the price advertised for a house is over or under-estimated.\n", - "\n", - "\n", - "### What is the objective of the machine learning model?\n", - "\n", - "We aim to minimise the difference between the real price and the price estimated by our model. We will evaluate model performance with the:\n", - "\n", - "1. mean squared error (mse)\n", - "2. root squared of the mean squared error (rmse)\n", - "3. r-squared (r2).\n", - "\n", - "\n", - "### How do I download the dataset?\n", - "\n", - "- Visit the [Kaggle Website](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data).\n", - "\n", - "- Remember to **log in**\n", - "\n", - "- Scroll down to the bottom of the page, and click on the link **'train.csv'**, and then click the 'download' blue button towards the right of the screen, to download the dataset.\n", - "\n", - "- The download the file called **'test.csv'** and save it in the directory with the notebooks.\n", - "\n", - "\n", - "**Note the following:**\n", - "\n", - "- You need to be logged in to Kaggle in order to download the datasets.\n", - "- You need to accept the terms and conditions of the competition to download the dataset\n", - "- If you save the file to the directory with the jupyter notebook, then you can run the code as it is written here." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reproducibility: Setting the seed\n", - "\n", - "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# to handle datasets\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# for plotting\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# for the yeo-johnson transformation\n", - "import scipy.stats as stats\n", - "\n", - "# to divide train and test set\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# feature scaling\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "\n", - "# to save the trained scaler class\n", - "import joblib\n", - "\n", - "# to visualise al the columns in the dataframe\n", - "pd.pandas.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1460, 81)\n" - ] - }, - { - "data": { - "text/html": [ - "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
\n", - "
" - ], - "text/plain": [ - " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 1 60 RL 65.0 8450 Pave NaN Reg \n", - "1 2 20 RL 80.0 9600 Pave NaN Reg \n", - "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", - "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", - "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", - "\n", - " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", - "0 Lvl AllPub Inside Gtl CollgCr Norm \n", - "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", - "2 Lvl AllPub Inside Gtl CollgCr Norm \n", - "3 Lvl AllPub Corner Gtl Crawfor Norm \n", - "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", - "\n", - " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", - "0 Norm 1Fam 2Story 7 5 2003 \n", - "1 Norm 1Fam 1Story 6 8 1976 \n", - "2 Norm 1Fam 2Story 7 5 2001 \n", - "3 Norm 1Fam 2Story 7 5 1915 \n", - "4 Norm 1Fam 2Story 8 5 2000 \n", - "\n", - " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", - "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", - "1 1976 Gable CompShg MetalSd MetalSd None \n", - "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", - "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", - "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", - "\n", - " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", - "0 196.0 Gd TA PConc Gd TA No \n", - "1 0.0 TA TA CBlock Gd TA Gd \n", - "2 162.0 Gd TA PConc Gd TA Mn \n", - "3 0.0 TA TA BrkTil TA Gd No \n", - "4 350.0 Gd TA PConc Gd TA Av \n", - "\n", - " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", - "0 GLQ 706 Unf 0 150 856 \n", - "1 ALQ 978 Unf 0 284 1262 \n", - "2 GLQ 486 Unf 0 434 920 \n", - "3 ALQ 216 Unf 0 540 756 \n", - "4 GLQ 655 Unf 0 490 1145 \n", - "\n", - " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", - "0 GasA Ex Y SBrkr 856 854 0 \n", - "1 GasA Ex Y SBrkr 1262 0 0 \n", - "2 GasA Ex Y SBrkr 920 866 0 \n", - "3 GasA Gd Y SBrkr 961 756 0 \n", - "4 GasA Ex Y SBrkr 1145 1053 0 \n", - "\n", - " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", - "0 1710 1 0 2 1 3 \n", - "1 1262 0 1 2 0 3 \n", - "2 1786 1 0 2 1 3 \n", - "3 1717 1 0 1 0 3 \n", - "4 2198 1 0 2 1 4 \n", - "\n", - " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", - "0 1 Gd 8 Typ 0 NaN \n", - "1 1 TA 6 Typ 1 TA \n", - "2 1 Gd 6 Typ 1 TA \n", - "3 1 Gd 7 Typ 1 Gd \n", - "4 1 Gd 9 Typ 1 TA \n", - "\n", - " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", - "0 Attchd 2003.0 RFn 2 548 TA \n", - "1 Attchd 1976.0 RFn 2 460 TA \n", - "2 Attchd 2001.0 RFn 2 608 TA \n", - "3 Detchd 1998.0 Unf 3 642 TA \n", - "4 Attchd 2000.0 RFn 3 836 TA \n", - "\n", - " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", - "0 TA Y 0 61 0 0 \n", - "1 TA Y 298 0 0 0 \n", - "2 TA Y 0 42 0 0 \n", - "3 TA Y 0 35 272 0 \n", - "4 TA Y 192 84 0 0 \n", - "\n", - " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", - "0 0 0 NaN NaN NaN 0 2 2008 \n", - "1 0 0 NaN NaN NaN 0 5 2007 \n", - "2 0 0 NaN NaN NaN 0 9 2008 \n", - "3 0 0 NaN NaN NaN 0 2 2006 \n", - "4 0 0 NaN NaN NaN 0 12 2008 \n", - "\n", - " SaleType SaleCondition SalePrice \n", - "0 WD Normal 208500 \n", - "1 WD Normal 181500 \n", - "2 WD Normal 223500 \n", - "3 WD Abnorml 140000 \n", - "4 WD Normal 250000 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load dataset\n", - "data = pd.read_csv('train.csv')\n", - "\n", - "# rows and columns of the data\n", - "print(data.shape)\n", - "\n", - "# visualise the dataset\n", - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Separate dataset into train and test\n", - "\n", - "It is important to separate our data intro training and testing set. \n", - "\n", - "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n", - "\n", - "Our feature engineering techniques will learn:\n", - "\n", - "- mean\n", - "- mode\n", - "- exponents for the yeo-johnson\n", - "- category frequency\n", - "- and category to number mappings\n", - "\n", - "from the train set.\n", - "\n", - "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1314, 79), (146, 79))" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's separate into train and test set\n", - "# Remember to set the seed (random_state for this sklearn function)\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n", - " data['SalePrice'], # target\n", - " test_size=0.1, # portion of dataset to allocate to test set\n", - " random_state=0, # we are setting the seed here\n", - ")\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Feature Engineering\n", - "\n", - "In the following cells, we will engineer the variables of the House Price Dataset so that we tackle:\n", - "\n", - "1. Missing values\n", - "2. Temporal variables\n", - "3. Non-Gaussian distributed variables\n", - "4. Categorical variables: remove rare labels\n", - "5. Categorical variables: convert strings to numbers\n", - "5. Put the variables in a similar scale" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Target\n", - "\n", - "We apply the logarithm" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "y_train = np.log(y_train)\n", - "y_test = np.log(y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Missing values\n", - "\n", - "### Categorical variables\n", - "\n", - "We will replace missing values with the string \"missing\" in those variables with a lot of missing data. \n", - "\n", - "Alternatively, we will replace missing data with the most frequent category in those variables that contain fewer observations without values. \n", - "\n", - "This is common practice." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "44" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let's identify the categorical variables\n", - "# we will capture those of type object\n", - "\n", - "cat_vars = [var for var in data.columns if data[var].dtype == 'O']\n", - "\n", - "# MSSubClass is also categorical by definition, despite its numeric values\n", - "# (you can find the definitions of the variables in the data_description.txt\n", - "# file available on Kaggle, in the same website where you downloaded the data)\n", - "\n", - "# lets add MSSubClass to the list of categorical variables\n", - "cat_vars = cat_vars + ['MSSubClass']\n", - "\n", - "# cast all variables as categorical\n", - "X_train[cat_vars] = X_train[cat_vars].astype('O')\n", - "X_test[cat_vars] = X_test[cat_vars].astype('O')\n", - "\n", - "# number of categorical variables\n", - "len(cat_vars)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "PoolQC 0.995434\n", - "MiscFeature 0.961187\n", - "Alley 0.938356\n", - "Fence 0.814307\n", - "FireplaceQu 0.472603\n", - "GarageType 0.056317\n", - "GarageFinish 0.056317\n", - "GarageQual 0.056317\n", - "GarageCond 0.056317\n", - "BsmtExposure 0.025114\n", - "BsmtFinType2 0.025114\n", - "BsmtQual 0.024353\n", - "BsmtCond 0.024353\n", - "BsmtFinType1 0.024353\n", - "MasVnrType 0.004566\n", - "Electrical 0.000761\n", - "dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# make a list of the categorical variables that contain missing values\n", - "\n", - "cat_vars_with_na = [\n", - " var for var in cat_vars\n", - " if X_train[var].isnull().sum() > 0\n", - "]\n", - "\n", - "# print percentage of missing values per variable\n", - "X_train[cat_vars_with_na ].isnull().mean().sort_values(ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# variables to impute with the string missing\n", - "with_string_missing = [\n", - " var for var in cat_vars_with_na if X_train[var].isnull().mean() > 0.1]\n", - "\n", - "# variables to impute with the most frequent category\n", - "with_frequent_category = [\n", - " var for var in cat_vars_with_na if X_train[var].isnull().mean() < 0.1]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with_string_missing" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# replace missing values with new label: \"Missing\"\n", - "\n", - "X_train[with_string_missing] = X_train[with_string_missing].fillna('Missing')\n", - "X_test[with_string_missing] = X_test[with_string_missing].fillna('Missing')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MasVnrType None\n", - "BsmtQual TA\n", - "BsmtCond TA\n", - "BsmtExposure No\n", - "BsmtFinType1 Unf\n", - "BsmtFinType2 Unf\n", - "Electrical SBrkr\n", - "GarageType Attchd\n", - "GarageFinish Unf\n", - "GarageQual TA\n", - "GarageCond TA\n" - ] - } - ], - "source": [ - "for var in with_frequent_category:\n", - " \n", - " # there can be more than 1 mode in a variable\n", - " # we take the first one with [0] \n", - " mode = X_train[var].mode()[0]\n", - " \n", - " print(var, mode)\n", - " \n", - " X_train[var].fillna(mode, inplace=True)\n", - " X_test[var].fillna(mode, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Alley 0\n", - "MasVnrType 0\n", - "BsmtQual 0\n", - "BsmtCond 0\n", - "BsmtExposure 0\n", - "BsmtFinType1 0\n", - "BsmtFinType2 0\n", - "Electrical 0\n", - "FireplaceQu 0\n", - "GarageType 0\n", - "GarageFinish 0\n", - "GarageQual 0\n", - "GarageCond 0\n", - "PoolQC 0\n", - "Fence 0\n", - "MiscFeature 0\n", - "dtype: int64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check that we have no missing information in the engineered variables\n", - "\n", - "X_train[cat_vars_with_na].isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check that test set does not contain null values in the engineered variables\n", - "\n", - "[var for var in cat_vars_with_na if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Numerical variables\n", - "\n", - "To engineer missing values in numerical variables, we will:\n", - "\n", - "- add a binary missing indicator variable\n", - "- and then replace the missing values in the original variable with the mean" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "35" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now let's identify the numerical variables\n", - "\n", - "num_vars = [\n", - " var for var in X_train.columns if var not in cat_vars and var != 'SalePrice'\n", - "]\n", - "\n", - "# number of numerical variables\n", - "len(num_vars)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LotFrontage 0.177321\n", - "MasVnrArea 0.004566\n", - "GarageYrBlt 0.056317\n", - "dtype: float64" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# make a list with the numerical variables that contain missing values\n", - "vars_with_na = [\n", - " var for var in num_vars\n", - " if X_train[var].isnull().sum() > 0\n", - "]\n", - "\n", - "# print percentage of missing values per variable\n", - "X_train[vars_with_na].isnull().mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LotFrontage 69.87974098057354\n", - "MasVnrArea 103.7974006116208\n", - "GarageYrBlt 1978.2959677419356\n" - ] - }, - { - "data": { - "text/plain": [ - "LotFrontage 0\n", - "MasVnrArea 0\n", - "GarageYrBlt 0\n", - "dtype: int64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# replace missing values as we described above\n", - "\n", - "for var in vars_with_na:\n", - "\n", - " # calculate the mean using the train set\n", - " mean_val = X_train[var].mean()\n", - " \n", - " print(var, mean_val)\n", - "\n", - " # add binary missing indicator (in train and test)\n", - " X_train[var + '_na'] = np.where(X_train[var].isnull(), 1, 0)\n", - " X_test[var + '_na'] = np.where(X_test[var].isnull(), 1, 0)\n", - "\n", - " # replace missing values by the mean\n", - " # (in train and test)\n", - " X_train[var].fillna(mean_val, inplace=True)\n", - " X_test[var].fillna(mean_val, inplace=True)\n", - "\n", - "# check that we have no more missing values in the engineered variables\n", - "X_train[vars_with_na].isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check that test set does not contain null values in the engineered variables\n", - "\n", - "[var for var in vars_with_na if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " LotFrontage_na MasVnrArea_na GarageYrBlt_na\n", - "930 0 0 0\n", - "656 0 0 0\n", - "45 0 0 0\n", - "1348 1 0 0\n", - "55 0 0 0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the binary missing indicator variables\n", - "\n", - "X_train[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Temporal variables\n", - "\n", - "### Capture elapsed time\n", - "\n", - "We learned in the previous notebook, that there are 4 variables that refer to the years in which the house or the garage were built or remodeled. \n", - "\n", - "We will capture the time elapsed between those variables and the year in which the house was sold:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "def elapsed_years(df, var):\n", - " # capture difference between the year variable\n", - " # and the year in which the house was sold\n", - " df[var] = df['YrSold'] - df[var]\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:\n", - " X_train = elapsed_years(X_train, var)\n", - " X_test = elapsed_years(X_test, var)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# now we drop YrSold\n", - "X_train.drop(['YrSold'], axis=1, inplace=True)\n", - "X_test.drop(['YrSold'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Numerical variable transformation\n", - "\n", - "### Logarithmic transformation\n", - "\n", - "In the previous notebook, we observed that the numerical variables are not normally distributed.\n", - "\n", - "We will transform with the logarightm the positive numerical variables in order to get a more Gaussian-like distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n", - " X_train[var] = np.log(X_train[var])\n", - " X_test[var] = np.log(X_test[var])" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check that test set does not contain null values in the engineered variables\n", - "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# same for train set\n", - "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_train[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Yeo-Johnson transformation\n", - "\n", - "We will apply the Yeo-Johnson transformation to LotArea." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-12.55283001172003\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\stats\\morestats.py:1476: RuntimeWarning: divide by zero encountered in log\n", - " loglike = -n_samples / 2 * np.log(trans.var(axis=0))\n", - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2555: RuntimeWarning: invalid value encountered in double_scalars\n", - " w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom\n", - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2148: RuntimeWarning: invalid value encountered in double_scalars\n", - " tmp1 = (x - w) * (fx - fv)\n", - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2149: RuntimeWarning: invalid value encountered in double_scalars\n", - " tmp2 = (x - v) * (fx - fw)\n" - ] - } - ], - "source": [ - "# the yeo-johnson transformation learns the best exponent to transform the variable\n", - "# it needs to learn it from the train set: \n", - "X_train['LotArea'], param = stats.yeojohnson(X_train['LotArea'])\n", - "\n", - "# and then apply the transformation to the test set with the same\n", - "# parameter: see who this time we pass param as argument to the \n", - "# yeo-johnson\n", - "X_test['LotArea'] = stats.yeojohnson(X_test['LotArea'], lmbda=param)\n", - "\n", - "print(param)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the train set\n", - "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the test set\n", - "[var for var in X_train.columns if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Binarize skewed variables\n", - "\n", - "There were a few variables very skewed, we would transform those into binary variables." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "skewed = [\n", - " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", - " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", - "]\n", - "\n", - "for var in skewed:\n", - " \n", - " # map the variable values into 0 and 1\n", - " X_train[var] = np.where(X_train[var]==0, 0, 1)\n", - " X_test[var] = np.where(X_test[var]==0, 0, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Categorical variables\n", - "\n", - "### Apply mappings\n", - "\n", - "These are variables which values have an assigned order, related to quality. For more information, check Kaggle website." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# re-map strings to numbers, which determine quality\n", - "\n", - "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", - "\n", - "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", - " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", - " 'GarageQual', 'GarageCond',\n", - " ]\n", - "\n", - "for var in qual_vars:\n", - " X_train[var] = X_train[var].map(qual_mappings)\n", - " X_test[var] = X_test[var].map(qual_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", - "\n", - "var = 'BsmtExposure'\n", - "\n", - "X_train[var] = X_train[var].map(exposure_mappings)\n", - "X_test[var] = X_test[var].map(exposure_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", - "\n", - "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n", - "\n", - "for var in finish_vars:\n", - " X_train[var] = X_train[var].map(finish_mappings)\n", - " X_test[var] = X_test[var].map(finish_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", - "\n", - "var = 'GarageFinish'\n", - "\n", - "X_train[var] = X_train[var].map(garage_mappings)\n", - "X_test[var] = X_test[var].map(garage_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n", - "\n", - "var = 'Fence'\n", - "\n", - "X_train[var] = X_train[var].map(fence_mappings)\n", - "X_test[var] = X_test[var].map(fence_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the train set\n", - "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Removing Rare Labels\n", - "\n", - "For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations. That is, all values of categorical variables that are shared by less than 1% of houses, well be replaced by the string \"Rare\".\n", - "\n", - "To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "30" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# capture all quality variables\n", - "\n", - "qual_vars = qual_vars + finish_vars + ['BsmtExposure','GarageFinish','Fence']\n", - "\n", - "# capture the remaining categorical variables\n", - "# (those that we did not re-map)\n", - "\n", - "cat_others = [\n", - " var for var in cat_vars if var not in qual_vars\n", - "]\n", - "\n", - "len(cat_others)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSZoning Index(['FV', 'RH', 'RL', 'RM'], dtype='object', name='MSZoning')\n", - "\n", - "Street Index(['Pave'], dtype='object', name='Street')\n", - "\n", - "Alley Index(['Grvl', 'Missing', 'Pave'], dtype='object', name='Alley')\n", - "\n", - "LotShape Index(['IR1', 'IR2', 'Reg'], dtype='object', name='LotShape')\n", - "\n", - "LandContour Index(['Bnk', 'HLS', 'Low', 'Lvl'], dtype='object', name='LandContour')\n", - "\n", - "Utilities Index(['AllPub'], dtype='object', name='Utilities')\n", - "\n", - "LotConfig Index(['Corner', 'CulDSac', 'FR2', 'Inside'], dtype='object', name='LotConfig')\n", - "\n", - "LandSlope Index(['Gtl', 'Mod'], dtype='object', name='LandSlope')\n", - "\n", - "Neighborhood Index(['Blmngtn', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor',\n", - " 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NWAmes',\n", - " 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW',\n", - " 'Somerst', 'StoneBr', 'Timber'],\n", - " dtype='object', name='Neighborhood')\n", - "\n", - "Condition1 Index(['Artery', 'Feedr', 'Norm', 'PosN', 'RRAn'], dtype='object', name='Condition1')\n", - "\n", - "Condition2 Index(['Norm'], dtype='object', name='Condition2')\n", - "\n", - "BldgType Index(['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE'], dtype='object', name='BldgType')\n", - "\n", - "HouseStyle Index(['1.5Fin', '1Story', '2Story', 'SFoyer', 'SLvl'], dtype='object', name='HouseStyle')\n", - "\n", - "RoofStyle Index(['Gable', 'Hip'], dtype='object', name='RoofStyle')\n", - "\n", - "RoofMatl Index(['CompShg'], dtype='object', name='RoofMatl')\n", - "\n", - "Exterior1st Index(['AsbShng', 'BrkFace', 'CemntBd', 'HdBoard', 'MetalSd', 'Plywood',\n", - " 'Stucco', 'VinylSd', 'Wd Sdng', 'WdShing'],\n", - " dtype='object', name='Exterior1st')\n", - "\n", - "Exterior2nd Index(['AsbShng', 'BrkFace', 'CmentBd', 'HdBoard', 'MetalSd', 'Plywood',\n", - " 'Stucco', 'VinylSd', 'Wd Sdng', 'Wd Shng'],\n", - " dtype='object', name='Exterior2nd')\n", - "\n", - "MasVnrType Index(['BrkFace', 'None', 'Stone'], dtype='object', name='MasVnrType')\n", - "\n", - "Foundation Index(['BrkTil', 'CBlock', 'PConc', 'Slab'], dtype='object', name='Foundation')\n", - "\n", - "Heating Index(['GasA', 'GasW'], dtype='object', name='Heating')\n", - "\n", - "CentralAir Index(['N', 'Y'], dtype='object', name='CentralAir')\n", - "\n", - "Electrical Index(['FuseA', 'FuseF', 'SBrkr'], dtype='object', name='Electrical')\n", - "\n", - "Functional Index(['Min1', 'Min2', 'Mod', 'Typ'], dtype='object', name='Functional')\n", - "\n", - "GarageType Index(['Attchd', 'Basment', 'BuiltIn', 'Detchd'], dtype='object', name='GarageType')\n", - "\n", - "PavedDrive Index(['N', 'P', 'Y'], dtype='object', name='PavedDrive')\n", - "\n", - "PoolQC Index(['Missing'], dtype='object', name='PoolQC')\n", - "\n", - "MiscFeature Index(['Missing', 'Shed'], dtype='object', name='MiscFeature')\n", - "\n", - "SaleType Index(['COD', 'New', 'WD'], dtype='object', name='SaleType')\n", - "\n", - "SaleCondition Index(['Abnorml', 'Family', 'Normal', 'Partial'], dtype='object', name='SaleCondition')\n", - "\n", - "MSSubClass Int64Index([20, 30, 50, 60, 70, 75, 80, 85, 90, 120, 160, 190], dtype='int64', name='MSSubClass')\n", - "\n" - ] - } - ], - "source": [ - "def find_frequent_labels(df, var, rare_perc):\n", - " \n", - " # function finds the labels that are shared by more than\n", - " # a certain % of the houses in the dataset\n", - "\n", - " df = df.copy()\n", - "\n", - " tmp = df.groupby(var)[var].count() / len(df)\n", - "\n", - " return tmp[tmp > rare_perc].index\n", - "\n", - "\n", - "for var in cat_others:\n", - " \n", - " # find the frequent categories\n", - " frequent_ls = find_frequent_labels(X_train, var, 0.01)\n", - " \n", - " print(var, frequent_ls)\n", - " print()\n", - " \n", - " # replace rare categories by the string \"Rare\"\n", - " X_train[var] = np.where(X_train[var].isin(\n", - " frequent_ls), X_train[var], 'Rare')\n", - " \n", - " X_test[var] = np.where(X_test[var].isin(\n", - " frequent_ls), X_test[var], 'Rare')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Encoding of categorical variables\n", - "\n", - "Next, we need to transform the strings of the categorical variables into numbers. \n", - "\n", - "We will do it so that we capture the monotonic relationship between the label and the target.\n", - "\n", - "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# this function will assign discrete values to the strings of the variables,\n", - "# so that the smaller value corresponds to the category that shows the smaller\n", - "# mean house sale price\n", - "\n", - "def replace_categories(train, test, y_train, var, target):\n", - " \n", - " tmp = pd.concat([X_train, y_train], axis=1)\n", - " \n", - " # order the categories in a variable from that with the lowest\n", - " # house sale price, to that with the highest\n", - " ordered_labels = tmp.groupby([var])[target].mean().sort_values().index\n", - "\n", - " # create a dictionary of ordered categories to integer values\n", - " ordinal_label = {k: i for i, k in enumerate(ordered_labels, 0)}\n", - " \n", - " print(var, ordinal_label)\n", - " print()\n", - "\n", - " # use the dictionary to replace the categorical strings by integers\n", - " train[var] = train[var].map(ordinal_label)\n", - " test[var] = test[var].map(ordinal_label)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSZoning {'Rare': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4}\n", - "\n", - "Street {'Rare': 0, 'Pave': 1}\n", - "\n", - "Alley {'Grvl': 0, 'Pave': 1, 'Missing': 2}\n", - "\n", - "LotShape {'Reg': 0, 'IR1': 1, 'Rare': 2, 'IR2': 3}\n", - "\n", - "LandContour {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3}\n", - "\n", - "Utilities {'Rare': 0, 'AllPub': 1}\n", - "\n", - "LotConfig {'Inside': 0, 'FR2': 1, 'Corner': 2, 'Rare': 3, 'CulDSac': 4}\n", - "\n", - "LandSlope {'Gtl': 0, 'Mod': 1, 'Rare': 2}\n", - "\n", - "Neighborhood {'IDOTRR': 0, 'MeadowV': 1, 'BrDale': 2, 'Edwards': 3, 'BrkSide': 4, 'OldTown': 5, 'Sawyer': 6, 'SWISU': 7, 'NAmes': 8, 'Mitchel': 9, 'SawyerW': 10, 'Rare': 11, 'NWAmes': 12, 'Gilbert': 13, 'Blmngtn': 14, 'CollgCr': 15, 'Crawfor': 16, 'ClearCr': 17, 'Somerst': 18, 'Timber': 19, 'StoneBr': 20, 'NridgHt': 21, 'NoRidge': 22}\n", - "\n", - "Condition1 {'Artery': 0, 'Feedr': 1, 'Norm': 2, 'RRAn': 3, 'Rare': 4, 'PosN': 5}\n", - "\n", - "Condition2 {'Rare': 0, 'Norm': 1}\n", - "\n", - "BldgType {'2fmCon': 0, 'Duplex': 1, 'Twnhs': 2, '1Fam': 3, 'TwnhsE': 4}\n", - "\n", - "HouseStyle {'SFoyer': 0, '1.5Fin': 1, 'Rare': 2, '1Story': 3, 'SLvl': 4, '2Story': 5}\n", - "\n", - "RoofStyle {'Gable': 0, 'Rare': 1, 'Hip': 2}\n", - "\n", - "RoofMatl {'CompShg': 0, 'Rare': 1}\n", - "\n", - "Exterior1st {'AsbShng': 0, 'Wd Sdng': 1, 'WdShing': 2, 'MetalSd': 3, 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7, 'BrkFace': 8, 'CemntBd': 9, 'VinylSd': 10}\n", - "\n", - "Exterior2nd {'AsbShng': 0, 'Wd Sdng': 1, 'MetalSd': 2, 'Wd Shng': 3, 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7, 'BrkFace': 8, 'CmentBd': 9, 'VinylSd': 10}\n", - "\n", - "MasVnrType {'Rare': 0, 'None': 1, 'BrkFace': 2, 'Stone': 3}\n", - "\n", - "Foundation {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare': 3, 'PConc': 4}\n", - "\n", - "Heating {'Rare': 0, 'GasW': 1, 'GasA': 2}\n", - "\n", - "CentralAir {'N': 0, 'Y': 1}\n", - "\n", - "Electrical {'Rare': 0, 'FuseF': 1, 'FuseA': 2, 'SBrkr': 3}\n", - "\n", - "Functional {'Rare': 0, 'Min2': 1, 'Mod': 2, 'Min1': 3, 'Typ': 4}\n", - "\n", - "GarageType {'Rare': 0, 'Detchd': 1, 'Basment': 2, 'Attchd': 3, 'BuiltIn': 4}\n", - "\n", - "PavedDrive {'N': 0, 'P': 1, 'Y': 2}\n", - "\n", - "PoolQC {'Missing': 0, 'Rare': 1}\n", - "\n", - "MiscFeature {'Rare': 0, 'Shed': 1, 'Missing': 2}\n", - "\n", - "SaleType {'COD': 0, 'Rare': 1, 'WD': 2, 'New': 3}\n", - "\n", - "SaleCondition {'Rare': 0, 'Abnorml': 1, 'Family': 2, 'Normal': 3, 'Partial': 4}\n", - "\n", - "MSSubClass {30: 0, 'Rare': 1, 190: 2, 90: 3, 160: 4, 50: 5, 85: 6, 70: 7, 80: 8, 20: 9, 75: 10, 120: 11, 60: 12}\n", - "\n" - ] - } - ], - "source": [ - "for var in cat_others:\n", - " replace_categories(X_train, X_test, y_train, var, 'SalePrice')" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the train set\n", - "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the test set\n", - "[var for var in X_test.columns if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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eAfymsaoiImLY7c4jR4+iOjGNpJcAjzZWVUREDLtOHzn6PmAJ8GxJP6SaD3HewItERMRI1um9mFZLOpPqkaMijxyNiBj1BnvkaPujRvvkkaMREaPcYHsQpUeN9skjRyMiRrEmJ8pFRMQI1ulJaiS9HjgROLSvzfZHmygqIiKGX0eXuUpaQPVUufdSnaT+Q+DYBuuKiIhh1uk8iJfafjvwiO2PAKcDJzRXVkREDLdOA+KJ+ufjkiZTPU70mGZKioiI/UGn5yC+IekI4P8Cq+q2zzRSUURE7BcGmwfxYuB+25fUnycAa4E7gb9pvryIiBgugx1i+jTwKwBJLwM+Xrc9Sv0Ut4iIGJ0GO8R0YMuDes4HFtpeDCxueQhQRESMQoPtQRwoqS9EXgF8r6Wv4zkUEREx8gz2R/6LwA8kPUR1JdMtAJKOJ7f7jogY1Qbcg7D9l8AFVE+UO8O2W5Z770DLSpom6SZJvZLWSZpbGHOOpDskrZG0UtIZLX2/qdvXSFqyu79YRETsnUEPE9m+rdB2dwfr3gFcUN8qfCKwStJS270tY74LLLFtSScBXwJm1H1P2D6lg+1EREQDOp0ot9tsb7K9un6/DVgPTGkbs71lr2Q89RPrIiJi+DUWEK0kTQdOBZYX+t4k6U7gm8AftXQdWh92uk3SufuizoiI+J3GA6KeXLcYmGd7a3u/7etszwDOBS5p6TrWdg/wVuBySc/uZ/1z6iBZuXnz5qH/BSIixqhGA0LSOKpwuHqwp8/Zvhl4lqRJ9eeN9c97ge9T7YGUlltou8d2T1dX11CWHxExpjUWEJIELALW276snzHH1+OQ9ELgEGCLpCMlHVK3TwJmAb2ldURERDOanOw2C5gNrG2ZdT0f6AawvQB4M/B2Sb+mmmdxfn1F0/OAT0vaSRViH2+7+ikiIhrWWEDYXkb1cKGBxlwKXFpovxV4QUOlRUREB/bJVUwRETHyJCAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioqixgJA0TdJNknolrZM0tzDmHEl3SFojaaWkM1r63iHpx/XrHU3VGRERZY09kxrYAVxge7WkicAqSUtt97aM+S6wxLYlnQR8CZgh6enAxUAP4HrZJbYfabDeiIho0dgehO1NtlfX77cB64EpbWO223b9cTxVGAC8Blhq++E6FJYCZzdVa0RE7GqfnIOQNB04FVhe6HuTpDuBbwJ/VDdPAe5vGbaBtnCJiIhmNR4QkiYAi4F5tre299u+zvYM4Fzgkj1Y/5z6/MXKzZs373W9ERFRaTQgJI2jCoerbV870FjbNwPPkjQJ2AhMa+meWreVlltou8d2T1dX1xBVHhERTV7FJGARsN72Zf2MOb4eh6QXAocAW4AbgVdLOlLSkcCr67aIiNhHmryKaRYwG1graU3dNh/oBrC9AHgz8HZJvwaeAM6vT1o/LOkSYEW93EdtP9xgrRER0aaxgLC9DNAgYy4FLu2n7yrgqgZKi4iIDmQmdUREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqKosYCQNE3STZJ6Ja2TNLcw5m2S7pC0VtKtkk5u6buvbl8jaWVTdUZERFljz6QGdgAX2F4taSKwStJS270tY34KnGn7EUmvBRYCp7X0v9z2Qw3WGBER/WgsIGxvAjbV77dJWg9MAXpbxtzasshtwNSm6omIiN2zT85BSJoOnAosH2DYHwM3tHw28G1JqyTNabC8iIgoaPIQEwCSJgCLgXm2t/Yz5uVUAXFGS/MZtjdKegawVNKdtm8uLDsHmAPQ3d095PVHRIxVje5BSBpHFQ5X2762nzEnAZ8BzrG9pa/d9sb654PAdcDM0vK2F9rusd3T1dU11L9CRMSY1eRVTAIWAettX9bPmG7gWmC27btb2sfXJ7aRNB54NfCjpmqNiIhdNXmIaRYwG1graU3dNh/oBrC9APgQcBTwqSpP2GG7BzgauK5uOwj4gu1vNVhrRES0afIqpmWABhnzJ8CfFNrvBU7edYmIiNhXMpM6IiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIosYCQtI0STdJ6pW0TtLcwpi3SbpD0lpJt0o6uaXvbEl3SbpH0kVN1RkREWUHNbjuHcAFtldLmgiskrTUdm/LmJ8CZ9p+RNJrgYXAaZIOBD4JvArYAKyQtKRt2YiIaFBjexC2N9leXb/fBqwHprSNudX2I/XH24Cp9fuZwD2277X9K+Aa4Jymao2IiF01uQfxW5KmA6cCywcY9sfADfX7KcD9LX0bgNP6WfccYE79cbuku/aq2OZNAh4ayhXq0qFc24iT73No5fscWkP6fTb0XR7bX0fjASFpArAYmGd7az9jXk4VEGfs7vptL6Q6NDUiSFppu2e46xgt8n0OrXyfQ2ukf5+NBoSkcVThcLXta/sZcxLwGeC1trfUzRuBaS3DptZtERGxjzR5FZOARcB625f1M6YbuBaYbfvulq4VwHMkHSfpYOAtwJKmao2IiF01uQcxC5gNrJW0pm6bD3QD2F4AfAg4CvhUlSfssN1je4ek9wA3AgcCV9le12Ct+9KIORw2QuT7HFr5PofWiP4+ZXu4a4iIiP1QZlJHRERRAiIiIooSEBERUbRPJsqNZZJmUM0C75tFvhFYYnv98FUVUan/+5wCLLe9vaX9bNvfGr7KRh5JMwHbXiHp+cDZwJ22rx/m0vZY9iAaJOn9VLcJEfAv9UvAF3MDwqEl6V3DXcNII+m/A18D3gv8SFLr7Ww+NjxVjUySLgb+DrhS0l8BVwDjgYskfWBYi9sLuYqpQZLuBk60/eu29oOBdbafMzyVjT6Sfm67e7jrGEkkrQVOt729vh3OV4B/sP23kv7V9qnDW+HIUX+XpwCHAL8AptreKukwqr2zk4azvj2VQ0zN2glMBn7W1n5M3Re7QdId/XUBR+/LWkaJA/oOK9m+T9JZwFckHUv1nUbndtj+DfC4pJ/03VbI9hOSRuz/6wmIZs0Dvivpx/zu5oPdwPHAe4arqBHsaOA1wCNt7QJu3ffljHj/JukU22sA6j2JNwBXAS8Y1spGnl9JOtz248CL+holPY0R/I/BHGJqmKQDqG5f3nqSekX9r43YDZIWAZ+1vazQ9wXbbx2GskYsSVOp/uX7i0LfLNs/HIayRiRJh9h+stA+CTjG9tphKGuvJSAiIqIoVzFFRERRAiIiIooSEDFmSNo++KjdXueHJV3Y8vlCSXdKWiNphaS37+F6z5L00qGrNGL3JSAihoikPwNeBcy0fQrwCvb8ctGzgCEJCEm5WjH2SAIixjRJb5S0XNK/SvqOpKPr9g9LukrS9yXdW8867lvmA5LulrQMeG7L6uYD7265Bn6r7c/Xy7yi3sbaer2H1O33SfqIpNV134x60tqfAf+j3hP5fUnTJX1P0h2Svls/bAtJn5N0Xktt2+ufZ0m6RdISoLfBrzBGsQREjHXLgJfUs4avAf6ipW8G1byLmcDFksZJehHVEw5PAV4HvBhA0u8BE23f274BSYcCnwPOt/0CqvlH724Z8pDtFwJXAhfavg9YAPyN7VNs3wJ8Avh8PSP3aqrbOgzmhcBc2yd08kVEtEtAxFg3FbixvlXC/wRObOn7pu0nbT8EPEg1Ue/3getsP17vKXTyKNznAj9teazu54GXtfT3Pa99FTC9n3WcDnyhfv8PwBkdbPdfbP+0g3ERRQmIGOs+AVxR/8v+T4FDW/paJz79hgHuPFCHxXZJz9qDGvq2M+A2+rGD+v/jelLmwS19j+1BLRG/lYCIse5pVLPbAd7RwfibgXMlHSZpIvDGlr6/Aj5ZH25C0oT6Kqa7gOmSjq/HzQZ+MMh2tgETWz7fSnVoC+BtwC31+/v43a0d/gAY18HvENGRBESMJYdL2tDyeh/wYeDLklYBDw22AturgX8CbgduAFa0dF8J3ASskPQjqj/iO23/O/Cuejtrqe7Ns2CQTX0deFPfSWqqW3K/q75h4Wxgbj3u74EzJd1OdRgqew0xZHKrjYiIKMoeREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIii/w+QxFp/3cR8GQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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bV5xFvdbzemdSSxpIlhymV2nbBFwjCWAYcLikVmA5cFCu3fbAXXXGamZmXahegribtc+izj+veSa1sk/9aUBLREwuapPf+S3pCuDGiLgh7aQ+T9LQVH0o8G91xmpmZl2o3pnUJ3Ri3WOBCcBCSc2p7ExgVFr3lBr9rpJ0LjA3FZ0TEas6MRYzM2unhg5zlbQ1cB6wXUQcJuldwAERMa1aTETcS3Zp8IZExPEVzy8DLms03szMulajJ8pdAdwGbJeeP0p2jwgzM+ulGk0Qw9LF+t4EiIhW4I3SRmVmZj2uPbcc3ZJsxzSS9gdeKm1UZmbW4xq95ehXgZnAOyT9N9n5EEfXDjEzsw1Zo9diWiDpQLJbjgrfctTMrNerd8vRyluNtvEtR83Merl6M4iiW4228S1Hzcx6sTJPlDMzsw1YozupkfQR4N3ARm1lEXFOGYMyM7Oe19BhrpKmkN1V7iSyndSfBHYocVxmZtbDGj0P4n0RcRzwYkR8GzgA2LW8YZmZWU9rNEG8ln6+Kmk7stuJblvOkMzMbH3Q6D6IGyVtDnwPmJ/KLi1lRGZmtl6odx7EPsBTEXFuej4EWAg8DPxn+cMzM7OeUm8T0yXA3wAkvR84P5W9BEwtd2hmZtaT6m1i6p+7Uc8xwNSImAHMyN0EyMzMeqF6M4j+ktqSyAeBO3N1DZ9DYWZmG556H/JXA3dLep7sSKbfAUjaGV/u28ysV6s5g4iIfwdOJbuj3LiIiFzcSbViJY2UNFvSYkmLJE0qaHOEpAclNUuaJ2lcru6NVN4saWZ7X5iZmXVO3c1EEXF/QdmjDay7FTg1XSp8U2C+pFkRsTjX5rfAzIgISbsD1wJjUt1rEbFnA/2YmVkJGj1Rrt0iYkVELEjLLwMtwIiKNmtys5LBpDvWmZlZzystQeRJGg3sBcwpqDtK0sPATcDnc1Ubpc1O90s6sjvGaWZmbyk9QaST62YAp0TE6sr6iLg+IsYARwLn5qp2iIgm4DPAhZLeUWX9E1Mimbdy5cqufwFmZn1UqQlC0kCy5DC93t3nIuIeYCdJw9Lz5ennUuAushlIUdzUiGiKiKbhw4d35fDNzPq00hKEJAHTgJaImFylzc6pHZL2BgYBL0gaKmlQKh8GjAUWF63DzMzKUebJbmOBCcDC3FnXZwKjACJiCvAJ4DhJfyc7z+KYdETTbsAlkt4kS2LnVxz9ZGZmJSstQUTEvWQ3F6rV5gLggoLy+4D3lDQ0MzNrQLccxWRmZhseJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQk4QZmZWyAnCzMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQk4QZmZWyAnCzMwKOUGYmVkhJwgzMytUWoKQNFLSbEmLJS2SNKmgzRGSHpTULGmepHG5us9J+lN6fK6scZqZWbHS7kkNtAKnRsQCSZsC8yXNiojFuTa/BWZGREjaHbgWGCNpC+BsoAmIFDszIl4scbxmZpZT2gwiIlZExIK0/DLQAoyoaLMmIiI9HUyWDAA+DMyKiFUpKcwCxpc1VjMzW1e37IOQNBrYC5hTUHeUpIeBm4DPp+IRwFO5ZsuoSC5mZlau0hOEpCHADOCUiFhdWR8R10fEGOBI4NwOrH9i2n8xb+XKlZ0er5mZZUpNEJIGkiWH6RFxXa22EXEPsJOkYcByYGSuevtUVhQ3NSKaIqJp+PDhXTRyMzMr8ygmAdOAloiYXKXNzqkdkvYGBgEvALcBh0oaKmkocGgqMzOzblLmUUxjgQnAQknNqexMYBRAREwBPgEcJ+nvwGvAMWmn9SpJ5wJzU9w5EbGqxLGamVmF0hJERNwLqE6bC4ALqtRdBlxWwtDMzKwBPpPazMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQk4QZmZWyAnCzMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQk4QZmZWyAnCzMwKOUGYmVmh0hKEpJGSZktaLGmRpEkFbT4r6UFJCyXdJ2mPXN3jqbxZ0ryyxmlmZsVKuyc10AqcGhELJG0KzJc0KyIW59o8BhwYES9KOgyYCuyXqz84Ip4vcYxmZlZFaQkiIlYAK9Lyy5JagBHA4lyb+3Ih9wPblzUeMzNrn27ZByFpNLAXMKdGsy8At+SeB3C7pPmSJpY4PDMzK1DmJiYAJA0BZgCnRMTqKm0OJksQ43LF4yJiuaStgFmSHo6IewpiJwITAUaNGtXl4zcz66tKnUFIGkiWHKZHxHVV2uwOXAocEREvtJVHxPL08zngemDfoviImBoRTRHRNHz48K5+CWZmfVaZRzEJmAa0RMTkKm1GAdcBEyLi0Vz54LRjG0mDgUOBh8oaq5mZravMTUxjgQnAQknNqexMYBRAREwBvglsCVyc5RNaI6IJ2Bq4PpUNAK6KiFtLHKuZmVUo8yimewHVaXMicGJB+VJgj3UjzMysu/hMajMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMysUGkJQtJISbMlLZa0SNKkgjaflfSgpIWS7pO0R65uvKRHJC2RdEZZ4zQzs2IDSlx3K3BqRCyQtCkwX9KsiFica/MYcGBEvCjpMGAqsJ+k/sCPgQ8By4C5kmZWxJqZWYlKm0FExIqIWJCWXwZagBEVbe6LiBfT0/uB7dPyvsCSiFgaEX8DrgGOKGusZma2LkVE+Z1Io4F7gH+IiNVV2pwGjImIEyUdDYyPiBNT3QRgv4j4ckHcRGBievpO4JEqwxgGPN+B4Xc0rqdi3Wfv6rMzse6zd/XZmdhacTtExPDCmogo9QEMAeYDH6/R5mCyGcaW6fnRwKW5+gnARZ0cx7zujOupWPfZu/rc0MbrPtfP2I7GlbkPAkkDgRnA9Ii4rkqb3YFLgcMi4oVUvBwYmWu2fSozM7NuUuZRTAKmAS0RMblKm1HAdcCEiHg0VzUX2EXSjpLeBhwLzCxrrGZmtq4yZxBjyTYNLZTUnMrOBEYBRMQU4JvAlsDFWT6hNSKaIqJV0peB24D+wGURsaiT45nazXE9Fes+e1efnYl1n72rz87EdiiuW3ZSm5nZhsdnUpuZWSEnCDMzK+QEYWZmhUo9zLWnSBpDduZ125nby4GZEdHSDf2OAOZExJpc+fiIuLVG3L5ARMRcSe8CxgMPR8TNHRjDzyLiuHbGjCM7e/2hiLi9Ttv9yI5MWy1pY+AMYG9gMXBeRLxUI/Zk4PqIeKqd42s7ku3piLhD0meA95GdOzM1Iv5eI3Yn4ONkh02/ATwKXBVVTtg0s7f0uhmEpNPJLs0h4A/pIeDqzl70T9IJNepOBn4NnAQ8JCl/aZDzasSdDfwI+Imk7wIXAYOBMyR9o854ZlY8fgN8vO15jbg/5Ja/mPrcFDi7gffoMuDVtPxDYDPgglR2eZ3Yc4E5kn4n6V8kFZ+9ua7LgY8AkyRdCXwSmAPsQ3YOTaH0O5kCbJTaDiJLFPdLOqjBvvskSVv1QJ9bdnefZZO0maTzJT0saZWkFyS1pLLNO7jOW+rUv13SdyVdmb5M5esubldnHT2jb319kH1DHFhQ/jbgT51c95M16hYCQ9LyaGAeMCk9/2OduP7AJsBq4O2pfGPgwTrjWQD8HDgIODD9XJGWD6wR98fc8lxgeFoeDCys02dLvv+KuuY6sX8k+1JyKNk5MiuBW4HPAZvWiHsw/RwAPAv0T89V6z1qe2/T8ibAXWl5VK3fSS5+M+B84GFgFfAC2azlfGDzDv4N3VKn/u3Ad4Ergc9U1F1cI24b4CdkF7ncEvhWev3XAtvW6XOLiseWwOPAUGCLGnHjK96racCDwFXA1nX6PB8YlpabgKXAEuCJOn+7C4CzgHd04L1vAman/5mRwCzgpfQ/sFed2CHAOcCiFLOS7Ppxx9eJuw04Hdim4nd1OnB7jbi9qzzeC6yo0+eM9P4eSXb+2AxgUNv71573rDduYnoT2I7sDy1v21RXk6QHq1UBW9cI7Rdps1JEPJ6+of5K0g4ptprWiHgDeFXSnyNt+oiI1yTVG28TMAn4BvC1iGiW9FpE3F0nrp+koWQf1oqIlanPVyS11ol9SNIJEXE58ICkpoiYJ2lXoOqmniQi4k3gduD2dKb9YcCngf8Aqs0o+qXNTIPJPug3I/vAHgQMrNPnALJNS4PI/smJiCdT3/VcC9wJHBQRzwBI2oYsoV1LlujWIWnvKusTsGedPi8H/kT2T/15SZ8gSxSvA/vXiLsCuInsPZoNTAcOJ/uQmELti10+z7r/LyPIPowD2KlK3HlkCR7gB2RfTv6RbJPeJanvaj4SEW2z1e8Dx0S2iXVXsgTTVCVuKLA5MFvSM8DVwC8i4ukafbW5GDg7xd8HfCUiPiTpg6nugBqx04HrgQ8DnyJ7n68BzpK0a0ScWSVudERckC9If0sXSPp8jf7mAndT/NmxeY04yJLnJ9LyDWlLxJ2SPlYnbl3tzcLr+4Ns+/0S4Bayk0Omkv0RLyH3jadG/LNk/8Q7VDxGk20DrxZ3J7BnRdkA4GfAGzXi5gCbpOV+ufLNaDDbk12K5Jdkm4qqznJy7R8n+8b2WPq5bSofQv1ZwGZkH0Z/TmP/e1rH3cAedWL/WKNukxp1X0l9PAGcDPwW+CnZN+Sza8RNIvtG+1OyWcAJqXw4cE8D79MjHax7I/09zC54vFanz+aK598A/pvsW33VvwfWnhU+WWudBbGnpv+R9+TKHmvg/VlQrY8G+mwBBqTl+yvqqs5iK/r8P2Qf7M+k93Zio39/Be9R1b/NVP9AxfO56Wc/sv2F1eJuB75ObkZF9kXzdOCOGnEPAbtUqXuqgfe2X0XZ8WSznyfq/V7XimtP4w3lkX5p+wOfSI/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\n", 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\n", 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\n", 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I7TPb3l8GXNbp8hERMbg6nSj3VeBa4ID6/b1Uz4iIiIidVKcBMba+Wd/TALY3AX9orKqIiOi6rXnk6AuoTkwj6XXA441VFRERXdfpI0c/AMwHXiLpx1TzIU7d8iIRETGcdXovpiWSjqV65KjII0cjInZ6Az1ytP1Ro33yyNGIiJ3cQHsQpUeN9skjRyMidmJNTpSLiIhhrNOT1Eh6C3Ao8Jy+Ntsfb6KoiIjovo4uc5U0i+qpcudQnaT+T8CBDdYVERFd1uk8iD+2fQbwmO1/BI4GXtpcWRER0W2dBsTG+vfvJB1A9TjR/ZspKSIidgSdnoP4rqTnAZ8CFtdtX2mkooiI2CEMNA/itcCDti+s348BlgF3A59uvryIiOiWgQ4xfQl4EkDS64GL6rbHqZ/iFhERO6eBDjHt2vKgntOA2bbnAfNaHgIUERE7oYH2IHaV1Bcifwbc0NLX8RyKiIgYfgb6I/9N4GZJj1BdyfQjAEkHkdt9R0Ts1La4B2H7E8B5VE+UO8a2W5Y7Z0vLSpoo6UZJvZKWS5pRGHOypLskLZW0SNIxLX1/qNuXSpq/tR8sIiK2z4CHiWzfXmi7t4N1bwLOq28VvjewWNIC270tY64H5tu2pFcB3wKm1H0bbR/ewXYiIqIBnU6U22q2V9teUr9eD6wAxreN2dCyVzKa+ol1ERHRfY0FRCtJk4EjgDsKfW+XdDfwPeCvWrqeUx92ul3SKUNRZ0REPKPxgKgn180DzrW9rr3f9lW2pwCnABe2dB1ouwd4F/AZSS/pZ/3T6yBZtGbNmsH/ABERI1SjASFpFFU4zB3o6XO2bwFeLGls/X5V/ft+4CaqPZDScrNt99juGTdu3GCWHxExojUWEJIEzAFW2L6knzEH1eOQ9GpgD2CtpH0k7VG3jwWmAr2ldURERDOanOw2FZgGLGuZdT0TmARgexbwF8AZkp6immdxWn1F0yHAlyQ9TRViF7Vd/RQREQ1rLCBs30r1cKEtjbkYuLjQfhvwyoZKi4iIDgzJVUwRETH8JCAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioqixgJA0UdKNknolLZc0ozDmZEl3SVoqaZGkY1r63iPp5/XPe5qqMyIiyhp7JjWwCTjP9hJJewOLJS2w3dsy5npgvm1LehXwLWCKpOcDFwA9gOtl59t+rMF6IyKiRWN7ELZX215Sv14PrADGt43ZYNv129FUYQDwJmCB7UfrUFgAnNhUrRERsbkhOQchaTJwBHBHoe/tku4Gvgf8Vd08HniwZdhK2sIlIiKa1XhASBoDzAPOtb2uvd/2VbanAKcAF27D+qfX5y8WrVmzZrvrjYiISqMBIWkUVTjMtX3llsbavgV4saSxwCpgYkv3hLqttNxs2z22e8aNGzdIlUdERJNXMQmYA6ywfUk/Yw6qxyHp1cAewFrgWuAESftI2gc4oW6LiIgh0uRVTFOBacAySUvrtpnAJADbs4C/AM6Q9BSwETitPmn9qKQLgYX1ch+3/WiDtUZERJvGAsL2rYAGGHMxcHE/fZcBlzVQWkREdCAzqSMioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFjQWEpImSbpTUK2m5pBmFMe+WdJekZZJuk3RYS98DdftSSYuaqjMiIsoaeyY1sAk4z/YSSXsDiyUtsN3bMuaXwLG2H5N0EjAbOKql/w22H2mwxoiI6EdjAWF7NbC6fr1e0gpgPNDbMua2lkVuByY0VU9ERGydITkHIWkycARwxxaGvQ/4fst7A9dJWixpeoPlRUREQZOHmACQNAaYB5xre10/Y95AFRDHtDQfY3uVpH2BBZLutn1LYdnpwHSASZMmDXr9EREjVaN7EJJGUYXDXNtX9jPmVcBXgJNtr+1rt72q/v0wcBVwZGl527Nt99juGTdu3GB/hIiIEavJq5gEzAFW2L6knzGTgCuBabbvbWkfXZ/YRtJo4ATgZ03VGhERm2vyENNUYBqwTNLSum0mMAnA9izgY8ALgC9UecIm2z3AfsBVddtuwDds/6DBWiMiok2TVzHdCmiAMWcBZxXa7wcO23yJiIgYKplJHRERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFFjASFpoqQbJfVKWi5pRmHMuyXdJWmZpNskHdbSd6KkeyTdJ+n8puqMiIiy3Rpc9ybgPNtLJO0NLJa0wHZvy5hfAsfafkzSScBs4ChJuwKfB94IrAQWSprftmxERDSosT0I26ttL6lfrwdWAOPbxtxm+7H67e3AhPr1kcB9tu+3/SRwOXByU7VGRMTmZLv5jUiTgVuAV9he18+YDwJTbJ8l6VTgRNtn1X3TgKNsn11YbjowvX77MuCeBj5CyVjgkSHaVjfk8w1v+XzD11B/tgNtjyt1NHmICQBJY4B5wLlbCIc3AO8Djtna9dueTXVoakhJWmS7Z6i3O1Ty+Ya3fL7ha0f6bI0GhKRRVOEw1/aV/Yx5FfAV4CTba+vmVcDElmET6raIiBgiTV7FJGAOsML2Jf2MmQRcCUyzfW9L10LgYEkvkrQ7cDowv6laIyJic03uQUwFpgHLJC2t22YCkwBszwI+BrwA+EKVJ2yy3WN7k6SzgWuBXYHLbC9vsNZtMeSHtYZYPt/wls83fO0wn21ITlJHRMTwk5nUERFRlICIiIiiBERERBQ1Pg9iZyFpCtVs7r7Z4KuA+bZXdK+q6FT9v9944A7bG1raT7T9g+5VNjgkHQnY9kJJLwdOBO62fU2XSxt0kv6P7TO6XUcTJB1DdSeJn9m+ruv15CT1wCR9BHgn1S0/VtbNE6guv73c9kXdqq1pkt5r+5+7Xcf2kPRfgb+jut3L4cAM2/9a9y2x/eoulrfdJF0AnET1hW8BcBRwI9W9zK61/YkulrddJLVf3i7gDcANALbfNuRFDSJJ/2b7yPr1X1P9d3oVcALwnW7/bUlAdEDSvcChtp9qa98dWG774O5U1jxJv7Y9qdt1bA9Jy4CjbW+ob/vybeDrtj8r6ae2j+huhdun/nyHA3sAvwEm2F4naU+qPaZXdbO+7SFpCdBLNZnWVAHxTaovZ9i+uXvVbb/W//4kLQTebHuNpNHA7bZf2c36coipM08DBwC/amvfv+4b1iTd1V8XsN9Q1tKQXfoOK9l+QNJxwLclHUj1GYe7Tbb/APxO0i/6bmlje6Ok4f7fZw8wA/go8CHbSyVtHO7B0GIXSftQnQ+W7TUAtp+QtKm7pSUgOnUucL2knwMP1m2TgIOAzW4gOAztB7wJeKytXcBtQ1/OoHtI0uG2lwLUexJvBS4DuvoNbZA8KWkv278DXtPXKOm5DPMvMLafBj4t6Yr690PsXH+3ngsspvr/miXtb3t1fQ+7rn95ySGmDknaherkUetJ6oX1N7dhTdIc4J9t31ro+4btd3WhrEEjaQLVt+zfFPqm2v5xF8oaNJL2sP37QvtYYH/by7pQViMkvQWYantmt2tpkqS9gP1s/7KrdSQgIiKiJPMgIiKiKAERERFFCYgYsSRtaHt/pqRLh2jbb5X0U0l3SuqV9Dd1+yn1RLeBlr9J0g7xUJnYee1MVwNEDAv1g7RmA0faXilpD2By3X0K8F2qa/8juip7EBEFkiZLukHSXZKurx9uhaSv1s9M7xu3of69v6RbJC2V9DNJf1K3nyDpJ5KWSLqivnxxb6ovZ2sBbP/e9j2S/hh4G/BP9XpeUk8U69vWwa3vW9pL24jYbgmIGMn2rP8QL60favXxlr7/DXytnoU8F/jcAOt6F9VtLQ4HDgOW1peZ/gNwfH07j0XAB2w/SvWExF9J+qakd0vaxfZtdfuHbB9u+xfA45IOr7fxXuBZtz3pbxvb9K8R0SaHmGIk21j/QQeqcxBUM3cBjgbeUb/+OvCpAda1ELisPnx0dT3j91jg5cCP6ycm7g78BMD2WZJeCRwPfJDqvklnFtb7FeC9kj4AnEY1F6fV6/rbRsT2SkBEbJ1N1Hve9eTJ3QFs3yLp9cBbgK9KuoRqZvoC2+8sraiewLZM0teBX1IOiHnABVQ3p1tse21bv7a0jYjtkUNMEWW3Ud8QDng38KP69QM8czuLtwGjAOr7Oj1k+8tU3/pfDdwOTJV0UD1mtKSXShpT3w+qz+E8c5+v9VTnKACw/R9Uz2b/Im2Hl2rFbWzTJ45ok4CIKDuH6tDOXcA0qhvGAXwZOFbSnVSHoZ6o248D7pT0U6pDQZ+tb7x2JvDNej0/AaZQfev/sKR76nMf/8gzew+XAx+qL4F9Sd02l+qeSps9H2AL24jYbrnVRsQOTtIHgefa/u/driVGlpyDiNiBSboKeAnwp92uJUae7EFERERRzkFERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqLo/wGqXRHrHDcNSgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let me show you what I mean by monotonic relationship\n", - "# between labels and target\n", - "\n", - "def analyse_vars(train, y_train, var):\n", - " \n", - " # function plots median house sale price per encoded\n", - " # category\n", - " \n", - " tmp = pd.concat([X_train, np.log(y_train)], axis=1)\n", - " \n", - " tmp.groupby(var)['SalePrice'].median().plot.bar()\n", - " plt.title(var)\n", - " plt.ylim(2.2, 2.6)\n", - " plt.ylabel('SalePrice')\n", - " plt.show()\n", - " \n", - "for var in cat_others:\n", - " analyse_vars(X_train, y_train, var)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The monotonic relationship is particularly clear for the variables MSZoning and Neighborhood. Note how, the higher the integer that now represents the category, the higher the mean house sale price.\n", - "\n", - "(remember that the target is log-transformed, that is why the differences seem so small)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Scaling\n", - "\n", - "For use in linear models, features need to be either scaled. We will scale features to the minimum and maximum values:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# create scaler\n", - "scaler = MinMaxScaler()\n", - "\n", - "# fit the scaler to the train set\n", - "scaler.fit(X_train) \n", - "\n", - "# transform the train and test set\n", - "\n", - "# sklearn returns numpy arrays, so we wrap the\n", - "# array with a pandas dataframe\n", - "\n", - "X_train = pd.DataFrame(\n", - " scaler.transform(X_train),\n", - " columns=X_train.columns\n", - ")\n", - "\n", - "X_test = pd.DataFrame(\n", - " scaler.transform(X_test),\n", - " columns=X_train.columns\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldSaleTypeSaleConditionLotFrontage_naMasVnrArea_naGarageYrBlt_na
00.7500000.750.4611710.01.01.00.3333331.0000001.00.00.00.8636360.41.00.750.60.7777780.500.0147060.0491800.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666670.6666671.00.0028350.00.00.6734790.2399351.01.001.01.00.5597600.00.00.5232500.0000000.00.6666670.00.3750.3333330.6666670.4166671.00.0000000.00.750.0186921.00.750.4301830.50.51.00.1166860.0329070.00.00.00.00.00.001.00.00.5454550.6666670.750.00.00.0
10.7500000.750.4560660.01.01.00.3333330.3333331.00.00.00.3636360.41.00.750.60.4444440.750.3602940.0491800.00.00.60.60.6666670.033750.6666670.50.50.3333330.6666670.0000000.80.1428070.00.00.1147240.1723401.01.001.01.00.4345390.00.00.4061960.3333330.00.3333330.50.3750.3333330.6666670.2500001.00.0000000.00.750.4579440.50.250.2200280.50.51.00.0000000.0000000.00.00.00.00.00.751.00.00.6363640.6666670.750.00.00.0
20.9166670.750.3946990.01.01.00.0000000.3333331.00.00.00.9545450.41.01.000.60.8888890.500.0367650.0983611.00.00.30.20.6666670.257501.0000000.51.01.0000000.6666670.0000001.00.0807940.00.00.6019510.2867431.01.001.01.00.6272050.00.00.5862960.3333330.00.6666670.00.2500.3333331.0000000.3333331.00.3333330.80.750.0467290.50.500.4062060.50.51.00.2287050.1499090.00.00.00.00.00.001.00.00.0909090.6666670.750.00.00.0
30.7500000.750.4450020.01.01.00.6666670.6666671.00.00.00.4545450.41.00.750.60.6666670.500.0661760.1639340.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666671.0000001.00.2556700.00.00.0181140.2425531.01.001.01.00.5669200.00.00.5299430.3333330.00.6666670.00.3750.3333330.6666670.2500001.00.3333330.40.750.0841120.50.500.3624820.50.51.00.4690780.0457040.00.00.00.00.00.001.00.00.6363640.6666670.751.00.00.0
40.7500000.750.5776580.01.01.00.3333330.3333331.00.00.00.3636360.41.00.750.60.5555560.500.3235290.7377050.00.00.60.70.6666670.170000.3333330.50.50.3333330.6666670.0000000.60.0868180.00.00.4342780.2332241.00.751.01.00.5490260.00.00.5132160.0000000.00.6666670.00.3750.3333330.3333330.4166671.00.3333330.80.750.4112150.50.500.4062060.50.51.00.0000000.0000000.01.00.00.00.00.001.00.00.5454550.6666670.750.00.00.0
\n", - "
" - ], - "text/plain": [ - " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 0.750000 0.75 0.461171 0.0 1.0 1.0 0.333333 \n", - "1 0.750000 0.75 0.456066 0.0 1.0 1.0 0.333333 \n", - "2 0.916667 0.75 0.394699 0.0 1.0 1.0 0.000000 \n", - "3 0.750000 0.75 0.445002 0.0 1.0 1.0 0.666667 \n", - "4 0.750000 0.75 0.577658 0.0 1.0 1.0 0.333333 \n", - "\n", - " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", - "0 1.000000 1.0 0.0 0.0 0.863636 0.4 \n", - "1 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", - "2 0.333333 1.0 0.0 0.0 0.954545 0.4 \n", - "3 0.666667 1.0 0.0 0.0 0.454545 0.4 \n", - "4 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", - "\n", - " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", - "0 1.0 0.75 0.6 0.777778 0.50 0.014706 \n", - "1 1.0 0.75 0.6 0.444444 0.75 0.360294 \n", - "2 1.0 1.00 0.6 0.888889 0.50 0.036765 \n", - "3 1.0 0.75 0.6 0.666667 0.50 0.066176 \n", - "4 1.0 0.75 0.6 0.555556 0.50 0.323529 \n", - "\n", - " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", - "0 0.049180 0.0 0.0 1.0 1.0 0.333333 \n", - "1 0.049180 0.0 0.0 0.6 0.6 0.666667 \n", - "2 0.098361 1.0 0.0 0.3 0.2 0.666667 \n", - "3 0.163934 0.0 0.0 1.0 1.0 0.333333 \n", - "4 0.737705 0.0 0.0 0.6 0.7 0.666667 \n", - "\n", - " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond \\\n", - "0 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", - "1 0.03375 0.666667 0.5 0.5 0.333333 0.666667 \n", - "2 0.25750 1.000000 0.5 1.0 1.000000 0.666667 \n", - "3 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", - "4 0.17000 0.333333 0.5 0.5 0.333333 0.666667 \n", - "\n", - " BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 \\\n", - "0 0.666667 1.0 0.002835 0.0 0.0 \n", - "1 0.000000 0.8 0.142807 0.0 0.0 \n", - "2 0.000000 1.0 0.080794 0.0 0.0 \n", - "3 1.000000 1.0 0.255670 0.0 0.0 \n", - "4 0.000000 0.6 0.086818 0.0 0.0 \n", - "\n", - " BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical \\\n", - "0 0.673479 0.239935 1.0 1.00 1.0 1.0 \n", - "1 0.114724 0.172340 1.0 1.00 1.0 1.0 \n", - "2 0.601951 0.286743 1.0 1.00 1.0 1.0 \n", - "3 0.018114 0.242553 1.0 1.00 1.0 1.0 \n", - "4 0.434278 0.233224 1.0 0.75 1.0 1.0 \n", - "\n", - " 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath \\\n", - "0 0.559760 0.0 0.0 0.523250 0.000000 0.0 \n", - "1 0.434539 0.0 0.0 0.406196 0.333333 0.0 \n", - "2 0.627205 0.0 0.0 0.586296 0.333333 0.0 \n", - "3 0.566920 0.0 0.0 0.529943 0.333333 0.0 \n", - "4 0.549026 0.0 0.0 0.513216 0.000000 0.0 \n", - "\n", - " FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd \\\n", - "0 0.666667 0.0 0.375 0.333333 0.666667 0.416667 \n", - "1 0.333333 0.5 0.375 0.333333 0.666667 0.250000 \n", - "2 0.666667 0.0 0.250 0.333333 1.000000 0.333333 \n", - "3 0.666667 0.0 0.375 0.333333 0.666667 0.250000 \n", - "4 0.666667 0.0 0.375 0.333333 0.333333 0.416667 \n", - "\n", - " Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish \\\n", - "0 1.0 0.000000 0.0 0.75 0.018692 1.0 \n", - "1 1.0 0.000000 0.0 0.75 0.457944 0.5 \n", - "2 1.0 0.333333 0.8 0.75 0.046729 0.5 \n", - "3 1.0 0.333333 0.4 0.75 0.084112 0.5 \n", - "4 1.0 0.333333 0.8 0.75 0.411215 0.5 \n", - "\n", - " GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF \\\n", - "0 0.75 0.430183 0.5 0.5 1.0 0.116686 \n", - "1 0.25 0.220028 0.5 0.5 1.0 0.000000 \n", - "2 0.50 0.406206 0.5 0.5 1.0 0.228705 \n", - "3 0.50 0.362482 0.5 0.5 1.0 0.469078 \n", - "4 0.50 0.406206 0.5 0.5 1.0 0.000000 \n", - "\n", - " OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC \\\n", - "0 0.032907 0.0 0.0 0.0 0.0 0.0 \n", - "1 0.000000 0.0 0.0 0.0 0.0 0.0 \n", - "2 0.149909 0.0 0.0 0.0 0.0 0.0 \n", - "3 0.045704 0.0 0.0 0.0 0.0 0.0 \n", - "4 0.000000 0.0 1.0 0.0 0.0 0.0 \n", - "\n", - " Fence MiscFeature MiscVal MoSold SaleType SaleCondition \\\n", - "0 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", - "1 0.75 1.0 0.0 0.636364 0.666667 0.75 \n", - "2 0.00 1.0 0.0 0.090909 0.666667 0.75 \n", - "3 0.00 1.0 0.0 0.636364 0.666667 0.75 \n", - "4 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", - "\n", - " LotFrontage_na MasVnrArea_na GarageYrBlt_na \n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 \n", - "3 1.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 " - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "# let's now save the train and test sets for the next notebook!\n", - "\n", - "X_train.to_csv('xtrain.csv', index=False)\n", - "X_test.to_csv('xtest.csv', index=False)\n", - "\n", - "y_train.to_csv('ytrain.csv', index=False)\n", - "y_test.to_csv('ytest.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['minmax_scaler.joblib']" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now let's save the scaler\n", - "\n", - "joblib.dump(scaler, 'minmax_scaler.joblib') " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "That concludes the feature engineering section.\n", - "\n", - "# Additional Resources\n", - "\n", - "- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n", - "- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n", - "- [Feature Engineering for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-engineering-for-machine-learning/) - Article\n", - "- [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) - Article" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "583px", - "left": "0px", - "right": "1324px", - "top": "107px", - "width": "212px" - }, - "toc_section_display": "block", - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Pipeline - Feature Engineering\n", + "\n", + "In the following notebooks, we will go through the implementation of each one of the steps in the Machine Learning Pipeline. \n", + "\n", + "We will discuss:\n", + "\n", + "1. Data Analysis\n", + "2. **Feature Engineering**\n", + "3. Feature Selection\n", + "4. Model Training\n", + "5. Obtaining Predictions / Scoring\n", + "\n", + "\n", + "We will use the house price dataset available on [Kaggle.com](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data). See below for more details.\n", + "\n", + "===================================================================================================\n", + "\n", + "## Predicting Sale Price of Houses\n", + "\n", + "The aim of the project is to build a machine learning model to predict the sale price of homes based on different explanatory variables describing aspects of residential houses.\n", + "\n", + "\n", + "### Why is this important? \n", + "\n", + "Predicting house prices is useful to identify fruitful investments, or to determine whether the price advertised for a house is over or under-estimated.\n", + "\n", + "\n", + "### What is the objective of the machine learning model?\n", + "\n", + "We aim to minimise the difference between the real price and the price estimated by our model. We will evaluate model performance with the:\n", + "\n", + "1. mean squared error (mse)\n", + "2. root squared of the mean squared error (rmse)\n", + "3. r-squared (r2).\n", + "\n", + "\n", + "### How do I download the dataset?\n", + "\n", + "- Visit the [Kaggle Website](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data).\n", + "\n", + "- Remember to **log in**\n", + "\n", + "- Scroll down to the bottom of the page, and click on the link **'train.csv'**, and then click the 'download' blue button towards the right of the screen, to download the dataset.\n", + "\n", + "- The download the file called **'test.csv'** and save it in the directory with the notebooks.\n", + "\n", + "\n", + "**Note the following:**\n", + "\n", + "- You need to be logged in to Kaggle in order to download the datasets.\n", + "- You need to accept the terms and conditions of the competition to download the dataset\n", + "- If you save the file to the directory with the jupyter notebook, then you can run the code as it is written here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reproducibility: Setting the seed\n", + "\n", + "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# to handle datasets\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# for plotting\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# for the yeo-johnson transformation\n", + "import scipy.stats as stats\n", + "\n", + "# to divide train and test set\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# feature scaling\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# to save the trained scaler class\n", + "import joblib\n", + "\n", + "# to visualise al the columns in the dataframe\n", + "pd.pandas.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n" + ] + }, + { + "data": { + "text/html": [ + "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
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" + ], + "text/plain": [ + " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 1 60 RL 65.0 8450 Pave NaN Reg \n", + "1 2 20 RL 80.0 9600 Pave NaN Reg \n", + "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", + "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", + "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", + "\n", + " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", + "0 Lvl AllPub Inside Gtl CollgCr Norm \n", + "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", + "2 Lvl AllPub Inside Gtl CollgCr Norm \n", + "3 Lvl AllPub Corner Gtl Crawfor Norm \n", + "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", + "\n", + " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", + "0 Norm 1Fam 2Story 7 5 2003 \n", + "1 Norm 1Fam 1Story 6 8 1976 \n", + "2 Norm 1Fam 2Story 7 5 2001 \n", + "3 Norm 1Fam 2Story 7 5 1915 \n", + "4 Norm 1Fam 2Story 8 5 2000 \n", + "\n", + " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", + "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", + "1 1976 Gable CompShg MetalSd MetalSd None \n", + "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", + "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", + "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", + "\n", + " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", + "0 196.0 Gd TA PConc Gd TA No \n", + "1 0.0 TA TA CBlock Gd TA Gd \n", + "2 162.0 Gd TA PConc Gd TA Mn \n", + "3 0.0 TA TA BrkTil TA Gd No \n", + "4 350.0 Gd TA PConc Gd TA Av \n", + "\n", + " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", + "0 GLQ 706 Unf 0 150 856 \n", + "1 ALQ 978 Unf 0 284 1262 \n", + "2 GLQ 486 Unf 0 434 920 \n", + "3 ALQ 216 Unf 0 540 756 \n", + "4 GLQ 655 Unf 0 490 1145 \n", + "\n", + " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", + "0 GasA Ex Y SBrkr 856 854 0 \n", + "1 GasA Ex Y SBrkr 1262 0 0 \n", + "2 GasA Ex Y SBrkr 920 866 0 \n", + "3 GasA Gd Y SBrkr 961 756 0 \n", + "4 GasA Ex Y SBrkr 1145 1053 0 \n", + "\n", + " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", + "0 1710 1 0 2 1 3 \n", + "1 1262 0 1 2 0 3 \n", + "2 1786 1 0 2 1 3 \n", + "3 1717 1 0 1 0 3 \n", + "4 2198 1 0 2 1 4 \n", + "\n", + " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", + "0 1 Gd 8 Typ 0 NaN \n", + "1 1 TA 6 Typ 1 TA \n", + "2 1 Gd 6 Typ 1 TA \n", + "3 1 Gd 7 Typ 1 Gd \n", + "4 1 Gd 9 Typ 1 TA \n", + "\n", + " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", + "0 Attchd 2003.0 RFn 2 548 TA \n", + "1 Attchd 1976.0 RFn 2 460 TA \n", + "2 Attchd 2001.0 RFn 2 608 TA \n", + "3 Detchd 1998.0 Unf 3 642 TA \n", + "4 Attchd 2000.0 RFn 3 836 TA \n", + "\n", + " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", + "0 TA Y 0 61 0 0 \n", + "1 TA Y 298 0 0 0 \n", + "2 TA Y 0 42 0 0 \n", + "3 TA Y 0 35 272 0 \n", + "4 TA Y 192 84 0 0 \n", + "\n", + " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", + "0 0 0 NaN NaN NaN 0 2 2008 \n", + "1 0 0 NaN NaN NaN 0 5 2007 \n", + "2 0 0 NaN NaN NaN 0 9 2008 \n", + "3 0 0 NaN NaN NaN 0 2 2006 \n", + "4 0 0 NaN NaN NaN 0 12 2008 \n", + "\n", + " SaleType SaleCondition SalePrice \n", + "0 WD Normal 208500 \n", + "1 WD Normal 181500 \n", + "2 WD Normal 223500 \n", + "3 WD Abnorml 140000 \n", + "4 WD Normal 250000 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load dataset\n", + "data = pd.read_csv('train.csv')\n", + "\n", + "# rows and columns of the data\n", + "print(data.shape)\n", + "\n", + "# visualise the dataset\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Separate dataset into train and test\n", + "\n", + "It is important to separate our data intro training and testing set. \n", + "\n", + "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n", + "\n", + "Our feature engineering techniques will learn:\n", + "\n", + "- mean\n", + "- mode\n", + "- exponents for the yeo-johnson\n", + "- category frequency\n", + "- and category to number mappings\n", + "\n", + "from the train set.\n", + "\n", + "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1314, 79), (146, 79))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's separate into train and test set\n", + "# Remember to set the seed (random_state for this sklearn function)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n", + " data['SalePrice'], # target\n", + " test_size=0.1, # portion of dataset to allocate to test set\n", + " random_state=0, # we are setting the seed here\n", + ")\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Engineering\n", + "\n", + "In the following cells, we will engineer the variables of the House Price Dataset so that we tackle:\n", + "\n", + "1. Missing values\n", + "2. Temporal variables\n", + "3. Non-Gaussian distributed variables\n", + "4. Categorical variables: remove rare labels\n", + "5. Categorical variables: convert strings to numbers\n", + "5. Put the variables in a similar scale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Target\n", + "\n", + "We apply the logarithm" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "y_train = np.log(y_train)\n", + "y_test = np.log(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Missing values\n", + "\n", + "### Categorical variables\n", + "\n", + "We will replace missing values with the string \"missing\" in those variables with a lot of missing data. \n", + "\n", + "Alternatively, we will replace missing data with the most frequent category in those variables that contain fewer observations without values. \n", + "\n", + "This is common practice." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "44" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's identify the categorical variables\n", + "# we will capture those of type object\n", + "\n", + "cat_vars = [var for var in data.columns if data[var].dtype == 'O']\n", + "\n", + "# MSSubClass is also categorical by definition, despite its numeric values\n", + "# (you can find the definitions of the variables in the data_description.txt\n", + "# file available on Kaggle, in the same website where you downloaded the data)\n", + "\n", + "# lets add MSSubClass to the list of categorical variables\n", + "cat_vars = cat_vars + ['MSSubClass']\n", + "\n", + "# cast all variables as categorical\n", + "X_train[cat_vars] = X_train[cat_vars].astype('O')\n", + "X_test[cat_vars] = X_test[cat_vars].astype('O')\n", + "\n", + "# number of categorical variables\n", + "len(cat_vars)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PoolQC 0.995434\n", + "MiscFeature 0.961187\n", + "Alley 0.938356\n", + "Fence 0.814307\n", + "FireplaceQu 0.472603\n", + "GarageType 0.056317\n", + "GarageFinish 0.056317\n", + "GarageQual 0.056317\n", + "GarageCond 0.056317\n", + "BsmtExposure 0.025114\n", + "BsmtFinType2 0.025114\n", + "BsmtQual 0.024353\n", + "BsmtCond 0.024353\n", + "BsmtFinType1 0.024353\n", + "MasVnrType 0.004566\n", + "Electrical 0.000761\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make a list of the categorical variables that contain missing values\n", + "\n", + "cat_vars_with_na = [\n", + " var for var in cat_vars\n", + " if X_train[var].isnull().sum() > 0\n", + "]\n", + "\n", + "# print percentage of missing values per variable\n", + "X_train[cat_vars_with_na ].isnull().mean().sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# variables to impute with the string missing\n", + "with_string_missing = [\n", + " var for var in cat_vars_with_na if X_train[var].isnull().mean() > 0.1]\n", + "\n", + "# variables to impute with the most frequent category\n", + "with_frequent_category = [\n", + " var for var in cat_vars_with_na if X_train[var].isnull().mean() < 0.1]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with_string_missing" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# replace missing values with new label: \"Missing\"\n", + "\n", + "X_train[with_string_missing] = X_train[with_string_missing].fillna('Missing')\n", + "X_test[with_string_missing] = X_test[with_string_missing].fillna('Missing')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MasVnrType None\n", + "BsmtQual TA\n", + "BsmtCond TA\n", + "BsmtExposure No\n", + "BsmtFinType1 Unf\n", + "BsmtFinType2 Unf\n", + "Electrical SBrkr\n", + "GarageType Attchd\n", + "GarageFinish Unf\n", + "GarageQual TA\n", + "GarageCond TA\n" + ] + } + ], + "source": [ + "for var in with_frequent_category:\n", + " \n", + " # there can be more than 1 mode in a variable\n", + " # we take the first one with [0] \n", + " mode = X_train[var].mode()[0]\n", + " \n", + " print(var, mode)\n", + " \n", + " X_train[var].fillna(mode, inplace=True)\n", + " X_test[var].fillna(mode, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Alley 0\n", + "MasVnrType 0\n", + "BsmtQual 0\n", + "BsmtCond 0\n", + "BsmtExposure 0\n", + "BsmtFinType1 0\n", + "BsmtFinType2 0\n", + "Electrical 0\n", + "FireplaceQu 0\n", + "GarageType 0\n", + "GarageFinish 0\n", + "GarageQual 0\n", + "GarageCond 0\n", + "PoolQC 0\n", + "Fence 0\n", + "MiscFeature 0\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check that we have no missing information in the engineered variables\n", + "\n", + "X_train[cat_vars_with_na].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check that test set does not contain null values in the engineered variables\n", + "\n", + "[var for var in cat_vars_with_na if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Numerical variables\n", + "\n", + "To engineer missing values in numerical variables, we will:\n", + "\n", + "- add a binary missing indicator variable\n", + "- and then replace the missing values in the original variable with the mean" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "35" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now let's identify the numerical variables\n", + "\n", + "num_vars = [\n", + " var for var in X_train.columns if var not in cat_vars and var != 'SalePrice'\n", + "]\n", + "\n", + "# number of numerical variables\n", + "len(num_vars)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LotFrontage 0.177321\n", + "MasVnrArea 0.004566\n", + "GarageYrBlt 0.056317\n", + "dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make a list with the numerical variables that contain missing values\n", + "vars_with_na = [\n", + " var for var in num_vars\n", + " if X_train[var].isnull().sum() > 0\n", + "]\n", + "\n", + "# print percentage of missing values per variable\n", + "X_train[vars_with_na].isnull().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LotFrontage 69.87974098057354\n", + "MasVnrArea 103.7974006116208\n", + "GarageYrBlt 1978.2959677419356\n" + ] + }, + { + "data": { + "text/plain": [ + "LotFrontage 0\n", + "MasVnrArea 0\n", + "GarageYrBlt 0\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# replace missing values as we described above\n", + "\n", + "for var in vars_with_na:\n", + "\n", + " # calculate the mean using the train set\n", + " mean_val = X_train[var].mean()\n", + " \n", + " print(var, mean_val)\n", + "\n", + " # add binary missing indicator (in train and test)\n", + " X_train[var + '_na'] = np.where(X_train[var].isnull(), 1, 0)\n", + " X_test[var + '_na'] = np.where(X_test[var].isnull(), 1, 0)\n", + "\n", + " # replace missing values by the mean\n", + " # (in train and test)\n", + " X_train[var].fillna(mean_val, inplace=True)\n", + " X_test[var].fillna(mean_val, inplace=True)\n", + "\n", + "# check that we have no more missing values in the engineered variables\n", + "X_train[vars_with_na].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check that test set does not contain null values in the engineered variables\n", + "\n", + "[var for var in vars_with_na if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " LotFrontage_na MasVnrArea_na GarageYrBlt_na\n", + "930 0 0 0\n", + "656 0 0 0\n", + "45 0 0 0\n", + "1348 1 0 0\n", + "55 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the binary missing indicator variables\n", + "\n", + "X_train[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Temporal variables\n", + "\n", + "### Capture elapsed time\n", + "\n", + "We learned in the previous notebook, that there are 4 variables that refer to the years in which the house or the garage were built or remodeled. \n", + "\n", + "We will capture the time elapsed between those variables and the year in which the house was sold:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def elapsed_years(df, var):\n", + " # capture difference between the year variable\n", + " # and the year in which the house was sold\n", + " df[var] = df['YrSold'] - df[var]\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:\n", + " X_train = elapsed_years(X_train, var)\n", + " X_test = elapsed_years(X_test, var)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# now we drop YrSold\n", + "X_train.drop(['YrSold'], axis=1, inplace=True)\n", + "X_test.drop(['YrSold'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Numerical variable transformation\n", + "\n", + "### Logarithmic transformation\n", + "\n", + "In the previous notebook, we observed that the numerical variables are not normally distributed.\n", + "\n", + "We will transform with the logarightm the positive numerical variables in order to get a more Gaussian-like distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n", + " X_train[var] = np.log(X_train[var])\n", + " X_test[var] = np.log(X_test[var])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check that test set does not contain null values in the engineered variables\n", + "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same for train set\n", + "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_train[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Yeo-Johnson transformation\n", + "\n", + "We will apply the Yeo-Johnson transformation to LotArea." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-12.55283001172003\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\stats\\morestats.py:1476: RuntimeWarning: divide by zero encountered in log\n", + " loglike = -n_samples / 2 * np.log(trans.var(axis=0))\n", + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2555: RuntimeWarning: invalid value encountered in double_scalars\n", + " w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom\n", + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2148: RuntimeWarning: invalid value encountered in double_scalars\n", + " tmp1 = (x - w) * (fx - fv)\n", + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2149: RuntimeWarning: invalid value encountered in double_scalars\n", + " tmp2 = (x - v) * (fx - fw)\n" + ] + } + ], + "source": [ + "# the yeo-johnson transformation learns the best exponent to transform the variable\n", + "# it needs to learn it from the train set: \n", + "X_train['LotArea'], param = stats.yeojohnson(X_train['LotArea'])\n", + "\n", + "# and then apply the transformation to the test set with the same\n", + "# parameter: see who this time we pass param as argument to the \n", + "# yeo-johnson\n", + "X_test['LotArea'] = stats.yeojohnson(X_test['LotArea'], lmbda=param)\n", + "\n", + "print(param)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the train set\n", + "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the test set\n", + "[var for var in X_train.columns if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Binarize skewed variables\n", + "\n", + "There were a few variables very skewed, we would transform those into binary variables." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "skewed = [\n", + " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", + " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", + "]\n", + "\n", + "for var in skewed:\n", + " \n", + " # map the variable values into 0 and 1\n", + " X_train[var] = np.where(X_train[var]==0, 0, 1)\n", + " X_test[var] = np.where(X_test[var]==0, 0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Categorical variables\n", + "\n", + "### Apply mappings\n", + "\n", + "These are variables which values have an assigned order, related to quality. For more information, check Kaggle website." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# re-map strings to numbers, which determine quality\n", + "\n", + "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", + "\n", + "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", + " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", + " 'GarageQual', 'GarageCond',\n", + " ]\n", + "\n", + "for var in qual_vars:\n", + " X_train[var] = X_train[var].map(qual_mappings)\n", + " X_test[var] = X_test[var].map(qual_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", + "\n", + "var = 'BsmtExposure'\n", + "\n", + "X_train[var] = X_train[var].map(exposure_mappings)\n", + "X_test[var] = X_test[var].map(exposure_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", + "\n", + "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n", + "\n", + "for var in finish_vars:\n", + " X_train[var] = X_train[var].map(finish_mappings)\n", + " X_test[var] = X_test[var].map(finish_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", + "\n", + "var = 'GarageFinish'\n", + "\n", + "X_train[var] = X_train[var].map(garage_mappings)\n", + "X_test[var] = X_test[var].map(garage_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n", + "\n", + "var = 'Fence'\n", + "\n", + "X_train[var] = X_train[var].map(fence_mappings)\n", + "X_test[var] = X_test[var].map(fence_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the train set\n", + "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Rare Labels\n", + "\n", + "For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations. That is, all values of categorical variables that are shared by less than 1% of houses, well be replaced by the string \"Rare\".\n", + "\n", + "To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# capture all quality variables\n", + "\n", + "qual_vars = qual_vars + finish_vars + ['BsmtExposure','GarageFinish','Fence']\n", + "\n", + "# capture the remaining categorical variables\n", + "# (those that we did not re-map)\n", + "\n", + "cat_others = [\n", + " var for var in cat_vars if var not in qual_vars\n", + "]\n", + "\n", + "len(cat_others)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSZoning Index(['FV', 'RH', 'RL', 'RM'], dtype='object', name='MSZoning')\n", + "\n", + "Street Index(['Pave'], dtype='object', name='Street')\n", + "\n", + "Alley Index(['Grvl', 'Missing', 'Pave'], dtype='object', name='Alley')\n", + "\n", + "LotShape Index(['IR1', 'IR2', 'Reg'], dtype='object', name='LotShape')\n", + "\n", + "LandContour Index(['Bnk', 'HLS', 'Low', 'Lvl'], dtype='object', name='LandContour')\n", + "\n", + "Utilities Index(['AllPub'], dtype='object', name='Utilities')\n", + "\n", + "LotConfig Index(['Corner', 'CulDSac', 'FR2', 'Inside'], dtype='object', name='LotConfig')\n", + "\n", + "LandSlope Index(['Gtl', 'Mod'], dtype='object', name='LandSlope')\n", + "\n", + "Neighborhood Index(['Blmngtn', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor',\n", + " 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NWAmes',\n", + " 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW',\n", + " 'Somerst', 'StoneBr', 'Timber'],\n", + " dtype='object', name='Neighborhood')\n", + "\n", + "Condition1 Index(['Artery', 'Feedr', 'Norm', 'PosN', 'RRAn'], dtype='object', name='Condition1')\n", + "\n", + "Condition2 Index(['Norm'], dtype='object', name='Condition2')\n", + "\n", + "BldgType Index(['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE'], dtype='object', name='BldgType')\n", + "\n", + "HouseStyle Index(['1.5Fin', '1Story', '2Story', 'SFoyer', 'SLvl'], dtype='object', name='HouseStyle')\n", + "\n", + "RoofStyle Index(['Gable', 'Hip'], dtype='object', name='RoofStyle')\n", + "\n", + "RoofMatl Index(['CompShg'], dtype='object', name='RoofMatl')\n", + "\n", + "Exterior1st Index(['AsbShng', 'BrkFace', 'CemntBd', 'HdBoard', 'MetalSd', 'Plywood',\n", + " 'Stucco', 'VinylSd', 'Wd Sdng', 'WdShing'],\n", + " dtype='object', name='Exterior1st')\n", + "\n", + "Exterior2nd Index(['AsbShng', 'BrkFace', 'CmentBd', 'HdBoard', 'MetalSd', 'Plywood',\n", + " 'Stucco', 'VinylSd', 'Wd Sdng', 'Wd Shng'],\n", + " dtype='object', name='Exterior2nd')\n", + "\n", + "MasVnrType Index(['BrkFace', 'None', 'Stone'], dtype='object', name='MasVnrType')\n", + "\n", + "Foundation Index(['BrkTil', 'CBlock', 'PConc', 'Slab'], dtype='object', name='Foundation')\n", + "\n", + "Heating Index(['GasA', 'GasW'], dtype='object', name='Heating')\n", + "\n", + "CentralAir Index(['N', 'Y'], dtype='object', name='CentralAir')\n", + "\n", + "Electrical Index(['FuseA', 'FuseF', 'SBrkr'], dtype='object', name='Electrical')\n", + "\n", + "Functional Index(['Min1', 'Min2', 'Mod', 'Typ'], dtype='object', name='Functional')\n", + "\n", + "GarageType Index(['Attchd', 'Basment', 'BuiltIn', 'Detchd'], dtype='object', name='GarageType')\n", + "\n", + "PavedDrive Index(['N', 'P', 'Y'], dtype='object', name='PavedDrive')\n", + "\n", + "PoolQC Index(['Missing'], dtype='object', name='PoolQC')\n", + "\n", + "MiscFeature Index(['Missing', 'Shed'], dtype='object', name='MiscFeature')\n", + "\n", + "SaleType Index(['COD', 'New', 'WD'], dtype='object', name='SaleType')\n", + "\n", + "SaleCondition Index(['Abnorml', 'Family', 'Normal', 'Partial'], dtype='object', name='SaleCondition')\n", + "\n", + "MSSubClass Int64Index([20, 30, 50, 60, 70, 75, 80, 85, 90, 120, 160, 190], dtype='int64', name='MSSubClass')\n", + "\n" + ] + } + ], + "source": [ + "def find_frequent_labels(df, var, rare_perc):\n", + " \n", + " # function finds the labels that are shared by more than\n", + " # a certain % of the houses in the dataset\n", + "\n", + " df = df.copy()\n", + "\n", + " tmp = df.groupby(var)[var].count() / len(df)\n", + "\n", + " return tmp[tmp > rare_perc].index\n", + "\n", + "\n", + "for var in cat_others:\n", + " \n", + " # find the frequent categories\n", + " frequent_ls = find_frequent_labels(X_train, var, 0.01)\n", + " \n", + " print(var, frequent_ls)\n", + " print()\n", + " \n", + " # replace rare categories by the string \"Rare\"\n", + " X_train[var] = np.where(X_train[var].isin(\n", + " frequent_ls), X_train[var], 'Rare')\n", + " \n", + " X_test[var] = np.where(X_test[var].isin(\n", + " frequent_ls), X_test[var], 'Rare')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Encoding of categorical variables\n", + "\n", + "Next, we need to transform the strings of the categorical variables into numbers. \n", + "\n", + "We will do it so that we capture the monotonic relationship between the label and the target.\n", + "\n", + "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# this function will assign discrete values to the strings of the variables,\n", + "# so that the smaller value corresponds to the category that shows the smaller\n", + "# mean house sale price\n", + "\n", + "def replace_categories(train, test, y_train, var, target):\n", + " \n", + " tmp = pd.concat([X_train, y_train], axis=1)\n", + " \n", + " # order the categories in a variable from that with the lowest\n", + " # house sale price, to that with the highest\n", + " ordered_labels = tmp.groupby([var])[target].mean().sort_values().index\n", + "\n", + " # create a dictionary of ordered categories to integer values\n", + " ordinal_label = {k: i for i, k in enumerate(ordered_labels, 0)}\n", + " \n", + " print(var, ordinal_label)\n", + " print()\n", + "\n", + " # use the dictionary to replace the categorical strings by integers\n", + " train[var] = train[var].map(ordinal_label)\n", + " test[var] = test[var].map(ordinal_label)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSZoning {'Rare': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4}\n", + "\n", + "Street {'Rare': 0, 'Pave': 1}\n", + "\n", + "Alley {'Grvl': 0, 'Pave': 1, 'Missing': 2}\n", + "\n", + "LotShape {'Reg': 0, 'IR1': 1, 'Rare': 2, 'IR2': 3}\n", + "\n", + "LandContour {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3}\n", + "\n", + "Utilities {'Rare': 0, 'AllPub': 1}\n", + "\n", + "LotConfig {'Inside': 0, 'FR2': 1, 'Corner': 2, 'Rare': 3, 'CulDSac': 4}\n", + "\n", + "LandSlope {'Gtl': 0, 'Mod': 1, 'Rare': 2}\n", + "\n", + "Neighborhood {'IDOTRR': 0, 'MeadowV': 1, 'BrDale': 2, 'Edwards': 3, 'BrkSide': 4, 'OldTown': 5, 'Sawyer': 6, 'SWISU': 7, 'NAmes': 8, 'Mitchel': 9, 'SawyerW': 10, 'Rare': 11, 'NWAmes': 12, 'Gilbert': 13, 'Blmngtn': 14, 'CollgCr': 15, 'Crawfor': 16, 'ClearCr': 17, 'Somerst': 18, 'Timber': 19, 'StoneBr': 20, 'NridgHt': 21, 'NoRidge': 22}\n", + "\n", + "Condition1 {'Artery': 0, 'Feedr': 1, 'Norm': 2, 'RRAn': 3, 'Rare': 4, 'PosN': 5}\n", + "\n", + "Condition2 {'Rare': 0, 'Norm': 1}\n", + "\n", + "BldgType {'2fmCon': 0, 'Duplex': 1, 'Twnhs': 2, '1Fam': 3, 'TwnhsE': 4}\n", + "\n", + "HouseStyle {'SFoyer': 0, '1.5Fin': 1, 'Rare': 2, '1Story': 3, 'SLvl': 4, '2Story': 5}\n", + "\n", + "RoofStyle {'Gable': 0, 'Rare': 1, 'Hip': 2}\n", + "\n", + "RoofMatl {'CompShg': 0, 'Rare': 1}\n", + "\n", + "Exterior1st {'AsbShng': 0, 'Wd Sdng': 1, 'WdShing': 2, 'MetalSd': 3, 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7, 'BrkFace': 8, 'CemntBd': 9, 'VinylSd': 10}\n", + "\n", + "Exterior2nd {'AsbShng': 0, 'Wd Sdng': 1, 'MetalSd': 2, 'Wd Shng': 3, 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7, 'BrkFace': 8, 'CmentBd': 9, 'VinylSd': 10}\n", + "\n", + "MasVnrType {'Rare': 0, 'None': 1, 'BrkFace': 2, 'Stone': 3}\n", + "\n", + "Foundation {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare': 3, 'PConc': 4}\n", + "\n", + "Heating {'Rare': 0, 'GasW': 1, 'GasA': 2}\n", + "\n", + "CentralAir {'N': 0, 'Y': 1}\n", + "\n", + "Electrical {'Rare': 0, 'FuseF': 1, 'FuseA': 2, 'SBrkr': 3}\n", + "\n", + "Functional {'Rare': 0, 'Min2': 1, 'Mod': 2, 'Min1': 3, 'Typ': 4}\n", + "\n", + "GarageType {'Rare': 0, 'Detchd': 1, 'Basment': 2, 'Attchd': 3, 'BuiltIn': 4}\n", + "\n", + "PavedDrive {'N': 0, 'P': 1, 'Y': 2}\n", + "\n", + "PoolQC {'Missing': 0, 'Rare': 1}\n", + "\n", + "MiscFeature {'Rare': 0, 'Shed': 1, 'Missing': 2}\n", + "\n", + "SaleType {'COD': 0, 'Rare': 1, 'WD': 2, 'New': 3}\n", + "\n", + "SaleCondition {'Rare': 0, 'Abnorml': 1, 'Family': 2, 'Normal': 3, 'Partial': 4}\n", + "\n", + "MSSubClass {30: 0, 'Rare': 1, 190: 2, 90: 3, 160: 4, 50: 5, 85: 6, 70: 7, 80: 8, 20: 9, 75: 10, 120: 11, 60: 12}\n", + "\n" + ] + } + ], + "source": [ + "for var in cat_others:\n", + " replace_categories(X_train, X_test, y_train, var, 'SalePrice')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the train set\n", + "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the test set\n", + "[var for var in X_test.columns if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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nRF4HfH2ob8prKCRJTz2T/SP/aeAbSe5jcCXTNwGSvABv9y1JB7UJZxBV9SFgGYMnyp1RVTW037sn2jfJ/CRrk2xJsjnJRY0x5ya5NcnGJOuSnDHU91jXvjHJ6n39YJKkJ2fSr4mq6qZG2+1TOPZuYFl3q/CjgfVJ1lTVlqExXwNWV1UleRlwJbCw63uoql4+hfeRJPVgqgvl9llV3VNVG7rtncBWYN6YMbuGZiVH0T2xTpI083oLiGFJFgCnADc3+t6c5DbgC8DvDHU9vfva6aYk501HnZKkf9F7QHSL61YBS6tqx9j+qrq6qhYC5wEfHOo6qapGgbcCH0nyC+Mcf0kXJOu2b9++/z+AJB2ieg2IJLMZhMMVkz19rqquA56f5Lju9bbu953AtQxmIK39VlbVaFWNzp07d3+WL0mHtN4CIkmAy4CtVbV8nDEv6MaR5BXAEcD9SY5NckTXfhywCNjSOoYkqR99LnZbBCwGNg2tur4EGAGoqhXAbwJvS/Iog3UWF3RXNL0Y+ESSxxmE2IfHXP0kSepZbwFRVdczeLjQRGMuBS5ttN8AvLSn0iRJUzAtVzFJkp56DAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSU28BkWR+krVJtiTZnOSixphzk9yaZGOSdUnOGOp7e5Lvdz9v76tOSVJbb8+kBnYDy6pqQ5KjgfVJ1lTVlqExXwNWV1UleRlwJbAwyTOB9wOjQHX7rq6qB3usV5I0pLcZRFXdU1Ubuu2dwFZg3pgxu6qqupdHMQgDgDcAa6rqgS4U1gBn91WrJGlv03IOIskC4BTg5kbfm5PcBnwB+J2ueR5w19CwuxkTLpKkfvUeEEnmAKuApVW1Y2x/VV1dVQuB84APPoHjL+nOX6zbvn37k65XkjTQa0Akmc0gHK6oqqsmGltV1wHPT3IcsA2YP9R9YtfW2m9lVY1W1ejcuXP3U+WSpD6vYgpwGbC1qpaPM+YF3TiSvAI4Argf+ApwVpJjkxwLnNW1SZKmSZ9XMS0CFgObkmzs2i4BRgCqagXwm8DbkjwKPARc0J20fiDJB4Fbuv0+UFUP9FirJGmM3gKiqq4HMsmYS4FLx+m7HLi8h9IkSVPgSmpJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWrqLSCSzE+yNsmWJJuTXNQY81tJbk2yKckNSU4e6vth174xybq+6pQktfX2TGpgN7CsqjYkORpYn2RNVW0ZGvMD4DVV9WCSc4CVwGlD/WdW1X091ihJGkdvAVFV9wD3dNs7k2wF5gFbhsbcMLTLTcCJfdUjSdo303IOIskC4BTg5gmG/S7wpaHXBVyTZH2SJT2WJ0lq6PMrJgCSzAFWAUurasc4Y85kEBBnDDWfUVXbkjwbWJPktqq6rrHvEmAJwMjIyH6vX5IOVb3OIJLMZhAOV1TVVeOMeRnwSeDcqrp/T3tVbet+3wtcDZza2r+qVlbVaFWNzp07d39/BEk6ZPV5FVOAy4CtVbV8nDEjwFXA4qq6faj9qO7ENkmOAs4CvttXrZKkvfX5FdMiYDGwKcnGru0SYASgqlYA7wOeBXxskCfsrqpR4Hjg6q7tMOBvqurLPdYqSRqjz6uYrgcyyZjfA36v0X4ncPLee0iSposrqSVJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ19RYQSeYnWZtkS5LNSS5qjPmtJLcm2ZTkhiQnD/WdneR7Se5IcnFfdUqS2g7r8di7gWVVtSHJ0cD6JGuqasvQmB8Ar6mqB5OcA6wETksyC/go8HrgbuCWJKvH7CtJ6lFvM4iquqeqNnTbO4GtwLwxY26oqge7lzcBJ3bbpwJ3VNWdVfUI8Bng3L5qlSTtrc8ZxD9LsgA4Bbh5gmG/C3yp254H3DXUdzdw2jjHXgIs6V7uSvK9J1Ws9jgOuG+mi5hMLp3pCjRD/Pvcf04ar6P3gEgyB1gFLK2qHeOMOZNBQJyxr8evqpUMvprSfpRkXVWNznQdUot/n9Oj14BIMptBOFxRVVeNM+ZlwCeBc6rq/q55GzB/aNiJXZskaZr0eRVTgMuArVW1fJwxI8BVwOKqun2o6xbghUmel+Rw4EJgdV+1SpL21ucMYhGwGNiUZGPXdgkwAlBVK4D3Ac8CPjbIE3ZX1WhV7U7yLuArwCzg8qra3GOt2ptf2+lA5t/nNEhVzXQNkqQDkCupJUlNBoQkqcmAkCQ1TctCOR34kixksFp9z2r3bcDqqto6c1VJmknOIESS9zC4nUmAb3c/AT7tjRJ1IEvy2zNdw8HMq5hEktuBl1TVo2PaDwc2V9ULZ6YyaWJJflRVIzNdx8HKr5gE8DhwAvCPY9qf2/VJMybJreN1AcdPZy2HGgNCAEuBryX5Pv9yk8QR4AXAu2aqKKlzPPAG4MEx7QFumP5yDh0GhKiqLyd5EYPbrA+fpL6lqh6bucokAD4PzKmqjWM7klw77dUcQjwHIUlq8iomSVKTASFJajIgpH2U5E+SbE5ya5KNSU5LsjTJM/bje+zX40lPhOcgpH2Q5HRgOfDaqno4yXHA4Qyuphmtqr0eg5lk1r6e7E/yw/GOJ00XZxDSvnkucF9VPQzQ/QN+PoN1JGuTrAVIsivJnyf5DnB6kv+Q5NvdjOMTSWZ1485KcmOSDUk+m2ROkj8cezxpJjiDkPZB94z164FnAF8F/raqvjH2f/xJCrigqq5M8mLgz4DfqKpHk3wMuAn4IoMnKp5TVT/tbnlyRFV9wBmEDgSug5D2QVXtSvJK4FeAM4G/Hed+VY8xeB47wOuAVwK3dE9OPBK4F3g18EvAt7r2w4Ebe/0A0j4wIKR91J1PuBa4Nskm4O2NYf9v6LxDgL+uqj8eHpDk14E1VfWWPuuVnijPQUj7IMkvJhm+eeHLGdzDaidw9Di7fQ04P8mzu2M8M8lJDL5mWpTkBV37Ud2KdiY5njQtnEFI+2YO8BdJjgF2A3cAS4C3AF9O8k9VdebwDlW1Jcl7gWuSPA14FPiDqropyTsY3Fb9iG74e4HbgZXjHU+aLp6kliQ1+RWTJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU3/H2u0e0B+rcsiAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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kSdoZTPYcxNeSPB34JDDUtH2+k4okSTuFie6DeDlwV1V9tFmeC6wBbgH+V/flSZJmykSHmD4H/A4gyZ8An2jafkXzFjdJ0q5pokNMu/W9qOcUYFlVXQ5c3vcSIEnSLmiiGcRuSUZC5NXAd/v6Jn0PhSTpyWeiX/JfAr6X5F56VzJ9HyDJ4fi4b0napY07g6iq/wGcRe+NcsdUVfWt957x1k2yIMnVSdYlWZvkjJYxJya5OcnqJKuSHNPX92jTvjrJim39wSRJO2bCw0RVdUNL222T2PYW4KzmUeH7AENJVlbVur4x3wFWVFUleTHwZeCIpu/hqnrJJPYjSerAZG+U22ZVdXdVDTffHwTWA/NHjXmob1ayN80b6yRJM6+zgOiXZCFwFHBjS98bk9wCfB14Z1/Xns1hpxuSnDQddUqSfq/zgGhurrscOLOqNo/ur6orquoI4CTgo31dh1TVIPAW4MIkh42x/SVNkKzatGnT1P8AkjRLdRoQSebQC4dLJ3r7XFVdCzw7yf7N8sbmzzuAa+jNQNrWW1ZVg1U1OG/evKksX5Jmtc4CIkmAi4D1VXXBGGMOb8aR5KXAHsB9SfZNskfTvj+wCFjXtg1JUje6vNltEbAYWNN31/U5wABAVS0F3gy8Nckj9O6zOKW5oun5wOeSPEYvxD4x6uonSVLHOguIqrqO3suFxhtzPnB+S/v1wIs6Kk2SNAnTchWTJOnJx4CQJLUyICRJrQwISVIrA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1MqAkCS16iwgkixIcnWSdUnWJjmjZcyJSW5OsjrJqiTH9PW9LclPms/buqpTktSus3dSA1uAs6pqOMk+wFCSlVW1rm/Md4AVVVVJXgx8GTgiyTOADwODQDXrrqiqBzqsV5LUp7MZRFXdXVXDzfcHgfXA/FFjHqqqahb3phcGAK8HVlbV/U0orASO76pWSdLWpuUcRJKFwFHAjS19b0xyC/B14J1N83zgrr5hGxgVLpKkbnUeEEnmApcDZ1bV5tH9VXVFVR0BnAR8dDu2v6Q5f7Fq06ZNO1yvJKmn04BIModeOFxaVcvHG1tV1wLPTrI/sBFY0Nd9cNPWtt6yqhqsqsF58+ZNUeWSpC6vYgpwEbC+qi4YY8zhzTiSvBTYA7gP+BbwuiT7JtkXeF3TJkmaJl1exbQIWAysSbK6aTsHGACoqqXAm4G3JnkEeBg4pTlpfX+SjwI3NeudV1X3d1irJGmUzgKiqq4DMsGY84Hzx+i7GLi4g9IkSZPgndSSpFYGhCSplQEhSWplQEiSWhkQkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWBoQkqVVnAZFkQZKrk6xLsjbJGS1jTktyc5I1Sa5PcmRf351N++okq7qqU5LUrrN3UgNbgLOqajjJPsBQkpVVta5vzM+AV1XVA0lOAJYBr+jrP66q7u2wRknSGDoLiKq6G7i7+f5gkvXAfGBd35jr+1a5ATi4q3okSdtmWs5BJFkIHAXcOM6wdwFX9S0X8O0kQ0mWdFieJKlFl4eYAEgyF7gcOLOqNo8x5jh6AXFMX/MxVbUxyTOBlUluqaprW9ZdAiwBGBgYmPL6JWm26nQGkWQOvXC4tKqWjzHmxcDngROr6r6R9qra2Px5D3AFcHTb+lW1rKoGq2pw3rx5U/0jSNKs1eVVTAEuAtZX1QVjjBkAlgOLq+q2vva9mxPbJNkbeB3w465qlSRtrctDTIuAxcCaJKubtnOAAYCqWgqcC+wHfKaXJ2ypqkHgAOCKpm134ItV9c0Oa5UkjdLlVUzXAZlgzF8Bf9XSfgdw5NZrSJKmi3dSS5JaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWBoQkqVVnAZFkQZKrk6xLsjbJGS1jTktyc5I1Sa5PcmRf3/FJbk1ye5Kzu6pTktRu9w63vQU4q6qGk+wDDCVZWVXr+sb8DHhVVT2Q5ARgGfCKJLsBnwZeC2wAbkqyYtS6kqQOdTaDqKq7q2q4+f4gsB6YP2rM9VX1QLN4A3Bw8/1o4PaquqOqfgdcBpzYVa2SpK11OYN4XJKFwFHAjeMMexdwVfN9PnBXX98G4BVjbHsJsKRZfCjJrTtUrEbsD9w700VMJOfPdAWaIf79nDqHjNXReUAkmQtcDpxZVZvHGHMcvYA4Zlu3X1XL6B2a0hRKsqqqBme6DqmNfz+nR6cBkWQOvXC4tKqWjzHmxcDngROq6r6meSOwoG/YwU2bJGmadHkVU4CLgPVVdcEYYwaA5cDiqrqtr+sm4DlJDk3yVOBUYEVXtUqSttblDGIRsBhYk2R103YOMABQVUuBc4H9gM/08oQtVTVYVVuSnA58C9gNuLiq1nZYq7bmYTvtzPz7OQ1SVTNdgyRpJ+Sd1JKkVgaEJKmVASFJajUtN8pp55fkCHp3q4/c7b4RWFFV62euKkkzyRmESPJBeo8zCfDD5hPgSz4oUTuzJO+Y6Rp2ZV7FJJLcBrywqh4Z1f5UYG1VPWdmKpPGl+QXVTUw03XsqjzEJIDHgGcBPx/VflDTJ82YJDeP1QUcMJ21zDYGhADOBL6T5Cf8/iGJA8DhwOkzVZTUOAB4PfDAqPYA109/ObOHASGq6ptJnkvvMev9J6lvqqpHZ64yCYCvAXOravXojiTXTHs1s4jnICRJrbyKSZLUyoCQJLUyIDSrJTkwyWVJfppkKMk3mvMxO7LNY5N8rfn+FyP3kiQ5KckL+sadl+Q127mP05LcnGRNkuuTHLkjNUttPEmtWat5Z8kVwBeq6tSm7Uh6V83cNt66k1VVK/j9u0xOonfCdV3Td+4ObPpnwKuq6oEkJ9B7/HXra3ml7eUMQrPZccAjzbtJAKiqHwHXJfm7JD9u/oV+Cjw+M7gmyT8muSXJpU3IkOT4pm0YeNPI9pK8PcmnkvwR8BfA3yVZneSwJJckObkZ9+ok/6/Z38VJ9mja70zykSTDTd8RTZ3XV9XIZZ830HvrojSlDAjNZv8BGGppfxPwEuBI4DX0fqkf1PQdRe++kRcAzwYWJdkT+HvgDcDLgANHb7Cqrqc3k3h/Vb2kqn460tesfwlwSlW9iN7M/t19q99bVS8FPgu8r6XedwFXTeonlraBASFt7RjgS1X1aFX9K/A94OVN3w+rakNVPQasBhYCRwA/q6qfVO+68X/Yxv09r1l/5LDWF4A/6esfeZ/7ULO/xyU5jl5AfHAb9ylNyIDQbLaW3r/4t8Vv+74/yvScxxvZ5xP2l+TFwOeBE6vqvmmoQ7OMAaHZ7LvAHkmWjDQ0v3T/DTglyW5J5tH71/wPx9nOLcDCJIc1y385xrgHgX1a2m9t1j+8WV5Mb9YypiQD9GYWi/tmHtKUMiA0azWHg94IvKa5zHUt8LfAF4GbgR/RC5EPVNW/jLOdfweWAF9vTlLfM8bQy4D3NyejDxu1/juAryRZQ+8BiUvH2MaIc4H9gM80J71XTfwTS9vGR21Iklo5g5AktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1Or/A/471w36suxLAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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URER0X0eXuUpaQvVUufdQnaR+M3Bkg3VFRESXdToP4vdsnwncb/sjwMuA5zZXVkREdFunAbGz/vdBSUdQPU70mc2UFBERE0Gn5yC+LulQ4FPA+rrt841UFBERE8JY8yBeAtxp+2P1+2nABuBm4NPNlxcREd0y1iGmzwEPA0j6feC8uu0B6qe4RUTE/mmsQ0wHtDyo5wxgqe3lwPKWhwBFRMR+aKw9iAMkDYfIHwLXtPR1PIciIiKeeMb6Jf9l4DuS7qG6kum7AJKeQ273HRGxXxt1D8L2x4GzqZ4od5Jtt6z3ntHWlTRb0rWSBiVtlLSwMOY0STdJGpC0TtJJLX2P1O0Dklbu6Q8WERF7Z8zDRLZvLLTd0sG2h4Cz61uFTwfWS1ple7BlzLeBlbYt6VjgK8Axdd9O23M7+JyIiGhApxPl9pjtLbb76+XtwCZgZtuYHS17JYdQP7EuIiK6r7GAaCVpDnA8sKbQ9yZJNwNXAH/e0nVQfdjpRklvHI86IyJil8YDop5ctxxYZHtbe7/ty2wfA7wR+FhL15G2+4A/Ay6Q9OwRtr+gDpJ1W7du3fc/QETEJNVoQEiaShUOy8Z6+pzt64FnSZpRv99c/3s7cB3VHkhpvaW2+2z39fT07MvyIyImtcYCQpKAi4BNthePMOY59TgkvQg4ELhX0mGSDqzbZwDzgMHSNiIiohlNTnabB8wHNrTMuj4H6AWwvQT4U+BMSb+hmmdxRn1F0/OAz0l6lCrEzmu7+ikiIhrWWEDYXk31cKHRxpwPnF9ovwF4YUOlRUREB8blKqaIiHjiSUBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFFjASFptqRrJQ1K2ihpYWHMaZJukjQgaZ2kk1r6zpJ0a/06q6k6IyKirLFnUgNDwNm2+yVNB9ZLWmV7sGXMt4GVti3pWOArwDGSngqcC/QBrtddafv+BuuNiIgWje1B2N5iu79e3g5sAma2jdlh2/XbQ6jCAODVwCrb99WhsAo4talaIyJid+NyDkLSHOB4YE2h702SbgauAP68bp4J3Nky7C7awiUiIprVeEBImgYsBxbZ3tbeb/sy28cAbwQ+9ji2v6A+f7Fu69ate11vRERUGg0ISVOpwmGZ7RWjjbV9PfAsSTOAzcDslu5ZdVtpvaW2+2z39fT07KPKIyKiyauYBFwEbLK9eIQxz6nHIelFwIHAvcDVwKskHSbpMOBVdVtERIyTJq9imgfMBzZIGqjbzgF6AWwvAf4UOFPSb4CdwBn1Sev7JH0MWFuv91Hb9zVYa0REtGksIGyvBjTGmPOB80fouxi4uIHSIiKiA5lJHRERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChqLCAkzZZ0raRBSRslLSyMeZukmyRtkHSDpONa+u6o2wckrWuqzoiIKGvsmdTAEHC27X5J04H1klbZHmwZ8xPgZNv3S3oNsBQ4saX/FbbvabDGiIgYQWMBYXsLsKVe3i5pEzATGGwZc0PLKjcCs5qqJyIi9sy4nIOQNAc4HlgzyrB3At9oeW/gm5LWS1rQYHkREVHQ5CEmACRNA5YDi2xvG2HMK6gC4qSW5pNsb5b0dGCVpJttX19YdwGwAKC3t3ef1x8RMVk1ugchaSpVOCyzvWKEMccCnwdOs33vcLvtzfW/dwOXASeU1re91Haf7b6enp59/SNERExaTV7FJOAiYJPtxSOM6QVWAPNt39LSfkh9YhtJhwCvAn7YVK0REbG7Jg8xzQPmAxskDdRt5wC9ALaXAB8CngZ8tsoThmz3AYcDl9VtU4B/tX1Vg7VGRESbJq9iWg1ojDHvAt5VaL8dOG73NSIiYrxkJnVERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFjQWEpNmSrpU0KGmjpIWFMW+TdJOkDZJukHRcS9+pkn4k6TZJH2iqzoiIKJvS4LaHgLNt90uaDqyXtMr2YMuYnwAn275f0muApcCJkg4APgO8ErgLWCtpZdu6ERHRoMb2IGxvsd1fL28HNgEz28bcYPv++u2NwKx6+QTgNtu3234YuAQ4ralaIyJid7Ld/IdIc4Drgf9qe9sIY94LHGP7XZJOB061/a66bz5wou13F9ZbACyo3/4u8KMGfoQ9MQO4p8s1TBT5LnbJd7FLvotdJsJ3caTtnlJHk4eYAJA0DVgOLBolHF4BvBM4aU+3b3sp1aGpCUHSOtt93a5jIsh3sUu+i13yXewy0b+LRgNC0lSqcFhme8UIY44FPg+8xva9dfNmYHbLsFl1W0REjJMmr2IScBGwyfbiEcb0AiuA+bZvaelaCxwt6ShJTwbeAqxsqtaIiNhdk3sQ84D5wAZJA3XbOUAvgO0lwIeApwGfrfKEIdt9tockvRu4GjgAuNj2xgZr3ZcmzOGuCSDfxS75LnbJd7HLhP4uxuUkdUREPPFkJnVERBQlICIioigBERERRY3Pg9jfSTqGapb38CzxzcBK25u6V1V0W/3fxUxgje0dLe2n2r6qe5WNP0knALa9VtLzgVOBm21f2eXSukrSF22f2e06RpOT1HtB0vuBt1LdCuSuunkW1WW5l9g+r1u1TSSS3mH7/3a7jvEi6W+Bv6G6vcxcYKHtf6/7+m2/qIvljStJ5wKvofpjdBVwInAt1X3Wrrb98S6WN24ktV+mL+AVwDUAtv943IvqQAJiL0i6BXiB7d+0tT8Z2Gj76O5UNrFI+pnt3m7XMV4kbQBeZntHfZuZrwJfsv0Pkv7T9vHdrXD81N/FXOBA4BfALNvbJB1MtXd1bDfrGy+S+oFBqknBpgqIL1P9MYnt73SvupHlENPeeRQ4AvhpW/sz675JQ9JNI3UBh49nLRPAk4YPK9m+Q9IpwFclHUn1fUwmQ7YfAR6U9OPh2+3Y3ilpMv0/0gcsBP4e+DvbA5J2TtRgGJaA2DuLgG9LuhW4s27rBZ4D7HZjwf3c4cCrgfvb2gXcMP7ldNUvJc21PQBQ70m8HrgYeGFXKxt/D0t6iu0HgRcPN0r6HSbRH1G2HwU+LenS+t9f8gT4/TvhC5zIbF8l6blUtydvPUm9tv6raTL5OjBt+JdiK0nXjXs13XUm1fNQfsv2EHCmpM91p6Su+X3bD8Fvf0kOmwqc1Z2Susf2XcCbJb0OKN68dCLJOYiIiCjKPIiIiChKQERERFECIiYtSY9IGmh5zWnws94u6cIxxpwi6fda3v+VpAk9kSr2bzlJHZPZTttzu11Ei1OAHdRXfdW3xI/omuxBRLSQNFfSjZJuknSZpMPq9usk9dXLMyTdUS+/XdIKSVdJulXSp1q29Q5Jt0j6PtXzUYbb3yBpjaT/lPQtSYfXey9/Bfz3em/m5ZI+XD+rfay6zpf0/fqzXj5OX1VMAgmImMwObjm8dFnd9kXg/fUM3w3AuR1sZy5wBtUchzMkzZb0TOAjVMFwEvD8lvGrgZfWM6ovAd5n+w5gCfBp23Ntf7ftM0ara4rtE6jm5XRSb0RHcogpJrPHHGKqJ28d2jK79Z+BSzvYzrdtP1BvYxA4EpgBXGd7a93+b8Bz6/GzgH+rQ+TJwE9G23gHdQ0/7309MKeDeiM6kj2IiM4Msev/l4Pa+h5qWX6Esf/w+kfgQtsvBP6ysL09Nfz5nXx2RMcSEBG1ei/g/pbj+POB4b/a72DXrSJO72Bza4CTJT1N0lTgzS19v0M14x4eO5t4OzB9D+uKaEz+2oh4rLOAJZKeAtwOvKNu/9/AVyQtAK4YayO2t0j6MPA94FfAQEv3h4FLJd1Pdbvno+r2r1Hd1O804D0d1hXRmNxqIyIiinKIKSIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERETR/wdkj2TFXdhN9QAAAABJRU5ErkJggg==\n", 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SpP3Tzk4xHdz3op7zgIVVtQhY1PcSIEnSfmhnRxAHJ9kWIr8NfKOvb+A5FJKkfc/O/sj/LfDNJA/Su5PpWwBJjsfHfUvSfm2HRxBV9WfAxfTeKHd6VVXfeu/d0bpJpidZmmQ4yZokF7WMOSvJHUlWJVme5PS+viea9lVJFo/1B5Mk7Z6dniaqqtta2u4eYNtbgYubR4VPAVYkWVJVw31jvg4srqpK8jLgs8Cspu+xqjp5gP1Ikjow6ES5MauqDVW1svm+GVgLTBsxZkvfUcnhNG+skyRNvM4Col+SmcApwO0tfeckuRP4EvB7fV1Pb0473Zbk7PGoU5L0K50HRDO5bhEwr6o2jeyvquuqahZwNnBpX9exVTUEvB34SJJfH2X7c5sgWb5x48Y9/wNI0gGq04BIMoleOFyzs7fPVdXNwAuSHNksr2/+vRe4id4RSNt6C6tqqKqGpk6duifLl6QDWmcBkSTAVcDaqlowypjjm3EkeTlwKPBQkiOSHNq0HwnMBobbtiFJ6kaXk91mA3OA1X2zrucDMwCq6grgrcAFSX5Bb57Fec0dTScCVyZ5kl6IXTbi7idJUsc6C4iquoXey4V2NOZy4PKW9luBl3ZUmiRpAONyF5Mkad9jQEiSWhkQkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWBoQkqZUBIUlqZUBIklp1FhBJpidZmmQ4yZokF7WMOSvJHUlWJVme5PS+vnck+X7zeUdXdUqS2nX2TmpgK3BxVa1MMgVYkWRJVQ33jfk6sLiqKsnLgM8Cs5I8G/gTYAioZt3FVfVIh/VKkvp0dgRRVRuqamXzfTOwFpg2YsyWqqpm8XB6YQDwBmBJVT3chMIS4MyuapUkbW9crkEkmQmcAtze0ndOkjuBLwG/1zRPA+7vG7aOEeEiSepW5wGRZDKwCJhXVZtG9lfVdVU1CzgbuHQXtj+3uX6xfOPGjbtdrySpp9OASDKJXjhcU1XX7mhsVd0MvCDJkcB6YHpf9zFNW9t6C6tqqKqGpk6duocqlyR1eRdTgKuAtVW1YJQxxzfjSPJy4FDgIeBG4PVJjkhyBPD6pk2SNE66vItpNjAHWJ1kVdM2H5gBUFVXAG8FLkjyC+Ax4LzmovXDSS4FljXrfaiqHu6wVknSCJ0FRFXdAmQnYy4HLh+l72rg6g5KkyQNwJnUkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKlVZwGRZHqSpUmGk6xJclHLmN9NckeS1UluTXJSX999TfuqJMu7qlOS1K6zd1IDW4GLq2plkinAiiRLqmq4b8wPgFdX1SNJ3ggsBF7V1/+aqnqwwxolSaPoLCCqagOwofm+OclaYBow3Dfm1r5VbgOO6aoeSdLYjMs1iCQzgVOA23cw7PeBG/qWC/hqkhVJ5nZYniSpRZenmABIMhlYBMyrqk2jjHkNvYA4va/59Kpan+S5wJIkd1bVzS3rzgXmAsyYMWOP1y9JB6pOjyCSTKIXDtdU1bWjjHkZ8CngrKp6aFt7Va1v/n0AuA44tW39qlpYVUNVNTR16tQ9/SNI0gGry7uYAlwFrK2qBaOMmQFcC8ypqrv72g9vLmyT5HDg9cD3uqpVkrS9Lk8xzQbmAKuTrGra5gMzAKrqCuCDwHOAT/TyhK1VNQQcBVzXtB0C/E1VfaXDWiVJI3R5F9MtQHYy5l3Au1ra7wVO2n4NSdJ4cSa1JKmVASFJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWnUWEEmmJ1maZDjJmiQXtYz53SR3JFmd5NYkJ/X1nZnkriT3JLmkqzolSe0O6XDbW4GLq2plkinAiiRLqmq4b8wPgFdX1SNJ3ggsBF6V5GDg48DrgHXAsiSLR6wrSepQZ0cQVbWhqlY23zcDa4FpI8bcWlWPNIu3Acc0308F7qmqe6vqceAzwFld1SpJ2l6XRxC/lGQmcApw+w6G/T5wQ/N9GnB/X9864FWjbHsuMLdZ3JLkrt0qdu91JPDgeO0sl4/Xng4Y/v72beP2+5uA392xo3V0HhBJJgOLgHlVtWmUMa+hFxCnj3X7VbWQ3qmp/VqS5VU1NNF1aNf4+9u3Hai/v04DIskkeuFwTVVdO8qYlwGfAt5YVQ81zeuB6X3DjmnaJEnjpMu7mAJcBaytqgWjjJkBXAvMqaq7+7qWASckOS7J04DzgcVd1SpJ2l6XRxCzgTnA6iSrmrb5wAyAqroC+CDwHOATvTxha1UNVdXWJO8BbgQOBq6uqjUd1rov2O9Po+3n/P3t2w7I31+qaqJrkCTthZxJLUlqZUBIkloZEJKkVuMyUU5jl2QWvdnj22afrwcWV9XaiatK2v81//emAbdX1Za+9jOr6isTV9n48whiL5TkA/QeLxLgH5tPgL/1wYX7tiTvnOgaNLok7wP+Hngv8L0k/Y/4+fDEVDVxvItpL5TkbuAlVfWLEe1PA9ZU1QkTU5l2V5IfVtWMia5D7ZKsBk6rqi3NI4I+D/yfqvrLJN+pqlMmtsLx5SmmvdOTwNHAP49of37Tp71YkjtG6wKOGs9aNGYHbTutVFX3JTkD+HySY+n9/g4oBsTeaR7w9STf51cPLZwBHA+8Z6KK0sCOAt4APDKiPcCt41+OxuBfkpxcVasAmiOJtwBXAy+d0MomgAGxF6qqryR5Ib3HnvdfpF5WVU9MXGUa0BeBydv+yPRLctO4V6OxuIDeu2x+qaq2AhckuXJiSpo4XoOQJLXyLiZJUisDQpLUyoCQRpFky4jlC5N8bBe3dXKSN/Ut/zvntGhvZ0BI4+Nk4JcBUVWLq+qyiStH2jkDQtoFSaYmWZRkWfOZ3bSfmuTbSb6T5NYkL2omOH4IOC/JqiTn9R+NJPl0ko824+9Ncm7TflCSTyS5M8mSJF/e1ieNB29zlUZ3WN/LrgCeza/ebPiXwF9U1S3NmxFvBE4E7gR+s3np1WuBD1fVW5N8EBiqqvdA73TViH09n9472Wc1+/g88DvATODFwHOBtfTux5fGhQEhje6xqjp520LzR33bi+tfC7y4eRMiwK8lmQw8E/jrJCcABUwacF/XV9WTwHCSbbOtTwc+17T/OMnS3flhpLEyIKRdcxDwG1X1//obm9NGS6vqnOZZPjcNuL2f929mj1Qo7SavQUi75qv0nvgJ9O5Sar4+k96sd4AL+8ZvBqaMcR//ALy1uRZxFHDGrhQq7SoDQto17wOGktyRZBj4T037nwP/M8l3eOoR+lJ6p6RWJTlvwH0sAtYBw8D/BVYCj+6R6qUB+KgNaS+WZHLzwLjn0HsvyOyq+vFE16UDg9cgpL3bF5M8C3gacKnhoPHkEYQkqZXXICRJrQwISVIrA0KS1MqAkCS1MiAkSa0MCElSq/8Ph558v4HmmU4AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let me show you what I mean by monotonic relationship\n", + "# between labels and target\n", + "\n", + "def analyse_vars(train, y_train, var):\n", + " \n", + " # function plots median house sale price per encoded\n", + " # category\n", + " \n", + " tmp = pd.concat([X_train, np.log(y_train)], axis=1)\n", + " \n", + " tmp.groupby(var)['SalePrice'].median().plot.bar()\n", + " plt.title(var)\n", + " plt.ylim(2.2, 2.6)\n", + " plt.ylabel('SalePrice')\n", + " plt.show()\n", + " \n", + "for var in cat_others:\n", + " analyse_vars(X_train, y_train, var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The monotonic relationship is particularly clear for the variables MSZoning and Neighborhood. Note how, the higher the integer that now represents the category, the higher the mean house sale price.\n", + "\n", + "(remember that the target is log-transformed, that is why the differences seem so small)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature Scaling\n", + "\n", + "For use in linear models, features need to be either scaled. We will scale features to the minimum and maximum values:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# create scaler\n", + "scaler = MinMaxScaler()\n", + "\n", + "# fit the scaler to the train set\n", + "scaler.fit(X_train) \n", + "\n", + "# transform the train and test set\n", + "\n", + "# sklearn returns numpy arrays, so we wrap the\n", + "# array with a pandas dataframe\n", + "\n", + "X_train = pd.DataFrame(\n", + " scaler.transform(X_train),\n", + " columns=X_train.columns\n", + ")\n", + "\n", + "X_test = pd.DataFrame(\n", + " scaler.transform(X_test),\n", + " columns=X_train.columns\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldSaleTypeSaleConditionLotFrontage_naMasVnrArea_naGarageYrBlt_na
00.7500000.750.4611710.01.01.00.3333331.0000001.00.00.00.8636360.41.00.750.60.7777780.500.0147060.0491800.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666670.6666671.00.0028350.00.00.6734790.2399351.01.001.01.00.5597600.00.00.5232500.0000000.00.6666670.00.3750.3333330.6666670.4166671.00.0000000.00.750.0186921.00.750.4301830.50.51.00.1166860.0329070.00.00.00.00.00.001.00.00.5454550.6666670.750.00.00.0
10.7500000.750.4560660.01.01.00.3333330.3333331.00.00.00.3636360.41.00.750.60.4444440.750.3602940.0491800.00.00.60.60.6666670.033750.6666670.50.50.3333330.6666670.0000000.80.1428070.00.00.1147240.1723401.01.001.01.00.4345390.00.00.4061960.3333330.00.3333330.50.3750.3333330.6666670.2500001.00.0000000.00.750.4579440.50.250.2200280.50.51.00.0000000.0000000.00.00.00.00.00.751.00.00.6363640.6666670.750.00.00.0
20.9166670.750.3946990.01.01.00.0000000.3333331.00.00.00.9545450.41.01.000.60.8888890.500.0367650.0983611.00.00.30.20.6666670.257501.0000000.51.01.0000000.6666670.0000001.00.0807940.00.00.6019510.2867431.01.001.01.00.6272050.00.00.5862960.3333330.00.6666670.00.2500.3333331.0000000.3333331.00.3333330.80.750.0467290.50.500.4062060.50.51.00.2287050.1499090.00.00.00.00.00.001.00.00.0909090.6666670.750.00.00.0
30.7500000.750.4450020.01.01.00.6666670.6666671.00.00.00.4545450.41.00.750.60.6666670.500.0661760.1639340.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666671.0000001.00.2556700.00.00.0181140.2425531.01.001.01.00.5669200.00.00.5299430.3333330.00.6666670.00.3750.3333330.6666670.2500001.00.3333330.40.750.0841120.50.500.3624820.50.51.00.4690780.0457040.00.00.00.00.00.001.00.00.6363640.6666670.751.00.00.0
40.7500000.750.5776580.01.01.00.3333330.3333331.00.00.00.3636360.41.00.750.60.5555560.500.3235290.7377050.00.00.60.70.6666670.170000.3333330.50.50.3333330.6666670.0000000.60.0868180.00.00.4342780.2332241.00.751.01.00.5490260.00.00.5132160.0000000.00.6666670.00.3750.3333330.3333330.4166671.00.3333330.80.750.4112150.50.500.4062060.50.51.00.0000000.0000000.01.00.00.00.00.001.00.00.5454550.6666670.750.00.00.0
\n", + "
" + ], + "text/plain": [ + " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 0.750000 0.75 0.461171 0.0 1.0 1.0 0.333333 \n", + "1 0.750000 0.75 0.456066 0.0 1.0 1.0 0.333333 \n", + "2 0.916667 0.75 0.394699 0.0 1.0 1.0 0.000000 \n", + "3 0.750000 0.75 0.445002 0.0 1.0 1.0 0.666667 \n", + "4 0.750000 0.75 0.577658 0.0 1.0 1.0 0.333333 \n", + "\n", + " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", + "0 1.000000 1.0 0.0 0.0 0.863636 0.4 \n", + "1 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", + "2 0.333333 1.0 0.0 0.0 0.954545 0.4 \n", + "3 0.666667 1.0 0.0 0.0 0.454545 0.4 \n", + "4 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", + "\n", + " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", + "0 1.0 0.75 0.6 0.777778 0.50 0.014706 \n", + "1 1.0 0.75 0.6 0.444444 0.75 0.360294 \n", + "2 1.0 1.00 0.6 0.888889 0.50 0.036765 \n", + "3 1.0 0.75 0.6 0.666667 0.50 0.066176 \n", + "4 1.0 0.75 0.6 0.555556 0.50 0.323529 \n", + "\n", + " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", + "0 0.049180 0.0 0.0 1.0 1.0 0.333333 \n", + "1 0.049180 0.0 0.0 0.6 0.6 0.666667 \n", + "2 0.098361 1.0 0.0 0.3 0.2 0.666667 \n", + "3 0.163934 0.0 0.0 1.0 1.0 0.333333 \n", + "4 0.737705 0.0 0.0 0.6 0.7 0.666667 \n", + "\n", + " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond \\\n", + "0 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", + "1 0.03375 0.666667 0.5 0.5 0.333333 0.666667 \n", + "2 0.25750 1.000000 0.5 1.0 1.000000 0.666667 \n", + "3 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", + "4 0.17000 0.333333 0.5 0.5 0.333333 0.666667 \n", + "\n", + " BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 \\\n", + "0 0.666667 1.0 0.002835 0.0 0.0 \n", + "1 0.000000 0.8 0.142807 0.0 0.0 \n", + "2 0.000000 1.0 0.080794 0.0 0.0 \n", + "3 1.000000 1.0 0.255670 0.0 0.0 \n", + "4 0.000000 0.6 0.086818 0.0 0.0 \n", + "\n", + " BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical \\\n", + "0 0.673479 0.239935 1.0 1.00 1.0 1.0 \n", + "1 0.114724 0.172340 1.0 1.00 1.0 1.0 \n", + "2 0.601951 0.286743 1.0 1.00 1.0 1.0 \n", + "3 0.018114 0.242553 1.0 1.00 1.0 1.0 \n", + "4 0.434278 0.233224 1.0 0.75 1.0 1.0 \n", + "\n", + " 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath \\\n", + "0 0.559760 0.0 0.0 0.523250 0.000000 0.0 \n", + "1 0.434539 0.0 0.0 0.406196 0.333333 0.0 \n", + "2 0.627205 0.0 0.0 0.586296 0.333333 0.0 \n", + "3 0.566920 0.0 0.0 0.529943 0.333333 0.0 \n", + "4 0.549026 0.0 0.0 0.513216 0.000000 0.0 \n", + "\n", + " FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd \\\n", + "0 0.666667 0.0 0.375 0.333333 0.666667 0.416667 \n", + "1 0.333333 0.5 0.375 0.333333 0.666667 0.250000 \n", + "2 0.666667 0.0 0.250 0.333333 1.000000 0.333333 \n", + "3 0.666667 0.0 0.375 0.333333 0.666667 0.250000 \n", + "4 0.666667 0.0 0.375 0.333333 0.333333 0.416667 \n", + "\n", + " Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish \\\n", + "0 1.0 0.000000 0.0 0.75 0.018692 1.0 \n", + "1 1.0 0.000000 0.0 0.75 0.457944 0.5 \n", + "2 1.0 0.333333 0.8 0.75 0.046729 0.5 \n", + "3 1.0 0.333333 0.4 0.75 0.084112 0.5 \n", + "4 1.0 0.333333 0.8 0.75 0.411215 0.5 \n", + "\n", + " GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF \\\n", + "0 0.75 0.430183 0.5 0.5 1.0 0.116686 \n", + "1 0.25 0.220028 0.5 0.5 1.0 0.000000 \n", + "2 0.50 0.406206 0.5 0.5 1.0 0.228705 \n", + "3 0.50 0.362482 0.5 0.5 1.0 0.469078 \n", + "4 0.50 0.406206 0.5 0.5 1.0 0.000000 \n", + "\n", + " OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC \\\n", + "0 0.032907 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.000000 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.149909 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.045704 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.000000 0.0 1.0 0.0 0.0 0.0 \n", + "\n", + " Fence MiscFeature MiscVal MoSold SaleType SaleCondition \\\n", + "0 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", + "1 0.75 1.0 0.0 0.636364 0.666667 0.75 \n", + "2 0.00 1.0 0.0 0.090909 0.666667 0.75 \n", + "3 0.00 1.0 0.0 0.636364 0.666667 0.75 \n", + "4 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", + "\n", + " LotFrontage_na MasVnrArea_na GarageYrBlt_na \n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 1.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# let's now save the train and test sets for the next notebook!\n", + "\n", + "X_train.to_csv('xtrain.csv', index=False)\n", + "X_test.to_csv('xtest.csv', index=False)\n", + "\n", + "y_train.to_csv('ytrain.csv', index=False)\n", + "y_test.to_csv('ytest.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['minmax_scaler.joblib']" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now let's save the scaler\n", + "\n", + "joblib.dump(scaler, 'minmax_scaler.joblib') " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "That concludes the feature engineering section.\n", + "\n", + "# Additional Resources\n", + "\n", + "- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n", + "- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n", + "- [Feature Engineering for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-engineering-for-machine-learning/) - Article\n", + "- [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) - Article" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "583px", + "left": "0px", + "right": "1324px", + "top": "107px", + "width": "212px" + }, + "toc_section_display": "block", + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb b/section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb index c29935ae2..9f2f9848a 100644 --- a/section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb +++ b/section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb @@ -1,993 +1,993 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Machine Learning Pipeline - Feature Selection\n", - "\n", - "In this notebook, we pick up the transformed datasets that we saved in the previous notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reproducibility: Setting the seed\n", - "\n", - "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# to handle datasets\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# for plotting\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# to build the models\n", - "from sklearn.linear_model import Lasso\n", - "from sklearn.feature_selection import SelectFromModel\n", - "\n", - "# to visualise al the columns in the dataframe\n", - "pd.pandas.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " SalePrice\n", - "0 12.211060\n", - "1 11.887931\n", - "2 12.675764\n", - "3 12.278393\n", - "4 12.103486" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the target (remember that the target is log transformed)\n", - "y_train = pd.read_csv('ytrain.csv')\n", - "y_test = pd.read_csv('ytest.csv')\n", - "\n", - "y_train.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature Selection\n", - "\n", - "Let's go ahead and select a subset of the most predictive features. There is an element of randomness in the Lasso regression, so remember to set the seed." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "SelectFromModel(estimator=Lasso(alpha=0.001, random_state=0))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# We will do the model fitting and feature selection\n", - "# altogether in a few lines of code\n", - "\n", - "# first, we specify the Lasso Regression model, and we\n", - "# select a suitable alpha (equivalent of penalty).\n", - "# The bigger the alpha the less features that will be selected.\n", - "\n", - "# Then we use the selectFromModel object from sklearn, which\n", - "# will select automatically the features which coefficients are non-zero\n", - "\n", - "# remember to set the seed, the random state in this function\n", - "sel_ = SelectFromModel(Lasso(alpha=0.001, random_state=0))\n", - "\n", - "# train Lasso model and select features\n", - "sel_.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sel_.get_support().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ True, True, True, False, False, False, True, True, False,\n", - " True, False, True, False, False, False, False, True, True,\n", - " False, True, True, False, True, False, False, False, True,\n", - " False, True, True, False, True, True, False, False, False,\n", - " False, False, False, True, True, False, True, True, False,\n", - " True, True, False, False, True, False, False, True, True,\n", - " True, True, True, False, False, True, True, True, False,\n", - " False, True, True, False, False, False, True, False, False,\n", - " False, False, False, False, False, True, False, False, False])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let's visualise those features that were selected.\n", - "# (selected features marked with True)\n", - "\n", - "sel_.get_support()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total features: 81\n", - "selected features: 36\n", - "features with coefficients shrank to zero: 45\n" - ] - } - ], - "source": [ - "# let's print the number of total and selected features\n", - "\n", - "# this is how we can make a list of the selected features\n", - "selected_feats = X_train.columns[(sel_.get_support())]\n", - "\n", - "# let's print some stats\n", - "print('total features: {}'.format((X_train.shape[1])))\n", - "print('selected features: {}'.format(len(selected_feats)))\n", - "print('features with coefficients shrank to zero: {}'.format(\n", - " np.sum(sel_.estimator_.coef_ == 0)))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['MSSubClass', 'MSZoning', 'LotFrontage', 'LotShape', 'LandContour',\n", - " 'LotConfig', 'Neighborhood', 'OverallQual', 'OverallCond',\n", - " 'YearRemodAdd', 'RoofStyle', 'Exterior1st', 'ExterQual', 'Foundation',\n", - " 'BsmtQual', 'BsmtExposure', 'BsmtFinType1', 'HeatingQC', 'CentralAir',\n", - " '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath',\n", - " 'KitchenQual', 'TotRmsAbvGrd', 'Functional', 'Fireplaces',\n", - " 'FireplaceQu', 'GarageFinish', 'GarageCars', 'GarageArea', 'PavedDrive',\n", - " 'WoodDeckSF', 'ScreenPorch', 'SaleCondition'],\n", - " dtype='object')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# print the selected features\n", - "selected_feats" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "pd.Series(selected_feats).to_csv('selected_features.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "# Additional Resources\n", - "\n", - "- [Feature Selection for Machine Learning](https://www.trainindata.com/p/feature-selection-for-machine-learning) - Online Course\n", - "- [Feature Selection in Machine Learning with Python](https://leanpub.com/feature-selection-in-machine-learning/) - Book\n", - "- [Feature Selection for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-selection-for-machine-learning/) - Article" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "583px", - "left": "0px", - "right": "1324px", - "top": "107px", - "width": "212px" - }, - "toc_section_display": "block", - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Pipeline - Feature Selection\n", + "\n", + "In this notebook, we pick up the transformed datasets that we saved in the previous notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reproducibility: Setting the seed\n", + "\n", + "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# to handle datasets\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# for plotting\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# to build the models\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.feature_selection import SelectFromModel\n", + "\n", + "# to visualise al the columns in the dataframe\n", + "pd.pandas.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " SalePrice\n", + "0 12.211060\n", + "1 11.887931\n", + "2 12.675764\n", + "3 12.278393\n", + "4 12.103486" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the target (remember that the target is log transformed)\n", + "y_train = pd.read_csv('ytrain.csv')\n", + "y_test = pd.read_csv('ytest.csv')\n", + "\n", + "y_train.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Selection\n", + "\n", + "Let's go ahead and select a subset of the most predictive features. There is an element of randomness in the Lasso regression, so remember to set the seed." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SelectFromModel(estimator=Lasso(alpha=0.001, random_state=0))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We will do the model fitting and feature selection\n", + "# altogether in a few lines of code\n", + "\n", + "# first, we specify the Lasso Regression model, and we\n", + "# select a suitable alpha (equivalent of penalty).\n", + "# The bigger the alpha the less features that will be selected.\n", + "\n", + "# Then we use the selectFromModel object from sklearn, which\n", + "# will select automatically the features which coefficients are non-zero\n", + "\n", + "# remember to set the seed, the random state in this function\n", + "sel_ = SelectFromModel(Lasso(alpha=0.001, random_state=0))\n", + "\n", + "# train Lasso model and select features\n", + "sel_.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "36" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sel_.get_support().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, False, False, False, True, True, False,\n", + " True, False, True, False, False, False, False, True, True,\n", + " False, True, True, False, True, False, False, False, True,\n", + " False, True, True, False, True, True, False, False, False,\n", + " False, False, False, True, True, False, True, True, False,\n", + " True, True, False, False, True, False, False, True, True,\n", + " True, True, True, False, False, True, True, True, False,\n", + " False, True, True, False, False, False, True, False, False,\n", + " False, False, False, False, False, True, False, False, False])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's visualise those features that were selected.\n", + "# (selected features marked with True)\n", + "\n", + "sel_.get_support()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total features: 81\n", + "selected features: 36\n", + "features with coefficients shrank to zero: 45\n" + ] + } + ], + "source": [ + "# let's print the number of total and selected features\n", + "\n", + "# this is how we can make a list of the selected features\n", + "selected_feats = X_train.columns[(sel_.get_support())]\n", + "\n", + "# let's print some stats\n", + "print('total features: {}'.format((X_train.shape[1])))\n", + "print('selected features: {}'.format(len(selected_feats)))\n", + "print('features with coefficients shrank to zero: {}'.format(\n", + " np.sum(sel_.estimator_.coef_ == 0)))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['MSSubClass', 'MSZoning', 'LotFrontage', 'LotShape', 'LandContour',\n", + " 'LotConfig', 'Neighborhood', 'OverallQual', 'OverallCond',\n", + " 'YearRemodAdd', 'RoofStyle', 'Exterior1st', 'ExterQual', 'Foundation',\n", + " 'BsmtQual', 'BsmtExposure', 'BsmtFinType1', 'HeatingQC', 'CentralAir',\n", + " '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath',\n", + " 'KitchenQual', 'TotRmsAbvGrd', 'Functional', 'Fireplaces',\n", + " 'FireplaceQu', 'GarageFinish', 'GarageCars', 'GarageArea', 'PavedDrive',\n", + " 'WoodDeckSF', 'ScreenPorch', 'SaleCondition'],\n", + " dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# print the selected features\n", + "selected_feats" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "pd.Series(selected_feats).to_csv('selected_features.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Additional Resources\n", + "\n", + "- [Feature Selection for Machine Learning](https://www.trainindata.com/p/feature-selection-for-machine-learning) - Online Course\n", + "- [Feature Selection in Machine Learning with Python](https://leanpub.com/feature-selection-in-machine-learning/) - Book\n", + "- [Feature Selection for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-selection-for-machine-learning/) - Article" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "583px", + "left": "0px", + "right": "1324px", + "top": "107px", + "width": "212px" + }, + "toc_section_display": "block", + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/04-machine-learning-pipeline-model-training.ipynb b/section-04-research-and-development/04-machine-learning-pipeline-model-training.ipynb index bc52dcdae..1fac786e0 100644 --- a/section-04-research-and-development/04-machine-learning-pipeline-model-training.ipynb +++ b/section-04-research-and-development/04-machine-learning-pipeline-model-training.ipynb @@ -1,1321 +1,1321 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Machine Learning Pipeline - Model Training\n", - "\n", - "In this notebook, we pick up the transformed datasets and the selected variables that we saved in the previous notebooks." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reproducibility: Setting the seed\n", - "\n", - "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# to handle datasets\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# for plotting\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# to save the model\n", - "import joblib\n", - "\n", - "# to build the model\n", - "from sklearn.linear_model import Lasso\n", - "\n", - "# to evaluate the model\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "\n", - "# to visualise al the columns in the dataframe\n", - "pd.pandas.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldSaleTypeSaleConditionLotFrontage_naMasVnrArea_naGarageYrBlt_na
00.7500000.750.4611710.01.01.00.3333331.0000001.00.00.00.8636360.41.00.750.60.7777780.500.0147060.0491800.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666670.6666671.00.0028350.00.00.6734790.2399351.01.001.01.00.5597600.00.00.5232500.0000000.00.6666670.00.3750.3333330.6666670.4166671.00.0000000.00.750.0186921.00.750.4301830.50.51.00.1166860.0329070.00.00.00.00.00.001.00.00.5454550.6666670.750.00.00.0
10.7500000.750.4560660.01.01.00.3333330.3333331.00.00.00.3636360.41.00.750.60.4444440.750.3602940.0491800.00.00.60.60.6666670.033750.6666670.50.50.3333330.6666670.0000000.80.1428070.00.00.1147240.1723401.01.001.01.00.4345390.00.00.4061960.3333330.00.3333330.50.3750.3333330.6666670.2500001.00.0000000.00.750.4579440.50.250.2200280.50.51.00.0000000.0000000.00.00.00.00.00.751.00.00.6363640.6666670.750.00.00.0
20.9166670.750.3946990.01.01.00.0000000.3333331.00.00.00.9545450.41.01.000.60.8888890.500.0367650.0983611.00.00.30.20.6666670.257501.0000000.51.01.0000000.6666670.0000001.00.0807940.00.00.6019510.2867431.01.001.01.00.6272050.00.00.5862960.3333330.00.6666670.00.2500.3333331.0000000.3333331.00.3333330.80.750.0467290.50.500.4062060.50.51.00.2287050.1499090.00.00.00.00.00.001.00.00.0909090.6666670.750.00.00.0
30.7500000.750.4450020.01.01.00.6666670.6666671.00.00.00.4545450.41.00.750.60.6666670.500.0661760.1639340.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666671.0000001.00.2556700.00.00.0181140.2425531.01.001.01.00.5669200.00.00.5299430.3333330.00.6666670.00.3750.3333330.6666670.2500001.00.3333330.40.750.0841120.50.500.3624820.50.51.00.4690780.0457040.00.00.00.00.00.001.00.00.6363640.6666670.751.00.00.0
40.7500000.750.5776580.01.01.00.3333330.3333331.00.00.00.3636360.41.00.750.60.5555560.500.3235290.7377050.00.00.60.70.6666670.170000.3333330.50.50.3333330.6666670.0000000.60.0868180.00.00.4342780.2332241.00.751.01.00.5490260.00.00.5132160.0000000.00.6666670.00.3750.3333330.3333330.4166671.00.3333330.80.750.4112150.50.500.4062060.50.51.00.0000000.0000000.01.00.00.00.00.001.00.00.5454550.6666670.750.00.00.0
\n", - "
" - ], - "text/plain": [ - " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 0.750000 0.75 0.461171 0.0 1.0 1.0 0.333333 \n", - "1 0.750000 0.75 0.456066 0.0 1.0 1.0 0.333333 \n", - "2 0.916667 0.75 0.394699 0.0 1.0 1.0 0.000000 \n", - "3 0.750000 0.75 0.445002 0.0 1.0 1.0 0.666667 \n", - "4 0.750000 0.75 0.577658 0.0 1.0 1.0 0.333333 \n", - "\n", - " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", - "0 1.000000 1.0 0.0 0.0 0.863636 0.4 \n", - "1 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", - "2 0.333333 1.0 0.0 0.0 0.954545 0.4 \n", - "3 0.666667 1.0 0.0 0.0 0.454545 0.4 \n", - "4 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", - "\n", - " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", - "0 1.0 0.75 0.6 0.777778 0.50 0.014706 \n", - "1 1.0 0.75 0.6 0.444444 0.75 0.360294 \n", - "2 1.0 1.00 0.6 0.888889 0.50 0.036765 \n", - "3 1.0 0.75 0.6 0.666667 0.50 0.066176 \n", - "4 1.0 0.75 0.6 0.555556 0.50 0.323529 \n", - "\n", - " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", - "0 0.049180 0.0 0.0 1.0 1.0 0.333333 \n", - "1 0.049180 0.0 0.0 0.6 0.6 0.666667 \n", - "2 0.098361 1.0 0.0 0.3 0.2 0.666667 \n", - "3 0.163934 0.0 0.0 1.0 1.0 0.333333 \n", - "4 0.737705 0.0 0.0 0.6 0.7 0.666667 \n", - "\n", - " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond \\\n", - "0 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", - "1 0.03375 0.666667 0.5 0.5 0.333333 0.666667 \n", - "2 0.25750 1.000000 0.5 1.0 1.000000 0.666667 \n", - "3 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", - "4 0.17000 0.333333 0.5 0.5 0.333333 0.666667 \n", - "\n", - " BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 \\\n", - "0 0.666667 1.0 0.002835 0.0 0.0 \n", - "1 0.000000 0.8 0.142807 0.0 0.0 \n", - "2 0.000000 1.0 0.080794 0.0 0.0 \n", - "3 1.000000 1.0 0.255670 0.0 0.0 \n", - "4 0.000000 0.6 0.086818 0.0 0.0 \n", - "\n", - " BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical \\\n", - "0 0.673479 0.239935 1.0 1.00 1.0 1.0 \n", - "1 0.114724 0.172340 1.0 1.00 1.0 1.0 \n", - "2 0.601951 0.286743 1.0 1.00 1.0 1.0 \n", - "3 0.018114 0.242553 1.0 1.00 1.0 1.0 \n", - "4 0.434278 0.233224 1.0 0.75 1.0 1.0 \n", - "\n", - " 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath \\\n", - "0 0.559760 0.0 0.0 0.523250 0.000000 0.0 \n", - "1 0.434539 0.0 0.0 0.406196 0.333333 0.0 \n", - "2 0.627205 0.0 0.0 0.586296 0.333333 0.0 \n", - "3 0.566920 0.0 0.0 0.529943 0.333333 0.0 \n", - "4 0.549026 0.0 0.0 0.513216 0.000000 0.0 \n", - "\n", - " FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd \\\n", - "0 0.666667 0.0 0.375 0.333333 0.666667 0.416667 \n", - "1 0.333333 0.5 0.375 0.333333 0.666667 0.250000 \n", - "2 0.666667 0.0 0.250 0.333333 1.000000 0.333333 \n", - "3 0.666667 0.0 0.375 0.333333 0.666667 0.250000 \n", - "4 0.666667 0.0 0.375 0.333333 0.333333 0.416667 \n", - "\n", - " Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish \\\n", - "0 1.0 0.000000 0.0 0.75 0.018692 1.0 \n", - "1 1.0 0.000000 0.0 0.75 0.457944 0.5 \n", - "2 1.0 0.333333 0.8 0.75 0.046729 0.5 \n", - "3 1.0 0.333333 0.4 0.75 0.084112 0.5 \n", - "4 1.0 0.333333 0.8 0.75 0.411215 0.5 \n", - "\n", - " GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF \\\n", - "0 0.75 0.430183 0.5 0.5 1.0 0.116686 \n", - "1 0.25 0.220028 0.5 0.5 1.0 0.000000 \n", - "2 0.50 0.406206 0.5 0.5 1.0 0.228705 \n", - "3 0.50 0.362482 0.5 0.5 1.0 0.469078 \n", - "4 0.50 0.406206 0.5 0.5 1.0 0.000000 \n", - "\n", - " OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC \\\n", - "0 0.032907 0.0 0.0 0.0 0.0 0.0 \n", - "1 0.000000 0.0 0.0 0.0 0.0 0.0 \n", - "2 0.149909 0.0 0.0 0.0 0.0 0.0 \n", - "3 0.045704 0.0 0.0 0.0 0.0 0.0 \n", - "4 0.000000 0.0 1.0 0.0 0.0 0.0 \n", - "\n", - " Fence MiscFeature MiscVal MoSold SaleType SaleCondition \\\n", - "0 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", - "1 0.75 1.0 0.0 0.636364 0.666667 0.75 \n", - "2 0.00 1.0 0.0 0.090909 0.666667 0.75 \n", - "3 0.00 1.0 0.0 0.636364 0.666667 0.75 \n", - "4 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", - "\n", - " LotFrontage_na MasVnrArea_na GarageYrBlt_na \n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 \n", - "3 1.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the train and test set with the engineered variables\n", - "\n", - "# we built and saved these datasets in a previous notebook.\n", - "# If you haven't done so, go ahead and check the previous notebooks (step 2)\n", - "# to find out how to create these datasets\n", - "\n", - "X_train = pd.read_csv('xtrain.csv')\n", - "X_test = pd.read_csv('xtest.csv')\n", - "\n", - "X_train.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SalePrice
012.211060
111.887931
212.675764
312.278393
412.103486
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" - ], - "text/plain": [ - " SalePrice\n", - "0 12.211060\n", - "1 11.887931\n", - "2 12.675764\n", - "3 12.278393\n", - "4 12.103486" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the target (remember that the target is log transformed)\n", - "y_train = pd.read_csv('ytrain.csv')\n", - "y_test = pd.read_csv('ytest.csv')\n", - "\n", - "y_train.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['MSSubClass',\n", - " 'MSZoning',\n", - " 'LotFrontage',\n", - " 'LotShape',\n", - " 'LandContour',\n", - " 'LotConfig',\n", - " 'Neighborhood',\n", - " 'OverallQual',\n", - " 'OverallCond',\n", - " 'YearRemodAdd',\n", - " 'RoofStyle',\n", - " 'Exterior1st',\n", - " 'ExterQual',\n", - " 'Foundation',\n", - " 'BsmtQual',\n", - " 'BsmtExposure',\n", - " 'BsmtFinType1',\n", - " 'HeatingQC',\n", - " 'CentralAir',\n", - " '1stFlrSF',\n", - " '2ndFlrSF',\n", - " 'GrLivArea',\n", - " 'BsmtFullBath',\n", - " 'HalfBath',\n", - " 'KitchenQual',\n", - " 'TotRmsAbvGrd',\n", - " 'Functional',\n", - " 'Fireplaces',\n", - " 'FireplaceQu',\n", - " 'GarageFinish',\n", - " 'GarageCars',\n", - " 'GarageArea',\n", - " 'PavedDrive',\n", - " 'WoodDeckSF',\n", - " 'ScreenPorch',\n", - " 'SaleCondition']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the pre-selected features\n", - "# ==============================\n", - "\n", - "# we selected the features in the previous notebook (step 3)\n", - "\n", - "# if you haven't done so, go ahead and visit the previous notebook\n", - "# to find out how to select the features\n", - "\n", - "features = pd.read_csv('selected_features.csv')\n", - "features = features['0'].to_list() \n", - "\n", - "# display final feature set\n", - "features" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# reduce the train and test set to the selected features\n", - "\n", - "X_train = X_train[features]\n", - "X_test = X_test[features]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Regularised linear regression: Lasso\n", - "\n", - "Remember to set the seed." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Lasso(alpha=0.001, random_state=0)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set up the model\n", - "# remember to set the random_state / seed\n", - "\n", - "lin_model = Lasso(alpha=0.001, random_state=0)\n", - "\n", - "# train the model\n", - "\n", - "lin_model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train mse: 781396538\n", - "train rmse: 27953\n", - "train r2: 0.8748530463468015\n", - "\n", - "test mse: 1060767982\n", - "test rmse: 32569\n", - "test r2: 0.8456417073258413\n", - "\n", - "Average house price: 163000\n" - ] - } - ], - "source": [ - "# evaluate the model:\n", - "# ====================\n", - "\n", - "# remember that we log transformed the output (SalePrice)\n", - "# in our feature engineering notebook (step 2).\n", - "\n", - "# In order to get the true performance of the Lasso\n", - "# we need to transform both the target and the predictions\n", - "# back to the original house prices values.\n", - "\n", - "# We will evaluate performance using the mean squared error and\n", - "# the root of the mean squared error and r2\n", - "\n", - "# make predictions for train set\n", - "pred = lin_model.predict(X_train)\n", - "\n", - "# determine mse, rmse and r2\n", - "print('train mse: {}'.format(int(\n", - " mean_squared_error(np.exp(y_train), np.exp(pred)))))\n", - "print('train rmse: {}'.format(int(\n", - " mean_squared_error(np.exp(y_train), np.exp(pred), squared=False))))\n", - "print('train r2: {}'.format(\n", - " r2_score(np.exp(y_train), np.exp(pred))))\n", - "print()\n", - "\n", - "# make predictions for test set\n", - "pred = lin_model.predict(X_test)\n", - "\n", - "# determine mse, rmse and r2\n", - "print('test mse: {}'.format(int(\n", - " mean_squared_error(np.exp(y_test), np.exp(pred)))))\n", - "print('test rmse: {}'.format(int(\n", - " mean_squared_error(np.exp(y_test), np.exp(pred), squared=False))))\n", - "print('test r2: {}'.format(\n", - " r2_score(np.exp(y_test), np.exp(pred))))\n", - "print()\n", - "\n", - "print('Average house price: ', int(np.exp(y_train).median()))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Evaluation of Lasso Predictions')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's evaluate our predictions respect to the real sale price\n", - "plt.scatter(y_test, lin_model.predict(X_test))\n", - "plt.xlabel('True House Price')\n", - "plt.ylabel('Predicted House Price')\n", - "plt.title('Evaluation of Lasso Predictions')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that our model is doing a pretty good job at estimating house prices." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SalePrice
012.209188
111.798104
211.608236
312.165251
411.385092
......
14111.884489
14212.287653
14311.921718
14411.598727
14512.017331
\n", - "

146 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " SalePrice\n", - "0 12.209188\n", - "1 11.798104\n", - "2 11.608236\n", - "3 12.165251\n", - "4 11.385092\n", - ".. ...\n", - "141 11.884489\n", - "142 12.287653\n", - "143 11.921718\n", - "144 11.598727\n", - "145 12.017331\n", - "\n", - "[146 rows x 1 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test.reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 12.148226\n", - "1 11.919326\n", - "2 11.677107\n", - "3 12.304289\n", - "4 11.447473\n", - " ... \n", - "141 11.775100\n", - "142 12.316546\n", - "143 11.955957\n", - "144 11.757571\n", - "145 12.072691\n", - "Length: 146, dtype: float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let's evaluate the distribution of the errors: \n", - "# they should be fairly normally distributed\n", - "\n", - "y_test.reset_index(drop=True, inplace=True)\n", - "\n", - "preds = pd.Series(lin_model.predict(X_test))\n", - "\n", - "preds" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's evaluate the distribution of the errors: \n", - "# they should be fairly normally distributed\n", - "\n", - "errors = y_test['SalePrice'] - preds\n", - "errors.hist(bins=30)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The distribution of the errors follows quite closely a gaussian distribution. That suggests that our model is doing a good job as well." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature importance" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Feature Importance')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Finally, just for fun, let's look at the feature importance\n", - "\n", - "importance = pd.Series(np.abs(lin_model.coef_.ravel()))\n", - "importance.index = features\n", - "importance.sort_values(inplace=True, ascending=False)\n", - "importance.plot.bar(figsize=(18,6))\n", - "plt.ylabel('Lasso Coefficients')\n", - "plt.title('Feature Importance')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save the Model" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['linear_regression.joblib']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# we are happy to our model, so we save it to be able\n", - "# to score new data\n", - "\n", - "joblib.dump(lin_model, 'linear_regression.joblib') " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Additional Resources\n", - "\n", - "\n", - "## Feature Engineering\n", - "\n", - "- [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) - Online Course\n", - "- [Packt Feature Engineering Cookbook](https://www.packtpub.com/data/python-feature-engineering-cookbook) - Book\n", - "- [Feature Engineering for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-engineering-for-machine-learning-a-comprehensive-overview-a7ad04c896f8) - Article\n", - "- [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) - Article\n", - "\n", - "## Feature Selection\n", - "\n", - "- [Feature Selection for Machine Learning](https://www.udemy.com/course/feature-selection-for-machine-learning/?referralCode=186501DF5D93F48C4F71) - Online Course\n", - "- [Feature Selection for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-selection-for-machine-learning-a-comprehensive-overview-bd571db5dd2d) - Article\n", - "\n", - "## Machine Learning\n", - "\n", - "- [Best Resources to Learn Machine Learning](https://trainindata.medium.com/find-out-the-best-resources-to-learn-machine-learning-cd560beec2b7) - Article\n", - "- [Machine Learning with Imbalanced Data](https://www.udemy.com/course/machine-learning-with-imbalanced-data/?referralCode=F30537642DA57D19ED83) - Online Course" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "583px", - "left": "0px", - "right": "1324px", - "top": "107px", - "width": "212px" - }, - "toc_section_display": "block", - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Pipeline - Model Training\n", + "\n", + "In this notebook, we pick up the transformed datasets and the selected variables that we saved in the previous notebooks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reproducibility: Setting the seed\n", + "\n", + "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# to handle datasets\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# for plotting\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# to save the model\n", + "import joblib\n", + "\n", + "# to build the model\n", + "from sklearn.linear_model import Lasso\n", + "\n", + "# to evaluate the model\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "\n", + "# to visualise al the columns in the dataframe\n", + "pd.pandas.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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212.675764
312.278393
412.103486
\n", + "
" + ], + "text/plain": [ + " SalePrice\n", + "0 12.211060\n", + "1 11.887931\n", + "2 12.675764\n", + "3 12.278393\n", + "4 12.103486" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the target (remember that the target is log transformed)\n", + "y_train = pd.read_csv('ytrain.csv')\n", + "y_test = pd.read_csv('ytest.csv')\n", + "\n", + "y_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['MSSubClass',\n", + " 'MSZoning',\n", + " 'LotFrontage',\n", + " 'LotShape',\n", + " 'LandContour',\n", + " 'LotConfig',\n", + " 'Neighborhood',\n", + " 'OverallQual',\n", + " 'OverallCond',\n", + " 'YearRemodAdd',\n", + " 'RoofStyle',\n", + " 'Exterior1st',\n", + " 'ExterQual',\n", + " 'Foundation',\n", + " 'BsmtQual',\n", + " 'BsmtExposure',\n", + " 'BsmtFinType1',\n", + " 'HeatingQC',\n", + " 'CentralAir',\n", + " '1stFlrSF',\n", + " '2ndFlrSF',\n", + " 'GrLivArea',\n", + " 'BsmtFullBath',\n", + " 'HalfBath',\n", + " 'KitchenQual',\n", + " 'TotRmsAbvGrd',\n", + " 'Functional',\n", + " 'Fireplaces',\n", + " 'FireplaceQu',\n", + " 'GarageFinish',\n", + " 'GarageCars',\n", + " 'GarageArea',\n", + " 'PavedDrive',\n", + " 'WoodDeckSF',\n", + " 'ScreenPorch',\n", + " 'SaleCondition']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the pre-selected features\n", + "# ==============================\n", + "\n", + "# we selected the features in the previous notebook (step 3)\n", + "\n", + "# if you haven't done so, go ahead and visit the previous notebook\n", + "# to find out how to select the features\n", + "\n", + "features = pd.read_csv('selected_features.csv')\n", + "features = features['0'].to_list() \n", + "\n", + "# display final feature set\n", + "features" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# reduce the train and test set to the selected features\n", + "\n", + "X_train = X_train[features]\n", + "X_test = X_test[features]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Regularised linear regression: Lasso\n", + "\n", + "Remember to set the seed." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Lasso(alpha=0.001, random_state=0)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# set up the model\n", + "# remember to set the random_state / seed\n", + "\n", + "lin_model = Lasso(alpha=0.001, random_state=0)\n", + "\n", + "# train the model\n", + "\n", + "lin_model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train mse: 781396538\n", + "train rmse: 27953\n", + "train r2: 0.8748530463468015\n", + "\n", + "test mse: 1060767982\n", + "test rmse: 32569\n", + "test r2: 0.8456417073258413\n", + "\n", + "Average house price: 163000\n" + ] + } + ], + "source": [ + "# evaluate the model:\n", + "# ====================\n", + "\n", + "# remember that we log transformed the output (SalePrice)\n", + "# in our feature engineering notebook (step 2).\n", + "\n", + "# In order to get the true performance of the Lasso\n", + "# we need to transform both the target and the predictions\n", + "# back to the original house prices values.\n", + "\n", + "# We will evaluate performance using the mean squared error and\n", + "# the root of the mean squared error and r2\n", + "\n", + "# make predictions for train set\n", + "pred = lin_model.predict(X_train)\n", + "\n", + "# determine mse, rmse and r2\n", + "print('train mse: {}'.format(int(\n", + " mean_squared_error(np.exp(y_train), np.exp(pred)))))\n", + "print('train rmse: {}'.format(int(\n", + " mean_squared_error(np.exp(y_train), np.exp(pred), squared=False))))\n", + "print('train r2: {}'.format(\n", + " r2_score(np.exp(y_train), np.exp(pred))))\n", + "print()\n", + "\n", + "# make predictions for test set\n", + "pred = lin_model.predict(X_test)\n", + "\n", + "# determine mse, rmse and r2\n", + "print('test mse: {}'.format(int(\n", + " mean_squared_error(np.exp(y_test), np.exp(pred)))))\n", + "print('test rmse: {}'.format(int(\n", + " mean_squared_error(np.exp(y_test), np.exp(pred), squared=False))))\n", + "print('test r2: {}'.format(\n", + " r2_score(np.exp(y_test), np.exp(pred))))\n", + "print()\n", + "\n", + "print('Average house price: ', int(np.exp(y_train).median()))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Evaluation of Lasso Predictions')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's evaluate our predictions respect to the real sale price\n", + "plt.scatter(y_test, lin_model.predict(X_test))\n", + "plt.xlabel('True House Price')\n", + "plt.ylabel('Predicted House Price')\n", + "plt.title('Evaluation of Lasso Predictions')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that our model is doing a pretty good job at estimating house prices." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SalePrice
012.209188
111.798104
211.608236
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......
14111.884489
14212.287653
14311.921718
14411.598727
14512.017331
\n", + "

146 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " SalePrice\n", + "0 12.209188\n", + "1 11.798104\n", + "2 11.608236\n", + "3 12.165251\n", + "4 11.385092\n", + ".. ...\n", + "141 11.884489\n", + "142 12.287653\n", + "143 11.921718\n", + "144 11.598727\n", + "145 12.017331\n", + "\n", + "[146 rows x 1 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 12.148226\n", + "1 11.919326\n", + "2 11.677107\n", + "3 12.304289\n", + "4 11.447473\n", + " ... \n", + "141 11.775100\n", + "142 12.316546\n", + "143 11.955957\n", + "144 11.757571\n", + "145 12.072691\n", + "Length: 146, dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's evaluate the distribution of the errors: \n", + "# they should be fairly normally distributed\n", + "\n", + "y_test.reset_index(drop=True, inplace=True)\n", + "\n", + "preds = pd.Series(lin_model.predict(X_test))\n", + "\n", + "preds" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rIi6rK5QkabBRPtT4COCDwO8BzwPOj4jn1RVMkrS2UfbAXwp8KzO/k5mPAgvAOfXEkiQNEpk53B0jzgXOzMw/7lx/I/AbmfmWrvXmgfnO1VOBbwwft5ITgYfGPMawmpwNmp2vydmg2fmanA2ana8p2Z6dmU/vXjj22QgzczuwfdzjHBIROzKzPanx1qPJ2aDZ+ZqcDZqdr8nZoNn5mpwNRjuEsgc4edX1kzrLJEkTMEqBfxU4JSJ+LSKOAs4DrqsnliRpkKEPoWTmwYh4C/DvwBHAlZl5T23JhjexwzVDaHI2aHa+JmeDZudrcjZodr4mZxv+j5iSpI3lmZiSVCgLXJIKVXyBR8QJEXFjRNzf+ff4Hus8OyLuiIg7I+KeiHhTg7K9KCJu7eS6KyLeMIlsVfN11vt8RPwwIq6fQKY1p2eIiCdHxCc6t98eEdPjzrSObK/oPM8Ods6TmKgK+f48Iu7tPM9uiohnNyjbmyJiqfMa/fKkz+quOi1IRPxBRGRENOOthZlZ9BfwPuCyzuXLgPf2WOco4Mmdy1PALuBZDcn2XOCUzuVnAQ8CxzXlsevcdjrw+8D1Y85zBPBt4Dmd/7OvAc/rWudPgA91Lp8HfGJCj1WVbNPAC4CPAudOItc6880BR3cuv7lhj91TV10+G/h8kx67znrHArcAtwHtSf7/9vsqfg+cldP3r+5cvhp4ffcKmfloZv6sc/XJTO43jyrZvpmZ93cuPwDsAx53xtVG5QPIzJuAH08gT5XpGVZnvhY4PSKiCdkyc1dm3gX8YgJ5hsl3c2b+pHP1NlbO3WhKth+tunoMMMl3V1SdFuSvgfcCP51gtjUdDgXeyswHO5e/D7R6rRQRJ0fEXcD3WNnTfKAp2Q6JiJeysgfw7XEH61hXvgnYwsr/zyG7O8t6rpOZB4H9wNMakm0jrTffJcC/jTXRL1XKFhF/GhHfZuU3w7dNKBtUyBcRLwZOzswbJphroLGfSl+HiPgi8IweN71r9ZXMzIjo+ZM7M78HvCAingV8NiKuzcy9TcjW2c4zgX8GLsrM2vbg6sqnw0dEXAi0gVdudJbVMvODwAcj4g+BvwAu2uBIAETEk4D3AxdvcJTHKaLAM/OMfrdFxN6IeGZmPtgpwX0DtvVARNwN/A4rv4JveLaIeCpwA/CuzLxt1Ex155ugKtMzHFpnd0QcCWwG/qch2TZSpXwRcQYrP7xfueqwYiOyrbIA/ONYEz3WoHzHAqcBi52jdc8ArouIszNzx8RS9nA4HEK5jl/+pL4I+Fz3ChFxUkRs6lw+Hvhtxj8rYtVsRwGfAT6amSP/QFmngfkmrMr0DKsznwt8KTt/YWpAto00MF9E/DrwT8DZmTnJH9ZVsp2y6upZwP1NyZeZ+zPzxMyczsxpVv5+sOHlDRwW70J5GnATK//hXwRO6CxvAx/uXH41cBcrf12+C5hvULYLgZ8Dd676elFT8nWu/yfwA+AAK8cHf3eMmV4LfJOVvwO8q7Psr1h5wQA8Bfgk8C3gK8BzJvhcG5TtJZ3H5xFWfiu4Z1LZKub7IrB31fPsugZl+wBwTyfXzcDzm/TYda27SEPeheKp9JJUqMPhEIokPSFZ4JJUKAtckgplgUtSoSxwSSqUBS5JhbLAJalQ/wca0qGrKXDJYgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's evaluate the distribution of the errors: \n", + "# they should be fairly normally distributed\n", + "\n", + "errors = y_test['SalePrice'] - preds\n", + "errors.hist(bins=30)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The distribution of the errors follows quite closely a gaussian distribution. That suggests that our model is doing a good job as well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Feature Importance')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Finally, just for fun, let's look at the feature importance\n", + "\n", + "importance = pd.Series(np.abs(lin_model.coef_.ravel()))\n", + "importance.index = features\n", + "importance.sort_values(inplace=True, ascending=False)\n", + "importance.plot.bar(figsize=(18,6))\n", + "plt.ylabel('Lasso Coefficients')\n", + "plt.title('Feature Importance')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['linear_regression.joblib']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we are happy to our model, so we save it to be able\n", + "# to score new data\n", + "\n", + "joblib.dump(lin_model, 'linear_regression.joblib') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Additional Resources\n", + "\n", + "\n", + "## Feature Engineering\n", + "\n", + "- [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) - Online Course\n", + "- [Packt Feature Engineering Cookbook](https://www.packtpub.com/data/python-feature-engineering-cookbook) - Book\n", + "- [Feature Engineering for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-engineering-for-machine-learning-a-comprehensive-overview-a7ad04c896f8) - Article\n", + "- [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) - Article\n", + "\n", + "## Feature Selection\n", + "\n", + "- [Feature Selection for Machine Learning](https://www.udemy.com/course/feature-selection-for-machine-learning/?referralCode=186501DF5D93F48C4F71) - Online Course\n", + "- [Feature Selection for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-selection-for-machine-learning-a-comprehensive-overview-bd571db5dd2d) - Article\n", + "\n", + "## Machine Learning\n", + "\n", + "- [Best Resources to Learn Machine Learning](https://trainindata.medium.com/find-out-the-best-resources-to-learn-machine-learning-cd560beec2b7) - Article\n", + "- [Machine Learning with Imbalanced Data](https://www.udemy.com/course/machine-learning-with-imbalanced-data/?referralCode=F30537642DA57D19ED83) - Online Course" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "583px", + "left": "0px", + "right": "1324px", + "top": "107px", + "width": "212px" + }, + "toc_section_display": "block", + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/05-machine-learning-pipeline-scoring-new-data.ipynb b/section-04-research-and-development/05-machine-learning-pipeline-scoring-new-data.ipynb index aaa9bc08e..8e60cdb3c 100644 --- a/section-04-research-and-development/05-machine-learning-pipeline-scoring-new-data.ipynb +++ b/section-04-research-and-development/05-machine-learning-pipeline-scoring-new-data.ipynb @@ -1,1419 +1,1419 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Machine Learning Pipeline - Scoring New Data\n", - "\n", - "Let's imagine that a colleague from the business department comes and asks us to score the data from last months customers. They want to be sure that our model is working appropriately in the most recent data that the organization has.\n", - "\n", - "**How would you go about to score the new data?** Try to give it a go. There is more than 1 way of doing it.\n", - "\n", - "Below we present one potential solution.\n", - "\n", - "What could we have done better?" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# to handle datasets\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# for plotting\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# for the yeo-johnson transformation\n", - "import scipy.stats as stats\n", - "\n", - "# to save the model\n", - "import joblib" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1459, 80)\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 1461 20 RH 80.0 11622 Pave NaN Reg \n", - "1 1462 20 RL 81.0 14267 Pave NaN IR1 \n", - "2 1463 60 RL 74.0 13830 Pave NaN IR1 \n", - "3 1464 60 RL 78.0 9978 Pave NaN IR1 \n", - "4 1465 120 RL 43.0 5005 Pave NaN IR1 \n", - "\n", - " LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence MiscFeature \\\n", - "0 Lvl AllPub ... 120 0 NaN MnPrv NaN \n", - "1 Lvl AllPub ... 0 0 NaN NaN Gar2 \n", - "2 Lvl AllPub ... 0 0 NaN MnPrv NaN \n", - "3 Lvl AllPub ... 0 0 NaN NaN NaN \n", - "4 HLS AllPub ... 144 0 NaN NaN NaN \n", - "\n", - " MiscVal MoSold YrSold SaleType SaleCondition \n", - "0 0 6 2010 WD Normal \n", - "1 12500 6 2010 WD Normal \n", - "2 0 3 2010 WD Normal \n", - "3 0 6 2010 WD Normal \n", - "4 0 1 2010 WD Normal \n", - "\n", - "[5 rows x 80 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the unseen / new dataset\n", - "data = pd.read_csv('test.csv')\n", - "\n", - "# rows and columns of the data\n", - "print(data.shape)\n", - "\n", - "# visualise the dataset\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1459, 79)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# drop the id variable\n", - "\n", - "data.drop('Id', axis=1, inplace=True)\n", - "\n", - "data.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Feature Engineering\n", - "\n", - "First we need to transform the data. Below the list of transformations that we did during the Feature Engineering phase:\n", - "\n", - "1. Missing values\n", - "2. Temporal variables\n", - "3. Non-Gaussian distributed variables\n", - "4. Categorical variables: remove rare labels\n", - "5. Categorical variables: convert strings to numbers\n", - "6. Put the variables in a similar scale" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Missing values\n", - "\n", - "### Categorical variables\n", - "\n", - "- Replace missing values with the string \"missing\" in those variables with a lot of missing data. \n", - "- Replace missing data with the most frequent category in those variables that contain fewer observations without values. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# first we needed to cast MSSubClass as object\n", - "\n", - "data['MSSubClass'] = data['MSSubClass'].astype('O')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# list of different groups of categorical variables\n", - "\n", - "with_string_missing = ['Alley', 'FireplaceQu',\n", - " 'PoolQC', 'Fence', 'MiscFeature']\n", - "\n", - "# ==================\n", - "# we copy this dictionary from the Feature-engineering notebook\n", - "# note that we needed to hard-code this by hand\n", - "\n", - "# the key is the variable and the value is its most frequent category\n", - "\n", - "# what if we re-train the model and the below values change?\n", - "# ==================\n", - "\n", - "with_frequent_category = {\n", - " 'MasVnrType': 'None',\n", - " 'BsmtQual': 'TA',\n", - " 'BsmtCond': 'TA',\n", - " 'BsmtExposure': 'No',\n", - " 'BsmtFinType1': 'Unf',\n", - " 'BsmtFinType2': 'Unf',\n", - " 'Electrical': 'SBrkr',\n", - " 'GarageType': 'Attchd',\n", - " 'GarageFinish': 'Unf',\n", - " 'GarageQual': 'TA',\n", - " 'GarageCond': 'TA',\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# replace missing values with new label: \"Missing\"\n", - "\n", - "data[with_string_missing] = data[with_string_missing].fillna('Missing')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# replace missing values with the most frequent category\n", - "\n", - "for var in with_frequent_category.keys():\n", - " data[var].fillna(with_frequent_category[var], inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Numerical variables\n", - "\n", - "To engineer missing values in numerical variables, we will:\n", - "\n", - "- add a binary missing value indicator variable\n", - "- and then replace the missing values in the original variable with the mean" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# this is the dictionary of numerical variable with missing data\n", - "# and its mean, as determined from the training set in the\n", - "# Feature Engineering notebook\n", - "\n", - "# note how we needed to hard code the values\n", - "\n", - "vars_with_na = {\n", - " 'LotFrontage': 69.87974098057354,\n", - " 'MasVnrArea': 103.7974006116208,\n", - " 'GarageYrBlt': 1978.2959677419356,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LotFrontage 0\n", - "MasVnrArea 0\n", - "GarageYrBlt 0\n", - "dtype: int64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# replace missing values as we described above\n", - "\n", - "for var in vars_with_na.keys():\n", - "\n", - " # add binary missing indicator (in train and test)\n", - " data[var + '_na'] = np.where(data[var].isnull(), 1, 0)\n", - "\n", - " # replace missing values by the mean\n", - " # (in train and test)\n", - " data[var].fillna(vars_with_na[var], inplace=True)\n", - "\n", - "data[vars_with_na].isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " LotFrontage_na MasVnrArea_na GarageYrBlt_na\n", - "0 0 0 0\n", - "1 0 0 0\n", - "2 0 0 0\n", - "3 0 0 0\n", - "4 0 0 0" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the binary missing indicator variables\n", - "\n", - "data[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Temporal variables\n", - "\n", - "### Capture elapsed time\n", - "\n", - "We need to capture the time elapsed between those variables and the year in which the house was sold:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def elapsed_years(df, var):\n", - " # capture difference between the year variable\n", - " # and the year in which the house was sold\n", - " df[var] = df['YrSold'] - df[var]\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:\n", - " data = elapsed_years(data, var)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# now we drop YrSold\n", - "data.drop(['YrSold'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Numerical variable transformation\n", - "\n", - "### Logarithmic transformation\n", - "\n", - "We will transform with the logarithm the positive numerical variables in order to get a more Gaussian-like distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n", - " data[var] = np.log(data[var])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Yeo-Johnson transformation\n", - "\n", - "We will apply the Yeo-Johnson transformation to LotArea." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# note how we use the lambda that we learned from the train set\n", - "# in the notebook on Feature Engineering.\n", - "\n", - "# Note that we need to hard code this value\n", - "\n", - "data['LotArea'] = stats.yeojohnson(data['LotArea'], lmbda=-12.55283001172003)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Binarize skewed variables\n", - "\n", - "There were a few variables very skewed, we would transform those into binary variables." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "skewed = [\n", - " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", - " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", - "]\n", - "\n", - "for var in skewed:\n", - " \n", - " # map the variable values into 0 and 1\n", - " data[var] = np.where(data[var]==0, 0, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Categorical variables\n", - "\n", - "### Apply mappings\n", - "\n", - "We remap variables with specific meanings into a numerical scale." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# re-map strings to numbers, which determine quality\n", - "\n", - "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", - "\n", - "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", - " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", - " 'GarageQual', 'GarageCond',\n", - " ]\n", - "\n", - "for var in qual_vars:\n", - " data[var] = data[var].map(qual_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", - "\n", - "var = 'BsmtExposure'\n", - "\n", - "data[var] = data[var].map(exposure_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", - "\n", - "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n", - "\n", - "for var in finish_vars:\n", - " data[var] = data[var].map(finish_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", - "\n", - "var = 'GarageFinish'\n", - "\n", - "data[var] = data[var].map(garage_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n", - "\n", - "var = 'Fence'\n", - "\n", - "data[var] = data[var].map(fence_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['MSZoning',\n", - " 'Utilities',\n", - " 'Exterior1st',\n", - " 'Exterior2nd',\n", - " 'BsmtFinSF1',\n", - " 'BsmtUnfSF',\n", - " 'TotalBsmtSF',\n", - " 'BsmtFullBath',\n", - " 'BsmtHalfBath',\n", - " 'KitchenQual',\n", - " 'Functional',\n", - " 'GarageCars',\n", - " 'GarageArea',\n", - " 'SaleType']" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the data set\n", - "\n", - "with_null = [var for var in data.columns if data[var].isnull().sum() > 0]\n", - "\n", - "with_null" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Surprise**\n", - "\n", - "There are quite a few variables with missing data!!" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# did those have missing data in the train set?\n", - "\n", - "[var for var in with_null if var in list(\n", - " with_frequent_category.keys())+with_string_missing+list(vars_with_na.keys())]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**IMPORTANT**\n", - "\n", - "In the new data, we have a bunch of variables that contain missing information, that we did not anticipate." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Removing Rare Labels\n", - "\n", - "For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations into a \"Rare\" string." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# create a dictionary with the most frequent categories per variable\n", - "\n", - "# note the amount of hard coding that I need to do.\n", - "\n", - "# Can you think of an alternative? Perhaps we could have save this as a numpy pickle\n", - "# and load it here, instead of hard-coding.\n", - "\n", - "# But that means that we need to go back to the Feature Engineering notebook, and change\n", - "# the code so that we store the pickle. So there is still some code changes that we need\n", - "\n", - "frequent_ls = {\n", - " 'MSZoning': ['FV', 'RH', 'RL', 'RM'],\n", - " 'Street': ['Pave'],\n", - " 'Alley': ['Grvl', 'Missing', 'Pave'],\n", - " 'LotShape': ['IR1', 'IR2', 'Reg'],\n", - " 'LandContour': ['Bnk', 'HLS', 'Low', 'Lvl'],\n", - " 'Utilities': ['AllPub'],\n", - " 'LotConfig': ['Corner', 'CulDSac', 'FR2', 'Inside'],\n", - " 'LandSlope': ['Gtl', 'Mod'],\n", - " 'Neighborhood': ['Blmngtn', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor',\n", - " 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NWAmes',\n", - " 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW',\n", - " 'Somerst', 'StoneBr', 'Timber'],\n", - "\n", - " 'Condition1': ['Artery', 'Feedr', 'Norm', 'PosN', 'RRAn'],\n", - " 'Condition2': ['Norm'],\n", - " 'BldgType': ['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE'],\n", - " 'HouseStyle': ['1.5Fin', '1Story', '2Story', 'SFoyer', 'SLvl'],\n", - " 'RoofStyle': ['Gable', 'Hip'],\n", - " 'RoofMatl': ['CompShg'],\n", - " 'Exterior1st': ['AsbShng', 'BrkFace', 'CemntBd', 'HdBoard', 'MetalSd', 'Plywood',\n", - " 'Stucco', 'VinylSd', 'Wd Sdng', 'WdShing'],\n", - "\n", - " 'Exterior2nd': ['AsbShng', 'BrkFace', 'CmentBd', 'HdBoard', 'MetalSd', 'Plywood',\n", - " 'Stucco', 'VinylSd', 'Wd Sdng', 'Wd Shng'],\n", - "\n", - " 'MasVnrType': ['BrkFace', 'None', 'Stone'],\n", - " 'Foundation': ['BrkTil', 'CBlock', 'PConc', 'Slab'],\n", - " 'Heating': ['GasA', 'GasW'],\n", - " 'CentralAir': ['N', 'Y'],\n", - " 'Electrical': ['FuseA', 'FuseF', 'SBrkr'],\n", - " 'Functional': ['Min1', 'Min2', 'Mod', 'Typ'],\n", - " 'GarageType': ['Attchd', 'Basment', 'BuiltIn', 'Detchd'],\n", - " 'PavedDrive': ['N', 'P', 'Y'],\n", - " 'PoolQC': ['Missing'],\n", - " 'MiscFeature': ['Missing', 'Shed'],\n", - " 'SaleType': ['COD', 'New', 'WD'],\n", - " 'SaleCondition': ['Abnorml', 'Family', 'Normal', 'Partial'],\n", - " 'MSSubClass': ['20', '30', '50', '60', '70', '75', '80', '85', '90', '120', '160', '190'],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "for var in frequent_ls.keys():\n", - " \n", - " # replace rare categories by the string \"Rare\"\n", - " data[var] = np.where(data[var].isin(\n", - " frequent_ls), data[var], 'Rare')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Encoding of categorical variables\n", - "\n", - "Next, we need to transform the strings of the categorical variables into numbers. " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# we need the mappings learned from the train set. Otherwise, our model is going\n", - "# to produce inaccurate results\n", - "\n", - "# note the amount of hard coding that we need to do.\n", - "\n", - "# Can you think of an alternative? \n", - "\n", - "# Perhaps we could have save this as a numpy pickle\n", - "# and load it here, instead of hard-coding.\n", - "\n", - "# But that means that we need to go back to the Feature Engineering notebook, and change\n", - "# the code so that we store the pickle. So there is still some code changes that we need\n", - "\n", - "ordinal_mappings = {\n", - " 'MSZoning': {'Rare': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4},\n", - " 'Street': {'Rare': 0, 'Pave': 1},\n", - " 'Alley': {'Grvl': 0, 'Pave': 1, 'Missing': 2},\n", - " 'LotShape': {'Reg': 0, 'IR1': 1, 'Rare': 2, 'IR2': 3},\n", - " 'LandContour': {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3},\n", - " 'Utilities': {'Rare': 0, 'AllPub': 1},\n", - " 'LotConfig': {'Inside': 0, 'FR2': 1, 'Corner': 2, 'Rare': 3, 'CulDSac': 4},\n", - " 'LandSlope': {'Gtl': 0, 'Mod': 1, 'Rare': 2},\n", - " 'Neighborhood': {'IDOTRR': 0, 'MeadowV': 1, 'BrDale': 2, 'Edwards': 3,\n", - " 'BrkSide': 4, 'OldTown': 5, 'Sawyer': 6, 'SWISU': 7,\n", - " 'NAmes': 8, 'Mitchel': 9, 'SawyerW': 10, 'Rare': 11,\n", - " 'NWAmes': 12, 'Gilbert': 13, 'Blmngtn': 14, 'CollgCr': 15,\n", - " 'Crawfor': 16, 'ClearCr': 17, 'Somerst': 18, 'Timber': 19,\n", - " 'StoneBr': 20, 'NridgHt': 21, 'NoRidge': 22},\n", - " \n", - " 'Condition1': {'Artery': 0, 'Feedr': 1, 'Norm': 2, 'RRAn': 3, 'Rare': 4, 'PosN': 5},\n", - " 'Condition2': {'Rare': 0, 'Norm': 1},\n", - " 'BldgType': {'2fmCon': 0, 'Duplex': 1, 'Twnhs': 2, '1Fam': 3, 'TwnhsE': 4},\n", - " 'HouseStyle': {'SFoyer': 0, '1.5Fin': 1, 'Rare': 2, '1Story': 3, 'SLvl': 4, '2Story': 5},\n", - " 'RoofStyle': {'Gable': 0, 'Rare': 1, 'Hip': 2},\n", - " 'RoofMatl': {'CompShg': 0, 'Rare': 1},\n", - " 'Exterior1st': {'AsbShng': 0, 'Wd Sdng': 1, 'WdShing': 2, 'MetalSd': 3,\n", - " 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7,\n", - " 'BrkFace': 8, 'CemntBd': 9, 'VinylSd': 10},\n", - " \n", - " 'Exterior2nd': {'AsbShng': 0, 'Wd Sdng': 1, 'MetalSd': 2, 'Wd Shng': 3,\n", - " 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7,\n", - " 'BrkFace': 8, 'CmentBd': 9, 'VinylSd': 10},\n", - " \n", - " 'MasVnrType': {'Rare': 0, 'None': 1, 'BrkFace': 2, 'Stone': 3},\n", - " 'Foundation': {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare': 3, 'PConc': 4},\n", - " 'Heating': {'Rare': 0, 'GasW': 1, 'GasA': 2},\n", - " 'CentralAir': {'N': 0, 'Y': 1},\n", - " 'Electrical': {'Rare': 0, 'FuseF': 1, 'FuseA': 2, 'SBrkr': 3},\n", - " 'Functional': {'Rare': 0, 'Min2': 1, 'Mod': 2, 'Min1': 3, 'Typ': 4},\n", - " 'GarageType': {'Rare': 0, 'Detchd': 1, 'Basment': 2, 'Attchd': 3, 'BuiltIn': 4},\n", - " 'PavedDrive': {'N': 0, 'P': 1, 'Y': 2},\n", - " 'PoolQC': {'Missing': 0, 'Rare': 1},\n", - " 'MiscFeature': {'Rare': 0, 'Shed': 1, 'Missing': 2},\n", - " 'SaleType': {'COD': 0, 'Rare': 1, 'WD': 2, 'New': 3},\n", - " 'SaleCondition': {'Rare': 0, 'Abnorml': 1, 'Family': 2, 'Normal': 3, 'Partial': 4},\n", - " 'MSSubClass': {'30': 0, 'Rare': 1, '190': 2, '90': 3, '160': 4, '50': 5, '85': 6,\n", - " '70': 7, '80': 8, '20': 9, '75': 10, '120': 11, '60': 12},\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "for var in ordinal_mappings.keys():\n", - "\n", - " ordinal_label = ordinal_mappings[var]\n", - "\n", - " # use the dictionary to replace the categorical strings by integers\n", - " data[var] = data[var].map(ordinal_label)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "13" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the data set\n", - "\n", - "with_null = [var for var in data.columns if data[var].isnull().sum() > 0]\n", - "\n", - "len(with_null)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# there is missing data in a lot of the variables.\n", - "\n", - "# unfortunately, the scaler wil not work with missing data, so\n", - "# we need to fill those values\n", - "\n", - "# in the real world, we would try to understand where they are coming from\n", - "# and why they were not present in the training set\n", - "\n", - "# here I will just fill them in quickly to proceed with the demo\n", - "\n", - "data.fillna(0, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Scaling\n", - "\n", - "We will scale features to the minimum and maximum values:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# load the scaler we saved in the notebook on Feature Engineering\n", - "\n", - "# fortunataly, we were smart and we saved it, but this is an easy step\n", - "# to forget\n", - "\n", - "scaler = joblib.load('minmax_scaler.joblib') \n", - "\n", - "data = pd.DataFrame(\n", - " scaler.transform(data),\n", - " columns=data.columns\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 0.083333 0.0 0.495064 0.0 0.0 0.0 0.666667 \n", - "1 0.083333 0.0 0.499662 0.0 0.0 0.0 0.666667 \n", - "2 0.083333 0.0 0.466207 0.0 0.0 0.0 0.666667 \n", - "3 0.083333 0.0 0.485693 0.0 0.0 0.0 0.666667 \n", - "4 0.083333 0.0 0.265271 0.0 0.0 0.0 0.666667 \n", - "\n", - " LandContour Utilities LotConfig ... PoolQC Fence MiscFeature \\\n", - "0 0.0 0.0 0.75 ... 1.0 0.75 0.0 \n", - "1 0.0 0.0 0.75 ... 1.0 0.00 0.0 \n", - "2 0.0 0.0 0.75 ... 1.0 0.75 0.0 \n", - "3 0.0 0.0 0.75 ... 1.0 0.00 0.0 \n", - "4 0.0 0.0 0.75 ... 1.0 0.00 0.0 \n", - "\n", - " MiscVal MoSold SaleType SaleCondition LotFrontage_na MasVnrArea_na \\\n", - "0 0.0 0.454545 0.333333 0.0 0.0 0.0 \n", - "1 1.0 0.454545 0.333333 0.0 0.0 0.0 \n", - "2 0.0 0.181818 0.333333 0.0 0.0 0.0 \n", - "3 0.0 0.454545 0.333333 0.0 0.0 0.0 \n", - "4 0.0 0.000000 0.333333 0.0 0.0 0.0 \n", - "\n", - " GarageYrBlt_na \n", - "0 0.0 \n", - "1 0.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 \n", - "\n", - "[5 rows x 81 columns]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1459, 36)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the pre-selected features\n", - "# ==============================\n", - "\n", - "features = pd.read_csv('selected_features.csv')\n", - "features = features['0'].to_list() \n", - "\n", - "# reduce the train and test set to the selected features\n", - "data = data[features]\n", - "\n", - "data.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that we engineered so many variables, when we are actually going to feed only 31 to the model.\n", - "\n", - "**What could we do differently?**\n", - "\n", - "We could have, of course, engineered only the variables that we are going to use in the model. But that means:\n", - "\n", - "- identifying which variables we need\n", - "- identifying which transformation we need per variable\n", - "- redefining our dictionaries accordingly\n", - "- retraining the MinMaxScaler only on the selected variables (at the moment, it is trained on the entire dataset)\n", - "\n", - "That means, that we need to create extra code to train the scaler only on the selected variables. Probably removing the scaler from the Feature Engineering notebook and passing it onto the Feature Selection one.\n", - "\n", - "We need to be really careful in re-writing the code here to make sure we do not forget or engineer wrongly any of the variables." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# now let's load the trained model\n", - "\n", - "lin_model = joblib.load('linear_regression.joblib') \n", - "\n", - "# let's obtain the predictions\n", - "pred = lin_model.predict(data)\n", - "\n", - "# let's plot the predicted sale prices\n", - "pd.Series(np.exp(pred)).hist(bins=50)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "What shortcomings, inconvenience and problems did you find when scoring new data?\n", - "\n", - "# List of problems\n", - "\n", - "- re-wrote a lot of code ==> repetitive\n", - "- hard coded a lot of parameters ==> if these change we need to re-write them again\n", - "- engineered a lot of variables that we actually do not need for the model\n", - "- additional variables present missing data, we do not know what to do with them\n", - "\n", - "We can minimize these hurdles by using Open-source. And we will see how in the next videos." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "583px", - "left": "0px", - "right": "1324px", - "top": "107px", - "width": "212px" - }, - "toc_section_display": "block", - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Pipeline - Scoring New Data\n", + "\n", + "Let's imagine that a colleague from the business department comes and asks us to score the data from last months customers. They want to be sure that our model is working appropriately in the most recent data that the organization has.\n", + "\n", + "**How would you go about to score the new data?** Try to give it a go. There is more than 1 way of doing it.\n", + "\n", + "Below we present one potential solution.\n", + "\n", + "What could we have done better?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# to handle datasets\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# for plotting\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# for the yeo-johnson transformation\n", + "import scipy.stats as stats\n", + "\n", + "# to save the model\n", + "import joblib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1459, 80)\n" + ] + }, + { + "data": { + "text/html": [ + "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...ScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleCondition
0146120RH80.011622PaveNaNRegLvlAllPub...1200NaNMnPrvNaN062010WDNormal
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" + ], + "text/plain": [ + " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 1461 20 RH 80.0 11622 Pave NaN Reg \n", + "1 1462 20 RL 81.0 14267 Pave NaN IR1 \n", + "2 1463 60 RL 74.0 13830 Pave NaN IR1 \n", + "3 1464 60 RL 78.0 9978 Pave NaN IR1 \n", + "4 1465 120 RL 43.0 5005 Pave NaN IR1 \n", + "\n", + " LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence MiscFeature \\\n", + "0 Lvl AllPub ... 120 0 NaN MnPrv NaN \n", + "1 Lvl AllPub ... 0 0 NaN NaN Gar2 \n", + "2 Lvl AllPub ... 0 0 NaN MnPrv NaN \n", + "3 Lvl AllPub ... 0 0 NaN NaN NaN \n", + "4 HLS AllPub ... 144 0 NaN NaN NaN \n", + "\n", + " MiscVal MoSold YrSold SaleType SaleCondition \n", + "0 0 6 2010 WD Normal \n", + "1 12500 6 2010 WD Normal \n", + "2 0 3 2010 WD Normal \n", + "3 0 6 2010 WD Normal \n", + "4 0 1 2010 WD Normal \n", + "\n", + "[5 rows x 80 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the unseen / new dataset\n", + "data = pd.read_csv('test.csv')\n", + "\n", + "# rows and columns of the data\n", + "print(data.shape)\n", + "\n", + "# visualise the dataset\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1459, 79)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop the id variable\n", + "\n", + "data.drop('Id', axis=1, inplace=True)\n", + "\n", + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Engineering\n", + "\n", + "First we need to transform the data. Below the list of transformations that we did during the Feature Engineering phase:\n", + "\n", + "1. Missing values\n", + "2. Temporal variables\n", + "3. Non-Gaussian distributed variables\n", + "4. Categorical variables: remove rare labels\n", + "5. Categorical variables: convert strings to numbers\n", + "6. Put the variables in a similar scale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Missing values\n", + "\n", + "### Categorical variables\n", + "\n", + "- Replace missing values with the string \"missing\" in those variables with a lot of missing data. \n", + "- Replace missing data with the most frequent category in those variables that contain fewer observations without values. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# first we needed to cast MSSubClass as object\n", + "\n", + "data['MSSubClass'] = data['MSSubClass'].astype('O')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# list of different groups of categorical variables\n", + "\n", + "with_string_missing = ['Alley', 'FireplaceQu',\n", + " 'PoolQC', 'Fence', 'MiscFeature']\n", + "\n", + "# ==================\n", + "# we copy this dictionary from the Feature-engineering notebook\n", + "# note that we needed to hard-code this by hand\n", + "\n", + "# the key is the variable and the value is its most frequent category\n", + "\n", + "# what if we re-train the model and the below values change?\n", + "# ==================\n", + "\n", + "with_frequent_category = {\n", + " 'MasVnrType': 'None',\n", + " 'BsmtQual': 'TA',\n", + " 'BsmtCond': 'TA',\n", + " 'BsmtExposure': 'No',\n", + " 'BsmtFinType1': 'Unf',\n", + " 'BsmtFinType2': 'Unf',\n", + " 'Electrical': 'SBrkr',\n", + " 'GarageType': 'Attchd',\n", + " 'GarageFinish': 'Unf',\n", + " 'GarageQual': 'TA',\n", + " 'GarageCond': 'TA',\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# replace missing values with new label: \"Missing\"\n", + "\n", + "data[with_string_missing] = data[with_string_missing].fillna('Missing')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# replace missing values with the most frequent category\n", + "\n", + "for var in with_frequent_category.keys():\n", + " data[var].fillna(with_frequent_category[var], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Numerical variables\n", + "\n", + "To engineer missing values in numerical variables, we will:\n", + "\n", + "- add a binary missing value indicator variable\n", + "- and then replace the missing values in the original variable with the mean" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# this is the dictionary of numerical variable with missing data\n", + "# and its mean, as determined from the training set in the\n", + "# Feature Engineering notebook\n", + "\n", + "# note how we needed to hard code the values\n", + "\n", + "vars_with_na = {\n", + " 'LotFrontage': 69.87974098057354,\n", + " 'MasVnrArea': 103.7974006116208,\n", + " 'GarageYrBlt': 1978.2959677419356,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LotFrontage 0\n", + "MasVnrArea 0\n", + "GarageYrBlt 0\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# replace missing values as we described above\n", + "\n", + "for var in vars_with_na.keys():\n", + "\n", + " # add binary missing indicator (in train and test)\n", + " data[var + '_na'] = np.where(data[var].isnull(), 1, 0)\n", + "\n", + " # replace missing values by the mean\n", + " # (in train and test)\n", + " data[var].fillna(vars_with_na[var], inplace=True)\n", + "\n", + "data[vars_with_na].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " LotFrontage_na MasVnrArea_na GarageYrBlt_na\n", + "0 0 0 0\n", + "1 0 0 0\n", + "2 0 0 0\n", + "3 0 0 0\n", + "4 0 0 0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the binary missing indicator variables\n", + "\n", + "data[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Temporal variables\n", + "\n", + "### Capture elapsed time\n", + "\n", + "We need to capture the time elapsed between those variables and the year in which the house was sold:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def elapsed_years(df, var):\n", + " # capture difference between the year variable\n", + " # and the year in which the house was sold\n", + " df[var] = df['YrSold'] - df[var]\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:\n", + " data = elapsed_years(data, var)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# now we drop YrSold\n", + "data.drop(['YrSold'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Numerical variable transformation\n", + "\n", + "### Logarithmic transformation\n", + "\n", + "We will transform with the logarithm the positive numerical variables in order to get a more Gaussian-like distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n", + " data[var] = np.log(data[var])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Yeo-Johnson transformation\n", + "\n", + "We will apply the Yeo-Johnson transformation to LotArea." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# note how we use the lambda that we learned from the train set\n", + "# in the notebook on Feature Engineering.\n", + "\n", + "# Note that we need to hard code this value\n", + "\n", + "data['LotArea'] = stats.yeojohnson(data['LotArea'], lmbda=-12.55283001172003)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Binarize skewed variables\n", + "\n", + "There were a few variables very skewed, we would transform those into binary variables." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "skewed = [\n", + " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", + " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", + "]\n", + "\n", + "for var in skewed:\n", + " \n", + " # map the variable values into 0 and 1\n", + " data[var] = np.where(data[var]==0, 0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Categorical variables\n", + "\n", + "### Apply mappings\n", + "\n", + "We remap variables with specific meanings into a numerical scale." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# re-map strings to numbers, which determine quality\n", + "\n", + "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", + "\n", + "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", + " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", + " 'GarageQual', 'GarageCond',\n", + " ]\n", + "\n", + "for var in qual_vars:\n", + " data[var] = data[var].map(qual_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", + "\n", + "var = 'BsmtExposure'\n", + "\n", + "data[var] = data[var].map(exposure_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", + "\n", + "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n", + "\n", + "for var in finish_vars:\n", + " data[var] = data[var].map(finish_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", + "\n", + "var = 'GarageFinish'\n", + "\n", + "data[var] = data[var].map(garage_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n", + "\n", + "var = 'Fence'\n", + "\n", + "data[var] = data[var].map(fence_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['MSZoning',\n", + " 'Utilities',\n", + " 'Exterior1st',\n", + " 'Exterior2nd',\n", + " 'BsmtFinSF1',\n", + " 'BsmtUnfSF',\n", + " 'TotalBsmtSF',\n", + " 'BsmtFullBath',\n", + " 'BsmtHalfBath',\n", + " 'KitchenQual',\n", + " 'Functional',\n", + " 'GarageCars',\n", + " 'GarageArea',\n", + " 'SaleType']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the data set\n", + "\n", + "with_null = [var for var in data.columns if data[var].isnull().sum() > 0]\n", + "\n", + "with_null" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Surprise**\n", + "\n", + "There are quite a few variables with missing data!!" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# did those have missing data in the train set?\n", + "\n", + "[var for var in with_null if var in list(\n", + " with_frequent_category.keys())+with_string_missing+list(vars_with_na.keys())]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**IMPORTANT**\n", + "\n", + "In the new data, we have a bunch of variables that contain missing information, that we did not anticipate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Rare Labels\n", + "\n", + "For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations into a \"Rare\" string." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# create a dictionary with the most frequent categories per variable\n", + "\n", + "# note the amount of hard coding that I need to do.\n", + "\n", + "# Can you think of an alternative? Perhaps we could have save this as a numpy pickle\n", + "# and load it here, instead of hard-coding.\n", + "\n", + "# But that means that we need to go back to the Feature Engineering notebook, and change\n", + "# the code so that we store the pickle. So there is still some code changes that we need\n", + "\n", + "frequent_ls = {\n", + " 'MSZoning': ['FV', 'RH', 'RL', 'RM'],\n", + " 'Street': ['Pave'],\n", + " 'Alley': ['Grvl', 'Missing', 'Pave'],\n", + " 'LotShape': ['IR1', 'IR2', 'Reg'],\n", + " 'LandContour': ['Bnk', 'HLS', 'Low', 'Lvl'],\n", + " 'Utilities': ['AllPub'],\n", + " 'LotConfig': ['Corner', 'CulDSac', 'FR2', 'Inside'],\n", + " 'LandSlope': ['Gtl', 'Mod'],\n", + " 'Neighborhood': ['Blmngtn', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor',\n", + " 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NWAmes',\n", + " 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW',\n", + " 'Somerst', 'StoneBr', 'Timber'],\n", + "\n", + " 'Condition1': ['Artery', 'Feedr', 'Norm', 'PosN', 'RRAn'],\n", + " 'Condition2': ['Norm'],\n", + " 'BldgType': ['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE'],\n", + " 'HouseStyle': ['1.5Fin', '1Story', '2Story', 'SFoyer', 'SLvl'],\n", + " 'RoofStyle': ['Gable', 'Hip'],\n", + " 'RoofMatl': ['CompShg'],\n", + " 'Exterior1st': ['AsbShng', 'BrkFace', 'CemntBd', 'HdBoard', 'MetalSd', 'Plywood',\n", + " 'Stucco', 'VinylSd', 'Wd Sdng', 'WdShing'],\n", + "\n", + " 'Exterior2nd': ['AsbShng', 'BrkFace', 'CmentBd', 'HdBoard', 'MetalSd', 'Plywood',\n", + " 'Stucco', 'VinylSd', 'Wd Sdng', 'Wd Shng'],\n", + "\n", + " 'MasVnrType': ['BrkFace', 'None', 'Stone'],\n", + " 'Foundation': ['BrkTil', 'CBlock', 'PConc', 'Slab'],\n", + " 'Heating': ['GasA', 'GasW'],\n", + " 'CentralAir': ['N', 'Y'],\n", + " 'Electrical': ['FuseA', 'FuseF', 'SBrkr'],\n", + " 'Functional': ['Min1', 'Min2', 'Mod', 'Typ'],\n", + " 'GarageType': ['Attchd', 'Basment', 'BuiltIn', 'Detchd'],\n", + " 'PavedDrive': ['N', 'P', 'Y'],\n", + " 'PoolQC': ['Missing'],\n", + " 'MiscFeature': ['Missing', 'Shed'],\n", + " 'SaleType': ['COD', 'New', 'WD'],\n", + " 'SaleCondition': ['Abnorml', 'Family', 'Normal', 'Partial'],\n", + " 'MSSubClass': ['20', '30', '50', '60', '70', '75', '80', '85', '90', '120', '160', '190'],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "for var in frequent_ls.keys():\n", + " \n", + " # replace rare categories by the string \"Rare\"\n", + " data[var] = np.where(data[var].isin(\n", + " frequent_ls), data[var], 'Rare')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Encoding of categorical variables\n", + "\n", + "Next, we need to transform the strings of the categorical variables into numbers. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# we need the mappings learned from the train set. Otherwise, our model is going\n", + "# to produce inaccurate results\n", + "\n", + "# note the amount of hard coding that we need to do.\n", + "\n", + "# Can you think of an alternative? \n", + "\n", + "# Perhaps we could have save this as a numpy pickle\n", + "# and load it here, instead of hard-coding.\n", + "\n", + "# But that means that we need to go back to the Feature Engineering notebook, and change\n", + "# the code so that we store the pickle. So there is still some code changes that we need\n", + "\n", + "ordinal_mappings = {\n", + " 'MSZoning': {'Rare': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4},\n", + " 'Street': {'Rare': 0, 'Pave': 1},\n", + " 'Alley': {'Grvl': 0, 'Pave': 1, 'Missing': 2},\n", + " 'LotShape': {'Reg': 0, 'IR1': 1, 'Rare': 2, 'IR2': 3},\n", + " 'LandContour': {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3},\n", + " 'Utilities': {'Rare': 0, 'AllPub': 1},\n", + " 'LotConfig': {'Inside': 0, 'FR2': 1, 'Corner': 2, 'Rare': 3, 'CulDSac': 4},\n", + " 'LandSlope': {'Gtl': 0, 'Mod': 1, 'Rare': 2},\n", + " 'Neighborhood': {'IDOTRR': 0, 'MeadowV': 1, 'BrDale': 2, 'Edwards': 3,\n", + " 'BrkSide': 4, 'OldTown': 5, 'Sawyer': 6, 'SWISU': 7,\n", + " 'NAmes': 8, 'Mitchel': 9, 'SawyerW': 10, 'Rare': 11,\n", + " 'NWAmes': 12, 'Gilbert': 13, 'Blmngtn': 14, 'CollgCr': 15,\n", + " 'Crawfor': 16, 'ClearCr': 17, 'Somerst': 18, 'Timber': 19,\n", + " 'StoneBr': 20, 'NridgHt': 21, 'NoRidge': 22},\n", + " \n", + " 'Condition1': {'Artery': 0, 'Feedr': 1, 'Norm': 2, 'RRAn': 3, 'Rare': 4, 'PosN': 5},\n", + " 'Condition2': {'Rare': 0, 'Norm': 1},\n", + " 'BldgType': {'2fmCon': 0, 'Duplex': 1, 'Twnhs': 2, '1Fam': 3, 'TwnhsE': 4},\n", + " 'HouseStyle': {'SFoyer': 0, '1.5Fin': 1, 'Rare': 2, '1Story': 3, 'SLvl': 4, '2Story': 5},\n", + " 'RoofStyle': {'Gable': 0, 'Rare': 1, 'Hip': 2},\n", + " 'RoofMatl': {'CompShg': 0, 'Rare': 1},\n", + " 'Exterior1st': {'AsbShng': 0, 'Wd Sdng': 1, 'WdShing': 2, 'MetalSd': 3,\n", + " 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7,\n", + " 'BrkFace': 8, 'CemntBd': 9, 'VinylSd': 10},\n", + " \n", + " 'Exterior2nd': {'AsbShng': 0, 'Wd Sdng': 1, 'MetalSd': 2, 'Wd Shng': 3,\n", + " 'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7,\n", + " 'BrkFace': 8, 'CmentBd': 9, 'VinylSd': 10},\n", + " \n", + " 'MasVnrType': {'Rare': 0, 'None': 1, 'BrkFace': 2, 'Stone': 3},\n", + " 'Foundation': {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare': 3, 'PConc': 4},\n", + " 'Heating': {'Rare': 0, 'GasW': 1, 'GasA': 2},\n", + " 'CentralAir': {'N': 0, 'Y': 1},\n", + " 'Electrical': {'Rare': 0, 'FuseF': 1, 'FuseA': 2, 'SBrkr': 3},\n", + " 'Functional': {'Rare': 0, 'Min2': 1, 'Mod': 2, 'Min1': 3, 'Typ': 4},\n", + " 'GarageType': {'Rare': 0, 'Detchd': 1, 'Basment': 2, 'Attchd': 3, 'BuiltIn': 4},\n", + " 'PavedDrive': {'N': 0, 'P': 1, 'Y': 2},\n", + " 'PoolQC': {'Missing': 0, 'Rare': 1},\n", + " 'MiscFeature': {'Rare': 0, 'Shed': 1, 'Missing': 2},\n", + " 'SaleType': {'COD': 0, 'Rare': 1, 'WD': 2, 'New': 3},\n", + " 'SaleCondition': {'Rare': 0, 'Abnorml': 1, 'Family': 2, 'Normal': 3, 'Partial': 4},\n", + " 'MSSubClass': {'30': 0, 'Rare': 1, '190': 2, '90': 3, '160': 4, '50': 5, '85': 6,\n", + " '70': 7, '80': 8, '20': 9, '75': 10, '120': 11, '60': 12},\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "for var in ordinal_mappings.keys():\n", + "\n", + " ordinal_label = ordinal_mappings[var]\n", + "\n", + " # use the dictionary to replace the categorical strings by integers\n", + " data[var] = data[var].map(ordinal_label)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the data set\n", + "\n", + "with_null = [var for var in data.columns if data[var].isnull().sum() > 0]\n", + "\n", + "len(with_null)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# there is missing data in a lot of the variables.\n", + "\n", + "# unfortunately, the scaler wil not work with missing data, so\n", + "# we need to fill those values\n", + "\n", + "# in the real world, we would try to understand where they are coming from\n", + "# and why they were not present in the training set\n", + "\n", + "# here I will just fill them in quickly to proceed with the demo\n", + "\n", + "data.fillna(0, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature Scaling\n", + "\n", + "We will scale features to the minimum and maximum values:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# load the scaler we saved in the notebook on Feature Engineering\n", + "\n", + "# fortunataly, we were smart and we saved it, but this is an easy step\n", + "# to forget\n", + "\n", + "scaler = joblib.load('minmax_scaler.joblib') \n", + "\n", + "data = pd.DataFrame(\n", + " scaler.transform(data),\n", + " columns=data.columns\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 81 columns

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" + ], + "text/plain": [ + " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 0.083333 0.0 0.495064 0.0 0.0 0.0 0.666667 \n", + "1 0.083333 0.0 0.499662 0.0 0.0 0.0 0.666667 \n", + "2 0.083333 0.0 0.466207 0.0 0.0 0.0 0.666667 \n", + "3 0.083333 0.0 0.485693 0.0 0.0 0.0 0.666667 \n", + "4 0.083333 0.0 0.265271 0.0 0.0 0.0 0.666667 \n", + "\n", + " LandContour Utilities LotConfig ... PoolQC Fence MiscFeature \\\n", + "0 0.0 0.0 0.75 ... 1.0 0.75 0.0 \n", + "1 0.0 0.0 0.75 ... 1.0 0.00 0.0 \n", + "2 0.0 0.0 0.75 ... 1.0 0.75 0.0 \n", + "3 0.0 0.0 0.75 ... 1.0 0.00 0.0 \n", + "4 0.0 0.0 0.75 ... 1.0 0.00 0.0 \n", + "\n", + " MiscVal MoSold SaleType SaleCondition LotFrontage_na MasVnrArea_na \\\n", + "0 0.0 0.454545 0.333333 0.0 0.0 0.0 \n", + "1 1.0 0.454545 0.333333 0.0 0.0 0.0 \n", + "2 0.0 0.181818 0.333333 0.0 0.0 0.0 \n", + "3 0.0 0.454545 0.333333 0.0 0.0 0.0 \n", + "4 0.0 0.000000 0.333333 0.0 0.0 0.0 \n", + "\n", + " GarageYrBlt_na \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "\n", + "[5 rows x 81 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1459, 36)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the pre-selected features\n", + "# ==============================\n", + "\n", + "features = pd.read_csv('selected_features.csv')\n", + "features = features['0'].to_list() \n", + "\n", + "# reduce the train and test set to the selected features\n", + "data = data[features]\n", + "\n", + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we engineered so many variables, when we are actually going to feed only 31 to the model.\n", + "\n", + "**What could we do differently?**\n", + "\n", + "We could have, of course, engineered only the variables that we are going to use in the model. But that means:\n", + "\n", + "- identifying which variables we need\n", + "- identifying which transformation we need per variable\n", + "- redefining our dictionaries accordingly\n", + "- retraining the MinMaxScaler only on the selected variables (at the moment, it is trained on the entire dataset)\n", + "\n", + "That means, that we need to create extra code to train the scaler only on the selected variables. Probably removing the scaler from the Feature Engineering notebook and passing it onto the Feature Selection one.\n", + "\n", + "We need to be really careful in re-writing the code here to make sure we do not forget or engineer wrongly any of the variables." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# now let's load the trained model\n", + "\n", + "lin_model = joblib.load('linear_regression.joblib') \n", + "\n", + "# let's obtain the predictions\n", + "pred = lin_model.predict(data)\n", + "\n", + "# let's plot the predicted sale prices\n", + "pd.Series(np.exp(pred)).hist(bins=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "What shortcomings, inconvenience and problems did you find when scoring new data?\n", + "\n", + "# List of problems\n", + "\n", + "- re-wrote a lot of code ==> repetitive\n", + "- hard coded a lot of parameters ==> if these change we need to re-write them again\n", + "- engineered a lot of variables that we actually do not need for the model\n", + "- additional variables present missing data, we do not know what to do with them\n", + "\n", + "We can minimize these hurdles by using Open-source. And we will see how in the next videos." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "583px", + "left": "0px", + "right": "1324px", + "top": "107px", + "width": "212px" + }, + "toc_section_display": "block", + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/06-feature-engineering-with-open-source.ipynb b/section-04-research-and-development/06-feature-engineering-with-open-source.ipynb index 2d25751b3..ef2875dff 100644 --- a/section-04-research-and-development/06-feature-engineering-with-open-source.ipynb +++ b/section-04-research-and-development/06-feature-engineering-with-open-source.ipynb @@ -1,3358 +1,3358 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Feature Engineering with Open-Source\n", - "\n", - "In this notebook, we will reproduce the Feature Engineering Pipeline from the notebook 2 (02-Machine-Learning-Pipeline-Feature-Engineering), but we will replace, whenever possible, the manually created functions by open-source classes, and hopefully understand the value they bring forward." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reproducibility: Setting the seed\n", - "\n", - "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# data manipulation and plotting\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# for saving the pipeline\n", - "import joblib\n", - "\n", - "# from Scikit-learn\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import MinMaxScaler, Binarizer\n", - "\n", - "# from feature-engine\n", - "from feature_engine.imputation import (\n", - " AddMissingIndicator,\n", - " MeanMedianImputer,\n", - " CategoricalImputer,\n", - ")\n", - "\n", - "from feature_engine.encoding import (\n", - " RareLabelEncoder,\n", - " OrdinalEncoder,\n", - ")\n", - "\n", - "from feature_engine.transformation import (\n", - " LogTransformer,\n", - " YeoJohnsonTransformer,\n", - ")\n", - "\n", - "from feature_engine.selection import DropFeatures\n", - "from feature_engine.wrappers import SklearnTransformerWrapper\n", - "\n", - "# to visualise al the columns in the dataframe\n", - "pd.pandas.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1460, 81)\n" - ] - }, - { - "data": { - "text/html": [ - "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
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" - ], - "text/plain": [ - " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 1 60 RL 65.0 8450 Pave NaN Reg \n", - "1 2 20 RL 80.0 9600 Pave NaN Reg \n", - "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", - "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", - "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", - "\n", - " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", - "0 Lvl AllPub Inside Gtl CollgCr Norm \n", - "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", - "2 Lvl AllPub Inside Gtl CollgCr Norm \n", - "3 Lvl AllPub Corner Gtl Crawfor Norm \n", - "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", - "\n", - " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", - "0 Norm 1Fam 2Story 7 5 2003 \n", - "1 Norm 1Fam 1Story 6 8 1976 \n", - "2 Norm 1Fam 2Story 7 5 2001 \n", - "3 Norm 1Fam 2Story 7 5 1915 \n", - "4 Norm 1Fam 2Story 8 5 2000 \n", - "\n", - " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", - "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", - "1 1976 Gable CompShg MetalSd MetalSd None \n", - "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", - "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", - "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", - "\n", - " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", - "0 196.0 Gd TA PConc Gd TA No \n", - "1 0.0 TA TA CBlock Gd TA Gd \n", - "2 162.0 Gd TA PConc Gd TA Mn \n", - "3 0.0 TA TA BrkTil TA Gd No \n", - "4 350.0 Gd TA PConc Gd TA Av \n", - "\n", - " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", - "0 GLQ 706 Unf 0 150 856 \n", - "1 ALQ 978 Unf 0 284 1262 \n", - "2 GLQ 486 Unf 0 434 920 \n", - "3 ALQ 216 Unf 0 540 756 \n", - "4 GLQ 655 Unf 0 490 1145 \n", - "\n", - " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", - "0 GasA Ex Y SBrkr 856 854 0 \n", - "1 GasA Ex Y SBrkr 1262 0 0 \n", - "2 GasA Ex Y SBrkr 920 866 0 \n", - "3 GasA Gd Y SBrkr 961 756 0 \n", - "4 GasA Ex Y SBrkr 1145 1053 0 \n", - "\n", - " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", - "0 1710 1 0 2 1 3 \n", - "1 1262 0 1 2 0 3 \n", - "2 1786 1 0 2 1 3 \n", - "3 1717 1 0 1 0 3 \n", - "4 2198 1 0 2 1 4 \n", - "\n", - " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", - "0 1 Gd 8 Typ 0 NaN \n", - "1 1 TA 6 Typ 1 TA \n", - "2 1 Gd 6 Typ 1 TA \n", - "3 1 Gd 7 Typ 1 Gd \n", - "4 1 Gd 9 Typ 1 TA \n", - "\n", - " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", - "0 Attchd 2003.0 RFn 2 548 TA \n", - "1 Attchd 1976.0 RFn 2 460 TA \n", - "2 Attchd 2001.0 RFn 2 608 TA \n", - "3 Detchd 1998.0 Unf 3 642 TA \n", - "4 Attchd 2000.0 RFn 3 836 TA \n", - "\n", - " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", - "0 TA Y 0 61 0 0 \n", - "1 TA Y 298 0 0 0 \n", - "2 TA Y 0 42 0 0 \n", - "3 TA Y 0 35 272 0 \n", - "4 TA Y 192 84 0 0 \n", - "\n", - " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", - "0 0 0 NaN NaN NaN 0 2 2008 \n", - "1 0 0 NaN NaN NaN 0 5 2007 \n", - "2 0 0 NaN NaN NaN 0 9 2008 \n", - "3 0 0 NaN NaN NaN 0 2 2006 \n", - "4 0 0 NaN NaN NaN 0 12 2008 \n", - "\n", - " SaleType SaleCondition SalePrice \n", - "0 WD Normal 208500 \n", - "1 WD Normal 181500 \n", - "2 WD Normal 223500 \n", - "3 WD Abnorml 140000 \n", - "4 WD Normal 250000 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load dataset\n", - "data = pd.read_csv('train.csv')\n", - "\n", - "# rows and columns of the data\n", - "print(data.shape)\n", - "\n", - "# visualise the dataset\n", - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Separate dataset into train and test\n", - "\n", - "It is important to separate our data intro training and testing set. \n", - "\n", - "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n", - "\n", - "Our feature engineering techniques will learn:\n", - "\n", - "- mean\n", - "- mode\n", - "- exponents for the yeo-johnson\n", - "- category frequency\n", - "- and category to number mappings\n", - "\n", - "from the train set.\n", - "\n", - "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1314, 79), (146, 79))" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's separate into train and test set\n", - "# Remember to set the seed (random_state for this sklearn function)\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n", - " data['SalePrice'], # target\n", - " test_size=0.1, # portion of dataset to allocate to test set\n", - " random_state=0, # we are setting the seed here\n", - ")\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Feature Engineering\n", - "\n", - "In the following cells, we will engineer the variables of the House Price Dataset so that we tackle:\n", - "\n", - "1. Missing values\n", - "2. Temporal variables\n", - "3. Non-Gaussian distributed variables\n", - "4. Categorical variables: remove rare labels\n", - "5. Categorical variables: convert strings to numbers\n", - "5. Standardize the values of the variables to the same range" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Target\n", - "\n", - "We apply the logarithm" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "y_train = np.log(y_train)\n", - "y_test = np.log(y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Missing values\n", - "\n", - "### Categorical variables\n", - "\n", - "We will replace missing values with the string \"missing\" in those variables with a lot of missing data. \n", - "\n", - "Alternatively, we will replace missing data with the most frequent category in those variables that contain fewer observations without values. \n", - "\n", - "This is common practice." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "44" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let's identify the categorical variables\n", - "# we will capture those of type object\n", - "\n", - "cat_vars = [var for var in data.columns if data[var].dtype == 'O']\n", - "\n", - "# MSSubClass is also categorical by definition, despite its numeric values\n", - "# (you can find the definitions of the variables in the data_description.txt\n", - "# file available on Kaggle, in the same website where you downloaded the data)\n", - "\n", - "# lets add MSSubClass to the list of categorical variables\n", - "cat_vars = cat_vars + ['MSSubClass']\n", - "\n", - "# cast all variables as categorical\n", - "X_train[cat_vars] = X_train[cat_vars].astype('O')\n", - "X_test[cat_vars] = X_test[cat_vars].astype('O')\n", - "\n", - "# number of categorical variables\n", - "len(cat_vars)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "PoolQC 0.995434\n", - "MiscFeature 0.961187\n", - "Alley 0.938356\n", - "Fence 0.814307\n", - "FireplaceQu 0.472603\n", - "GarageType 0.056317\n", - "GarageFinish 0.056317\n", - "GarageQual 0.056317\n", - "GarageCond 0.056317\n", - "BsmtExposure 0.025114\n", - "BsmtFinType2 0.025114\n", - "BsmtQual 0.024353\n", - "BsmtCond 0.024353\n", - "BsmtFinType1 0.024353\n", - "MasVnrType 0.004566\n", - "Electrical 0.000761\n", - "dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# make a list of the categorical variables that contain missing values\n", - "\n", - "cat_vars_with_na = [\n", - " var for var in cat_vars\n", - " if X_train[var].isnull().sum() > 0\n", - "]\n", - "\n", - "# print percentage of missing values per variable\n", - "X_train[cat_vars_with_na ].isnull().mean().sort_values(ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# variables to impute with the string missing\n", - "with_string_missing = [\n", - " var for var in cat_vars_with_na if X_train[var].isnull().mean() > 0.1]\n", - "\n", - "# variables to impute with the most frequent category\n", - "with_frequent_category = [\n", - " var for var in cat_vars_with_na if X_train[var].isnull().mean() < 0.1]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# I print the values here, because it makes it easier for\n", - "# later when we need to add this values to a config file for \n", - "# deployment\n", - "\n", - "with_string_missing" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['MasVnrType',\n", - " 'BsmtQual',\n", - " 'BsmtCond',\n", - " 'BsmtExposure',\n", - " 'BsmtFinType1',\n", - " 'BsmtFinType2',\n", - " 'Electrical',\n", - " 'GarageType',\n", - " 'GarageFinish',\n", - " 'GarageQual',\n", - " 'GarageCond']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with_frequent_category" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Alley': 'Missing',\n", - " 'FireplaceQu': 'Missing',\n", - " 'PoolQC': 'Missing',\n", - " 'Fence': 'Missing',\n", - " 'MiscFeature': 'Missing'}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# replace missing values with new label: \"Missing\"\n", - "\n", - "# set up the class\n", - "cat_imputer_missing = CategoricalImputer(\n", - " imputation_method='missing', variables=with_string_missing)\n", - "\n", - "# fit the class to the train set\n", - "cat_imputer_missing.fit(X_train)\n", - "\n", - "# the class learns and stores the parameters\n", - "cat_imputer_missing.imputer_dict_" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# replace NA by missing\n", - "\n", - "# IMPORTANT: note that we could store this class with joblib\n", - "X_train = cat_imputer_missing.transform(X_train)\n", - "X_test = cat_imputer_missing.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'MasVnrType': 'None',\n", - " 'BsmtQual': 'TA',\n", - " 'BsmtCond': 'TA',\n", - " 'BsmtExposure': 'No',\n", - " 'BsmtFinType1': 'Unf',\n", - " 'BsmtFinType2': 'Unf',\n", - " 'Electrical': 'SBrkr',\n", - " 'GarageType': 'Attchd',\n", - " 'GarageFinish': 'Unf',\n", - " 'GarageQual': 'TA',\n", - " 'GarageCond': 'TA'}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# replace missing values with most frequent category\n", - "\n", - "# set up the class\n", - "cat_imputer_frequent = CategoricalImputer(\n", - " imputation_method='frequent', variables=with_frequent_category)\n", - "\n", - "# fit the class to the train set\n", - "cat_imputer_frequent.fit(X_train)\n", - "\n", - "# the class learns and stores the parameters\n", - "cat_imputer_frequent.imputer_dict_" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# replace NA by missing\n", - "\n", - "# IMPORTANT: note that we could store this class with joblib\n", - "X_train = cat_imputer_frequent.transform(X_train)\n", - "X_test = cat_imputer_frequent.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Alley 0\n", - "MasVnrType 0\n", - "BsmtQual 0\n", - "BsmtCond 0\n", - "BsmtExposure 0\n", - "BsmtFinType1 0\n", - "BsmtFinType2 0\n", - "Electrical 0\n", - "FireplaceQu 0\n", - "GarageType 0\n", - "GarageFinish 0\n", - "GarageQual 0\n", - "GarageCond 0\n", - "PoolQC 0\n", - "Fence 0\n", - "MiscFeature 0\n", - "dtype: int64" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check that we have no missing information in the engineered variables\n", - "\n", - "X_train[cat_vars_with_na].isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check that test set does not contain null values in the engineered variables\n", - "\n", - "[var for var in cat_vars_with_na if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Numerical variables\n", - "\n", - "To engineer missing values in numerical variables, we will:\n", - "\n", - "- add a binary missing indicator variable\n", - "- and then replace the missing values in the original variable with the mean" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "35" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now let's identify the numerical variables\n", - "\n", - "num_vars = [\n", - " var for var in X_train.columns if var not in cat_vars and var != 'SalePrice'\n", - "]\n", - "\n", - "# number of numerical variables\n", - "len(num_vars)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LotFrontage 0.177321\n", - "MasVnrArea 0.004566\n", - "GarageYrBlt 0.056317\n", - "dtype: float64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# make a list with the numerical variables that contain missing values\n", - "vars_with_na = [\n", - " var for var in num_vars\n", - " if X_train[var].isnull().sum() > 0\n", - "]\n", - "\n", - "# print percentage of missing values per variable\n", - "X_train[vars_with_na].isnull().mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# print, makes my life easier when I want to create the config\n", - "vars_with_na" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " LotFrontage_na MasVnrArea_na GarageYrBlt_na\n", - "930 0 0 0\n", - "656 0 0 0\n", - "45 0 0 0\n", - "1348 1 0 0\n", - "55 0 0 0" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# add missing indicator\n", - "\n", - "missing_ind = AddMissingIndicator(variables=vars_with_na)\n", - "\n", - "missing_ind.fit(X_train)\n", - "\n", - "X_train = missing_ind.transform(X_train)\n", - "X_test = missing_ind.transform(X_test)\n", - "\n", - "# check the binary missing indicator variables\n", - "X_train[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'LotFrontage': 69.87974098057354,\n", - " 'MasVnrArea': 103.7974006116208,\n", - " 'GarageYrBlt': 1978.2959677419356}" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# then replace missing data with the mean\n", - "\n", - "# set the imputer\n", - "mean_imputer = MeanMedianImputer(\n", - " imputation_method='mean', variables=vars_with_na)\n", - "\n", - "# learn and store parameters from train set\n", - "mean_imputer.fit(X_train)\n", - "\n", - "# the stored parameters\n", - "mean_imputer.imputer_dict_" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LotFrontage 0\n", - "MasVnrArea 0\n", - "GarageYrBlt 0\n", - "dtype: int64" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train = mean_imputer.transform(X_train)\n", - "X_test = mean_imputer.transform(X_test)\n", - "\n", - "# IMPORTANT: note that we could save the imputers with joblib\n", - "\n", - "# check that we have no more missing values in the engineered variables\n", - "X_train[vars_with_na].isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check that test set does not contain null values in the engineered variables\n", - "\n", - "[var for var in vars_with_na if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Temporal variables\n", - "\n", - "### Capture elapsed time\n", - "\n", - "There is in Feature-engine 2 classes that allow us to perform the 2 transformations below:\n", - "\n", - "- [CombineWithFeatureReference](https://feature-engine.readthedocs.io/en/latest/creation/CombineWithReferenceFeature.html) to capture elapsed time\n", - "- [DropFeatures](https://feature-engine.readthedocs.io/en/latest/selection/DropFeatures.html) to drop the unwanted features\n", - "\n", - "We will do the first one manually, so we take the opportunity to create 1 class ourselves for the course. For the second operation, we will use the DropFeatures class." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "def elapsed_years(df, var):\n", - " # capture difference between the year variable\n", - " # and the year in which the house was sold\n", - " df[var] = df['YrSold'] - df[var]\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:\n", - " X_train = elapsed_years(X_train, var)\n", - " X_test = elapsed_years(X_test, var)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# now we drop YrSold\n", - "drop_features = DropFeatures(features_to_drop=['YrSold'])\n", - "\n", - "X_train = drop_features.fit_transform(X_train)\n", - "X_test = drop_features.transform(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Numerical variable transformation\n", - "\n", - "### Logarithmic transformation\n", - "\n", - "In the previous notebook, we observed that the numerical variables are not normally distributed.\n", - "\n", - "We will transform with the logarightm the positive numerical variables in order to get a more Gaussian-like distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "log_transformer = LogTransformer(\n", - " variables=[\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"])\n", - "\n", - "X_train = log_transformer.fit_transform(X_train)\n", - "X_test = log_transformer.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check that test set does not contain null values in the engineered variables\n", - "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# same for train set\n", - "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_train[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Yeo-Johnson transformation\n", - "\n", - "We will apply the Yeo-Johnson transformation to LotArea." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\stats\\morestats.py:1476: RuntimeWarning: divide by zero encountered in log\n", - " loglike = -n_samples / 2 * np.log(trans.var(axis=0))\n", - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2555: RuntimeWarning: invalid value encountered in double_scalars\n", - " w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom\n", - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2148: RuntimeWarning: invalid value encountered in double_scalars\n", - " tmp1 = (x - w) * (fx - fv)\n", - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2149: RuntimeWarning: invalid value encountered in double_scalars\n", - " tmp2 = (x - v) * (fx - fw)\n" - ] - }, - { - "data": { - "text/plain": [ - "{'LotArea': -12.55283001172003}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "yeo_transformer = YeoJohnsonTransformer(\n", - " variables=['LotArea'])\n", - "\n", - "X_train = yeo_transformer.fit_transform(X_train)\n", - "X_test = yeo_transformer.transform(X_test)\n", - "\n", - "# the learned parameter\n", - "yeo_transformer.lambda_dict_" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the train set\n", - "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the test set\n", - "[var for var in X_train.columns if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Binarize skewed variables\n", - "\n", - "There were a few variables very skewed, we would transform those into binary variables.\n", - "\n", - "We can perform the below transformation with open source. We can use the [Binarizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html) from Scikit-learn, in combination with the [SklearnWrapper](https://feature-engine.readthedocs.io/en/latest/wrappers/Wrapper.html) from Feature-engine to be able to apply the transformation only to a subset of features.\n", - "\n", - "Instead, we are going to do it manually, to give us another opportunity to code the class as an in-house package later in the course." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " BsmtFinSF2 LowQualFinSF EnclosedPorch 3SsnPorch ScreenPorch MiscVal\n", - "930 0 0 0 0 0 0\n", - "656 0 0 0 0 0 0\n", - "45 0 0 0 0 0 0\n", - "1348 0 0 0 0 0 0\n", - "55 0 0 0 1 0 0" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "skewed = [\n", - " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", - " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", - "]\n", - "\n", - "binarizer = SklearnTransformerWrapper(\n", - " transformer=Binarizer(threshold=0), variables=skewed\n", - ")\n", - "\n", - "\n", - "X_train = binarizer.fit_transform(X_train)\n", - "X_test = binarizer.transform(X_test)\n", - "\n", - "X_train[skewed].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Categorical variables\n", - "\n", - "### Apply mappings\n", - "\n", - "These are variables which values have an assigned order, related to quality. For more information, check Kaggle website." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# re-map strings to numbers, which determine quality\n", - "\n", - "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", - "\n", - "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", - " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", - " 'GarageQual', 'GarageCond',\n", - " ]\n", - "\n", - "for var in qual_vars:\n", - " X_train[var] = X_train[var].map(qual_mappings)\n", - " X_test[var] = X_test[var].map(qual_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", - "\n", - "var = 'BsmtExposure'\n", - "\n", - "X_train[var] = X_train[var].map(exposure_mappings)\n", - "X_test[var] = X_test[var].map(exposure_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", - "\n", - "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n", - "\n", - "for var in finish_vars:\n", - " X_train[var] = X_train[var].map(finish_mappings)\n", - " X_test[var] = X_test[var].map(finish_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", - "\n", - "var = 'GarageFinish'\n", - "\n", - "X_train[var] = X_train[var].map(garage_mappings)\n", - "X_test[var] = X_test[var].map(garage_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n", - "\n", - "var = 'Fence'\n", - "\n", - "X_train[var] = X_train[var].map(fence_mappings)\n", - "X_test[var] = X_test[var].map(fence_mappings)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the train set\n", - "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Removing Rare Labels\n", - "\n", - "For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations. That is, all values of categorical variables that are shared by less than 1% of houses, well be replaced by the string \"Rare\".\n", - "\n", - "To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "30" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# capture all quality variables\n", - "\n", - "qual_vars = qual_vars + finish_vars + ['BsmtExposure','GarageFinish','Fence']\n", - "\n", - "# capture the remaining categorical variables\n", - "# (those that we did not re-map)\n", - "\n", - "cat_others = [\n", - " var for var in cat_vars if var not in qual_vars\n", - "]\n", - "\n", - "len(cat_others)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['MSZoning',\n", - " 'Street',\n", - " 'Alley',\n", - " 'LotShape',\n", - " 'LandContour',\n", - " 'Utilities',\n", - " 'LotConfig',\n", - " 'LandSlope',\n", - " 'Neighborhood',\n", - " 'Condition1',\n", - " 'Condition2',\n", - " 'BldgType',\n", - " 'HouseStyle',\n", - " 'RoofStyle',\n", - " 'RoofMatl',\n", - " 'Exterior1st',\n", - " 'Exterior2nd',\n", - " 'MasVnrType',\n", - " 'Foundation',\n", - " 'Heating',\n", - " 'CentralAir',\n", - " 'Electrical',\n", - " 'Functional',\n", - " 'GarageType',\n", - " 'PavedDrive',\n", - " 'PoolQC',\n", - " 'MiscFeature',\n", - " 'SaleType',\n", - " 'SaleCondition',\n", - " 'MSSubClass']" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cat_others" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'MSZoning': Index(['RL', 'RM', 'FV', 'RH'], dtype='object'),\n", - " 'Street': Index(['Pave'], dtype='object'),\n", - " 'Alley': Index(['Missing', 'Grvl', 'Pave'], dtype='object'),\n", - " 'LotShape': Index(['Reg', 'IR1', 'IR2'], dtype='object'),\n", - " 'LandContour': Index(['Lvl', 'Bnk', 'HLS', 'Low'], dtype='object'),\n", - " 'Utilities': Index(['AllPub'], dtype='object'),\n", - " 'LotConfig': Index(['Inside', 'Corner', 'CulDSac', 'FR2'], dtype='object'),\n", - " 'LandSlope': Index(['Gtl', 'Mod'], dtype='object'),\n", - " 'Neighborhood': Index(['NAmes', 'CollgCr', 'OldTown', 'Edwards', 'Somerst', 'NridgHt',\n", - " 'Gilbert', 'Sawyer', 'NWAmes', 'BrkSide', 'SawyerW', 'Crawfor',\n", - " 'Mitchel', 'Timber', 'NoRidge', 'IDOTRR', 'ClearCr', 'SWISU', 'StoneBr',\n", - " 'MeadowV', 'Blmngtn', 'BrDale'],\n", - " dtype='object'),\n", - " 'Condition1': Index(['Norm', 'Feedr', 'Artery', 'RRAn', 'PosN'], dtype='object'),\n", - " 'Condition2': Index(['Norm'], dtype='object'),\n", - " 'BldgType': Index(['1Fam', 'TwnhsE', 'Duplex', 'Twnhs', '2fmCon'], dtype='object'),\n", - " 'HouseStyle': Index(['1Story', '2Story', '1.5Fin', 'SLvl', 'SFoyer'], dtype='object'),\n", - " 'RoofStyle': Index(['Gable', 'Hip'], dtype='object'),\n", - " 'RoofMatl': Index(['CompShg'], dtype='object'),\n", - " 'Exterior1st': Index(['VinylSd', 'HdBoard', 'Wd Sdng', 'MetalSd', 'Plywood', 'CemntBd',\n", - " 'BrkFace', 'Stucco', 'WdShing', 'AsbShng'],\n", - " dtype='object'),\n", - " 'Exterior2nd': Index(['VinylSd', 'Wd Sdng', 'HdBoard', 'MetalSd', 'Plywood', 'CmentBd',\n", - " 'Wd Shng', 'BrkFace', 'Stucco', 'AsbShng'],\n", - " dtype='object'),\n", - " 'MasVnrType': Index(['None', 'BrkFace', 'Stone'], dtype='object'),\n", - " 'Foundation': Index(['PConc', 'CBlock', 'BrkTil', 'Slab'], dtype='object'),\n", - " 'Heating': Index(['GasA', 'GasW'], dtype='object'),\n", - " 'CentralAir': Index(['Y', 'N'], dtype='object'),\n", - " 'Electrical': Index(['SBrkr', 'FuseA', 'FuseF'], dtype='object'),\n", - " 'Functional': Index(['Typ', 'Min2', 'Min1', 'Mod'], dtype='object'),\n", - " 'GarageType': Index(['Attchd', 'Detchd', 'BuiltIn', 'Basment'], dtype='object'),\n", - " 'PavedDrive': Index(['Y', 'N', 'P'], dtype='object'),\n", - " 'PoolQC': Index(['Missing'], dtype='object'),\n", - " 'MiscFeature': Index(['Missing', 'Shed'], dtype='object'),\n", - " 'SaleType': Index(['WD', 'New', 'COD'], dtype='object'),\n", - " 'SaleCondition': Index(['Normal', 'Partial', 'Abnorml', 'Family'], dtype='object'),\n", - " 'MSSubClass': Int64Index([20, 60, 50, 120, 30, 160, 70, 80, 90, 190, 75, 85], dtype='int64')}" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rare_encoder = RareLabelEncoder(tol=0.01, n_categories=1, variables=cat_others)\n", - "\n", - "# find common labels\n", - "rare_encoder.fit(X_train)\n", - "\n", - "# the common labels are stored, we can save the class\n", - "# and then use it later :)\n", - "rare_encoder.encoder_dict_" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = rare_encoder.transform(X_train)\n", - "X_test = rare_encoder.transform(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Encoding of categorical variables\n", - "\n", - "Next, we need to transform the strings of the categorical variables into numbers. \n", - "\n", - "We will do it so that we capture the monotonic relationship between the label and the target.\n", - "\n", - "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'MSZoning': {'Rare': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4},\n", - " 'Street': {'Rare': 0, 'Pave': 1},\n", - " 'Alley': {'Grvl': 0, 'Pave': 1, 'Missing': 2},\n", - " 'LotShape': {'Reg': 0, 'IR1': 1, 'Rare': 2, 'IR2': 3},\n", - " 'LandContour': {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3},\n", - " 'Utilities': {'Rare': 0, 'AllPub': 1},\n", - " 'LotConfig': {'Inside': 0, 'FR2': 1, 'Corner': 2, 'Rare': 3, 'CulDSac': 4},\n", - " 'LandSlope': {'Gtl': 0, 'Mod': 1, 'Rare': 2},\n", - " 'Neighborhood': {'IDOTRR': 0,\n", - " 'MeadowV': 1,\n", - " 'BrDale': 2,\n", - " 'Edwards': 3,\n", - " 'BrkSide': 4,\n", - " 'OldTown': 5,\n", - " 'Sawyer': 6,\n", - " 'SWISU': 7,\n", - " 'NAmes': 8,\n", - " 'Mitchel': 9,\n", - " 'SawyerW': 10,\n", - " 'Rare': 11,\n", - " 'NWAmes': 12,\n", - " 'Gilbert': 13,\n", - " 'Blmngtn': 14,\n", - " 'CollgCr': 15,\n", - " 'Crawfor': 16,\n", - " 'ClearCr': 17,\n", - " 'Somerst': 18,\n", - " 'Timber': 19,\n", - " 'StoneBr': 20,\n", - " 'NridgHt': 21,\n", - " 'NoRidge': 22},\n", - " 'Condition1': {'Artery': 0,\n", - " 'Feedr': 1,\n", - " 'Norm': 2,\n", - " 'RRAn': 3,\n", - " 'Rare': 4,\n", - " 'PosN': 5},\n", - " 'Condition2': {'Rare': 0, 'Norm': 1},\n", - " 'BldgType': {'2fmCon': 0, 'Duplex': 1, 'Twnhs': 2, '1Fam': 3, 'TwnhsE': 4},\n", - " 'HouseStyle': {'SFoyer': 0,\n", - " '1.5Fin': 1,\n", - " 'Rare': 2,\n", - " '1Story': 3,\n", - " 'SLvl': 4,\n", - " '2Story': 5},\n", - " 'RoofStyle': {'Gable': 0, 'Rare': 1, 'Hip': 2},\n", - " 'RoofMatl': {'CompShg': 0, 'Rare': 1},\n", - " 'Exterior1st': {'AsbShng': 0,\n", - " 'Wd Sdng': 1,\n", - " 'WdShing': 2,\n", - " 'MetalSd': 3,\n", - " 'Stucco': 4,\n", - " 'Rare': 5,\n", - " 'HdBoard': 6,\n", - " 'Plywood': 7,\n", - " 'BrkFace': 8,\n", - " 'CemntBd': 9,\n", - " 'VinylSd': 10},\n", - " 'Exterior2nd': {'AsbShng': 0,\n", - " 'Wd Sdng': 1,\n", - " 'MetalSd': 2,\n", - " 'Wd Shng': 3,\n", - " 'Stucco': 4,\n", - " 'Rare': 5,\n", - " 'HdBoard': 6,\n", - " 'Plywood': 7,\n", - " 'BrkFace': 8,\n", - " 'CmentBd': 9,\n", - " 'VinylSd': 10},\n", - " 'MasVnrType': {'Rare': 0, 'None': 1, 'BrkFace': 2, 'Stone': 3},\n", - " 'Foundation': {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare': 3, 'PConc': 4},\n", - " 'Heating': {'Rare': 0, 'GasW': 1, 'GasA': 2},\n", - " 'CentralAir': {'N': 0, 'Y': 1},\n", - " 'Electrical': {'Rare': 0, 'FuseF': 1, 'FuseA': 2, 'SBrkr': 3},\n", - " 'Functional': {'Rare': 0, 'Min2': 1, 'Mod': 2, 'Min1': 3, 'Typ': 4},\n", - " 'GarageType': {'Rare': 0,\n", - " 'Detchd': 1,\n", - " 'Basment': 2,\n", - " 'Attchd': 3,\n", - " 'BuiltIn': 4},\n", - " 'PavedDrive': {'N': 0, 'P': 1, 'Y': 2},\n", - " 'PoolQC': {'Missing': 0, 'Rare': 1},\n", - " 'MiscFeature': {'Rare': 0, 'Shed': 1, 'Missing': 2},\n", - " 'SaleType': {'COD': 0, 'Rare': 1, 'WD': 2, 'New': 3},\n", - " 'SaleCondition': {'Rare': 0,\n", - " 'Abnorml': 1,\n", - " 'Family': 2,\n", - " 'Normal': 3,\n", - " 'Partial': 4},\n", - " 'MSSubClass': {30: 0,\n", - " 'Rare': 1,\n", - " 190: 2,\n", - " 90: 3,\n", - " 160: 4,\n", - " 50: 5,\n", - " 85: 6,\n", - " 70: 7,\n", - " 80: 8,\n", - " 20: 9,\n", - " 75: 10,\n", - " 120: 11,\n", - " 60: 12}}" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set up the encoder\n", - "cat_encoder = OrdinalEncoder(encoding_method='ordered', variables=cat_others)\n", - "\n", - "# create the mappings\n", - "cat_encoder.fit(X_train, y_train)\n", - "\n", - "# mappings are stored and class can be saved\n", - "cat_encoder.encoder_dict_" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = cat_encoder.transform(X_train)\n", - "X_test = cat_encoder.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the train set\n", - "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the test set\n", - "[var for var in X_test.columns if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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cfNy3JO3RJjyCqKo/Bs5k8Ea5E6qqhrZ730TbJlmY5OokG5KsT3JGY8wpSW5OsjbJqiQnDPU92rWvTbJiR3+YJGnXTHqaqKpuaLTdNoV9bwXO7B4VfiCwOsnKqtowNOarwIqqqiRHAZ8Fjuz6HqmqF0/heyRJPZjqRLkdVlX3VNWabvkhYCMwf2TMw0NHJXPp3lgnSZp9vQXEsCSLgWOAGxt9b0pyC/BF4DeGup7SnXa6IckbZ6JOSdLP9R4Q3eS6S4GlVbVltL+qLq+qI4E3AmcPdR1WVWPA24CPJfmFcfa/pAuSVZs3b57+HyBJe6leAyLJHAbhcPFkb5+rqmuB5yQ5pFvf1P29A7iGwRFIa7vlVTVWVWPz5s2bzvIlaa/WW0AkCXAhsLGqzhtnzHO7cSR5CbAfcH+Sg5Ls17UfAhwPbGjtQ5LUjz4nux0PnAasG5p1fRawCKCqlgFvBt6R5GcM5lm8pbuj6QXA+UkeYxBi54zc/SRJ6llvAVFV1zF4udBEY84Fzm20Xw/8Yk+lSZKmYEbuYpIkPfEYEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqSm3gIiycIkVyfZkGR9kjMaY05JcnOStUlWJTlhqO+dSb7dfd7ZV52SpLbe3kkNbAXOrKo1SQ4EVidZWVUbhsZ8FVhRVZXkKOCzwJFJng58GBgDqtt2RVU92GO9kqQhvR1BVNU9VbWmW34I2AjMHxnzcFVVtzqXQRgAvBZYWVUPdKGwEji5r1olSdubkWsQSRYDxwA3NvrelOQW4IvAb3TN84G7hobdzUi4SJL61XtAJDkAuBRYWlVbRvur6vKqOhJ4I3D2Tux/SXf9YtXmzZt3uV5J0kCvAZFkDoNwuLiqLptobFVdCzwnySHAJmDhUPeCrq213fKqGquqsXnz5k1T5ZKkPu9iCnAhsLGqzhtnzHO7cSR5CbAfcD9wJXBSkoOSHASc1LVJkmZIn3cxHQ+cBqxLsrZrOwtYBFBVy4A3A+9I8jPgEeAt3UXrB5KcDdzUbffRqnqgx1olSSN6C4iqug7IJGPOBc4dp+8i4KIeSpMkTYEzqSVJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqam3gEiyMMnVSTYkWZ/kjMaYtye5Ocm6JNcnOXqo786ufW2SVX3VKUlq6+2d1MBW4MyqWpPkQGB1kpVVtWFozHeBV1TVg0leBywHjhvqf2VV3ddjjZKkcfQWEFV1D3BPt/xQko3AfGDD0Jjrhza5AVjQVz2SpB0zI9cgkiwGjgFunGDYbwJXDK0X8JUkq5Ms6bE8SVJDn6eYAEhyAHApsLSqtowz5pUMAuKEoeYTqmpTkmcAK5PcUlXXNrZdAiwBWLRo0bTXL0l7q16PIJLMYRAOF1fVZeOMOQq4ADilqu7f1l5Vm7q/9wKXA8e2tq+q5VU1VlVj8+bNm+6fIEl7rT7vYgpwIbCxqs4bZ8wi4DLgtKq6bah9bndhmyRzgZOAb/VVqyRpe32eYjoeOA1Yl2Rt13YWsAigqpYBHwIOBj45yBO2VtUY8Ezg8q5tX+DTVfXlHmuVJI3o8y6m64BMMuZdwLsa7XcAR2+/hSRppjiTWpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVJTbwGRZGGSq5NsSLI+yRmNMW9PcnOSdUmuT3L0UN/JSW5NcnuSD/ZVpySpbd8e970VOLOq1iQ5EFidZGVVbRga813gFVX1YJLXAcuB45LsA3wCeA1wN3BTkhUj20qSetTbEURV3VNVa7rlh4CNwPyRMddX1YPd6g3Agm75WOD2qrqjqn4KXAKc0letkqTt9XkE8U+SLAaOAW6cYNhvAld0y/OBu4b67gaOG2ffS4Al3erDSW7dpWK1zSHAfbNdxGRy7mxXoFniv8/pc9h4Hb0HRJIDgEuBpVW1ZZwxr2QQECfs6P6rajmDU1OaRklWVdXYbNchtfjvc2b0GhBJ5jAIh4ur6rJxxhwFXAC8rqru75o3AQuHhi3o2iRJM6TPu5gCXAhsrKrzxhmzCLgMOK2qbhvqugk4IsnhSZ4MvBVY0VetkqTt9XkEcTxwGrAuydqu7SxgEUBVLQM+BBwMfHKQJ2ytqrGq2prkvcCVwD7ARVW1vsdatT1P22l35r/PGZCqmu0aJEm7IWdSS5KaDAhJUpMBIUlqmpGJctr9JTmSwWz1bbPdNwErqmrj7FUlaTZ5BCGSfIDB40wC/F33CfAZH5So3VmSX5/tGvZk3sUkktwGvKiqfjbS/mRgfVUdMTuVSRNL8v2qWjTbdeypPMUkgMeAQ4HvjbQ/u+uTZk2Sm8frAp45k7XsbQwIASwFvprk2/z8IYmLgOcC752toqTOM4HXAg+OtAe4fubL2XsYEKKqvpzkeQwesz58kfqmqnp09iqTAPgCcEBVrR3tSHLNjFezF/EahCSpybuYJElNBoQkqcmAkCaQZHGSb420fSTJ+5OcnuTQofYLkrywW74zySHd8vVD+3rb0PixJH8+M79E2nEGhLTzTmdwezAAVfWuqtowOqiqfrlbXAy8bah9VVX9p55rlHaaASHtvDHg4iRrk+yf5Jok270GM8nD3eI5wL/oxv9OkhOTfKEbMzfJRUn+Lsn/TXJK1/6irm1tkpuTOGlRM8aAkHbeKuDtVfXiqnpkCuM/CHy9G/9nI32/B1xVVccCrwT+a5K5wHuA/1ZVL2YQSHdPX/nSxJwHIU1svPvAp/v+8JOAf5Xk/d36UxhMVvwG8HtJFgCXVdW3p/l7pXEZENLE7gcOGml7OvDdaf6eAG+uqltH2jcmuRF4PfClJO+uqqum+bulJk8xSROoqoeBe5K8CiDJ04GTgeuAh4ADd2B3E42/EnhfupezJzmm+/sc4I6q+nPgb4CjduZ3SDvDgJAm9w7gD5KsBa4C/rCqvgN8Cli27SL1FPZzM/Bokm8m+Z2RvrOBOcDNSdZ36wD/FvhW993/HPifu/pjpKnyURuSpCaPICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlq+v+RX0cTtXuk6QAAAABJRU5ErkJggg==\n", 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\n", 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dLxIRES9knd6LqUfS6VSPHBV55GhExKg30CNH2x812iePHI2IGOUG2oMoPWq0Tx45GhExijU5US4iIl7AOj1JjaSzgdcAB/a12f50E0VFRMTw6+gyV0nzqZ4qdyHVSep3AzMarCsiIoZZp/Mg/qPtC4Attv8HcCpwbHNlRUTEcOs0IHbUfz4p6Uiqx4ke0UxJERExEnR6DuI6SQcDfwmsrtu+1khFERExIgw0D+Ik4CHbl9efJwJrgbuBzzdfXkREDJeBDjF9FfgVgKTfBD5Xtz1O/RS3iIgYnQY6xDSu5UE97wEW2F4CLGl5CFBERIxCA+1BjJPUFyJvBm5u6et4DkVERLzwDPRL/h+AH0p6hOpKph8BSDqa3O47ImJU2+0ehO3PABdTPVHuNNtuWe7C3S0rabqkWyT1Slon6aLCmHMk3SVpjaRVkk5r6Xumbl8jadlgv1hEROydAQ8T2b6z0HZvB+veCVxc3yp8ErBa0nLbvS1jfgAss21JxwNXA8fVfTtsv66D7URERAM6nSg3aLY32e6p328D1gNT28Zsb9krmUD9xLqIiBh+jQVEK0kzgROBFYW+d0m6G/gu8HstXQfWh53ulHTuUNQZERG/1nhA1JPrlgBzbW9t77d9re3jgHOBy1u6ZtjuBt4LfEHSK/tZ/5w6SFZt3rx533+BiIgxqtGAkDSeKhwWD/T0Odu3Aa+QNLn+vLH+837gVqo9kNJyC2x32+6eMmXKviw/ImJMaywgJAlYCKy3Pa+fMUfX45D0euBFwKOSDpH0orp9MjAL6C2tIyIimtHkZLdZwGxgbcus60uBLgDb84HfAS6Q9DTVPIv31Fc0vRr4qqRnqULsc21XP0VERMMaCwjbt1M9XGh3Y64Arii03wG8tqHSIiKiA0NyFVNERLzwJCAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioqixgJA0XdItknolrZN0UWHMOZLukrRG0ipJp7X0vV/ST+vX+5uqMyIiyhp7JjWwE7jYdo+kScBqSctt97aM+QGwzLYlHQ9cDRwn6WXAZUA34HrZZba3NFhvRES0aGwPwvYm2z31+23AemBq25jttl1/nEAVBgBvBZbbfqwOheXAWU3VGhERuxqScxCSZgInAisKfe+SdDfwXeD36uapwEMtwzbQFi4REdGsxgNC0kRgCTDX9tb2ftvX2j4OOBe4fA/WP6c+f7Fq8+bNe11vRERUGg0ISeOpwmGx7aW7G2v7NuAVkiYDG4HpLd3T6rbScgtsd9vunjJlyj6qPCIimryKScBCYL3tef2MOboeh6TXAy8CHgVuBM6UdIikQ4Az67aIiBgiTV7FNAuYDayVtKZuuxToArA9H/gd4AJJTwM7gPfUJ60fk3Q5sLJe7tO2H2uw1oiIaNNYQNi+HdAAY64AruinbxGwqIHSIiKiA5lJHRERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChqLCAkTZd0i6ReSeskXVQY8z5Jd0laK+kOSSe09D1Qt6+RtKqpOiMioqyxZ1IDO4GLbfdImgSslrTcdm/LmJ8Dp9veIultwALglJb+N9l+pMEaIyKiH40FhO1NwKb6/TZJ64GpQG/LmDtaFrkTmNZUPRERMThDcg5C0kzgRGDFboZ9CPhey2cDN0laLWlOg+VFRERBk4eYAJA0EVgCzLW9tZ8xb6IKiNNamk+zvVHSYcBySXfbvq2w7BxgDkBXV9c+rz8iYqxqdA9C0niqcFhse2k/Y44HvgacY/vRvnbbG+s/HwauBU4uLW97ge1u291TpkzZ118hImLMavIqJgELgfW25/UzpgtYCsy2fW9L+4T6xDaSJgBnAj9pqtaIiNhVk4eYZgGzgbWS1tRtlwJdALbnA58EDgW+XOUJO213A4cD19Zt+wPftH1Dg7VGRESbJq9iuh3QAGM+DHy40H4/cMKuS0RExFDJTOqIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKGgsISdMl3SKpV9I6SRcVxrxP0l2S1kq6Q9IJLX1nSbpH0n2SLmmqzoiIKNu/wXXvBC623SNpErBa0nLbvS1jfg6cbnuLpLcBC4BTJI0DvgS8BdgArJS0rG3ZiIhoUGN7ELY32e6p328D1gNT28bcYXtL/fFOYFr9/mTgPtv32/4VcBVwTlO1RkTErprcg3iOpJnAicCK3Qz7EPC9+v1U4KGWvg3AKf2sew4wp/64XdI9e1XsyDUZeGSoNqYrhmpLY0Z+fi9sQ/bzG4af3Yz+OhoPCEkTgSXAXNtb+xnzJqqAOG2w67e9gOrQ1KgmaZXt7uGuI/ZMfn4vbGP159doQEgaTxUOi20v7WfM8cDXgLfZfrRu3ghMbxk2rW6LiIgh0uRVTAIWAuttz+tnTBewFJht+96WrpXAMZKOknQAcD6wrKlaIyJiV03uQcwCZgNrJa2p2y4FugBszwc+CRwKfLnKE3ba7ra9U9LHgBuBccAi2+sarPWFYNQfRhvl8vN7YRuTPz/ZHu4aIiJiBMpM6oiIKEpAREREUQIiIiKKhmSiXAyepOOoZo/3zT7fCCyzvX74qooY/ep/e1OBFba3t7SfZfuG4ats6GUPYgSS9Amq24sI+L/1S8A/5MaFL2ySPjjcNUT/JP0h8I/AhcBPJLXe4uezw1PV8MlVTCOQpHuB19h+uq39AGCd7WOGp7LYW5J+YbtruOuIMklrgVNtb69vEXQN8He2/5ekf7F94vBWOLRyiGlkehY4Eniwrf2Iui9GMEl39dcFHD6UtcSg7dd3WMn2A5LOAK6RNIPq5zemJCBGprnADyT9lF/ftLALOBr42HAVFR07HHgrsKWtXcAdQ19ODMK/S3qd7TUA9Z7EO4BFwGuHtbJhkIAYgWzfIOlYqtuet56kXmn7meGrLDp0HTCx75dMK0m3Dnk1MRgXUD3L5jm2dwIXSPrq8JQ0fHIOIiIiinIVU0REFCUgIiKiKAERY56k7QOPGvQ6PyXp4/X7N0paIWmNpPWSPlW3f0DSlft62xH7Sk5SRzTvb4D/YvvHksYBrxrugiI6kT2IiAJJ76z/1/8vkr4v6fC6/VOSFkm6VdL99czbvmX+VNK9km7n+SFwGLAJwPYztnsL25sp6WZJd0n6Qf0wLSR9Q9J8Savqdb+jbh8n6X9KWlkv85EG/zpijEpARJTdDryxnjl7FfDHLX3HUc1zOBm4TNJ4SW+gevLh64C3Aye1jP88cI+kayV9RNKBhe19Efgb28cDi4H/3dI3s97W2cD8evkPAY/bPqne1u9LOmovv3PE8+QQU0TZNOBbko4ADgB+3tL3XdtPAU9JephqYtx/Aq61/SSApOcekWv705IWA2cC7wV+FzijbXunAr9dv/874C9b+q62/SzwU0n3UwXUmcDxks6rx7wUOKatzoi9koCIKPsiMM/2svp2C59q6Xuq5f0zdPDvyPbPgK9I+mtgs6RDB1FL+2QlU83KvtD2jYNYT8Sg5BBTRNlLqWavA7y/g/G3AedKOkjSJOCdfR2Szlb90HWq/+U/A/yybfk7qA5RAbwP+FFL37sl7SfplcArgHuontf+UUnj620cK2lCp18uohPZg4iAF0va0PJ5HtUew7clbQFuBnZ7fN92j6RvAT8GHgZWtnTPBj4v6Umq2zi8z/Yzv84MoLq99Ncl/XdgM9B6W/BfUN3y/SXAf7X9/yR9jercRE8dPpuBcwfzpSMGklttRIxgkr4BXGf7muGuJcaeHGKKiIii7EFERERR9iAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVH0/wG3zvMxIKcJwAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let me show you what I mean by monotonic relationship\n", - "# between labels and target\n", - "\n", - "def analyse_vars(train, y_train, var):\n", - " \n", - " # function plots median house sale price per encoded\n", - " # category\n", - " \n", - " tmp = pd.concat([X_train, np.log(y_train)], axis=1)\n", - " \n", - " tmp.groupby(var)['SalePrice'].median().plot.bar()\n", - " plt.title(var)\n", - " plt.ylim(2.2, 2.6)\n", - " plt.ylabel('SalePrice')\n", - " plt.show()\n", - " \n", - "for var in cat_others:\n", - " analyse_vars(X_train, y_train, var)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The monotonic relationship is particularly clear for the variables MSZoning and Neighborhood. Note how, the higher the integer that now represents the category, the higher the mean house sale price.\n", - "\n", - "(remember that the target is log-transformed, that is why the differences seem so small)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Scaling\n", - "\n", - "For use in linear models, features need to be either scaled. We will scale features to the minimum and maximum values:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# create scaler\n", - "scaler = MinMaxScaler()\n", - "\n", - "# fit the scaler to the train set\n", - "scaler.fit(X_train) \n", - "\n", - "# transform the train and test set\n", - "\n", - "# sklearn returns numpy arrays, so we wrap the\n", - "# array with a pandas dataframe\n", - "\n", - "X_train = pd.DataFrame(\n", - " scaler.transform(X_train),\n", - " columns=X_train.columns\n", - ")\n", - "\n", - "X_test = pd.DataFrame(\n", - " scaler.transform(X_test),\n", - " columns=X_train.columns\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldSaleTypeSaleConditionLotFrontage_naMasVnrArea_naGarageYrBlt_na
00.7500000.750.4611710.01.01.00.3333331.0000001.00.00.00.8636360.41.00.750.60.7777780.500.0147060.0491800.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666670.6666671.00.0028350.00.00.6734790.2399351.01.001.01.00.5597600.00.00.5232500.0000000.00.6666670.00.3750.3333330.6666670.4166671.00.0000000.00.750.0186921.00.750.4301830.50.51.00.1166860.0329070.00.00.00.00.00.001.00.00.5454550.6666670.750.00.00.0
10.7500000.750.4560660.01.01.00.3333330.3333331.00.00.00.3636360.41.00.750.60.4444440.750.3602940.0491800.00.00.60.60.6666670.033750.6666670.50.50.3333330.6666670.0000000.80.1428070.00.00.1147240.1723401.01.001.01.00.4345390.00.00.4061960.3333330.00.3333330.50.3750.3333330.6666670.2500001.00.0000000.00.750.4579440.50.250.2200280.50.51.00.0000000.0000000.00.00.00.00.00.751.00.00.6363640.6666670.750.00.00.0
20.9166670.750.3946990.01.01.00.0000000.3333331.00.00.00.9545450.41.01.000.60.8888890.500.0367650.0983611.00.00.30.20.6666670.257501.0000000.51.01.0000000.6666670.0000001.00.0807940.00.00.6019510.2867431.01.001.01.00.6272050.00.00.5862960.3333330.00.6666670.00.2500.3333331.0000000.3333331.00.3333330.80.750.0467290.50.500.4062060.50.51.00.2287050.1499090.00.00.00.00.00.001.00.00.0909090.6666670.750.00.00.0
30.7500000.750.4450020.01.01.00.6666670.6666671.00.00.00.4545450.41.00.750.60.6666670.500.0661760.1639340.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666671.0000001.00.2556700.00.00.0181140.2425531.01.001.01.00.5669200.00.00.5299430.3333330.00.6666670.00.3750.3333330.6666670.2500001.00.3333330.40.750.0841120.50.500.3624820.50.51.00.4690780.0457040.00.00.00.00.00.001.00.00.6363640.6666670.751.00.00.0
40.7500000.750.5776580.01.01.00.3333330.3333331.00.00.00.3636360.41.00.750.60.5555560.500.3235290.7377050.00.00.60.70.6666670.170000.3333330.50.50.3333330.6666670.0000000.60.0868180.00.00.4342780.2332241.00.751.01.00.5490260.00.00.5132160.0000000.00.6666670.00.3750.3333330.3333330.4166671.00.3333330.80.750.4112150.50.500.4062060.50.51.00.0000000.0000000.01.00.00.00.00.001.00.00.5454550.6666670.750.00.00.0
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" - ], - "text/plain": [ - " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 0.750000 0.75 0.461171 0.0 1.0 1.0 0.333333 \n", - "1 0.750000 0.75 0.456066 0.0 1.0 1.0 0.333333 \n", - "2 0.916667 0.75 0.394699 0.0 1.0 1.0 0.000000 \n", - "3 0.750000 0.75 0.445002 0.0 1.0 1.0 0.666667 \n", - "4 0.750000 0.75 0.577658 0.0 1.0 1.0 0.333333 \n", - "\n", - " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", - "0 1.000000 1.0 0.0 0.0 0.863636 0.4 \n", - "1 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", - "2 0.333333 1.0 0.0 0.0 0.954545 0.4 \n", - "3 0.666667 1.0 0.0 0.0 0.454545 0.4 \n", - "4 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", - "\n", - " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", - "0 1.0 0.75 0.6 0.777778 0.50 0.014706 \n", - "1 1.0 0.75 0.6 0.444444 0.75 0.360294 \n", - "2 1.0 1.00 0.6 0.888889 0.50 0.036765 \n", - "3 1.0 0.75 0.6 0.666667 0.50 0.066176 \n", - "4 1.0 0.75 0.6 0.555556 0.50 0.323529 \n", - "\n", - " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", - "0 0.049180 0.0 0.0 1.0 1.0 0.333333 \n", - "1 0.049180 0.0 0.0 0.6 0.6 0.666667 \n", - "2 0.098361 1.0 0.0 0.3 0.2 0.666667 \n", - "3 0.163934 0.0 0.0 1.0 1.0 0.333333 \n", - "4 0.737705 0.0 0.0 0.6 0.7 0.666667 \n", - "\n", - " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond \\\n", - "0 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", - "1 0.03375 0.666667 0.5 0.5 0.333333 0.666667 \n", - "2 0.25750 1.000000 0.5 1.0 1.000000 0.666667 \n", - "3 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", - "4 0.17000 0.333333 0.5 0.5 0.333333 0.666667 \n", - "\n", - " BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 \\\n", - "0 0.666667 1.0 0.002835 0.0 0.0 \n", - "1 0.000000 0.8 0.142807 0.0 0.0 \n", - "2 0.000000 1.0 0.080794 0.0 0.0 \n", - "3 1.000000 1.0 0.255670 0.0 0.0 \n", - "4 0.000000 0.6 0.086818 0.0 0.0 \n", - "\n", - " BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical \\\n", - "0 0.673479 0.239935 1.0 1.00 1.0 1.0 \n", - "1 0.114724 0.172340 1.0 1.00 1.0 1.0 \n", - "2 0.601951 0.286743 1.0 1.00 1.0 1.0 \n", - "3 0.018114 0.242553 1.0 1.00 1.0 1.0 \n", - "4 0.434278 0.233224 1.0 0.75 1.0 1.0 \n", - "\n", - " 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath \\\n", - "0 0.559760 0.0 0.0 0.523250 0.000000 0.0 \n", - "1 0.434539 0.0 0.0 0.406196 0.333333 0.0 \n", - "2 0.627205 0.0 0.0 0.586296 0.333333 0.0 \n", - "3 0.566920 0.0 0.0 0.529943 0.333333 0.0 \n", - "4 0.549026 0.0 0.0 0.513216 0.000000 0.0 \n", - "\n", - " FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd \\\n", - "0 0.666667 0.0 0.375 0.333333 0.666667 0.416667 \n", - "1 0.333333 0.5 0.375 0.333333 0.666667 0.250000 \n", - "2 0.666667 0.0 0.250 0.333333 1.000000 0.333333 \n", - "3 0.666667 0.0 0.375 0.333333 0.666667 0.250000 \n", - "4 0.666667 0.0 0.375 0.333333 0.333333 0.416667 \n", - "\n", - " Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish \\\n", - "0 1.0 0.000000 0.0 0.75 0.018692 1.0 \n", - "1 1.0 0.000000 0.0 0.75 0.457944 0.5 \n", - "2 1.0 0.333333 0.8 0.75 0.046729 0.5 \n", - "3 1.0 0.333333 0.4 0.75 0.084112 0.5 \n", - "4 1.0 0.333333 0.8 0.75 0.411215 0.5 \n", - "\n", - " GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF \\\n", - "0 0.75 0.430183 0.5 0.5 1.0 0.116686 \n", - "1 0.25 0.220028 0.5 0.5 1.0 0.000000 \n", - "2 0.50 0.406206 0.5 0.5 1.0 0.228705 \n", - "3 0.50 0.362482 0.5 0.5 1.0 0.469078 \n", - "4 0.50 0.406206 0.5 0.5 1.0 0.000000 \n", - "\n", - " OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC \\\n", - "0 0.032907 0.0 0.0 0.0 0.0 0.0 \n", - "1 0.000000 0.0 0.0 0.0 0.0 0.0 \n", - "2 0.149909 0.0 0.0 0.0 0.0 0.0 \n", - "3 0.045704 0.0 0.0 0.0 0.0 0.0 \n", - "4 0.000000 0.0 1.0 0.0 0.0 0.0 \n", - "\n", - " Fence MiscFeature MiscVal MoSold SaleType SaleCondition \\\n", - "0 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", - "1 0.75 1.0 0.0 0.636364 0.666667 0.75 \n", - "2 0.00 1.0 0.0 0.090909 0.666667 0.75 \n", - "3 0.00 1.0 0.0 0.636364 0.666667 0.75 \n", - "4 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", - "\n", - " LotFrontage_na MasVnrArea_na GarageYrBlt_na \n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 \n", - "3 1.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "# Conclusion\n", - "\n", - "We now have several classes with parameters learned from the training dataset, that we can store and retrieve at a later stage, so that when a colleague comes with new data, we are in a better position to score it faster.\n", - "\n", - "Still:\n", - "\n", - "- we would need to save each class\n", - "- then we could load each class\n", - "- and apply each transformation individually.\n", - "\n", - "Which sounds like a lot of work.\n", - "\n", - "The good news is, we can reduce the amount of work, if we set up all the transformations within a pipeline.\n", - "\n", - "**IMPORTANT**\n", - "\n", - "In order to set up the entire feature transformation within a pipeline, we still need to create a class that can be used within a pipeline to map the categorical variables with the arbitrary mappings, and also, to capture elapsed time between the temporal variables.\n", - "\n", - "We will take that opportunity to create an in-house package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "583px", - "left": "0px", - "right": "1324px", - "top": "107px", - "width": "212px" - }, - "toc_section_display": "block", - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Engineering with Open-Source\n", + "\n", + "In this notebook, we will reproduce the Feature Engineering Pipeline from the notebook 2 (02-Machine-Learning-Pipeline-Feature-Engineering), but we will replace, whenever possible, the manually created functions by open-source classes, and hopefully understand the value they bring forward." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reproducibility: Setting the seed\n", + "\n", + "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# data manipulation and plotting\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# for saving the pipeline\n", + "import joblib\n", + "\n", + "# from Scikit-learn\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import MinMaxScaler, Binarizer\n", + "\n", + "# from feature-engine\n", + "from feature_engine.imputation import (\n", + " AddMissingIndicator,\n", + " MeanMedianImputer,\n", + " CategoricalImputer,\n", + ")\n", + "\n", + "from feature_engine.encoding import (\n", + " RareLabelEncoder,\n", + " OrdinalEncoder,\n", + ")\n", + "\n", + "from feature_engine.transformation import (\n", + " LogTransformer,\n", + " YeoJohnsonTransformer,\n", + ")\n", + "\n", + "from feature_engine.selection import DropFeatures\n", + "from feature_engine.wrappers import SklearnTransformerWrapper\n", + "\n", + "# to visualise al the columns in the dataframe\n", + "pd.pandas.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n" + ] + }, + { + "data": { + "text/html": [ + "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
\n", + "
" + ], + "text/plain": [ + " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 1 60 RL 65.0 8450 Pave NaN Reg \n", + "1 2 20 RL 80.0 9600 Pave NaN Reg \n", + "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", + "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", + "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", + "\n", + " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", + "0 Lvl AllPub Inside Gtl CollgCr Norm \n", + "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", + "2 Lvl AllPub Inside Gtl CollgCr Norm \n", + "3 Lvl AllPub Corner Gtl Crawfor Norm \n", + "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", + "\n", + " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", + "0 Norm 1Fam 2Story 7 5 2003 \n", + "1 Norm 1Fam 1Story 6 8 1976 \n", + "2 Norm 1Fam 2Story 7 5 2001 \n", + "3 Norm 1Fam 2Story 7 5 1915 \n", + "4 Norm 1Fam 2Story 8 5 2000 \n", + "\n", + " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", + "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", + "1 1976 Gable CompShg MetalSd MetalSd None \n", + "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", + "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", + "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", + "\n", + " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", + "0 196.0 Gd TA PConc Gd TA No \n", + "1 0.0 TA TA CBlock Gd TA Gd \n", + "2 162.0 Gd TA PConc Gd TA Mn \n", + "3 0.0 TA TA BrkTil TA Gd No \n", + "4 350.0 Gd TA PConc Gd TA Av \n", + "\n", + " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", + "0 GLQ 706 Unf 0 150 856 \n", + "1 ALQ 978 Unf 0 284 1262 \n", + "2 GLQ 486 Unf 0 434 920 \n", + "3 ALQ 216 Unf 0 540 756 \n", + "4 GLQ 655 Unf 0 490 1145 \n", + "\n", + " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", + "0 GasA Ex Y SBrkr 856 854 0 \n", + "1 GasA Ex Y SBrkr 1262 0 0 \n", + "2 GasA Ex Y SBrkr 920 866 0 \n", + "3 GasA Gd Y SBrkr 961 756 0 \n", + "4 GasA Ex Y SBrkr 1145 1053 0 \n", + "\n", + " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", + "0 1710 1 0 2 1 3 \n", + "1 1262 0 1 2 0 3 \n", + "2 1786 1 0 2 1 3 \n", + "3 1717 1 0 1 0 3 \n", + "4 2198 1 0 2 1 4 \n", + "\n", + " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", + "0 1 Gd 8 Typ 0 NaN \n", + "1 1 TA 6 Typ 1 TA \n", + "2 1 Gd 6 Typ 1 TA \n", + "3 1 Gd 7 Typ 1 Gd \n", + "4 1 Gd 9 Typ 1 TA \n", + "\n", + " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", + "0 Attchd 2003.0 RFn 2 548 TA \n", + "1 Attchd 1976.0 RFn 2 460 TA \n", + "2 Attchd 2001.0 RFn 2 608 TA \n", + "3 Detchd 1998.0 Unf 3 642 TA \n", + "4 Attchd 2000.0 RFn 3 836 TA \n", + "\n", + " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", + "0 TA Y 0 61 0 0 \n", + "1 TA Y 298 0 0 0 \n", + "2 TA Y 0 42 0 0 \n", + "3 TA Y 0 35 272 0 \n", + "4 TA Y 192 84 0 0 \n", + "\n", + " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", + "0 0 0 NaN NaN NaN 0 2 2008 \n", + "1 0 0 NaN NaN NaN 0 5 2007 \n", + "2 0 0 NaN NaN NaN 0 9 2008 \n", + "3 0 0 NaN NaN NaN 0 2 2006 \n", + "4 0 0 NaN NaN NaN 0 12 2008 \n", + "\n", + " SaleType SaleCondition SalePrice \n", + "0 WD Normal 208500 \n", + "1 WD Normal 181500 \n", + "2 WD Normal 223500 \n", + "3 WD Abnorml 140000 \n", + "4 WD Normal 250000 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load dataset\n", + "data = pd.read_csv('train.csv')\n", + "\n", + "# rows and columns of the data\n", + "print(data.shape)\n", + "\n", + "# visualise the dataset\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Separate dataset into train and test\n", + "\n", + "It is important to separate our data intro training and testing set. \n", + "\n", + "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n", + "\n", + "Our feature engineering techniques will learn:\n", + "\n", + "- mean\n", + "- mode\n", + "- exponents for the yeo-johnson\n", + "- category frequency\n", + "- and category to number mappings\n", + "\n", + "from the train set.\n", + "\n", + "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1314, 79), (146, 79))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's separate into train and test set\n", + "# Remember to set the seed (random_state for this sklearn function)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n", + " data['SalePrice'], # target\n", + " test_size=0.1, # portion of dataset to allocate to test set\n", + " random_state=0, # we are setting the seed here\n", + ")\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Engineering\n", + "\n", + "In the following cells, we will engineer the variables of the House Price Dataset so that we tackle:\n", + "\n", + "1. Missing values\n", + "2. Temporal variables\n", + "3. Non-Gaussian distributed variables\n", + "4. Categorical variables: remove rare labels\n", + "5. Categorical variables: convert strings to numbers\n", + "5. Standardize the values of the variables to the same range" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Target\n", + "\n", + "We apply the logarithm" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "y_train = np.log(y_train)\n", + "y_test = np.log(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Missing values\n", + "\n", + "### Categorical variables\n", + "\n", + "We will replace missing values with the string \"missing\" in those variables with a lot of missing data. \n", + "\n", + "Alternatively, we will replace missing data with the most frequent category in those variables that contain fewer observations without values. \n", + "\n", + "This is common practice." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "44" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's identify the categorical variables\n", + "# we will capture those of type object\n", + "\n", + "cat_vars = [var for var in data.columns if data[var].dtype == 'O']\n", + "\n", + "# MSSubClass is also categorical by definition, despite its numeric values\n", + "# (you can find the definitions of the variables in the data_description.txt\n", + "# file available on Kaggle, in the same website where you downloaded the data)\n", + "\n", + "# lets add MSSubClass to the list of categorical variables\n", + "cat_vars = cat_vars + ['MSSubClass']\n", + "\n", + "# cast all variables as categorical\n", + "X_train[cat_vars] = X_train[cat_vars].astype('O')\n", + "X_test[cat_vars] = X_test[cat_vars].astype('O')\n", + "\n", + "# number of categorical variables\n", + "len(cat_vars)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PoolQC 0.995434\n", + "MiscFeature 0.961187\n", + "Alley 0.938356\n", + "Fence 0.814307\n", + "FireplaceQu 0.472603\n", + "GarageType 0.056317\n", + "GarageFinish 0.056317\n", + "GarageQual 0.056317\n", + "GarageCond 0.056317\n", + "BsmtExposure 0.025114\n", + "BsmtFinType2 0.025114\n", + "BsmtQual 0.024353\n", + "BsmtCond 0.024353\n", + "BsmtFinType1 0.024353\n", + "MasVnrType 0.004566\n", + "Electrical 0.000761\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make a list of the categorical variables that contain missing values\n", + "\n", + "cat_vars_with_na = [\n", + " var for var in cat_vars\n", + " if X_train[var].isnull().sum() > 0\n", + "]\n", + "\n", + "# print percentage of missing values per variable\n", + "X_train[cat_vars_with_na ].isnull().mean().sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# variables to impute with the string missing\n", + "with_string_missing = [\n", + " var for var in cat_vars_with_na if X_train[var].isnull().mean() > 0.1]\n", + "\n", + "# variables to impute with the most frequent category\n", + "with_frequent_category = [\n", + " var for var in cat_vars_with_na if X_train[var].isnull().mean() < 0.1]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# I print the values here, because it makes it easier for\n", + "# later when we need to add this values to a config file for \n", + "# deployment\n", + "\n", + "with_string_missing" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['MasVnrType',\n", + " 'BsmtQual',\n", + " 'BsmtCond',\n", + " 'BsmtExposure',\n", + " 'BsmtFinType1',\n", + " 'BsmtFinType2',\n", + " 'Electrical',\n", + " 'GarageType',\n", + " 'GarageFinish',\n", + " 'GarageQual',\n", + " 'GarageCond']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with_frequent_category" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Alley': 'Missing',\n", + " 'FireplaceQu': 'Missing',\n", + " 'PoolQC': 'Missing',\n", + " 'Fence': 'Missing',\n", + " 'MiscFeature': 'Missing'}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# replace missing values with new label: \"Missing\"\n", + "\n", + "# set up the class\n", + "cat_imputer_missing = CategoricalImputer(\n", + " imputation_method='missing', variables=with_string_missing)\n", + "\n", + "# fit the class to the train set\n", + "cat_imputer_missing.fit(X_train)\n", + "\n", + "# the class learns and stores the parameters\n", + "cat_imputer_missing.imputer_dict_" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# replace NA by missing\n", + "\n", + "# IMPORTANT: note that we could store this class with joblib\n", + "X_train = cat_imputer_missing.transform(X_train)\n", + "X_test = cat_imputer_missing.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'MasVnrType': 'None',\n", + " 'BsmtQual': 'TA',\n", + " 'BsmtCond': 'TA',\n", + " 'BsmtExposure': 'No',\n", + " 'BsmtFinType1': 'Unf',\n", + " 'BsmtFinType2': 'Unf',\n", + " 'Electrical': 'SBrkr',\n", + " 'GarageType': 'Attchd',\n", + " 'GarageFinish': 'Unf',\n", + " 'GarageQual': 'TA',\n", + " 'GarageCond': 'TA'}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# replace missing values with most frequent category\n", + "\n", + "# set up the class\n", + "cat_imputer_frequent = CategoricalImputer(\n", + " imputation_method='frequent', variables=with_frequent_category)\n", + "\n", + "# fit the class to the train set\n", + "cat_imputer_frequent.fit(X_train)\n", + "\n", + "# the class learns and stores the parameters\n", + "cat_imputer_frequent.imputer_dict_" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# replace NA by missing\n", + "\n", + "# IMPORTANT: note that we could store this class with joblib\n", + "X_train = cat_imputer_frequent.transform(X_train)\n", + "X_test = cat_imputer_frequent.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Alley 0\n", + "MasVnrType 0\n", + "BsmtQual 0\n", + "BsmtCond 0\n", + "BsmtExposure 0\n", + "BsmtFinType1 0\n", + "BsmtFinType2 0\n", + "Electrical 0\n", + "FireplaceQu 0\n", + "GarageType 0\n", + "GarageFinish 0\n", + "GarageQual 0\n", + "GarageCond 0\n", + "PoolQC 0\n", + "Fence 0\n", + "MiscFeature 0\n", + "dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check that we have no missing information in the engineered variables\n", + "\n", + "X_train[cat_vars_with_na].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check that test set does not contain null values in the engineered variables\n", + "\n", + "[var for var in cat_vars_with_na if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Numerical variables\n", + "\n", + "To engineer missing values in numerical variables, we will:\n", + "\n", + "- add a binary missing indicator variable\n", + "- and then replace the missing values in the original variable with the mean" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "35" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now let's identify the numerical variables\n", + "\n", + "num_vars = [\n", + " var for var in X_train.columns if var not in cat_vars and var != 'SalePrice'\n", + "]\n", + "\n", + "# number of numerical variables\n", + "len(num_vars)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LotFrontage 0.177321\n", + "MasVnrArea 0.004566\n", + "GarageYrBlt 0.056317\n", + "dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make a list with the numerical variables that contain missing values\n", + "vars_with_na = [\n", + " var for var in num_vars\n", + " if X_train[var].isnull().sum() > 0\n", + "]\n", + "\n", + "# print percentage of missing values per variable\n", + "X_train[vars_with_na].isnull().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# print, makes my life easier when I want to create the config\n", + "vars_with_na" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " LotFrontage_na MasVnrArea_na GarageYrBlt_na\n", + "930 0 0 0\n", + "656 0 0 0\n", + "45 0 0 0\n", + "1348 1 0 0\n", + "55 0 0 0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add missing indicator\n", + "\n", + "missing_ind = AddMissingIndicator(variables=vars_with_na)\n", + "\n", + "missing_ind.fit(X_train)\n", + "\n", + "X_train = missing_ind.transform(X_train)\n", + "X_test = missing_ind.transform(X_test)\n", + "\n", + "# check the binary missing indicator variables\n", + "X_train[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'LotFrontage': 69.87974098057354,\n", + " 'MasVnrArea': 103.7974006116208,\n", + " 'GarageYrBlt': 1978.2959677419356}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# then replace missing data with the mean\n", + "\n", + "# set the imputer\n", + "mean_imputer = MeanMedianImputer(\n", + " imputation_method='mean', variables=vars_with_na)\n", + "\n", + "# learn and store parameters from train set\n", + "mean_imputer.fit(X_train)\n", + "\n", + "# the stored parameters\n", + "mean_imputer.imputer_dict_" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LotFrontage 0\n", + "MasVnrArea 0\n", + "GarageYrBlt 0\n", + "dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train = mean_imputer.transform(X_train)\n", + "X_test = mean_imputer.transform(X_test)\n", + "\n", + "# IMPORTANT: note that we could save the imputers with joblib\n", + "\n", + "# check that we have no more missing values in the engineered variables\n", + "X_train[vars_with_na].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check that test set does not contain null values in the engineered variables\n", + "\n", + "[var for var in vars_with_na if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Temporal variables\n", + "\n", + "### Capture elapsed time\n", + "\n", + "There is in Feature-engine 2 classes that allow us to perform the 2 transformations below:\n", + "\n", + "- [CombineWithFeatureReference](https://feature-engine.readthedocs.io/en/latest/creation/CombineWithReferenceFeature.html) to capture elapsed time\n", + "- [DropFeatures](https://feature-engine.readthedocs.io/en/latest/selection/DropFeatures.html) to drop the unwanted features\n", + "\n", + "We will do the first one manually, so we take the opportunity to create 1 class ourselves for the course. For the second operation, we will use the DropFeatures class." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def elapsed_years(df, var):\n", + " # capture difference between the year variable\n", + " # and the year in which the house was sold\n", + " df[var] = df['YrSold'] - df[var]\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:\n", + " X_train = elapsed_years(X_train, var)\n", + " X_test = elapsed_years(X_test, var)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# now we drop YrSold\n", + "drop_features = DropFeatures(features_to_drop=['YrSold'])\n", + "\n", + "X_train = drop_features.fit_transform(X_train)\n", + "X_test = drop_features.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Numerical variable transformation\n", + "\n", + "### Logarithmic transformation\n", + "\n", + "In the previous notebook, we observed that the numerical variables are not normally distributed.\n", + "\n", + "We will transform with the logarightm the positive numerical variables in order to get a more Gaussian-like distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "log_transformer = LogTransformer(\n", + " variables=[\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"])\n", + "\n", + "X_train = log_transformer.fit_transform(X_train)\n", + "X_test = log_transformer.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check that test set does not contain null values in the engineered variables\n", + "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same for train set\n", + "[var for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"] if X_train[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Yeo-Johnson transformation\n", + "\n", + "We will apply the Yeo-Johnson transformation to LotArea." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\stats\\morestats.py:1476: RuntimeWarning: divide by zero encountered in log\n", + " loglike = -n_samples / 2 * np.log(trans.var(axis=0))\n", + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2555: RuntimeWarning: invalid value encountered in double_scalars\n", + " w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom\n", + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2148: RuntimeWarning: invalid value encountered in double_scalars\n", + " tmp1 = (x - w) * (fx - fv)\n", + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2149: RuntimeWarning: invalid value encountered in double_scalars\n", + " tmp2 = (x - v) * (fx - fw)\n" + ] + }, + { + "data": { + "text/plain": [ + "{'LotArea': -12.55283001172003}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yeo_transformer = YeoJohnsonTransformer(\n", + " variables=['LotArea'])\n", + "\n", + "X_train = yeo_transformer.fit_transform(X_train)\n", + "X_test = yeo_transformer.transform(X_test)\n", + "\n", + "# the learned parameter\n", + "yeo_transformer.lambda_dict_" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the train set\n", + "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the test set\n", + "[var for var in X_train.columns if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Binarize skewed variables\n", + "\n", + "There were a few variables very skewed, we would transform those into binary variables.\n", + "\n", + "We can perform the below transformation with open source. We can use the [Binarizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html) from Scikit-learn, in combination with the [SklearnWrapper](https://feature-engine.readthedocs.io/en/latest/wrappers/Wrapper.html) from Feature-engine to be able to apply the transformation only to a subset of features.\n", + "\n", + "Instead, we are going to do it manually, to give us another opportunity to code the class as an in-house package later in the course." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " BsmtFinSF2 LowQualFinSF EnclosedPorch 3SsnPorch ScreenPorch MiscVal\n", + "930 0 0 0 0 0 0\n", + "656 0 0 0 0 0 0\n", + "45 0 0 0 0 0 0\n", + "1348 0 0 0 0 0 0\n", + "55 0 0 0 1 0 0" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "skewed = [\n", + " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", + " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", + "]\n", + "\n", + "binarizer = SklearnTransformerWrapper(\n", + " transformer=Binarizer(threshold=0), variables=skewed\n", + ")\n", + "\n", + "\n", + "X_train = binarizer.fit_transform(X_train)\n", + "X_test = binarizer.transform(X_test)\n", + "\n", + "X_train[skewed].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Categorical variables\n", + "\n", + "### Apply mappings\n", + "\n", + "These are variables which values have an assigned order, related to quality. For more information, check Kaggle website." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# re-map strings to numbers, which determine quality\n", + "\n", + "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", + "\n", + "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", + " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", + " 'GarageQual', 'GarageCond',\n", + " ]\n", + "\n", + "for var in qual_vars:\n", + " X_train[var] = X_train[var].map(qual_mappings)\n", + " X_test[var] = X_test[var].map(qual_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", + "\n", + "var = 'BsmtExposure'\n", + "\n", + "X_train[var] = X_train[var].map(exposure_mappings)\n", + "X_test[var] = X_test[var].map(exposure_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", + "\n", + "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n", + "\n", + "for var in finish_vars:\n", + " X_train[var] = X_train[var].map(finish_mappings)\n", + " X_test[var] = X_test[var].map(finish_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", + "\n", + "var = 'GarageFinish'\n", + "\n", + "X_train[var] = X_train[var].map(garage_mappings)\n", + "X_test[var] = X_test[var].map(garage_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n", + "\n", + "var = 'Fence'\n", + "\n", + "X_train[var] = X_train[var].map(fence_mappings)\n", + "X_test[var] = X_test[var].map(fence_mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the train set\n", + "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Rare Labels\n", + "\n", + "For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations. That is, all values of categorical variables that are shared by less than 1% of houses, well be replaced by the string \"Rare\".\n", + "\n", + "To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# capture all quality variables\n", + "\n", + "qual_vars = qual_vars + finish_vars + ['BsmtExposure','GarageFinish','Fence']\n", + "\n", + "# capture the remaining categorical variables\n", + "# (those that we did not re-map)\n", + "\n", + "cat_others = [\n", + " var for var in cat_vars if var not in qual_vars\n", + "]\n", + "\n", + "len(cat_others)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['MSZoning',\n", + " 'Street',\n", + " 'Alley',\n", + " 'LotShape',\n", + " 'LandContour',\n", + " 'Utilities',\n", + " 'LotConfig',\n", + " 'LandSlope',\n", + " 'Neighborhood',\n", + " 'Condition1',\n", + " 'Condition2',\n", + " 'BldgType',\n", + " 'HouseStyle',\n", + " 'RoofStyle',\n", + " 'RoofMatl',\n", + " 'Exterior1st',\n", + " 'Exterior2nd',\n", + " 'MasVnrType',\n", + " 'Foundation',\n", + " 'Heating',\n", + " 'CentralAir',\n", + " 'Electrical',\n", + " 'Functional',\n", + " 'GarageType',\n", + " 'PavedDrive',\n", + " 'PoolQC',\n", + " 'MiscFeature',\n", + " 'SaleType',\n", + " 'SaleCondition',\n", + " 'MSSubClass']" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_others" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'MSZoning': Index(['RL', 'RM', 'FV', 'RH'], dtype='object'),\n", + " 'Street': Index(['Pave'], dtype='object'),\n", + " 'Alley': Index(['Missing', 'Grvl', 'Pave'], dtype='object'),\n", + " 'LotShape': Index(['Reg', 'IR1', 'IR2'], dtype='object'),\n", + " 'LandContour': Index(['Lvl', 'Bnk', 'HLS', 'Low'], dtype='object'),\n", + " 'Utilities': Index(['AllPub'], dtype='object'),\n", + " 'LotConfig': Index(['Inside', 'Corner', 'CulDSac', 'FR2'], dtype='object'),\n", + " 'LandSlope': Index(['Gtl', 'Mod'], dtype='object'),\n", + " 'Neighborhood': Index(['NAmes', 'CollgCr', 'OldTown', 'Edwards', 'Somerst', 'NridgHt',\n", + " 'Gilbert', 'Sawyer', 'NWAmes', 'BrkSide', 'SawyerW', 'Crawfor',\n", + " 'Mitchel', 'Timber', 'NoRidge', 'IDOTRR', 'ClearCr', 'SWISU', 'StoneBr',\n", + " 'MeadowV', 'Blmngtn', 'BrDale'],\n", + " dtype='object'),\n", + " 'Condition1': Index(['Norm', 'Feedr', 'Artery', 'RRAn', 'PosN'], dtype='object'),\n", + " 'Condition2': Index(['Norm'], dtype='object'),\n", + " 'BldgType': Index(['1Fam', 'TwnhsE', 'Duplex', 'Twnhs', '2fmCon'], dtype='object'),\n", + " 'HouseStyle': Index(['1Story', '2Story', '1.5Fin', 'SLvl', 'SFoyer'], dtype='object'),\n", + " 'RoofStyle': Index(['Gable', 'Hip'], dtype='object'),\n", + " 'RoofMatl': Index(['CompShg'], dtype='object'),\n", + " 'Exterior1st': Index(['VinylSd', 'HdBoard', 'Wd Sdng', 'MetalSd', 'Plywood', 'CemntBd',\n", + " 'BrkFace', 'Stucco', 'WdShing', 'AsbShng'],\n", + " dtype='object'),\n", + " 'Exterior2nd': Index(['VinylSd', 'Wd Sdng', 'HdBoard', 'MetalSd', 'Plywood', 'CmentBd',\n", + " 'Wd Shng', 'BrkFace', 'Stucco', 'AsbShng'],\n", + " dtype='object'),\n", + " 'MasVnrType': Index(['None', 'BrkFace', 'Stone'], dtype='object'),\n", + " 'Foundation': Index(['PConc', 'CBlock', 'BrkTil', 'Slab'], dtype='object'),\n", + " 'Heating': Index(['GasA', 'GasW'], dtype='object'),\n", + " 'CentralAir': Index(['Y', 'N'], dtype='object'),\n", + " 'Electrical': Index(['SBrkr', 'FuseA', 'FuseF'], dtype='object'),\n", + " 'Functional': Index(['Typ', 'Min2', 'Min1', 'Mod'], dtype='object'),\n", + " 'GarageType': Index(['Attchd', 'Detchd', 'BuiltIn', 'Basment'], dtype='object'),\n", + " 'PavedDrive': Index(['Y', 'N', 'P'], dtype='object'),\n", + " 'PoolQC': Index(['Missing'], dtype='object'),\n", + " 'MiscFeature': Index(['Missing', 'Shed'], dtype='object'),\n", + " 'SaleType': Index(['WD', 'New', 'COD'], dtype='object'),\n", + " 'SaleCondition': Index(['Normal', 'Partial', 'Abnorml', 'Family'], dtype='object'),\n", + " 'MSSubClass': Int64Index([20, 60, 50, 120, 30, 160, 70, 80, 90, 190, 75, 85], dtype='int64')}" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rare_encoder = RareLabelEncoder(tol=0.01, n_categories=1, variables=cat_others)\n", + "\n", + "# find common labels\n", + "rare_encoder.fit(X_train)\n", + "\n", + "# the common labels are stored, we can save the class\n", + "# and then use it later :)\n", + "rare_encoder.encoder_dict_" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = rare_encoder.transform(X_train)\n", + "X_test = rare_encoder.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Encoding of categorical variables\n", + "\n", + "Next, we need to transform the strings of the categorical variables into numbers. \n", + "\n", + "We will do it so that we capture the monotonic relationship between the label and the target.\n", + "\n", + "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'MSZoning': {'Rare': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4},\n", + " 'Street': {'Rare': 0, 'Pave': 1},\n", + " 'Alley': {'Grvl': 0, 'Pave': 1, 'Missing': 2},\n", + " 'LotShape': {'Reg': 0, 'IR1': 1, 'Rare': 2, 'IR2': 3},\n", + " 'LandContour': {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3},\n", + " 'Utilities': {'Rare': 0, 'AllPub': 1},\n", + " 'LotConfig': {'Inside': 0, 'FR2': 1, 'Corner': 2, 'Rare': 3, 'CulDSac': 4},\n", + " 'LandSlope': {'Gtl': 0, 'Mod': 1, 'Rare': 2},\n", + " 'Neighborhood': {'IDOTRR': 0,\n", + " 'MeadowV': 1,\n", + " 'BrDale': 2,\n", + " 'Edwards': 3,\n", + " 'BrkSide': 4,\n", + " 'OldTown': 5,\n", + " 'Sawyer': 6,\n", + " 'SWISU': 7,\n", + " 'NAmes': 8,\n", + " 'Mitchel': 9,\n", + " 'SawyerW': 10,\n", + " 'Rare': 11,\n", + " 'NWAmes': 12,\n", + " 'Gilbert': 13,\n", + " 'Blmngtn': 14,\n", + " 'CollgCr': 15,\n", + " 'Crawfor': 16,\n", + " 'ClearCr': 17,\n", + " 'Somerst': 18,\n", + " 'Timber': 19,\n", + " 'StoneBr': 20,\n", + " 'NridgHt': 21,\n", + " 'NoRidge': 22},\n", + " 'Condition1': {'Artery': 0,\n", + " 'Feedr': 1,\n", + " 'Norm': 2,\n", + " 'RRAn': 3,\n", + " 'Rare': 4,\n", + " 'PosN': 5},\n", + " 'Condition2': {'Rare': 0, 'Norm': 1},\n", + " 'BldgType': {'2fmCon': 0, 'Duplex': 1, 'Twnhs': 2, '1Fam': 3, 'TwnhsE': 4},\n", + " 'HouseStyle': {'SFoyer': 0,\n", + " '1.5Fin': 1,\n", + " 'Rare': 2,\n", + " '1Story': 3,\n", + " 'SLvl': 4,\n", + " '2Story': 5},\n", + " 'RoofStyle': {'Gable': 0, 'Rare': 1, 'Hip': 2},\n", + " 'RoofMatl': {'CompShg': 0, 'Rare': 1},\n", + " 'Exterior1st': {'AsbShng': 0,\n", + " 'Wd Sdng': 1,\n", + " 'WdShing': 2,\n", + " 'MetalSd': 3,\n", + " 'Stucco': 4,\n", + " 'Rare': 5,\n", + " 'HdBoard': 6,\n", + " 'Plywood': 7,\n", + " 'BrkFace': 8,\n", + " 'CemntBd': 9,\n", + " 'VinylSd': 10},\n", + " 'Exterior2nd': {'AsbShng': 0,\n", + " 'Wd Sdng': 1,\n", + " 'MetalSd': 2,\n", + " 'Wd Shng': 3,\n", + " 'Stucco': 4,\n", + " 'Rare': 5,\n", + " 'HdBoard': 6,\n", + " 'Plywood': 7,\n", + " 'BrkFace': 8,\n", + " 'CmentBd': 9,\n", + " 'VinylSd': 10},\n", + " 'MasVnrType': {'Rare': 0, 'None': 1, 'BrkFace': 2, 'Stone': 3},\n", + " 'Foundation': {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare': 3, 'PConc': 4},\n", + " 'Heating': {'Rare': 0, 'GasW': 1, 'GasA': 2},\n", + " 'CentralAir': {'N': 0, 'Y': 1},\n", + " 'Electrical': {'Rare': 0, 'FuseF': 1, 'FuseA': 2, 'SBrkr': 3},\n", + " 'Functional': {'Rare': 0, 'Min2': 1, 'Mod': 2, 'Min1': 3, 'Typ': 4},\n", + " 'GarageType': {'Rare': 0,\n", + " 'Detchd': 1,\n", + " 'Basment': 2,\n", + " 'Attchd': 3,\n", + " 'BuiltIn': 4},\n", + " 'PavedDrive': {'N': 0, 'P': 1, 'Y': 2},\n", + " 'PoolQC': {'Missing': 0, 'Rare': 1},\n", + " 'MiscFeature': {'Rare': 0, 'Shed': 1, 'Missing': 2},\n", + " 'SaleType': {'COD': 0, 'Rare': 1, 'WD': 2, 'New': 3},\n", + " 'SaleCondition': {'Rare': 0,\n", + " 'Abnorml': 1,\n", + " 'Family': 2,\n", + " 'Normal': 3,\n", + " 'Partial': 4},\n", + " 'MSSubClass': {30: 0,\n", + " 'Rare': 1,\n", + " 190: 2,\n", + " 90: 3,\n", + " 160: 4,\n", + " 50: 5,\n", + " 85: 6,\n", + " 70: 7,\n", + " 80: 8,\n", + " 20: 9,\n", + " 75: 10,\n", + " 120: 11,\n", + " 60: 12}}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# set up the encoder\n", + "cat_encoder = OrdinalEncoder(encoding_method='ordered', variables=cat_others)\n", + "\n", + "# create the mappings\n", + "cat_encoder.fit(X_train, y_train)\n", + "\n", + "# mappings are stored and class can be saved\n", + "cat_encoder.encoder_dict_" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = cat_encoder.transform(X_train)\n", + "X_test = cat_encoder.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the train set\n", + "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the test set\n", + "[var for var in X_test.columns if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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P9xeTJO2bKQ8TVdVdLW33DrDvXcAlzaPCjwHGkqypqi19Y74GrK6qSvIq4HpgcdP3VFW9eoDvkSR1YNAb5aatqh6uqg3N8k5gKzB/3Jgn+2Ylc2neWCdJGr7OAqJfkkXAqcDalr63JfkO8EXgvX1dRzaHne5Kcu5M1ClJ+pnOA6K5uW4VsLyqdozvr6obq2oxcC5wWV/XwqoaBd4FfDTJL06w/2VNkKzfvn37/v8FJOkQ1WlAJJlDLxyunertc1V1O/CSJMc169uan/cDt9GbgbRtt7KqRqtqdN68efuzfEk6pHUWEEkCXA1sraorJhjz0mYcSV4DHAE8luTYJEc07ccBS4AtbfuQJHWjy5vdlgBLgU19d12vAEYAqupK4O3ABUl+Qu8+i3c0VzS9HPhkkmfphdhHxl39JEnqWGcBUVV30Hu50GRjLgcub2m/E3hlR6VJkgYwI1cxSZIOPAaEJKmVASFJamVASJJaGRCSpFYGhCSplQEhSWplQEiSWhkQkqRWBoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJaGRCSpFYGhCSpVWcBkWRBkluTbEmyOcnFLWPOSXJ3ko1J1ic5va/vwiTfbT4XdlWnJKldZ++kBnYBl1TVhiTHAGNJ1lTVlr4xXwNWV1UleRVwPbA4yfOBDwKjQDXbrq6qJzqsV5LUp7MZRFU9XFUbmuWdwFZg/rgxT1ZVNatz6YUBwJuBNVX1eBMKa4Czu6pVkrSnGTkHkWQRcCqwtqXvbUm+A3wReG/TPB94sG/YQ4wLF0lStzoPiCRHA6uA5VW1Y3x/Vd1YVYuBc4HL9mL/y5rzF+u3b9++z/VKkno6DYgkc+iFw7VVdcNkY6vqduAlSY4DtgEL+rpPbNratltZVaNVNTpv3rz9VLkkqcurmAJcDWytqismGPPSZhxJXgMcATwG3AycleTYJMcCZzVtkqQZ0uVVTEuApcCmJBubthXACEBVXQm8HbggyU+Ap4B3NCetH09yGbCu2e5DVfV4h7VKksbpLCCq6g4gU4y5HLh8gr5rgGs6KE2SNADvpJYktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrToLiCQLktyaZEuSzUkubhnz7iR3J9mU5M4kp/T1fa9p35hkfVd1SpLadfZOamAXcElVbUhyDDCWZE1Vbekb8wDwxqp6IslbgJXA6/v6z6yqRzusUZI0gc4CoqoeBh5ulncm2QrMB7b0jbmzb5O7gBO7qkeSND0zcg4iySLgVGDtJMN+E/hy33oBX0kylmRZh+VJklp0eYgJgCRHA6uA5VW1Y4IxZ9ILiNP7mk+vqm1JXgisSfKdqrq9ZdtlwDKAkZGR/V6/JB2qOp1BJJlDLxyuraobJhjzKuAq4Jyqemx3e1Vta34+AtwInNa2fVWtrKrRqhqdN2/e/v4VJOmQ1eVVTAGuBrZW1RUTjBkBbgCWVtW9fe1zmxPbJJkLnAV8u6taJUl76vIQ0xJgKbApycambQUwAlBVVwIfAF4AfKKXJ+yqqlHgeODGpu1w4NNVdVOHtUqSxunyKqY7gEwx5reA32ppvx84Zc8tJEkzxTupJUmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1KqzgEiyIMmtSbYk2Zzk4pYx705yd5JNSe5Mckpf39lJ7klyX5JLu6pTktTu8A73vQu4pKo2JDkGGEuypqq29I15AHhjVT2R5C3ASuD1SQ4DPg68CXgIWJdk9bhtJUkd6mwGUVUPV9WGZnknsBWYP27MnVX1RLN6F3Bis3wacF9V3V9VTwPXAed0VaskaU9dziB+Kski4FRg7STDfhP4crM8H3iwr+8h4PUT7HsZsKxZfTLJPftU7Ox1HPDoTH1ZLp+pbzpk+Pc7sM3Y328If7uFE3V0HhBJjgZWAcurascEY86kFxCnT3f/VbWS3qGpg1qS9VU1Ouw6tHf8+x3YDtW/X6cBkWQOvXC4tqpumGDMq4CrgLdU1WNN8zZgQd+wE5s2SdIM6fIqpgBXA1ur6ooJxowANwBLq+revq51wMlJTkryXOB8YHVXtUqS9tTlDGIJsBTYlGRj07YCGAGoqiuBDwAvAD7RyxN2VdVoVe1K8j7gZuAw4Jqq2txhrQeCg/4w2kHOv9+B7ZD8+6Wqhl2DJGkW8k5qSVIrA0KS1MqAkCS1mpEb5TR9SRbTu3t8993n24DVVbV1eFVJB7/mv735wNqqerKv/eyquml4lc08ZxCzUJL303u8SIBvNp8Af+mDCw9sSX5j2DVoYkl+B/g8cBHw7ST9j/j58HCqGh6vYpqFktwLvKKqfjKu/bnA5qo6eTiVaV8l+X5VjQy7DrVLsgl4Q1U92Twi6LPAX1TV/0ryt1V16nArnFkeYpqdngVOAP5+XPuLmz7NYknunqgLOH4ma9G0PWf3YaWq+l6SM4DPJllI7+93SDEgZqflwNeSfJefPbRwBHgp8L5hFaWBHQ+8GXhiXHuAO2e+HE3D/0ny6qraCNDMJN4KXAO8cqiVDYEBMQtV1U1JXkbvsef9J6nXVdUzw6tMA/oCcPTuf2T6JbltxqvRdFxA7102P1VVu4ALknxyOCUNj+cgJEmtvIpJktTKgJAktTIgpH2Q5Nwk1dxcRZJFSb7dLJ+R5AvDrVDaewaEtG/eCdzR/JQOKgaEtJea1+meTu91uedPMXZukmuSfDPJ3+6+QzfJ7Ule3TfujiSndFm3NCgDQtp75wA3NW9DfCzJaycZ+/vALVV1GnAm8D+SzKX31sX3ADSXNh9ZVd/qtmxpMAaEtPfeSe+ZWTQ/JzvMdBZwafN2xduAI+nd/PgZ4K3N+9vfC3yqo1qlafNGOWkvJHk+8MvAK5MUvVfjFvDxiTYB3l5V97Tsaw292civA5PNQqQZ5QxC2jvn0XuI28KqWlRVC4AHgAUTjL8ZuCjNy9eT9D/07SrgD+ndKT/+8RzS0BgQ0t55J3DjuLZVwO9NMP4yYA5wd5LNzToAVTUG7AD+tIM6pb3mozakIUtyAr3zEouryqf1atZwBiENUZILgLXA7xsOmm2cQUiSWjmDkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmt/j8/4ztZKln+GwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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k6H8GFgAvlfQzqushTu19lYiI2J51ei+mpZKOpXrkqMgjRyMidnh9PXK0/VGj3fLI0YiIHVxfexClR412yyNHIyJ2YE1eKBcREduxTk9SI+mtwKuAXbvLbH+2iUFFRMTQ62iaq6QLqZ4q92Gqk9TvBA5scFwRETHEOr0O4m9tnwk8ZftfgdcBBzc3rIiIGGqdBsSm+vvvJI2jepzoAc0MKSIiXgg6PQfxQ0l7Af8DWFKXXdTIiCIi4gWhr+sgjgQesf25+vVoYDlwN/DF5ocXERFDpa9DTF8H/ggg6e+BmXXZ08CcZocWERFDqa9DTCNaHtRzGjDH9nxgfstDgCIiYgfU1x7ECEndIXI8cH1LXcfXUERExPanrw/57wI3SXqcaibTTwEkvYzc7jsiYofW6x6E7c8D51E9Ue4Y225Z78O9rStpoqQbJK2UtELSuYU2J0u6U9IySYslHdNS92xdvkzSgv6+sYiI2DZ9HiayfVuh7N4Otr0ZOK++VfgewBJJC22vbGlzHbDAtiUdClwKTKnrNtk+vIN+IiKiAZ1eKNdvttfaXlovbwBWAePb2mxs2SsZRf3EuoiIGHqNBUQrSZOBI4DbC3Vvl3Q38CPg/S1Vu9aHnW6TdMpgjDMiIv6i8YCoL66bD8ywvb693vYVtqcApwCfa6k60HYX8C7gS5Je2sP2p9dBsnjdunUD/wYiIoapRgNC0kiqcJjX19PnbN8MvETSmPr1mvr7g8CNVHsgpfXm2O6y3TV27NiBHH5ExLDWWEBIEjAXWGV7dg9tXla3Q9JrgF2AJyTtLWmXunwMMBVYWdpGREQ0o8mL3aYC04DlLVddXwBMArB9IfCPwJmS/kR1ncVp9YymQ4CvS3qOKsRmts1+ioiIhjUWELZvoXq4UG9tZgGzCuW3Aq9uaGgREdGBQZnFFBER258EREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUNRYQkiZKukHSSkkrJJ1baHOypDslLZO0WNIxLXVnSbqv/jqrqXFGRERZY8+kBjYD59leKmkPYImkhbZXtrS5Dlhg25IOBS4FpkjaB/g00AW4XneB7acaHG9ERLRobA/C9lrbS+vlDcAqYHxbm422Xb8cRRUGAG8GFtp+sg6FhcCJTY01IiK2NCjnICRNBo4Abi/UvV3S3cCPgPfXxeOBR1qaraYtXCIiolmNB4Sk0cB8YIbt9e31tq+wPQU4BfjcVmx/en3+YvG6deu2ebwREVFpNCAkjaQKh3m2L++tre2bgZdIGgOsASa2VE+oy0rrzbHdZbtr7NixAzTyiIhochaTgLnAKtuze2jzsrodkl4D7AI8AVwDnCBpb0l7AyfUZRERMUianMU0FZgGLJe0rC67AJgEYPtC4B+BMyX9CdgEnFaftH5S0ueARfV6n7X9ZINjjYiINo0FhO1bAPXRZhYwq4e6i4GLGxhaRER0IFdSR0REUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooaCwhJEyXdIGmlpBWSzi20ebekOyUtl3SrpMNa6h6qy5dJWtzUOCMioqyxZ1IDm4HzbC+VtAewRNJC2ytb2vwKONb2U5JOAuYAR7fUv9724w2OMSIietBYQNheC6ytlzdIWgWMB1a2tLm1ZZXbgAlNjSciIvpnUM5BSJoMHAHc3kuzDwBXt7w2cK2kJZKmNzi8iIgoaPIQEwCSRgPzgRm21/fQ5vVUAXFMS/ExttdI2g9YKOlu2zcX1p0OTAeYNGnSgI8/ImK4anQPQtJIqnCYZ/vyHtocClwEnGz7ie5y22vq748BVwBHlda3Pcd2l+2usWPHDvRbiIgYtpqcxSRgLrDK9uwe2kwCLgem2b63pXxUfWIbSaOAE4C7mhprRERsqclDTFOBacByScvqsguASQC2LwQ+BewLfLXKEzbb7gL2B66oy3YGvmP7xw2ONSIi2jQ5i+kWQH20ORs4u1D+IHDYlmtERMRgyZXUERFRlICIiIiiBERERBQlICIioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFDUWEJImSrpB0kpJKySdW2jzbkl3Slou6VZJh7XUnSjpHkn3Szq/qXFGRETZzg1uezNwnu2lkvYAlkhaaHtlS5tfAcfafkrSScAc4GhJI4CvAG8CVgOLJC1oWzciIhrU2B6E7bW2l9bLG4BVwPi2Nrfafqp+eRswoV4+Crjf9oO2/whcApzc1FgjImJLst18J9Jk4Gbgr22v76HNR4Epts+WdCpwou2z67ppwNG2zymsNx2YXr98BXDPVg5zDPD4Vq67LYaq36HsO+95x+93KPvOe+6fA22PLVU0eYgJAEmjgfnAjF7C4fXAB4Bj+rt923OoDk1tE0mLbXdt63a2l36Hsu+85x2/36HsO+954DQaEJJGUoXDPNuX99DmUOAi4CTbT9TFa4CJLc0m1GURETFImpzFJGAusMr27B7aTAIuB6bZvrelahHwckkHSXoRcDqwoKmxRkTElprcg5gKTAOWS1pWl10ATAKwfSHwKWBf4KtVnrDZdpftzZLOAa4BRgAX217R4FhhAA5TbWf9DmXfec87fr9D2Xfe8wAZlJPUERGx/cmV1BERUZSAiIiIogREREQUNX4dxAuRpClUV2Z3X9m9Blhge9XQjapZ9XseD9xue2NL+Ym2f9xw30cBtr1I0iuBE4G7bV/VZL+Fcfxf22cOZp91v8dQ3R3gLtvXNtjP0VSzBtdL2g04H3gNsBL4gu2nG+z7I8AVth9pqo8e+u2e5fhr2z+R9C7gb6nu3DDH9p8a7PslwDuopuQ/C9wLfKen6722R8PuJLWkjwNnUN2+Y3VdPIHql+wS2zOHaFzvs/3Nhrb9EeBDVP9pDgfOtf39um6p7dc00W+9/U8DJ1H9MbIQOBq4geo+W9fY/nxD/bZPixbweuB6ANv/oYl+675/YfuoevmDVD/7K4ATgB809TsmaQVwWD0LcA7wO+B7wPF1+Tua6Lfu+2ngGeAB4LvAZbbXNdVfS7/zqH63dgd+C4ymmjp/PNXn21kN9fsR4G1Ud4h4C/DLuv+3A/9s+8Ym+h10tofVF1XKjyyUvwi4bwjH9XCD214OjK6XJwOLqUIC4JcNv6/lVFOVdwfWA39Vl+8G3Nlgv0uBbwPHAcfW39fWy8c2/J5/2bK8CBhbL48CljfY76rW999Wt6zp90x1yPoEquuf1gE/Bs4C9miw3zvr7zsDjwIj6tdq+PdreUtfuwM31suTBuH/1J7ATOBu4EngCao//mYCew1kX8PxHMRzwLhC+QF1XWPqW5uXvpYD+zfY9U6uDyvZfojqw/IkSbOp/iM1abPtZ23/DnjA9e637U00+/PuApYA/xV42tVfdJts32T7pgb7BdhJ0t6S9qX6K3YdgO1nqO5y3JS7JL2vXr5DUheApIOBxg611Gz7OdvX2v4A1f+xr1IdTnywwX53qg8z7UH1Qb1nXb4LMLLBfuEvh+h3odpzwfbDg9DvpcBTwHG297G9L9Xe8VN13YAZjucgZgDXSboP6D5eOgl4GbDFzQAH2P7Am6n+IVsJuLXBfh+VdLjtZQC2N0p6G3Ax8OoG+wX4o6Td64B4bXehpD1pMCBsPwd8UdJl9fdHGbzf9z2pwkmAJR1ge219X7ImA/ls4N8kfZLqxm0/l/QI1e/52Q32C23vy9Wx/wXAAkm7N9jvXKq/pEdQ/TFwmaQHgb+hOozclIuoHkNwO/B3wCwASWOp/qpv0mTbs1oLbP8GmCXp/QPZ0bA7BwEgaSeqk4atJ6kX2X624X7nAt+0fUuh7ju239VQvxOo/pL/TaFuqu2fNdFvvf1dbP+hUD4GOMD28qb6buvvrcBU2xcMRn89jGF3YH/bv2q4n78CDqIKxNW2H22yv7rPg/382+UMGknjAGz/WtJewBupDtn+ouF+XwUcQjX54O4m+2rr91rgJ8D/6f63lbQ/8F7gTbbfOGB9DceAiIjYXknam2qG2snAfnXxo1R7bDP9l2fsbHtfCYiIiB3DQM+GTEBEROwgJD1se9JAbW84nqSOiNhuSbqzpyoGeDZkAiIiYvsyaLMhExAREduXH1Jd+LqsvULSjQPZUc5BRERE0XC8kjoiIjqQgIiIiKIERAxrkp6VtKzl6/w+2m/VldiSLqpvdd6fdc6RdL8k11ee99Z2cn2r64gBk3MQMaxJ2mh7dFPt63VG9Pc2LpJGAIdSzVS5Eeiy/Xgv7Y8DPmr7bf3pJ6I32YOIaCNpT0n3SHpF/fq7kj4oaSawW72nMa+ue4+kX9RlX68/2JG0UdL/lHQH8DpJN7bcXfUMScsl3SVpVku/z1vH9i/ru++2j+/Ylj2eX0rag+pWz39Xl/1L0z+jGB4SEDHc7abnH2I6zdWT184BviXpdGBv29+wfT7VLcMPt/1uSYcAp1HdBPBwqqeKvbve7iiqp/cd1npzxvrGcrOAN1A9vOlISaf0tk7BR4EP1X3+HbCJ6t48P63H9sVt/7FE5DqIiE31B+3z2F4o6Z3AV4DDelj3eKpbmC+SBNVDkB6r654F5hfWOZLq4TLr4M9PRPt74Mpe1mn3M2B2ve7ltlfX/UcMqAREREF9S/hDqB7buTd/eTzt85pR3XL5E4W632/F7eM7Wsf2TEk/onrU5c8kvbmf/UR0JIeYIsr+heoxju8Cvimp+ylhf2pZvg44VdJ+AJL2kXRgH9v9BXCspDH1+YozgH494U7SS20vrx8aswiYAmygeqpaxIBJQMRw134OYmZ9cvps4DzbP6V6MP0n6/ZzgDslzbO9si6/tr6B2kKqR9f2yPZaqvMFNwB3AEtsf7/UVtJHJK0GJtR9XlRXzahPcN9J9SjRq4E7gWcl3ZGT1DFQMs01IiKKsgcRERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBQlICIiouj/A4VUz43wN/ArAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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qNSD2KzfrexzA9lbgsdaqioiIMbc9jxzdl+bENJJeCfyqtaoiImLM9frI0b8GFgG/L+l7NPMhTh1+lYiIeDrr9V5MyyUdT/PIUZFHjkZE7PZGeuRo96NGB+WRoxERu7mR9iBqjxodlEeORkTsxtqcKBcREU9jvZ6kRtIfA4cDzxpss/2JNoqKiIix19NlrpLm0zxV7gyak9T/AZjeYl0RETHGep0H8Ye2Twcesf1x4FXAC9srKyIixlqvAbGl/PyNpINoHid6YDslRUTErqDXcxDflLQ38LfAstJ2XisVRUTELmGkeRBHA/fbPru8nwysBH4MfKb98iIiYqyMdIjpXOBRAEmvBj5d2n5FeYpbRETsnkY6xDSh40E9pwELbC8EFnY8BCgiInZDI+1BTJA0GCJ/BFzX0dfzHIqIiHj6GelL/mvAdyU9RHMl000Akg4ht/uOiNitDbsHYfuTwFyaJ8odZ9sd650x3LqSpkm6XtJqSasknVkZc7Kk2yWtkLRU0nEdfY+V9hWSFm3vLxYRETtmxMNEtm+ttN3Vw7a3AnPLrcKnAMskLba9umPMd4BFti3pZcAlwGGlb4vtI3v4nIiIaEGvE+W2m+11tpeX5U3AGmBq15jNHXslkyhPrIuIiLHXWkB0kjQDOAq4rdL3Fkk/Br4FvLej61nlsNOtkk4ZjTojIuIJrQdEmVy3EJhje2N3v+3LbR8GnAKc3dE13fYA8A7gs5J+f4jtzy5BsnT9+vU7/xeIiBinWg0ISRNpwuHCkZ4+Z/tG4AWS9ivv15af9wA30OyB1NZbYHvA9kBfX9/OLD8iYlxrLSAkCTgfWGN73hBjDinjkPRy4JnABkn7SHpmad8PmAmsrm0jIiLa0eZkt5nALGBlx6zrs4B+ANvzgbcCp0v6Lc08i9PKFU0vBs6V9DhNiH266+qniIhoWWsBYftmmocLDTfmHOCcSvstwEtbKi0iInowKlcxRUTE008CIiIiqhIQERFRlYCIiIiqBERERFQlICIioioBERERVQmIiIioSkBERERVAiIiIqoSEBERUZWAiIiIqgRERERUJSAiIqIqAREREVUJiIiIqEpAREREVQIiIiKqWgsISdMkXS9ptaRVks6sjDlZ0u2SVkhaKum4jr53SfpJeb2rrTojIqKutWdSA1uBubaXS5oCLJO02PbqjjHfARbZtqSXAZcAh0l6LvBRYABwWXeR7UdarDciIjq0tgdhe53t5WV5E7AGmNo1ZrNtl7eTaMIA4A3AYtsPl1BYDJzYVq0REbGtUTkHIWkGcBRwW6XvLZJ+DHwLeG9pngrc3zHsAbrCJSIi2tV6QEiaDCwE5tje2N1v+3LbhwGnAGc/he3PLucvlq5fv36H642IiEarASFpIk04XGj7suHG2r4ReIGk/YC1wLSO7oNLW229BbYHbA/09fXtpMojIqLNq5gEnA+ssT1viDGHlHFIejnwTGADcDVwgqR9JO0DnFDaIiJilLR5FdNMYBawUtKK0nYW0A9gez7wVuB0Sb8FtgCnlZPWD0s6G1hS1vuE7YdbrDUiIrq0FhC2bwY0wphzgHOG6LsAuKCF0iIiogeZSR0REVUJiIiIqEpAREREVQIiIiKqEhAREVGVgIiIiKoEREREVCUgIiKiKgERERFVCYiIiKhKQERERFUCIiIiqhIQERFRlYCIiIiqBERERFQlICIioioBERERVQmIiIioai0gJE2TdL2k1ZJWSTqzMuadkm6XtFLSLZKO6Oi7r7SvkLS0rTojIqKutWdSA1uBubaXS5oCLJO02PbqjjH3AsfbfkTSScAC4NiO/tfafqjFGiMiYgitBYTtdcC6srxJ0hpgKrC6Y8wtHavcChzcVj0REbF9RuUchKQZwFHAbcMM+3Pgyo73Bq6RtEzS7BbLi4iIijYPMQEgaTKwEJhje+MQY15LExDHdTQfZ3utpP2BxZJ+bPvGyrqzgdkA/f39O73+iIjxqtU9CEkTacLhQtuXDTHmZcB5wMm2Nwy2215bfj4IXA4cU1vf9gLbA7YH+vr6dvavEBExbrV5FZOA84E1tucNMaYfuAyYZfuujvZJ5cQ2kiYBJwB3tFVrRERsq81DTDOBWcBKSStK21lAP4Dt+cBHgH2BLzZ5wlbbA8ABwOWlbU/gq7avarHWiIjo0uZVTDcDGmHMXwB/UWm/Bzhi2zUiImK0ZCZ1RERUJSAiIqIqAREREVUJiIiIqEpAREREVQIiIiKqEhAREVGVgIiIiKoEREREVCUgIiKiKgERERFVCYiIiKhKQERERFUCIiIiqhIQERFRlYCIiIiqBERERFQlICIioioBERERVa0FhKRpkq6XtFrSKklnVsa8U9LtklZKukXSER19J0q6U9Ldkj7cVp0REVG3Z4vb3grMtb1c0hRgmaTFtld3jLkXON72I5JOAhYAx0qaAHwBeD3wALBE0qKudSMiokWt7UHYXmd7eVneBKwBpnaNucX2I+XtrcDBZfkY4G7b99h+FLgIOLmtWiMiYltt7kH8jqQZwFHAbcMM+3PgyrI8Fbi/o+8B4Nghtj0bmF3ebpZ05w4Vu+vaD3hotD5M54zWJ40b+fs9vY3a328M/nbTh+poPSAkTQYWAnNsbxxizGtpAuK47d2+7QU0h6Z2a5KW2h4Y6zriqcnf7+ltvP79Wg0ISRNpwuFC25cNMeZlwHnASbY3lOa1wLSOYQeXtoiIGCVtXsUk4Hxgje15Q4zpBy4DZtm+q6NrCXCopOdLegbwdmBRW7VGRMS22tyDmAnMAlZKWlHazgL6AWzPBz4C7At8sckTttoesL1V0vuAq4EJwAW2V7VY69PBbn8YbTeXv9/T27j8+8n2WNcQERG7oMykjoiIqgRERERUJSAiIqJqVCbKxfaTdBjN7PHB2edrgUW214xdVRG7v/L/3lTgNtubO9pPtH3V2FU2+rIHsQuS9CGa24sI+EF5Cfhablz49CbpPWNdQwxN0vuBbwBnAHdI6rzFz6fGpqqxk6uYdkGS7gIOt/3brvZnAKtsHzo2lcWOkvQz2/1jXUfUSVoJvMr25nKLoK8D/2z7HyT90PZRY1vh6Mohpl3T48BBwE+72g8sfbELk3T7UF3AAaNZS2y3PQYPK9m+T9JrgK9Lmk7z9xtXEhC7pjnAdyT9hCduWtgPHAK8b6yKip4dALwBeKSrXcAto19ObIdfSDrS9gqAsifxJuAC4KVjWtkYSEDsgmxfJemFNLc97zxJvcT2Y2NXWfTom8DkwS+ZTpJuGPVqYnucTvMsm9+xvRU4XdK5Y1PS2Mk5iIiIqMpVTBERUZWAiIiIqgREjFuSHpO0QtIdki6V9OwWP+s+Sft1fe4qST+SNFdS9f9FSQdJ+npbdUUMJwER49kW20fa/gPgUeC/jPLnHg68HjgJ+Gj3IEl72v657VNHqa6IJ0lARDRuAg6R9GZJt0n6oaRrJR0gaY+yB7D34GBJPyl9fZIWSlpSXjNL/76Sril7CecxxDX0th+keab6+9R4t6RFkq6judR5hqQ7yjZvlXR4Rw03SBqQNEnSBZJ+UOo+ufZZEdsrARHjnqQ9af4VvxK4GXhlmTF7EfBB24/T3H7hLWX8scBPbf8C+AfgM7aPBt5K8/hcaPYIbi57CZdTHpRVY/semgdj7V+aXg6cavv4rqEXA28rNRwIHGh7KfA/getsHwO8Fvg7SZOe6n+PiEGZBxHj2V4dTzu8ieYRuS8CLi5fwM8A7i39F9M8AfHLNI/Avbi0vw54SXkiIsDvSZoMvBr4UwDb35LUPWluOIttP1xpvwS4hiZ83kZzGwiAE4A/kfSB8v5ZNIGUGzvGDklAxHi2xfaRnQ2SPgfMs72o3GbhY6Xr+zSHoPqAU4D/Vdr3oNnj+Neu7fRchKQXAI8BD5amX9fG2V4raYOklwGn8cQ5EwFvtX1nzx8a0YMcYop4sufQzFoHeNdgo5sZpZcD84A1tjeUrmto7vwJgKQjy+KNwDtK20nAPrUPK4EzH/i8e5u1ejHwQeA5tgfv+XQ1cIZKKkkaVzeUi/YkICKe7GPApZKWAQ919V0M/EeeOLwE8H5gQNLtklbzxL/qPw68WtIqmkNNP+tYZ6/By1yBa2lC5uM91vd1mkNcl3S0nQ1MBG4v2zy7x21FDCu32oiIiKrsQURERFUCIiIiqhIQERFRlYCIiIiqBERERFQlICIioioBERERVQmIiIio+v81OUEjhRrUfgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let me show you what I mean by monotonic relationship\n", + "# between labels and target\n", + "\n", + "def analyse_vars(train, y_train, var):\n", + " \n", + " # function plots median house sale price per encoded\n", + " # category\n", + " \n", + " tmp = pd.concat([X_train, np.log(y_train)], axis=1)\n", + " \n", + " tmp.groupby(var)['SalePrice'].median().plot.bar()\n", + " plt.title(var)\n", + " plt.ylim(2.2, 2.6)\n", + " plt.ylabel('SalePrice')\n", + " plt.show()\n", + " \n", + "for var in cat_others:\n", + " analyse_vars(X_train, y_train, var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The monotonic relationship is particularly clear for the variables MSZoning and Neighborhood. Note how, the higher the integer that now represents the category, the higher the mean house sale price.\n", + "\n", + "(remember that the target is log-transformed, that is why the differences seem so small)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature Scaling\n", + "\n", + "For use in linear models, features need to be either scaled. We will scale features to the minimum and maximum values:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# create scaler\n", + "scaler = MinMaxScaler()\n", + "\n", + "# fit the scaler to the train set\n", + "scaler.fit(X_train) \n", + "\n", + "# transform the train and test set\n", + "\n", + "# sklearn returns numpy arrays, so we wrap the\n", + "# array with a pandas dataframe\n", + "\n", + "X_train = pd.DataFrame(\n", + " scaler.transform(X_train),\n", + " columns=X_train.columns\n", + ")\n", + "\n", + "X_test = pd.DataFrame(\n", + " scaler.transform(X_test),\n", + " columns=X_train.columns\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldSaleTypeSaleConditionLotFrontage_naMasVnrArea_naGarageYrBlt_na
00.7500000.750.4611710.01.01.00.3333331.0000001.00.00.00.8636360.41.00.750.60.7777780.500.0147060.0491800.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666670.6666671.00.0028350.00.00.6734790.2399351.01.001.01.00.5597600.00.00.5232500.0000000.00.6666670.00.3750.3333330.6666670.4166671.00.0000000.00.750.0186921.00.750.4301830.50.51.00.1166860.0329070.00.00.00.00.00.001.00.00.5454550.6666670.750.00.00.0
10.7500000.750.4560660.01.01.00.3333330.3333331.00.00.00.3636360.41.00.750.60.4444440.750.3602940.0491800.00.00.60.60.6666670.033750.6666670.50.50.3333330.6666670.0000000.80.1428070.00.00.1147240.1723401.01.001.01.00.4345390.00.00.4061960.3333330.00.3333330.50.3750.3333330.6666670.2500001.00.0000000.00.750.4579440.50.250.2200280.50.51.00.0000000.0000000.00.00.00.00.00.751.00.00.6363640.6666670.750.00.00.0
20.9166670.750.3946990.01.01.00.0000000.3333331.00.00.00.9545450.41.01.000.60.8888890.500.0367650.0983611.00.00.30.20.6666670.257501.0000000.51.01.0000000.6666670.0000001.00.0807940.00.00.6019510.2867431.01.001.01.00.6272050.00.00.5862960.3333330.00.6666670.00.2500.3333331.0000000.3333331.00.3333330.80.750.0467290.50.500.4062060.50.51.00.2287050.1499090.00.00.00.00.00.001.00.00.0909090.6666670.750.00.00.0
30.7500000.750.4450020.01.01.00.6666670.6666671.00.00.00.4545450.41.00.750.60.6666670.500.0661760.1639340.00.01.01.00.3333330.000000.6666670.51.00.6666670.6666671.0000001.00.2556700.00.00.0181140.2425531.01.001.01.00.5669200.00.00.5299430.3333330.00.6666670.00.3750.3333330.6666670.2500001.00.3333330.40.750.0841120.50.500.3624820.50.51.00.4690780.0457040.00.00.00.00.00.001.00.00.6363640.6666670.751.00.00.0
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" + ], + "text/plain": [ + " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 0.750000 0.75 0.461171 0.0 1.0 1.0 0.333333 \n", + "1 0.750000 0.75 0.456066 0.0 1.0 1.0 0.333333 \n", + "2 0.916667 0.75 0.394699 0.0 1.0 1.0 0.000000 \n", + "3 0.750000 0.75 0.445002 0.0 1.0 1.0 0.666667 \n", + "4 0.750000 0.75 0.577658 0.0 1.0 1.0 0.333333 \n", + "\n", + " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", + "0 1.000000 1.0 0.0 0.0 0.863636 0.4 \n", + "1 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", + "2 0.333333 1.0 0.0 0.0 0.954545 0.4 \n", + "3 0.666667 1.0 0.0 0.0 0.454545 0.4 \n", + "4 0.333333 1.0 0.0 0.0 0.363636 0.4 \n", + "\n", + " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", + "0 1.0 0.75 0.6 0.777778 0.50 0.014706 \n", + "1 1.0 0.75 0.6 0.444444 0.75 0.360294 \n", + "2 1.0 1.00 0.6 0.888889 0.50 0.036765 \n", + "3 1.0 0.75 0.6 0.666667 0.50 0.066176 \n", + "4 1.0 0.75 0.6 0.555556 0.50 0.323529 \n", + "\n", + " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", + "0 0.049180 0.0 0.0 1.0 1.0 0.333333 \n", + "1 0.049180 0.0 0.0 0.6 0.6 0.666667 \n", + "2 0.098361 1.0 0.0 0.3 0.2 0.666667 \n", + "3 0.163934 0.0 0.0 1.0 1.0 0.333333 \n", + "4 0.737705 0.0 0.0 0.6 0.7 0.666667 \n", + "\n", + " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond \\\n", + "0 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", + "1 0.03375 0.666667 0.5 0.5 0.333333 0.666667 \n", + "2 0.25750 1.000000 0.5 1.0 1.000000 0.666667 \n", + "3 0.00000 0.666667 0.5 1.0 0.666667 0.666667 \n", + "4 0.17000 0.333333 0.5 0.5 0.333333 0.666667 \n", + "\n", + " BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 \\\n", + "0 0.666667 1.0 0.002835 0.0 0.0 \n", + "1 0.000000 0.8 0.142807 0.0 0.0 \n", + "2 0.000000 1.0 0.080794 0.0 0.0 \n", + "3 1.000000 1.0 0.255670 0.0 0.0 \n", + "4 0.000000 0.6 0.086818 0.0 0.0 \n", + "\n", + " BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical \\\n", + "0 0.673479 0.239935 1.0 1.00 1.0 1.0 \n", + "1 0.114724 0.172340 1.0 1.00 1.0 1.0 \n", + "2 0.601951 0.286743 1.0 1.00 1.0 1.0 \n", + "3 0.018114 0.242553 1.0 1.00 1.0 1.0 \n", + "4 0.434278 0.233224 1.0 0.75 1.0 1.0 \n", + "\n", + " 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath \\\n", + "0 0.559760 0.0 0.0 0.523250 0.000000 0.0 \n", + "1 0.434539 0.0 0.0 0.406196 0.333333 0.0 \n", + "2 0.627205 0.0 0.0 0.586296 0.333333 0.0 \n", + "3 0.566920 0.0 0.0 0.529943 0.333333 0.0 \n", + "4 0.549026 0.0 0.0 0.513216 0.000000 0.0 \n", + "\n", + " FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd \\\n", + "0 0.666667 0.0 0.375 0.333333 0.666667 0.416667 \n", + "1 0.333333 0.5 0.375 0.333333 0.666667 0.250000 \n", + "2 0.666667 0.0 0.250 0.333333 1.000000 0.333333 \n", + "3 0.666667 0.0 0.375 0.333333 0.666667 0.250000 \n", + "4 0.666667 0.0 0.375 0.333333 0.333333 0.416667 \n", + "\n", + " Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish \\\n", + "0 1.0 0.000000 0.0 0.75 0.018692 1.0 \n", + "1 1.0 0.000000 0.0 0.75 0.457944 0.5 \n", + "2 1.0 0.333333 0.8 0.75 0.046729 0.5 \n", + "3 1.0 0.333333 0.4 0.75 0.084112 0.5 \n", + "4 1.0 0.333333 0.8 0.75 0.411215 0.5 \n", + "\n", + " GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF \\\n", + "0 0.75 0.430183 0.5 0.5 1.0 0.116686 \n", + "1 0.25 0.220028 0.5 0.5 1.0 0.000000 \n", + "2 0.50 0.406206 0.5 0.5 1.0 0.228705 \n", + "3 0.50 0.362482 0.5 0.5 1.0 0.469078 \n", + "4 0.50 0.406206 0.5 0.5 1.0 0.000000 \n", + "\n", + " OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC \\\n", + "0 0.032907 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.000000 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.149909 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.045704 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.000000 0.0 1.0 0.0 0.0 0.0 \n", + "\n", + " Fence MiscFeature MiscVal MoSold SaleType SaleCondition \\\n", + "0 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", + "1 0.75 1.0 0.0 0.636364 0.666667 0.75 \n", + "2 0.00 1.0 0.0 0.090909 0.666667 0.75 \n", + "3 0.00 1.0 0.0 0.636364 0.666667 0.75 \n", + "4 0.00 1.0 0.0 0.545455 0.666667 0.75 \n", + "\n", + " LotFrontage_na MasVnrArea_na GarageYrBlt_na \n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 1.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Conclusion\n", + "\n", + "We now have several classes with parameters learned from the training dataset, that we can store and retrieve at a later stage, so that when a colleague comes with new data, we are in a better position to score it faster.\n", + "\n", + "Still:\n", + "\n", + "- we would need to save each class\n", + "- then we could load each class\n", + "- and apply each transformation individually.\n", + "\n", + "Which sounds like a lot of work.\n", + "\n", + "The good news is, we can reduce the amount of work, if we set up all the transformations within a pipeline.\n", + "\n", + "**IMPORTANT**\n", + "\n", + "In order to set up the entire feature transformation within a pipeline, we still need to create a class that can be used within a pipeline to map the categorical variables with the arbitrary mappings, and also, to capture elapsed time between the temporal variables.\n", + "\n", + "We will take that opportunity to create an in-house package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "583px", + "left": "0px", + "right": "1324px", + "top": "107px", + "width": "212px" + }, + "toc_section_display": "block", + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/07-feature-engineering-pipeline.ipynb b/section-04-research-and-development/07-feature-engineering-pipeline.ipynb index aafaeae09..a5dcad6f3 100644 --- a/section-04-research-and-development/07-feature-engineering-pipeline.ipynb +++ b/section-04-research-and-development/07-feature-engineering-pipeline.ipynb @@ -1,1850 +1,1850 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Feature Engineering with in house software\n", - "\n", - "In this notebook, we will set up all the feature engineering steps within a Scikit-learn pipeline utilizing the open source transformers plus those we developed in house." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reproducibility: Setting the seed\n", - "\n", - "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# data manipulation and plotting\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# for saving the pipeline\n", - "import joblib\n", - "\n", - "# from Scikit-learn\n", - "from sklearn.feature_selection import SelectFromModel\n", - "from sklearn.linear_model import Lasso\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import MinMaxScaler, Binarizer\n", - "\n", - "# from feature-engine\n", - "from feature_engine.imputation import (\n", - " AddMissingIndicator,\n", - " MeanMedianImputer,\n", - " CategoricalImputer,\n", - ")\n", - "\n", - "from feature_engine.encoding import (\n", - " RareLabelEncoder,\n", - " OrdinalEncoder,\n", - ")\n", - "\n", - "from feature_engine.transformation import (\n", - " LogTransformer,\n", - " YeoJohnsonTransformer,\n", - ")\n", - "\n", - "from feature_engine.selection import DropFeatures\n", - "from feature_engine.wrappers import SklearnTransformerWrapper\n", - "\n", - "import preprocessors as pp\n", - "\n", - "# to visualise al the columns in the dataframe\n", - "pd.pandas.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1460, 81)\n" - ] - }, - { - "data": { - "text/html": [ - "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
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" - ], - "text/plain": [ - " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 1 60 RL 65.0 8450 Pave NaN Reg \n", - "1 2 20 RL 80.0 9600 Pave NaN Reg \n", - "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", - "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", - "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", - "\n", - " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", - "0 Lvl AllPub Inside Gtl CollgCr Norm \n", - "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", - "2 Lvl AllPub Inside Gtl CollgCr Norm \n", - "3 Lvl AllPub Corner Gtl Crawfor Norm \n", - "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", - "\n", - " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", - "0 Norm 1Fam 2Story 7 5 2003 \n", - "1 Norm 1Fam 1Story 6 8 1976 \n", - "2 Norm 1Fam 2Story 7 5 2001 \n", - "3 Norm 1Fam 2Story 7 5 1915 \n", - "4 Norm 1Fam 2Story 8 5 2000 \n", - "\n", - " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", - "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", - "1 1976 Gable CompShg MetalSd MetalSd None \n", - "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", - "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", - "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", - "\n", - " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", - "0 196.0 Gd TA PConc Gd TA No \n", - "1 0.0 TA TA CBlock Gd TA Gd \n", - "2 162.0 Gd TA PConc Gd TA Mn \n", - "3 0.0 TA TA BrkTil TA Gd No \n", - "4 350.0 Gd TA PConc Gd TA Av \n", - "\n", - " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", - "0 GLQ 706 Unf 0 150 856 \n", - "1 ALQ 978 Unf 0 284 1262 \n", - "2 GLQ 486 Unf 0 434 920 \n", - "3 ALQ 216 Unf 0 540 756 \n", - "4 GLQ 655 Unf 0 490 1145 \n", - "\n", - " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", - "0 GasA Ex Y SBrkr 856 854 0 \n", - "1 GasA Ex Y SBrkr 1262 0 0 \n", - "2 GasA Ex Y SBrkr 920 866 0 \n", - "3 GasA Gd Y SBrkr 961 756 0 \n", - "4 GasA Ex Y SBrkr 1145 1053 0 \n", - "\n", - " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", - "0 1710 1 0 2 1 3 \n", - "1 1262 0 1 2 0 3 \n", - "2 1786 1 0 2 1 3 \n", - "3 1717 1 0 1 0 3 \n", - "4 2198 1 0 2 1 4 \n", - "\n", - " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", - "0 1 Gd 8 Typ 0 NaN \n", - "1 1 TA 6 Typ 1 TA \n", - "2 1 Gd 6 Typ 1 TA \n", - "3 1 Gd 7 Typ 1 Gd \n", - "4 1 Gd 9 Typ 1 TA \n", - "\n", - " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", - "0 Attchd 2003.0 RFn 2 548 TA \n", - "1 Attchd 1976.0 RFn 2 460 TA \n", - "2 Attchd 2001.0 RFn 2 608 TA \n", - "3 Detchd 1998.0 Unf 3 642 TA \n", - "4 Attchd 2000.0 RFn 3 836 TA \n", - "\n", - " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", - "0 TA Y 0 61 0 0 \n", - "1 TA Y 298 0 0 0 \n", - "2 TA Y 0 42 0 0 \n", - "3 TA Y 0 35 272 0 \n", - "4 TA Y 192 84 0 0 \n", - "\n", - " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", - "0 0 0 NaN NaN NaN 0 2 2008 \n", - "1 0 0 NaN NaN NaN 0 5 2007 \n", - "2 0 0 NaN NaN NaN 0 9 2008 \n", - "3 0 0 NaN NaN NaN 0 2 2006 \n", - "4 0 0 NaN NaN NaN 0 12 2008 \n", - "\n", - " SaleType SaleCondition SalePrice \n", - "0 WD Normal 208500 \n", - "1 WD Normal 181500 \n", - "2 WD Normal 223500 \n", - "3 WD Abnorml 140000 \n", - "4 WD Normal 250000 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load dataset\n", - "data = pd.read_csv('train.csv')\n", - "\n", - "# rows and columns of the data\n", - "print(data.shape)\n", - "\n", - "# visualise the dataset\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Cast MSSubClass as object\n", - "\n", - "data['MSSubClass'] = data['MSSubClass'].astype('O')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Separate dataset into train and test\n", - "\n", - "It is important to separate our data intro training and testing set. \n", - "\n", - "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n", - "\n", - "Our feature engineering techniques will learn:\n", - "\n", - "- mean\n", - "- mode\n", - "- exponents for the yeo-johnson\n", - "- category frequency\n", - "- and category to number mappings\n", - "\n", - "from the train set.\n", - "\n", - "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1314, 79), (146, 79))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's separate into train and test set\n", - "# Remember to set the seed (random_state for this sklearn function)\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n", - " data['SalePrice'], # target\n", - " test_size=0.1, # portion of dataset to allocate to test set\n", - " random_state=0, # we are setting the seed here\n", - ")\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Target\n", - "\n", - "We apply the logarithm" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "y_train = np.log(y_train)\n", - "y_test = np.log(y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Config" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# categorical variables with NA in train set\n", - "CATEGORICAL_VARS_WITH_NA_FREQUENT = ['MasVnrType',\n", - " 'BsmtQual',\n", - " 'BsmtCond',\n", - " 'BsmtExposure',\n", - " 'BsmtFinType1',\n", - " 'BsmtFinType2',\n", - " 'Electrical',\n", - " 'GarageType',\n", - " 'GarageFinish',\n", - " 'GarageQual',\n", - " 'GarageCond']\n", - "\n", - "\n", - "CATEGORICAL_VARS_WITH_NA_MISSING = [\n", - " 'Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']\n", - "\n", - "\n", - "# numerical variables with NA in train set\n", - "NUMERICAL_VARS_WITH_NA = ['LotFrontage', 'MasVnrArea', 'GarageYrBlt']\n", - "\n", - "\n", - "TEMPORAL_VARS = ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']\n", - "REF_VAR = \"YrSold\"\n", - "\n", - "\n", - "# variables to log transform\n", - "NUMERICALS_LOG_VARS = [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]\n", - "\n", - "NUMERICALS_YEO_VARS = ['LotArea']\n", - "\n", - "\n", - "BINARIZE_VARS = [\n", - " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", - " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", - "]\n", - "\n", - "# variables to map\n", - "QUAL_VARS = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", - " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", - " 'GarageQual', 'GarageCond',\n", - " ]\n", - "\n", - "EXPOSURE_VARS = ['BsmtExposure']\n", - "\n", - "FINISH_VARS = ['BsmtFinType1', 'BsmtFinType2']\n", - "\n", - "GARAGE_VARS = ['GarageFinish']\n", - "\n", - "FENCE_VARS = ['Fence']\n", - "\n", - "# categorical variables to encode\n", - "CATEGORICAL_VARS = [\n", - " 'MSZoning',\n", - " 'Street',\n", - " 'Alley',\n", - " 'LotShape',\n", - " 'LandContour',\n", - " 'Utilities',\n", - " 'LotConfig',\n", - " 'LandSlope',\n", - " 'Neighborhood',\n", - " 'Condition1',\n", - " 'Condition2',\n", - " 'BldgType',\n", - " 'HouseStyle',\n", - " 'RoofStyle',\n", - " 'RoofMatl',\n", - " 'Exterior1st',\n", - " 'Exterior2nd',\n", - " 'MasVnrType',\n", - " 'Foundation',\n", - " 'Heating',\n", - " 'CentralAir',\n", - " 'Electrical',\n", - " 'Functional',\n", - " 'GarageType',\n", - " 'PavedDrive',\n", - " 'PoolQC',\n", - " 'MiscFeature',\n", - " 'SaleType',\n", - " 'SaleCondition',\n", - " 'MSSubClass']\n", - "\n", - "\n", - "QUAL_MAPPINGS = {'Po': 1, 'Fa': 2, 'TA': 3,\n", - " 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", - "\n", - "EXPOSURE_MAPPINGS = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", - "\n", - "FINISH_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1,\n", - " 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", - "\n", - "GARAGE_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", - "\n", - "FENCE_MAPPINGS = {'Missing': 0, 'NA': 0,\n", - " 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pipeline - Feature engineering" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# set up the pipeline\n", - "price_pipe = Pipeline([\n", - "\n", - " # ===== IMPUTATION =====\n", - " # impute categorical variables with string missing\n", - " ('missing_imputation', CategoricalImputer(\n", - " imputation_method='missing', variables=CATEGORICAL_VARS_WITH_NA_MISSING)),\n", - "\n", - " ('frequent_imputation', CategoricalImputer(\n", - " imputation_method='frequent', variables=CATEGORICAL_VARS_WITH_NA_FREQUENT)),\n", - "\n", - " # add missing indicator\n", - " ('missing_indicator', AddMissingIndicator(variables=NUMERICAL_VARS_WITH_NA)),\n", - "\n", - " # impute numerical variables with the mean\n", - " ('mean_imputation', MeanMedianImputer(\n", - " imputation_method='mean', variables=NUMERICAL_VARS_WITH_NA\n", - " )),\n", - " \n", - " \n", - " # == TEMPORAL VARIABLES ====\n", - " ('elapsed_time', pp.TemporalVariableTransformer(\n", - " variables=TEMPORAL_VARS, reference_variable=REF_VAR)),\n", - "\n", - " ('drop_features', DropFeatures(features_to_drop=[REF_VAR])),\n", - "\n", - " \n", - "\n", - " # ==== VARIABLE TRANSFORMATION =====\n", - " ('log', LogTransformer(variables=NUMERICALS_LOG_VARS)),\n", - " \n", - " ('yeojohnson', YeoJohnsonTransformer(variables=NUMERICALS_YEO_VARS)),\n", - " \n", - " ('binarizer', SklearnTransformerWrapper(\n", - " transformer=Binarizer(threshold=0), variables=BINARIZE_VARS)),\n", - " \n", - "\n", - " # === mappers ===\n", - " ('mapper_qual', pp.Mapper(\n", - " variables=QUAL_VARS, mappings=QUAL_MAPPINGS)),\n", - "\n", - " ('mapper_exposure', pp.Mapper(\n", - " variables=EXPOSURE_VARS, mappings=EXPOSURE_MAPPINGS)),\n", - "\n", - " ('mapper_finish', pp.Mapper(\n", - " variables=FINISH_VARS, mappings=FINISH_MAPPINGS)),\n", - "\n", - " ('mapper_garage', pp.Mapper(\n", - " variables=GARAGE_VARS, mappings=GARAGE_MAPPINGS)),\n", - " \n", - " ('mapper_fence', pp.Mapper(\n", - " variables=FENCE_VARS, mappings=FENCE_MAPPINGS)),\n", - "\n", - "\n", - " # == CATEGORICAL ENCODING\n", - " ('rare_label_encoder', RareLabelEncoder(\n", - " tol=0.01, n_categories=1, variables=CATEGORICAL_VARS\n", - " )),\n", - "\n", - " # encode categorical and discrete variables using the target mean\n", - " ('categorical_encoder', OrdinalEncoder(\n", - " encoding_method='ordered', variables=CATEGORICAL_VARS)),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\stats\\morestats.py:1476: RuntimeWarning: divide by zero encountered in log\n", - " loglike = -n_samples / 2 * np.log(trans.var(axis=0))\n", - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2555: RuntimeWarning: invalid value encountered in double_scalars\n", - " w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom\n", - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2148: RuntimeWarning: invalid value encountered in double_scalars\n", - " tmp1 = (x - w) * (fx - fv)\n", - "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2149: RuntimeWarning: invalid value encountered in double_scalars\n", - " tmp2 = (x - v) * (fx - fw)\n" - ] - }, - { - "data": { - "text/plain": [ - "Pipeline(steps=[('missing_imputation',\n", - " CategoricalImputer(variables=['Alley', 'FireplaceQu', 'PoolQC',\n", - " 'Fence', 'MiscFeature'])),\n", - " ('frequent_imputation',\n", - " CategoricalImputer(imputation_method='frequent',\n", - " variables=['MasVnrType', 'BsmtQual',\n", - " 'BsmtCond', 'BsmtExposure',\n", - " 'BsmtFinType1', 'BsmtFinType2',\n", - " 'Electrical', 'GarageType',\n", - " 'GarageFinish', 'GarageQual',\n", - " 'GarageCon...\n", - " OrdinalEncoder(variables=['MSZoning', 'Street', 'Alley',\n", - " 'LotShape', 'LandContour',\n", - " 'Utilities', 'LotConfig',\n", - " 'LandSlope', 'Neighborhood',\n", - " 'Condition1', 'Condition2',\n", - " 'BldgType', 'HouseStyle',\n", - " 'RoofStyle', 'RoofMatl',\n", - " 'Exterior1st', 'Exterior2nd',\n", - " 'MasVnrType', 'Foundation',\n", - " 'Heating', 'CentralAir',\n", - " 'Electrical', 'Functional',\n", - " 'GarageType', 'PavedDrive', 'PoolQC',\n", - " 'MiscFeature', 'SaleType',\n", - " 'SaleCondition', 'MSSubClass']))])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# train the pipeline\n", - "price_pipe.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = price_pipe.transform(X_train)\n", - "X_test = price_pipe.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the train set\n", - "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check absence of na in the test set\n", - "[var for var in X_test.columns if X_test[var].isnull().sum() > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'MasVnrType': 'None',\n", - " 'BsmtQual': 'TA',\n", - " 'BsmtCond': 'TA',\n", - " 'BsmtExposure': 'No',\n", - " 'BsmtFinType1': 'Unf',\n", - " 'BsmtFinType2': 'Unf',\n", - " 'Electrical': 'SBrkr',\n", - " 'GarageType': 'Attchd',\n", - " 'GarageFinish': 'Unf',\n", - " 'GarageQual': 'TA',\n", - " 'GarageCond': 'TA'}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the parameters are learnt and stored in each step\n", - "# of the pipeline\n", - "\n", - "price_pipe.named_steps['frequent_imputation'].imputer_dict_" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "930 9 3 4.290459 0.079663 1 2 1 \n", - "656 9 3 4.276666 0.079663 1 2 1 \n", - "45 11 3 4.110874 0.079663 1 2 0 \n", - "1348 9 3 4.246776 0.079663 1 2 2 \n", - "55 9 3 4.605170 0.079663 1 2 1 \n", - "\n", - " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", - "930 3 1 0 0 19 2 \n", - "656 1 1 0 0 8 2 \n", - "45 1 1 0 0 21 2 \n", - "1348 2 1 0 0 10 2 \n", - "55 1 1 0 0 8 2 \n", - "\n", - " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", - "930 1 3 3 8 5 2 \n", - "656 1 3 3 5 7 49 \n", - "45 1 4 3 9 5 5 \n", - "1348 1 3 3 7 5 9 \n", - "55 1 3 3 6 5 44 \n", - "\n", - " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", - "930 2 0 0 10 10 1 \n", - "656 2 0 0 6 6 2 \n", - "45 5 2 0 3 2 2 \n", - "1348 9 0 0 10 10 1 \n", - "55 44 0 0 6 7 2 \n", - "\n", - " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond \\\n", - "930 0.0 4 3 4 4 3 \n", - "656 54.0 4 3 2 3 3 \n", - "45 412.0 5 3 4 5 3 \n", - "1348 0.0 4 3 4 4 3 \n", - "55 272.0 3 3 2 3 3 \n", - "\n", - " BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 \\\n", - "930 3 6 16 1 0 \n", - "656 1 5 806 1 0 \n", - "45 1 6 456 1 0 \n", - "1348 4 6 1443 1 0 \n", - "55 1 4 490 1 0 \n", - "\n", - " BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical \\\n", - "930 1450 1466 2 5 1 3 \n", - "656 247 1053 2 5 1 3 \n", - "45 1296 1752 2 5 1 3 \n", - "1348 39 1482 2 5 1 3 \n", - "55 935 1425 2 4 1 3 \n", - "\n", - " 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath \\\n", - "930 7.290293 0 0 7.290293 0 0 \n", - "656 6.959399 0 0 6.959399 1 0 \n", - "45 7.468513 0 0 7.468513 1 0 \n", - "1348 7.309212 0 0 7.309212 1 0 \n", - "55 7.261927 0 0 7.261927 0 0 \n", - "\n", - " FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual \\\n", - "930 2 0 3 1 4 \n", - "656 1 1 3 1 4 \n", - "45 2 0 2 1 5 \n", - "1348 2 0 3 1 4 \n", - "55 2 0 3 1 3 \n", - "\n", - " TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType \\\n", - "930 7 4 0 0 3 \n", - "656 5 4 0 0 3 \n", - "45 6 4 1 4 3 \n", - "1348 5 4 1 2 3 \n", - "55 7 4 1 4 3 \n", - "\n", - " GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", - "930 2.0 3 3 610 3 \n", - "656 49.0 2 1 312 3 \n", - "45 5.0 2 2 576 3 \n", - "1348 9.0 2 2 514 3 \n", - "55 44.0 2 2 576 3 \n", - "\n", - " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch \\\n", - "930 3 2 100 18 0 \n", - "656 3 2 0 0 0 \n", - "45 3 2 196 82 0 \n", - "1348 3 2 402 25 0 \n", - "55 3 2 0 0 0 \n", - "\n", - " 3SsnPorch ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal \\\n", - "930 0 0 0 0 0 2 0 \n", - "656 0 0 0 0 3 2 0 \n", - "45 0 0 0 0 0 2 0 \n", - "1348 0 0 0 0 0 2 0 \n", - "55 1 0 0 0 0 2 0 \n", - "\n", - " MoSold SaleType SaleCondition LotFrontage_na MasVnrArea_na \\\n", - "930 7 2 3 0 0 \n", - "656 8 2 3 0 0 \n", - "45 2 2 3 0 0 \n", - "1348 8 2 3 1 0 \n", - "55 7 2 3 0 0 \n", - "\n", - " GarageYrBlt_na \n", - "930 0 \n", - "656 0 \n", - "45 0 \n", - "1348 0 \n", - "55 0 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conclusion\n", - "\n", - "Now we have all the feature engineering steps in 1 pipeline.\n", - "\n", - "The next steps are:\n", - "\n", - "- Add the scaler and model to the pipeline\n", - "- Produce a final pipeline only with the selected features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "583px", - "left": "0px", - "right": "1324px", - "top": "107px", - "width": "212px" - }, - "toc_section_display": "block", - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Engineering with in house software\n", + "\n", + "In this notebook, we will set up all the feature engineering steps within a Scikit-learn pipeline utilizing the open source transformers plus those we developed in house." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reproducibility: Setting the seed\n", + "\n", + "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# data manipulation and plotting\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# for saving the pipeline\n", + "import joblib\n", + "\n", + "# from Scikit-learn\n", + "from sklearn.feature_selection import SelectFromModel\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import MinMaxScaler, Binarizer\n", + "\n", + "# from feature-engine\n", + "from feature_engine.imputation import (\n", + " AddMissingIndicator,\n", + " MeanMedianImputer,\n", + " CategoricalImputer,\n", + ")\n", + "\n", + "from feature_engine.encoding import (\n", + " RareLabelEncoder,\n", + " OrdinalEncoder,\n", + ")\n", + "\n", + "from feature_engine.transformation import (\n", + " LogTransformer,\n", + " YeoJohnsonTransformer,\n", + ")\n", + "\n", + "from feature_engine.selection import DropFeatures\n", + "from feature_engine.wrappers import SklearnTransformerWrapper\n", + "\n", + "import preprocessors as pp\n", + "\n", + "# to visualise al the columns in the dataframe\n", + "pd.pandas.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n" + ] + }, + { + "data": { + "text/html": [ + "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
\n", + "
" + ], + "text/plain": [ + " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 1 60 RL 65.0 8450 Pave NaN Reg \n", + "1 2 20 RL 80.0 9600 Pave NaN Reg \n", + "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", + "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", + "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", + "\n", + " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", + "0 Lvl AllPub Inside Gtl CollgCr Norm \n", + "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", + "2 Lvl AllPub Inside Gtl CollgCr Norm \n", + "3 Lvl AllPub Corner Gtl Crawfor Norm \n", + "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", + "\n", + " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", + "0 Norm 1Fam 2Story 7 5 2003 \n", + "1 Norm 1Fam 1Story 6 8 1976 \n", + "2 Norm 1Fam 2Story 7 5 2001 \n", + "3 Norm 1Fam 2Story 7 5 1915 \n", + "4 Norm 1Fam 2Story 8 5 2000 \n", + "\n", + " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", + "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", + "1 1976 Gable CompShg MetalSd MetalSd None \n", + "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", + "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", + "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", + "\n", + " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", + "0 196.0 Gd TA PConc Gd TA No \n", + "1 0.0 TA TA CBlock Gd TA Gd \n", + "2 162.0 Gd TA PConc Gd TA Mn \n", + "3 0.0 TA TA BrkTil TA Gd No \n", + "4 350.0 Gd TA PConc Gd TA Av \n", + "\n", + " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", + "0 GLQ 706 Unf 0 150 856 \n", + "1 ALQ 978 Unf 0 284 1262 \n", + "2 GLQ 486 Unf 0 434 920 \n", + "3 ALQ 216 Unf 0 540 756 \n", + "4 GLQ 655 Unf 0 490 1145 \n", + "\n", + " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", + "0 GasA Ex Y SBrkr 856 854 0 \n", + "1 GasA Ex Y SBrkr 1262 0 0 \n", + "2 GasA Ex Y SBrkr 920 866 0 \n", + "3 GasA Gd Y SBrkr 961 756 0 \n", + "4 GasA Ex Y SBrkr 1145 1053 0 \n", + "\n", + " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", + "0 1710 1 0 2 1 3 \n", + "1 1262 0 1 2 0 3 \n", + "2 1786 1 0 2 1 3 \n", + "3 1717 1 0 1 0 3 \n", + "4 2198 1 0 2 1 4 \n", + "\n", + " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", + "0 1 Gd 8 Typ 0 NaN \n", + "1 1 TA 6 Typ 1 TA \n", + "2 1 Gd 6 Typ 1 TA \n", + "3 1 Gd 7 Typ 1 Gd \n", + "4 1 Gd 9 Typ 1 TA \n", + "\n", + " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", + "0 Attchd 2003.0 RFn 2 548 TA \n", + "1 Attchd 1976.0 RFn 2 460 TA \n", + "2 Attchd 2001.0 RFn 2 608 TA \n", + "3 Detchd 1998.0 Unf 3 642 TA \n", + "4 Attchd 2000.0 RFn 3 836 TA \n", + "\n", + " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", + "0 TA Y 0 61 0 0 \n", + "1 TA Y 298 0 0 0 \n", + "2 TA Y 0 42 0 0 \n", + "3 TA Y 0 35 272 0 \n", + "4 TA Y 192 84 0 0 \n", + "\n", + " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", + "0 0 0 NaN NaN NaN 0 2 2008 \n", + "1 0 0 NaN NaN NaN 0 5 2007 \n", + "2 0 0 NaN NaN NaN 0 9 2008 \n", + "3 0 0 NaN NaN NaN 0 2 2006 \n", + "4 0 0 NaN NaN NaN 0 12 2008 \n", + "\n", + " SaleType SaleCondition SalePrice \n", + "0 WD Normal 208500 \n", + "1 WD Normal 181500 \n", + "2 WD Normal 223500 \n", + "3 WD Abnorml 140000 \n", + "4 WD Normal 250000 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load dataset\n", + "data = pd.read_csv('train.csv')\n", + "\n", + "# rows and columns of the data\n", + "print(data.shape)\n", + "\n", + "# visualise the dataset\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Cast MSSubClass as object\n", + "\n", + "data['MSSubClass'] = data['MSSubClass'].astype('O')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Separate dataset into train and test\n", + "\n", + "It is important to separate our data intro training and testing set. \n", + "\n", + "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n", + "\n", + "Our feature engineering techniques will learn:\n", + "\n", + "- mean\n", + "- mode\n", + "- exponents for the yeo-johnson\n", + "- category frequency\n", + "- and category to number mappings\n", + "\n", + "from the train set.\n", + "\n", + "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1314, 79), (146, 79))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's separate into train and test set\n", + "# Remember to set the seed (random_state for this sklearn function)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n", + " data['SalePrice'], # target\n", + " test_size=0.1, # portion of dataset to allocate to test set\n", + " random_state=0, # we are setting the seed here\n", + ")\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Target\n", + "\n", + "We apply the logarithm" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "y_train = np.log(y_train)\n", + "y_test = np.log(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Config" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# categorical variables with NA in train set\n", + "CATEGORICAL_VARS_WITH_NA_FREQUENT = ['MasVnrType',\n", + " 'BsmtQual',\n", + " 'BsmtCond',\n", + " 'BsmtExposure',\n", + " 'BsmtFinType1',\n", + " 'BsmtFinType2',\n", + " 'Electrical',\n", + " 'GarageType',\n", + " 'GarageFinish',\n", + " 'GarageQual',\n", + " 'GarageCond']\n", + "\n", + "\n", + "CATEGORICAL_VARS_WITH_NA_MISSING = [\n", + " 'Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']\n", + "\n", + "\n", + "# numerical variables with NA in train set\n", + "NUMERICAL_VARS_WITH_NA = ['LotFrontage', 'MasVnrArea', 'GarageYrBlt']\n", + "\n", + "\n", + "TEMPORAL_VARS = ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']\n", + "REF_VAR = \"YrSold\"\n", + "\n", + "\n", + "# variables to log transform\n", + "NUMERICALS_LOG_VARS = [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]\n", + "\n", + "NUMERICALS_YEO_VARS = ['LotArea']\n", + "\n", + "\n", + "BINARIZE_VARS = [\n", + " 'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n", + " '3SsnPorch', 'ScreenPorch', 'MiscVal'\n", + "]\n", + "\n", + "# variables to map\n", + "QUAL_VARS = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n", + " 'HeatingQC', 'KitchenQual', 'FireplaceQu',\n", + " 'GarageQual', 'GarageCond',\n", + " ]\n", + "\n", + "EXPOSURE_VARS = ['BsmtExposure']\n", + "\n", + "FINISH_VARS = ['BsmtFinType1', 'BsmtFinType2']\n", + "\n", + "GARAGE_VARS = ['GarageFinish']\n", + "\n", + "FENCE_VARS = ['Fence']\n", + "\n", + "# categorical variables to encode\n", + "CATEGORICAL_VARS = [\n", + " 'MSZoning',\n", + " 'Street',\n", + " 'Alley',\n", + " 'LotShape',\n", + " 'LandContour',\n", + " 'Utilities',\n", + " 'LotConfig',\n", + " 'LandSlope',\n", + " 'Neighborhood',\n", + " 'Condition1',\n", + " 'Condition2',\n", + " 'BldgType',\n", + " 'HouseStyle',\n", + " 'RoofStyle',\n", + " 'RoofMatl',\n", + " 'Exterior1st',\n", + " 'Exterior2nd',\n", + " 'MasVnrType',\n", + " 'Foundation',\n", + " 'Heating',\n", + " 'CentralAir',\n", + " 'Electrical',\n", + " 'Functional',\n", + " 'GarageType',\n", + " 'PavedDrive',\n", + " 'PoolQC',\n", + " 'MiscFeature',\n", + " 'SaleType',\n", + " 'SaleCondition',\n", + " 'MSSubClass']\n", + "\n", + "\n", + "QUAL_MAPPINGS = {'Po': 1, 'Fa': 2, 'TA': 3,\n", + " 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", + "\n", + "EXPOSURE_MAPPINGS = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", + "\n", + "FINISH_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1,\n", + " 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", + "\n", + "GARAGE_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", + "\n", + "FENCE_MAPPINGS = {'Missing': 0, 'NA': 0,\n", + " 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pipeline - Feature engineering" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# set up the pipeline\n", + "price_pipe = Pipeline([\n", + "\n", + " # ===== IMPUTATION =====\n", + " # impute categorical variables with string missing\n", + " ('missing_imputation', CategoricalImputer(\n", + " imputation_method='missing', variables=CATEGORICAL_VARS_WITH_NA_MISSING)),\n", + "\n", + " ('frequent_imputation', CategoricalImputer(\n", + " imputation_method='frequent', variables=CATEGORICAL_VARS_WITH_NA_FREQUENT)),\n", + "\n", + " # add missing indicator\n", + " ('missing_indicator', AddMissingIndicator(variables=NUMERICAL_VARS_WITH_NA)),\n", + "\n", + " # impute numerical variables with the mean\n", + " ('mean_imputation', MeanMedianImputer(\n", + " imputation_method='mean', variables=NUMERICAL_VARS_WITH_NA\n", + " )),\n", + " \n", + " \n", + " # == TEMPORAL VARIABLES ====\n", + " ('elapsed_time', pp.TemporalVariableTransformer(\n", + " variables=TEMPORAL_VARS, reference_variable=REF_VAR)),\n", + "\n", + " ('drop_features', DropFeatures(features_to_drop=[REF_VAR])),\n", + "\n", + " \n", + "\n", + " # ==== VARIABLE TRANSFORMATION =====\n", + " ('log', LogTransformer(variables=NUMERICALS_LOG_VARS)),\n", + " \n", + " ('yeojohnson', YeoJohnsonTransformer(variables=NUMERICALS_YEO_VARS)),\n", + " \n", + " ('binarizer', SklearnTransformerWrapper(\n", + " transformer=Binarizer(threshold=0), variables=BINARIZE_VARS)),\n", + " \n", + "\n", + " # === mappers ===\n", + " ('mapper_qual', pp.Mapper(\n", + " variables=QUAL_VARS, mappings=QUAL_MAPPINGS)),\n", + "\n", + " ('mapper_exposure', pp.Mapper(\n", + " variables=EXPOSURE_VARS, mappings=EXPOSURE_MAPPINGS)),\n", + "\n", + " ('mapper_finish', pp.Mapper(\n", + " variables=FINISH_VARS, mappings=FINISH_MAPPINGS)),\n", + "\n", + " ('mapper_garage', pp.Mapper(\n", + " variables=GARAGE_VARS, mappings=GARAGE_MAPPINGS)),\n", + " \n", + " ('mapper_fence', pp.Mapper(\n", + " variables=FENCE_VARS, mappings=FENCE_MAPPINGS)),\n", + "\n", + "\n", + " # == CATEGORICAL ENCODING\n", + " ('rare_label_encoder', RareLabelEncoder(\n", + " tol=0.01, n_categories=1, variables=CATEGORICAL_VARS\n", + " )),\n", + "\n", + " # encode categorical and discrete variables using the target mean\n", + " ('categorical_encoder', OrdinalEncoder(\n", + " encoding_method='ordered', variables=CATEGORICAL_VARS)),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\stats\\morestats.py:1476: RuntimeWarning: divide by zero encountered in log\n", + " loglike = -n_samples / 2 * np.log(trans.var(axis=0))\n", + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2555: RuntimeWarning: invalid value encountered in double_scalars\n", + " w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom\n", + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2148: RuntimeWarning: invalid value encountered in double_scalars\n", + " tmp1 = (x - w) * (fx - fv)\n", + "C:\\Users\\Sole\\Documents\\Repositories\\envs\\feml\\lib\\site-packages\\scipy\\optimize\\optimize.py:2149: RuntimeWarning: invalid value encountered in double_scalars\n", + " tmp2 = (x - v) * (fx - fw)\n" + ] + }, + { + "data": { + "text/plain": [ + "Pipeline(steps=[('missing_imputation',\n", + " CategoricalImputer(variables=['Alley', 'FireplaceQu', 'PoolQC',\n", + " 'Fence', 'MiscFeature'])),\n", + " ('frequent_imputation',\n", + " CategoricalImputer(imputation_method='frequent',\n", + " variables=['MasVnrType', 'BsmtQual',\n", + " 'BsmtCond', 'BsmtExposure',\n", + " 'BsmtFinType1', 'BsmtFinType2',\n", + " 'Electrical', 'GarageType',\n", + " 'GarageFinish', 'GarageQual',\n", + " 'GarageCon...\n", + " OrdinalEncoder(variables=['MSZoning', 'Street', 'Alley',\n", + " 'LotShape', 'LandContour',\n", + " 'Utilities', 'LotConfig',\n", + " 'LandSlope', 'Neighborhood',\n", + " 'Condition1', 'Condition2',\n", + " 'BldgType', 'HouseStyle',\n", + " 'RoofStyle', 'RoofMatl',\n", + " 'Exterior1st', 'Exterior2nd',\n", + " 'MasVnrType', 'Foundation',\n", + " 'Heating', 'CentralAir',\n", + " 'Electrical', 'Functional',\n", + " 'GarageType', 'PavedDrive', 'PoolQC',\n", + " 'MiscFeature', 'SaleType',\n", + " 'SaleCondition', 'MSSubClass']))])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# train the pipeline\n", + "price_pipe.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = price_pipe.transform(X_train)\n", + "X_test = price_pipe.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the train set\n", + "[var for var in X_train.columns if X_train[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check absence of na in the test set\n", + "[var for var in X_test.columns if X_test[var].isnull().sum() > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'MasVnrType': 'None',\n", + " 'BsmtQual': 'TA',\n", + " 'BsmtCond': 'TA',\n", + " 'BsmtExposure': 'No',\n", + " 'BsmtFinType1': 'Unf',\n", + " 'BsmtFinType2': 'Unf',\n", + " 'Electrical': 'SBrkr',\n", + " 'GarageType': 'Attchd',\n", + " 'GarageFinish': 'Unf',\n", + " 'GarageQual': 'TA',\n", + " 'GarageCond': 'TA'}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the parameters are learnt and stored in each step\n", + "# of the pipeline\n", + "\n", + "price_pipe.named_steps['frequent_imputation'].imputer_dict_" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "930 9 3 4.290459 0.079663 1 2 1 \n", + "656 9 3 4.276666 0.079663 1 2 1 \n", + "45 11 3 4.110874 0.079663 1 2 0 \n", + "1348 9 3 4.246776 0.079663 1 2 2 \n", + "55 9 3 4.605170 0.079663 1 2 1 \n", + "\n", + " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", + "930 3 1 0 0 19 2 \n", + "656 1 1 0 0 8 2 \n", + "45 1 1 0 0 21 2 \n", + "1348 2 1 0 0 10 2 \n", + "55 1 1 0 0 8 2 \n", + "\n", + " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", + "930 1 3 3 8 5 2 \n", + "656 1 3 3 5 7 49 \n", + "45 1 4 3 9 5 5 \n", + "1348 1 3 3 7 5 9 \n", + "55 1 3 3 6 5 44 \n", + "\n", + " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", + "930 2 0 0 10 10 1 \n", + "656 2 0 0 6 6 2 \n", + "45 5 2 0 3 2 2 \n", + "1348 9 0 0 10 10 1 \n", + "55 44 0 0 6 7 2 \n", + "\n", + " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond \\\n", + "930 0.0 4 3 4 4 3 \n", + "656 54.0 4 3 2 3 3 \n", + "45 412.0 5 3 4 5 3 \n", + "1348 0.0 4 3 4 4 3 \n", + "55 272.0 3 3 2 3 3 \n", + "\n", + " BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 \\\n", + "930 3 6 16 1 0 \n", + "656 1 5 806 1 0 \n", + "45 1 6 456 1 0 \n", + "1348 4 6 1443 1 0 \n", + "55 1 4 490 1 0 \n", + "\n", + " BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical \\\n", + "930 1450 1466 2 5 1 3 \n", + "656 247 1053 2 5 1 3 \n", + "45 1296 1752 2 5 1 3 \n", + "1348 39 1482 2 5 1 3 \n", + "55 935 1425 2 4 1 3 \n", + "\n", + " 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath \\\n", + "930 7.290293 0 0 7.290293 0 0 \n", + "656 6.959399 0 0 6.959399 1 0 \n", + "45 7.468513 0 0 7.468513 1 0 \n", + "1348 7.309212 0 0 7.309212 1 0 \n", + "55 7.261927 0 0 7.261927 0 0 \n", + "\n", + " FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual \\\n", + "930 2 0 3 1 4 \n", + "656 1 1 3 1 4 \n", + "45 2 0 2 1 5 \n", + "1348 2 0 3 1 4 \n", + "55 2 0 3 1 3 \n", + "\n", + " TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType \\\n", + "930 7 4 0 0 3 \n", + "656 5 4 0 0 3 \n", + "45 6 4 1 4 3 \n", + "1348 5 4 1 2 3 \n", + "55 7 4 1 4 3 \n", + "\n", + " GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", + "930 2.0 3 3 610 3 \n", + "656 49.0 2 1 312 3 \n", + "45 5.0 2 2 576 3 \n", + "1348 9.0 2 2 514 3 \n", + "55 44.0 2 2 576 3 \n", + "\n", + " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch \\\n", + "930 3 2 100 18 0 \n", + "656 3 2 0 0 0 \n", + "45 3 2 196 82 0 \n", + "1348 3 2 402 25 0 \n", + "55 3 2 0 0 0 \n", + "\n", + " 3SsnPorch ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal \\\n", + "930 0 0 0 0 0 2 0 \n", + "656 0 0 0 0 3 2 0 \n", + "45 0 0 0 0 0 2 0 \n", + "1348 0 0 0 0 0 2 0 \n", + "55 1 0 0 0 0 2 0 \n", + "\n", + " MoSold SaleType SaleCondition LotFrontage_na MasVnrArea_na \\\n", + "930 7 2 3 0 0 \n", + "656 8 2 3 0 0 \n", + "45 2 2 3 0 0 \n", + "1348 8 2 3 1 0 \n", + "55 7 2 3 0 0 \n", + "\n", + " GarageYrBlt_na \n", + "930 0 \n", + "656 0 \n", + "45 0 \n", + "1348 0 \n", + "55 0 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusion\n", + "\n", + "Now we have all the feature engineering steps in 1 pipeline.\n", + "\n", + "The next steps are:\n", + "\n", + "- Add the scaler and model to the pipeline\n", + "- Produce a final pipeline only with the selected features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "583px", + "left": "0px", + "right": "1324px", + "top": "107px", + "width": "212px" + }, + "toc_section_display": "block", + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb b/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb index b514e7786..433c137c3 100644 --- a/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb +++ b/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb @@ -1,1122 +1,1122 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Final Machine Learning Pipeline\n", - "\n", - "The pipeline features\n", - "\n", - "- open source classes\n", - "- in house package classes\n", - "- only uses the selected features\n", - "- we score new data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reproducibility: Setting the seed\n", - "\n", - "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# data manipulation and plotting\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# for saving the pipeline\n", - "import joblib\n", - "\n", - "# from Scikit-learn\n", - "from sklearn.linear_model import Lasso\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import MinMaxScaler, Binarizer\n", - "\n", - "# from feature-engine\n", - "from feature_engine.imputation import (\n", - " AddMissingIndicator,\n", - " MeanMedianImputer,\n", - " CategoricalImputer,\n", - ")\n", - "\n", - "from feature_engine.encoding import (\n", - " RareLabelEncoder,\n", - " OrdinalEncoder,\n", - ")\n", - "\n", - "from feature_engine.transformation import LogTransformer\n", - "\n", - "from feature_engine.selection import DropFeatures\n", - "from feature_engine.wrappers import SklearnTransformerWrapper\n", - "\n", - "import preprocessors as pp" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1460, 81)\n" - ] - }, - { - "data": { - "text/html": [ - "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...PoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPub...0NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPub...0NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPub...0NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPub...0NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPub...0NaNNaNNaN0122008WDNormal250000
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5 rows × 81 columns

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" - ], - "text/plain": [ - " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 1 60 RL 65.0 8450 Pave NaN Reg \n", - "1 2 20 RL 80.0 9600 Pave NaN Reg \n", - "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", - "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", - "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", - "\n", - " LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold \\\n", - "0 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n", - "1 Lvl AllPub ... 0 NaN NaN NaN 0 5 \n", - "2 Lvl AllPub ... 0 NaN NaN NaN 0 9 \n", - "3 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n", - "4 Lvl AllPub ... 0 NaN NaN NaN 0 12 \n", - "\n", - " YrSold SaleType SaleCondition SalePrice \n", - "0 2008 WD Normal 208500 \n", - "1 2007 WD Normal 181500 \n", - "2 2008 WD Normal 223500 \n", - "3 2006 WD Abnorml 140000 \n", - "4 2008 WD Normal 250000 \n", - "\n", - "[5 rows x 81 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load dataset\n", - "data = pd.read_csv('train.csv')\n", - "\n", - "# rows and columns of the data\n", - "print(data.shape)\n", - "\n", - "# visualise the dataset\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Cast MSSubClass as object\n", - "\n", - "data['MSSubClass'] = data['MSSubClass'].astype('O')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Separate dataset into train and test\n", - "\n", - "It is important to separate our data intro training and testing set. \n", - "\n", - "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n", - "\n", - "Our feature engineering techniques will learn:\n", - "\n", - "- mean\n", - "- mode\n", - "- exponents for the yeo-johnson\n", - "- category frequency\n", - "- and category to number mappings\n", - "\n", - "from the train set.\n", - "\n", - "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1314, 79), (146, 79))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's separate into train and test set\n", - "# Remember to set the seed (random_state for this sklearn function)\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n", - " data['SalePrice'], # target\n", - " test_size=0.1, # portion of dataset to allocate to test set\n", - " random_state=0, # we are setting the seed here\n", - ")\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Target\n", - "\n", - "We apply the logarithm" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "y_train = np.log(y_train)\n", - "y_test = np.log(y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# categorical variables with NA in train set\n", - "CATEGORICAL_VARS_WITH_NA_FREQUENT = ['BsmtQual', 'BsmtExposure',\n", - " 'BsmtFinType1', 'GarageFinish']\n", - "\n", - "\n", - "CATEGORICAL_VARS_WITH_NA_MISSING = ['FireplaceQu']\n", - "\n", - "\n", - "# numerical variables with NA in train set\n", - "NUMERICAL_VARS_WITH_NA = ['LotFrontage']\n", - "\n", - "\n", - "TEMPORAL_VARS = ['YearRemodAdd']\n", - "REF_VAR = \"YrSold\"\n", - "\n", - "# this variable is to calculate the temporal variable,\n", - "# can be dropped afterwards\n", - "DROP_FEATURES = [\"YrSold\"]\n", - "\n", - "# variables to log transform\n", - "NUMERICALS_LOG_VARS = [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]\n", - "\n", - "\n", - "# variables to binarize\n", - "BINARIZE_VARS = ['ScreenPorch']\n", - "\n", - "# variables to map\n", - "QUAL_VARS = ['ExterQual', 'BsmtQual',\n", - " 'HeatingQC', 'KitchenQual', 'FireplaceQu']\n", - "\n", - "EXPOSURE_VARS = ['BsmtExposure']\n", - "\n", - "FINISH_VARS = ['BsmtFinType1']\n", - "\n", - "GARAGE_VARS = ['GarageFinish']\n", - "\n", - "FENCE_VARS = ['Fence']\n", - "\n", - "\n", - "# categorical variables to encode\n", - "CATEGORICAL_VARS = ['MSSubClass', 'MSZoning', 'LotShape', 'LandContour',\n", - " 'LotConfig', 'Neighborhood', 'RoofStyle', 'Exterior1st',\n", - " 'Foundation', 'CentralAir', 'Functional', 'PavedDrive',\n", - " 'SaleCondition']\n", - "\n", - "\n", - "# variable mappings\n", - "QUAL_MAPPINGS = {'Po': 1, 'Fa': 2, 'TA': 3,\n", - " 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", - "\n", - "EXPOSURE_MAPPINGS = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", - "\n", - "FINISH_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1,\n", - " 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", - "\n", - "GARAGE_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", - "\n", - "\n", - "# the selected variables\n", - "FEATURES = [\n", - " 'MSSubClass',\n", - " 'MSZoning',\n", - " 'LotFrontage',\n", - " 'LotShape',\n", - " 'LandContour',\n", - " 'LotConfig',\n", - " 'Neighborhood',\n", - " 'OverallQual',\n", - " 'OverallCond',\n", - " 'YearRemodAdd',\n", - " 'RoofStyle',\n", - " 'Exterior1st',\n", - " 'ExterQual',\n", - " 'Foundation',\n", - " 'BsmtQual',\n", - " 'BsmtExposure',\n", - " 'BsmtFinType1',\n", - " 'HeatingQC',\n", - " 'CentralAir',\n", - " '1stFlrSF',\n", - " '2ndFlrSF',\n", - " 'GrLivArea',\n", - " 'BsmtFullBath',\n", - " 'HalfBath',\n", - " 'KitchenQual',\n", - " 'TotRmsAbvGrd',\n", - " 'Functional',\n", - " 'Fireplaces',\n", - " 'FireplaceQu',\n", - " 'GarageFinish',\n", - " 'GarageCars',\n", - " 'GarageArea',\n", - " 'PavedDrive',\n", - " 'WoodDeckSF',\n", - " 'ScreenPorch',\n", - " 'SaleCondition',\n", - " # this one is only to calculate temporal variable:\n", - " \"YrSold\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1314, 37), (146, 37))" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train = X_train[FEATURES]\n", - "X_test = X_test[FEATURES]\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pipeline - End-to-end\n", - "\n", - "We have 3 steps less, they are commented out. So the pipeline is also simpler:\n", - "\n", - "- the yeo-johnson transformation\n", - "- 1 of the mappings\n", - "- the selection procedure\n", - "\n", - "this makes the pipeline faster and easier to deploy." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# set up the pipeline\n", - "price_pipe = Pipeline([\n", - "\n", - " # ===== IMPUTATION =====\n", - " # impute categorical variables with string missing\n", - " ('missing_imputation', CategoricalImputer(\n", - " imputation_method='missing', variables=CATEGORICAL_VARS_WITH_NA_MISSING)),\n", - "\n", - " ('frequent_imputation', CategoricalImputer(\n", - " imputation_method='frequent', variables=CATEGORICAL_VARS_WITH_NA_FREQUENT)),\n", - "\n", - " # add missing indicator\n", - " ('missing_indicator', AddMissingIndicator(variables=NUMERICAL_VARS_WITH_NA)),\n", - "\n", - " # impute numerical variables with the mean\n", - " ('mean_imputation', MeanMedianImputer(\n", - " imputation_method='mean', variables=NUMERICAL_VARS_WITH_NA\n", - " )),\n", - " \n", - " \n", - " # == TEMPORAL VARIABLES ====\n", - " ('elapsed_time', pp.TemporalVariableTransformer(\n", - " variables=TEMPORAL_VARS, reference_variable=REF_VAR)),\n", - "\n", - " ('drop_features', DropFeatures(features_to_drop=[REF_VAR])),\n", - "\n", - " \n", - "\n", - " # ==== VARIABLE TRANSFORMATION =====\n", - " ('log', LogTransformer(variables=NUMERICALS_LOG_VARS)),\n", - " \n", - "# ('yeojohnson', YeoJohnsonTransformer(variables=NUMERICALS_YEO_VARS)),\n", - " \n", - " ('binarizer', SklearnTransformerWrapper(\n", - " transformer=Binarizer(threshold=0), variables=BINARIZE_VARS)),\n", - " \n", - "\n", - " # === mappers ===\n", - " ('mapper_qual', pp.Mapper(\n", - " variables=QUAL_VARS, mappings=QUAL_MAPPINGS)),\n", - "\n", - " ('mapper_exposure', pp.Mapper(\n", - " variables=EXPOSURE_VARS, mappings=EXPOSURE_MAPPINGS)),\n", - "\n", - " ('mapper_finish', pp.Mapper(\n", - " variables=FINISH_VARS, mappings=FINISH_MAPPINGS)),\n", - "\n", - " ('mapper_garage', pp.Mapper(\n", - " variables=GARAGE_VARS, mappings=GARAGE_MAPPINGS)),\n", - " \n", - "# ('mapper_fence', pp.Mapper(\n", - "# variables=FENCE_VARS, mappings=FENCE_MAPPINGS)),\n", - "\n", - "\n", - " # == CATEGORICAL ENCODING\n", - " ('rare_label_encoder', RareLabelEncoder(\n", - " tol=0.01, n_categories=1, variables=CATEGORICAL_VARS\n", - " )),\n", - "\n", - " # encode categorical and discrete variables using the target mean\n", - " ('categorical_encoder', OrdinalEncoder(\n", - " encoding_method='ordered', variables=CATEGORICAL_VARS)),\n", - " \n", - " \n", - " ('scaler', MinMaxScaler()),\n", - "# ('selector', SelectFromModel(Lasso(alpha=0.001, random_state=0))),\n", - " ('Lasso', Lasso(alpha=0.001, random_state=0)),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('missing_imputation',\n", - " CategoricalImputer(variables=['FireplaceQu'])),\n", - " ('frequent_imputation',\n", - " CategoricalImputer(imputation_method='frequent',\n", - " variables=['BsmtQual', 'BsmtExposure',\n", - " 'BsmtFinType1',\n", - " 'GarageFinish'])),\n", - " ('missing_indicator',\n", - " AddMissingIndicator(variables=['LotFrontage'])),\n", - " ('mean_imputation',\n", - " MeanMedianImputer(imputation_method=...\n", - " 'Foundation', 'CentralAir',\n", - " 'Functional', 'PavedDrive',\n", - " 'SaleCondition'])),\n", - " ('categorical_encoder',\n", - " OrdinalEncoder(variables=['MSSubClass', 'MSZoning', 'LotShape',\n", - " 'LandContour', 'LotConfig',\n", - " 'Neighborhood', 'RoofStyle',\n", - " 'Exterior1st', 'Foundation',\n", - " 'CentralAir', 'Functional',\n", - " 'PavedDrive', 'SaleCondition'])),\n", - " ('scaler', MinMaxScaler()),\n", - " ('Lasso', Lasso(alpha=0.001, random_state=0))])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# train the pipeline\n", - "price_pipe.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train mse: 781396630\n", - "train rmse: 27953\n", - "train r2: 0.8748530315439078\n", - "\n", - "test mse: 1060769014\n", - "test rmse: 32569\n", - "test r2: 0.8456415571208442\n", - "\n", - "Average house price: 163000\n" - ] - } - ], - "source": [ - "# evaluate the model:\n", - "# ====================\n", - "\n", - "# make predictions for train set\n", - "pred = price_pipe.predict(X_train)\n", - "\n", - "# determine mse, rmse and r2\n", - "print('train mse: {}'.format(int(\n", - " mean_squared_error(np.exp(y_train), np.exp(pred)))))\n", - "print('train rmse: {}'.format(int(\n", - " mean_squared_error(np.exp(y_train), np.exp(pred), squared=False))))\n", - "print('train r2: {}'.format(\n", - " r2_score(np.exp(y_train), np.exp(pred))))\n", - "print()\n", - "\n", - "# make predictions for test set\n", - "pred = price_pipe.predict(X_test)\n", - "\n", - "# determine mse, rmse and r2\n", - "print('test mse: {}'.format(int(\n", - " mean_squared_error(np.exp(y_test), np.exp(pred)))))\n", - "print('test rmse: {}'.format(int(\n", - " mean_squared_error(np.exp(y_test), np.exp(pred), squared=False))))\n", - "print('test r2: {}'.format(\n", - " r2_score(np.exp(y_test), np.exp(pred))))\n", - "print()\n", - "\n", - "print('Average house price: ', int(np.exp(y_train).median()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Identical results to when we did all the engineering manually." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Evaluation of Lasso Predictions')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's evaluate our predictions respect to the real sale price\n", - "plt.scatter(y_test, price_pipe.predict(X_test))\n", - "plt.xlabel('True House Price')\n", - "plt.ylabel('Predicted House Price')\n", - "plt.title('Evaluation of Lasso Predictions')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's evaluate the distribution of the errors: \n", - "# they should be fairly normally distributed\n", - "\n", - "y_test.reset_index(drop=True, inplace=True)\n", - "\n", - "preds = pd.Series(price_pipe.predict(X_test))\n", - "\n", - "errors = y_test - preds\n", - "errors.hist(bins=30)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['price_pipe.joblib']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now let's save the scaler\n", - "\n", - "joblib.dump(price_pipe, 'price_pipe.joblib') " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Score new data" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1459, 37)\n" - ] - } - ], - "source": [ - "# load the unseen / new dataset\n", - "data = pd.read_csv('test.csv')\n", - "\n", - "data.drop('Id', axis=1, inplace=True)\n", - "\n", - "data['MSSubClass'] = data['MSSubClass'].astype('O')\n", - "\n", - "data = data[FEATURES]\n", - "\n", - "print(data.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['MSZoning',\n", - " 'Exterior1st',\n", - " 'BsmtFullBath',\n", - " 'KitchenQual',\n", - " 'Functional',\n", - " 'GarageCars',\n", - " 'GarageArea']" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_vars_with_na = [\n", - " var for var in FEATURES\n", - " if var not in CATEGORICAL_VARS_WITH_NA_FREQUENT +\n", - " CATEGORICAL_VARS_WITH_NA_MISSING +\n", - " NUMERICAL_VARS_WITH_NA\n", - " and data[var].isnull().sum() > 0]\n", - "\n", - "new_vars_with_na" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " MSZoning Exterior1st BsmtFullBath KitchenQual Functional GarageCars \\\n", - "0 RH VinylSd 0.0 TA Typ 1.0 \n", - "1 RL Wd Sdng 0.0 Gd Typ 1.0 \n", - "2 RL VinylSd 0.0 TA Typ 2.0 \n", - "3 RL VinylSd 0.0 Gd Typ 2.0 \n", - "4 RL HdBoard 0.0 Gd Typ 2.0 \n", - "\n", - " GarageArea \n", - "0 730.0 \n", - "1 312.0 \n", - "2 482.0 \n", - "3 470.0 \n", - "4 506.0 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data[new_vars_with_na].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MSZoning 0.002742\n", - "Exterior1st 0.000685\n", - "BsmtFullBath 0.001371\n", - "KitchenQual 0.000685\n", - "Functional 0.001371\n", - "GarageCars 0.000685\n", - "GarageArea 0.000685\n", - "dtype: float64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data[new_vars_with_na].isnull().mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1449, 37)\n" - ] - } - ], - "source": [ - "data.dropna(subset=new_vars_with_na, inplace=True)\n", - "\n", - "print(data.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "new_preds = price_pipe.predict(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# let's plot the predicted sale prices\n", - "pd.Series(np.exp(new_preds)).hist(bins=50)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "# Conclusion\n", - "\n", - "Now we are ready for deployment!!!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "583px", - "left": "0px", - "right": "1324px", - "top": "107px", - "width": "212px" - }, - "toc_section_display": "block", - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Final Machine Learning Pipeline\n", + "\n", + "The pipeline features\n", + "\n", + "- open source classes\n", + "- in house package classes\n", + "- only uses the selected features\n", + "- we score new data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reproducibility: Setting the seed\n", + "\n", + "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# data manipulation and plotting\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# for saving the pipeline\n", + "import joblib\n", + "\n", + "# from Scikit-learn\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import MinMaxScaler, Binarizer\n", + "\n", + "# from feature-engine\n", + "from feature_engine.imputation import (\n", + " AddMissingIndicator,\n", + " MeanMedianImputer,\n", + " CategoricalImputer,\n", + ")\n", + "\n", + "from feature_engine.encoding import (\n", + " RareLabelEncoder,\n", + " OrdinalEncoder,\n", + ")\n", + "\n", + "from feature_engine.transformation import LogTransformer\n", + "\n", + "from feature_engine.selection import DropFeatures\n", + "from feature_engine.wrappers import SklearnTransformerWrapper\n", + "\n", + "import preprocessors as pp" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n" + ] + }, + { + "data": { + "text/html": [ + "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...PoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPub...0NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPub...0NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPub...0NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPub...0NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPub...0NaNNaNNaN0122008WDNormal250000
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5 rows × 81 columns

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" + ], + "text/plain": [ + " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 1 60 RL 65.0 8450 Pave NaN Reg \n", + "1 2 20 RL 80.0 9600 Pave NaN Reg \n", + "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", + "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", + "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", + "\n", + " LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold \\\n", + "0 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n", + "1 Lvl AllPub ... 0 NaN NaN NaN 0 5 \n", + "2 Lvl AllPub ... 0 NaN NaN NaN 0 9 \n", + "3 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n", + "4 Lvl AllPub ... 0 NaN NaN NaN 0 12 \n", + "\n", + " YrSold SaleType SaleCondition SalePrice \n", + "0 2008 WD Normal 208500 \n", + "1 2007 WD Normal 181500 \n", + "2 2008 WD Normal 223500 \n", + "3 2006 WD Abnorml 140000 \n", + "4 2008 WD Normal 250000 \n", + "\n", + "[5 rows x 81 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load dataset\n", + "data = pd.read_csv('train.csv')\n", + "\n", + "# rows and columns of the data\n", + "print(data.shape)\n", + "\n", + "# visualise the dataset\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Cast MSSubClass as object\n", + "\n", + "data['MSSubClass'] = data['MSSubClass'].astype('O')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Separate dataset into train and test\n", + "\n", + "It is important to separate our data intro training and testing set. \n", + "\n", + "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n", + "\n", + "Our feature engineering techniques will learn:\n", + "\n", + "- mean\n", + "- mode\n", + "- exponents for the yeo-johnson\n", + "- category frequency\n", + "- and category to number mappings\n", + "\n", + "from the train set.\n", + "\n", + "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1314, 79), (146, 79))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's separate into train and test set\n", + "# Remember to set the seed (random_state for this sklearn function)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n", + " data['SalePrice'], # target\n", + " test_size=0.1, # portion of dataset to allocate to test set\n", + " random_state=0, # we are setting the seed here\n", + ")\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Target\n", + "\n", + "We apply the logarithm" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "y_train = np.log(y_train)\n", + "y_test = np.log(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# categorical variables with NA in train set\n", + "CATEGORICAL_VARS_WITH_NA_FREQUENT = ['BsmtQual', 'BsmtExposure',\n", + " 'BsmtFinType1', 'GarageFinish']\n", + "\n", + "\n", + "CATEGORICAL_VARS_WITH_NA_MISSING = ['FireplaceQu']\n", + "\n", + "\n", + "# numerical variables with NA in train set\n", + "NUMERICAL_VARS_WITH_NA = ['LotFrontage']\n", + "\n", + "\n", + "TEMPORAL_VARS = ['YearRemodAdd']\n", + "REF_VAR = \"YrSold\"\n", + "\n", + "# this variable is to calculate the temporal variable,\n", + "# can be dropped afterwards\n", + "DROP_FEATURES = [\"YrSold\"]\n", + "\n", + "# variables to log transform\n", + "NUMERICALS_LOG_VARS = [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]\n", + "\n", + "\n", + "# variables to binarize\n", + "BINARIZE_VARS = ['ScreenPorch']\n", + "\n", + "# variables to map\n", + "QUAL_VARS = ['ExterQual', 'BsmtQual',\n", + " 'HeatingQC', 'KitchenQual', 'FireplaceQu']\n", + "\n", + "EXPOSURE_VARS = ['BsmtExposure']\n", + "\n", + "FINISH_VARS = ['BsmtFinType1']\n", + "\n", + "GARAGE_VARS = ['GarageFinish']\n", + "\n", + "FENCE_VARS = ['Fence']\n", + "\n", + "\n", + "# categorical variables to encode\n", + "CATEGORICAL_VARS = ['MSSubClass', 'MSZoning', 'LotShape', 'LandContour',\n", + " 'LotConfig', 'Neighborhood', 'RoofStyle', 'Exterior1st',\n", + " 'Foundation', 'CentralAir', 'Functional', 'PavedDrive',\n", + " 'SaleCondition']\n", + "\n", + "\n", + "# variable mappings\n", + "QUAL_MAPPINGS = {'Po': 1, 'Fa': 2, 'TA': 3,\n", + " 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n", + "\n", + "EXPOSURE_MAPPINGS = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n", + "\n", + "FINISH_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1,\n", + " 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n", + "\n", + "GARAGE_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n", + "\n", + "\n", + "# the selected variables\n", + "FEATURES = [\n", + " 'MSSubClass',\n", + " 'MSZoning',\n", + " 'LotFrontage',\n", + " 'LotShape',\n", + " 'LandContour',\n", + " 'LotConfig',\n", + " 'Neighborhood',\n", + " 'OverallQual',\n", + " 'OverallCond',\n", + " 'YearRemodAdd',\n", + " 'RoofStyle',\n", + " 'Exterior1st',\n", + " 'ExterQual',\n", + " 'Foundation',\n", + " 'BsmtQual',\n", + " 'BsmtExposure',\n", + " 'BsmtFinType1',\n", + " 'HeatingQC',\n", + " 'CentralAir',\n", + " '1stFlrSF',\n", + " '2ndFlrSF',\n", + " 'GrLivArea',\n", + " 'BsmtFullBath',\n", + " 'HalfBath',\n", + " 'KitchenQual',\n", + " 'TotRmsAbvGrd',\n", + " 'Functional',\n", + " 'Fireplaces',\n", + " 'FireplaceQu',\n", + " 'GarageFinish',\n", + " 'GarageCars',\n", + " 'GarageArea',\n", + " 'PavedDrive',\n", + " 'WoodDeckSF',\n", + " 'ScreenPorch',\n", + " 'SaleCondition',\n", + " # this one is only to calculate temporal variable:\n", + " \"YrSold\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1314, 37), (146, 37))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train = X_train[FEATURES]\n", + "X_test = X_test[FEATURES]\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pipeline - End-to-end\n", + "\n", + "We have 3 steps less, they are commented out. So the pipeline is also simpler:\n", + "\n", + "- the yeo-johnson transformation\n", + "- 1 of the mappings\n", + "- the selection procedure\n", + "\n", + "this makes the pipeline faster and easier to deploy." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# set up the pipeline\n", + "price_pipe = Pipeline([\n", + "\n", + " # ===== IMPUTATION =====\n", + " # impute categorical variables with string missing\n", + " ('missing_imputation', CategoricalImputer(\n", + " imputation_method='missing', variables=CATEGORICAL_VARS_WITH_NA_MISSING)),\n", + "\n", + " ('frequent_imputation', CategoricalImputer(\n", + " imputation_method='frequent', variables=CATEGORICAL_VARS_WITH_NA_FREQUENT)),\n", + "\n", + " # add missing indicator\n", + " ('missing_indicator', AddMissingIndicator(variables=NUMERICAL_VARS_WITH_NA)),\n", + "\n", + " # impute numerical variables with the mean\n", + " ('mean_imputation', MeanMedianImputer(\n", + " imputation_method='mean', variables=NUMERICAL_VARS_WITH_NA\n", + " )),\n", + " \n", + " \n", + " # == TEMPORAL VARIABLES ====\n", + " ('elapsed_time', pp.TemporalVariableTransformer(\n", + " variables=TEMPORAL_VARS, reference_variable=REF_VAR)),\n", + "\n", + " ('drop_features', DropFeatures(features_to_drop=[REF_VAR])),\n", + "\n", + " \n", + "\n", + " # ==== VARIABLE TRANSFORMATION =====\n", + " ('log', LogTransformer(variables=NUMERICALS_LOG_VARS)),\n", + " \n", + "# ('yeojohnson', YeoJohnsonTransformer(variables=NUMERICALS_YEO_VARS)),\n", + " \n", + " ('binarizer', SklearnTransformerWrapper(\n", + " transformer=Binarizer(threshold=0), variables=BINARIZE_VARS)),\n", + " \n", + "\n", + " # === mappers ===\n", + " ('mapper_qual', pp.Mapper(\n", + " variables=QUAL_VARS, mappings=QUAL_MAPPINGS)),\n", + "\n", + " ('mapper_exposure', pp.Mapper(\n", + " variables=EXPOSURE_VARS, mappings=EXPOSURE_MAPPINGS)),\n", + "\n", + " ('mapper_finish', pp.Mapper(\n", + " variables=FINISH_VARS, mappings=FINISH_MAPPINGS)),\n", + "\n", + " ('mapper_garage', pp.Mapper(\n", + " variables=GARAGE_VARS, mappings=GARAGE_MAPPINGS)),\n", + " \n", + "# ('mapper_fence', pp.Mapper(\n", + "# variables=FENCE_VARS, mappings=FENCE_MAPPINGS)),\n", + "\n", + "\n", + " # == CATEGORICAL ENCODING\n", + " ('rare_label_encoder', RareLabelEncoder(\n", + " tol=0.01, n_categories=1, variables=CATEGORICAL_VARS\n", + " )),\n", + "\n", + " # encode categorical and discrete variables using the target mean\n", + " ('categorical_encoder', OrdinalEncoder(\n", + " encoding_method='ordered', variables=CATEGORICAL_VARS)),\n", + " \n", + " \n", + " ('scaler', MinMaxScaler()),\n", + "# ('selector', SelectFromModel(Lasso(alpha=0.001, random_state=0))),\n", + " ('Lasso', Lasso(alpha=0.001, random_state=0)),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('missing_imputation',\n", + " CategoricalImputer(variables=['FireplaceQu'])),\n", + " ('frequent_imputation',\n", + " CategoricalImputer(imputation_method='frequent',\n", + " variables=['BsmtQual', 'BsmtExposure',\n", + " 'BsmtFinType1',\n", + " 'GarageFinish'])),\n", + " ('missing_indicator',\n", + " AddMissingIndicator(variables=['LotFrontage'])),\n", + " ('mean_imputation',\n", + " MeanMedianImputer(imputation_method=...\n", + " 'Foundation', 'CentralAir',\n", + " 'Functional', 'PavedDrive',\n", + " 'SaleCondition'])),\n", + " ('categorical_encoder',\n", + " OrdinalEncoder(variables=['MSSubClass', 'MSZoning', 'LotShape',\n", + " 'LandContour', 'LotConfig',\n", + " 'Neighborhood', 'RoofStyle',\n", + " 'Exterior1st', 'Foundation',\n", + " 'CentralAir', 'Functional',\n", + " 'PavedDrive', 'SaleCondition'])),\n", + " ('scaler', MinMaxScaler()),\n", + " ('Lasso', Lasso(alpha=0.001, random_state=0))])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# train the pipeline\n", + "price_pipe.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train mse: 781396630\n", + "train rmse: 27953\n", + "train r2: 0.8748530315439078\n", + "\n", + "test mse: 1060769014\n", + "test rmse: 32569\n", + "test r2: 0.8456415571208442\n", + "\n", + "Average house price: 163000\n" + ] + } + ], + "source": [ + "# evaluate the model:\n", + "# ====================\n", + "\n", + "# make predictions for train set\n", + "pred = price_pipe.predict(X_train)\n", + "\n", + "# determine mse, rmse and r2\n", + "print('train mse: {}'.format(int(\n", + " mean_squared_error(np.exp(y_train), np.exp(pred)))))\n", + "print('train rmse: {}'.format(int(\n", + " mean_squared_error(np.exp(y_train), np.exp(pred), squared=False))))\n", + "print('train r2: {}'.format(\n", + " r2_score(np.exp(y_train), np.exp(pred))))\n", + "print()\n", + "\n", + "# make predictions for test set\n", + "pred = price_pipe.predict(X_test)\n", + "\n", + "# determine mse, rmse and r2\n", + "print('test mse: {}'.format(int(\n", + " mean_squared_error(np.exp(y_test), np.exp(pred)))))\n", + "print('test rmse: {}'.format(int(\n", + " mean_squared_error(np.exp(y_test), np.exp(pred), squared=False))))\n", + "print('test r2: {}'.format(\n", + " r2_score(np.exp(y_test), np.exp(pred))))\n", + "print()\n", + "\n", + "print('Average house price: ', int(np.exp(y_train).median()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Identical results to when we did all the engineering manually." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Evaluation of Lasso Predictions')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's evaluate our predictions respect to the real sale price\n", + "plt.scatter(y_test, price_pipe.predict(X_test))\n", + "plt.xlabel('True House Price')\n", + "plt.ylabel('Predicted House Price')\n", + "plt.title('Evaluation of Lasso Predictions')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's evaluate the distribution of the errors: \n", + "# they should be fairly normally distributed\n", + "\n", + "y_test.reset_index(drop=True, inplace=True)\n", + "\n", + "preds = pd.Series(price_pipe.predict(X_test))\n", + "\n", + "errors = y_test - preds\n", + "errors.hist(bins=30)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['price_pipe.joblib']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now let's save the scaler\n", + "\n", + "joblib.dump(price_pipe, 'price_pipe.joblib') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Score new data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1459, 37)\n" + ] + } + ], + "source": [ + "# load the unseen / new dataset\n", + "data = pd.read_csv('test.csv')\n", + "\n", + "data.drop('Id', axis=1, inplace=True)\n", + "\n", + "data['MSSubClass'] = data['MSSubClass'].astype('O')\n", + "\n", + "data = data[FEATURES]\n", + "\n", + "print(data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['MSZoning',\n", + " 'Exterior1st',\n", + " 'BsmtFullBath',\n", + " 'KitchenQual',\n", + " 'Functional',\n", + " 'GarageCars',\n", + " 'GarageArea']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_vars_with_na = [\n", + " var for var in FEATURES\n", + " if var not in CATEGORICAL_VARS_WITH_NA_FREQUENT +\n", + " CATEGORICAL_VARS_WITH_NA_MISSING +\n", + " NUMERICAL_VARS_WITH_NA\n", + " and data[var].isnull().sum() > 0]\n", + "\n", + "new_vars_with_na" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " MSZoning Exterior1st BsmtFullBath KitchenQual Functional GarageCars \\\n", + "0 RH VinylSd 0.0 TA Typ 1.0 \n", + "1 RL Wd Sdng 0.0 Gd Typ 1.0 \n", + "2 RL VinylSd 0.0 TA Typ 2.0 \n", + "3 RL VinylSd 0.0 Gd Typ 2.0 \n", + "4 RL HdBoard 0.0 Gd Typ 2.0 \n", + "\n", + " GarageArea \n", + "0 730.0 \n", + "1 312.0 \n", + "2 482.0 \n", + "3 470.0 \n", + "4 506.0 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[new_vars_with_na].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MSZoning 0.002742\n", + "Exterior1st 0.000685\n", + "BsmtFullBath 0.001371\n", + "KitchenQual 0.000685\n", + "Functional 0.001371\n", + "GarageCars 0.000685\n", + "GarageArea 0.000685\n", + "dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[new_vars_with_na].isnull().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1449, 37)\n" + ] + } + ], + "source": [ + "data.dropna(subset=new_vars_with_na, inplace=True)\n", + "\n", + "print(data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "new_preds = price_pipe.predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Efq8s/z5wZ1m+AbinLF8IfAd4PbAReJbGBQanleU3AaeXbS4s+yx4jj7X46PAPwIPLtXHIajDYeC8eW0D/froa8EG4QGM8Yvh/jSwriyvA54uy58Hbpy/HXAj8Pmm9s+XtnXA95raX9tusXMMygN4gMa9gYa6FsAbgMeAt9P4ZOGq0v4O4KGy/BDwjrK8qmwXwO3A7U3Heqjs99q+pf328ojFztHH738DsAe4AnhwqT7WXIfSj8OcGO4D/foY6mmZRYxm5otl+YfAaFleD/ygabsXSttS7S8s0L7UOfqu/Dn9GzRGrENZizIV8ThwBNhNY4T5SmYeK5s09/+177msPwqcS+s1OneJc/TLXwN/Cvxfeb5UH2uuA0ACX4uI/dG4dQoM+OvD+7kvITMzInp6rehKnGO5ImIE+DLwR5n5kzLtBwxXLTLzf4G3RcQa4J+At/a3RysvIt4LHMnM/REx0efuDIJ3ZuZMRPwKsDsivte8chBfH47cT/RSRKwDKF+PlPbFbquwVPuGBdqXOkffRMTraAT73Zl5f2keylocl5mvAI/QmBpYExHHB0PN/X/tey7rVwM/pvUa/XiJc/TD5cD7IuIwjTu7XgHcwfDVAYDMnClfj9D4hX8pA/76MNxPtAs4/i72Zhrzz8fbP1jeCb8MOFr+XHoIeHdErC3vZL+bxhzhi8BPIuKy8s73B+cda6Fz9EXp313Awcz8dNOqYazFG8uInYg4k8Z7DwdphPz1ZbP5tTje/+uBh7MxQboLuKFcRbIRuIDGm2YL3p6j7LPYOVZcZt6emRsyc4xGHx/OzJsYsjoARMRZEXH28WUaP9dPMOivj36/UdHPB/BF4EXg5zTmuW6mMee3B3gG+FfgnLJt0PgnJM8CB4DxpuP8DnCoPD7U1D5efgieBf6GuU8EL3iOPtbhnTTmFL8LPF4eVw9pLX4N+HapxRPAn5f2N9EIpUPAl4DXl/YzyvNDZf2bmo71sfL9Pk25+qG0X03jiqRngY81tS94jn4/gAnmrpYZujqU/nynPJ483tdBf314+wFJqpDTMpJUIcNdkipkuEtShQx3SaqQ4S5JFTLcJalChrskVej/ARqgTW4lDD2YAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's plot the predicted sale prices\n", + "pd.Series(np.exp(new_preds)).hist(bins=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Conclusion\n", + "\n", + "Now we are ready for deployment!!!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "583px", + "left": "0px", + "right": "1324px", + "top": "107px", + "width": "212px" + }, + "toc_section_display": "block", + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/preprocessors.py b/section-04-research-and-development/preprocessors.py index 8611867be..ec6b7f24a 100644 --- a/section-04-research-and-development/preprocessors.py +++ b/section-04-research-and-development/preprocessors.py @@ -1,55 +1,55 @@ -import numpy as np -import pandas as pd - -from sklearn.base import BaseEstimator, TransformerMixin - - - -class TemporalVariableTransformer(BaseEstimator, TransformerMixin): - # Temporal elapsed time transformer - - def __init__(self, variables, reference_variable): - - if not isinstance(variables, list): - raise ValueError('variables should be a list') - - self.variables = variables - self.reference_variable = reference_variable - - def fit(self, X, y=None): - # we need this step to fit the sklearn pipeline - return self - - def transform(self, X): - - # so that we do not over-write the original dataframe - X = X.copy() - - for feature in self.variables: - X[feature] = X[self.reference_variable] - X[feature] - - return X - - - -# categorical missing value imputer -class Mapper(BaseEstimator, TransformerMixin): - - def __init__(self, variables, mappings): - - if not isinstance(variables, list): - raise ValueError('variables should be a list') - - self.variables = variables - self.mappings = mappings - - def fit(self, X, y=None): - # we need the fit statement to accomodate the sklearn pipeline - return self - - def transform(self, X): - X = X.copy() - for feature in self.variables: - X[feature] = X[feature].map(self.mappings) - +import numpy as np +import pandas as pd + +from sklearn.base import BaseEstimator, TransformerMixin + + + +class TemporalVariableTransformer(BaseEstimator, TransformerMixin): + # Temporal elapsed time transformer + + def __init__(self, variables, reference_variable): + + if not isinstance(variables, list): + raise ValueError('variables should be a list') + + self.variables = variables + self.reference_variable = reference_variable + + def fit(self, X, y=None): + # we need this step to fit the sklearn pipeline + return self + + def transform(self, X): + + # so that we do not over-write the original dataframe + X = X.copy() + + for feature in self.variables: + X[feature] = X[self.reference_variable] - X[feature] + + return X + + + +# categorical missing value imputer +class Mapper(BaseEstimator, TransformerMixin): + + def __init__(self, variables, mappings): + + if not isinstance(variables, list): + raise ValueError('variables should be a list') + + self.variables = variables + self.mappings = mappings + + def fit(self, X, y=None): + # we need the fit statement to accomodate the sklearn pipeline + return self + + def transform(self, X): + X = X.copy() + for feature in self.variables: + X[feature] = X[feature].map(self.mappings) + return X \ No newline at end of file diff --git a/section-04-research-and-development/preprocessors_bonus.py b/section-04-research-and-development/preprocessors_bonus.py index b33907823..efd16f7c0 100644 --- a/section-04-research-and-development/preprocessors_bonus.py +++ b/section-04-research-and-development/preprocessors_bonus.py @@ -1,90 +1,90 @@ -import numpy as np -import pandas as pd -from sklearn.base import BaseEstimator, TransformerMixin - - -class MeanImputer(BaseEstimator, TransformerMixin): - """Numerical missing value imputer.""" - - def __init__(self, variables): - if not isinstance(variables, list): - raise ValueError('variables should be a list') - self.variables = variables - - def fit(self, X, y=None): - # persist mean values in a dictionary - self.imputer_dict_ = X[self.variables].mean().to_dict() - return self - - def transform(self, X): - X = X.copy() - for feature in self.variables: - X[feature].fillna(self.imputer_dict_[feature], - inplace=True) - return X - - - -class RareLabelCategoricalEncoder(BaseEstimator, TransformerMixin): - """Groups infrequent categories into a single string""" - - def __init__(self, tol=0.05, variables): - - if not isinstance(variables, list): - raise ValueError('variables should be a list') - - self.tol = tol - self.variables = variables - - def fit(self, X, y=None): - # persist frequent labels in dictionary - self.encoder_dict_ = {} - - for var in self.variables: - # the encoder will learn the most frequent categories - t = pd.Series(X[var].value_counts(normalize=True) - # frequent labels: - self.encoder_dict_[var] = list(t[t >= self.tol].index) - - return self - - def transform(self, X): - X = X.copy() - for feature in self.variables: - X[feature] = np.where( - X[feature].isin(self.encoder_dict_[feature]), - X[feature], "Rare") - - return X - - -class CategoricalEncoder(BaseEstimator, TransformerMixin): - """String to numbers categorical encoder.""" - - def __init__(self, variables): - - if not isinstance(variables, list): - raise ValueError('variables should be a list') - - self.variables = variables - - def fit(self, X, y): - temp = pd.concat([X, y], axis=1) - temp.columns = list(X.columns) + ["target"] - - # persist transforming dictionary - self.encoder_dict_ = {} - - for var in self.variables: - t = temp.groupby([var])["target"].mean().sort_values(ascending=True).index - self.encoder_dict_[var] = {k: i for i, k in enumerate(t, 0)} - - return self - - def transform(self, X): - # encode labels - X = X.copy() - for feature in self.variables: - X[feature] = X[feature].map(self.encoder_dict_[feature]) - +import numpy as np +import pandas as pd +from sklearn.base import BaseEstimator, TransformerMixin + + +class MeanImputer(BaseEstimator, TransformerMixin): + """Numerical missing value imputer.""" + + def __init__(self, variables): + if not isinstance(variables, list): + raise ValueError('variables should be a list') + self.variables = variables + + def fit(self, X, y=None): + # persist mean values in a dictionary + self.imputer_dict_ = X[self.variables].mean().to_dict() + return self + + def transform(self, X): + X = X.copy() + for feature in self.variables: + X[feature].fillna(self.imputer_dict_[feature], + inplace=True) + return X + + + +class RareLabelCategoricalEncoder(BaseEstimator, TransformerMixin): + """Groups infrequent categories into a single string""" + + def __init__(self, tol=0.05, variables): + + if not isinstance(variables, list): + raise ValueError('variables should be a list') + + self.tol = tol + self.variables = variables + + def fit(self, X, y=None): + # persist frequent labels in dictionary + self.encoder_dict_ = {} + + for var in self.variables: + # the encoder will learn the most frequent categories + t = pd.Series(X[var].value_counts(normalize=True) + # frequent labels: + self.encoder_dict_[var] = list(t[t >= self.tol].index) + + return self + + def transform(self, X): + X = X.copy() + for feature in self.variables: + X[feature] = np.where( + X[feature].isin(self.encoder_dict_[feature]), + X[feature], "Rare") + + return X + + +class CategoricalEncoder(BaseEstimator, TransformerMixin): + """String to numbers categorical encoder.""" + + def __init__(self, variables): + + if not isinstance(variables, list): + raise ValueError('variables should be a list') + + self.variables = variables + + def fit(self, X, y): + temp = pd.concat([X, y], axis=1) + temp.columns = list(X.columns) + ["target"] + + # persist transforming dictionary + self.encoder_dict_ = {} + + for var in self.variables: + t = temp.groupby([var])["target"].mean().sort_values(ascending=True).index + self.encoder_dict_[var] = {k: i for i, k in enumerate(t, 0)} + + return self + + def transform(self, X): + # encode labels + X = X.copy() + for feature in self.variables: + X[feature] = X[feature].map(self.encoder_dict_[feature]) + return X \ No newline at end of file diff --git a/section-04-research-and-development/requirements.txt b/section-04-research-and-development/requirements.txt index aef524768..e01f7a47c 100644 --- a/section-04-research-and-development/requirements.txt +++ b/section-04-research-and-development/requirements.txt @@ -1,9 +1,9 @@ -feature-engine==1.0.2 -joblib==1.0.1 -matplotlib==3.3.4 -numpy==1.20.1 -pandas==1.2.2 -scikit-learn==0.24.1 -scipy==1.6.0 -seaborn==0.11.1 +feature-engine==1.0.2 +joblib==1.0.1 +matplotlib==3.3.4 +numpy==1.20.1 +pandas==1.2.2 +scikit-learn==0.24.1 +scipy==1.6.0 +seaborn==0.11.1 statsmodels==0.12.2 \ No newline at end of file diff --git a/section-04-research-and-development/titanic-assignment/01-predicting-survival-titanic-assignement.ipynb b/section-04-research-and-development/titanic-assignment/01-predicting-survival-titanic-assignement.ipynb index 13a218de6..c1bf22499 100644 --- a/section-04-research-and-development/titanic-assignment/01-predicting-survival-titanic-assignement.ipynb +++ b/section-04-research-and-development/titanic-assignment/01-predicting-survival-titanic-assignement.ipynb @@ -1,787 +1,787 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predicting Survival on the Titanic\n", - "\n", - "### History\n", - "Perhaps one of the most infamous shipwrecks in history, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 people on board. Interestingly, by analysing the probability of survival based on few attributes like gender, age, and social status, we can make very accurate predictions on which passengers would survive. Some groups of people were more likely to survive than others, such as women, children, and the upper-class. Therefore, we can learn about the society priorities and privileges at the time.\n", - "\n", - "### Assignment:\n", - "\n", - "Build a Machine Learning Pipeline, to engineer the features in the data set and predict who is more likely to Survive the catastrophe.\n", - "\n", - "Follow the Jupyter notebook below, and complete the missing bits of code, to achieve each one of the pipeline steps." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "\n", - "# to handle datasets\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# for visualization\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# to divide train and test set\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# feature scaling\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "# to build the models\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "# to evaluate the models\n", - "from sklearn.metrics import accuracy_score, roc_auc_score\n", - "\n", - "# to persist the model and the scaler\n", - "import joblib\n", - "\n", - "# to visualise al the columns in the dataframe\n", - "pd.pandas.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare the data set" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2?St Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.55C22 C26S11?Montreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale212113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale3012113781151.55C22 C26S?135Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
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" - ], - "text/plain": [ - " pclass survived name sex \\\n", - "0 1 1 Allen, Miss. Elisabeth Walton female \n", - "1 1 1 Allison, Master. Hudson Trevor male \n", - "2 1 0 Allison, Miss. Helen Loraine female \n", - "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", - "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", - "\n", - " age sibsp parch ticket fare cabin embarked boat body \\\n", - "0 29 0 0 24160 211.3375 B5 S 2 ? \n", - "1 0.9167 1 2 113781 151.55 C22 C26 S 11 ? \n", - "2 2 1 2 113781 151.55 C22 C26 S ? ? \n", - "3 30 1 2 113781 151.55 C22 C26 S ? 135 \n", - "4 25 1 2 113781 151.55 C22 C26 S ? ? \n", - "\n", - " home.dest \n", - "0 St Louis, MO \n", - "1 Montreal, PQ / Chesterville, ON \n", - "2 Montreal, PQ / Chesterville, ON \n", - "3 Montreal, PQ / Chesterville, ON \n", - "4 Montreal, PQ / Chesterville, ON " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the data - it is available open source and online\n", - "\n", - "data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')\n", - "\n", - "# display data\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# replace interrogation marks by NaN values\n", - "\n", - "data = data.replace('?', np.nan)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# retain only the first cabin if more than\n", - "# 1 are available per passenger\n", - "\n", - "def get_first_cabin(row):\n", - " try:\n", - " return row.split()[0]\n", - " except:\n", - " return np.nan\n", - " \n", - "data['cabin'] = data['cabin'].apply(get_first_cabin)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# extracts the title (Mr, Ms, etc) from the name variable\n", - "\n", - "def get_title(passenger):\n", - " line = passenger\n", - " if re.search('Mrs', line):\n", - " return 'Mrs'\n", - " elif re.search('Mr', line):\n", - " return 'Mr'\n", - " elif re.search('Miss', line):\n", - " return 'Miss'\n", - " elif re.search('Master', line):\n", - " return 'Master'\n", - " else:\n", - " return 'Other'\n", - " \n", - "data['title'] = data['name'].apply(get_title)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# cast numerical variables as floats\n", - "\n", - "data['fare'] = data['fare'].astype('float')\n", - "data['age'] = data['age'].astype('float')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pclasssurvivedsexagesibspparchfarecabinembarkedtitle
011female29.000000211.3375B5SMiss
111male0.916712151.5500C22SMaster
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310male30.000012151.5500C22SMr
410female25.000012151.5500C22SMrs
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" - ], - "text/plain": [ - " pclass survived sex age sibsp parch fare cabin embarked \\\n", - "0 1 1 female 29.0000 0 0 211.3375 B5 S \n", - "1 1 1 male 0.9167 1 2 151.5500 C22 S \n", - "2 1 0 female 2.0000 1 2 151.5500 C22 S \n", - "3 1 0 male 30.0000 1 2 151.5500 C22 S \n", - "4 1 0 female 25.0000 1 2 151.5500 C22 S \n", - "\n", - " title \n", - "0 Miss \n", - "1 Master \n", - "2 Miss \n", - "3 Mr \n", - "4 Mrs " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# drop unnecessary variables\n", - "\n", - "data.drop(labels=['name','ticket', 'boat', 'body','home.dest'], axis=1, inplace=True)\n", - "\n", - "# display data\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# save the data set\n", - "\n", - "data.to_csv('titanic.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Exploration\n", - "\n", - "### Find numerical and categorical variables" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "target = 'survived'" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "vars_num = # fill your code here\n", - "\n", - "vars_cat = # fill your code here\n", - "\n", - "print('Number of numerical variables: {}'.format(len(vars_num)))\n", - "print('Number of categorical variables: {}'.format(len(vars_cat)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find missing values in variables" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# first in numerical variables\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# now in categorical variables\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Determine cardinality of categorical variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Determine the distribution of numerical variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Separate data into train and test\n", - "\n", - "Use the code below for reproducibility. Don't change it." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " data.drop('survived', axis=1), # predictors\n", - " data['survived'], # target\n", - " test_size=0.2, # percentage of obs in test set\n", - " random_state=0) # seed to ensure reproducibility\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Engineering\n", - "\n", - "### Extract only the letter (and drop the number) from the variable Cabin" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fill in Missing data in numerical variables:\n", - "\n", - "- Add a binary missing indicator\n", - "- Fill NA in original variable with the median" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Replace Missing data in categorical variables with the string **Missing**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove rare labels in categorical variables\n", - "\n", - "- remove labels present in less than 5 % of the passengers" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perform one hot encoding of categorical variables into k-1 binary variables\n", - "\n", - "- k-1, means that if the variable contains 9 different categories, we create 8 different binary variables\n", - "- Remember to drop the original categorical variable (the one with the strings) after the encoding" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Scale the variables\n", - "\n", - "- Use the standard scaler from Scikit-learn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the Logistic Regression model\n", - "\n", - "- Set the regularization parameter to 0.0005\n", - "- Set the seed to 0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make predictions and evaluate model performance\n", - "\n", - "Determine:\n", - "- roc-auc\n", - "- accuracy\n", - "\n", - "**Important, remember that to determine the accuracy, you need the outcome 0, 1, referring to survived or not. But to determine the roc-auc you need the probability of survival.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it! Well done\n", - "\n", - "**Keep this code safe, as we will use this notebook later on, to build production code, in our next assignement!!**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predicting Survival on the Titanic\n", + "\n", + "### History\n", + "Perhaps one of the most infamous shipwrecks in history, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 people on board. Interestingly, by analysing the probability of survival based on few attributes like gender, age, and social status, we can make very accurate predictions on which passengers would survive. Some groups of people were more likely to survive than others, such as women, children, and the upper-class. Therefore, we can learn about the society priorities and privileges at the time.\n", + "\n", + "### Assignment:\n", + "\n", + "Build a Machine Learning Pipeline, to engineer the features in the data set and predict who is more likely to Survive the catastrophe.\n", + "\n", + "Follow the Jupyter notebook below, and complete the missing bits of code, to achieve each one of the pipeline steps." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "# to handle datasets\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# for visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# to divide train and test set\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# feature scaling\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# to build the models\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# to evaluate the models\n", + "from sklearn.metrics import accuracy_score, roc_auc_score\n", + "\n", + "# to persist the model and the scaler\n", + "import joblib\n", + "\n", + "# to visualise al the columns in the dataframe\n", + "pd.pandas.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare the data set" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2?St Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.55C22 C26S11?Montreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale212113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale3012113781151.55C22 C26S?135Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
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" + ], + "text/plain": [ + " pclass survived name sex \\\n", + "0 1 1 Allen, Miss. Elisabeth Walton female \n", + "1 1 1 Allison, Master. Hudson Trevor male \n", + "2 1 0 Allison, Miss. Helen Loraine female \n", + "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", + "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", + "\n", + " age sibsp parch ticket fare cabin embarked boat body \\\n", + "0 29 0 0 24160 211.3375 B5 S 2 ? \n", + "1 0.9167 1 2 113781 151.55 C22 C26 S 11 ? \n", + "2 2 1 2 113781 151.55 C22 C26 S ? ? \n", + "3 30 1 2 113781 151.55 C22 C26 S ? 135 \n", + "4 25 1 2 113781 151.55 C22 C26 S ? ? \n", + "\n", + " home.dest \n", + "0 St Louis, MO \n", + "1 Montreal, PQ / Chesterville, ON \n", + "2 Montreal, PQ / Chesterville, ON \n", + "3 Montreal, PQ / Chesterville, ON \n", + "4 Montreal, PQ / Chesterville, ON " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data - it is available open source and online\n", + "\n", + "data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')\n", + "\n", + "# display data\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# replace interrogation marks by NaN values\n", + "\n", + "data = data.replace('?', np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# retain only the first cabin if more than\n", + "# 1 are available per passenger\n", + "\n", + "def get_first_cabin(row):\n", + " try:\n", + " return row.split()[0]\n", + " except:\n", + " return np.nan\n", + " \n", + "data['cabin'] = data['cabin'].apply(get_first_cabin)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# extracts the title (Mr, Ms, etc) from the name variable\n", + "\n", + "def get_title(passenger):\n", + " line = passenger\n", + " if re.search('Mrs', line):\n", + " return 'Mrs'\n", + " elif re.search('Mr', line):\n", + " return 'Mr'\n", + " elif re.search('Miss', line):\n", + " return 'Miss'\n", + " elif re.search('Master', line):\n", + " return 'Master'\n", + " else:\n", + " return 'Other'\n", + " \n", + "data['title'] = data['name'].apply(get_title)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# cast numerical variables as floats\n", + "\n", + "data['fare'] = data['fare'].astype('float')\n", + "data['age'] = data['age'].astype('float')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " pclass survived sex age sibsp parch fare cabin embarked \\\n", + "0 1 1 female 29.0000 0 0 211.3375 B5 S \n", + "1 1 1 male 0.9167 1 2 151.5500 C22 S \n", + "2 1 0 female 2.0000 1 2 151.5500 C22 S \n", + "3 1 0 male 30.0000 1 2 151.5500 C22 S \n", + "4 1 0 female 25.0000 1 2 151.5500 C22 S \n", + "\n", + " title \n", + "0 Miss \n", + "1 Master \n", + "2 Miss \n", + "3 Mr \n", + "4 Mrs " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop unnecessary variables\n", + "\n", + "data.drop(labels=['name','ticket', 'boat', 'body','home.dest'], axis=1, inplace=True)\n", + "\n", + "# display data\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# save the data set\n", + "\n", + "data.to_csv('titanic.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration\n", + "\n", + "### Find numerical and categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "target = 'survived'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "vars_num = # fill your code here\n", + "\n", + "vars_cat = # fill your code here\n", + "\n", + "print('Number of numerical variables: {}'.format(len(vars_num)))\n", + "print('Number of categorical variables: {}'.format(len(vars_cat)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find missing values in variables" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# first in numerical variables\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# now in categorical variables\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Determine cardinality of categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Determine the distribution of numerical variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Separate data into train and test\n", + "\n", + "Use the code below for reproducibility. Don't change it." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data.drop('survived', axis=1), # predictors\n", + " data['survived'], # target\n", + " test_size=0.2, # percentage of obs in test set\n", + " random_state=0) # seed to ensure reproducibility\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature Engineering\n", + "\n", + "### Extract only the letter (and drop the number) from the variable Cabin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fill in Missing data in numerical variables:\n", + "\n", + "- Add a binary missing indicator\n", + "- Fill NA in original variable with the median" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Replace Missing data in categorical variables with the string **Missing**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove rare labels in categorical variables\n", + "\n", + "- remove labels present in less than 5 % of the passengers" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform one hot encoding of categorical variables into k-1 binary variables\n", + "\n", + "- k-1, means that if the variable contains 9 different categories, we create 8 different binary variables\n", + "- Remember to drop the original categorical variable (the one with the strings) after the encoding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scale the variables\n", + "\n", + "- Use the standard scaler from Scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the Logistic Regression model\n", + "\n", + "- Set the regularization parameter to 0.0005\n", + "- Set the seed to 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make predictions and evaluate model performance\n", + "\n", + "Determine:\n", + "- roc-auc\n", + "- accuracy\n", + "\n", + "**Important, remember that to determine the accuracy, you need the outcome 0, 1, referring to survived or not. But to determine the roc-auc you need the probability of survival.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it! Well done\n", + "\n", + "**Keep this code safe, as we will use this notebook later on, to build production code, in our next assignement!!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/titanic-assignment/02-predicting-survival-titanic-solution.ipynb b/section-04-research-and-development/titanic-assignment/02-predicting-survival-titanic-solution.ipynb index 0530a8a91..b5da8e438 100644 --- a/section-04-research-and-development/titanic-assignment/02-predicting-survival-titanic-solution.ipynb +++ b/section-04-research-and-development/titanic-assignment/02-predicting-survival-titanic-solution.ipynb @@ -1,1489 +1,1489 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predicting Survival on the Titanic\n", - "\n", - "### History\n", - "Perhaps one of the most infamous shipwrecks in history, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 people on board. Interestingly, by analysing the probability of survival based on few attributes like gender, age, and social status, we can make very accurate predictions on which passengers would survive. Some groups of people were more likely to survive than others, such as women, children, and the upper-class. Therefore, we can learn about the society priorities and privileges at the time.\n", - "\n", - "### Assignment:\n", - "\n", - "Build a Machine Learning Pipeline, to engineer the features in the data set and predict who is more likely to Survive the catastrophe.\n", - "\n", - "Follow the Jupyter notebook below, and complete the missing bits of code, to achieve each one of the pipeline steps." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "\n", - "# to handle datasets\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# for visualization\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# to divide train and test set\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# feature scaling\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "# to build the models\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "# to evaluate the models\n", - "from sklearn.metrics import accuracy_score, roc_auc_score\n", - "\n", - "# to persist the model and the scaler\n", - "import joblib\n", - "\n", - "# to visualise al the columns in the dataframe\n", - "pd.pandas.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare the data set" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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011Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2?St Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.55C22 C26S11?Montreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale212113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale3012113781151.55C22 C26S?135Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
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" - ], - "text/plain": [ - " pclass survived name sex \\\n", - "0 1 1 Allen, Miss. Elisabeth Walton female \n", - "1 1 1 Allison, Master. Hudson Trevor male \n", - "2 1 0 Allison, Miss. Helen Loraine female \n", - "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", - "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", - "\n", - " age sibsp parch ticket fare cabin embarked boat body \\\n", - "0 29 0 0 24160 211.3375 B5 S 2 ? \n", - "1 0.9167 1 2 113781 151.55 C22 C26 S 11 ? \n", - "2 2 1 2 113781 151.55 C22 C26 S ? ? \n", - "3 30 1 2 113781 151.55 C22 C26 S ? 135 \n", - "4 25 1 2 113781 151.55 C22 C26 S ? ? \n", - "\n", - " home.dest \n", - "0 St Louis, MO \n", - "1 Montreal, PQ / Chesterville, ON \n", - "2 Montreal, PQ / Chesterville, ON \n", - "3 Montreal, PQ / Chesterville, ON \n", - "4 Montreal, PQ / Chesterville, ON " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the data - it is available open source and online\n", - "\n", - "data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')\n", - "\n", - "# display data\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# replace interrogation marks by NaN values\n", - "\n", - "data = data.replace('?', np.nan)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# retain only the first cabin if more than\n", - "# 1 are available per passenger\n", - "\n", - "def get_first_cabin(row):\n", - " try:\n", - " return row.split()[0]\n", - " except:\n", - " return np.nan\n", - " \n", - "data['cabin'] = data['cabin'].apply(get_first_cabin)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# extracts the title (Mr, Ms, etc) from the name variable\n", - "\n", - "def get_title(passenger):\n", - " line = passenger\n", - " if re.search('Mrs', line):\n", - " return 'Mrs'\n", - " elif re.search('Mr', line):\n", - " return 'Mr'\n", - " elif re.search('Miss', line):\n", - " return 'Miss'\n", - " elif re.search('Master', line):\n", - " return 'Master'\n", - " else:\n", - " return 'Other'\n", - " \n", - "data['title'] = data['name'].apply(get_title)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# cast numerical variables as floats\n", - "\n", - "data['fare'] = data['fare'].astype('float')\n", - "data['age'] = data['age'].astype('float')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pclasssurvivedsexagesibspparchfarecabinembarkedtitle
011female29.000000211.3375B5SMiss
111male0.916712151.5500C22SMaster
210female2.000012151.5500C22SMiss
310male30.000012151.5500C22SMr
410female25.000012151.5500C22SMrs
\n", - "
" - ], - "text/plain": [ - " pclass survived sex age sibsp parch fare cabin embarked \\\n", - "0 1 1 female 29.0000 0 0 211.3375 B5 S \n", - "1 1 1 male 0.9167 1 2 151.5500 C22 S \n", - "2 1 0 female 2.0000 1 2 151.5500 C22 S \n", - "3 1 0 male 30.0000 1 2 151.5500 C22 S \n", - "4 1 0 female 25.0000 1 2 151.5500 C22 S \n", - "\n", - " title \n", - "0 Miss \n", - "1 Master \n", - "2 Miss \n", - "3 Mr \n", - "4 Mrs " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# drop unnecessary variables\n", - "\n", - "data.drop(labels=['name','ticket', 'boat', 'body','home.dest'], axis=1, inplace=True)\n", - "\n", - "# display data\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# save the data set\n", - "\n", - "data.to_csv('titanic.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Exploration\n", - "\n", - "### Find numerical and categorical variables" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "target = 'survived'" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of numerical variables: 5\n", - "Number of categorical variables: 4\n" - ] - } - ], - "source": [ - "vars_num = [c for c in data.columns if data[c].dtypes!='O' and c!=target]\n", - "\n", - "vars_cat = [c for c in data.columns if data[c].dtypes=='O']\n", - "\n", - "print('Number of numerical variables: {}'.format(len(vars_num)))\n", - "print('Number of categorical variables: {}'.format(len(vars_cat)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find missing values in variables" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pclass 0.000000\n", - "age 0.200917\n", - "sibsp 0.000000\n", - "parch 0.000000\n", - "fare 0.000764\n", - "dtype: float64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# first in numerical variables\n", - "\n", - "data[vars_num].isnull().mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "sex 0.000000\n", - "cabin 0.774637\n", - "embarked 0.001528\n", - "title 0.000000\n", - "dtype: float64" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now in categorical variables\n", - "\n", - "data[vars_cat].isnull().mean()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Determine cardinality of categorical variables" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "sex 2\n", - "cabin 181\n", - "embarked 3\n", - "title 5\n", - "dtype: int64" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data[vars_cat].nunique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Determine the distribution of numerical variables" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "data[vars_num].hist(bins=30, figsize=(10,10))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Separate data into train and test" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1047, 9), (262, 9))" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " data.drop('survived', axis=1), # predictors\n", - " data['survived'], # target\n", - " test_size=0.2, # percentage of obs in test set\n", - " random_state=0) # seed to ensure reproducibility\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Engineering\n", - "\n", - "### Extract only the letter (and drop the number) from the variable Cabin" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([nan, 'E', 'F', 'A', 'C', 'D', 'B', 'T', 'G'], dtype=object)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train['cabin'] = X_train['cabin'].str[0] # captures the first letter\n", - "X_test['cabin'] = X_test['cabin'].str[0] # captures the first letter\n", - "\n", - "X_train['cabin'].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fill in Missing data in numerical variables:\n", - "\n", - "- Add a binary missing indicator\n", - "- Fill NA in original variable with the median" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "age 0\n", - "fare 0\n", - "dtype: int64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "for var in ['age', 'fare']:\n", - "\n", - " # add missing indicator\n", - " X_train[var+'_NA'] = np.where(X_train[var].isnull(), 1, 0)\n", - " X_test[var+'_NA'] = np.where(X_test[var].isnull(), 1, 0)\n", - "\n", - " # replace NaN by median\n", - " median_val = X_train[var].median()\n", - "\n", - " X_train[var].fillna(median_val, inplace=True)\n", - " X_test[var].fillna(median_val, inplace=True)\n", - "\n", - "X_train[['age', 'fare']].isnull().sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Replace Missing data in categorical variables with the string **Missing**" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "X_train[vars_cat] = X_train[vars_cat].fillna('Missing')\n", - "X_test[vars_cat] = X_test[vars_cat].fillna('Missing')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pclass 0\n", - "sex 0\n", - "age 0\n", - "sibsp 0\n", - "parch 0\n", - "fare 0\n", - "cabin 0\n", - "embarked 0\n", - "title 0\n", - "age_NA 0\n", - "fare_NA 0\n", - "dtype: int64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pclass 0\n", - "sex 0\n", - "age 0\n", - "sibsp 0\n", - "parch 0\n", - "fare 0\n", - "cabin 0\n", - "embarked 0\n", - "title 0\n", - "age_NA 0\n", - "fare_NA 0\n", - "dtype: int64" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test.isnull().sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove rare labels in categorical variables\n", - "\n", - "- remove labels present in less than 5 % of the passengers" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def find_frequent_labels(df, var, rare_perc):\n", - " \n", - " # function finds the labels that are shared by more than\n", - " # a certain % of the passengers in the dataset\n", - " \n", - " df = df.copy()\n", - " \n", - " tmp = df.groupby(var)[var].count() / len(df)\n", - " \n", - " return tmp[tmp > rare_perc].index\n", - "\n", - "\n", - "for var in vars_cat:\n", - " \n", - " # find the frequent categories\n", - " frequent_ls = find_frequent_labels(X_train, var, 0.05)\n", - " \n", - " # replace rare categories by the string \"Rare\"\n", - " X_train[var] = np.where(X_train[var].isin(\n", - " frequent_ls), X_train[var], 'Rare')\n", - " \n", - " X_test[var] = np.where(X_test[var].isin(\n", - " frequent_ls), X_test[var], 'Rare')" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "sex 2\n", - "cabin 3\n", - "embarked 4\n", - "title 4\n", - "dtype: int64" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[vars_cat].nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "sex 2\n", - "cabin 3\n", - "embarked 3\n", - "title 4\n", - "dtype: int64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test[vars_cat].nunique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perform one hot encoding of categorical variables into k-1 binary variables\n", - "\n", - "- k-1, means that if the variable contains 9 different categories, we create 8 different binary variables\n", - "- Remember to drop the original categorical variable (the one with the strings) after the encoding" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1047, 16), (262, 15))" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "for var in vars_cat:\n", - " \n", - " # to create the binary variables, we use get_dummies from pandas\n", - " \n", - " X_train = pd.concat([X_train,\n", - " pd.get_dummies(X_train[var], prefix=var, drop_first=True)\n", - " ], axis=1)\n", - " \n", - " X_test = pd.concat([X_test,\n", - " pd.get_dummies(X_test[var], prefix=var, drop_first=True)\n", - " ], axis=1)\n", - " \n", - "\n", - "X_train.drop(labels=vars_cat, axis=1, inplace=True)\n", - "X_test.drop(labels=vars_cat, axis=1, inplace=True)\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " pclass age sibsp parch fare age_NA fare_NA sex_male \\\n", - "1118 3 25.0 0 0 7.9250 0 0 1 \n", - "44 1 41.0 0 0 134.5000 0 0 0 \n", - "1072 3 28.0 0 0 7.7333 1 0 1 \n", - "1130 3 18.0 0 0 7.7750 0 0 0 \n", - "574 2 29.0 1 0 21.0000 0 0 1 \n", - "\n", - " cabin_Missing cabin_Rare embarked_Q embarked_Rare embarked_S \\\n", - "1118 1 0 0 0 1 \n", - "44 0 1 0 0 0 \n", - "1072 1 0 1 0 0 \n", - "1130 1 0 0 0 1 \n", - "574 1 0 0 0 1 \n", - "\n", - " title_Mr title_Mrs title_Rare \n", - "1118 1 0 0 \n", - "44 0 0 0 \n", - "1072 1 0 0 \n", - "1130 0 0 0 \n", - "574 1 0 0 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Note that we have one less column in the test set\n", - "# this is because we had 1 less category in embarked.\n", - "\n", - "# we need to add that category manually to the test set\n", - "\n", - "X_train.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " pclass age sibsp parch fare age_NA fare_NA sex_male \\\n", - "1139 3 38.0 0 0 7.8958 0 0 1 \n", - "533 2 21.0 0 1 21.0000 0 0 0 \n", - "459 2 42.0 1 0 27.0000 0 0 1 \n", - "1150 3 28.0 0 0 14.5000 1 0 1 \n", - "393 2 25.0 0 0 31.5000 0 0 1 \n", - "\n", - " cabin_Missing cabin_Rare embarked_Q embarked_S title_Mr title_Mrs \\\n", - "1139 1 0 0 1 1 0 \n", - "533 1 0 0 1 0 0 \n", - "459 1 0 0 1 1 0 \n", - "1150 1 0 0 1 1 0 \n", - "393 1 0 0 1 1 0 \n", - "\n", - " title_Rare \n", - "1139 0 \n", - "533 0 \n", - "459 0 \n", - "1150 0 \n", - "393 0 " - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# we add 0 as values for all the observations, as Rare\n", - "# was not present in the test set\n", - "\n", - "X_test['embarked_Rare'] = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['pclass',\n", - " 'age',\n", - " 'sibsp',\n", - " 'parch',\n", - " 'fare',\n", - " 'age_NA',\n", - " 'fare_NA',\n", - " 'sex_male',\n", - " 'cabin_Missing',\n", - " 'cabin_Rare',\n", - " 'embarked_Q',\n", - " 'embarked_Rare',\n", - " 'embarked_S',\n", - " 'title_Mr',\n", - " 'title_Mrs',\n", - " 'title_Rare']" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Note that now embarked_Rare will be at the end of the test set\n", - "# so in order to pass the variables in the same order, we will\n", - "# create a variables variable:\n", - "\n", - "variables = [c for c in X_train.columns]\n", - "\n", - "variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Scale the variables\n", - "\n", - "- Use the standard scaler from Scikit-learn" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# create scaler\n", - "scaler = StandardScaler()\n", - "\n", - "# fit the scaler to the train set\n", - "scaler.fit(X_train[variables]) \n", - "\n", - "# transform the train and test set\n", - "X_train = scaler.transform(X_train[variables])\n", - "\n", - "X_test = scaler.transform(X_test[variables])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the Logistic Regression model\n", - "\n", - "- Set the regularization parameter to 0.0005\n", - "- Set the seed to 0" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=0.0005, random_state=0)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set up the model\n", - "# remember to set the random_state / seed\n", - "\n", - "model = LogisticRegression(C=0.0005, random_state=0)\n", - "\n", - "# train the model\n", - "model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make predictions and evaluate model performance\n", - "\n", - "Determine:\n", - "- roc-auc\n", - "- accuracy\n", - "\n", - "**Important, remember that to determine the accuracy, you need the outcome 0, 1, referring to survived or not. But to determine the roc-auc you need the probability of survival.**" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train roc-auc: 0.8431723338485316\n", - "train accuracy: 0.7125119388729704\n", - "\n", - "test roc-auc: 0.8354012345679012\n", - "test accuracy: 0.7022900763358778\n", - "\n" - ] - } - ], - "source": [ - "# make predictions for test set\n", - "class_ = model.predict(X_train)\n", - "pred = model.predict_proba(X_train)[:,1]\n", - "\n", - "# determine mse and rmse\n", - "print('train roc-auc: {}'.format(roc_auc_score(y_train, pred)))\n", - "print('train accuracy: {}'.format(accuracy_score(y_train, class_)))\n", - "print()\n", - "\n", - "# make predictions for test set\n", - "class_ = model.predict(X_test)\n", - "pred = model.predict_proba(X_test)[:,1]\n", - "\n", - "# determine mse and rmse\n", - "print('test roc-auc: {}'.format(roc_auc_score(y_test, pred)))\n", - "print('test accuracy: {}'.format(accuracy_score(y_test, class_)))\n", - "print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it! Well done\n", - "\n", - "**Keep this code safe, as we will use this notebook later on, to build production code, in our next assignement!!**" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predicting Survival on the Titanic\n", + "\n", + "### History\n", + "Perhaps one of the most infamous shipwrecks in history, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 people on board. Interestingly, by analysing the probability of survival based on few attributes like gender, age, and social status, we can make very accurate predictions on which passengers would survive. Some groups of people were more likely to survive than others, such as women, children, and the upper-class. Therefore, we can learn about the society priorities and privileges at the time.\n", + "\n", + "### Assignment:\n", + "\n", + "Build a Machine Learning Pipeline, to engineer the features in the data set and predict who is more likely to Survive the catastrophe.\n", + "\n", + "Follow the Jupyter notebook below, and complete the missing bits of code, to achieve each one of the pipeline steps." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "# to handle datasets\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# for visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# to divide train and test set\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# feature scaling\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# to build the models\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# to evaluate the models\n", + "from sklearn.metrics import accuracy_score, roc_auc_score\n", + "\n", + "# to persist the model and the scaler\n", + "import joblib\n", + "\n", + "# to visualise al the columns in the dataframe\n", + "pd.pandas.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare the data set" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2?St Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.55C22 C26S11?Montreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale212113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale3012113781151.55C22 C26S?135Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
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" + ], + "text/plain": [ + " pclass survived name sex \\\n", + "0 1 1 Allen, Miss. Elisabeth Walton female \n", + "1 1 1 Allison, Master. Hudson Trevor male \n", + "2 1 0 Allison, Miss. Helen Loraine female \n", + "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", + "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", + "\n", + " age sibsp parch ticket fare cabin embarked boat body \\\n", + "0 29 0 0 24160 211.3375 B5 S 2 ? \n", + "1 0.9167 1 2 113781 151.55 C22 C26 S 11 ? \n", + "2 2 1 2 113781 151.55 C22 C26 S ? ? \n", + "3 30 1 2 113781 151.55 C22 C26 S ? 135 \n", + "4 25 1 2 113781 151.55 C22 C26 S ? ? \n", + "\n", + " home.dest \n", + "0 St Louis, MO \n", + "1 Montreal, PQ / Chesterville, ON \n", + "2 Montreal, PQ / Chesterville, ON \n", + "3 Montreal, PQ / Chesterville, ON \n", + "4 Montreal, PQ / Chesterville, ON " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data - it is available open source and online\n", + "\n", + "data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')\n", + "\n", + "# display data\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# replace interrogation marks by NaN values\n", + "\n", + "data = data.replace('?', np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# retain only the first cabin if more than\n", + "# 1 are available per passenger\n", + "\n", + "def get_first_cabin(row):\n", + " try:\n", + " return row.split()[0]\n", + " except:\n", + " return np.nan\n", + " \n", + "data['cabin'] = data['cabin'].apply(get_first_cabin)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# extracts the title (Mr, Ms, etc) from the name variable\n", + "\n", + "def get_title(passenger):\n", + " line = passenger\n", + " if re.search('Mrs', line):\n", + " return 'Mrs'\n", + " elif re.search('Mr', line):\n", + " return 'Mr'\n", + " elif re.search('Miss', line):\n", + " return 'Miss'\n", + " elif re.search('Master', line):\n", + " return 'Master'\n", + " else:\n", + " return 'Other'\n", + " \n", + "data['title'] = data['name'].apply(get_title)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# cast numerical variables as floats\n", + "\n", + "data['fare'] = data['fare'].astype('float')\n", + "data['age'] = data['age'].astype('float')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pclasssurvivedsexagesibspparchfarecabinembarkedtitle
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111male0.916712151.5500C22SMaster
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310male30.000012151.5500C22SMr
410female25.000012151.5500C22SMrs
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" + ], + "text/plain": [ + " pclass survived sex age sibsp parch fare cabin embarked \\\n", + "0 1 1 female 29.0000 0 0 211.3375 B5 S \n", + "1 1 1 male 0.9167 1 2 151.5500 C22 S \n", + "2 1 0 female 2.0000 1 2 151.5500 C22 S \n", + "3 1 0 male 30.0000 1 2 151.5500 C22 S \n", + "4 1 0 female 25.0000 1 2 151.5500 C22 S \n", + "\n", + " title \n", + "0 Miss \n", + "1 Master \n", + "2 Miss \n", + "3 Mr \n", + "4 Mrs " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop unnecessary variables\n", + "\n", + "data.drop(labels=['name','ticket', 'boat', 'body','home.dest'], axis=1, inplace=True)\n", + "\n", + "# display data\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# save the data set\n", + "\n", + "data.to_csv('titanic.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration\n", + "\n", + "### Find numerical and categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "target = 'survived'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of numerical variables: 5\n", + "Number of categorical variables: 4\n" + ] + } + ], + "source": [ + "vars_num = [c for c in data.columns if data[c].dtypes!='O' and c!=target]\n", + "\n", + "vars_cat = [c for c in data.columns if data[c].dtypes=='O']\n", + "\n", + "print('Number of numerical variables: {}'.format(len(vars_num)))\n", + "print('Number of categorical variables: {}'.format(len(vars_cat)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find missing values in variables" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pclass 0.000000\n", + "age 0.200917\n", + "sibsp 0.000000\n", + "parch 0.000000\n", + "fare 0.000764\n", + "dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# first in numerical variables\n", + "\n", + "data[vars_num].isnull().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sex 0.000000\n", + "cabin 0.774637\n", + "embarked 0.001528\n", + "title 0.000000\n", + "dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now in categorical variables\n", + "\n", + "data[vars_cat].isnull().mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Determine cardinality of categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sex 2\n", + "cabin 181\n", + "embarked 3\n", + "title 5\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[vars_cat].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Determine the distribution of numerical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kSVJHDFaSJEkd8T5WWhCz3dgVvLmrJGk8eMRKkiSpIwYrSZKkjhisJEmSOuI1VpLUhyQHAfcDW6rq3CTHA7cAxwDrgNdV1Q8HWaP2zVzP/JP2h0esJKk/VwAbZsy/C7iuqk4AngIuG0hVkoaKwUqS5pBkOXAO8IE2H+BM4LbW5Cbg/IEUJ2moeCpQkub2HuBtwHPa/DHA01W1s81vBpbtacMkq4HVABMTE0xPT/f1hjt27Oi77bC48uSdczcCJg7rv+0omm3frd+yfa/bnbzsyHmqqDuj+HPZr676ZrCSpFkkORfYVlXrkkzt6/ZVtRZYCzA5OVlTU/29xPT0NP22HRaz3atupitP3sm168f3v58bzzp8r/tu1vv5XbznbYbJKP5c9qurvo3vT7YkdeN04NVJzgYOBZ4LXA8sTbKkHbVaDmwZYI2ShoTXWEnSLKrq6qpaXlUrgYuAu6vqYuAe4ILW7BLg9gGVKGmIGKwkaf9cBbw1yUZ611zdMOB6JA0BTwVKUp+qahqYbtMPA6cOsh6Nl7nuq+UzVUfDnEeskqxIck+Sh5I8mOSKtvzoJHcm+Wr7flRbniTvTbIxyReTnDLfnZAkSRoG/Ryx2glcWVWfT/IcYF2SO4E3AHdV1TVJ1gBr6B0afxVwYvt6OfD+9l2SNMK8U7k0tzmDVVVtBba26W8n2UDvfi3nAVOt2U30Do9f1ZZ/uKoKuDfJ0iTHtdeRJPVh/Zbts38039NC0lDap2uskqwEXgbcB0zMCEuPARNtehnw6IzNdt047xnBan9vmjfbjeVG/aZl43zjtbluCDjq/R7lfTfXjRpHuW+StND6DlZJjgA+Brylqr7Ve6JDT1VVktqXN97fm+a97+bb93pjuVG4udpsxvnGa7PtN3DfDdJcN3Wc7WaHkn7WXEcbNd76ut1CkmfRC1U3V9XH2+LHkxzX1h8HbGvLtwArZmzujfMkSdKi0M+nAkPv/iwbqurdM1bdQe+mePDMm+PdAby+fTrwNGC711dJkqTFoJ9TgacDrwPWJ3mgLXs7cA1wa5LLgEeAC9u6TwFnAxuB7wKXdlmwJEnSsOrnU4GfA7KX1av20L6Ayw+wLkmSpJHjI20kSZI6YrCSJEnqiMFKkiSpIwYrSZKkjhisJEmSOrJPj7SRJEmDMdtDsH125PAwWEmSNOYMZQvHYCVJc0iyAvgwvYfNF7C2qq5PcjTwUWAlsAm4sKqeWoia/I9SGk5eYyVJc9sJXFlVJwGnAZcnOQlYA9xVVScCd7V5SYuYwUqS5lBVW6vq823628AGYBlwHnBTa3YTcP5ACpQ0NDwVKEn7IMlK4GXAfcDEjIfMP0bvVOHu7VcDqwEmJiaYnp7u630mDoMrT965XzX2+x77an/r2d2B9G0UDKJ/c+3z2erZl5+XHTt2zNvP16B11TeDlST1KckRwMeAt1TVt5KfPka1qipJ7b5NVa0F1gJMTk7W1NRUX+/1vptv59r1+zdEb7q4v/fYV2+Y5bqufXHlyTv3u2+jYBD9m2ufz7bv9uXnZXp6mn5/hkdNV30b359sSepQkmfRC1U3V9XH2+LHkxxXVVuTHAdsG1yFWsxm+zCDFpbXWEnSHNI7NHUDsKGq3j1j1R3AJW36EuD2ha5N0nDxiJUkze104HXA+iQPtGVvB64Bbk1yGfAIcOFgypM0LAxWkjSHqvockL2sXrWQtfRjrtNC3udK/dr9Z+nKk3c+43otf5Z+lqcCJUmSOuIRK0laZLxru7riz9LPMlhJkn7CT5dJB8ZTgZIkSR0xWEmSJHXEU4GSJC1inv7tlkesJEmSOmKwkiRJ6ojBSpIkqSMGK0mSpI4YrCRJkjpisJIkSeqIt1uQJElDZZQflWOwkiRJnRvlcHQgPBUoSZLUkXkJVknOSvKVJBuTrJmP95CkYeB4J2mmzoNVkoOAfw28CjgJeG2Sk7p+H0kaNMc7Sbubj2usTgU2VtXDAEluAc4DHpqH95KkQXK8k/bDgTyfcL6ebXjjWYd38jqpqk5e6CcvmFwAnFVVb2zzrwNeXlVv2q3damB1m30h8JU+3+JY4ImOyh029m10jXP/9qVvL6iq581nMcOkn/HOsW6PxrlvMN79s289ex3rBvapwKpaC6zd1+2S3F9Vk/NQ0sDZt9E1zv0b574tBMe6nzXOfYPx7p99m9t8XLy+BVgxY355WyZJ48bxTtIzzEew+mvgxCTHJzkYuAi4Yx7eR5IGzfFO0jN0fiqwqnYmeRPwF8BBwAer6sEO32KfD6mPEPs2usa5f+PctwMyz+PdOP+7j3PfYLz7Z9/m0PnF65IkSYuVd16XJEnqiMFKkiSpI0MZrJJ8MMm2JF/ay/okeW97hMQXk5yy0DXurz76NpVke5IH2tc/X+ga91eSFUnuSfJQkgeTXLGHNiO57/rs2yjvu0OT/FWS/9r69/t7aHNIko+2fXdfkpUDKHVRGKfH5OztdyfJ0UnuTPLV9v2oQde6v5IclOQLST7R5o9vvyMb2+/MwYOucX8kWZrktiRfTrIhySvGZb8l+Z328/ilJB9pY2An+20ogxVwI3DWLOtfBZzYvlYD71+AmrpyI7P3DeAvq+ql7esPFqCmruwErqyqk4DTgMv38HiPUd13/fQNRnff/QA4s6peArwUOCvJabu1uQx4qqpOAK4D3rWwJS4OGb/H5Oztd2cNcFdVnQjc1eZH1RXAhhnz7wKua78rT9H73RlF1wOfrqoXAS+h18eR329JlgG/DUxW1YvpffDkIjrab0MZrKrqs8CTszQ5D/hw9dwLLE1y3MJUd2D66NvIqqqtVfX5Nv1ter+Ey3ZrNpL7rs++jay2P3a02We1r90/2XIecFObvg1YlSQLVOJi8pPH5FTVD4Fdj8kZSbP87sz8eboJOH8gBR6gJMuBc4APtPkAZ9L7HYER7VuSI4FXAjcAVNUPq+ppxmS/0bsrwmFJlgDPBrbS0X4bymDVh2XAozPmNzNG/8kBr2inZP5Dkl8adDH7o50mehlw326rRn7fzdI3GOF9105nPABsA+6sqr3uu6raCWwHjlnQIheHkf8d2ZvdfncmqmprW/UYMDGoug7Qe4C3AT9u88cAT7ffERjd/Xc88A3gQ+005weSHM4Y7Leq2gL8IfB1eoFqO7COjvbbqAarcfZ5es8gegnwPuDfD7acfZfkCOBjwFuq6luDrqdLc/RtpPddVf1dVb2U3t3DT03y4gGXpDEy2+9O9e77M3L3/klyLrCtqtYNupZ5sAQ4BXh/Vb0M+A67nfYb4f12FL0jb8cDzwcOZ+5LdPo2qsFqbB8jUVXf2nVKpqo+BTwrybEDLqtvSZ5Fb/C8uao+vocmI7vv5urbqO+7Xdrh/nv42YHmJ/uuHT4/Evjmgha3OIzs78je7OV35/FdlwG079sGVd8BOB14dZJN9E7ZnknvuqSl7XcERnf/bQY2zzhyfRu9oDUO++3XgK9V1Teq6kfAx+nty07226gGqzuA17dPmJ0GbJ9xaHKkJflvd123kuRUevtoJP7zanXfAGyoqnfvpdlI7rt++jbi++55SZa26cOAfwR8ebdmdwCXtOkLgLvLOwzPh7F6TM4svzszf54uAW5f6NoOVFVdXVXLq2olvf10d1VdTO8Pkwtas1Ht22PAo0le2BatAh5iDPYbvVOApyV5dvv53NW3TvbbUN55PclHgCngWOBx4B30Lqalqv64/UP8Eb2/qL8LXFpV9w+m2n3TR9/eBPwTep+k+R7w1qr6L4Opdt8k+RXgL4H1/PR6g7cDPw+jve/67Nso77t/QO9izYPoBcJbq+oPkvwBcH9V3ZHkUODf0btG5kngoqp6eGBFj7EkZ9O7dmfXY3LeOdiK9t8svzv3AbfS+x16BLiwqkb2gz1JpoDfrapzk/wCvSNYRwNfAH6zqn4wwPL2S5KX0rso/2DgYeBS2vjAiO+39G4p8z/TG6+/ALyR3jVVB7zfhjJYSZIkjaJRPRUoSZI0dAxWkiRJHTFYSZIkdcRgJUmS1BGDlSRJUkcMVpIkSR0xWEmSJHXEYCVJktQRg5UkSVJHDFaSJEkdMVhJkiR1xGAlSZLUEYOVJElSRwxWOmBJ3p7kA216ZZJKsmTQdUnSsErye0n+ZNB1qHv+56cDVlX/ctA1SJI0DDxiJUlSx9Lj/7GLkDtd+yTJVUm2JPl2kq8kWbWXQ9q/leRvk2xN8rsztj81yf1JvpXk8STvbst3nUJcvaftJGmhJNmU5OokDyV5KsmHkhya5Kgkn0jyjbb8E0mWz9huOsk7k/xn4LvALyT5pSR3JnmyjXlvn/FWByf5cBtPH0wyueCdVecMVupbkhcCbwL+YVU9B/gNYNNemp8BnAj8OnBVkl9ry68Hrq+q5wK/CNza53aStJAupjfG/SLw94B/Ru//zA8BLwB+Hvge8Ee7bfc6YDXwHOBx4D8CnwaeD5wA3DWj7auBW4ClwB17eC2NIIOV9sXfAYcAJyV5VlVtqqq/2Uvb36+q71TVenoD0Wvb8h8BJyQ5tqp2VNW9fW4nSQvpj6rq0ap6Engn8Nqq+mZVfayqvltV327Lf3W37W6sqgeraidwLvBYVV1bVd+vqm9X1X0z2n6uqj5VVX8H/DvgJQvRMc0vg5X6VlUbgbcAvwdsS3JLkufvpfmjM6YfoffXGsBl9P76+3KSv05ybp/bSdJC+pmxKMmzk/ybJI8k+RbwWWBpkoP2st0KYG9/fAI8NmP6u8ChfqJ69BmstE+q6k+r6lfoHQov4F17abpixvTPA3/btv9qVb0W+Lm27W1JDp9rO0laYHsai64EXgi8vF3O8Mq2PjPa1ozpR4FfmM8iNXwMVupbkhcmOTPJIcD36V1f8OO9NP8/2193vwRcCny0vcZvJnleVf0YeLq1/fFc20nSArs8yfIkRwP/lN5Y9Bx6497Tbfk75niNTwDHJXlLkkOSPCfJy+e3bA2awUr74hDgGuAJeoewfw64ei9t/xOwkd6Fmn9YVZ9py88CHkyyg96F7BdV1ff62E6SFtKfAp8BHqZ3Ou9fAO8BDqM3Bt5L76L0vWrXYf0j4H+gN2Z+ld4HdDTGUlVzt5LmWZKVwNeAZ7WLPiVpIJJsAt5YVf9x0LVo9HjESpIkqSMGK0mSpI54KlCSJKkjHrGSJEnqiMFKkiSpI0Nxh9djjz22Vq5c2Vfb73znOxx++OFzNxxB9m10jXP/9qVv69ate6KqnjfPJY2sxTjWjUM/7MNwGKY+zDbWDUWwWrlyJffff39fbaenp5mamprfggbEvo2uce7fvvQtySPzW81oW4xj3Tj0wz4Mh2Hqw2xjnacCJUmSOmKwkiRJ6ojBStKikuSDSbYl+dKMZUcnuTPJV9v3o9ryJHlvko1JvpjklBnbXNLafzXJJYPoi6ThY7CStNjcSO+ZlTOtAe6qqhPpPadyTVv+KuDE9rUaeD/0ghi9B/C+HDgVeMeuMCZpcTNYSVpUquqzwJO7LT4PuKlN3wScP2P5h6vnXmBpkuOA3wDurKonq+op4E5+NqxJWoSG4lOB+2L9lu28Yc0n97hu0zXnLHA1ksbERFVtbdOPARNtehnw6Ix2m9uyvS3/GUlW0zvaxcTEBNPT030VtO3J7bzv5tv3uv7kZUf29TqDtmPHjr77PKzsw3AYlT6MXLCSpPlUVZWks2d9VdVaYC3A5ORk9ftx8ffdfDvXrt/7EL3p4v5eZ9CG6SPy+8s+DIdR6YOnAiUJHm+n+Gjft7XlW4AVM9otb8v2tlzSImewkiS4A9j1yb5LgNtnLH99+3TgacD2dsrwL4BfT3JUu2j919sySYucpwIlLSpJPgJMAccm2Uzv033XALcmuQx4BLiwNf8UcDawEfgucClAVT2Z5P8C/rq1+4Oq2v2CeEmLkMFK0qJSVa/dy6pVe2hbwOV7eZ0PAh/ssDRJY8BTgZIkSR0xWEmSJHXEYCVJktQRg5UkSVJHDFaSJEkd6StYJfmdJA8m+VKSjyQ5NMnxSe5rT33/aJKDW9tD2vzGtn7lvPZAkiRpSMwZrJIsA34bmKyqFwMHARcB7wKuq6oTgKeAy9omlwFPteXXtXaSJEljr99TgUuAw5IsAZ4NbAXOBG5r63d/Gvyup8TfBqxKkk6qlSRJGmJz3iC0qrYk+UPg68D3gM8A64Cnq2pnazbzye4/eep7Ve1Msh04Bnhi5uvu7xPfJw6DK0/eucd1o/DU69mMypO798c49w3Gu3/j3DdJ6tqcwao9B+s84HjgaeDPgLMO9I3n44nvo/K0970ZlSd3749x7huMd//GuW+S1LV+TgX+GvC1qvpGVf0I+DhwOrC0nRqEZz7Z/SdPfW/rjwS+2WnVkiRJQ6ifYPV14LQkz27XSq0CHgLuAS5obXZ/Gvyup8RfANzdnrclSZI01uYMVlV1H72L0D8PrG/brAWuAt6aZCO9a6huaJvcABzTlr8VWDMPdUuSJA2dOa+xAqiqdwDv2G3xw8Cpe2j7feA1B16aJEnSaPHO65IkSR0xWEmSJHXEYCVJktQRg5UkSVJHDFaSJEkdMVhJkiR1xGAlSU2S30nyYJIvJflIkkOTHJ/kviQbk3w0ycGt7SFtfmNbv3LA5UsaAgYrSQKSLAN+G5isqhcDBwEXAe8CrquqE4CngMvaJpcBT7Xl17V2khY5g5Uk/dQS4LD2nNNnA1uBM+k9fQLgJuD8Nn1em6etX9Ue+yVpEevrzuuSNO6qakuSP6T3fNTvAZ8B1gFPV9XO1mwzsKxNLwMebdvuTLKd3uO9npj5uklWA6sBJiYmmJ6e7queicPgypN37nV9v68zaDt27BiZWvfGPgyHUemDwUqSgCRH0TsKdTzwNPBnwFkH+rpVtZbe81WZnJysqampvrZ73823c+36vQ/Rmy7u73UGbXp6mn77PKzsw3AYlT54KlCSen4N+FpVfaOqfgR8HDgdWNpODQIsB7a06S3ACoC2/kjgmwtbsqRhY7CSpJ6vA6cleXa7VmoV8BBwD3BBa3MJcHubvqPN09bfXVW1gPVKGkIGK0kCquo+ehehfx5YT298XAtcBbw1yUZ611Dd0Da5ATimLX8rsGbBi5Y0dLzGSpKaqnoH8I7dFj8MnLqHtt8HXrMQdUkaHR6xkiRJ6ojBSpIkqSMGK0mSpI4YrCRJkjpisJIkSeqIwUqSJKkjBitJkqSOGKwkSZI6YrCSJEnqSF/BKsnSJLcl+XKSDUlekeToJHcm+Wr7flRrmyTvTbIxyReTnDK/XZAkSRoO/R6xuh74dFW9CHgJsIHec7HuqqoTgbv46XOyXgWc2L5WA+/vtGJJkqQhNWewSnIk8Erag0er6odV9TRwHnBTa3YTcH6bPg/4cPXcCyxNclzHdUuSJA2dfh7CfDzwDeBDSV4CrAOuACaqamtr8xgw0aaXAY/O2H5zW7Z1xjKSrKZ3RIuJiQmmp6f7KnjiMLjy5J17XNfvawyrHTt2jHwf9mac+wbj3b9x7pskda2fYLUEOAV4c1Xdl+R6fnraD4CqqiS1L29cVWuBtQCTk5M1NTXV13bvu/l2rl2/57I3Xdzfawyr6elp+v13GDXj3DcY7/6Nc98kqWv9XGO1GdhcVfe1+dvoBa3Hd53ia9+3tfVbgBUztl/elkmSJI21OYNVVT0GPJrkhW3RKuAh4A7gkrbsEuD2Nn0H8Pr26cDTgO0zThlKkiSNrX5OBQK8Gbg5ycHAw8Cl9ELZrUkuAx4BLmxtPwWcDWwEvtvaSpIkjb2+glVVPQBM7mHVqj20LeDyAytLkiRp9HjndUmSpI4YrCSp8SkTkg6UwUqSfsqnTEg6IAYrScKnTEjqRr+fCpSkcTcyT5mA0XnSxDjcud8+DIdR6YPBSpJ6RuYpEzA6T5oYhzv324fhMCp98FSgJPX4lAlJB8xgJUn4lAlJ3fBUoCT9lE+ZkHRADFaS1PiUCUkHylOBkiRJHTFYSZIkdcRgJUmS1BGDlSRJUkcMVpIkSR0xWEmSJHXEYCVJktQRg5UkSVJHDFaSJEkdMVhJkiR1xGAlSZLUEYOVJElSRwxWkiRJHek7WCU5KMkXknyizR+f5L4kG5N8NMnBbfkhbX5jW79ynmqXJEkaKvtyxOoKYMOM+XcB11XVCcBTwGVt+WXAU235da2dJEnS2OsrWCVZDpwDfKDNBzgTuK01uQk4v02f1+Zp61e19pIkSWNtSZ/t3gO8DXhOmz8GeLqqdrb5zcCyNr0MeBSgqnYm2d7aPzHzBZOsBlYDTExMMD093VchE4fBlSfv3OO6fl9jWO3YsWPk+7A349w3GO/+jXPfJKlrcwarJOcC26pqXZKprt64qtYCawEmJydraqq/l37fzbdz7fo9l73p4v5eY1hNT0/T77/DqBnnvsF492+c+yZJXevniNXpwKuTnA0cCjwXuB5YmmRJO2q1HNjS2m8BVgCbkywBjgS+2XnlkiRJQ2bOa6yq6uqqWl5VK4GLgLur6mLgHuCC1uwS4PY2fUebp62/u6qq06olSZKG0IHcx+oq4K1JNtK7huqGtvwG4Ji2/K3AmgMrUZIWjreWkXQg9ilYVdV0VZ3bph+uqlOr6oSqek1V/aAt/36bP6Gtf3g+CpekeeKtZSTtN++8LkmNt5aRdKD6vd2CJC0G72EEbi0Do3N7mXG4XYd9GA6j0geDlSQxWreWgdG5vcw43K7DPgyHUemDwUqSery1jKQD5jVWkoS3lpHUDYOVJM3OW8tI6punAiVpN1U1DUy36YeBU/fQ5vvAaxa0MElDz2DVrFzzyVnXb7rmnAWqRJIkjSpPBUqSJHXEYCVJktQRg5UkSVJHDFaSJEkdMVhJkiR1xGAlSZLUEYOVJElSRwxWkiRJHTFYSZIkdcRgJUmS1BGDlSRJUkcMVpIkSR0xWEmSJHXEYCVJktQRg5UkSVJH5gxWSVYkuSfJQ0keTHJFW350kjuTfLV9P6otT5L3JtmY5ItJTpnvTkiSJA2Dfo5Y7QSurKqTgNOAy5OcBKwB7qqqE4G72jzAq4AT29dq4P2dVy1JkjSE5gxWVbW1qj7fpr8NbACWAecBN7VmNwHnt+nzgA9Xz73A0iTHdV24JEnSsFmyL42TrAReBtwHTFTV1rbqMWCiTS8DHp2x2ea2bOuMZSRZTe+IFhMTE0xPT/dVw8RhcOXJO/e4rt/X2JO9vWYXr92vHTt2LMj7DMI49w3Gu3/j3DdJ6lrfwSrJEcDHgLdU1beS/GRdVVWS2pc3rqq1wFqAycnJmpqa6mu79918O9eu33PZmy7u7zX25A1rPjnr+gN57X5NT0/T77/DqBnnvsF492+c+zZTkhXAh+n9kVjA2qq6PsnRwEeBlcAm4MKqeiq9QfB64Gzgu8Abdh3dl7R49fWpwCTPoheqbq6qj7fFj+86xde+b2vLtwArZmy+vC2TpGHm9aSSDlg/nwoMcAOwoarePWPVHcAlbfoS4PYZy1/fPh14GrB9xilDSRpKXk8qqQv9nAo8HXgdsD7JA23Z24FrgFuTXAY8AlzY1n2K3qHxjfQOj1/aZcGSNN+G/XpSWJjrPrswDtfo2YfhMCp9mDNYVdXngOxl9ao9tC/g8gOsS5IGYhSuJ4WFue6zC+NwjZ59GA6j0gfvvC5JjdeTSjpQBitJwutJJXVjn+5jJUljbNFcT7pyltvLbLrmnAWsRBo/BitJwutJJXXDU4GSJEkdMVhJkiR1xGAlSZLUEYOVJElSRwxWkiRJHTFYSZIkdcTbLUiSOrGn+2NdefJO3rDmk94fS4uGR6wkSZI6YrCSJEnqiMFKkiSpIwYrSZKkjhisJEmSOmKwkiRJ6ojBSpIkqSMGK0mSpI4YrCRJkjrindcHbOadinfdoXgX71QsSdJo8YiVJElSRwxWkiRJHZmXU4FJzgKuBw4CPlBV18zH+2j/7OlBqbvM1+nH9Vu2P+M050K9rzTfHO8kzdT5EaskBwH/GngVcBLw2iQndf0+kjRojneSdjcfR6xOBTZW1cMASW4BzgMemof3knSAZjuCCXDjWYcvUCUjyfFuwAZxBF6azXwEq2XAozPmNwMvn4f3keYMBQ6smmeOd2Nqvj6xPaggONvlGItpnFyIf/9UVScv9JMXTC4AzqqqN7b51wEvr6o37dZuNbC6zb4Q+Eqfb3Es8ERH5Q4b+za6xrl/+9K3F1TV8+azmGHSz3jnWDcW/bAPw2GY+rDXsW4+jlhtAVbMmF/elj1DVa0F1u7riye5v6om97+84WXfRtc492+c+9aBOce7xT7WjUM/7MNwGJU+zMftFv4aODHJ8UkOBi4C7piH95GkQXO8k/QMnR+xqqqdSd4E/AW9jx9/sKoe7Pp9JGnQHO8k7W5e7mNVVZ8CPjUfr81+HFIfIfZtdI1z/8a5bwdsHse7cfl3H4d+2IfhMBJ96PzidUmSpMXKR9pIkiR1ZKSCVZKzknwlycYkawZdT1eSrEhyT5KHkjyY5IpB19S1JAcl+UKSTwy6li4lWZrktiRfTrIhySsGXVNXkvxO+3n8UpKPJDl00DUtFqM+1o3TmDbqY9c4jFGjNhaNTLAa80dH7ASurKqTgNOAy8eob7tcAWwYdBHz4Hrg01X1IuAljEkfkywDfhuYrKoX07sw+6LBVrU4jMlYN05j2qiPXSM9Ro3iWDQywYoZj46oqh8Cux4dMfKqamtVfb5Nf5veD/6ywVbVnSTLgXOADwy6li4lORJ4JXADQFX9sKqeHmhR3VoCHJZkCfBs4G8HXM9iMfJj3biMaaM+do3RGDVSY9EoBas9PTpi5H5R55JkJfAy4L4Bl9Kl9wBvA3484Dq6djzwDeBD7VTBB5KMxYP1qmoL8IfA14GtwPaq+sxgq1o0xmqsG/Ex7T2M9tg18mPUKI5FoxSsxl6SI4CPAW+pqm8Nup4uJDkX2FZV6wZdyzxYApwCvL+qXgZ8Bxi562H2JMlR9I6SHA88Hzg8yW8OtiqNmlEe08Zk7Br5MWoUx6JRClZ9PSpnVCV5Fr0B6Oaq+vig6+nQ6cCrk2yid0rjzCR/MtiSOrMZ2FxVu/4Sv43eIDYOfg34WlV9o6p+BHwc+O8GXNNiMRZj3RiMaeMwdo3DGDVyY9EoBauxfXREktA7B76hqt496Hq6VFVXV9XyqlpJb5/dXVVD/ddGv6rqMeDRJC9si1YBDw2wpC59HTgtybPbz+cqRuyi1xE28mPdOIxp4zB2jckYNXJj0bzceX0+jPmjI04HXgesT/JAW/b2dkdnDbc3Aze3/wAfBi4dcD2dqKr7ktwGfJ7eJ7y+wIjc9XjUjclY55g2PEZ6jBrFscg7r0uSJHVklE4FSpIkDTWDlSRJUkcMVpIkSR0xWEmSJHXEYCVJktQRg5UkSVJHDFaSJEkdMVhJkiR1xGAlSZLUEYOVJElSRwxWkiRJHTFYSZIkdcRgJUmS1BGDlSRJUkcMVjogSV6Y5IEk307y24OuR5KkQVoy6AI08t4G3FNVLx10IZIkDZpHrHSgXgA8uK8bJTHUS5LGjsFK+y3J3cAZwB8l2ZHkiiRfSPKtJI8m+b0ZbVcmqSSXJfk6cHdb/ltJNiR5KslfJHnBYHojSdKBM1hpv1XVmcBfAm+qqiOA/wq8HlgKnAP8kyTn77bZrwJ/H/iNJOcBbwf+J+B57bU+siDFS5I0DwxW6kxVTVfV+qr6cVV9kV5I+tXdmv1eVX2nqr4H/GPgX1XVhqraCfxL4KUetZIkjSqDlTqT5OVJ7knyjSTb6QWnY3dr9uiM6RcA1yd5OsnTwJNAgGULUrAkSR0zWKlLfwrcAayoqiOBP6YXlGaqGdOPAv9bVS2d8XVYVf2XBapXkqROGazUpecAT1bV95OcCvwvc7T/Y+DqJL8EkOTIJK+Z7yIlSZovBit16X8H/iDJt4F/Dtw6W+Oq+nPgXcAtSb4FfAl41bxXKUnSPElVzd1KkiRJc/KIlSRJUkcMVpIkSR0xWEmSJHXEYCVJktSRoXgQ7rHHHlsrV67sq+13vvMdDj/88PktqCPWOj+sdX50Ueu6deueqKrndVSSJI2coQhWK1eu5P777++r7fT0NFNTU/NbUEesdX5Y6/zootYkj3RTjSSNJk8FSpIkdcRgJUmS1BGDlSRJUkcMVpIkSR0xWEmSJHVkKD4VuC/Wb9nOG9Z8co/rNl1zzgJXI0mS9FMesZIkSeqIwUqSJKkjBitJkqSOGKwkSZI6YrCSJEnqiMFKkiSpIwYrSZKkjhisJEmSOmKwkiRJ6ojBSpIkqSMGK0mSpI4YrCRJkjpisJIkSeqIwUqSJKkjBitJkqSOGKwkSZI6YrCSJEnqiMFKkiSpIwYrSZKkjhisJEmSOmKwkiRJ6ojBSpIkqSN9BaskS5PcluTLSTYkeUWSo5PcmeSr7ftRrW2SvDfJxiRfTHLK/HZBkiRpOPR7xOp64NNV9SLgJcAGYA1wV1WdCNzV5gFeBZzYvlYD7++0YkmSpCE1Z7BKciTwSuAGgKr6YVU9DZwH3NSa3QSc36bPAz5cPfcCS5Mc13HdkiRJQydVNXuD5KXAWuAheker1gFXAFuqamlrE+Cpqlqa5BPANVX1ubbuLuCqqrp/t9ddTe+IFhMTE798yy239FXwtie38/j39rzu5GVH9vUaC2XHjh0cccQRgy6jL9Y6PxZbrWeccca6qprsqCRJGjlL+mxzCvDmqrovyfX89LQfAFVVSWZPaLupqrX0AhuTk5M1NTXV13bvu/l2rl2/57I3XdzfayyU6elp+u3XoFnr/LBWSVpc+rnGajOwuarua/O30Qtaj+86xde+b2vrtwArZmy/vC2TJEkaa3MGq6p6DHg0yQvbolX0TgveAVzSll0C3N6m7wBe3z4deBqwvaq2dlu2JEnS8OnnVCDAm4GbkxwMPAxcSi+U3ZrkMuAR4MLW9lPA2cBG4LutrSRJ0tjrK1hV1QPAni5IXbWHtgVcfmBlSZIkjR7vvC5JktQRg5UkSVJHDFaSJEkdMVhJkiR1xGAlSZLUEYOVJElSRwxWkiRJHTFYSZIkdcRgJUmS1BGDlSRJUkcMVpIkSR0xWEmSJHXEYCVJktQRg5UkSVJHDFaSJEkdMVhJkiR1xGAlSZLUEYOVJElSRwxWkiRJHTFYSZIkdcRgJUmS1BGDlSRJUkcMVpIkSR0xWEmSJHXEYCVJktQRg5UkSVJHDFaSJEkdMVhJkiR1xGAlSZLUEYOVJElSR/oOVkkOSvKFJJ9o88cnuS/JxiQfTXJwW35Im9/Y1q+cp9olSZKGyr4csboC2DBj/l3AdVV1AvAUcFlbfhnwVFt+XWsnSZI09voKVkmWA+cAH2jzAc4EbmtNbgLOb9PntXna+lWtvSRJ0lhLVc3dKLkN+FfAc4DfBd4A3NuOSpFkBfAfqurFSb4EnFVVm9u6vwFeXlVP7Paaq4HVABMTE798yy239FXwtie38/j39rzu5GVH9vUaC2XHjh0cccQRgy6jL9Y6PxZbrWeccca6qprsqCRJGjlL5mqQ5FxgW1WtSzLV1RtX1VpgLcDk5GRNTfX30u+7+XauXb/nsjdd3N9rLJTp6Wn67degWev8sFZJWlzmDFbA6cCrk5wNHAo8F7geWJpkSVXtBJYDW1r7LcAKYHOSJcCRwDc7r1ySJGnIzHmNVVVdXVXLq2olcBFwd1VdDNwDXNCaXQLc3qbvaPO09XdXP+cbJUmSRtyB3MfqKuCtSTYCxwA3tOU3AMe05W8F1hxYiZIkSaOhn1OBP1FV08B0m34YOHUPbb4PvKaD2iRJkkaKd16XJEnqiMFKkiSpIwYrSZKkjhisJEmSOmKwkiRJ6ojBSpIkqSMGK0mSpI4YrCRJkjpisJIkSeqIwUqSJKkjBitJkqSOGKwkSZI6YrCSJEnqiMFKkiSpIwYrSZKkjhisJEmSOmKwkiRJ6ojBSpIkqSMGK0mSpI4sGXQBXVq55pOzrt90zTkLVIkkSVqMPGIlSZLUEYOVJElSRwxWkiRJHTFYSZIkdcRgJUmS1BGDlSRJUkcMVpIkSR0xWEmSJHXEYCVJktQRg5UkSVJH5gxWSVYkuSfJQ0keTHJFW350kjuTfLV9P6otT5L3JtmY5ItJTpnvTkiSJA2Dfo5Y7QSurKqTgNOAy5OcBKwB7qqqE4G72jzAq4AT29dq4P2dVy1JkjSE5gxWVbW1qj7fpr8NbACWAecBN7VmNwHnt+nzgA9Xz73A0iTHdV24JEnSsElV9d84WQl8Fngx8PWqWtqWB3iqqpYm+QRwTVV9rq27C7iqqu7f7bVW0zuixcTExC/fcsstfdWw7cntPP69vkt+hpOXHbl/G+6nHTt2cMQRRyzoe+4va50fi63WM844Y11VTXZUkiSNnCX9NkxyBPAx4C1V9a1eluqpqkrSf0LrbbMWWAswOTlZU1NTfW33vptv59r1fZf9DJsu7u89ujI9PU2//Ro0a50f1ipJi0tfnwpM8ix6oermqvp4W/z4rlN87fu2tnwLsGLG5svbMkmSpLHWz6cCA9wAbKiqd89YdQdwSZu+BLh9xvLXt08HngZsr6qtHdYsSZI0lPo5p3Y68DpgfZIH2rK3A9cAtya5DHgEuLCt+xRwNrAR+C5waZcFS5IkDas5g1W7CD17Wb1qD+0LuPwA65IkSRo53nldkiSpIwYrSZKkjhisJEmSOmKwkiRJ6ojBSpIkqSMGK0mSpI4YrCRJkjpisJIkSeqIwUqSJKkjBitJkqSOGKwkSZI6YrCSJEnqiMFKkiSpIwYrSZKkjhisJEmSOmKwkiRJ6ojBSpIkqSMGK0mSpI4YrCRJkjpisJIkSerIkkEXMCxWrvnkfm+76ZpzOqxEkiSNqkUVrA4kPEmSJM3FU4GSJEkdMVhJkiR1xGAlSZLUEYOVJElSRwxWkiRJHTFYSZIkdcRgJUmS1BGDlSRJUkcMVpIkSR2ZlzuvJzkLuB44CPhAVV0zH+8zLPZ2R/crT97JG+bpbu/z9Ri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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data[vars_num].hist(bins=30, figsize=(10,10))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Separate data into train and test" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1047, 9), (262, 9))" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data.drop('survived', axis=1), # predictors\n", + " data['survived'], # target\n", + " test_size=0.2, # percentage of obs in test set\n", + " random_state=0) # seed to ensure reproducibility\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature Engineering\n", + "\n", + "### Extract only the letter (and drop the number) from the variable Cabin" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([nan, 'E', 'F', 'A', 'C', 'D', 'B', 'T', 'G'], dtype=object)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train['cabin'] = X_train['cabin'].str[0] # captures the first letter\n", + "X_test['cabin'] = X_test['cabin'].str[0] # captures the first letter\n", + "\n", + "X_train['cabin'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fill in Missing data in numerical variables:\n", + "\n", + "- Add a binary missing indicator\n", + "- Fill NA in original variable with the median" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age 0\n", + "fare 0\n", + "dtype: int64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for var in ['age', 'fare']:\n", + "\n", + " # add missing indicator\n", + " X_train[var+'_NA'] = np.where(X_train[var].isnull(), 1, 0)\n", + " X_test[var+'_NA'] = np.where(X_test[var].isnull(), 1, 0)\n", + "\n", + " # replace NaN by median\n", + " median_val = X_train[var].median()\n", + "\n", + " X_train[var].fillna(median_val, inplace=True)\n", + " X_test[var].fillna(median_val, inplace=True)\n", + "\n", + "X_train[['age', 'fare']].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Replace Missing data in categorical variables with the string **Missing**" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "X_train[vars_cat] = X_train[vars_cat].fillna('Missing')\n", + "X_test[vars_cat] = X_test[vars_cat].fillna('Missing')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pclass 0\n", + "sex 0\n", + "age 0\n", + "sibsp 0\n", + "parch 0\n", + "fare 0\n", + "cabin 0\n", + "embarked 0\n", + "title 0\n", + "age_NA 0\n", + "fare_NA 0\n", + "dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pclass 0\n", + "sex 0\n", + "age 0\n", + "sibsp 0\n", + "parch 0\n", + "fare 0\n", + "cabin 0\n", + "embarked 0\n", + "title 0\n", + "age_NA 0\n", + "fare_NA 0\n", + "dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove rare labels in categorical variables\n", + "\n", + "- remove labels present in less than 5 % of the passengers" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def find_frequent_labels(df, var, rare_perc):\n", + " \n", + " # function finds the labels that are shared by more than\n", + " # a certain % of the passengers in the dataset\n", + " \n", + " df = df.copy()\n", + " \n", + " tmp = df.groupby(var)[var].count() / len(df)\n", + " \n", + " return tmp[tmp > rare_perc].index\n", + "\n", + "\n", + "for var in vars_cat:\n", + " \n", + " # find the frequent categories\n", + " frequent_ls = find_frequent_labels(X_train, var, 0.05)\n", + " \n", + " # replace rare categories by the string \"Rare\"\n", + " X_train[var] = np.where(X_train[var].isin(\n", + " frequent_ls), X_train[var], 'Rare')\n", + " \n", + " X_test[var] = np.where(X_test[var].isin(\n", + " frequent_ls), X_test[var], 'Rare')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sex 2\n", + "cabin 3\n", + "embarked 4\n", + "title 4\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train[vars_cat].nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sex 2\n", + "cabin 3\n", + "embarked 3\n", + "title 4\n", + "dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test[vars_cat].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform one hot encoding of categorical variables into k-1 binary variables\n", + "\n", + "- k-1, means that if the variable contains 9 different categories, we create 8 different binary variables\n", + "- Remember to drop the original categorical variable (the one with the strings) after the encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1047, 16), (262, 15))" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for var in vars_cat:\n", + " \n", + " # to create the binary variables, we use get_dummies from pandas\n", + " \n", + " X_train = pd.concat([X_train,\n", + " pd.get_dummies(X_train[var], prefix=var, drop_first=True)\n", + " ], axis=1)\n", + " \n", + " X_test = pd.concat([X_test,\n", + " pd.get_dummies(X_test[var], prefix=var, drop_first=True)\n", + " ], axis=1)\n", + " \n", + "\n", + "X_train.drop(labels=vars_cat, axis=1, inplace=True)\n", + "X_test.drop(labels=vars_cat, axis=1, inplace=True)\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " pclass age sibsp parch fare age_NA fare_NA sex_male \\\n", + "1118 3 25.0 0 0 7.9250 0 0 1 \n", + "44 1 41.0 0 0 134.5000 0 0 0 \n", + "1072 3 28.0 0 0 7.7333 1 0 1 \n", + "1130 3 18.0 0 0 7.7750 0 0 0 \n", + "574 2 29.0 1 0 21.0000 0 0 1 \n", + "\n", + " cabin_Missing cabin_Rare embarked_Q embarked_Rare embarked_S \\\n", + "1118 1 0 0 0 1 \n", + "44 0 1 0 0 0 \n", + "1072 1 0 1 0 0 \n", + "1130 1 0 0 0 1 \n", + "574 1 0 0 0 1 \n", + "\n", + " title_Mr title_Mrs title_Rare \n", + "1118 1 0 0 \n", + "44 0 0 0 \n", + "1072 1 0 0 \n", + "1130 0 0 0 \n", + "574 1 0 0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Note that we have one less column in the test set\n", + "# this is because we had 1 less category in embarked.\n", + "\n", + "# we need to add that category manually to the test set\n", + "\n", + "X_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " pclass age sibsp parch fare age_NA fare_NA sex_male \\\n", + "1139 3 38.0 0 0 7.8958 0 0 1 \n", + "533 2 21.0 0 1 21.0000 0 0 0 \n", + "459 2 42.0 1 0 27.0000 0 0 1 \n", + "1150 3 28.0 0 0 14.5000 1 0 1 \n", + "393 2 25.0 0 0 31.5000 0 0 1 \n", + "\n", + " cabin_Missing cabin_Rare embarked_Q embarked_S title_Mr title_Mrs \\\n", + "1139 1 0 0 1 1 0 \n", + "533 1 0 0 1 0 0 \n", + "459 1 0 0 1 1 0 \n", + "1150 1 0 0 1 1 0 \n", + "393 1 0 0 1 1 0 \n", + "\n", + " title_Rare \n", + "1139 0 \n", + "533 0 \n", + "459 0 \n", + "1150 0 \n", + "393 0 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# we add 0 as values for all the observations, as Rare\n", + "# was not present in the test set\n", + "\n", + "X_test['embarked_Rare'] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['pclass',\n", + " 'age',\n", + " 'sibsp',\n", + " 'parch',\n", + " 'fare',\n", + " 'age_NA',\n", + " 'fare_NA',\n", + " 'sex_male',\n", + " 'cabin_Missing',\n", + " 'cabin_Rare',\n", + " 'embarked_Q',\n", + " 'embarked_Rare',\n", + " 'embarked_S',\n", + " 'title_Mr',\n", + " 'title_Mrs',\n", + " 'title_Rare']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Note that now embarked_Rare will be at the end of the test set\n", + "# so in order to pass the variables in the same order, we will\n", + "# create a variables variable:\n", + "\n", + "variables = [c for c in X_train.columns]\n", + "\n", + "variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scale the variables\n", + "\n", + "- Use the standard scaler from Scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# create scaler\n", + "scaler = StandardScaler()\n", + "\n", + "# fit the scaler to the train set\n", + "scaler.fit(X_train[variables]) \n", + "\n", + "# transform the train and test set\n", + "X_train = scaler.transform(X_train[variables])\n", + "\n", + "X_test = scaler.transform(X_test[variables])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the Logistic Regression model\n", + "\n", + "- Set the regularization parameter to 0.0005\n", + "- Set the seed to 0" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=0.0005, random_state=0)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# set up the model\n", + "# remember to set the random_state / seed\n", + "\n", + "model = LogisticRegression(C=0.0005, random_state=0)\n", + "\n", + "# train the model\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make predictions and evaluate model performance\n", + "\n", + "Determine:\n", + "- roc-auc\n", + "- accuracy\n", + "\n", + "**Important, remember that to determine the accuracy, you need the outcome 0, 1, referring to survived or not. But to determine the roc-auc you need the probability of survival.**" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train roc-auc: 0.8431723338485316\n", + "train accuracy: 0.7125119388729704\n", + "\n", + "test roc-auc: 0.8354012345679012\n", + "test accuracy: 0.7022900763358778\n", + "\n" + ] + } + ], + "source": [ + "# make predictions for test set\n", + "class_ = model.predict(X_train)\n", + "pred = model.predict_proba(X_train)[:,1]\n", + "\n", + "# determine mse and rmse\n", + "print('train roc-auc: {}'.format(roc_auc_score(y_train, pred)))\n", + "print('train accuracy: {}'.format(accuracy_score(y_train, class_)))\n", + "print()\n", + "\n", + "# make predictions for test set\n", + "class_ = model.predict(X_test)\n", + "pred = model.predict_proba(X_test)[:,1]\n", + "\n", + "# determine mse and rmse\n", + "print('test roc-auc: {}'.format(roc_auc_score(y_test, pred)))\n", + "print('test accuracy: {}'.format(accuracy_score(y_test, class_)))\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it! Well done\n", + "\n", + "**Keep this code safe, as we will use this notebook later on, to build production code, in our next assignement!!**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/titanic-assignment/03-titanic-survival-pipeline-assignment.ipynb b/section-04-research-and-development/titanic-assignment/03-titanic-survival-pipeline-assignment.ipynb index 9a46fe51b..806f05a68 100644 --- a/section-04-research-and-development/titanic-assignment/03-titanic-survival-pipeline-assignment.ipynb +++ b/section-04-research-and-development/titanic-assignment/03-titanic-survival-pipeline-assignment.ipynb @@ -1,732 +1,732 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predicting Survival on the Titanic\n", - "\n", - "### History\n", - "Perhaps one of the most infamous shipwrecks in history, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 people on board. Interestingly, by analysing the probability of survival based on few attributes like gender, age, and social status, we can make very accurate predictions on which passengers would survive. Some groups of people were more likely to survive than others, such as women, children, and the upper-class. Therefore, we can learn about the society priorities and privileges at the time.\n", - "\n", - "### Assignment:\n", - "\n", - "Build a Machine Learning Pipeline, to engineer the features in the data set and predict who is more likely to Survive the catastrophe.\n", - "\n", - "Follow the Jupyter notebook below, and complete the missing bits of code, to achieve each one of the pipeline steps." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "\n", - "# to handle datasets\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# for visualization\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# to divide train and test set\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# feature scaling\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "# to build the models\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "# to evaluate the models\n", - "from sklearn.metrics import accuracy_score, roc_auc_score\n", - "\n", - "# to persist the model and the scaler\n", - "import joblib\n", - "\n", - "# ========== NEW IMPORTS ========\n", - "# Respect to notebook 02-Predicting-Survival-Titanic-Solution\n", - "\n", - "# pipeline\n", - "\n", - "\n", - "# for the preprocessors\n", - "\n", - "\n", - "# for imputation\n", - "\n", - "\n", - "# for encoding categorical variables\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare the data set" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2?St Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.55C22 C26S11?Montreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale212113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale3012113781151.55C22 C26S?135Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
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" - ], - "text/plain": [ - " pclass survived name sex \\\n", - "0 1 1 Allen, Miss. Elisabeth Walton female \n", - "1 1 1 Allison, Master. Hudson Trevor male \n", - "2 1 0 Allison, Miss. Helen Loraine female \n", - "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", - "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", - "\n", - " age sibsp parch ticket fare cabin embarked boat body \\\n", - "0 29 0 0 24160 211.3375 B5 S 2 ? \n", - "1 0.9167 1 2 113781 151.55 C22 C26 S 11 ? \n", - "2 2 1 2 113781 151.55 C22 C26 S ? ? \n", - "3 30 1 2 113781 151.55 C22 C26 S ? 135 \n", - "4 25 1 2 113781 151.55 C22 C26 S ? ? \n", - "\n", - " home.dest \n", - "0 St Louis, MO \n", - "1 Montreal, PQ / Chesterville, ON \n", - "2 Montreal, PQ / Chesterville, ON \n", - "3 Montreal, PQ / Chesterville, ON \n", - "4 Montreal, PQ / Chesterville, ON " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the data - it is available open source and online\n", - "\n", - "data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')\n", - "\n", - "# display data\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# replace interrogation marks by NaN values\n", - "\n", - "data = data.replace('?', np.nan)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# retain only the first cabin if more than\n", - "# 1 are available per passenger\n", - "\n", - "def get_first_cabin(row):\n", - " try:\n", - " return row.split()[0]\n", - " except:\n", - " return np.nan\n", - " \n", - "data['cabin'] = data['cabin'].apply(get_first_cabin)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# extracts the title (Mr, Ms, etc) from the name variable\n", - "\n", - "def get_title(passenger):\n", - " line = passenger\n", - " if re.search('Mrs', line):\n", - " return 'Mrs'\n", - " elif re.search('Mr', line):\n", - " return 'Mr'\n", - " elif re.search('Miss', line):\n", - " return 'Miss'\n", - " elif re.search('Master', line):\n", - " return 'Master'\n", - " else:\n", - " return 'Other'\n", - " \n", - "data['title'] = data['name'].apply(get_title)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# cast numerical variables as floats\n", - "\n", - "data['fare'] = data['fare'].astype('float')\n", - "data['age'] = data['age'].astype('float')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " pclass survived sex age sibsp parch fare cabin embarked \\\n", - "0 1 1 female 29.0000 0 0 211.3375 B5 S \n", - "1 1 1 male 0.9167 1 2 151.5500 C22 S \n", - "2 1 0 female 2.0000 1 2 151.5500 C22 S \n", - "3 1 0 male 30.0000 1 2 151.5500 C22 S \n", - "4 1 0 female 25.0000 1 2 151.5500 C22 S \n", - "\n", - " title \n", - "0 Miss \n", - "1 Master \n", - "2 Miss \n", - "3 Mr \n", - "4 Mrs " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# drop unnecessary variables\n", - "\n", - "data.drop(labels=['name','ticket', 'boat', 'body','home.dest'], axis=1, inplace=True)\n", - "\n", - "# display data\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# # save the data set\n", - "\n", - "# data.to_csv('titanic.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Begin Assignment\n", - "\n", - "## Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# list of variables to be used in the pipeline's transformers\n", - "\n", - "NUMERICAL_VARIABLES = \n", - "\n", - "CATEGORICAL_VARIABLES = \n", - "\n", - "CABIN = " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Separate data into train and test" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1047, 9), (262, 9))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " data.drop('survived', axis=1), # predictors\n", - " data['survived'], # target\n", - " test_size=0.2, # percentage of obs in test set\n", - " random_state=0) # seed to ensure reproducibility\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessors\n", - "\n", - "### Class to extract the letter from the variable Cabin" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "class ExtractLetterTransformer():\n", - " # Extract fist letter of variable\n", - "\n", - " def __init__():\n", - " \n", - "\n", - "\n", - " def fit():\n", - "\n", - " \n", - "\n", - " def transform():\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pipeline\n", - "\n", - "- Impute categorical variables with string missing\n", - "- Add a binary missing indicator to numerical variables with missing data\n", - "- Fill NA in original numerical variable with the median\n", - "- Extract first letter from cabin\n", - "- Group rare Categories\n", - "- Perform One hot encoding\n", - "- Scale features with standard scaler\n", - "- Fit a Logistic regression" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# set up the pipeline\n", - "titanic_pipe = Pipeline([\n", - "\n", - " # ===== IMPUTATION =====\n", - " # impute categorical variables with string 'missing'\n", - " ('categorical_imputation', ),\n", - "\n", - " # add missing indicator to numerical variables\n", - " ('missing_indicator', ),\n", - "\n", - " # impute numerical variables with the median\n", - " ('median_imputation', ),\n", - "\n", - "\n", - " # Extract first letter from cabin\n", - " ('extract_letter', ),\n", - "\n", - "\n", - " # == CATEGORICAL ENCODING ======\n", - " # remove categories present in less than 5% of the observations (0.05)\n", - " # group them in one category called 'Rare'\n", - " ('rare_label_encoder', ),\n", - "\n", - "\n", - " # encode categorical variables using one hot encoding into k-1 variables\n", - " ('categorical_encoder', ),\n", - "\n", - " # scale using standardization\n", - " ('scaler', ),\n", - "\n", - " # logistic regression (use C=0.0005 and random_state=0)\n", - " ('Logit', ),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('categorical_imputation',\n", - " CategoricalImputer(variables=['sex', 'cabin', 'embarked',\n", - " 'title'])),\n", - " ('missing_indicator',\n", - " AddMissingIndicator(variables=['age', 'fare'])),\n", - " ('median_imputation',\n", - " MeanMedianImputer(variables=['age', 'fare'])),\n", - " ('extract_letter',\n", - " ExtractLetterTransformer(variables=['cabin'])),\n", - " ('rare_label_encoder',\n", - " RareLabelEncoder(n_categories=1,\n", - " variables=['sex', 'cabin', 'embarked',\n", - " 'title'])),\n", - " ('categorical_encoder',\n", - " OneHotEncoder(drop_last=True,\n", - " variables=['sex', 'cabin', 'embarked',\n", - " 'title'])),\n", - " ('scaler', StandardScaler()),\n", - " ('Logit', LogisticRegression(C=0.0005, random_state=0))])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# train the pipeline\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make predictions and evaluate model performance\n", - "\n", - "Determine:\n", - "- roc-auc\n", - "- accuracy\n", - "\n", - "**Important, remember that to determine the accuracy, you need the outcome 0, 1, referring to survived or not. But to determine the roc-auc you need the probability of survival.**" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train roc-auc: 0.8450386398763523\n", - "train accuracy: 0.7220630372492837\n", - "\n", - "test roc-auc: 0.8354629629629629\n", - "test accuracy: 0.7137404580152672\n", - "\n" - ] - } - ], - "source": [ - "# make predictions for train set\n", - "class_ = \n", - "pred = \n", - "\n", - "# determine mse and rmse\n", - "print('train roc-auc: {}'.format(roc_auc_score(y_train, pred)))\n", - "print('train accuracy: {}'.format(accuracy_score(y_train, class_)))\n", - "print()\n", - "\n", - "# make predictions for test set\n", - "class_ = \n", - "pred = \n", - "\n", - "# determine mse and rmse\n", - "print('test roc-auc: {}'.format(roc_auc_score(y_test, pred)))\n", - "print('test accuracy: {}'.format(accuracy_score(y_test, class_)))\n", - "print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it! Well done\n", - "\n", - "**Keep this code safe, as we will use this notebook later on, to build production code, in our next assignement!!**" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predicting Survival on the Titanic\n", + "\n", + "### History\n", + "Perhaps one of the most infamous shipwrecks in history, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 people on board. Interestingly, by analysing the probability of survival based on few attributes like gender, age, and social status, we can make very accurate predictions on which passengers would survive. Some groups of people were more likely to survive than others, such as women, children, and the upper-class. Therefore, we can learn about the society priorities and privileges at the time.\n", + "\n", + "### Assignment:\n", + "\n", + "Build a Machine Learning Pipeline, to engineer the features in the data set and predict who is more likely to Survive the catastrophe.\n", + "\n", + "Follow the Jupyter notebook below, and complete the missing bits of code, to achieve each one of the pipeline steps." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "# to handle datasets\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# for visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# to divide train and test set\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# feature scaling\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# to build the models\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# to evaluate the models\n", + "from sklearn.metrics import accuracy_score, roc_auc_score\n", + "\n", + "# to persist the model and the scaler\n", + "import joblib\n", + "\n", + "# ========== NEW IMPORTS ========\n", + "# Respect to notebook 02-Predicting-Survival-Titanic-Solution\n", + "\n", + "# pipeline\n", + "\n", + "\n", + "# for the preprocessors\n", + "\n", + "\n", + "# for imputation\n", + "\n", + "\n", + "# for encoding categorical variables\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare the data set" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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011Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2?St Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.55C22 C26S11?Montreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale212113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale3012113781151.55C22 C26S?135Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
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" + ], + "text/plain": [ + " pclass survived name sex \\\n", + "0 1 1 Allen, Miss. Elisabeth Walton female \n", + "1 1 1 Allison, Master. Hudson Trevor male \n", + "2 1 0 Allison, Miss. Helen Loraine female \n", + "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", + "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", + "\n", + " age sibsp parch ticket fare cabin embarked boat body \\\n", + "0 29 0 0 24160 211.3375 B5 S 2 ? \n", + "1 0.9167 1 2 113781 151.55 C22 C26 S 11 ? \n", + "2 2 1 2 113781 151.55 C22 C26 S ? ? \n", + "3 30 1 2 113781 151.55 C22 C26 S ? 135 \n", + "4 25 1 2 113781 151.55 C22 C26 S ? ? \n", + "\n", + " home.dest \n", + "0 St Louis, MO \n", + "1 Montreal, PQ / Chesterville, ON \n", + "2 Montreal, PQ / Chesterville, ON \n", + "3 Montreal, PQ / Chesterville, ON \n", + "4 Montreal, PQ / Chesterville, ON " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data - it is available open source and online\n", + "\n", + "data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')\n", + "\n", + "# display data\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# replace interrogation marks by NaN values\n", + "\n", + "data = data.replace('?', np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# retain only the first cabin if more than\n", + "# 1 are available per passenger\n", + "\n", + "def get_first_cabin(row):\n", + " try:\n", + " return row.split()[0]\n", + " except:\n", + " return np.nan\n", + " \n", + "data['cabin'] = data['cabin'].apply(get_first_cabin)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# extracts the title (Mr, Ms, etc) from the name variable\n", + "\n", + "def get_title(passenger):\n", + " line = passenger\n", + " if re.search('Mrs', line):\n", + " return 'Mrs'\n", + " elif re.search('Mr', line):\n", + " return 'Mr'\n", + " elif re.search('Miss', line):\n", + " return 'Miss'\n", + " elif re.search('Master', line):\n", + " return 'Master'\n", + " else:\n", + " return 'Other'\n", + " \n", + "data['title'] = data['name'].apply(get_title)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# cast numerical variables as floats\n", + "\n", + "data['fare'] = data['fare'].astype('float')\n", + "data['age'] = data['age'].astype('float')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " pclass survived sex age sibsp parch fare cabin embarked \\\n", + "0 1 1 female 29.0000 0 0 211.3375 B5 S \n", + "1 1 1 male 0.9167 1 2 151.5500 C22 S \n", + "2 1 0 female 2.0000 1 2 151.5500 C22 S \n", + "3 1 0 male 30.0000 1 2 151.5500 C22 S \n", + "4 1 0 female 25.0000 1 2 151.5500 C22 S \n", + "\n", + " title \n", + "0 Miss \n", + "1 Master \n", + "2 Miss \n", + "3 Mr \n", + "4 Mrs " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop unnecessary variables\n", + "\n", + "data.drop(labels=['name','ticket', 'boat', 'body','home.dest'], axis=1, inplace=True)\n", + "\n", + "# display data\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# # save the data set\n", + "\n", + "# data.to_csv('titanic.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Begin Assignment\n", + "\n", + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# list of variables to be used in the pipeline's transformers\n", + "\n", + "NUMERICAL_VARIABLES = \n", + "\n", + "CATEGORICAL_VARIABLES = \n", + "\n", + "CABIN = " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Separate data into train and test" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1047, 9), (262, 9))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data.drop('survived', axis=1), # predictors\n", + " data['survived'], # target\n", + " test_size=0.2, # percentage of obs in test set\n", + " random_state=0) # seed to ensure reproducibility\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessors\n", + "\n", + "### Class to extract the letter from the variable Cabin" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "class ExtractLetterTransformer():\n", + " # Extract fist letter of variable\n", + "\n", + " def __init__():\n", + " \n", + "\n", + "\n", + " def fit():\n", + "\n", + " \n", + "\n", + " def transform():\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pipeline\n", + "\n", + "- Impute categorical variables with string missing\n", + "- Add a binary missing indicator to numerical variables with missing data\n", + "- Fill NA in original numerical variable with the median\n", + "- Extract first letter from cabin\n", + "- Group rare Categories\n", + "- Perform One hot encoding\n", + "- Scale features with standard scaler\n", + "- Fit a Logistic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# set up the pipeline\n", + "titanic_pipe = Pipeline([\n", + "\n", + " # ===== IMPUTATION =====\n", + " # impute categorical variables with string 'missing'\n", + " ('categorical_imputation', ),\n", + "\n", + " # add missing indicator to numerical variables\n", + " ('missing_indicator', ),\n", + "\n", + " # impute numerical variables with the median\n", + " ('median_imputation', ),\n", + "\n", + "\n", + " # Extract first letter from cabin\n", + " ('extract_letter', ),\n", + "\n", + "\n", + " # == CATEGORICAL ENCODING ======\n", + " # remove categories present in less than 5% of the observations (0.05)\n", + " # group them in one category called 'Rare'\n", + " ('rare_label_encoder', ),\n", + "\n", + "\n", + " # encode categorical variables using one hot encoding into k-1 variables\n", + " ('categorical_encoder', ),\n", + "\n", + " # scale using standardization\n", + " ('scaler', ),\n", + "\n", + " # logistic regression (use C=0.0005 and random_state=0)\n", + " ('Logit', ),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('categorical_imputation',\n", + " CategoricalImputer(variables=['sex', 'cabin', 'embarked',\n", + " 'title'])),\n", + " ('missing_indicator',\n", + " AddMissingIndicator(variables=['age', 'fare'])),\n", + " ('median_imputation',\n", + " MeanMedianImputer(variables=['age', 'fare'])),\n", + " ('extract_letter',\n", + " ExtractLetterTransformer(variables=['cabin'])),\n", + " ('rare_label_encoder',\n", + " RareLabelEncoder(n_categories=1,\n", + " variables=['sex', 'cabin', 'embarked',\n", + " 'title'])),\n", + " ('categorical_encoder',\n", + " OneHotEncoder(drop_last=True,\n", + " variables=['sex', 'cabin', 'embarked',\n", + " 'title'])),\n", + " ('scaler', StandardScaler()),\n", + " ('Logit', LogisticRegression(C=0.0005, random_state=0))])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# train the pipeline\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make predictions and evaluate model performance\n", + "\n", + "Determine:\n", + "- roc-auc\n", + "- accuracy\n", + "\n", + "**Important, remember that to determine the accuracy, you need the outcome 0, 1, referring to survived or not. But to determine the roc-auc you need the probability of survival.**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train roc-auc: 0.8450386398763523\n", + "train accuracy: 0.7220630372492837\n", + "\n", + "test roc-auc: 0.8354629629629629\n", + "test accuracy: 0.7137404580152672\n", + "\n" + ] + } + ], + "source": [ + "# make predictions for train set\n", + "class_ = \n", + "pred = \n", + "\n", + "# determine mse and rmse\n", + "print('train roc-auc: {}'.format(roc_auc_score(y_train, pred)))\n", + "print('train accuracy: {}'.format(accuracy_score(y_train, class_)))\n", + "print()\n", + "\n", + "# make predictions for test set\n", + "class_ = \n", + "pred = \n", + "\n", + "# determine mse and rmse\n", + "print('test roc-auc: {}'.format(roc_auc_score(y_test, pred)))\n", + "print('test accuracy: {}'.format(accuracy_score(y_test, class_)))\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it! Well done\n", + "\n", + "**Keep this code safe, as we will use this notebook later on, to build production code, in our next assignement!!**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-04-research-and-development/titanic-assignment/04-titanic-survival-pipeline-solution.ipynb b/section-04-research-and-development/titanic-assignment/04-titanic-survival-pipeline-solution.ipynb index 3aba602e3..464477287 100644 --- a/section-04-research-and-development/titanic-assignment/04-titanic-survival-pipeline-solution.ipynb +++ b/section-04-research-and-development/titanic-assignment/04-titanic-survival-pipeline-solution.ipynb @@ -1,750 +1,750 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predicting Survival on the Titanic\n", - "\n", - "### History\n", - "Perhaps one of the most infamous shipwrecks in history, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 people on board. Interestingly, by analysing the probability of survival based on few attributes like gender, age, and social status, we can make very accurate predictions on which passengers would survive. Some groups of people were more likely to survive than others, such as women, children, and the upper-class. Therefore, we can learn about the society priorities and privileges at the time.\n", - "\n", - "### Assignment:\n", - "\n", - "Build a Machine Learning Pipeline, to engineer the features in the data set and predict who is more likely to Survive the catastrophe.\n", - "\n", - "Follow the Jupyter notebook below, and complete the missing bits of code, to achieve each one of the pipeline steps." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "\n", - "# to handle datasets\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "# for visualization\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# to divide train and test set\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# feature scaling\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "# to build the models\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "# to evaluate the models\n", - "from sklearn.metrics import accuracy_score, roc_auc_score\n", - "\n", - "# to persist the model and the scaler\n", - "import joblib\n", - "\n", - "# ========== NEW IMPORTS ========\n", - "# Respect to notebook 02-Predicting-Survival-Titanic-Solution\n", - "\n", - "# pipeline\n", - "from sklearn.pipeline import Pipeline\n", - "\n", - "# for the preprocessors\n", - "from sklearn.base import BaseEstimator, TransformerMixin\n", - "\n", - "# for imputation\n", - "from feature_engine.imputation import (\n", - " CategoricalImputer,\n", - " AddMissingIndicator,\n", - " MeanMedianImputer)\n", - "\n", - "# for encoding categorical variables\n", - "from feature_engine.encoding import (\n", - " RareLabelEncoder,\n", - " OneHotEncoder\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare the data set" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2?St Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.55C22 C26S11?Montreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale212113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale3012113781151.55C22 C26S?135Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
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" - ], - "text/plain": [ - " pclass survived name sex \\\n", - "0 1 1 Allen, Miss. Elisabeth Walton female \n", - "1 1 1 Allison, Master. Hudson Trevor male \n", - "2 1 0 Allison, Miss. Helen Loraine female \n", - "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", - "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", - "\n", - " age sibsp parch ticket fare cabin embarked boat body \\\n", - "0 29 0 0 24160 211.3375 B5 S 2 ? \n", - "1 0.9167 1 2 113781 151.55 C22 C26 S 11 ? \n", - "2 2 1 2 113781 151.55 C22 C26 S ? ? \n", - "3 30 1 2 113781 151.55 C22 C26 S ? 135 \n", - "4 25 1 2 113781 151.55 C22 C26 S ? ? \n", - "\n", - " home.dest \n", - "0 St Louis, MO \n", - "1 Montreal, PQ / Chesterville, ON \n", - "2 Montreal, PQ / Chesterville, ON \n", - "3 Montreal, PQ / Chesterville, ON \n", - "4 Montreal, PQ / Chesterville, ON " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the data - it is available open source and online\n", - "\n", - "data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')\n", - "\n", - "# display data\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# replace interrogation marks by NaN values\n", - "\n", - "data = data.replace('?', np.nan)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# retain only the first cabin if more than\n", - "# 1 are available per passenger\n", - "\n", - "def get_first_cabin(row):\n", - " try:\n", - " return row.split()[0]\n", - " except:\n", - " return np.nan\n", - " \n", - "data['cabin'] = data['cabin'].apply(get_first_cabin)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# extracts the title (Mr, Ms, etc) from the name variable\n", - "\n", - "def get_title(passenger):\n", - " line = passenger\n", - " if re.search('Mrs', line):\n", - " return 'Mrs'\n", - " elif re.search('Mr', line):\n", - " return 'Mr'\n", - " elif re.search('Miss', line):\n", - " return 'Miss'\n", - " elif re.search('Master', line):\n", - " return 'Master'\n", - " else:\n", - " return 'Other'\n", - " \n", - "data['title'] = data['name'].apply(get_title)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# cast numerical variables as floats\n", - "\n", - "data['fare'] = data['fare'].astype('float')\n", - "data['age'] = data['age'].astype('float')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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310male30.000012151.5500C22SMr
410female25.000012151.5500C22SMrs
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" - ], - "text/plain": [ - " pclass survived sex age sibsp parch fare cabin embarked \\\n", - "0 1 1 female 29.0000 0 0 211.3375 B5 S \n", - "1 1 1 male 0.9167 1 2 151.5500 C22 S \n", - "2 1 0 female 2.0000 1 2 151.5500 C22 S \n", - "3 1 0 male 30.0000 1 2 151.5500 C22 S \n", - "4 1 0 female 25.0000 1 2 151.5500 C22 S \n", - "\n", - " title \n", - "0 Miss \n", - "1 Master \n", - "2 Miss \n", - "3 Mr \n", - "4 Mrs " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# drop unnecessary variables\n", - "\n", - "data.drop(labels=['name','ticket', 'boat', 'body','home.dest'], axis=1, inplace=True)\n", - "\n", - "# display data\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# # save the data set\n", - "\n", - "# data.to_csv('titanic.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# list of variables to be used in the pipeline's transformers\n", - "\n", - "NUMERICAL_VARIABLES = ['age', 'fare']\n", - "\n", - "CATEGORICAL_VARIABLES = ['sex', 'cabin', 'embarked', 'title']\n", - "\n", - "CABIN = ['cabin']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Separate data into train and test" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1047, 9), (262, 9))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " data.drop('survived', axis=1), # predictors\n", - " data['survived'], # target\n", - " test_size=0.2, # percentage of obs in test set\n", - " random_state=0) # seed to ensure reproducibility\n", - "\n", - "X_train.shape, X_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessors\n", - "\n", - "### Class to extract the letter from the variable Cabin" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "class ExtractLetterTransformer(BaseEstimator, TransformerMixin):\n", - " # Extract fist letter of variable\n", - "\n", - " def __init__(self, variables):\n", - " \n", - " if not isinstance(variables, list):\n", - " raise ValueError('variables should be a list')\n", - " \n", - " self.variables = variables\n", - "\n", - " def fit(self, X, y=None):\n", - " # we need this step to fit the sklearn pipeline\n", - " return self\n", - "\n", - " def transform(self, X):\n", - "\n", - " # so that we do not over-write the original dataframe\n", - " X = X.copy()\n", - " \n", - " for feature in self.variables:\n", - " X[feature] = X[feature].str[0]\n", - "\n", - " return X" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pipeline\n", - "\n", - "- Impute categorical variables with string missing\n", - "- Add a binary missing indicator to numerical variables with missing data\n", - "- Fill NA in original numerical variable with the median\n", - "- Extract first letter from cabin\n", - "- Group rare Categories\n", - "- Perform One hot encoding\n", - "- Scale features with standard scaler\n", - "- Fit a Logistic regression" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# set up the pipeline\n", - "titanic_pipe = Pipeline([\n", - "\n", - " # ===== IMPUTATION =====\n", - " # impute categorical variables with string missing\n", - " ('categorical_imputation', CategoricalImputer(\n", - " imputation_method='missing', variables=CATEGORICAL_VARIABLES)),\n", - "\n", - " # add missing indicator to numerical variables\n", - " ('missing_indicator', AddMissingIndicator(variables=NUMERICAL_VARIABLES)),\n", - "\n", - " # impute numerical variables with the median\n", - " ('median_imputation', MeanMedianImputer(\n", - " imputation_method='median', variables=NUMERICAL_VARIABLES)),\n", - "\n", - "\n", - " # Extract letter from cabin\n", - " ('extract_letter', ExtractLetterTransformer(variables=CABIN)),\n", - "\n", - "\n", - " # == CATEGORICAL ENCODING ======\n", - " # remove categories present in less than 5% of the observations (0.05)\n", - " # group them in one category called 'Rare'\n", - " ('rare_label_encoder', RareLabelEncoder(\n", - " tol=0.05, n_categories=1, variables=CATEGORICAL_VARIABLES)),\n", - "\n", - "\n", - " # encode categorical variables using one hot encoding into k-1 variables\n", - " ('categorical_encoder', OneHotEncoder(\n", - " drop_last=True, variables=CATEGORICAL_VARIABLES)),\n", - "\n", - " # scale\n", - " ('scaler', StandardScaler()),\n", - "\n", - " ('Logit', LogisticRegression(C=0.0005, random_state=0)),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('categorical_imputation',\n", - " CategoricalImputer(variables=['sex', 'cabin', 'embarked',\n", - " 'title'])),\n", - " ('missing_indicator',\n", - " AddMissingIndicator(variables=['age', 'fare'])),\n", - " ('median_imputation',\n", - " MeanMedianImputer(variables=['age', 'fare'])),\n", - " ('extract_letter',\n", - " ExtractLetterTransformer(variables=['cabin'])),\n", - " ('rare_label_encoder',\n", - " RareLabelEncoder(n_categories=1,\n", - " variables=['sex', 'cabin', 'embarked',\n", - " 'title'])),\n", - " ('categorical_encoder',\n", - " OneHotEncoder(drop_last=True,\n", - " variables=['sex', 'cabin', 'embarked',\n", - " 'title'])),\n", - " ('scaler', StandardScaler()),\n", - " ('Logit', LogisticRegression(C=0.0005, random_state=0))])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# train the pipeline\n", - "titanic_pipe.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make predictions and evaluate model performance\n", - "\n", - "Determine:\n", - "- roc-auc\n", - "- accuracy\n", - "\n", - "**Important, remember that to determine the accuracy, you need the outcome 0, 1, referring to survived or not. But to determine the roc-auc you need the probability of survival.**" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train roc-auc: 0.8450386398763523\n", - "train accuracy: 0.7220630372492837\n", - "\n", - "test roc-auc: 0.8354629629629629\n", - "test accuracy: 0.7137404580152672\n", - "\n" - ] - } - ], - "source": [ - "# make predictions for train set\n", - "class_ = titanic_pipe.predict(X_train)\n", - "pred = titanic_pipe.predict_proba(X_train)[:,1]\n", - "\n", - "# determine mse and rmse\n", - "print('train roc-auc: {}'.format(roc_auc_score(y_train, pred)))\n", - "print('train accuracy: {}'.format(accuracy_score(y_train, class_)))\n", - "print()\n", - "\n", - "# make predictions for test set\n", - "class_ = titanic_pipe.predict(X_test)\n", - "pred = titanic_pipe.predict_proba(X_test)[:,1]\n", - "\n", - "# determine mse and rmse\n", - "print('test roc-auc: {}'.format(roc_auc_score(y_test, pred)))\n", - "print('test accuracy: {}'.format(accuracy_score(y_test, class_)))\n", - "print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it! Well done\n", - "\n", - "**Keep this code safe, as we will use this notebook later on, to build production code, in our next assignement!!**" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "feml", - "language": "python", - "name": "feml" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predicting Survival on the Titanic\n", + "\n", + "### History\n", + "Perhaps one of the most infamous shipwrecks in history, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 people on board. Interestingly, by analysing the probability of survival based on few attributes like gender, age, and social status, we can make very accurate predictions on which passengers would survive. Some groups of people were more likely to survive than others, such as women, children, and the upper-class. Therefore, we can learn about the society priorities and privileges at the time.\n", + "\n", + "### Assignment:\n", + "\n", + "Build a Machine Learning Pipeline, to engineer the features in the data set and predict who is more likely to Survive the catastrophe.\n", + "\n", + "Follow the Jupyter notebook below, and complete the missing bits of code, to achieve each one of the pipeline steps." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "# to handle datasets\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# for visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# to divide train and test set\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# feature scaling\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# to build the models\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# to evaluate the models\n", + "from sklearn.metrics import accuracy_score, roc_auc_score\n", + "\n", + "# to persist the model and the scaler\n", + "import joblib\n", + "\n", + "# ========== NEW IMPORTS ========\n", + "# Respect to notebook 02-Predicting-Survival-Titanic-Solution\n", + "\n", + "# pipeline\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "# for the preprocessors\n", + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "\n", + "# for imputation\n", + "from feature_engine.imputation import (\n", + " CategoricalImputer,\n", + " AddMissingIndicator,\n", + " MeanMedianImputer)\n", + "\n", + "# for encoding categorical variables\n", + "from feature_engine.encoding import (\n", + " RareLabelEncoder,\n", + " OneHotEncoder\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare the data set" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2?St Louis, MO
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410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.55C22 C26S??Montreal, PQ / Chesterville, ON
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" + ], + "text/plain": [ + " pclass survived name sex \\\n", + "0 1 1 Allen, Miss. Elisabeth Walton female \n", + "1 1 1 Allison, Master. Hudson Trevor male \n", + "2 1 0 Allison, Miss. Helen Loraine female \n", + "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", + "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", + "\n", + " age sibsp parch ticket fare cabin embarked boat body \\\n", + "0 29 0 0 24160 211.3375 B5 S 2 ? \n", + "1 0.9167 1 2 113781 151.55 C22 C26 S 11 ? \n", + "2 2 1 2 113781 151.55 C22 C26 S ? ? \n", + "3 30 1 2 113781 151.55 C22 C26 S ? 135 \n", + "4 25 1 2 113781 151.55 C22 C26 S ? ? \n", + "\n", + " home.dest \n", + "0 St Louis, MO \n", + "1 Montreal, PQ / Chesterville, ON \n", + "2 Montreal, PQ / Chesterville, ON \n", + "3 Montreal, PQ / Chesterville, ON \n", + "4 Montreal, PQ / Chesterville, ON " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data - it is available open source and online\n", + "\n", + "data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')\n", + "\n", + "# display data\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# replace interrogation marks by NaN values\n", + "\n", + "data = data.replace('?', np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# retain only the first cabin if more than\n", + "# 1 are available per passenger\n", + "\n", + "def get_first_cabin(row):\n", + " try:\n", + " return row.split()[0]\n", + " except:\n", + " return np.nan\n", + " \n", + "data['cabin'] = data['cabin'].apply(get_first_cabin)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# extracts the title (Mr, Ms, etc) from the name variable\n", + "\n", + "def get_title(passenger):\n", + " line = passenger\n", + " if re.search('Mrs', line):\n", + " return 'Mrs'\n", + " elif re.search('Mr', line):\n", + " return 'Mr'\n", + " elif re.search('Miss', line):\n", + " return 'Miss'\n", + " elif re.search('Master', line):\n", + " return 'Master'\n", + " else:\n", + " return 'Other'\n", + " \n", + "data['title'] = data['name'].apply(get_title)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# cast numerical variables as floats\n", + "\n", + "data['fare'] = data['fare'].astype('float')\n", + "data['age'] = data['age'].astype('float')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " pclass survived sex age sibsp parch fare cabin embarked \\\n", + "0 1 1 female 29.0000 0 0 211.3375 B5 S \n", + "1 1 1 male 0.9167 1 2 151.5500 C22 S \n", + "2 1 0 female 2.0000 1 2 151.5500 C22 S \n", + "3 1 0 male 30.0000 1 2 151.5500 C22 S \n", + "4 1 0 female 25.0000 1 2 151.5500 C22 S \n", + "\n", + " title \n", + "0 Miss \n", + "1 Master \n", + "2 Miss \n", + "3 Mr \n", + "4 Mrs " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop unnecessary variables\n", + "\n", + "data.drop(labels=['name','ticket', 'boat', 'body','home.dest'], axis=1, inplace=True)\n", + "\n", + "# display data\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# # save the data set\n", + "\n", + "# data.to_csv('titanic.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# list of variables to be used in the pipeline's transformers\n", + "\n", + "NUMERICAL_VARIABLES = ['age', 'fare']\n", + "\n", + "CATEGORICAL_VARIABLES = ['sex', 'cabin', 'embarked', 'title']\n", + "\n", + "CABIN = ['cabin']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Separate data into train and test" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1047, 9), (262, 9))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data.drop('survived', axis=1), # predictors\n", + " data['survived'], # target\n", + " test_size=0.2, # percentage of obs in test set\n", + " random_state=0) # seed to ensure reproducibility\n", + "\n", + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessors\n", + "\n", + "### Class to extract the letter from the variable Cabin" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "class ExtractLetterTransformer(BaseEstimator, TransformerMixin):\n", + " # Extract fist letter of variable\n", + "\n", + " def __init__(self, variables):\n", + " \n", + " if not isinstance(variables, list):\n", + " raise ValueError('variables should be a list')\n", + " \n", + " self.variables = variables\n", + "\n", + " def fit(self, X, y=None):\n", + " # we need this step to fit the sklearn pipeline\n", + " return self\n", + "\n", + " def transform(self, X):\n", + "\n", + " # so that we do not over-write the original dataframe\n", + " X = X.copy()\n", + " \n", + " for feature in self.variables:\n", + " X[feature] = X[feature].str[0]\n", + "\n", + " return X" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pipeline\n", + "\n", + "- Impute categorical variables with string missing\n", + "- Add a binary missing indicator to numerical variables with missing data\n", + "- Fill NA in original numerical variable with the median\n", + "- Extract first letter from cabin\n", + "- Group rare Categories\n", + "- Perform One hot encoding\n", + "- Scale features with standard scaler\n", + "- Fit a Logistic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# set up the pipeline\n", + "titanic_pipe = Pipeline([\n", + "\n", + " # ===== IMPUTATION =====\n", + " # impute categorical variables with string missing\n", + " ('categorical_imputation', CategoricalImputer(\n", + " imputation_method='missing', variables=CATEGORICAL_VARIABLES)),\n", + "\n", + " # add missing indicator to numerical variables\n", + " ('missing_indicator', AddMissingIndicator(variables=NUMERICAL_VARIABLES)),\n", + "\n", + " # impute numerical variables with the median\n", + " ('median_imputation', MeanMedianImputer(\n", + " imputation_method='median', variables=NUMERICAL_VARIABLES)),\n", + "\n", + "\n", + " # Extract letter from cabin\n", + " ('extract_letter', ExtractLetterTransformer(variables=CABIN)),\n", + "\n", + "\n", + " # == CATEGORICAL ENCODING ======\n", + " # remove categories present in less than 5% of the observations (0.05)\n", + " # group them in one category called 'Rare'\n", + " ('rare_label_encoder', RareLabelEncoder(\n", + " tol=0.05, n_categories=1, variables=CATEGORICAL_VARIABLES)),\n", + "\n", + "\n", + " # encode categorical variables using one hot encoding into k-1 variables\n", + " ('categorical_encoder', OneHotEncoder(\n", + " drop_last=True, variables=CATEGORICAL_VARIABLES)),\n", + "\n", + " # scale\n", + " ('scaler', StandardScaler()),\n", + "\n", + " ('Logit', LogisticRegression(C=0.0005, random_state=0)),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('categorical_imputation',\n", + " CategoricalImputer(variables=['sex', 'cabin', 'embarked',\n", + " 'title'])),\n", + " ('missing_indicator',\n", + " AddMissingIndicator(variables=['age', 'fare'])),\n", + " ('median_imputation',\n", + " MeanMedianImputer(variables=['age', 'fare'])),\n", + " ('extract_letter',\n", + " ExtractLetterTransformer(variables=['cabin'])),\n", + " ('rare_label_encoder',\n", + " RareLabelEncoder(n_categories=1,\n", + " variables=['sex', 'cabin', 'embarked',\n", + " 'title'])),\n", + " ('categorical_encoder',\n", + " OneHotEncoder(drop_last=True,\n", + " variables=['sex', 'cabin', 'embarked',\n", + " 'title'])),\n", + " ('scaler', StandardScaler()),\n", + " ('Logit', LogisticRegression(C=0.0005, random_state=0))])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# train the pipeline\n", + "titanic_pipe.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make predictions and evaluate model performance\n", + "\n", + "Determine:\n", + "- roc-auc\n", + "- accuracy\n", + "\n", + "**Important, remember that to determine the accuracy, you need the outcome 0, 1, referring to survived or not. But to determine the roc-auc you need the probability of survival.**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train roc-auc: 0.8450386398763523\n", + "train accuracy: 0.7220630372492837\n", + "\n", + "test roc-auc: 0.8354629629629629\n", + "test accuracy: 0.7137404580152672\n", + "\n" + ] + } + ], + "source": [ + "# make predictions for train set\n", + "class_ = titanic_pipe.predict(X_train)\n", + "pred = titanic_pipe.predict_proba(X_train)[:,1]\n", + "\n", + "# determine mse and rmse\n", + "print('train roc-auc: {}'.format(roc_auc_score(y_train, pred)))\n", + "print('train accuracy: {}'.format(accuracy_score(y_train, class_)))\n", + "print()\n", + "\n", + "# make predictions for test set\n", + "class_ = titanic_pipe.predict(X_test)\n", + "pred = titanic_pipe.predict_proba(X_test)[:,1]\n", + "\n", + "# determine mse and rmse\n", + "print('test roc-auc: {}'.format(roc_auc_score(y_test, pred)))\n", + "print('test accuracy: {}'.format(accuracy_score(y_test, class_)))\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it! Well done\n", + "\n", + "**Keep this code safe, as we will use this notebook later on, to build production code, in our next assignement!!**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "feml", + "language": "python", + "name": "feml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section-05-production-model-package/MANIFEST.in b/section-05-production-model-package/MANIFEST.in index 507089640..ad7def636 100644 --- a/section-05-production-model-package/MANIFEST.in +++ b/section-05-production-model-package/MANIFEST.in @@ -1,18 +1,18 @@ -include *.txt -include *.md -include *.pkl -recursive-include ./regression_model/* - -include regression_model/datasets/train.csv -include regression_model/datasets/test.csv -include regression_model/trained_models/*.pkl -include regression_model/VERSION -include regression_model/config.yml - -include ./requirements/requirements.txt -include ./requirements/test_requirements.txt -exclude *.log -exclude *.cfg - -recursive-exclude * __pycache__ +include *.txt +include *.md +include *.pkl +recursive-include ./regression_model/* + +include regression_model/datasets/train.csv +include regression_model/datasets/test.csv +include regression_model/trained_models/*.pkl +include regression_model/VERSION +include regression_model/config.yml + +include ./requirements/requirements.txt +include ./requirements/test_requirements.txt +exclude *.log +exclude *.cfg + +recursive-exclude * __pycache__ recursive-exclude * *.py[co] \ No newline at end of file diff --git a/section-05-production-model-package/mypy.ini b/section-05-production-model-package/mypy.ini index 9f1b46b12..e499513b5 100644 --- a/section-05-production-model-package/mypy.ini +++ b/section-05-production-model-package/mypy.ini @@ -1,14 +1,14 @@ -[mypy] -# warn_unreachable = True -warn_unused_ignores = True -follow_imports = skip -show_error_context = True -warn_incomplete_stub = True -ignore_missing_imports = True -check_untyped_defs = True -cache_dir = /dev/null -# Cannot enable this one as we still allow defining functions without any types. -# disallow_untyped_defs = True -warn_redundant_casts = True -warn_unused_configs = True +[mypy] +# warn_unreachable = True +warn_unused_ignores = True +follow_imports = skip +show_error_context = True +warn_incomplete_stub = True +ignore_missing_imports = True +check_untyped_defs = True +cache_dir = /dev/null +# Cannot enable this one as we still allow defining functions without any types. +# disallow_untyped_defs = True +warn_redundant_casts = True +warn_unused_configs = True strict_optional = True \ No newline at end of file diff --git a/section-05-production-model-package/pyproject.toml b/section-05-production-model-package/pyproject.toml index 31a46cadd..29227b4db 100644 --- a/section-05-production-model-package/pyproject.toml +++ b/section-05-production-model-package/pyproject.toml @@ -1,48 +1,48 @@ -[build-system] -requires = [ - "setuptools>=42", - "wheel" -] -build-backend = "setuptools.build_meta" - -[tool.pytest.ini_options] -minversion = "2.0" -addopts = "-rfEX -p pytester --strict-markers" -python_files = ["test_*.py", "*_test.py"] -python_classes = ["Test", "Acceptance"] -python_functions = ["test"] -# NOTE: "doc" is not included here, but gets tested explicitly via "doctesting". -testpaths = ["tests"] -xfail_strict = true -filterwarnings = [ - "error", - "default:Using or importing the ABCs:DeprecationWarning:unittest2.*", - # produced by older pyparsing<=2.2.0. - "default:Using or importing the ABCs:DeprecationWarning:pyparsing.*", - "default:the imp module is deprecated in favour of importlib:DeprecationWarning:nose.*", - # distutils is deprecated in 3.10, scheduled for removal in 3.12 - "ignore:The distutils package is deprecated:DeprecationWarning", - # produced by python3.6/site.py itself (3.6.7 on Travis, could not trigger it with 3.6.8)." - "ignore:.*U.*mode is deprecated:DeprecationWarning:(?!(pytest|_pytest))", - # produced by pytest-xdist - "ignore:.*type argument to addoption.*:DeprecationWarning", - # produced on execnet (pytest-xdist) - "ignore:.*inspect.getargspec.*deprecated, use inspect.signature.*:DeprecationWarning", - # pytest's own futurewarnings - "ignore::pytest.PytestExperimentalApiWarning", - # Do not cause SyntaxError for invalid escape sequences in py37. - # Those are caught/handled by pyupgrade, and not easy to filter with the - # module being the filename (with .py removed). - "default:invalid escape sequence:DeprecationWarning", - # ignore use of unregistered marks, because we use many to test the implementation - "ignore::_pytest.warning_types.PytestUnknownMarkWarning", -] - -[tool.black] -target-version = ['py311'] - -[tool.isort] -profile = "black" -line_length = 100 -lines_between_sections = 1 -skip = "migrations" +[build-system] +requires = [ + "setuptools>=42", + "wheel" +] +build-backend = "setuptools.build_meta" + +[tool.pytest.ini_options] +minversion = "2.0" +addopts = "-rfEX -p pytester --strict-markers" +python_files = ["test_*.py", "*_test.py"] +python_classes = ["Test", "Acceptance"] +python_functions = ["test"] +# NOTE: "doc" is not included here, but gets tested explicitly via "doctesting". +testpaths = ["tests"] +xfail_strict = true +filterwarnings = [ + "error", + "default:Using or importing the ABCs:DeprecationWarning:unittest2.*", + # produced by older pyparsing<=2.2.0. + "default:Using or importing the ABCs:DeprecationWarning:pyparsing.*", + "default:the imp module is deprecated in favour of importlib:DeprecationWarning:nose.*", + # distutils is deprecated in 3.10, scheduled for removal in 3.12 + "ignore:The distutils package is deprecated:DeprecationWarning", + # produced by python3.6/site.py itself (3.6.7 on Travis, could not trigger it with 3.6.8)." + "ignore:.*U.*mode is deprecated:DeprecationWarning:(?!(pytest|_pytest))", + # produced by pytest-xdist + "ignore:.*type argument to addoption.*:DeprecationWarning", + # produced on execnet (pytest-xdist) + "ignore:.*inspect.getargspec.*deprecated, use inspect.signature.*:DeprecationWarning", + # pytest's own futurewarnings + "ignore::pytest.PytestExperimentalApiWarning", + # Do not cause SyntaxError for invalid escape sequences in py37. + # Those are caught/handled by pyupgrade, and not easy to filter with the + # module being the filename (with .py removed). + "default:invalid escape sequence:DeprecationWarning", + # ignore use of unregistered marks, because we use many to test the implementation + "ignore::_pytest.warning_types.PytestUnknownMarkWarning", +] + +[tool.black] +target-version = ['py311'] + +[tool.isort] +profile = "black" +line_length = 100 +lines_between_sections = 1 +skip = "migrations" diff --git a/section-05-production-model-package/regression_model/VERSION b/section-05-production-model-package/regression_model/VERSION index 8acdd82b7..84576eaa9 100644 --- a/section-05-production-model-package/regression_model/VERSION +++ b/section-05-production-model-package/regression_model/VERSION @@ -1 +1 @@ -0.0.1 +0.0.1 diff --git a/section-05-production-model-package/regression_model/__init__.py b/section-05-production-model-package/regression_model/__init__.py index 79c79a020..c4c8d0f55 100644 --- a/section-05-production-model-package/regression_model/__init__.py +++ b/section-05-production-model-package/regression_model/__init__.py @@ -1,17 +1,17 @@ -import logging - -from regression_model.config.core import PACKAGE_ROOT, config - -# It is strongly advised that you do not add any handlers other than -# NullHandler to your library’s loggers. This is because the configuration -# of handlers is the prerogative of the application developer who uses your -# library. The application developer knows their target audience and what -# handlers are most appropriate for their application: if you add handlers -# ‘under the hood’, you might well interfere with their ability to carry out -# unit tests and deliver logs which suit their requirements. -# https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library -logging.getLogger(config.app_config.package_name).addHandler(logging.NullHandler()) - - -with open(PACKAGE_ROOT / "VERSION") as version_file: - __version__ = version_file.read().strip() +import logging + +from regression_model.config.core import PACKAGE_ROOT, config + +# It is strongly advised that you do not add any handlers other than +# NullHandler to your library’s loggers. This is because the configuration +# of handlers is the prerogative of the application developer who uses your +# library. The application developer knows their target audience and what +# handlers are most appropriate for their application: if you add handlers +# ‘under the hood’, you might well interfere with their ability to carry out +# unit tests and deliver logs which suit their requirements. +# https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library +logging.getLogger(config.app_config.package_name).addHandler(logging.NullHandler()) + + +with open(PACKAGE_ROOT / "VERSION") as version_file: + __version__ = version_file.read().strip() diff --git a/section-05-production-model-package/regression_model/config.yml b/section-05-production-model-package/regression_model/config.yml index 6715787a0..282ddb276 100644 --- a/section-05-production-model-package/regression_model/config.yml +++ b/section-05-production-model-package/regression_model/config.yml @@ -1,162 +1,162 @@ -# Package Overview -package_name: regression_model - -# Data Files -training_data_file: train.csv -test_data_file: test.csv - -# Variables -# The variable we are attempting to predict (sale price) -target: SalePrice - -pipeline_name: regression_model -pipeline_save_file: regression_model_output_v - -# Will cause syntax errors since they begin with numbers -variables_to_rename: - 1stFlrSF: FirstFlrSF - 2ndFlrSF: SecondFlrSF - 3SsnPorch: ThreeSsnPortch - -features: - - MSSubClass - - MSZoning - - LotFrontage - - LotShape - - LandContour - - LotConfig - - Neighborhood - - OverallQual - - OverallCond - - YearRemodAdd - - RoofStyle - - Exterior1st - - ExterQual - - Foundation - - BsmtQual - - BsmtExposure - - BsmtFinType1 - - HeatingQC - - CentralAir - - FirstFlrSF # renamed - - SecondFlrSF # renamed - - GrLivArea - - BsmtFullBath - - HalfBath - - KitchenQual - - TotRmsAbvGrd - - Functional - - Fireplaces - - FireplaceQu - - GarageFinish - - GarageCars - - GarageArea - - PavedDrive - - WoodDeckSF - - ScreenPorch - - SaleCondition - # this one is only to calculate temporal variable: - - YrSold - -# set train/test split -test_size: 0.1 - -# to set the random seed -random_state: 0 - -alpha: 0.001 - -# categorical variables with NA in train set -categorical_vars_with_na_frequent: - - BsmtQual - - BsmtExposure - - BsmtFinType1 - - GarageFinish - -categorical_vars_with_na_missing: - - FireplaceQu - -numerical_vars_with_na: - - LotFrontage - -temporal_vars: - - YearRemodAdd - -ref_var: YrSold - - -# variables to log transform -numericals_log_vars: - - LotFrontage - - FirstFlrSF - - GrLivArea - -binarize_vars: - - ScreenPorch - -# variables to map -qual_vars: - - ExterQual - - BsmtQual - - HeatingQC - - KitchenQual - - FireplaceQu - -exposure_vars: - - BsmtExposure - -finish_vars: - - BsmtFinType1 - -garage_vars: - - GarageFinish - -categorical_vars: - - MSSubClass - - MSZoning - - LotShape - - LandContour - - LotConfig - - Neighborhood - - RoofStyle - - Exterior1st - - Foundation - - CentralAir - - Functional - - PavedDrive - - SaleCondition - -# variable mappings -qual_mappings: - Po: 1 - Fa: 2 - TA: 3 - Gd: 4 - Ex: 5 - Missing: 0 - NA: 0 - -exposure_mappings: - No: 1 - Mn: 2 - Av: 3 - Gd: 4 - - -finish_mappings: - Missing: 0 - NA: 0 - Unf: 1 - LwQ: 2 - Rec: 3 - BLQ: 4 - ALQ: 5 - GLQ: 6 - - -garage_mappings: - Missing: 0 - NA: 0 - Unf: 1 - RFn: 2 - Fin: 3 +# Package Overview +package_name: regression_model + +# Data Files +training_data_file: train.csv +test_data_file: test.csv + +# Variables +# The variable we are attempting to predict (sale price) +target: SalePrice + +pipeline_name: regression_model +pipeline_save_file: regression_model_output_v + +# Will cause syntax errors since they begin with numbers +variables_to_rename: + 1stFlrSF: FirstFlrSF + 2ndFlrSF: SecondFlrSF + 3SsnPorch: ThreeSsnPortch + +features: + - MSSubClass + - MSZoning + - LotFrontage + - LotShape + - LandContour + - LotConfig + - Neighborhood + - OverallQual + - OverallCond + - YearRemodAdd + - RoofStyle + - Exterior1st + - ExterQual + - Foundation + - BsmtQual + - BsmtExposure + - BsmtFinType1 + - HeatingQC + - CentralAir + - FirstFlrSF # renamed + - SecondFlrSF # renamed + - GrLivArea + - BsmtFullBath + - HalfBath + - KitchenQual + - TotRmsAbvGrd + - Functional + - Fireplaces + - FireplaceQu + - GarageFinish + - GarageCars + - GarageArea + - PavedDrive + - WoodDeckSF + - ScreenPorch + - SaleCondition + # this one is only to calculate temporal variable: + - YrSold + +# set train/test split +test_size: 0.1 + +# to set the random seed +random_state: 0 + +alpha: 0.001 + +# categorical variables with NA in train set +categorical_vars_with_na_frequent: + - BsmtQual + - BsmtExposure + - BsmtFinType1 + - GarageFinish + +categorical_vars_with_na_missing: + - FireplaceQu + +numerical_vars_with_na: + - LotFrontage + +temporal_vars: + - YearRemodAdd + +ref_var: YrSold + + +# variables to log transform +numericals_log_vars: + - LotFrontage + - FirstFlrSF + - GrLivArea + +binarize_vars: + - ScreenPorch + +# variables to map +qual_vars: + - ExterQual + - BsmtQual + - HeatingQC + - KitchenQual + - FireplaceQu + +exposure_vars: + - BsmtExposure + +finish_vars: + - BsmtFinType1 + +garage_vars: + - GarageFinish + +categorical_vars: + - MSSubClass + - MSZoning + - LotShape + - LandContour + - LotConfig + - Neighborhood + - RoofStyle + - Exterior1st + - Foundation + - CentralAir + - Functional + - PavedDrive + - SaleCondition + +# variable mappings +qual_mappings: + Po: 1 + Fa: 2 + TA: 3 + Gd: 4 + Ex: 5 + Missing: 0 + NA: 0 + +exposure_mappings: + No: 1 + Mn: 2 + Av: 3 + Gd: 4 + + +finish_mappings: + Missing: 0 + NA: 0 + Unf: 1 + LwQ: 2 + Rec: 3 + BLQ: 4 + ALQ: 5 + GLQ: 6 + + +garage_mappings: + Missing: 0 + NA: 0 + Unf: 1 + RFn: 2 + Fin: 3 diff --git a/section-05-production-model-package/regression_model/config/core.py b/section-05-production-model-package/regression_model/config/core.py index f321864f9..7263a84ed 100644 --- a/section-05-production-model-package/regression_model/config/core.py +++ b/section-05-production-model-package/regression_model/config/core.py @@ -1,99 +1,99 @@ -from pathlib import Path -from typing import Dict, List, Optional, Sequence - -from pydantic import BaseModel -from strictyaml import YAML, load - -import regression_model - -# Project Directories -PACKAGE_ROOT = Path(regression_model.__file__).resolve().parent -ROOT = PACKAGE_ROOT.parent -CONFIG_FILE_PATH = PACKAGE_ROOT / "config.yml" -DATASET_DIR = PACKAGE_ROOT / "datasets" -TRAINED_MODEL_DIR = PACKAGE_ROOT / "trained_models" - - -class AppConfig(BaseModel): - """ - Application-level config. - """ - - package_name: str - training_data_file: str - test_data_file: str - pipeline_save_file: str - - -class ModelConfig(BaseModel): - """ - All configuration relevant to model - training and feature engineering. - """ - - target: str - variables_to_rename: Dict - features: List[str] - test_size: float - random_state: int - alpha: float - categorical_vars_with_na_frequent: List[str] - categorical_vars_with_na_missing: List[str] - numerical_vars_with_na: List[str] - temporal_vars: List[str] - ref_var: str - numericals_log_vars: Sequence[str] - binarize_vars: Sequence[str] - qual_vars: List[str] - exposure_vars: List[str] - finish_vars: List[str] - garage_vars: List[str] - categorical_vars: Sequence[str] - qual_mappings: Dict[str, int] - exposure_mappings: Dict[str, int] - garage_mappings: Dict[str, int] - finish_mappings: Dict[str, int] - - -class Config(BaseModel): - """Master config object.""" - - app_config: AppConfig - model_config: ModelConfig - - -def find_config_file() -> Path: - """Locate the configuration file.""" - if CONFIG_FILE_PATH.is_file(): - return CONFIG_FILE_PATH - raise Exception(f"Config not found at {CONFIG_FILE_PATH!r}") - - -def fetch_config_from_yaml(cfg_path: Optional[Path] = None) -> YAML: - """Parse YAML containing the package configuration.""" - - if not cfg_path: - cfg_path = find_config_file() - - if cfg_path: - with open(cfg_path, "r") as conf_file: - parsed_config = load(conf_file.read()) - return parsed_config - raise OSError(f"Did not find config file at path: {cfg_path}") - - -def create_and_validate_config(parsed_config: YAML = None) -> Config: - """Run validation on config values.""" - if parsed_config is None: - parsed_config = fetch_config_from_yaml() - - # specify the data attribute from the strictyaml YAML type. - _config = Config( - app_config=AppConfig(**parsed_config.data), - model_config=ModelConfig(**parsed_config.data), - ) - - return _config - - -config = create_and_validate_config() +from pathlib import Path +from typing import Dict, List, Optional, Sequence + +from pydantic import BaseModel +from strictyaml import YAML, load + +import regression_model + +# Project Directories +PACKAGE_ROOT = Path(regression_model.__file__).resolve().parent +ROOT = PACKAGE_ROOT.parent +CONFIG_FILE_PATH = PACKAGE_ROOT / "config.yml" +DATASET_DIR = PACKAGE_ROOT / "datasets" +TRAINED_MODEL_DIR = PACKAGE_ROOT / "trained_models" + + +class AppConfig(BaseModel): + """ + Application-level config. + """ + + package_name: str + training_data_file: str + test_data_file: str + pipeline_save_file: str + + +class ModelConfig(BaseModel): + """ + All configuration relevant to model + training and feature engineering. + """ + + target: str + variables_to_rename: Dict + features: List[str] + test_size: float + random_state: int + alpha: float + categorical_vars_with_na_frequent: List[str] + categorical_vars_with_na_missing: List[str] + numerical_vars_with_na: List[str] + temporal_vars: List[str] + ref_var: str + numericals_log_vars: Sequence[str] + binarize_vars: Sequence[str] + qual_vars: List[str] + exposure_vars: List[str] + finish_vars: List[str] + garage_vars: List[str] + categorical_vars: Sequence[str] + qual_mappings: Dict[str, int] + exposure_mappings: Dict[str, int] + garage_mappings: Dict[str, int] + finish_mappings: Dict[str, int] + + +class Config(BaseModel): + """Master config object.""" + + app_config: AppConfig + model_config: ModelConfig + + +def find_config_file() -> Path: + """Locate the configuration file.""" + if CONFIG_FILE_PATH.is_file(): + return CONFIG_FILE_PATH + raise Exception(f"Config not found at {CONFIG_FILE_PATH!r}") + + +def fetch_config_from_yaml(cfg_path: Optional[Path] = None) -> YAML: + """Parse YAML containing the package configuration.""" + + if not cfg_path: + cfg_path = find_config_file() + + if cfg_path: + with open(cfg_path, "r") as conf_file: + parsed_config = load(conf_file.read()) + return parsed_config + raise OSError(f"Did not find config file at path: {cfg_path}") + + +def create_and_validate_config(parsed_config: YAML = None) -> Config: + """Run validation on config values.""" + if parsed_config is None: + parsed_config = fetch_config_from_yaml() + + # specify the data attribute from the strictyaml YAML type. + _config = Config( + app_config=AppConfig(**parsed_config.data), + model_config=ModelConfig(**parsed_config.data), + ) + + return _config + + +config = create_and_validate_config() diff --git a/section-05-production-model-package/regression_model/pipeline.py b/section-05-production-model-package/regression_model/pipeline.py index 6fc888e01..757c43f60 100644 --- a/section-05-production-model-package/regression_model/pipeline.py +++ b/section-05-production-model-package/regression_model/pipeline.py @@ -1,115 +1,115 @@ -from feature_engine.encoding import OrdinalEncoder, RareLabelEncoder -from feature_engine.imputation import AddMissingIndicator, CategoricalImputer, MeanMedianImputer -from feature_engine.selection import DropFeatures -from feature_engine.transformation import LogTransformer -from feature_engine.wrappers import SklearnTransformerWrapper -from sklearn.linear_model import Lasso -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import Binarizer, MinMaxScaler - -from regression_model.config.core import config -from regression_model.processing import features as pp - -price_pipe = Pipeline( - [ - # ===== IMPUTATION ===== - # impute categorical variables with string missing - ( - "missing_imputation", - CategoricalImputer( - imputation_method="missing", - variables=config.model_config.categorical_vars_with_na_missing, - ), - ), - ( - "frequent_imputation", - CategoricalImputer( - imputation_method="frequent", - variables=config.model_config.categorical_vars_with_na_frequent, - ), - ), - # add missing indicator - ( - "missing_indicator", - AddMissingIndicator(variables=config.model_config.numerical_vars_with_na), - ), - # impute numerical variables with the mean - ( - "mean_imputation", - MeanMedianImputer( - imputation_method="mean", - variables=config.model_config.numerical_vars_with_na, - ), - ), - # == TEMPORAL VARIABLES ==== - ( - "elapsed_time", - pp.TemporalVariableTransformer( - variables=config.model_config.temporal_vars, - reference_variable=config.model_config.ref_var, - ), - ), - ("drop_features", DropFeatures(features_to_drop=[config.model_config.ref_var])), - # ==== VARIABLE TRANSFORMATION ===== - ("log", LogTransformer(variables=config.model_config.numericals_log_vars)), - ( - "binarizer", - SklearnTransformerWrapper( - transformer=Binarizer(threshold=0), - variables=config.model_config.binarize_vars, - ), - ), - # === mappers === - ( - "mapper_qual", - pp.Mapper( - variables=config.model_config.qual_vars, - mappings=config.model_config.qual_mappings, - ), - ), - ( - "mapper_exposure", - pp.Mapper( - variables=config.model_config.exposure_vars, - mappings=config.model_config.exposure_mappings, - ), - ), - ( - "mapper_finish", - pp.Mapper( - variables=config.model_config.finish_vars, - mappings=config.model_config.finish_mappings, - ), - ), - ( - "mapper_garage", - pp.Mapper( - variables=config.model_config.garage_vars, - mappings=config.model_config.garage_mappings, - ), - ), - # == CATEGORICAL ENCODING - ( - "rare_label_encoder", - RareLabelEncoder( - tol=0.01, n_categories=1, variables=config.model_config.categorical_vars - ), - ), - # encode categorical variables using the target mean - ( - "categorical_encoder", - OrdinalEncoder( - encoding_method="ordered", - variables=config.model_config.categorical_vars, - ), - ), - ("scaler", MinMaxScaler()), - ( - "Lasso", - Lasso( - alpha=config.model_config.alpha, - random_state=config.model_config.random_state, - ), - ), - ] -) +from feature_engine.encoding import OrdinalEncoder, RareLabelEncoder +from feature_engine.imputation import AddMissingIndicator, CategoricalImputer, MeanMedianImputer +from feature_engine.selection import DropFeatures +from feature_engine.transformation import LogTransformer +from feature_engine.wrappers import SklearnTransformerWrapper +from sklearn.linear_model import Lasso +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import Binarizer, MinMaxScaler + +from regression_model.config.core import config +from regression_model.processing import features as pp + +price_pipe = Pipeline( + [ + # ===== IMPUTATION ===== + # impute categorical variables with string missing + ( + "missing_imputation", + CategoricalImputer( + imputation_method="missing", + variables=config.model_config.categorical_vars_with_na_missing, + ), + ), + ( + "frequent_imputation", + CategoricalImputer( + imputation_method="frequent", + variables=config.model_config.categorical_vars_with_na_frequent, + ), + ), + # add missing indicator + ( + "missing_indicator", + AddMissingIndicator(variables=config.model_config.numerical_vars_with_na), + ), + # impute numerical variables with the mean + ( + "mean_imputation", + MeanMedianImputer( + imputation_method="mean", + variables=config.model_config.numerical_vars_with_na, + ), + ), + # == TEMPORAL VARIABLES ==== + ( + "elapsed_time", + pp.TemporalVariableTransformer( + variables=config.model_config.temporal_vars, + reference_variable=config.model_config.ref_var, + ), + ), + ("drop_features", DropFeatures(features_to_drop=[config.model_config.ref_var])), + # ==== VARIABLE TRANSFORMATION ===== + ("log", LogTransformer(variables=config.model_config.numericals_log_vars)), + ( + "binarizer", + SklearnTransformerWrapper( + transformer=Binarizer(threshold=0), + variables=config.model_config.binarize_vars, + ), + ), + # === mappers === + ( + "mapper_qual", + pp.Mapper( + variables=config.model_config.qual_vars, + mappings=config.model_config.qual_mappings, + ), + ), + ( + "mapper_exposure", + pp.Mapper( + variables=config.model_config.exposure_vars, + mappings=config.model_config.exposure_mappings, + ), + ), + ( + "mapper_finish", + pp.Mapper( + variables=config.model_config.finish_vars, + mappings=config.model_config.finish_mappings, + ), + ), + ( + "mapper_garage", + pp.Mapper( + variables=config.model_config.garage_vars, + mappings=config.model_config.garage_mappings, + ), + ), + # == CATEGORICAL ENCODING + ( + "rare_label_encoder", + RareLabelEncoder( + tol=0.01, n_categories=1, variables=config.model_config.categorical_vars + ), + ), + # encode categorical variables using the target mean + ( + "categorical_encoder", + OrdinalEncoder( + encoding_method="ordered", + variables=config.model_config.categorical_vars, + ), + ), + ("scaler", MinMaxScaler()), + ( + "Lasso", + Lasso( + alpha=config.model_config.alpha, + random_state=config.model_config.random_state, + ), + ), + ] +) diff --git a/section-05-production-model-package/regression_model/predict.py b/section-05-production-model-package/regression_model/predict.py index d27739780..166d7761a 100644 --- a/section-05-production-model-package/regression_model/predict.py +++ b/section-05-production-model-package/regression_model/predict.py @@ -1,35 +1,35 @@ -import typing as t - -import numpy as np -import pandas as pd - -from regression_model import __version__ as _version -from regression_model.config.core import config -from regression_model.processing.data_manager import load_pipeline -from regression_model.processing.validation import validate_inputs - -pipeline_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" -_price_pipe = load_pipeline(file_name=pipeline_file_name) - - -def make_prediction( - *, - input_data: t.Union[pd.DataFrame, dict], -) -> dict: - """Make a prediction using a saved model pipeline.""" - - data = pd.DataFrame(input_data) - validated_data, errors = validate_inputs(input_data=data) - results = {"predictions": None, "version": _version, "errors": errors} - - if not errors: - predictions = _price_pipe.predict( - X=validated_data[config.model_config.features] - ) - results = { - "predictions": [np.exp(pred) for pred in predictions], # type: ignore - "version": _version, - "errors": errors, - } - - return results +import typing as t + +import numpy as np +import pandas as pd + +from regression_model import __version__ as _version +from regression_model.config.core import config +from regression_model.processing.data_manager import load_pipeline +from regression_model.processing.validation import validate_inputs + +pipeline_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" +_price_pipe = load_pipeline(file_name=pipeline_file_name) + + +def make_prediction( + *, + input_data: t.Union[pd.DataFrame, dict], +) -> dict: + """Make a prediction using a saved model pipeline.""" + + data = pd.DataFrame(input_data) + validated_data, errors = validate_inputs(input_data=data) + results = {"predictions": None, "version": _version, "errors": errors} + + if not errors: + predictions = _price_pipe.predict( + X=validated_data[config.model_config.features] + ) + results = { + "predictions": [np.exp(pred) for pred in predictions], # type: ignore + "version": _version, + "errors": errors, + } + + return results diff --git a/section-05-production-model-package/regression_model/processing/data_manager.py b/section-05-production-model-package/regression_model/processing/data_manager.py index fa5a54942..bb696b086 100644 --- a/section-05-production-model-package/regression_model/processing/data_manager.py +++ b/section-05-production-model-package/regression_model/processing/data_manager.py @@ -1,55 +1,55 @@ -import typing as t -from pathlib import Path - -import joblib -import pandas as pd -from sklearn.pipeline import Pipeline - -from regression_model import __version__ as _version -from regression_model.config.core import DATASET_DIR, TRAINED_MODEL_DIR, config - - -def load_dataset(*, file_name: str) -> pd.DataFrame: - dataframe = pd.read_csv(Path(f"{DATASET_DIR}/{file_name}")) - dataframe["MSSubClass"] = dataframe["MSSubClass"].astype("O") - - # rename variables beginning with numbers to avoid syntax errors later - transformed = dataframe.rename(columns=config.model_config.variables_to_rename) - return transformed - - -def save_pipeline(*, pipeline_to_persist: Pipeline) -> None: - """Persist the pipeline. - Saves the versioned model, and overwrites any previous - saved models. This ensures that when the package is - published, there is only one trained model that can be - called, and we know exactly how it was built. - """ - - # Prepare versioned save file name - save_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" - save_path = TRAINED_MODEL_DIR / save_file_name - - remove_old_pipelines(files_to_keep=[save_file_name]) - joblib.dump(pipeline_to_persist, save_path) - - -def load_pipeline(*, file_name: str) -> Pipeline: - """Load a persisted pipeline.""" - - file_path = TRAINED_MODEL_DIR / file_name - trained_model = joblib.load(filename=file_path) - return trained_model - - -def remove_old_pipelines(*, files_to_keep: t.List[str]) -> None: - """ - Remove old model pipelines. - This is to ensure there is a simple one-to-one - mapping between the package version and the model - version to be imported and used by other applications. - """ - do_not_delete = files_to_keep + ["__init__.py"] - for model_file in TRAINED_MODEL_DIR.iterdir(): - if model_file.name not in do_not_delete: - model_file.unlink() +import typing as t +from pathlib import Path + +import joblib +import pandas as pd +from sklearn.pipeline import Pipeline + +from regression_model import __version__ as _version +from regression_model.config.core import DATASET_DIR, TRAINED_MODEL_DIR, config + + +def load_dataset(*, file_name: str) -> pd.DataFrame: + dataframe = pd.read_csv(Path(f"{DATASET_DIR}/{file_name}")) + dataframe["MSSubClass"] = dataframe["MSSubClass"].astype("O") + + # rename variables beginning with numbers to avoid syntax errors later + transformed = dataframe.rename(columns=config.model_config.variables_to_rename) + return transformed + + +def save_pipeline(*, pipeline_to_persist: Pipeline) -> None: + """Persist the pipeline. + Saves the versioned model, and overwrites any previous + saved models. This ensures that when the package is + published, there is only one trained model that can be + called, and we know exactly how it was built. + """ + + # Prepare versioned save file name + save_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" + save_path = TRAINED_MODEL_DIR / save_file_name + + remove_old_pipelines(files_to_keep=[save_file_name]) + joblib.dump(pipeline_to_persist, save_path) + + +def load_pipeline(*, file_name: str) -> Pipeline: + """Load a persisted pipeline.""" + + file_path = TRAINED_MODEL_DIR / file_name + trained_model = joblib.load(filename=file_path) + return trained_model + + +def remove_old_pipelines(*, files_to_keep: t.List[str]) -> None: + """ + Remove old model pipelines. + This is to ensure there is a simple one-to-one + mapping between the package version and the model + version to be imported and used by other applications. + """ + do_not_delete = files_to_keep + ["__init__.py"] + for model_file in TRAINED_MODEL_DIR.iterdir(): + if model_file.name not in do_not_delete: + model_file.unlink() diff --git a/section-05-production-model-package/regression_model/processing/features.py b/section-05-production-model-package/regression_model/processing/features.py index ae05559fb..7d6ba4eb2 100644 --- a/section-05-production-model-package/regression_model/processing/features.py +++ b/section-05-production-model-package/regression_model/processing/features.py @@ -1,53 +1,53 @@ -from typing import List - -import pandas as pd -from sklearn.base import BaseEstimator, TransformerMixin - - -class TemporalVariableTransformer(BaseEstimator, TransformerMixin): - """Temporal elapsed time transformer.""" - - def __init__(self, variables: List[str], reference_variable: str): - - if not isinstance(variables, list): - raise ValueError("variables should be a list") - - self.variables = variables - self.reference_variable = reference_variable - - def fit(self, X: pd.DataFrame, y: pd.Series = None): - # we need this step to fit the sklearn pipeline - return self - - def transform(self, X: pd.DataFrame) -> pd.DataFrame: - - # so that we do not over-write the original dataframe - X = X.copy() - - for feature in self.variables: - X[feature] = X[self.reference_variable] - X[feature] - - return X - - -class Mapper(BaseEstimator, TransformerMixin): - """Categorical variable mapper.""" - - def __init__(self, variables: List[str], mappings: dict): - - if not isinstance(variables, list): - raise ValueError("variables should be a list") - - self.variables = variables - self.mappings = mappings - - def fit(self, X: pd.DataFrame, y: pd.Series = None): - # we need the fit statement to accomodate the sklearn pipeline - return self - - def transform(self, X: pd.DataFrame) -> pd.DataFrame: - X = X.copy() - for feature in self.variables: - X[feature] = X[feature].map(self.mappings) - - return X +from typing import List + +import pandas as pd +from sklearn.base import BaseEstimator, TransformerMixin + + +class TemporalVariableTransformer(BaseEstimator, TransformerMixin): + """Temporal elapsed time transformer.""" + + def __init__(self, variables: List[str], reference_variable: str): + + if not isinstance(variables, list): + raise ValueError("variables should be a list") + + self.variables = variables + self.reference_variable = reference_variable + + def fit(self, X: pd.DataFrame, y: pd.Series = None): + # we need this step to fit the sklearn pipeline + return self + + def transform(self, X: pd.DataFrame) -> pd.DataFrame: + + # so that we do not over-write the original dataframe + X = X.copy() + + for feature in self.variables: + X[feature] = X[self.reference_variable] - X[feature] + + return X + + +class Mapper(BaseEstimator, TransformerMixin): + """Categorical variable mapper.""" + + def __init__(self, variables: List[str], mappings: dict): + + if not isinstance(variables, list): + raise ValueError("variables should be a list") + + self.variables = variables + self.mappings = mappings + + def fit(self, X: pd.DataFrame, y: pd.Series = None): + # we need the fit statement to accomodate the sklearn pipeline + return self + + def transform(self, X: pd.DataFrame) -> pd.DataFrame: + X = X.copy() + for feature in self.variables: + X[feature] = X[feature].map(self.mappings) + + return X diff --git a/section-05-production-model-package/regression_model/processing/validation.py b/section-05-production-model-package/regression_model/processing/validation.py index 8e7ce56d0..79bf82fca 100644 --- a/section-05-production-model-package/regression_model/processing/validation.py +++ b/section-05-production-model-package/regression_model/processing/validation.py @@ -1,132 +1,132 @@ -from typing import List, Optional, Tuple - -import numpy as np -import pandas as pd -from pydantic import BaseModel, ValidationError - -from regression_model.config.core import config - - -def drop_na_inputs(*, input_data: pd.DataFrame) -> pd.DataFrame: - """Check model inputs for na values and filter.""" - validated_data = input_data.copy() - new_vars_with_na = [ - var - for var in config.model_config.features - if var - not in config.model_config.categorical_vars_with_na_frequent - + config.model_config.categorical_vars_with_na_missing - + config.model_config.numerical_vars_with_na - and validated_data[var].isnull().sum() > 0 - ] - validated_data.dropna(subset=new_vars_with_na, inplace=True) - - return validated_data - - -def validate_inputs(*, input_data: pd.DataFrame) -> Tuple[pd.DataFrame, Optional[dict]]: - """Check model inputs for unprocessable values.""" - - # convert syntax error field names (beginning with numbers) - input_data.rename(columns=config.model_config.variables_to_rename, inplace=True) - input_data["MSSubClass"] = input_data["MSSubClass"].astype("O") - relevant_data = input_data[config.model_config.features].copy() - validated_data = drop_na_inputs(input_data=relevant_data) - errors = None - - try: - # replace numpy nans so that pydantic can validate - MultipleHouseDataInputs( - inputs=validated_data.replace({np.nan: None}).to_dict(orient="records") - ) - except ValidationError as error: - errors = error.json() - - return validated_data, errors - - -class HouseDataInputSchema(BaseModel): - Alley: Optional[str] - BedroomAbvGr: Optional[int] - BldgType: Optional[str] - BsmtCond: Optional[str] - BsmtExposure: Optional[str] - BsmtFinSF1: Optional[float] - BsmtFinSF2: Optional[float] - BsmtFinType1: Optional[str] - BsmtFinType2: Optional[str] - BsmtFullBath: Optional[float] - BsmtHalfBath: Optional[float] - BsmtQual: Optional[str] - BsmtUnfSF: Optional[float] - CentralAir: Optional[str] - Condition1: Optional[str] - Condition2: Optional[str] - Electrical: Optional[str] - EnclosedPorch: Optional[int] - ExterCond: Optional[str] - ExterQual: Optional[str] - Exterior1st: Optional[str] - Exterior2nd: Optional[str] - Fence: Optional[str] - FireplaceQu: Optional[str] - Fireplaces: Optional[int] - Foundation: Optional[str] - FullBath: Optional[int] - Functional: Optional[str] - GarageArea: Optional[float] - GarageCars: Optional[float] - GarageCond: Optional[str] - GarageFinish: Optional[str] - GarageQual: Optional[str] - GarageType: Optional[str] - GarageYrBlt: Optional[float] - GrLivArea: Optional[int] - HalfBath: Optional[int] - Heating: Optional[str] - HeatingQC: Optional[str] - HouseStyle: Optional[str] - Id: Optional[int] - KitchenAbvGr: Optional[int] - KitchenQual: Optional[str] - LandContour: Optional[str] - LandSlope: Optional[str] - LotArea: Optional[int] - LotConfig: Optional[str] - LotFrontage: Optional[float] - LotShape: Optional[str] - LowQualFinSF: Optional[int] - MSSubClass: Optional[int] - MSZoning: Optional[str] - MasVnrArea: Optional[float] - MasVnrType: Optional[str] - MiscFeature: Optional[str] - MiscVal: Optional[int] - MoSold: Optional[int] - Neighborhood: Optional[str] - OpenPorchSF: Optional[int] - OverallCond: Optional[int] - OverallQual: Optional[int] - PavedDrive: Optional[str] - PoolArea: Optional[int] - PoolQC: Optional[str] - RoofMatl: Optional[str] - RoofStyle: Optional[str] - SaleCondition: Optional[str] - SaleType: Optional[str] - ScreenPorch: Optional[int] - Street: Optional[str] - TotRmsAbvGrd: Optional[int] - TotalBsmtSF: Optional[float] - Utilities: Optional[str] - WoodDeckSF: Optional[int] - YearBuilt: Optional[int] - YearRemodAdd: Optional[int] - YrSold: Optional[int] - FirstFlrSF: Optional[int] # renamed - SecondFlrSF: Optional[int] # renamed - ThreeSsnPortch: Optional[int] # renamed - - -class MultipleHouseDataInputs(BaseModel): - inputs: List[HouseDataInputSchema] +from typing import List, Optional, Tuple + +import numpy as np +import pandas as pd +from pydantic import BaseModel, ValidationError + +from regression_model.config.core import config + + +def drop_na_inputs(*, input_data: pd.DataFrame) -> pd.DataFrame: + """Check model inputs for na values and filter.""" + validated_data = input_data.copy() + new_vars_with_na = [ + var + for var in config.model_config.features + if var + not in config.model_config.categorical_vars_with_na_frequent + + config.model_config.categorical_vars_with_na_missing + + config.model_config.numerical_vars_with_na + and validated_data[var].isnull().sum() > 0 + ] + validated_data.dropna(subset=new_vars_with_na, inplace=True) + + return validated_data + + +def validate_inputs(*, input_data: pd.DataFrame) -> Tuple[pd.DataFrame, Optional[dict]]: + """Check model inputs for unprocessable values.""" + + # convert syntax error field names (beginning with numbers) + input_data.rename(columns=config.model_config.variables_to_rename, inplace=True) + input_data["MSSubClass"] = input_data["MSSubClass"].astype("O") + relevant_data = input_data[config.model_config.features].copy() + validated_data = drop_na_inputs(input_data=relevant_data) + errors = None + + try: + # replace numpy nans so that pydantic can validate + MultipleHouseDataInputs( + inputs=validated_data.replace({np.nan: None}).to_dict(orient="records") + ) + except ValidationError as error: + errors = error.json() + + return validated_data, errors + + +class HouseDataInputSchema(BaseModel): + Alley: Optional[str] + BedroomAbvGr: Optional[int] + BldgType: Optional[str] + BsmtCond: Optional[str] + BsmtExposure: Optional[str] + BsmtFinSF1: Optional[float] + BsmtFinSF2: Optional[float] + BsmtFinType1: Optional[str] + BsmtFinType2: Optional[str] + BsmtFullBath: Optional[float] + BsmtHalfBath: Optional[float] + BsmtQual: Optional[str] + BsmtUnfSF: Optional[float] + CentralAir: Optional[str] + Condition1: Optional[str] + Condition2: Optional[str] + Electrical: Optional[str] + EnclosedPorch: Optional[int] + ExterCond: Optional[str] + ExterQual: Optional[str] + Exterior1st: Optional[str] + Exterior2nd: Optional[str] + Fence: Optional[str] + FireplaceQu: Optional[str] + Fireplaces: Optional[int] + Foundation: Optional[str] + FullBath: Optional[int] + Functional: Optional[str] + GarageArea: Optional[float] + GarageCars: Optional[float] + GarageCond: Optional[str] + GarageFinish: Optional[str] + GarageQual: Optional[str] + GarageType: Optional[str] + GarageYrBlt: Optional[float] + GrLivArea: Optional[int] + HalfBath: Optional[int] + Heating: Optional[str] + HeatingQC: Optional[str] + HouseStyle: Optional[str] + Id: Optional[int] + KitchenAbvGr: Optional[int] + KitchenQual: Optional[str] + LandContour: Optional[str] + LandSlope: Optional[str] + LotArea: Optional[int] + LotConfig: Optional[str] + LotFrontage: Optional[float] + LotShape: Optional[str] + LowQualFinSF: Optional[int] + MSSubClass: Optional[int] + MSZoning: Optional[str] + MasVnrArea: Optional[float] + MasVnrType: Optional[str] + MiscFeature: Optional[str] + MiscVal: Optional[int] + MoSold: Optional[int] + Neighborhood: Optional[str] + OpenPorchSF: Optional[int] + OverallCond: Optional[int] + OverallQual: Optional[int] + PavedDrive: Optional[str] + PoolArea: Optional[int] + PoolQC: Optional[str] + RoofMatl: Optional[str] + RoofStyle: Optional[str] + SaleCondition: Optional[str] + SaleType: Optional[str] + ScreenPorch: Optional[int] + Street: Optional[str] + TotRmsAbvGrd: Optional[int] + TotalBsmtSF: Optional[float] + Utilities: Optional[str] + WoodDeckSF: Optional[int] + YearBuilt: Optional[int] + YearRemodAdd: Optional[int] + YrSold: Optional[int] + FirstFlrSF: Optional[int] # renamed + SecondFlrSF: Optional[int] # renamed + ThreeSsnPortch: Optional[int] # renamed + + +class MultipleHouseDataInputs(BaseModel): + inputs: List[HouseDataInputSchema] diff --git a/section-05-production-model-package/regression_model/train_pipeline.py b/section-05-production-model-package/regression_model/train_pipeline.py index 95243a421..ecd58697d 100644 --- a/section-05-production-model-package/regression_model/train_pipeline.py +++ b/section-05-production-model-package/regression_model/train_pipeline.py @@ -1,33 +1,33 @@ -import numpy as np -from config.core import config -from pipeline import price_pipe -from processing.data_manager import load_dataset, save_pipeline -from sklearn.model_selection import train_test_split - - -def run_training() -> None: - """Train the model.""" - - # read training data - data = load_dataset(file_name=config.app_config.training_data_file) - - # divide train and test - X_train, X_test, y_train, y_test = train_test_split( - data[config.model_config.features], # predictors - data[config.model_config.target], - test_size=config.model_config.test_size, - # we are setting the random seed here - # for reproducibility - random_state=config.model_config.random_state, - ) - y_train = np.log(y_train) - - # fit model - price_pipe.fit(X_train, y_train) - - # persist trained model - save_pipeline(pipeline_to_persist=price_pipe) - - -if __name__ == "__main__": - run_training() +import numpy as np +from config.core import config +from pipeline import price_pipe +from processing.data_manager import load_dataset, save_pipeline +from sklearn.model_selection import train_test_split + + +def run_training() -> None: + """Train the model.""" + + # read training data + data = load_dataset(file_name=config.app_config.training_data_file) + + # divide train and test + X_train, X_test, y_train, y_test = train_test_split( + data[config.model_config.features], # predictors + data[config.model_config.target], + test_size=config.model_config.test_size, + # we are setting the random seed here + # for reproducibility + random_state=config.model_config.random_state, + ) + y_train = np.log(y_train) + + # fit model + price_pipe.fit(X_train, y_train) + + # persist trained model + save_pipeline(pipeline_to_persist=price_pipe) + + +if __name__ == "__main__": + run_training() diff --git a/section-05-production-model-package/requirements/requirements.txt b/section-05-production-model-package/requirements/requirements.txt index 0fbffd3a6..eb9c31686 100644 --- a/section-05-production-model-package/requirements/requirements.txt +++ b/section-05-production-model-package/requirements/requirements.txt @@ -1,11 +1,11 @@ -# We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) -# to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small -# updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. -numpy>=1.21.0,<2.0.0 -pandas>=1.3.5,<2.0.0 -pydantic>=1.8.1,<2.0.0 -scikit-learn>=1.1.3,<2.0.0 -strictyaml>=1.3.2,<2.0.0 -ruamel.yaml>=0.16.12,<1.0.0 -feature-engine>=1.0.2,<1.6.0 # breaking change in v1.6.0 +# We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) +# to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small +# updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. +numpy>=1.21.0,<2.0.0 +pandas>=1.3.5,<2.0.0 +pydantic>=1.8.1,<2.0.0 +scikit-learn>=1.1.3,<2.0.0 +strictyaml>=1.3.2,<2.0.0 +ruamel.yaml>=0.16.12,<1.0.0 +feature-engine>=1.0.2,<1.6.0 # breaking change in v1.6.0 joblib>=1.0.1,<2.0.0 \ No newline at end of file diff --git a/section-05-production-model-package/requirements/test_requirements.txt b/section-05-production-model-package/requirements/test_requirements.txt index e69019391..b080f909c 100644 --- a/section-05-production-model-package/requirements/test_requirements.txt +++ b/section-05-production-model-package/requirements/test_requirements.txt @@ -1,4 +1,4 @@ --r requirements.txt - -# testing requirements -pytest>=7.2.0,<8.0.0 +-r requirements.txt + +# testing requirements +pytest>=7.2.0,<8.0.0 diff --git a/section-05-production-model-package/requirements/typing_requirements.txt b/section-05-production-model-package/requirements/typing_requirements.txt index 667cc2e4d..59619752c 100644 --- a/section-05-production-model-package/requirements/typing_requirements.txt +++ b/section-05-production-model-package/requirements/typing_requirements.txt @@ -1,5 +1,5 @@ -# repo maintenance tooling -black>=22.12.0,<23.0.0 -flake8>=6.0.0,<7.0.0 -mypy>=0.991,<1.0.0 +# repo maintenance tooling +black>=22.12.0,<23.0.0 +flake8>=6.0.0,<7.0.0 +mypy>=0.991,<1.0.0 isort>=5.11.4,<6.0.0 \ No newline at end of file diff --git a/section-05-production-model-package/setup.py b/section-05-production-model-package/setup.py index 440d8d43c..b773d41cc 100644 --- a/section-05-production-model-package/setup.py +++ b/section-05-production-model-package/setup.py @@ -1,69 +1,69 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from pathlib import Path - -from setuptools import find_packages, setup - -# Package meta-data. -NAME = 'tid-regression-model' -DESCRIPTION = "Example regression model package from Train In Data." -URL = "https://github.com/trainindata/testing-and-monitoring-ml-deployments" -EMAIL = "christopher.samiullah@protonmail.com" -AUTHOR = "ChristopherGS" -REQUIRES_PYTHON = ">=3.6.0" - - -# The rest you shouldn't have to touch too much :) -# ------------------------------------------------ -# Except, perhaps the License and Trove Classifiers! -# If you do change the License, remember to change the -# Trove Classifier for that! -long_description = DESCRIPTION - -# Load the package's VERSION file as a dictionary. -about = {} -ROOT_DIR = Path(__file__).resolve().parent -REQUIREMENTS_DIR = ROOT_DIR / 'requirements' -PACKAGE_DIR = ROOT_DIR / 'regression_model' -with open(PACKAGE_DIR / "VERSION") as f: - _version = f.read().strip() - about["__version__"] = _version - - -# What packages are required for this module to be executed? -def list_reqs(fname="requirements.txt"): - with open(REQUIREMENTS_DIR / fname) as fd: - return fd.read().splitlines() - -# Where the magic happens: -setup( - name=NAME, - version=about["__version__"], - description=DESCRIPTION, - long_description=long_description, - long_description_content_type="text/markdown", - author=AUTHOR, - author_email=EMAIL, - python_requires=REQUIRES_PYTHON, - url=URL, - packages=find_packages(exclude=("tests",)), - package_data={"regression_model": ["VERSION"]}, - install_requires=list_reqs(), - extras_require={}, - include_package_data=True, - license="BSD-3", - classifiers=[ - # Trove classifiers - # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers - "License :: OSI Approved :: MIT License", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.6", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: Implementation :: CPython", - "Programming Language :: Python :: Implementation :: PyPy", - ], +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +from pathlib import Path + +from setuptools import find_packages, setup + +# Package meta-data. +NAME = 'tid-regression-model' +DESCRIPTION = "Example regression model package from Train In Data." +URL = "https://github.com/trainindata/testing-and-monitoring-ml-deployments" +EMAIL = "christopher.samiullah@protonmail.com" +AUTHOR = "ChristopherGS" +REQUIRES_PYTHON = ">=3.6.0" + + +# The rest you shouldn't have to touch too much :) +# ------------------------------------------------ +# Except, perhaps the License and Trove Classifiers! +# If you do change the License, remember to change the +# Trove Classifier for that! +long_description = DESCRIPTION + +# Load the package's VERSION file as a dictionary. +about = {} +ROOT_DIR = Path(__file__).resolve().parent +REQUIREMENTS_DIR = ROOT_DIR / 'requirements' +PACKAGE_DIR = ROOT_DIR / 'regression_model' +with open(PACKAGE_DIR / "VERSION") as f: + _version = f.read().strip() + about["__version__"] = _version + + +# What packages are required for this module to be executed? +def list_reqs(fname="requirements.txt"): + with open(REQUIREMENTS_DIR / fname) as fd: + return fd.read().splitlines() + +# Where the magic happens: +setup( + name=NAME, + version=about["__version__"], + description=DESCRIPTION, + long_description=long_description, + long_description_content_type="text/markdown", + author=AUTHOR, + author_email=EMAIL, + python_requires=REQUIRES_PYTHON, + url=URL, + packages=find_packages(exclude=("tests",)), + package_data={"regression_model": ["VERSION"]}, + install_requires=list_reqs(), + extras_require={}, + include_package_data=True, + license="BSD-3", + classifiers=[ + # Trove classifiers + # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers + "License :: OSI Approved :: MIT License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: Implementation :: PyPy", + ], ) \ No newline at end of file diff --git a/section-05-production-model-package/tests/conftest.py b/section-05-production-model-package/tests/conftest.py index a4014d5f4..db7332de2 100644 --- a/section-05-production-model-package/tests/conftest.py +++ b/section-05-production-model-package/tests/conftest.py @@ -1,9 +1,9 @@ -import pytest - -from regression_model.config.core import config -from regression_model.processing.data_manager import load_dataset - - -@pytest.fixture() -def sample_input_data(): - return load_dataset(file_name=config.app_config.test_data_file) +import pytest + +from regression_model.config.core import config +from regression_model.processing.data_manager import load_dataset + + +@pytest.fixture() +def sample_input_data(): + return load_dataset(file_name=config.app_config.test_data_file) diff --git a/section-05-production-model-package/tests/test_features.py b/section-05-production-model-package/tests/test_features.py index f3cd92832..6dd7ae2ea 100644 --- a/section-05-production-model-package/tests/test_features.py +++ b/section-05-production-model-package/tests/test_features.py @@ -1,17 +1,17 @@ -from regression_model.config.core import config -from regression_model.processing.features import TemporalVariableTransformer - - -def test_temporal_variable_transformer(sample_input_data): - # Given - transformer = TemporalVariableTransformer( - variables=config.model_config.temporal_vars, # YearRemodAdd - reference_variable=config.model_config.ref_var, - ) - assert sample_input_data["YearRemodAdd"].iat[0] == 1961 - - # When - subject = transformer.fit_transform(sample_input_data) - - # Then - assert subject["YearRemodAdd"].iat[0] == 49 +from regression_model.config.core import config +from regression_model.processing.features import TemporalVariableTransformer + + +def test_temporal_variable_transformer(sample_input_data): + # Given + transformer = TemporalVariableTransformer( + variables=config.model_config.temporal_vars, # YearRemodAdd + reference_variable=config.model_config.ref_var, + ) + assert sample_input_data["YearRemodAdd"].iat[0] == 1961 + + # When + subject = transformer.fit_transform(sample_input_data) + + # Then + assert subject["YearRemodAdd"].iat[0] == 49 diff --git a/section-05-production-model-package/tests/test_prediction.py b/section-05-production-model-package/tests/test_prediction.py index afbc508d0..d7321b9ed 100644 --- a/section-05-production-model-package/tests/test_prediction.py +++ b/section-05-production-model-package/tests/test_prediction.py @@ -1,22 +1,22 @@ -import math - -import numpy as np - -from regression_model.predict import make_prediction - - -def test_make_prediction(sample_input_data): - # Given - expected_first_prediction_value = 113422 - expected_no_predictions = 1449 - - # When - result = make_prediction(input_data=sample_input_data) - - # Then - predictions = result.get("predictions") - assert isinstance(predictions, list) - assert isinstance(predictions[0], np.float64) - assert result.get("errors") is None - assert len(predictions) == expected_no_predictions - assert math.isclose(predictions[0], expected_first_prediction_value, abs_tol=100) +import math + +import numpy as np + +from regression_model.predict import make_prediction + + +def test_make_prediction(sample_input_data): + # Given + expected_first_prediction_value = 113422 + expected_no_predictions = 1449 + + # When + result = make_prediction(input_data=sample_input_data) + + # Then + predictions = result.get("predictions") + assert isinstance(predictions, list) + assert isinstance(predictions[0], np.float64) + assert result.get("errors") is None + assert len(predictions) == expected_no_predictions + assert math.isclose(predictions[0], expected_first_prediction_value, abs_tol=100) diff --git a/section-05-production-model-package/tox.ini b/section-05-production-model-package/tox.ini index ffc4c60c8..a68d2b651 100644 --- a/section-05-production-model-package/tox.ini +++ b/section-05-production-model-package/tox.ini @@ -1,55 +1,55 @@ -# Tox is a generic virtualenv management and test command line tool. Its goal is to -# standardize testing in Python. We will be using it extensively in this course. - -# Using Tox we can (on multiple operating systems): -# + Eliminate PYTHONPATH challenges when running scripts/tests -# + Eliminate virtualenv setup confusion -# + Streamline steps such as model training, model publishing - - -[tox] -min_version = 4 -envlist = test_package, checks -skipsdist = True - -[testenv] -basepython = python -install_command = pip install {opts} {packages} -allowlist_externals = train - -setenv = - PYTHONPATH=. - PYTHONHASHSEED=0 - -[testenv:test_package] -envdir = {toxworkdir}/test_package -deps = - -r{toxinidir}/requirements/test_requirements.txt -commands= - python regression_model/train_pipeline.py - pytest \ - -s \ - -vv \ - {posargs:tests/} - -[testenv:train] -envdir = {toxworkdir}/test_package -deps = - {[testenv:test_package]deps} -commands= - python regression_model/train_pipeline.py - - -[testenv:checks] -envdir = {toxworkdir}/checks -deps = - -r{toxinidir}/requirements/typing_requirements.txt -commands = - flake8 regression_model tests - isort regression_model tests - {posargs:mypy regression_model} - - -[flake8] -exclude = .git,env +# Tox is a generic virtualenv management and test command line tool. Its goal is to +# standardize testing in Python. We will be using it extensively in this course. + +# Using Tox we can (on multiple operating systems): +# + Eliminate PYTHONPATH challenges when running scripts/tests +# + Eliminate virtualenv setup confusion +# + Streamline steps such as model training, model publishing + + +[tox] +min_version = 4 +envlist = test_package, checks +skipsdist = True + +[testenv] +basepython = python +install_command = pip install {opts} {packages} +allowlist_externals = train + +setenv = + PYTHONPATH=. + PYTHONHASHSEED=0 + +[testenv:test_package] +envdir = {toxworkdir}/test_package +deps = + -r{toxinidir}/requirements/test_requirements.txt +commands= + python regression_model/train_pipeline.py + pytest \ + -s \ + -vv \ + {posargs:tests/} + +[testenv:train] +envdir = {toxworkdir}/test_package +deps = + {[testenv:test_package]deps} +commands= + python regression_model/train_pipeline.py + + +[testenv:checks] +envdir = {toxworkdir}/checks +deps = + -r{toxinidir}/requirements/typing_requirements.txt +commands = + flake8 regression_model tests + isort regression_model tests + {posargs:mypy regression_model} + + +[flake8] +exclude = .git,env max-line-length = 100 \ No newline at end of file diff --git a/section-06-model-serving-api/house-prices-api/app/__init__.py b/section-06-model-serving-api/house-prices-api/app/__init__.py index 3b93d0be0..b5ca99eb0 100644 --- a/section-06-model-serving-api/house-prices-api/app/__init__.py +++ b/section-06-model-serving-api/house-prices-api/app/__init__.py @@ -1 +1 @@ -__version__ = "0.0.2" +__version__ = "0.0.2" diff --git a/section-06-model-serving-api/house-prices-api/app/api.py b/section-06-model-serving-api/house-prices-api/app/api.py index de9559faf..1bdd9c0fc 100644 --- a/section-06-model-serving-api/house-prices-api/app/api.py +++ b/section-06-model-serving-api/house-prices-api/app/api.py @@ -1,49 +1,49 @@ -import json -from typing import Any - -import numpy as np -import pandas as pd -from fastapi import APIRouter, HTTPException -from fastapi.encoders import jsonable_encoder -from loguru import logger -from regression_model import __version__ as model_version -from regression_model.predict import make_prediction - -from app import __version__, schemas -from app.config import settings - -api_router = APIRouter() - - -@api_router.get("/health", response_model=schemas.Health, status_code=200) -def health() -> dict: - """ - Root Get - """ - health = schemas.Health( - name=settings.PROJECT_NAME, api_version=__version__, model_version=model_version - ) - - return health.dict() - - -@api_router.post("/predict", response_model=schemas.PredictionResults, status_code=200) -async def predict(input_data: schemas.MultipleHouseDataInputs) -> Any: - """ - Make house price predictions with the TID regression model - """ - - input_df = pd.DataFrame(jsonable_encoder(input_data.inputs)) - - # Advanced: You can improve performance of your API by rewriting the - # `make prediction` function to be async and using await here. - logger.info(f"Making prediction on inputs: {input_data.inputs}") - results = make_prediction(input_data=input_df.replace({np.nan: None})) - - if results["errors"] is not None: - logger.warning(f"Prediction validation error: {results.get('errors')}") - raise HTTPException(status_code=400, detail=json.loads(results["errors"])) - - logger.info(f"Prediction results: {results.get('predictions')}") - - return results +import json +from typing import Any + +import numpy as np +import pandas as pd +from fastapi import APIRouter, HTTPException +from fastapi.encoders import jsonable_encoder +from loguru import logger +from regression_model import __version__ as model_version +from regression_model.predict import make_prediction + +from app import __version__, schemas +from app.config import settings + +api_router = APIRouter() + + +@api_router.get("/health", response_model=schemas.Health, status_code=200) +def health() -> dict: + """ + Root Get + """ + health = schemas.Health( + name=settings.PROJECT_NAME, api_version=__version__, model_version=model_version + ) + + return health.dict() + + +@api_router.post("/predict", response_model=schemas.PredictionResults, status_code=200) +async def predict(input_data: schemas.MultipleHouseDataInputs) -> Any: + """ + Make house price predictions with the TID regression model + """ + + input_df = pd.DataFrame(jsonable_encoder(input_data.inputs)) + + # Advanced: You can improve performance of your API by rewriting the + # `make prediction` function to be async and using await here. + logger.info(f"Making prediction on inputs: {input_data.inputs}") + results = make_prediction(input_data=input_df.replace({np.nan: None})) + + if results["errors"] is not None: + logger.warning(f"Prediction validation error: {results.get('errors')}") + raise HTTPException(status_code=400, detail=json.loads(results["errors"])) + + logger.info(f"Prediction results: {results.get('predictions')}") + + return results diff --git a/section-06-model-serving-api/house-prices-api/app/config.py b/section-06-model-serving-api/house-prices-api/app/config.py index 9dc62e5fb..7233dcfc1 100644 --- a/section-06-model-serving-api/house-prices-api/app/config.py +++ b/section-06-model-serving-api/house-prices-api/app/config.py @@ -1,70 +1,70 @@ -import logging -import sys -from types import FrameType -from typing import List, cast - -from loguru import logger -from pydantic import AnyHttpUrl, BaseSettings - - -class LoggingSettings(BaseSettings): - LOGGING_LEVEL: int = logging.INFO # logging levels are type int - - -class Settings(BaseSettings): - API_V1_STR: str = "/api/v1" - - # Meta - logging: LoggingSettings = LoggingSettings() - - # BACKEND_CORS_ORIGINS is a comma-separated list of origins - # e.g: http://localhost,http://localhost:4200,http://localhost:3000 - BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [ - "http://localhost:3000", # type: ignore - "http://localhost:8000", # type: ignore - "https://localhost:3000", # type: ignore - "https://localhost:8000", # type: ignore - ] - - PROJECT_NAME: str = "House Price Prediction API" - - class Config: - case_sensitive = True - - -# See: https://loguru.readthedocs.io/en/stable/overview.html#entirely-compatible-with-standard-logging # noqa -class InterceptHandler(logging.Handler): - def emit(self, record: logging.LogRecord) -> None: # pragma: no cover - # Get corresponding Loguru level if it exists - try: - level = logger.level(record.levelname).name - except ValueError: - level = str(record.levelno) - - # Find caller from where originated the logged message - frame, depth = logging.currentframe(), 2 - while frame.f_code.co_filename == logging.__file__: # noqa: WPS609 - frame = cast(FrameType, frame.f_back) - depth += 1 - - logger.opt(depth=depth, exception=record.exc_info).log( - level, - record.getMessage(), - ) - - -def setup_app_logging(config: Settings) -> None: - """Prepare custom logging for our application.""" - - LOGGERS = ("uvicorn.asgi", "uvicorn.access") - logging.getLogger().handlers = [InterceptHandler()] - for logger_name in LOGGERS: - logging_logger = logging.getLogger(logger_name) - logging_logger.handlers = [InterceptHandler(level=config.logging.LOGGING_LEVEL)] - - logger.configure( - handlers=[{"sink": sys.stderr, "level": config.logging.LOGGING_LEVEL}] - ) - - -settings = Settings() +import logging +import sys +from types import FrameType +from typing import List, cast + +from loguru import logger +from pydantic import AnyHttpUrl, BaseSettings + + +class LoggingSettings(BaseSettings): + LOGGING_LEVEL: int = logging.INFO # logging levels are type int + + +class Settings(BaseSettings): + API_V1_STR: str = "/api/v1" + + # Meta + logging: LoggingSettings = LoggingSettings() + + # BACKEND_CORS_ORIGINS is a comma-separated list of origins + # e.g: http://localhost,http://localhost:4200,http://localhost:3000 + BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [ + "http://localhost:3000", # type: ignore + "http://localhost:8000", # type: ignore + "https://localhost:3000", # type: ignore + "https://localhost:8000", # type: ignore + ] + + PROJECT_NAME: str = "House Price Prediction API" + + class Config: + case_sensitive = True + + +# See: https://loguru.readthedocs.io/en/stable/overview.html#entirely-compatible-with-standard-logging # noqa +class InterceptHandler(logging.Handler): + def emit(self, record: logging.LogRecord) -> None: # pragma: no cover + # Get corresponding Loguru level if it exists + try: + level = logger.level(record.levelname).name + except ValueError: + level = str(record.levelno) + + # Find caller from where originated the logged message + frame, depth = logging.currentframe(), 2 + while frame.f_code.co_filename == logging.__file__: # noqa: WPS609 + frame = cast(FrameType, frame.f_back) + depth += 1 + + logger.opt(depth=depth, exception=record.exc_info).log( + level, + record.getMessage(), + ) + + +def setup_app_logging(config: Settings) -> None: + """Prepare custom logging for our application.""" + + LOGGERS = ("uvicorn.asgi", "uvicorn.access") + logging.getLogger().handlers = [InterceptHandler()] + for logger_name in LOGGERS: + logging_logger = logging.getLogger(logger_name) + logging_logger.handlers = [InterceptHandler(level=config.logging.LOGGING_LEVEL)] + + logger.configure( + handlers=[{"sink": sys.stderr, "level": config.logging.LOGGING_LEVEL}] + ) + + +settings = Settings() diff --git a/section-06-model-serving-api/house-prices-api/app/main.py b/section-06-model-serving-api/house-prices-api/app/main.py index b55d34402..902eb649f 100644 --- a/section-06-model-serving-api/house-prices-api/app/main.py +++ b/section-06-model-serving-api/house-prices-api/app/main.py @@ -1,58 +1,58 @@ -from typing import Any - -from fastapi import APIRouter, FastAPI, Request -from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import HTMLResponse -from loguru import logger - -from app.api import api_router -from app.config import settings, setup_app_logging - -# setup logging as early as possible -setup_app_logging(config=settings) - - -app = FastAPI( - title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json" -) - -root_router = APIRouter() - - -@root_router.get("/") -def index(request: Request) -> Any: - """Basic HTML response.""" - body = ( - "" - "" - "

Welcome to the API

" - "
" - "Check the docs: here" - "
" - "" - "" - ) - - return HTMLResponse(content=body) - - -app.include_router(api_router, prefix=settings.API_V1_STR) -app.include_router(root_router) - -# Set all CORS enabled origins -if settings.BACKEND_CORS_ORIGINS: - app.add_middleware( - CORSMiddleware, - allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - ) - - -if __name__ == "__main__": - # Use this for debugging purposes only - logger.warning("Running in development mode. Do not run like this in production.") - import uvicorn - - uvicorn.run(app, host="localhost", port=8001, log_level="debug") +from typing import Any + +from fastapi import APIRouter, FastAPI, Request +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import HTMLResponse +from loguru import logger + +from app.api import api_router +from app.config import settings, setup_app_logging + +# setup logging as early as possible +setup_app_logging(config=settings) + + +app = FastAPI( + title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json" +) + +root_router = APIRouter() + + +@root_router.get("/") +def index(request: Request) -> Any: + """Basic HTML response.""" + body = ( + "" + "" + "

Welcome to the API

" + "
" + "Check the docs: here" + "
" + "" + "" + ) + + return HTMLResponse(content=body) + + +app.include_router(api_router, prefix=settings.API_V1_STR) +app.include_router(root_router) + +# Set all CORS enabled origins +if settings.BACKEND_CORS_ORIGINS: + app.add_middleware( + CORSMiddleware, + allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + +if __name__ == "__main__": + # Use this for debugging purposes only + logger.warning("Running in development mode. Do not run like this in production.") + import uvicorn + + uvicorn.run(app, host="localhost", port=8001, log_level="debug") diff --git a/section-06-model-serving-api/house-prices-api/app/schemas/__init__.py b/section-06-model-serving-api/house-prices-api/app/schemas/__init__.py index f0e08e102..fac77b131 100644 --- a/section-06-model-serving-api/house-prices-api/app/schemas/__init__.py +++ b/section-06-model-serving-api/house-prices-api/app/schemas/__init__.py @@ -1,2 +1,2 @@ -from .health import Health -from .predict import MultipleHouseDataInputs, PredictionResults +from .health import Health +from .predict import MultipleHouseDataInputs, PredictionResults diff --git a/section-06-model-serving-api/house-prices-api/app/schemas/health.py b/section-06-model-serving-api/house-prices-api/app/schemas/health.py index bede1e8a5..b7f801c6c 100644 --- a/section-06-model-serving-api/house-prices-api/app/schemas/health.py +++ b/section-06-model-serving-api/house-prices-api/app/schemas/health.py @@ -1,7 +1,7 @@ -from pydantic import BaseModel - - -class Health(BaseModel): - name: str - api_version: str - model_version: str +from pydantic import BaseModel + + +class Health(BaseModel): + name: str + api_version: str + model_version: str diff --git a/section-06-model-serving-api/house-prices-api/app/schemas/predict.py b/section-06-model-serving-api/house-prices-api/app/schemas/predict.py index e3b668312..42241ac39 100644 --- a/section-06-model-serving-api/house-prices-api/app/schemas/predict.py +++ b/section-06-model-serving-api/house-prices-api/app/schemas/predict.py @@ -1,103 +1,103 @@ -from typing import Any, List, Optional - -from pydantic import BaseModel -from regression_model.processing.validation import HouseDataInputSchema - - -class PredictionResults(BaseModel): - errors: Optional[Any] - version: str - predictions: Optional[List[float]] - - -class MultipleHouseDataInputs(BaseModel): - inputs: List[HouseDataInputSchema] - - class Config: - schema_extra = { - "example": { - "inputs": [ - { - "MSSubClass": 20, - "MSZoning": "RH", - "LotFrontage": 80.0, - "LotArea": 11622, - "Street": "Pave", - "Alley": None, - "LotShape": "Reg", - "LandContour": "Lvl", - "Utilities": "AllPub", - "LotConfig": "Inside", - "LandSlope": "Gtl", - "Neighborhood": "NAmes", - "Condition1": "Feedr", - "Condition2": "Norm", - "BldgType": "1Fam", - "HouseStyle": "1Story", - "OverallQual": 5, - "OverallCond": 6, - "YearBuilt": 1961, - "YearRemodAdd": 1961, - "RoofStyle": "Gable", - "RoofMatl": "CompShg", - "Exterior1st": "VinylSd", - "Exterior2nd": "VinylSd", - "MasVnrType": "None", - "MasVnrArea": 0.0, - "ExterQual": "TA", - "ExterCond": "TA", - "Foundation": "CBlock", - "BsmtQual": "TA", - "BsmtCond": "TA", - "BsmtExposure": "No", - "BsmtFinType1": "Rec", - "BsmtFinSF1": 468.0, - "BsmtFinType2": "LwQ", - "BsmtFinSF2": 144.0, - "BsmtUnfSF": 270.0, - "TotalBsmtSF": 882.0, - "Heating": "GasA", - "HeatingQC": "TA", - "CentralAir": "Y", - "Electrical": "SBrkr", - "FirstFlrSF": 896, - "SecondFlrSF": 0, - "LowQualFinSF": 0, - "GrLivArea": 896, - "BsmtFullBath": 0.0, - "BsmtHalfBath": 0.0, - "FullBath": 1, - "HalfBath": 0, - "BedroomAbvGr": 2, - "KitchenAbvGr": 1, - "KitchenQual": "TA", - "TotRmsAbvGrd": 5, - "Functional": "Typ", - "Fireplaces": 0, - "FireplaceQu": None, - "GarageType": "Attchd", - "GarageYrBlt": 1961.0, - "GarageFinish": "Unf", - "GarageCars": 1.0, - "GarageArea": 730.0, - "GarageQual": "TA", - "GarageCond": "TA", - "PavedDrive": "Y", - "WoodDeckSF": 140, - "OpenPorchSF": 0, - "EnclosedPorch": 0, - "ThreeSsnPortch": 0, - "ScreenPorch": 120, - "PoolArea": 0, - "PoolQC": None, - "Fence": "MnPrv", - "MiscFeature": None, - "MiscVal": 0, - "MoSold": 6, - "YrSold": 2010, - "SaleType": "WD", - "SaleCondition": "Normal", - } - ] - } - } +from typing import Any, List, Optional + +from pydantic import BaseModel +from regression_model.processing.validation import HouseDataInputSchema + + +class PredictionResults(BaseModel): + errors: Optional[Any] + version: str + predictions: Optional[List[float]] + + +class MultipleHouseDataInputs(BaseModel): + inputs: List[HouseDataInputSchema] + + class Config: + schema_extra = { + "example": { + "inputs": [ + { + "MSSubClass": 20, + "MSZoning": "RH", + "LotFrontage": 80.0, + "LotArea": 11622, + "Street": "Pave", + "Alley": None, + "LotShape": "Reg", + "LandContour": "Lvl", + "Utilities": "AllPub", + "LotConfig": "Inside", + "LandSlope": "Gtl", + "Neighborhood": "NAmes", + "Condition1": "Feedr", + "Condition2": "Norm", + "BldgType": "1Fam", + "HouseStyle": "1Story", + "OverallQual": 5, + "OverallCond": 6, + "YearBuilt": 1961, + "YearRemodAdd": 1961, + "RoofStyle": "Gable", + "RoofMatl": "CompShg", + "Exterior1st": "VinylSd", + "Exterior2nd": "VinylSd", + "MasVnrType": "None", + "MasVnrArea": 0.0, + "ExterQual": "TA", + "ExterCond": "TA", + "Foundation": "CBlock", + "BsmtQual": "TA", + "BsmtCond": "TA", + "BsmtExposure": "No", + "BsmtFinType1": "Rec", + "BsmtFinSF1": 468.0, + "BsmtFinType2": "LwQ", + "BsmtFinSF2": 144.0, + "BsmtUnfSF": 270.0, + "TotalBsmtSF": 882.0, + "Heating": "GasA", + "HeatingQC": "TA", + "CentralAir": "Y", + "Electrical": "SBrkr", + "FirstFlrSF": 896, + "SecondFlrSF": 0, + "LowQualFinSF": 0, + "GrLivArea": 896, + "BsmtFullBath": 0.0, + "BsmtHalfBath": 0.0, + "FullBath": 1, + "HalfBath": 0, + "BedroomAbvGr": 2, + "KitchenAbvGr": 1, + "KitchenQual": "TA", + "TotRmsAbvGrd": 5, + "Functional": "Typ", + "Fireplaces": 0, + "FireplaceQu": None, + "GarageType": "Attchd", + "GarageYrBlt": 1961.0, + "GarageFinish": "Unf", + "GarageCars": 1.0, + "GarageArea": 730.0, + "GarageQual": "TA", + "GarageCond": "TA", + "PavedDrive": "Y", + "WoodDeckSF": 140, + "OpenPorchSF": 0, + "EnclosedPorch": 0, + "ThreeSsnPortch": 0, + "ScreenPorch": 120, + "PoolArea": 0, + "PoolQC": None, + "Fence": "MnPrv", + "MiscFeature": None, + "MiscVal": 0, + "MoSold": 6, + "YrSold": 2010, + "SaleType": "WD", + "SaleCondition": "Normal", + } + ] + } + } diff --git a/section-06-model-serving-api/house-prices-api/app/tests/conftest.py b/section-06-model-serving-api/house-prices-api/app/tests/conftest.py index b87469ec0..1e7d8f0f8 100644 --- a/section-06-model-serving-api/house-prices-api/app/tests/conftest.py +++ b/section-06-model-serving-api/house-prices-api/app/tests/conftest.py @@ -1,21 +1,21 @@ -from typing import Generator - -import pandas as pd -import pytest -from fastapi.testclient import TestClient -from regression_model.config.core import config -from regression_model.processing.data_manager import load_dataset - -from app.main import app - - -@pytest.fixture(scope="module") -def test_data() -> pd.DataFrame: - return load_dataset(file_name=config.app_config.test_data_file) - - -@pytest.fixture() -def client() -> Generator: - with TestClient(app) as _client: - yield _client - app.dependency_overrides = {} +from typing import Generator + +import pandas as pd +import pytest +from fastapi.testclient import TestClient +from regression_model.config.core import config +from regression_model.processing.data_manager import load_dataset + +from app.main import app + + +@pytest.fixture(scope="module") +def test_data() -> pd.DataFrame: + return load_dataset(file_name=config.app_config.test_data_file) + + +@pytest.fixture() +def client() -> Generator: + with TestClient(app) as _client: + yield _client + app.dependency_overrides = {} diff --git a/section-06-model-serving-api/house-prices-api/app/tests/test_api.py b/section-06-model-serving-api/house-prices-api/app/tests/test_api.py index 833fcadb5..21be33f2a 100644 --- a/section-06-model-serving-api/house-prices-api/app/tests/test_api.py +++ b/section-06-model-serving-api/house-prices-api/app/tests/test_api.py @@ -1,26 +1,26 @@ -import math - -import numpy as np -import pandas as pd -from fastapi.testclient import TestClient - - -def test_make_prediction(client: TestClient, test_data: pd.DataFrame) -> None: - # Given - payload = { - # ensure pydantic plays well with np.nan - "inputs": test_data.replace({np.nan: None}).to_dict(orient="records") - } - - # When - response = client.post( - "http://localhost:8001/api/v1/predict", - json=payload, - ) - - # Then - assert response.status_code == 200 - prediction_data = response.json() - assert prediction_data["predictions"] - assert prediction_data["errors"] is None - assert math.isclose(prediction_data["predictions"][0], 113422, rel_tol=100) +import math + +import numpy as np +import pandas as pd +from fastapi.testclient import TestClient + + +def test_make_prediction(client: TestClient, test_data: pd.DataFrame) -> None: + # Given + payload = { + # ensure pydantic plays well with np.nan + "inputs": test_data.replace({np.nan: None}).to_dict(orient="records") + } + + # When + response = client.post( + "http://localhost:8001/api/v1/predict", + json=payload, + ) + + # Then + assert response.status_code == 200 + prediction_data = response.json() + assert prediction_data["predictions"] + assert prediction_data["errors"] is None + assert math.isclose(prediction_data["predictions"][0], 113422, rel_tol=100) diff --git a/section-06-model-serving-api/house-prices-api/mypy.ini b/section-06-model-serving-api/house-prices-api/mypy.ini index 19273b9c1..84250acaf 100644 --- a/section-06-model-serving-api/house-prices-api/mypy.ini +++ b/section-06-model-serving-api/house-prices-api/mypy.ini @@ -1,4 +1,4 @@ -[mypy] -plugins = pydantic.mypy -ignore_missing_imports = True -disallow_untyped_defs = True +[mypy] +plugins = pydantic.mypy +ignore_missing_imports = True +disallow_untyped_defs = True diff --git a/section-06-model-serving-api/house-prices-api/requirements.txt b/section-06-model-serving-api/house-prices-api/requirements.txt index c6f00d968..7266fba74 100644 --- a/section-06-model-serving-api/house-prices-api/requirements.txt +++ b/section-06-model-serving-api/house-prices-api/requirements.txt @@ -1,9 +1,9 @@ -uvicorn>=0.20.0,<0.30.0 -fastapi>=0.88.0,<1.0.0 -python-multipart>=0.0.5,<0.1.0 -pydantic>=1.10.4,<1.12.0 -typing_extensions>=4.2.0,<5.0.0 -loguru>=0.5.3,<1.0.0 -# We will explain this in the course -tid-regression-model>=3.2.0 +uvicorn>=0.20.0,<0.30.0 +fastapi>=0.88.0,<1.0.0 +python-multipart>=0.0.5,<0.1.0 +pydantic>=1.10.4,<1.12.0 +typing_extensions>=4.2.0,<5.0.0 +loguru>=0.5.3,<1.0.0 +# We will explain this in the course +tid-regression-model>=3.2.0 feature-engine>=1.0.2,<1.6.0 # breaking change in v1.6.0 \ No newline at end of file diff --git a/section-06-model-serving-api/house-prices-api/test_requirements.txt b/section-06-model-serving-api/house-prices-api/test_requirements.txt index 52881a5c0..1ed08278f 100644 --- a/section-06-model-serving-api/house-prices-api/test_requirements.txt +++ b/section-06-model-serving-api/house-prices-api/test_requirements.txt @@ -1,6 +1,6 @@ --r requirements.txt - -# testing requirements -pytest>=7.2.0,<8.0.0 -requests>=2.28.0,<2.50.0 -httpx>=0.23.2,<0.50.0 +-r requirements.txt + +# testing requirements +pytest>=7.2.0,<8.0.0 +requests>=2.28.0,<2.50.0 +httpx>=0.23.2,<0.50.0 diff --git a/section-06-model-serving-api/house-prices-api/tox.ini b/section-06-model-serving-api/house-prices-api/tox.ini index 90f683c99..f4c8bb1b5 100644 --- a/section-06-model-serving-api/house-prices-api/tox.ini +++ b/section-06-model-serving-api/house-prices-api/tox.ini @@ -1,59 +1,59 @@ -# Tox is a generic virtualenv management and test command line tool. Its goal is to -# standardize testing in Python. We will be using it extensively in this course. - -# Using Tox we can (on multiple operating systems): -# + Eliminate PYTHONPATH challenges when running scripts/tests -# + Eliminate virtualenv setup confusion -# + Streamline steps such as model training, model publishing - -[pytest] -log_cli_level=WARNING - -[tox] -min_version = 4 -envlist = test_app, checks -skipsdist = True - -[testenv] -install_command = pip install {opts} {packages} - -[testenv:test_app] -deps = - -rtest_requirements.txt - -setenv = - PYTHONPATH=. - PYTHONHASHSEED=0 - -commands= - pytest \ - -vv \ - {posargs:app/tests/} - - -[testenv:run] -envdir = {toxworkdir}/test_app -deps = - {[testenv:test_app]deps} - -setenv = - {[testenv:test_app]setenv} - -commands= - python app/main.py - - -[testenv:checks] -envdir = {toxworkdir}/checks -deps = - -r{toxinidir}/typing_requirements.txt -commands = - flake8 app - isort app - black app - {posargs:mypy app} - - -[flake8] -exclude = .git,__pycache__,__init__.py,.mypy_cache,.pytest_cache,.venv,alembic +# Tox is a generic virtualenv management and test command line tool. Its goal is to +# standardize testing in Python. We will be using it extensively in this course. + +# Using Tox we can (on multiple operating systems): +# + Eliminate PYTHONPATH challenges when running scripts/tests +# + Eliminate virtualenv setup confusion +# + Streamline steps such as model training, model publishing + +[pytest] +log_cli_level=WARNING + +[tox] +min_version = 4 +envlist = test_app, checks +skipsdist = True + +[testenv] +install_command = pip install {opts} {packages} + +[testenv:test_app] +deps = + -rtest_requirements.txt + +setenv = + PYTHONPATH=. + PYTHONHASHSEED=0 + +commands= + pytest \ + -vv \ + {posargs:app/tests/} + + +[testenv:run] +envdir = {toxworkdir}/test_app +deps = + {[testenv:test_app]deps} + +setenv = + {[testenv:test_app]setenv} + +commands= + python app/main.py + + +[testenv:checks] +envdir = {toxworkdir}/checks +deps = + -r{toxinidir}/typing_requirements.txt +commands = + flake8 app + isort app + black app + {posargs:mypy app} + + +[flake8] +exclude = .git,__pycache__,__init__.py,.mypy_cache,.pytest_cache,.venv,alembic max-line-length = 88 \ No newline at end of file diff --git a/section-06-model-serving-api/house-prices-api/typing_requirements.txt b/section-06-model-serving-api/house-prices-api/typing_requirements.txt index c75846478..1c4228e7e 100644 --- a/section-06-model-serving-api/house-prices-api/typing_requirements.txt +++ b/section-06-model-serving-api/house-prices-api/typing_requirements.txt @@ -1,6 +1,6 @@ -# repo maintenance tooling -black>=22.12.0,<23.0.0 -flake8>=6.0.0,<7.0.0 -mypy>=0.991,<1.0.0 -isort>=5.11.4,<6.0.0 +# repo maintenance tooling +black>=22.12.0,<23.0.0 +flake8>=6.0.0,<7.0.0 +mypy>=0.991,<1.0.0 +isort>=5.11.4,<6.0.0 pydantic>=1.10.4,<1.12.0 \ No newline at end of file diff --git a/section-07-ci-and-publishing/house-prices-api/app/__init__.py b/section-07-ci-and-publishing/house-prices-api/app/__init__.py index b1a19e323..3bd88fb03 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/__init__.py +++ b/section-07-ci-and-publishing/house-prices-api/app/__init__.py @@ -1 +1 @@ -__version__ = "0.0.5" +__version__ = "0.0.8" diff --git a/section-07-ci-and-publishing/house-prices-api/app/api.py b/section-07-ci-and-publishing/house-prices-api/app/api.py index de9559faf..1bdd9c0fc 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/api.py +++ b/section-07-ci-and-publishing/house-prices-api/app/api.py @@ -1,49 +1,49 @@ -import json -from typing import Any - -import numpy as np -import pandas as pd -from fastapi import APIRouter, HTTPException -from fastapi.encoders import jsonable_encoder -from loguru import logger -from regression_model import __version__ as model_version -from regression_model.predict import make_prediction - -from app import __version__, schemas -from app.config import settings - -api_router = APIRouter() - - -@api_router.get("/health", response_model=schemas.Health, status_code=200) -def health() -> dict: - """ - Root Get - """ - health = schemas.Health( - name=settings.PROJECT_NAME, api_version=__version__, model_version=model_version - ) - - return health.dict() - - -@api_router.post("/predict", response_model=schemas.PredictionResults, status_code=200) -async def predict(input_data: schemas.MultipleHouseDataInputs) -> Any: - """ - Make house price predictions with the TID regression model - """ - - input_df = pd.DataFrame(jsonable_encoder(input_data.inputs)) - - # Advanced: You can improve performance of your API by rewriting the - # `make prediction` function to be async and using await here. - logger.info(f"Making prediction on inputs: {input_data.inputs}") - results = make_prediction(input_data=input_df.replace({np.nan: None})) - - if results["errors"] is not None: - logger.warning(f"Prediction validation error: {results.get('errors')}") - raise HTTPException(status_code=400, detail=json.loads(results["errors"])) - - logger.info(f"Prediction results: {results.get('predictions')}") - - return results +import json +from typing import Any + +import numpy as np +import pandas as pd +from fastapi import APIRouter, HTTPException +from fastapi.encoders import jsonable_encoder +from loguru import logger +from regression_model import __version__ as model_version +from regression_model.predict import make_prediction + +from app import __version__, schemas +from app.config import settings + +api_router = APIRouter() + + +@api_router.get("/health", response_model=schemas.Health, status_code=200) +def health() -> dict: + """ + Root Get + """ + health = schemas.Health( + name=settings.PROJECT_NAME, api_version=__version__, model_version=model_version + ) + + return health.dict() + + +@api_router.post("/predict", response_model=schemas.PredictionResults, status_code=200) +async def predict(input_data: schemas.MultipleHouseDataInputs) -> Any: + """ + Make house price predictions with the TID regression model + """ + + input_df = pd.DataFrame(jsonable_encoder(input_data.inputs)) + + # Advanced: You can improve performance of your API by rewriting the + # `make prediction` function to be async and using await here. + logger.info(f"Making prediction on inputs: {input_data.inputs}") + results = make_prediction(input_data=input_df.replace({np.nan: None})) + + if results["errors"] is not None: + logger.warning(f"Prediction validation error: {results.get('errors')}") + raise HTTPException(status_code=400, detail=json.loads(results["errors"])) + + logger.info(f"Prediction results: {results.get('predictions')}") + + return results diff --git a/section-07-ci-and-publishing/house-prices-api/app/config.py b/section-07-ci-and-publishing/house-prices-api/app/config.py index 9dc62e5fb..7233dcfc1 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/config.py +++ b/section-07-ci-and-publishing/house-prices-api/app/config.py @@ -1,70 +1,70 @@ -import logging -import sys -from types import FrameType -from typing import List, cast - -from loguru import logger -from pydantic import AnyHttpUrl, BaseSettings - - -class LoggingSettings(BaseSettings): - LOGGING_LEVEL: int = logging.INFO # logging levels are type int - - -class Settings(BaseSettings): - API_V1_STR: str = "/api/v1" - - # Meta - logging: LoggingSettings = LoggingSettings() - - # BACKEND_CORS_ORIGINS is a comma-separated list of origins - # e.g: http://localhost,http://localhost:4200,http://localhost:3000 - BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [ - "http://localhost:3000", # type: ignore - "http://localhost:8000", # type: ignore - "https://localhost:3000", # type: ignore - "https://localhost:8000", # type: ignore - ] - - PROJECT_NAME: str = "House Price Prediction API" - - class Config: - case_sensitive = True - - -# See: https://loguru.readthedocs.io/en/stable/overview.html#entirely-compatible-with-standard-logging # noqa -class InterceptHandler(logging.Handler): - def emit(self, record: logging.LogRecord) -> None: # pragma: no cover - # Get corresponding Loguru level if it exists - try: - level = logger.level(record.levelname).name - except ValueError: - level = str(record.levelno) - - # Find caller from where originated the logged message - frame, depth = logging.currentframe(), 2 - while frame.f_code.co_filename == logging.__file__: # noqa: WPS609 - frame = cast(FrameType, frame.f_back) - depth += 1 - - logger.opt(depth=depth, exception=record.exc_info).log( - level, - record.getMessage(), - ) - - -def setup_app_logging(config: Settings) -> None: - """Prepare custom logging for our application.""" - - LOGGERS = ("uvicorn.asgi", "uvicorn.access") - logging.getLogger().handlers = [InterceptHandler()] - for logger_name in LOGGERS: - logging_logger = logging.getLogger(logger_name) - logging_logger.handlers = [InterceptHandler(level=config.logging.LOGGING_LEVEL)] - - logger.configure( - handlers=[{"sink": sys.stderr, "level": config.logging.LOGGING_LEVEL}] - ) - - -settings = Settings() +import logging +import sys +from types import FrameType +from typing import List, cast + +from loguru import logger +from pydantic import AnyHttpUrl, BaseSettings + + +class LoggingSettings(BaseSettings): + LOGGING_LEVEL: int = logging.INFO # logging levels are type int + + +class Settings(BaseSettings): + API_V1_STR: str = "/api/v1" + + # Meta + logging: LoggingSettings = LoggingSettings() + + # BACKEND_CORS_ORIGINS is a comma-separated list of origins + # e.g: http://localhost,http://localhost:4200,http://localhost:3000 + BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [ + "http://localhost:3000", # type: ignore + "http://localhost:8000", # type: ignore + "https://localhost:3000", # type: ignore + "https://localhost:8000", # type: ignore + ] + + PROJECT_NAME: str = "House Price Prediction API" + + class Config: + case_sensitive = True + + +# See: https://loguru.readthedocs.io/en/stable/overview.html#entirely-compatible-with-standard-logging # noqa +class InterceptHandler(logging.Handler): + def emit(self, record: logging.LogRecord) -> None: # pragma: no cover + # Get corresponding Loguru level if it exists + try: + level = logger.level(record.levelname).name + except ValueError: + level = str(record.levelno) + + # Find caller from where originated the logged message + frame, depth = logging.currentframe(), 2 + while frame.f_code.co_filename == logging.__file__: # noqa: WPS609 + frame = cast(FrameType, frame.f_back) + depth += 1 + + logger.opt(depth=depth, exception=record.exc_info).log( + level, + record.getMessage(), + ) + + +def setup_app_logging(config: Settings) -> None: + """Prepare custom logging for our application.""" + + LOGGERS = ("uvicorn.asgi", "uvicorn.access") + logging.getLogger().handlers = [InterceptHandler()] + for logger_name in LOGGERS: + logging_logger = logging.getLogger(logger_name) + logging_logger.handlers = [InterceptHandler(level=config.logging.LOGGING_LEVEL)] + + logger.configure( + handlers=[{"sink": sys.stderr, "level": config.logging.LOGGING_LEVEL}] + ) + + +settings = Settings() diff --git a/section-07-ci-and-publishing/house-prices-api/app/main.py b/section-07-ci-and-publishing/house-prices-api/app/main.py index b55d34402..902eb649f 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/main.py +++ b/section-07-ci-and-publishing/house-prices-api/app/main.py @@ -1,58 +1,58 @@ -from typing import Any - -from fastapi import APIRouter, FastAPI, Request -from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import HTMLResponse -from loguru import logger - -from app.api import api_router -from app.config import settings, setup_app_logging - -# setup logging as early as possible -setup_app_logging(config=settings) - - -app = FastAPI( - title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json" -) - -root_router = APIRouter() - - -@root_router.get("/") -def index(request: Request) -> Any: - """Basic HTML response.""" - body = ( - "" - "" - "

Welcome to the API

" - "
" - "Check the docs: here" - "
" - "" - "" - ) - - return HTMLResponse(content=body) - - -app.include_router(api_router, prefix=settings.API_V1_STR) -app.include_router(root_router) - -# Set all CORS enabled origins -if settings.BACKEND_CORS_ORIGINS: - app.add_middleware( - CORSMiddleware, - allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - ) - - -if __name__ == "__main__": - # Use this for debugging purposes only - logger.warning("Running in development mode. Do not run like this in production.") - import uvicorn - - uvicorn.run(app, host="localhost", port=8001, log_level="debug") +from typing import Any + +from fastapi import APIRouter, FastAPI, Request +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import HTMLResponse +from loguru import logger + +from app.api import api_router +from app.config import settings, setup_app_logging + +# setup logging as early as possible +setup_app_logging(config=settings) + + +app = FastAPI( + title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json" +) + +root_router = APIRouter() + + +@root_router.get("/") +def index(request: Request) -> Any: + """Basic HTML response.""" + body = ( + "" + "" + "

Welcome to the API

" + "
" + "Check the docs: here" + "
" + "" + "" + ) + + return HTMLResponse(content=body) + + +app.include_router(api_router, prefix=settings.API_V1_STR) +app.include_router(root_router) + +# Set all CORS enabled origins +if settings.BACKEND_CORS_ORIGINS: + app.add_middleware( + CORSMiddleware, + allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + +if __name__ == "__main__": + # Use this for debugging purposes only + logger.warning("Running in development mode. Do not run like this in production.") + import uvicorn + + uvicorn.run(app, host="localhost", port=8001, log_level="debug") diff --git a/section-07-ci-and-publishing/house-prices-api/app/schemas/__init__.py b/section-07-ci-and-publishing/house-prices-api/app/schemas/__init__.py index f0e08e102..fac77b131 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/schemas/__init__.py +++ b/section-07-ci-and-publishing/house-prices-api/app/schemas/__init__.py @@ -1,2 +1,2 @@ -from .health import Health -from .predict import MultipleHouseDataInputs, PredictionResults +from .health import Health +from .predict import MultipleHouseDataInputs, PredictionResults diff --git a/section-07-ci-and-publishing/house-prices-api/app/schemas/health.py b/section-07-ci-and-publishing/house-prices-api/app/schemas/health.py index bede1e8a5..b7f801c6c 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/schemas/health.py +++ b/section-07-ci-and-publishing/house-prices-api/app/schemas/health.py @@ -1,7 +1,7 @@ -from pydantic import BaseModel - - -class Health(BaseModel): - name: str - api_version: str - model_version: str +from pydantic import BaseModel + + +class Health(BaseModel): + name: str + api_version: str + model_version: str diff --git a/section-07-ci-and-publishing/house-prices-api/app/schemas/predict.py b/section-07-ci-and-publishing/house-prices-api/app/schemas/predict.py index e3b668312..42241ac39 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/schemas/predict.py +++ b/section-07-ci-and-publishing/house-prices-api/app/schemas/predict.py @@ -1,103 +1,103 @@ -from typing import Any, List, Optional - -from pydantic import BaseModel -from regression_model.processing.validation import HouseDataInputSchema - - -class PredictionResults(BaseModel): - errors: Optional[Any] - version: str - predictions: Optional[List[float]] - - -class MultipleHouseDataInputs(BaseModel): - inputs: List[HouseDataInputSchema] - - class Config: - schema_extra = { - "example": { - "inputs": [ - { - "MSSubClass": 20, - "MSZoning": "RH", - "LotFrontage": 80.0, - "LotArea": 11622, - "Street": "Pave", - "Alley": None, - "LotShape": "Reg", - "LandContour": "Lvl", - "Utilities": "AllPub", - "LotConfig": "Inside", - "LandSlope": "Gtl", - "Neighborhood": "NAmes", - "Condition1": "Feedr", - "Condition2": "Norm", - "BldgType": "1Fam", - "HouseStyle": "1Story", - "OverallQual": 5, - "OverallCond": 6, - "YearBuilt": 1961, - "YearRemodAdd": 1961, - "RoofStyle": "Gable", - "RoofMatl": "CompShg", - "Exterior1st": "VinylSd", - "Exterior2nd": "VinylSd", - "MasVnrType": "None", - "MasVnrArea": 0.0, - "ExterQual": "TA", - "ExterCond": "TA", - "Foundation": "CBlock", - "BsmtQual": "TA", - "BsmtCond": "TA", - "BsmtExposure": "No", - "BsmtFinType1": "Rec", - "BsmtFinSF1": 468.0, - "BsmtFinType2": "LwQ", - "BsmtFinSF2": 144.0, - "BsmtUnfSF": 270.0, - "TotalBsmtSF": 882.0, - "Heating": "GasA", - "HeatingQC": "TA", - "CentralAir": "Y", - "Electrical": "SBrkr", - "FirstFlrSF": 896, - "SecondFlrSF": 0, - "LowQualFinSF": 0, - "GrLivArea": 896, - "BsmtFullBath": 0.0, - "BsmtHalfBath": 0.0, - "FullBath": 1, - "HalfBath": 0, - "BedroomAbvGr": 2, - "KitchenAbvGr": 1, - "KitchenQual": "TA", - "TotRmsAbvGrd": 5, - "Functional": "Typ", - "Fireplaces": 0, - "FireplaceQu": None, - "GarageType": "Attchd", - "GarageYrBlt": 1961.0, - "GarageFinish": "Unf", - "GarageCars": 1.0, - "GarageArea": 730.0, - "GarageQual": "TA", - "GarageCond": "TA", - "PavedDrive": "Y", - "WoodDeckSF": 140, - "OpenPorchSF": 0, - "EnclosedPorch": 0, - "ThreeSsnPortch": 0, - "ScreenPorch": 120, - "PoolArea": 0, - "PoolQC": None, - "Fence": "MnPrv", - "MiscFeature": None, - "MiscVal": 0, - "MoSold": 6, - "YrSold": 2010, - "SaleType": "WD", - "SaleCondition": "Normal", - } - ] - } - } +from typing import Any, List, Optional + +from pydantic import BaseModel +from regression_model.processing.validation import HouseDataInputSchema + + +class PredictionResults(BaseModel): + errors: Optional[Any] + version: str + predictions: Optional[List[float]] + + +class MultipleHouseDataInputs(BaseModel): + inputs: List[HouseDataInputSchema] + + class Config: + schema_extra = { + "example": { + "inputs": [ + { + "MSSubClass": 20, + "MSZoning": "RH", + "LotFrontage": 80.0, + "LotArea": 11622, + "Street": "Pave", + "Alley": None, + "LotShape": "Reg", + "LandContour": "Lvl", + "Utilities": "AllPub", + "LotConfig": "Inside", + "LandSlope": "Gtl", + "Neighborhood": "NAmes", + "Condition1": "Feedr", + "Condition2": "Norm", + "BldgType": "1Fam", + "HouseStyle": "1Story", + "OverallQual": 5, + "OverallCond": 6, + "YearBuilt": 1961, + "YearRemodAdd": 1961, + "RoofStyle": "Gable", + "RoofMatl": "CompShg", + "Exterior1st": "VinylSd", + "Exterior2nd": "VinylSd", + "MasVnrType": "None", + "MasVnrArea": 0.0, + "ExterQual": "TA", + "ExterCond": "TA", + "Foundation": "CBlock", + "BsmtQual": "TA", + "BsmtCond": "TA", + "BsmtExposure": "No", + "BsmtFinType1": "Rec", + "BsmtFinSF1": 468.0, + "BsmtFinType2": "LwQ", + "BsmtFinSF2": 144.0, + "BsmtUnfSF": 270.0, + "TotalBsmtSF": 882.0, + "Heating": "GasA", + "HeatingQC": "TA", + "CentralAir": "Y", + "Electrical": "SBrkr", + "FirstFlrSF": 896, + "SecondFlrSF": 0, + "LowQualFinSF": 0, + "GrLivArea": 896, + "BsmtFullBath": 0.0, + "BsmtHalfBath": 0.0, + "FullBath": 1, + "HalfBath": 0, + "BedroomAbvGr": 2, + "KitchenAbvGr": 1, + "KitchenQual": "TA", + "TotRmsAbvGrd": 5, + "Functional": "Typ", + "Fireplaces": 0, + "FireplaceQu": None, + "GarageType": "Attchd", + "GarageYrBlt": 1961.0, + "GarageFinish": "Unf", + "GarageCars": 1.0, + "GarageArea": 730.0, + "GarageQual": "TA", + "GarageCond": "TA", + "PavedDrive": "Y", + "WoodDeckSF": 140, + "OpenPorchSF": 0, + "EnclosedPorch": 0, + "ThreeSsnPortch": 0, + "ScreenPorch": 120, + "PoolArea": 0, + "PoolQC": None, + "Fence": "MnPrv", + "MiscFeature": None, + "MiscVal": 0, + "MoSold": 6, + "YrSold": 2010, + "SaleType": "WD", + "SaleCondition": "Normal", + } + ] + } + } diff --git a/section-07-ci-and-publishing/house-prices-api/app/tests/conftest.py b/section-07-ci-and-publishing/house-prices-api/app/tests/conftest.py index b87469ec0..1e7d8f0f8 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/tests/conftest.py +++ b/section-07-ci-and-publishing/house-prices-api/app/tests/conftest.py @@ -1,21 +1,21 @@ -from typing import Generator - -import pandas as pd -import pytest -from fastapi.testclient import TestClient -from regression_model.config.core import config -from regression_model.processing.data_manager import load_dataset - -from app.main import app - - -@pytest.fixture(scope="module") -def test_data() -> pd.DataFrame: - return load_dataset(file_name=config.app_config.test_data_file) - - -@pytest.fixture() -def client() -> Generator: - with TestClient(app) as _client: - yield _client - app.dependency_overrides = {} +from typing import Generator + +import pandas as pd +import pytest +from fastapi.testclient import TestClient +from regression_model.config.core import config +from regression_model.processing.data_manager import load_dataset + +from app.main import app + + +@pytest.fixture(scope="module") +def test_data() -> pd.DataFrame: + return load_dataset(file_name=config.app_config.test_data_file) + + +@pytest.fixture() +def client() -> Generator: + with TestClient(app) as _client: + yield _client + app.dependency_overrides = {} diff --git a/section-07-ci-and-publishing/house-prices-api/app/tests/test_api.py b/section-07-ci-and-publishing/house-prices-api/app/tests/test_api.py index 833fcadb5..21be33f2a 100644 --- a/section-07-ci-and-publishing/house-prices-api/app/tests/test_api.py +++ b/section-07-ci-and-publishing/house-prices-api/app/tests/test_api.py @@ -1,26 +1,26 @@ -import math - -import numpy as np -import pandas as pd -from fastapi.testclient import TestClient - - -def test_make_prediction(client: TestClient, test_data: pd.DataFrame) -> None: - # Given - payload = { - # ensure pydantic plays well with np.nan - "inputs": test_data.replace({np.nan: None}).to_dict(orient="records") - } - - # When - response = client.post( - "http://localhost:8001/api/v1/predict", - json=payload, - ) - - # Then - assert response.status_code == 200 - prediction_data = response.json() - assert prediction_data["predictions"] - assert prediction_data["errors"] is None - assert math.isclose(prediction_data["predictions"][0], 113422, rel_tol=100) +import math + +import numpy as np +import pandas as pd +from fastapi.testclient import TestClient + + +def test_make_prediction(client: TestClient, test_data: pd.DataFrame) -> None: + # Given + payload = { + # ensure pydantic plays well with np.nan + "inputs": test_data.replace({np.nan: None}).to_dict(orient="records") + } + + # When + response = client.post( + "http://localhost:8001/api/v1/predict", + json=payload, + ) + + # Then + assert response.status_code == 200 + prediction_data = response.json() + assert prediction_data["predictions"] + assert prediction_data["errors"] is None + assert math.isclose(prediction_data["predictions"][0], 113422, rel_tol=100) diff --git a/section-07-ci-and-publishing/house-prices-api/mypy.ini b/section-07-ci-and-publishing/house-prices-api/mypy.ini index 19273b9c1..84250acaf 100644 --- a/section-07-ci-and-publishing/house-prices-api/mypy.ini +++ b/section-07-ci-and-publishing/house-prices-api/mypy.ini @@ -1,4 +1,4 @@ -[mypy] -plugins = pydantic.mypy -ignore_missing_imports = True -disallow_untyped_defs = True +[mypy] +plugins = pydantic.mypy +ignore_missing_imports = True +disallow_untyped_defs = True diff --git a/section-07-ci-and-publishing/house-prices-api/requirements.txt b/section-07-ci-and-publishing/house-prices-api/requirements.txt index c6f00d968..7266fba74 100644 --- a/section-07-ci-and-publishing/house-prices-api/requirements.txt +++ b/section-07-ci-and-publishing/house-prices-api/requirements.txt @@ -1,9 +1,9 @@ -uvicorn>=0.20.0,<0.30.0 -fastapi>=0.88.0,<1.0.0 -python-multipart>=0.0.5,<0.1.0 -pydantic>=1.10.4,<1.12.0 -typing_extensions>=4.2.0,<5.0.0 -loguru>=0.5.3,<1.0.0 -# We will explain this in the course -tid-regression-model>=3.2.0 +uvicorn>=0.20.0,<0.30.0 +fastapi>=0.88.0,<1.0.0 +python-multipart>=0.0.5,<0.1.0 +pydantic>=1.10.4,<1.12.0 +typing_extensions>=4.2.0,<5.0.0 +loguru>=0.5.3,<1.0.0 +# We will explain this in the course +tid-regression-model>=3.2.0 feature-engine>=1.0.2,<1.6.0 # breaking change in v1.6.0 \ No newline at end of file diff --git a/section-07-ci-and-publishing/house-prices-api/test_requirements.txt b/section-07-ci-and-publishing/house-prices-api/test_requirements.txt index 52881a5c0..1ed08278f 100644 --- a/section-07-ci-and-publishing/house-prices-api/test_requirements.txt +++ b/section-07-ci-and-publishing/house-prices-api/test_requirements.txt @@ -1,6 +1,6 @@ --r requirements.txt - -# testing requirements -pytest>=7.2.0,<8.0.0 -requests>=2.28.0,<2.50.0 -httpx>=0.23.2,<0.50.0 +-r requirements.txt + +# testing requirements +pytest>=7.2.0,<8.0.0 +requests>=2.28.0,<2.50.0 +httpx>=0.23.2,<0.50.0 diff --git a/section-07-ci-and-publishing/house-prices-api/tox.ini b/section-07-ci-and-publishing/house-prices-api/tox.ini index c7318e209..58a24fb94 100644 --- a/section-07-ci-and-publishing/house-prices-api/tox.ini +++ b/section-07-ci-and-publishing/house-prices-api/tox.ini @@ -1,58 +1,58 @@ -# Tox is a generic virtualenv management and test command line tool. Its goal is to -# standardize testing in Python. We will be using it extensively in this course. - -# Using Tox we can (on multiple operating systems): -# + Eliminate PYTHONPATH challenges when running scripts/tests -# + Eliminate virtualenv setup confusion -# + Streamline steps such as model training, model publishing - -[pytest] -log_cli_level=WARNING - -[tox] -envlist = test_app, checks -skipsdist = True - -[testenv] -install_command = pip install {opts} {packages} - -[testenv:test_app] -deps = - -rtest_requirements.txt - -setenv = - PYTHONPATH=. - PYTHONHASHSEED=0 - -commands= - pytest \ - -vv \ - {posargs:app/tests/} - - -[testenv:run] -envdir = {toxworkdir}/test_app -deps = - {[testenv:test_app]deps} - -setenv = - {[testenv:test_app]setenv} - -commands= - python app/main.py - - -[testenv:checks] -envdir = {toxworkdir}/checks -deps = - -r{toxinidir}/typing_requirements.txt -commands = - flake8 app - isort app - black app - {posargs:mypy app} - - -[flake8] -exclude = .git,__pycache__,__init__.py,.mypy_cache,.pytest_cache,.venv,alembic +# Tox is a generic virtualenv management and test command line tool. Its goal is to +# standardize testing in Python. We will be using it extensively in this course. + +# Using Tox we can (on multiple operating systems): +# + Eliminate PYTHONPATH challenges when running scripts/tests +# + Eliminate virtualenv setup confusion +# + Streamline steps such as model training, model publishing + +[pytest] +log_cli_level=WARNING + +[tox] +envlist = test_app, checks +skipsdist = True + +[testenv] +install_command = pip install {opts} {packages} + +[testenv:test_app] +deps = + -rtest_requirements.txt + +setenv = + PYTHONPATH=. + PYTHONHASHSEED=0 + +commands= + pytest \ + -vv \ + {posargs:app/tests/} + + +[testenv:run] +envdir = {toxworkdir}/test_app +deps = + {[testenv:test_app]deps} + +setenv = + {[testenv:test_app]setenv} + +commands= + python app/main.py + + +[testenv:checks] +envdir = {toxworkdir}/checks +deps = + -r{toxinidir}/typing_requirements.txt +commands = + flake8 app + isort app + black app + {posargs:mypy app} + + +[flake8] +exclude = .git,__pycache__,__init__.py,.mypy_cache,.pytest_cache,.venv,alembic max-line-length = 88 \ No newline at end of file diff --git a/section-07-ci-and-publishing/house-prices-api/typing_requirements.txt b/section-07-ci-and-publishing/house-prices-api/typing_requirements.txt index c75846478..1c4228e7e 100644 --- a/section-07-ci-and-publishing/house-prices-api/typing_requirements.txt +++ b/section-07-ci-and-publishing/house-prices-api/typing_requirements.txt @@ -1,6 +1,6 @@ -# repo maintenance tooling -black>=22.12.0,<23.0.0 -flake8>=6.0.0,<7.0.0 -mypy>=0.991,<1.0.0 -isort>=5.11.4,<6.0.0 +# repo maintenance tooling +black>=22.12.0,<23.0.0 +flake8>=6.0.0,<7.0.0 +mypy>=0.991,<1.0.0 +isort>=5.11.4,<6.0.0 pydantic>=1.10.4,<1.12.0 \ No newline at end of file diff --git a/section-07-ci-and-publishing/model-package/MANIFEST.in b/section-07-ci-and-publishing/model-package/MANIFEST.in index 507089640..ad7def636 100644 --- a/section-07-ci-and-publishing/model-package/MANIFEST.in +++ b/section-07-ci-and-publishing/model-package/MANIFEST.in @@ -1,18 +1,18 @@ -include *.txt -include *.md -include *.pkl -recursive-include ./regression_model/* - -include regression_model/datasets/train.csv -include regression_model/datasets/test.csv -include regression_model/trained_models/*.pkl -include regression_model/VERSION -include regression_model/config.yml - -include ./requirements/requirements.txt -include ./requirements/test_requirements.txt -exclude *.log -exclude *.cfg - -recursive-exclude * __pycache__ +include *.txt +include *.md +include *.pkl +recursive-include ./regression_model/* + +include regression_model/datasets/train.csv +include regression_model/datasets/test.csv +include regression_model/trained_models/*.pkl +include regression_model/VERSION +include regression_model/config.yml + +include ./requirements/requirements.txt +include ./requirements/test_requirements.txt +exclude *.log +exclude *.cfg + +recursive-exclude * __pycache__ recursive-exclude * *.py[co] \ No newline at end of file diff --git a/section-07-ci-and-publishing/model-package/mypy.ini b/section-07-ci-and-publishing/model-package/mypy.ini index 9f1b46b12..e499513b5 100644 --- a/section-07-ci-and-publishing/model-package/mypy.ini +++ b/section-07-ci-and-publishing/model-package/mypy.ini @@ -1,14 +1,14 @@ -[mypy] -# warn_unreachable = True -warn_unused_ignores = True -follow_imports = skip -show_error_context = True -warn_incomplete_stub = True -ignore_missing_imports = True -check_untyped_defs = True -cache_dir = /dev/null -# Cannot enable this one as we still allow defining functions without any types. -# disallow_untyped_defs = True -warn_redundant_casts = True -warn_unused_configs = True +[mypy] +# warn_unreachable = True +warn_unused_ignores = True +follow_imports = skip +show_error_context = True +warn_incomplete_stub = True +ignore_missing_imports = True +check_untyped_defs = True +cache_dir = /dev/null +# Cannot enable this one as we still allow defining functions without any types. +# disallow_untyped_defs = True +warn_redundant_casts = True +warn_unused_configs = True strict_optional = True \ No newline at end of file diff --git a/section-07-ci-and-publishing/model-package/publish_model.sh b/section-07-ci-and-publishing/model-package/publish_model.sh index 9a1cad78a..b479b1428 100755 --- a/section-07-ci-and-publishing/model-package/publish_model.sh +++ b/section-07-ci-and-publishing/model-package/publish_model.sh @@ -1,44 +1,44 @@ -#!/bin/bash - -# Building packages and uploading them to a Gemfury repository - -GEMFURY_URL=$GEMFURY_PUSH_URL - -set -e - -DIRS="$@" -BASE_DIR=$(pwd) -SETUP="setup.py" - -warn() { - echo "$@" 1>&2 -} - -die() { - warn "$@" - exit 1 -} - -build() { - DIR="${1/%\//}" - echo "Checking directory $DIR" - cd "$BASE_DIR/$DIR" - [ ! -e $SETUP ] && warn "No $SETUP file, skipping" && return - PACKAGE_NAME=$(python $SETUP --fullname) - echo "Package $PACKAGE_NAME" - python "$SETUP" sdist bdist_wheel || die "Building package $PACKAGE_NAME failed" - for X in $(ls dist) - do - curl -F package=@"dist/$X" "$GEMFURY_URL" || die "Uploading package $PACKAGE_NAME failed on file dist/$X" - done -} - -if [ -n "$DIRS" ]; then - for dir in $DIRS; do - build $dir - done -else - ls -d */ | while read dir; do - build $dir - done +#!/bin/bash + +# Building packages and uploading them to a Gemfury repository + +GEMFURY_URL=$GEMFURY_PUSH_URL + +set -e + +DIRS="$@" +BASE_DIR=$(pwd) +SETUP="setup.py" + +warn() { + echo "$@" 1>&2 +} + +die() { + warn "$@" + exit 1 +} + +build() { + DIR="${1/%\//}" + echo "Checking directory $DIR" + cd "$BASE_DIR/$DIR" + [ ! -e $SETUP ] && warn "No $SETUP file, skipping" && return + PACKAGE_NAME=$(python $SETUP --fullname) + echo "Package $PACKAGE_NAME" + python "$SETUP" sdist bdist_wheel || die "Building package $PACKAGE_NAME failed" + for X in $(ls dist) + do + curl -F package=@"dist/$X" "$GEMFURY_URL" || die "Uploading package $PACKAGE_NAME failed on file dist/$X" + done +} + +if [ -n "$DIRS" ]; then + for dir in $DIRS; do + build $dir + done +else + ls -d */ | while read dir; do + build $dir + done fi \ No newline at end of file diff --git a/section-07-ci-and-publishing/model-package/pyproject.toml b/section-07-ci-and-publishing/model-package/pyproject.toml index 31a46cadd..29227b4db 100644 --- a/section-07-ci-and-publishing/model-package/pyproject.toml +++ b/section-07-ci-and-publishing/model-package/pyproject.toml @@ -1,48 +1,48 @@ -[build-system] -requires = [ - "setuptools>=42", - "wheel" -] -build-backend = "setuptools.build_meta" - -[tool.pytest.ini_options] -minversion = "2.0" -addopts = "-rfEX -p pytester --strict-markers" -python_files = ["test_*.py", "*_test.py"] -python_classes = ["Test", "Acceptance"] -python_functions = ["test"] -# NOTE: "doc" is not included here, but gets tested explicitly via "doctesting". -testpaths = ["tests"] -xfail_strict = true -filterwarnings = [ - "error", - "default:Using or importing the ABCs:DeprecationWarning:unittest2.*", - # produced by older pyparsing<=2.2.0. - "default:Using or importing the ABCs:DeprecationWarning:pyparsing.*", - "default:the imp module is deprecated in favour of importlib:DeprecationWarning:nose.*", - # distutils is deprecated in 3.10, scheduled for removal in 3.12 - "ignore:The distutils package is deprecated:DeprecationWarning", - # produced by python3.6/site.py itself (3.6.7 on Travis, could not trigger it with 3.6.8)." - "ignore:.*U.*mode is deprecated:DeprecationWarning:(?!(pytest|_pytest))", - # produced by pytest-xdist - "ignore:.*type argument to addoption.*:DeprecationWarning", - # produced on execnet (pytest-xdist) - "ignore:.*inspect.getargspec.*deprecated, use inspect.signature.*:DeprecationWarning", - # pytest's own futurewarnings - "ignore::pytest.PytestExperimentalApiWarning", - # Do not cause SyntaxError for invalid escape sequences in py37. - # Those are caught/handled by pyupgrade, and not easy to filter with the - # module being the filename (with .py removed). - "default:invalid escape sequence:DeprecationWarning", - # ignore use of unregistered marks, because we use many to test the implementation - "ignore::_pytest.warning_types.PytestUnknownMarkWarning", -] - -[tool.black] -target-version = ['py311'] - -[tool.isort] -profile = "black" -line_length = 100 -lines_between_sections = 1 -skip = "migrations" +[build-system] +requires = [ + "setuptools>=42", + "wheel" +] +build-backend = "setuptools.build_meta" + +[tool.pytest.ini_options] +minversion = "2.0" +addopts = "-rfEX -p pytester --strict-markers" +python_files = ["test_*.py", "*_test.py"] +python_classes = ["Test", "Acceptance"] +python_functions = ["test"] +# NOTE: "doc" is not included here, but gets tested explicitly via "doctesting". +testpaths = ["tests"] +xfail_strict = true +filterwarnings = [ + "error", + "default:Using or importing the ABCs:DeprecationWarning:unittest2.*", + # produced by older pyparsing<=2.2.0. + "default:Using or importing the ABCs:DeprecationWarning:pyparsing.*", + "default:the imp module is deprecated in favour of importlib:DeprecationWarning:nose.*", + # distutils is deprecated in 3.10, scheduled for removal in 3.12 + "ignore:The distutils package is deprecated:DeprecationWarning", + # produced by python3.6/site.py itself (3.6.7 on Travis, could not trigger it with 3.6.8)." + "ignore:.*U.*mode is deprecated:DeprecationWarning:(?!(pytest|_pytest))", + # produced by pytest-xdist + "ignore:.*type argument to addoption.*:DeprecationWarning", + # produced on execnet (pytest-xdist) + "ignore:.*inspect.getargspec.*deprecated, use inspect.signature.*:DeprecationWarning", + # pytest's own futurewarnings + "ignore::pytest.PytestExperimentalApiWarning", + # Do not cause SyntaxError for invalid escape sequences in py37. + # Those are caught/handled by pyupgrade, and not easy to filter with the + # module being the filename (with .py removed). + "default:invalid escape sequence:DeprecationWarning", + # ignore use of unregistered marks, because we use many to test the implementation + "ignore::_pytest.warning_types.PytestUnknownMarkWarning", +] + +[tool.black] +target-version = ['py311'] + +[tool.isort] +profile = "black" +line_length = 100 +lines_between_sections = 1 +skip = "migrations" diff --git a/section-07-ci-and-publishing/model-package/regression_model/VERSION b/section-07-ci-and-publishing/model-package/regression_model/VERSION index 7636e7565..ed7511af9 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/VERSION +++ b/section-07-ci-and-publishing/model-package/regression_model/VERSION @@ -1 +1 @@ -4.0.5 +4.0.5 diff --git a/section-07-ci-and-publishing/model-package/regression_model/__init__.py b/section-07-ci-and-publishing/model-package/regression_model/__init__.py index 79c79a020..c4c8d0f55 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/__init__.py +++ b/section-07-ci-and-publishing/model-package/regression_model/__init__.py @@ -1,17 +1,17 @@ -import logging - -from regression_model.config.core import PACKAGE_ROOT, config - -# It is strongly advised that you do not add any handlers other than -# NullHandler to your library’s loggers. This is because the configuration -# of handlers is the prerogative of the application developer who uses your -# library. The application developer knows their target audience and what -# handlers are most appropriate for their application: if you add handlers -# ‘under the hood’, you might well interfere with their ability to carry out -# unit tests and deliver logs which suit their requirements. -# https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library -logging.getLogger(config.app_config.package_name).addHandler(logging.NullHandler()) - - -with open(PACKAGE_ROOT / "VERSION") as version_file: - __version__ = version_file.read().strip() +import logging + +from regression_model.config.core import PACKAGE_ROOT, config + +# It is strongly advised that you do not add any handlers other than +# NullHandler to your library’s loggers. This is because the configuration +# of handlers is the prerogative of the application developer who uses your +# library. The application developer knows their target audience and what +# handlers are most appropriate for their application: if you add handlers +# ‘under the hood’, you might well interfere with their ability to carry out +# unit tests and deliver logs which suit their requirements. +# https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library +logging.getLogger(config.app_config.package_name).addHandler(logging.NullHandler()) + + +with open(PACKAGE_ROOT / "VERSION") as version_file: + __version__ = version_file.read().strip() diff --git a/section-07-ci-and-publishing/model-package/regression_model/config.yml b/section-07-ci-and-publishing/model-package/regression_model/config.yml index 6715787a0..282ddb276 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/config.yml +++ b/section-07-ci-and-publishing/model-package/regression_model/config.yml @@ -1,162 +1,162 @@ -# Package Overview -package_name: regression_model - -# Data Files -training_data_file: train.csv -test_data_file: test.csv - -# Variables -# The variable we are attempting to predict (sale price) -target: SalePrice - -pipeline_name: regression_model -pipeline_save_file: regression_model_output_v - -# Will cause syntax errors since they begin with numbers -variables_to_rename: - 1stFlrSF: FirstFlrSF - 2ndFlrSF: SecondFlrSF - 3SsnPorch: ThreeSsnPortch - -features: - - MSSubClass - - MSZoning - - LotFrontage - - LotShape - - LandContour - - LotConfig - - Neighborhood - - OverallQual - - OverallCond - - YearRemodAdd - - RoofStyle - - Exterior1st - - ExterQual - - Foundation - - BsmtQual - - BsmtExposure - - BsmtFinType1 - - HeatingQC - - CentralAir - - FirstFlrSF # renamed - - SecondFlrSF # renamed - - GrLivArea - - BsmtFullBath - - HalfBath - - KitchenQual - - TotRmsAbvGrd - - Functional - - Fireplaces - - FireplaceQu - - GarageFinish - - GarageCars - - GarageArea - - PavedDrive - - WoodDeckSF - - ScreenPorch - - SaleCondition - # this one is only to calculate temporal variable: - - YrSold - -# set train/test split -test_size: 0.1 - -# to set the random seed -random_state: 0 - -alpha: 0.001 - -# categorical variables with NA in train set -categorical_vars_with_na_frequent: - - BsmtQual - - BsmtExposure - - BsmtFinType1 - - GarageFinish - -categorical_vars_with_na_missing: - - FireplaceQu - -numerical_vars_with_na: - - LotFrontage - -temporal_vars: - - YearRemodAdd - -ref_var: YrSold - - -# variables to log transform -numericals_log_vars: - - LotFrontage - - FirstFlrSF - - GrLivArea - -binarize_vars: - - ScreenPorch - -# variables to map -qual_vars: - - ExterQual - - BsmtQual - - HeatingQC - - KitchenQual - - FireplaceQu - -exposure_vars: - - BsmtExposure - -finish_vars: - - BsmtFinType1 - -garage_vars: - - GarageFinish - -categorical_vars: - - MSSubClass - - MSZoning - - LotShape - - LandContour - - LotConfig - - Neighborhood - - RoofStyle - - Exterior1st - - Foundation - - CentralAir - - Functional - - PavedDrive - - SaleCondition - -# variable mappings -qual_mappings: - Po: 1 - Fa: 2 - TA: 3 - Gd: 4 - Ex: 5 - Missing: 0 - NA: 0 - -exposure_mappings: - No: 1 - Mn: 2 - Av: 3 - Gd: 4 - - -finish_mappings: - Missing: 0 - NA: 0 - Unf: 1 - LwQ: 2 - Rec: 3 - BLQ: 4 - ALQ: 5 - GLQ: 6 - - -garage_mappings: - Missing: 0 - NA: 0 - Unf: 1 - RFn: 2 - Fin: 3 +# Package Overview +package_name: regression_model + +# Data Files +training_data_file: train.csv +test_data_file: test.csv + +# Variables +# The variable we are attempting to predict (sale price) +target: SalePrice + +pipeline_name: regression_model +pipeline_save_file: regression_model_output_v + +# Will cause syntax errors since they begin with numbers +variables_to_rename: + 1stFlrSF: FirstFlrSF + 2ndFlrSF: SecondFlrSF + 3SsnPorch: ThreeSsnPortch + +features: + - MSSubClass + - MSZoning + - LotFrontage + - LotShape + - LandContour + - LotConfig + - Neighborhood + - OverallQual + - OverallCond + - YearRemodAdd + - RoofStyle + - Exterior1st + - ExterQual + - Foundation + - BsmtQual + - BsmtExposure + - BsmtFinType1 + - HeatingQC + - CentralAir + - FirstFlrSF # renamed + - SecondFlrSF # renamed + - GrLivArea + - BsmtFullBath + - HalfBath + - KitchenQual + - TotRmsAbvGrd + - Functional + - Fireplaces + - FireplaceQu + - GarageFinish + - GarageCars + - GarageArea + - PavedDrive + - WoodDeckSF + - ScreenPorch + - SaleCondition + # this one is only to calculate temporal variable: + - YrSold + +# set train/test split +test_size: 0.1 + +# to set the random seed +random_state: 0 + +alpha: 0.001 + +# categorical variables with NA in train set +categorical_vars_with_na_frequent: + - BsmtQual + - BsmtExposure + - BsmtFinType1 + - GarageFinish + +categorical_vars_with_na_missing: + - FireplaceQu + +numerical_vars_with_na: + - LotFrontage + +temporal_vars: + - YearRemodAdd + +ref_var: YrSold + + +# variables to log transform +numericals_log_vars: + - LotFrontage + - FirstFlrSF + - GrLivArea + +binarize_vars: + - ScreenPorch + +# variables to map +qual_vars: + - ExterQual + - BsmtQual + - HeatingQC + - KitchenQual + - FireplaceQu + +exposure_vars: + - BsmtExposure + +finish_vars: + - BsmtFinType1 + +garage_vars: + - GarageFinish + +categorical_vars: + - MSSubClass + - MSZoning + - LotShape + - LandContour + - LotConfig + - Neighborhood + - RoofStyle + - Exterior1st + - Foundation + - CentralAir + - Functional + - PavedDrive + - SaleCondition + +# variable mappings +qual_mappings: + Po: 1 + Fa: 2 + TA: 3 + Gd: 4 + Ex: 5 + Missing: 0 + NA: 0 + +exposure_mappings: + No: 1 + Mn: 2 + Av: 3 + Gd: 4 + + +finish_mappings: + Missing: 0 + NA: 0 + Unf: 1 + LwQ: 2 + Rec: 3 + BLQ: 4 + ALQ: 5 + GLQ: 6 + + +garage_mappings: + Missing: 0 + NA: 0 + Unf: 1 + RFn: 2 + Fin: 3 diff --git a/section-07-ci-and-publishing/model-package/regression_model/config/core.py b/section-07-ci-and-publishing/model-package/regression_model/config/core.py index f5f354b19..898dace9b 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/config/core.py +++ b/section-07-ci-and-publishing/model-package/regression_model/config/core.py @@ -1,99 +1,99 @@ -from pathlib import Path -from typing import Dict, List, Sequence - -from pydantic import BaseModel -from strictyaml import YAML, load - -import regression_model - -# Project Directories -PACKAGE_ROOT = Path(regression_model.__file__).resolve().parent -ROOT = PACKAGE_ROOT.parent -CONFIG_FILE_PATH = PACKAGE_ROOT / "config.yml" -DATASET_DIR = PACKAGE_ROOT / "datasets" -TRAINED_MODEL_DIR = PACKAGE_ROOT / "trained_models" - - -class AppConfig(BaseModel): - """ - Application-level config. - """ - - package_name: str - training_data_file: str - test_data_file: str - pipeline_save_file: str - - -class ModelConfig(BaseModel): - """ - All configuration relevant to model - training and feature engineering. - """ - - target: str - variables_to_rename: Dict - features: List[str] - test_size: float - random_state: int - alpha: float - categorical_vars_with_na_frequent: List[str] - categorical_vars_with_na_missing: List[str] - numerical_vars_with_na: List[str] - temporal_vars: List[str] - ref_var: str - numericals_log_vars: Sequence[str] - binarize_vars: Sequence[str] - qual_vars: List[str] - exposure_vars: List[str] - finish_vars: List[str] - garage_vars: List[str] - categorical_vars: Sequence[str] - qual_mappings: Dict[str, int] - exposure_mappings: Dict[str, int] - garage_mappings: Dict[str, int] - finish_mappings: Dict[str, int] - - -class Config(BaseModel): - """Master config object.""" - - app_config: AppConfig - model_config: ModelConfig - - -def find_config_file() -> Path: - """Locate the configuration file.""" - if CONFIG_FILE_PATH.is_file(): - return CONFIG_FILE_PATH - raise Exception(f"Config not found at {CONFIG_FILE_PATH!r}") - - -def fetch_config_from_yaml(cfg_path: Path = None) -> YAML: - """Parse YAML containing the package configuration.""" - - if not cfg_path: - cfg_path = find_config_file() - - if cfg_path: - with open(cfg_path, "r") as conf_file: - parsed_config = load(conf_file.read()) - return parsed_config - raise OSError(f"Did not find config file at path: {cfg_path}") - - -def create_and_validate_config(parsed_config: YAML = None) -> Config: - """Run validation on config values.""" - if parsed_config is None: - parsed_config = fetch_config_from_yaml() - - # specify the data attribute from the strictyaml YAML type. - _config = Config( - app_config=AppConfig(**parsed_config.data), - model_config=ModelConfig(**parsed_config.data), - ) - - return _config - - -config = create_and_validate_config() +from pathlib import Path +from typing import Dict, List, Sequence + +from pydantic import BaseModel +from strictyaml import YAML, load + +import regression_model + +# Project Directories +PACKAGE_ROOT = Path(regression_model.__file__).resolve().parent +ROOT = PACKAGE_ROOT.parent +CONFIG_FILE_PATH = PACKAGE_ROOT / "config.yml" +DATASET_DIR = PACKAGE_ROOT / "datasets" +TRAINED_MODEL_DIR = PACKAGE_ROOT / "trained_models" + + +class AppConfig(BaseModel): + """ + Application-level config. + """ + + package_name: str + training_data_file: str + test_data_file: str + pipeline_save_file: str + + +class ModelConfig(BaseModel): + """ + All configuration relevant to model + training and feature engineering. + """ + + target: str + variables_to_rename: Dict + features: List[str] + test_size: float + random_state: int + alpha: float + categorical_vars_with_na_frequent: List[str] + categorical_vars_with_na_missing: List[str] + numerical_vars_with_na: List[str] + temporal_vars: List[str] + ref_var: str + numericals_log_vars: Sequence[str] + binarize_vars: Sequence[str] + qual_vars: List[str] + exposure_vars: List[str] + finish_vars: List[str] + garage_vars: List[str] + categorical_vars: Sequence[str] + qual_mappings: Dict[str, int] + exposure_mappings: Dict[str, int] + garage_mappings: Dict[str, int] + finish_mappings: Dict[str, int] + + +class Config(BaseModel): + """Master config object.""" + + app_config: AppConfig + model_config: ModelConfig + + +def find_config_file() -> Path: + """Locate the configuration file.""" + if CONFIG_FILE_PATH.is_file(): + return CONFIG_FILE_PATH + raise Exception(f"Config not found at {CONFIG_FILE_PATH!r}") + + +def fetch_config_from_yaml(cfg_path: Path = None) -> YAML: + """Parse YAML containing the package configuration.""" + + if not cfg_path: + cfg_path = find_config_file() + + if cfg_path: + with open(cfg_path, "r") as conf_file: + parsed_config = load(conf_file.read()) + return parsed_config + raise OSError(f"Did not find config file at path: {cfg_path}") + + +def create_and_validate_config(parsed_config: YAML = None) -> Config: + """Run validation on config values.""" + if parsed_config is None: + parsed_config = fetch_config_from_yaml() + + # specify the data attribute from the strictyaml YAML type. + _config = Config( + app_config=AppConfig(**parsed_config.data), + model_config=ModelConfig(**parsed_config.data), + ) + + return _config + + +config = create_and_validate_config() diff --git a/section-07-ci-and-publishing/model-package/regression_model/pipeline.py b/section-07-ci-and-publishing/model-package/regression_model/pipeline.py index e6153a6b8..55b8f4f05 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/pipeline.py +++ b/section-07-ci-and-publishing/model-package/regression_model/pipeline.py @@ -1,119 +1,119 @@ -from feature_engine.encoding import OrdinalEncoder, RareLabelEncoder -from feature_engine.imputation import ( - AddMissingIndicator, - CategoricalImputer, - MeanMedianImputer, -) -from feature_engine.selection import DropFeatures -from feature_engine.transformation import LogTransformer -from feature_engine.wrappers import SklearnTransformerWrapper -from sklearn.linear_model import Lasso -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import Binarizer, MinMaxScaler - -from regression_model.config.core import config -from regression_model.processing import features as pp - -price_pipe = Pipeline( - [ - # ===== IMPUTATION ===== - # impute categorical variables with string missing - ( - "missing_imputation", - CategoricalImputer( - imputation_method="missing", - variables=config.model_config.categorical_vars_with_na_missing, - ), - ), - ( - "frequent_imputation", - CategoricalImputer( - imputation_method="frequent", - variables=config.model_config.categorical_vars_with_na_frequent, - ), - ), - # add missing indicator - ( - "missing_indicator", - AddMissingIndicator(variables=config.model_config.numerical_vars_with_na), - ), - # impute numerical variables with the mean - ( - "mean_imputation", - MeanMedianImputer( - imputation_method="mean", - variables=config.model_config.numerical_vars_with_na, - ), - ), - # == TEMPORAL VARIABLES ==== - ( - "elapsed_time", - pp.TemporalVariableTransformer( - variables=config.model_config.temporal_vars, - reference_variable=config.model_config.ref_var, - ), - ), - ("drop_features", DropFeatures(features_to_drop=[config.model_config.ref_var])), - # ==== VARIABLE TRANSFORMATION ===== - ("log", LogTransformer(variables=config.model_config.numericals_log_vars)), - ( - "binarizer", - SklearnTransformerWrapper( - transformer=Binarizer(threshold=0), - variables=config.model_config.binarize_vars, - ), - ), - # === mappers === - ( - "mapper_qual", - pp.Mapper( - variables=config.model_config.qual_vars, - mappings=config.model_config.qual_mappings, - ), - ), - ( - "mapper_exposure", - pp.Mapper( - variables=config.model_config.exposure_vars, - mappings=config.model_config.exposure_mappings, - ), - ), - ( - "mapper_finish", - pp.Mapper( - variables=config.model_config.finish_vars, - mappings=config.model_config.finish_mappings, - ), - ), - ( - "mapper_garage", - pp.Mapper( - variables=config.model_config.garage_vars, - mappings=config.model_config.garage_mappings, - ), - ), - # == CATEGORICAL ENCODING - ( - "rare_label_encoder", - RareLabelEncoder( - tol=0.01, n_categories=1, variables=config.model_config.categorical_vars - ), - ), - # encode categorical variables using the target mean - ( - "categorical_encoder", - OrdinalEncoder( - encoding_method="ordered", - variables=config.model_config.categorical_vars, - ), - ), - ("scaler", MinMaxScaler()), - ( - "Lasso", - Lasso( - alpha=config.model_config.alpha, - random_state=config.model_config.random_state, - ), - ), - ] -) +from feature_engine.encoding import OrdinalEncoder, RareLabelEncoder +from feature_engine.imputation import ( + AddMissingIndicator, + CategoricalImputer, + MeanMedianImputer, +) +from feature_engine.selection import DropFeatures +from feature_engine.transformation import LogTransformer +from feature_engine.wrappers import SklearnTransformerWrapper +from sklearn.linear_model import Lasso +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import Binarizer, MinMaxScaler + +from regression_model.config.core import config +from regression_model.processing import features as pp + +price_pipe = Pipeline( + [ + # ===== IMPUTATION ===== + # impute categorical variables with string missing + ( + "missing_imputation", + CategoricalImputer( + imputation_method="missing", + variables=config.model_config.categorical_vars_with_na_missing, + ), + ), + ( + "frequent_imputation", + CategoricalImputer( + imputation_method="frequent", + variables=config.model_config.categorical_vars_with_na_frequent, + ), + ), + # add missing indicator + ( + "missing_indicator", + AddMissingIndicator(variables=config.model_config.numerical_vars_with_na), + ), + # impute numerical variables with the mean + ( + "mean_imputation", + MeanMedianImputer( + imputation_method="mean", + variables=config.model_config.numerical_vars_with_na, + ), + ), + # == TEMPORAL VARIABLES ==== + ( + "elapsed_time", + pp.TemporalVariableTransformer( + variables=config.model_config.temporal_vars, + reference_variable=config.model_config.ref_var, + ), + ), + ("drop_features", DropFeatures(features_to_drop=[config.model_config.ref_var])), + # ==== VARIABLE TRANSFORMATION ===== + ("log", LogTransformer(variables=config.model_config.numericals_log_vars)), + ( + "binarizer", + SklearnTransformerWrapper( + transformer=Binarizer(threshold=0), + variables=config.model_config.binarize_vars, + ), + ), + # === mappers === + ( + "mapper_qual", + pp.Mapper( + variables=config.model_config.qual_vars, + mappings=config.model_config.qual_mappings, + ), + ), + ( + "mapper_exposure", + pp.Mapper( + variables=config.model_config.exposure_vars, + mappings=config.model_config.exposure_mappings, + ), + ), + ( + "mapper_finish", + pp.Mapper( + variables=config.model_config.finish_vars, + mappings=config.model_config.finish_mappings, + ), + ), + ( + "mapper_garage", + pp.Mapper( + variables=config.model_config.garage_vars, + mappings=config.model_config.garage_mappings, + ), + ), + # == CATEGORICAL ENCODING + ( + "rare_label_encoder", + RareLabelEncoder( + tol=0.01, n_categories=1, variables=config.model_config.categorical_vars + ), + ), + # encode categorical variables using the target mean + ( + "categorical_encoder", + OrdinalEncoder( + encoding_method="ordered", + variables=config.model_config.categorical_vars, + ), + ), + ("scaler", MinMaxScaler()), + ( + "Lasso", + Lasso( + alpha=config.model_config.alpha, + random_state=config.model_config.random_state, + ), + ), + ] +) diff --git a/section-07-ci-and-publishing/model-package/regression_model/predict.py b/section-07-ci-and-publishing/model-package/regression_model/predict.py index d27739780..166d7761a 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/predict.py +++ b/section-07-ci-and-publishing/model-package/regression_model/predict.py @@ -1,35 +1,35 @@ -import typing as t - -import numpy as np -import pandas as pd - -from regression_model import __version__ as _version -from regression_model.config.core import config -from regression_model.processing.data_manager import load_pipeline -from regression_model.processing.validation import validate_inputs - -pipeline_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" -_price_pipe = load_pipeline(file_name=pipeline_file_name) - - -def make_prediction( - *, - input_data: t.Union[pd.DataFrame, dict], -) -> dict: - """Make a prediction using a saved model pipeline.""" - - data = pd.DataFrame(input_data) - validated_data, errors = validate_inputs(input_data=data) - results = {"predictions": None, "version": _version, "errors": errors} - - if not errors: - predictions = _price_pipe.predict( - X=validated_data[config.model_config.features] - ) - results = { - "predictions": [np.exp(pred) for pred in predictions], # type: ignore - "version": _version, - "errors": errors, - } - - return results +import typing as t + +import numpy as np +import pandas as pd + +from regression_model import __version__ as _version +from regression_model.config.core import config +from regression_model.processing.data_manager import load_pipeline +from regression_model.processing.validation import validate_inputs + +pipeline_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" +_price_pipe = load_pipeline(file_name=pipeline_file_name) + + +def make_prediction( + *, + input_data: t.Union[pd.DataFrame, dict], +) -> dict: + """Make a prediction using a saved model pipeline.""" + + data = pd.DataFrame(input_data) + validated_data, errors = validate_inputs(input_data=data) + results = {"predictions": None, "version": _version, "errors": errors} + + if not errors: + predictions = _price_pipe.predict( + X=validated_data[config.model_config.features] + ) + results = { + "predictions": [np.exp(pred) for pred in predictions], # type: ignore + "version": _version, + "errors": errors, + } + + return results diff --git a/section-07-ci-and-publishing/model-package/regression_model/processing/data_manager.py b/section-07-ci-and-publishing/model-package/regression_model/processing/data_manager.py index fa5a54942..bb696b086 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/processing/data_manager.py +++ b/section-07-ci-and-publishing/model-package/regression_model/processing/data_manager.py @@ -1,55 +1,55 @@ -import typing as t -from pathlib import Path - -import joblib -import pandas as pd -from sklearn.pipeline import Pipeline - -from regression_model import __version__ as _version -from regression_model.config.core import DATASET_DIR, TRAINED_MODEL_DIR, config - - -def load_dataset(*, file_name: str) -> pd.DataFrame: - dataframe = pd.read_csv(Path(f"{DATASET_DIR}/{file_name}")) - dataframe["MSSubClass"] = dataframe["MSSubClass"].astype("O") - - # rename variables beginning with numbers to avoid syntax errors later - transformed = dataframe.rename(columns=config.model_config.variables_to_rename) - return transformed - - -def save_pipeline(*, pipeline_to_persist: Pipeline) -> None: - """Persist the pipeline. - Saves the versioned model, and overwrites any previous - saved models. This ensures that when the package is - published, there is only one trained model that can be - called, and we know exactly how it was built. - """ - - # Prepare versioned save file name - save_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" - save_path = TRAINED_MODEL_DIR / save_file_name - - remove_old_pipelines(files_to_keep=[save_file_name]) - joblib.dump(pipeline_to_persist, save_path) - - -def load_pipeline(*, file_name: str) -> Pipeline: - """Load a persisted pipeline.""" - - file_path = TRAINED_MODEL_DIR / file_name - trained_model = joblib.load(filename=file_path) - return trained_model - - -def remove_old_pipelines(*, files_to_keep: t.List[str]) -> None: - """ - Remove old model pipelines. - This is to ensure there is a simple one-to-one - mapping between the package version and the model - version to be imported and used by other applications. - """ - do_not_delete = files_to_keep + ["__init__.py"] - for model_file in TRAINED_MODEL_DIR.iterdir(): - if model_file.name not in do_not_delete: - model_file.unlink() +import typing as t +from pathlib import Path + +import joblib +import pandas as pd +from sklearn.pipeline import Pipeline + +from regression_model import __version__ as _version +from regression_model.config.core import DATASET_DIR, TRAINED_MODEL_DIR, config + + +def load_dataset(*, file_name: str) -> pd.DataFrame: + dataframe = pd.read_csv(Path(f"{DATASET_DIR}/{file_name}")) + dataframe["MSSubClass"] = dataframe["MSSubClass"].astype("O") + + # rename variables beginning with numbers to avoid syntax errors later + transformed = dataframe.rename(columns=config.model_config.variables_to_rename) + return transformed + + +def save_pipeline(*, pipeline_to_persist: Pipeline) -> None: + """Persist the pipeline. + Saves the versioned model, and overwrites any previous + saved models. This ensures that when the package is + published, there is only one trained model that can be + called, and we know exactly how it was built. + """ + + # Prepare versioned save file name + save_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl" + save_path = TRAINED_MODEL_DIR / save_file_name + + remove_old_pipelines(files_to_keep=[save_file_name]) + joblib.dump(pipeline_to_persist, save_path) + + +def load_pipeline(*, file_name: str) -> Pipeline: + """Load a persisted pipeline.""" + + file_path = TRAINED_MODEL_DIR / file_name + trained_model = joblib.load(filename=file_path) + return trained_model + + +def remove_old_pipelines(*, files_to_keep: t.List[str]) -> None: + """ + Remove old model pipelines. + This is to ensure there is a simple one-to-one + mapping between the package version and the model + version to be imported and used by other applications. + """ + do_not_delete = files_to_keep + ["__init__.py"] + for model_file in TRAINED_MODEL_DIR.iterdir(): + if model_file.name not in do_not_delete: + model_file.unlink() diff --git a/section-07-ci-and-publishing/model-package/regression_model/processing/features.py b/section-07-ci-and-publishing/model-package/regression_model/processing/features.py index ae05559fb..7d6ba4eb2 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/processing/features.py +++ b/section-07-ci-and-publishing/model-package/regression_model/processing/features.py @@ -1,53 +1,53 @@ -from typing import List - -import pandas as pd -from sklearn.base import BaseEstimator, TransformerMixin - - -class TemporalVariableTransformer(BaseEstimator, TransformerMixin): - """Temporal elapsed time transformer.""" - - def __init__(self, variables: List[str], reference_variable: str): - - if not isinstance(variables, list): - raise ValueError("variables should be a list") - - self.variables = variables - self.reference_variable = reference_variable - - def fit(self, X: pd.DataFrame, y: pd.Series = None): - # we need this step to fit the sklearn pipeline - return self - - def transform(self, X: pd.DataFrame) -> pd.DataFrame: - - # so that we do not over-write the original dataframe - X = X.copy() - - for feature in self.variables: - X[feature] = X[self.reference_variable] - X[feature] - - return X - - -class Mapper(BaseEstimator, TransformerMixin): - """Categorical variable mapper.""" - - def __init__(self, variables: List[str], mappings: dict): - - if not isinstance(variables, list): - raise ValueError("variables should be a list") - - self.variables = variables - self.mappings = mappings - - def fit(self, X: pd.DataFrame, y: pd.Series = None): - # we need the fit statement to accomodate the sklearn pipeline - return self - - def transform(self, X: pd.DataFrame) -> pd.DataFrame: - X = X.copy() - for feature in self.variables: - X[feature] = X[feature].map(self.mappings) - - return X +from typing import List + +import pandas as pd +from sklearn.base import BaseEstimator, TransformerMixin + + +class TemporalVariableTransformer(BaseEstimator, TransformerMixin): + """Temporal elapsed time transformer.""" + + def __init__(self, variables: List[str], reference_variable: str): + + if not isinstance(variables, list): + raise ValueError("variables should be a list") + + self.variables = variables + self.reference_variable = reference_variable + + def fit(self, X: pd.DataFrame, y: pd.Series = None): + # we need this step to fit the sklearn pipeline + return self + + def transform(self, X: pd.DataFrame) -> pd.DataFrame: + + # so that we do not over-write the original dataframe + X = X.copy() + + for feature in self.variables: + X[feature] = X[self.reference_variable] - X[feature] + + return X + + +class Mapper(BaseEstimator, TransformerMixin): + """Categorical variable mapper.""" + + def __init__(self, variables: List[str], mappings: dict): + + if not isinstance(variables, list): + raise ValueError("variables should be a list") + + self.variables = variables + self.mappings = mappings + + def fit(self, X: pd.DataFrame, y: pd.Series = None): + # we need the fit statement to accomodate the sklearn pipeline + return self + + def transform(self, X: pd.DataFrame) -> pd.DataFrame: + X = X.copy() + for feature in self.variables: + X[feature] = X[feature].map(self.mappings) + + return X diff --git a/section-07-ci-and-publishing/model-package/regression_model/processing/validation.py b/section-07-ci-and-publishing/model-package/regression_model/processing/validation.py index 8e7ce56d0..79bf82fca 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/processing/validation.py +++ b/section-07-ci-and-publishing/model-package/regression_model/processing/validation.py @@ -1,132 +1,132 @@ -from typing import List, Optional, Tuple - -import numpy as np -import pandas as pd -from pydantic import BaseModel, ValidationError - -from regression_model.config.core import config - - -def drop_na_inputs(*, input_data: pd.DataFrame) -> pd.DataFrame: - """Check model inputs for na values and filter.""" - validated_data = input_data.copy() - new_vars_with_na = [ - var - for var in config.model_config.features - if var - not in config.model_config.categorical_vars_with_na_frequent - + config.model_config.categorical_vars_with_na_missing - + config.model_config.numerical_vars_with_na - and validated_data[var].isnull().sum() > 0 - ] - validated_data.dropna(subset=new_vars_with_na, inplace=True) - - return validated_data - - -def validate_inputs(*, input_data: pd.DataFrame) -> Tuple[pd.DataFrame, Optional[dict]]: - """Check model inputs for unprocessable values.""" - - # convert syntax error field names (beginning with numbers) - input_data.rename(columns=config.model_config.variables_to_rename, inplace=True) - input_data["MSSubClass"] = input_data["MSSubClass"].astype("O") - relevant_data = input_data[config.model_config.features].copy() - validated_data = drop_na_inputs(input_data=relevant_data) - errors = None - - try: - # replace numpy nans so that pydantic can validate - MultipleHouseDataInputs( - inputs=validated_data.replace({np.nan: None}).to_dict(orient="records") - ) - except ValidationError as error: - errors = error.json() - - return validated_data, errors - - -class HouseDataInputSchema(BaseModel): - Alley: Optional[str] - BedroomAbvGr: Optional[int] - BldgType: Optional[str] - BsmtCond: Optional[str] - BsmtExposure: Optional[str] - BsmtFinSF1: Optional[float] - BsmtFinSF2: Optional[float] - BsmtFinType1: Optional[str] - BsmtFinType2: Optional[str] - BsmtFullBath: Optional[float] - BsmtHalfBath: Optional[float] - BsmtQual: Optional[str] - BsmtUnfSF: Optional[float] - CentralAir: Optional[str] - Condition1: Optional[str] - Condition2: Optional[str] - Electrical: Optional[str] - EnclosedPorch: Optional[int] - ExterCond: Optional[str] - ExterQual: Optional[str] - Exterior1st: Optional[str] - Exterior2nd: Optional[str] - Fence: Optional[str] - FireplaceQu: Optional[str] - Fireplaces: Optional[int] - Foundation: Optional[str] - FullBath: Optional[int] - Functional: Optional[str] - GarageArea: Optional[float] - GarageCars: Optional[float] - GarageCond: Optional[str] - GarageFinish: Optional[str] - GarageQual: Optional[str] - GarageType: Optional[str] - GarageYrBlt: Optional[float] - GrLivArea: Optional[int] - HalfBath: Optional[int] - Heating: Optional[str] - HeatingQC: Optional[str] - HouseStyle: Optional[str] - Id: Optional[int] - KitchenAbvGr: Optional[int] - KitchenQual: Optional[str] - LandContour: Optional[str] - LandSlope: Optional[str] - LotArea: Optional[int] - LotConfig: Optional[str] - LotFrontage: Optional[float] - LotShape: Optional[str] - LowQualFinSF: Optional[int] - MSSubClass: Optional[int] - MSZoning: Optional[str] - MasVnrArea: Optional[float] - MasVnrType: Optional[str] - MiscFeature: Optional[str] - MiscVal: Optional[int] - MoSold: Optional[int] - Neighborhood: Optional[str] - OpenPorchSF: Optional[int] - OverallCond: Optional[int] - OverallQual: Optional[int] - PavedDrive: Optional[str] - PoolArea: Optional[int] - PoolQC: Optional[str] - RoofMatl: Optional[str] - RoofStyle: Optional[str] - SaleCondition: Optional[str] - SaleType: Optional[str] - ScreenPorch: Optional[int] - Street: Optional[str] - TotRmsAbvGrd: Optional[int] - TotalBsmtSF: Optional[float] - Utilities: Optional[str] - WoodDeckSF: Optional[int] - YearBuilt: Optional[int] - YearRemodAdd: Optional[int] - YrSold: Optional[int] - FirstFlrSF: Optional[int] # renamed - SecondFlrSF: Optional[int] # renamed - ThreeSsnPortch: Optional[int] # renamed - - -class MultipleHouseDataInputs(BaseModel): - inputs: List[HouseDataInputSchema] +from typing import List, Optional, Tuple + +import numpy as np +import pandas as pd +from pydantic import BaseModel, ValidationError + +from regression_model.config.core import config + + +def drop_na_inputs(*, input_data: pd.DataFrame) -> pd.DataFrame: + """Check model inputs for na values and filter.""" + validated_data = input_data.copy() + new_vars_with_na = [ + var + for var in config.model_config.features + if var + not in config.model_config.categorical_vars_with_na_frequent + + config.model_config.categorical_vars_with_na_missing + + config.model_config.numerical_vars_with_na + and validated_data[var].isnull().sum() > 0 + ] + validated_data.dropna(subset=new_vars_with_na, inplace=True) + + return validated_data + + +def validate_inputs(*, input_data: pd.DataFrame) -> Tuple[pd.DataFrame, Optional[dict]]: + """Check model inputs for unprocessable values.""" + + # convert syntax error field names (beginning with numbers) + input_data.rename(columns=config.model_config.variables_to_rename, inplace=True) + input_data["MSSubClass"] = input_data["MSSubClass"].astype("O") + relevant_data = input_data[config.model_config.features].copy() + validated_data = drop_na_inputs(input_data=relevant_data) + errors = None + + try: + # replace numpy nans so that pydantic can validate + MultipleHouseDataInputs( + inputs=validated_data.replace({np.nan: None}).to_dict(orient="records") + ) + except ValidationError as error: + errors = error.json() + + return validated_data, errors + + +class HouseDataInputSchema(BaseModel): + Alley: Optional[str] + BedroomAbvGr: Optional[int] + BldgType: Optional[str] + BsmtCond: Optional[str] + BsmtExposure: Optional[str] + BsmtFinSF1: Optional[float] + BsmtFinSF2: Optional[float] + BsmtFinType1: Optional[str] + BsmtFinType2: Optional[str] + BsmtFullBath: Optional[float] + BsmtHalfBath: Optional[float] + BsmtQual: Optional[str] + BsmtUnfSF: Optional[float] + CentralAir: Optional[str] + Condition1: Optional[str] + Condition2: Optional[str] + Electrical: Optional[str] + EnclosedPorch: Optional[int] + ExterCond: Optional[str] + ExterQual: Optional[str] + Exterior1st: Optional[str] + Exterior2nd: Optional[str] + Fence: Optional[str] + FireplaceQu: Optional[str] + Fireplaces: Optional[int] + Foundation: Optional[str] + FullBath: Optional[int] + Functional: Optional[str] + GarageArea: Optional[float] + GarageCars: Optional[float] + GarageCond: Optional[str] + GarageFinish: Optional[str] + GarageQual: Optional[str] + GarageType: Optional[str] + GarageYrBlt: Optional[float] + GrLivArea: Optional[int] + HalfBath: Optional[int] + Heating: Optional[str] + HeatingQC: Optional[str] + HouseStyle: Optional[str] + Id: Optional[int] + KitchenAbvGr: Optional[int] + KitchenQual: Optional[str] + LandContour: Optional[str] + LandSlope: Optional[str] + LotArea: Optional[int] + LotConfig: Optional[str] + LotFrontage: Optional[float] + LotShape: Optional[str] + LowQualFinSF: Optional[int] + MSSubClass: Optional[int] + MSZoning: Optional[str] + MasVnrArea: Optional[float] + MasVnrType: Optional[str] + MiscFeature: Optional[str] + MiscVal: Optional[int] + MoSold: Optional[int] + Neighborhood: Optional[str] + OpenPorchSF: Optional[int] + OverallCond: Optional[int] + OverallQual: Optional[int] + PavedDrive: Optional[str] + PoolArea: Optional[int] + PoolQC: Optional[str] + RoofMatl: Optional[str] + RoofStyle: Optional[str] + SaleCondition: Optional[str] + SaleType: Optional[str] + ScreenPorch: Optional[int] + Street: Optional[str] + TotRmsAbvGrd: Optional[int] + TotalBsmtSF: Optional[float] + Utilities: Optional[str] + WoodDeckSF: Optional[int] + YearBuilt: Optional[int] + YearRemodAdd: Optional[int] + YrSold: Optional[int] + FirstFlrSF: Optional[int] # renamed + SecondFlrSF: Optional[int] # renamed + ThreeSsnPortch: Optional[int] # renamed + + +class MultipleHouseDataInputs(BaseModel): + inputs: List[HouseDataInputSchema] diff --git a/section-07-ci-and-publishing/model-package/regression_model/train_pipeline.py b/section-07-ci-and-publishing/model-package/regression_model/train_pipeline.py index 95243a421..ecd58697d 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/train_pipeline.py +++ b/section-07-ci-and-publishing/model-package/regression_model/train_pipeline.py @@ -1,33 +1,33 @@ -import numpy as np -from config.core import config -from pipeline import price_pipe -from processing.data_manager import load_dataset, save_pipeline -from sklearn.model_selection import train_test_split - - -def run_training() -> None: - """Train the model.""" - - # read training data - data = load_dataset(file_name=config.app_config.training_data_file) - - # divide train and test - X_train, X_test, y_train, y_test = train_test_split( - data[config.model_config.features], # predictors - data[config.model_config.target], - test_size=config.model_config.test_size, - # we are setting the random seed here - # for reproducibility - random_state=config.model_config.random_state, - ) - y_train = np.log(y_train) - - # fit model - price_pipe.fit(X_train, y_train) - - # persist trained model - save_pipeline(pipeline_to_persist=price_pipe) - - -if __name__ == "__main__": - run_training() +import numpy as np +from config.core import config +from pipeline import price_pipe +from processing.data_manager import load_dataset, save_pipeline +from sklearn.model_selection import train_test_split + + +def run_training() -> None: + """Train the model.""" + + # read training data + data = load_dataset(file_name=config.app_config.training_data_file) + + # divide train and test + X_train, X_test, y_train, y_test = train_test_split( + data[config.model_config.features], # predictors + data[config.model_config.target], + test_size=config.model_config.test_size, + # we are setting the random seed here + # for reproducibility + random_state=config.model_config.random_state, + ) + y_train = np.log(y_train) + + # fit model + price_pipe.fit(X_train, y_train) + + # persist trained model + save_pipeline(pipeline_to_persist=price_pipe) + + +if __name__ == "__main__": + run_training() diff --git a/section-07-ci-and-publishing/model-package/requirements/requirements.txt b/section-07-ci-and-publishing/model-package/requirements/requirements.txt index 0fbffd3a6..eb9c31686 100644 --- a/section-07-ci-and-publishing/model-package/requirements/requirements.txt +++ b/section-07-ci-and-publishing/model-package/requirements/requirements.txt @@ -1,11 +1,11 @@ -# We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) -# to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small -# updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. -numpy>=1.21.0,<2.0.0 -pandas>=1.3.5,<2.0.0 -pydantic>=1.8.1,<2.0.0 -scikit-learn>=1.1.3,<2.0.0 -strictyaml>=1.3.2,<2.0.0 -ruamel.yaml>=0.16.12,<1.0.0 -feature-engine>=1.0.2,<1.6.0 # breaking change in v1.6.0 +# We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) +# to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small +# updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. +numpy>=1.21.0,<2.0.0 +pandas>=1.3.5,<2.0.0 +pydantic>=1.8.1,<2.0.0 +scikit-learn>=1.1.3,<2.0.0 +strictyaml>=1.3.2,<2.0.0 +ruamel.yaml>=0.16.12,<1.0.0 +feature-engine>=1.0.2,<1.6.0 # breaking change in v1.6.0 joblib>=1.0.1,<2.0.0 \ No newline at end of file diff --git a/section-07-ci-and-publishing/model-package/requirements/test_requirements.txt b/section-07-ci-and-publishing/model-package/requirements/test_requirements.txt index e69019391..b080f909c 100644 --- a/section-07-ci-and-publishing/model-package/requirements/test_requirements.txt +++ b/section-07-ci-and-publishing/model-package/requirements/test_requirements.txt @@ -1,4 +1,4 @@ --r requirements.txt - -# testing requirements -pytest>=7.2.0,<8.0.0 +-r requirements.txt + +# testing requirements +pytest>=7.2.0,<8.0.0 diff --git a/section-07-ci-and-publishing/model-package/requirements/typing_requirements.txt b/section-07-ci-and-publishing/model-package/requirements/typing_requirements.txt index 667cc2e4d..59619752c 100644 --- a/section-07-ci-and-publishing/model-package/requirements/typing_requirements.txt +++ b/section-07-ci-and-publishing/model-package/requirements/typing_requirements.txt @@ -1,5 +1,5 @@ -# repo maintenance tooling -black>=22.12.0,<23.0.0 -flake8>=6.0.0,<7.0.0 -mypy>=0.991,<1.0.0 +# repo maintenance tooling +black>=22.12.0,<23.0.0 +flake8>=6.0.0,<7.0.0 +mypy>=0.991,<1.0.0 isort>=5.11.4,<6.0.0 \ No newline at end of file diff --git a/section-07-ci-and-publishing/model-package/setup.py b/section-07-ci-and-publishing/model-package/setup.py index bf62537cd..9455c9d74 100644 --- a/section-07-ci-and-publishing/model-package/setup.py +++ b/section-07-ci-and-publishing/model-package/setup.py @@ -1,69 +1,69 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from pathlib import Path - -from setuptools import find_packages, setup - -# Package meta-data. -NAME = 'tid-regression-model' -DESCRIPTION = "Example regression model package from Train In Data." -URL = "https://github.com/trainindata/testing-and-monitoring-ml-deployments" -EMAIL = "christopher.samiullah@protonmail.com" -AUTHOR = "ChristopherGS" -REQUIRES_PYTHON = ">=3.7.0" - - -# The rest you shouldn't have to touch too much :) -# ------------------------------------------------ -# Except, perhaps the License and Trove Classifiers! -# If you do change the License, remember to change the -# Trove Classifier for that! -long_description = DESCRIPTION - -# Load the package's VERSION file as a dictionary. -about = {} -ROOT_DIR = Path(__file__).resolve().parent -REQUIREMENTS_DIR = ROOT_DIR / 'requirements' -PACKAGE_DIR = ROOT_DIR / 'regression_model' -with open(PACKAGE_DIR / "VERSION") as f: - _version = f.read().strip() - about["__version__"] = _version - - -# What packages are required for this module to be executed? -def list_reqs(fname="requirements.txt"): - with open(REQUIREMENTS_DIR / fname) as fd: - return fd.read().splitlines() - -# Where the magic happens: -setup( - name=NAME, - version=about["__version__"], - description=DESCRIPTION, - long_description=long_description, - long_description_content_type="text/markdown", - author=AUTHOR, - author_email=EMAIL, - python_requires=REQUIRES_PYTHON, - url=URL, - packages=find_packages(exclude=("tests",)), - package_data={"regression_model": ["VERSION"]}, - install_requires=list_reqs(), - extras_require={}, - include_package_data=True, - license="BSD-3", - classifiers=[ - # Trove classifiers - # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers - "License :: OSI Approved :: MIT License", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.6", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: Implementation :: CPython", - "Programming Language :: Python :: Implementation :: PyPy", - ], +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +from pathlib import Path + +from setuptools import find_packages, setup + +# Package meta-data. +NAME = 'tid-regression-model' +DESCRIPTION = "Example regression model package from Train In Data." +URL = "https://github.com/trainindata/testing-and-monitoring-ml-deployments" +EMAIL = "christopher.samiullah@protonmail.com" +AUTHOR = "ChristopherGS" +REQUIRES_PYTHON = ">=3.7.0" + + +# The rest you shouldn't have to touch too much :) +# ------------------------------------------------ +# Except, perhaps the License and Trove Classifiers! +# If you do change the License, remember to change the +# Trove Classifier for that! +long_description = DESCRIPTION + +# Load the package's VERSION file as a dictionary. +about = {} +ROOT_DIR = Path(__file__).resolve().parent +REQUIREMENTS_DIR = ROOT_DIR / 'requirements' +PACKAGE_DIR = ROOT_DIR / 'regression_model' +with open(PACKAGE_DIR / "VERSION") as f: + _version = f.read().strip() + about["__version__"] = _version + + +# What packages are required for this module to be executed? +def list_reqs(fname="requirements.txt"): + with open(REQUIREMENTS_DIR / fname) as fd: + return fd.read().splitlines() + +# Where the magic happens: +setup( + name=NAME, + version=about["__version__"], + description=DESCRIPTION, + long_description=long_description, + long_description_content_type="text/markdown", + author=AUTHOR, + author_email=EMAIL, + python_requires=REQUIRES_PYTHON, + url=URL, + packages=find_packages(exclude=("tests",)), + package_data={"regression_model": ["VERSION"]}, + install_requires=list_reqs(), + extras_require={}, + include_package_data=True, + license="BSD-3", + classifiers=[ + # Trove classifiers + # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers + "License :: OSI Approved :: MIT License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: Implementation :: PyPy", + ], ) \ No newline at end of file diff --git a/section-07-ci-and-publishing/model-package/tests/conftest.py b/section-07-ci-and-publishing/model-package/tests/conftest.py index a4014d5f4..db7332de2 100644 --- a/section-07-ci-and-publishing/model-package/tests/conftest.py +++ b/section-07-ci-and-publishing/model-package/tests/conftest.py @@ -1,9 +1,9 @@ -import pytest - -from regression_model.config.core import config -from regression_model.processing.data_manager import load_dataset - - -@pytest.fixture() -def sample_input_data(): - return load_dataset(file_name=config.app_config.test_data_file) +import pytest + +from regression_model.config.core import config +from regression_model.processing.data_manager import load_dataset + + +@pytest.fixture() +def sample_input_data(): + return load_dataset(file_name=config.app_config.test_data_file) diff --git a/section-07-ci-and-publishing/model-package/tests/test_features.py b/section-07-ci-and-publishing/model-package/tests/test_features.py index f3cd92832..6dd7ae2ea 100644 --- a/section-07-ci-and-publishing/model-package/tests/test_features.py +++ b/section-07-ci-and-publishing/model-package/tests/test_features.py @@ -1,17 +1,17 @@ -from regression_model.config.core import config -from regression_model.processing.features import TemporalVariableTransformer - - -def test_temporal_variable_transformer(sample_input_data): - # Given - transformer = TemporalVariableTransformer( - variables=config.model_config.temporal_vars, # YearRemodAdd - reference_variable=config.model_config.ref_var, - ) - assert sample_input_data["YearRemodAdd"].iat[0] == 1961 - - # When - subject = transformer.fit_transform(sample_input_data) - - # Then - assert subject["YearRemodAdd"].iat[0] == 49 +from regression_model.config.core import config +from regression_model.processing.features import TemporalVariableTransformer + + +def test_temporal_variable_transformer(sample_input_data): + # Given + transformer = TemporalVariableTransformer( + variables=config.model_config.temporal_vars, # YearRemodAdd + reference_variable=config.model_config.ref_var, + ) + assert sample_input_data["YearRemodAdd"].iat[0] == 1961 + + # When + subject = transformer.fit_transform(sample_input_data) + + # Then + assert subject["YearRemodAdd"].iat[0] == 49 diff --git a/section-07-ci-and-publishing/model-package/tests/test_prediction.py b/section-07-ci-and-publishing/model-package/tests/test_prediction.py index afbc508d0..d7321b9ed 100644 --- a/section-07-ci-and-publishing/model-package/tests/test_prediction.py +++ b/section-07-ci-and-publishing/model-package/tests/test_prediction.py @@ -1,22 +1,22 @@ -import math - -import numpy as np - -from regression_model.predict import make_prediction - - -def test_make_prediction(sample_input_data): - # Given - expected_first_prediction_value = 113422 - expected_no_predictions = 1449 - - # When - result = make_prediction(input_data=sample_input_data) - - # Then - predictions = result.get("predictions") - assert isinstance(predictions, list) - assert isinstance(predictions[0], np.float64) - assert result.get("errors") is None - assert len(predictions) == expected_no_predictions - assert math.isclose(predictions[0], expected_first_prediction_value, abs_tol=100) +import math + +import numpy as np + +from regression_model.predict import make_prediction + + +def test_make_prediction(sample_input_data): + # Given + expected_first_prediction_value = 113422 + expected_no_predictions = 1449 + + # When + result = make_prediction(input_data=sample_input_data) + + # Then + predictions = result.get("predictions") + assert isinstance(predictions, list) + assert isinstance(predictions[0], np.float64) + assert result.get("errors") is None + assert len(predictions) == expected_no_predictions + assert math.isclose(predictions[0], expected_first_prediction_value, abs_tol=100) diff --git a/section-07-ci-and-publishing/model-package/tox.ini b/section-07-ci-and-publishing/model-package/tox.ini index d93e00636..0cc85ed67 100644 --- a/section-07-ci-and-publishing/model-package/tox.ini +++ b/section-07-ci-and-publishing/model-package/tox.ini @@ -1,95 +1,95 @@ -# Tox is a generic virtualenv management and test command line tool. Its goal is to -# standardize testing in Python. We will be using it extensively in this course. - -# Using Tox we can (on multiple operating systems): -# + Eliminate PYTHONPATH challenges when running scripts/tests -# + Eliminate virtualenv setup confusion -# + Streamline steps such as model training, model publishing - - -[tox] -min_version = 4 -envlist = test_package, checks -skipsdist = True - -[testenv] -basepython = python -install_command = pip install {opts} {packages} -allowlist_externals = train,python - -passenv = - KAGGLE_USERNAME - KAGGLE_KEY - GEMFURY_PUSH_URL - -[testenv:test_package] -allowlist_externals = python -deps = - -rrequirements/test_requirements.txt - -setenv = - PYTHONPATH=. - PYTHONHASHSEED=0 - -commands= - python regression_model/train_pipeline.py - pytest \ - -s \ - -vv \ - {posargs:tests/} - -[testenv:train] -envdir = {toxworkdir}/test_package -deps = - {[testenv:test_package]deps} - -setenv = - {[testenv:test_package]setenv} - -commands= - python regression_model/train_pipeline.py - -[testenv:fetch_data] -envdir = {toxworkdir}/test_package -allowlist_externals = unzip -deps = - kaggle<1.6.0 - -setenv = - {[testenv:test_package]setenv} - -commands= - # fetch - kaggle competitions download -c house-prices-advanced-regression-techniques -p ./regression_model/datasets - # unzip - unzip ./regression_model/datasets/house-prices-advanced-regression-techniques.zip -d ./regression_model/datasets - - -[testenv:publish_model] -envdir = {toxworkdir}/test_package -allowlist_externals = * -deps = - {[testenv:test_package]deps} - -setenv = - {[testenv:test_package]setenv} - -commands= - python regression_model/train_pipeline.py - ./publish_model.sh . - - -[testenv:checks] -envdir = {toxworkdir}/checks -deps = - -r{toxinidir}/requirements/typing_requirements.txt -commands = - flake8 regression_model tests - isort regression_model tests - black regression_model tests - {posargs:mypy regression_model} - - -[flake8] -exclude = .git,env +# Tox is a generic virtualenv management and test command line tool. Its goal is to +# standardize testing in Python. We will be using it extensively in this course. + +# Using Tox we can (on multiple operating systems): +# + Eliminate PYTHONPATH challenges when running scripts/tests +# + Eliminate virtualenv setup confusion +# + Streamline steps such as model training, model publishing + + +[tox] +min_version = 4 +envlist = test_package, checks +skipsdist = True + +[testenv] +basepython = python +install_command = pip install {opts} {packages} +allowlist_externals = train,python + +passenv = + KAGGLE_USERNAME + KAGGLE_KEY + GEMFURY_PUSH_URL + +[testenv:test_package] +allowlist_externals = python +deps = + -rrequirements/test_requirements.txt + +setenv = + PYTHONPATH=. + PYTHONHASHSEED=0 + +commands= + python regression_model/train_pipeline.py + pytest \ + -s \ + -vv \ + {posargs:tests/} + +[testenv:train] +envdir = {toxworkdir}/test_package +deps = + {[testenv:test_package]deps} + +setenv = + {[testenv:test_package]setenv} + +commands= + python regression_model/train_pipeline.py + +[testenv:fetch_data] +envdir = {toxworkdir}/test_package +allowlist_externals = unzip +deps = + kaggle<1.6.0 + +setenv = + {[testenv:test_package]setenv} + +commands= + # fetch + kaggle competitions download -c house-prices-advanced-regression-techniques -p ./regression_model/datasets + # unzip + unzip ./regression_model/datasets/house-prices-advanced-regression-techniques.zip -d ./regression_model/datasets + + +[testenv:publish_model] +envdir = {toxworkdir}/test_package +allowlist_externals = * +deps = + {[testenv:test_package]deps} + +setenv = + {[testenv:test_package]setenv} + +commands= + python regression_model/train_pipeline.py + ./publish_model.sh . + + +[testenv:checks] +envdir = {toxworkdir}/checks +deps = + -r{toxinidir}/requirements/typing_requirements.txt +commands = + flake8 regression_model tests + isort regression_model tests + black regression_model tests + {posargs:mypy regression_model} + + +[flake8] +exclude = .git,env max-line-length = 90 \ No newline at end of file diff --git a/section-08-deploying-with-containers/.dockerignore b/section-08-deploying-with-containers/.dockerignore index 632f30de5..a1b13a97d 100644 --- a/section-08-deploying-with-containers/.dockerignore +++ b/section-08-deploying-with-containers/.dockerignore @@ -1,10 +1,10 @@ -jupyter_notebooks* -*/env* -*/venv* -.circleci* -packages/regression_model -*.env -*.log -.git -.gitignore +jupyter_notebooks* +*/env* +*/venv* +.circleci* +packages/regression_model +*.env +*.log +.git +.gitignore .tox \ No newline at end of file diff --git a/section-08-deploying-with-containers/Dockerfile b/section-08-deploying-with-containers/Dockerfile index bde85149f..78ad64c5d 100644 --- a/section-08-deploying-with-containers/Dockerfile +++ b/section-08-deploying-with-containers/Dockerfile @@ -1,22 +1,22 @@ -FROM python:3.11 - -# Create the user that will run the app -RUN adduser --disabled-password --gecos '' ml-api-user - -WORKDIR /opt/house-prices-api - -ARG PIP_EXTRA_INDEX_URL - -# Install requirements, including from Gemfury -ADD ./house-prices-api /opt/house-prices-api/ -RUN pip install --upgrade pip -RUN pip install -r /opt/house-prices-api/requirements.txt - -RUN chmod +x /opt/house-prices-api/run.sh -RUN chown -R ml-api-user:ml-api-user ./ - -USER ml-api-user - -EXPOSE 8001 - -CMD ["bash", "./run.sh"] +FROM python:3.11 + +# Create the user that will run the app +RUN adduser --disabled-password --gecos '' ml-api-user + +WORKDIR /opt/house-prices-api + +ARG PIP_EXTRA_INDEX_URL + +# Install requirements, including from Gemfury +ADD ./house-prices-api /opt/house-prices-api/ +RUN pip install --upgrade pip +RUN pip install -r /opt/house-prices-api/requirements.txt + +RUN chmod +x /opt/house-prices-api/run.sh +RUN chown -R ml-api-user:ml-api-user ./ + +USER ml-api-user + +EXPOSE 8001 + +CMD ["bash", "./run.sh"] diff --git a/section-08-deploying-with-containers/house-prices-api/app/__init__.py b/section-08-deploying-with-containers/house-prices-api/app/__init__.py index 3b93d0be0..b5ca99eb0 100644 --- a/section-08-deploying-with-containers/house-prices-api/app/__init__.py +++ b/section-08-deploying-with-containers/house-prices-api/app/__init__.py @@ -1 +1 @@ -__version__ = "0.0.2" +__version__ = "0.0.2" diff --git a/section-08-deploying-with-containers/house-prices-api/app/api.py b/section-08-deploying-with-containers/house-prices-api/app/api.py index de9559faf..1bdd9c0fc 100644 --- a/section-08-deploying-with-containers/house-prices-api/app/api.py +++ b/section-08-deploying-with-containers/house-prices-api/app/api.py @@ -1,49 +1,49 @@ -import json -from typing import Any - -import numpy as np -import pandas as pd -from fastapi import APIRouter, HTTPException -from fastapi.encoders import jsonable_encoder -from loguru import logger -from regression_model import __version__ as model_version -from regression_model.predict import make_prediction - -from app import __version__, schemas -from app.config import settings - -api_router = APIRouter() - - -@api_router.get("/health", response_model=schemas.Health, status_code=200) -def health() -> dict: - """ - Root Get - """ - health = schemas.Health( - name=settings.PROJECT_NAME, api_version=__version__, model_version=model_version - ) - - return health.dict() - - -@api_router.post("/predict", response_model=schemas.PredictionResults, status_code=200) -async def predict(input_data: schemas.MultipleHouseDataInputs) -> Any: - """ - Make house price predictions with the TID regression model - """ - - input_df = pd.DataFrame(jsonable_encoder(input_data.inputs)) - - # Advanced: You can improve performance of your API by rewriting the - # `make prediction` function to be async and using await here. - logger.info(f"Making prediction on inputs: {input_data.inputs}") - results = make_prediction(input_data=input_df.replace({np.nan: None})) - - if results["errors"] is not None: - logger.warning(f"Prediction validation error: {results.get('errors')}") - raise HTTPException(status_code=400, detail=json.loads(results["errors"])) - - logger.info(f"Prediction results: {results.get('predictions')}") - - return results +import json +from typing import Any + +import numpy as np +import pandas as pd +from fastapi import APIRouter, HTTPException +from fastapi.encoders import jsonable_encoder +from loguru import logger +from regression_model import __version__ as model_version +from regression_model.predict import make_prediction + +from app import __version__, schemas +from app.config import settings + +api_router = APIRouter() + + +@api_router.get("/health", response_model=schemas.Health, status_code=200) +def health() -> dict: + """ + Root Get + """ + health = schemas.Health( + name=settings.PROJECT_NAME, api_version=__version__, model_version=model_version + ) + + return health.dict() + + +@api_router.post("/predict", response_model=schemas.PredictionResults, status_code=200) +async def predict(input_data: schemas.MultipleHouseDataInputs) -> Any: + """ + Make house price predictions with the TID regression model + """ + + input_df = pd.DataFrame(jsonable_encoder(input_data.inputs)) + + # Advanced: You can improve performance of your API by rewriting the + # `make prediction` function to be async and using await here. + logger.info(f"Making prediction on inputs: {input_data.inputs}") + results = make_prediction(input_data=input_df.replace({np.nan: None})) + + if results["errors"] is not None: + logger.warning(f"Prediction validation error: {results.get('errors')}") + raise HTTPException(status_code=400, detail=json.loads(results["errors"])) + + logger.info(f"Prediction results: {results.get('predictions')}") + + return results diff --git a/section-08-deploying-with-containers/house-prices-api/app/config.py b/section-08-deploying-with-containers/house-prices-api/app/config.py index 9dc62e5fb..7233dcfc1 100644 --- a/section-08-deploying-with-containers/house-prices-api/app/config.py +++ b/section-08-deploying-with-containers/house-prices-api/app/config.py @@ -1,70 +1,70 @@ -import logging -import sys -from types import FrameType -from typing import List, cast - -from loguru import logger -from pydantic import AnyHttpUrl, BaseSettings - - -class LoggingSettings(BaseSettings): - LOGGING_LEVEL: int = logging.INFO # logging levels are type int - - -class Settings(BaseSettings): - API_V1_STR: str = "/api/v1" - - # Meta - logging: LoggingSettings = LoggingSettings() - - # BACKEND_CORS_ORIGINS is a comma-separated list of origins - # e.g: http://localhost,http://localhost:4200,http://localhost:3000 - BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [ - "http://localhost:3000", # type: ignore - "http://localhost:8000", # type: ignore - "https://localhost:3000", # type: ignore - "https://localhost:8000", # type: ignore - ] - - PROJECT_NAME: str = "House Price Prediction API" - - class Config: - case_sensitive = True - - -# See: https://loguru.readthedocs.io/en/stable/overview.html#entirely-compatible-with-standard-logging # noqa -class InterceptHandler(logging.Handler): - def emit(self, record: logging.LogRecord) -> None: # pragma: no cover - # Get corresponding Loguru level if it exists - try: - level = logger.level(record.levelname).name - except ValueError: - level = str(record.levelno) - - # Find caller from where originated the logged message - frame, depth = logging.currentframe(), 2 - while frame.f_code.co_filename == logging.__file__: # noqa: WPS609 - frame = cast(FrameType, frame.f_back) - depth += 1 - - logger.opt(depth=depth, exception=record.exc_info).log( - level, - record.getMessage(), - ) - - -def setup_app_logging(config: Settings) -> None: - """Prepare custom logging for our application.""" - - LOGGERS = ("uvicorn.asgi", "uvicorn.access") - logging.getLogger().handlers = [InterceptHandler()] - for logger_name in LOGGERS: - logging_logger = logging.getLogger(logger_name) - logging_logger.handlers = [InterceptHandler(level=config.logging.LOGGING_LEVEL)] - - logger.configure( - handlers=[{"sink": sys.stderr, "level": config.logging.LOGGING_LEVEL}] - ) - - -settings = Settings() +import logging +import sys +from types import FrameType +from typing import List, cast + +from loguru import logger +from pydantic import AnyHttpUrl, BaseSettings + + +class LoggingSettings(BaseSettings): + LOGGING_LEVEL: int = logging.INFO # logging levels are type int + + +class Settings(BaseSettings): + API_V1_STR: str = "/api/v1" + + # Meta + logging: LoggingSettings = LoggingSettings() + + # BACKEND_CORS_ORIGINS is a comma-separated list of origins + # e.g: http://localhost,http://localhost:4200,http://localhost:3000 + BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [ + "http://localhost:3000", # type: ignore + "http://localhost:8000", # type: ignore + "https://localhost:3000", # type: ignore + "https://localhost:8000", # type: ignore + ] + + PROJECT_NAME: str = "House Price Prediction API" + + class Config: + case_sensitive = True + + +# See: https://loguru.readthedocs.io/en/stable/overview.html#entirely-compatible-with-standard-logging # noqa +class InterceptHandler(logging.Handler): + def emit(self, record: logging.LogRecord) -> None: # pragma: no cover + # Get corresponding Loguru level if it exists + try: + level = logger.level(record.levelname).name + except ValueError: + level = str(record.levelno) + + # Find caller from where originated the logged message + frame, depth = logging.currentframe(), 2 + while frame.f_code.co_filename == logging.__file__: # noqa: WPS609 + frame = cast(FrameType, frame.f_back) + depth += 1 + + logger.opt(depth=depth, exception=record.exc_info).log( + level, + record.getMessage(), + ) + + +def setup_app_logging(config: Settings) -> None: + """Prepare custom logging for our application.""" + + LOGGERS = ("uvicorn.asgi", "uvicorn.access") + logging.getLogger().handlers = [InterceptHandler()] + for logger_name in LOGGERS: + logging_logger = logging.getLogger(logger_name) + logging_logger.handlers = [InterceptHandler(level=config.logging.LOGGING_LEVEL)] + + logger.configure( + handlers=[{"sink": sys.stderr, "level": config.logging.LOGGING_LEVEL}] + ) + + +settings = Settings() diff --git a/section-08-deploying-with-containers/house-prices-api/app/main.py b/section-08-deploying-with-containers/house-prices-api/app/main.py index b55d34402..902eb649f 100644 --- a/section-08-deploying-with-containers/house-prices-api/app/main.py +++ b/section-08-deploying-with-containers/house-prices-api/app/main.py @@ -1,58 +1,58 @@ -from typing import Any - -from fastapi import APIRouter, FastAPI, Request -from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import HTMLResponse -from loguru import logger - -from app.api import api_router -from app.config import settings, setup_app_logging - -# setup logging as early as possible -setup_app_logging(config=settings) - - -app = FastAPI( - title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json" -) - -root_router = APIRouter() - - -@root_router.get("/") -def index(request: Request) -> Any: - """Basic HTML response.""" - body = ( - "" - "" - "

Welcome to the API

" - "
" - "Check the docs: here" - "
" - "" - "" - ) - - return HTMLResponse(content=body) - - -app.include_router(api_router, prefix=settings.API_V1_STR) -app.include_router(root_router) - -# Set all CORS enabled origins -if settings.BACKEND_CORS_ORIGINS: - app.add_middleware( - CORSMiddleware, - allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - ) - - -if __name__ == "__main__": - # Use this for debugging purposes only - logger.warning("Running in development mode. Do not run like this in production.") - import uvicorn - - uvicorn.run(app, host="localhost", port=8001, log_level="debug") +from typing import Any + +from fastapi import APIRouter, FastAPI, Request +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import HTMLResponse +from loguru import logger + +from app.api import api_router +from app.config import settings, setup_app_logging + +# setup logging as early as possible +setup_app_logging(config=settings) + + +app = FastAPI( + title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json" +) + +root_router = APIRouter() + + +@root_router.get("/") +def index(request: Request) -> Any: + """Basic HTML response.""" + body = ( + "" + "" + "

Welcome to the API

" + "
" + "Check the docs: here" + "
" + "" + "" + ) + + return HTMLResponse(content=body) + + +app.include_router(api_router, prefix=settings.API_V1_STR) +app.include_router(root_router) + +# Set all CORS enabled origins +if settings.BACKEND_CORS_ORIGINS: + app.add_middleware( + CORSMiddleware, + allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + +if __name__ == "__main__": + # Use this for debugging purposes only + logger.warning("Running in development mode. Do not run like this in production.") + import uvicorn + + uvicorn.run(app, host="localhost", port=8001, log_level="debug") diff --git a/section-08-deploying-with-containers/house-prices-api/app/schemas/__init__.py b/section-08-deploying-with-containers/house-prices-api/app/schemas/__init__.py index f0e08e102..fac77b131 100644 --- a/section-08-deploying-with-containers/house-prices-api/app/schemas/__init__.py +++ b/section-08-deploying-with-containers/house-prices-api/app/schemas/__init__.py @@ -1,2 +1,2 @@ -from .health import Health -from .predict import MultipleHouseDataInputs, PredictionResults +from .health import Health +from .predict import MultipleHouseDataInputs, PredictionResults diff --git a/section-08-deploying-with-containers/house-prices-api/app/schemas/health.py b/section-08-deploying-with-containers/house-prices-api/app/schemas/health.py index bede1e8a5..b7f801c6c 100644 --- a/section-08-deploying-with-containers/house-prices-api/app/schemas/health.py +++ b/section-08-deploying-with-containers/house-prices-api/app/schemas/health.py @@ -1,7 +1,7 @@ -from pydantic import BaseModel - - -class Health(BaseModel): - name: str - api_version: str - model_version: str +from pydantic import BaseModel + + +class Health(BaseModel): + name: str + api_version: str + model_version: str diff --git a/section-08-deploying-with-containers/house-prices-api/app/schemas/predict.py b/section-08-deploying-with-containers/house-prices-api/app/schemas/predict.py index e3b668312..42241ac39 100644 --- a/section-08-deploying-with-containers/house-prices-api/app/schemas/predict.py +++ b/section-08-deploying-with-containers/house-prices-api/app/schemas/predict.py @@ -1,103 +1,103 @@ -from typing import Any, List, Optional - -from pydantic import BaseModel -from regression_model.processing.validation import HouseDataInputSchema - - -class PredictionResults(BaseModel): - errors: Optional[Any] - version: str - predictions: Optional[List[float]] - - -class MultipleHouseDataInputs(BaseModel): - inputs: List[HouseDataInputSchema] - - class Config: - schema_extra = { - "example": { - "inputs": [ - { - "MSSubClass": 20, - "MSZoning": "RH", - "LotFrontage": 80.0, - "LotArea": 11622, - "Street": "Pave", - "Alley": None, - "LotShape": "Reg", - "LandContour": "Lvl", - "Utilities": "AllPub", - "LotConfig": "Inside", - "LandSlope": "Gtl", - "Neighborhood": "NAmes", - "Condition1": "Feedr", - "Condition2": "Norm", - "BldgType": "1Fam", - "HouseStyle": "1Story", - "OverallQual": 5, - "OverallCond": 6, - "YearBuilt": 1961, - "YearRemodAdd": 1961, - "RoofStyle": "Gable", - "RoofMatl": "CompShg", - "Exterior1st": "VinylSd", - "Exterior2nd": "VinylSd", - "MasVnrType": "None", - "MasVnrArea": 0.0, - "ExterQual": "TA", - "ExterCond": "TA", - "Foundation": "CBlock", - "BsmtQual": "TA", - "BsmtCond": "TA", - "BsmtExposure": "No", - "BsmtFinType1": "Rec", - "BsmtFinSF1": 468.0, - "BsmtFinType2": "LwQ", - "BsmtFinSF2": 144.0, - "BsmtUnfSF": 270.0, - "TotalBsmtSF": 882.0, - "Heating": "GasA", - "HeatingQC": "TA", - "CentralAir": "Y", - "Electrical": "SBrkr", - "FirstFlrSF": 896, - "SecondFlrSF": 0, - "LowQualFinSF": 0, - "GrLivArea": 896, - "BsmtFullBath": 0.0, - "BsmtHalfBath": 0.0, - "FullBath": 1, - "HalfBath": 0, - "BedroomAbvGr": 2, - "KitchenAbvGr": 1, - "KitchenQual": "TA", - "TotRmsAbvGrd": 5, - "Functional": "Typ", - "Fireplaces": 0, - "FireplaceQu": None, - "GarageType": "Attchd", - "GarageYrBlt": 1961.0, - "GarageFinish": "Unf", - "GarageCars": 1.0, - "GarageArea": 730.0, - "GarageQual": "TA", - "GarageCond": "TA", - "PavedDrive": "Y", - "WoodDeckSF": 140, - "OpenPorchSF": 0, - "EnclosedPorch": 0, - "ThreeSsnPortch": 0, - "ScreenPorch": 120, - "PoolArea": 0, - "PoolQC": None, - "Fence": "MnPrv", - "MiscFeature": None, - "MiscVal": 0, - "MoSold": 6, - "YrSold": 2010, - "SaleType": "WD", - "SaleCondition": "Normal", - } - ] - } - } +from typing import Any, List, Optional + +from pydantic import BaseModel +from regression_model.processing.validation import HouseDataInputSchema + + +class PredictionResults(BaseModel): + errors: Optional[Any] + version: str + predictions: Optional[List[float]] + + +class MultipleHouseDataInputs(BaseModel): + inputs: List[HouseDataInputSchema] + + class Config: + schema_extra = { + "example": { + "inputs": [ + { + "MSSubClass": 20, + "MSZoning": "RH", + "LotFrontage": 80.0, + "LotArea": 11622, + "Street": "Pave", + "Alley": None, + "LotShape": "Reg", + "LandContour": "Lvl", + "Utilities": "AllPub", + "LotConfig": "Inside", + "LandSlope": "Gtl", + "Neighborhood": "NAmes", + "Condition1": "Feedr", + "Condition2": "Norm", + "BldgType": "1Fam", + "HouseStyle": "1Story", + "OverallQual": 5, + "OverallCond": 6, + "YearBuilt": 1961, + "YearRemodAdd": 1961, + "RoofStyle": "Gable", + "RoofMatl": "CompShg", + "Exterior1st": "VinylSd", + "Exterior2nd": "VinylSd", + "MasVnrType": "None", + "MasVnrArea": 0.0, + "ExterQual": "TA", + "ExterCond": "TA", + "Foundation": "CBlock", + "BsmtQual": "TA", + "BsmtCond": "TA", + "BsmtExposure": "No", + "BsmtFinType1": "Rec", + "BsmtFinSF1": 468.0, + "BsmtFinType2": "LwQ", + "BsmtFinSF2": 144.0, + "BsmtUnfSF": 270.0, + "TotalBsmtSF": 882.0, + "Heating": "GasA", + "HeatingQC": "TA", + "CentralAir": "Y", + "Electrical": "SBrkr", + "FirstFlrSF": 896, + "SecondFlrSF": 0, + "LowQualFinSF": 0, + "GrLivArea": 896, + "BsmtFullBath": 0.0, + "BsmtHalfBath": 0.0, + "FullBath": 1, + "HalfBath": 0, + "BedroomAbvGr": 2, + "KitchenAbvGr": 1, + "KitchenQual": "TA", + "TotRmsAbvGrd": 5, + "Functional": "Typ", + "Fireplaces": 0, + "FireplaceQu": None, + "GarageType": "Attchd", + "GarageYrBlt": 1961.0, + "GarageFinish": "Unf", + "GarageCars": 1.0, + "GarageArea": 730.0, + "GarageQual": "TA", + "GarageCond": "TA", + "PavedDrive": "Y", + "WoodDeckSF": 140, + "OpenPorchSF": 0, + "EnclosedPorch": 0, + "ThreeSsnPortch": 0, + "ScreenPorch": 120, + "PoolArea": 0, + "PoolQC": None, + "Fence": "MnPrv", + "MiscFeature": None, + "MiscVal": 0, + "MoSold": 6, + "YrSold": 2010, + "SaleType": "WD", + "SaleCondition": "Normal", + } + ] + } + } diff --git a/section-08-deploying-with-containers/house-prices-api/app/tests/conftest.py b/section-08-deploying-with-containers/house-prices-api/app/tests/conftest.py index b87469ec0..1e7d8f0f8 100644 --- a/section-08-deploying-with-containers/house-prices-api/app/tests/conftest.py +++ b/section-08-deploying-with-containers/house-prices-api/app/tests/conftest.py @@ -1,21 +1,21 @@ -from typing import Generator - -import pandas as pd -import pytest -from fastapi.testclient import TestClient -from regression_model.config.core import config -from regression_model.processing.data_manager import load_dataset - -from app.main import app - - -@pytest.fixture(scope="module") -def test_data() -> pd.DataFrame: - return load_dataset(file_name=config.app_config.test_data_file) - - -@pytest.fixture() -def client() -> Generator: - with TestClient(app) as _client: - yield _client - app.dependency_overrides = {} +from typing import Generator + +import pandas as pd +import pytest +from fastapi.testclient import TestClient +from regression_model.config.core import config +from regression_model.processing.data_manager import load_dataset + +from app.main import app + + +@pytest.fixture(scope="module") +def test_data() -> pd.DataFrame: + return load_dataset(file_name=config.app_config.test_data_file) + + +@pytest.fixture() +def client() -> Generator: + with TestClient(app) as _client: + yield _client + app.dependency_overrides = {} diff --git a/section-08-deploying-with-containers/house-prices-api/app/tests/test_api.py b/section-08-deploying-with-containers/house-prices-api/app/tests/test_api.py index 833fcadb5..21be33f2a 100644 --- a/section-08-deploying-with-containers/house-prices-api/app/tests/test_api.py +++ b/section-08-deploying-with-containers/house-prices-api/app/tests/test_api.py @@ -1,26 +1,26 @@ -import math - -import numpy as np -import pandas as pd -from fastapi.testclient import TestClient - - -def test_make_prediction(client: TestClient, test_data: pd.DataFrame) -> None: - # Given - payload = { - # ensure pydantic plays well with np.nan - "inputs": test_data.replace({np.nan: None}).to_dict(orient="records") - } - - # When - response = client.post( - "http://localhost:8001/api/v1/predict", - json=payload, - ) - - # Then - assert response.status_code == 200 - prediction_data = response.json() - assert prediction_data["predictions"] - assert prediction_data["errors"] is None - assert math.isclose(prediction_data["predictions"][0], 113422, rel_tol=100) +import math + +import numpy as np +import pandas as pd +from fastapi.testclient import TestClient + + +def test_make_prediction(client: TestClient, test_data: pd.DataFrame) -> None: + # Given + payload = { + # ensure pydantic plays well with np.nan + "inputs": test_data.replace({np.nan: None}).to_dict(orient="records") + } + + # When + response = client.post( + "http://localhost:8001/api/v1/predict", + json=payload, + ) + + # Then + assert response.status_code == 200 + prediction_data = response.json() + assert prediction_data["predictions"] + assert prediction_data["errors"] is None + assert math.isclose(prediction_data["predictions"][0], 113422, rel_tol=100) diff --git a/section-08-deploying-with-containers/house-prices-api/mypy.ini b/section-08-deploying-with-containers/house-prices-api/mypy.ini index 19273b9c1..84250acaf 100644 --- a/section-08-deploying-with-containers/house-prices-api/mypy.ini +++ b/section-08-deploying-with-containers/house-prices-api/mypy.ini @@ -1,4 +1,4 @@ -[mypy] -plugins = pydantic.mypy -ignore_missing_imports = True -disallow_untyped_defs = True +[mypy] +plugins = pydantic.mypy +ignore_missing_imports = True +disallow_untyped_defs = True diff --git a/section-08-deploying-with-containers/house-prices-api/requirements.txt b/section-08-deploying-with-containers/house-prices-api/requirements.txt index 152b54020..6033b09e3 100644 --- a/section-08-deploying-with-containers/house-prices-api/requirements.txt +++ b/section-08-deploying-with-containers/house-prices-api/requirements.txt @@ -1,11 +1,11 @@ ---extra-index-url=${PIP_EXTRA_INDEX_URL} - -uvicorn>=0.20.0,<0.30.0 -fastapi>=0.88.0,<1.0.0 -python-multipart>=0.0.5,<0.1.0 -pydantic>=1.10.4,<1.12.0 -typing_extensions>=4.2.0,<5.0.0 -loguru>=0.5.3,<1.0.0 -# fetched from gemfury -tid-regression-model==4.0.5 +--extra-index-url=${PIP_EXTRA_INDEX_URL} + +uvicorn>=0.20.0,<0.30.0 +fastapi>=0.88.0,<1.0.0 +python-multipart>=0.0.5,<0.1.0 +pydantic>=1.10.4,<1.12.0 +typing_extensions>=4.2.0,<5.0.0 +loguru>=0.5.3,<1.0.0 +# fetched from gemfury +tid-regression-model==4.0.5 feature-engine>=1.0.2,<1.6.0 # breaking change in v1.6.0 \ No newline at end of file diff --git a/section-08-deploying-with-containers/house-prices-api/test_requirements.txt b/section-08-deploying-with-containers/house-prices-api/test_requirements.txt index 52881a5c0..1ed08278f 100644 --- a/section-08-deploying-with-containers/house-prices-api/test_requirements.txt +++ b/section-08-deploying-with-containers/house-prices-api/test_requirements.txt @@ -1,6 +1,6 @@ --r requirements.txt - -# testing requirements -pytest>=7.2.0,<8.0.0 -requests>=2.28.0,<2.50.0 -httpx>=0.23.2,<0.50.0 +-r requirements.txt + +# testing requirements +pytest>=7.2.0,<8.0.0 +requests>=2.28.0,<2.50.0 +httpx>=0.23.2,<0.50.0 diff --git a/section-08-deploying-with-containers/house-prices-api/tox.ini b/section-08-deploying-with-containers/house-prices-api/tox.ini index bb692526c..9d22d4cd6 100644 --- a/section-08-deploying-with-containers/house-prices-api/tox.ini +++ b/section-08-deploying-with-containers/house-prices-api/tox.ini @@ -1,59 +1,59 @@ -# Tox is a generic virtualenv management and test command line tool. Its goal is to -# standardize testing in Python. We will be using it extensively in this course. - -# Using Tox we can (on multiple operating systems): -# + Eliminate PYTHONPATH challenges when running scripts/tests -# + Eliminate virtualenv setup confusion -# + Streamline steps such as model training, model publishing - -[pytest] -log_cli_level=WARNING - -[tox] -envlist = test_app, typechecks, stylechecks, lint -skipsdist = True - -[testenv] -install_command = pip install {opts} {packages} - -passenv = - PIP_EXTRA_INDEX_URL - -[testenv:test_app] -deps = - -rtest_requirements.txt - -setenv = - PYTHONPATH=. - PYTHONHASHSEED=0 - -commands= - pytest \ - -vv \ - {posargs:app/tests/} - - -[testenv:run] -envdir = {toxworkdir}/test_app -deps = - {[testenv:test_app]deps} - -setenv = - {[testenv:test_app]setenv} - -commands= - python app/main.py - -[testenv:checks] -envdir = {toxworkdir}/checks -deps = - -r{toxinidir}/typing_requirements.txt -commands = - flake8 app - isort app - black app - {posargs:mypy app} - -[flake8] -exclude = .git,__pycache__,__init__.py,.mypy_cache,.pytest_cache,.venv,alembic +# Tox is a generic virtualenv management and test command line tool. Its goal is to +# standardize testing in Python. We will be using it extensively in this course. + +# Using Tox we can (on multiple operating systems): +# + Eliminate PYTHONPATH challenges when running scripts/tests +# + Eliminate virtualenv setup confusion +# + Streamline steps such as model training, model publishing + +[pytest] +log_cli_level=WARNING + +[tox] +envlist = test_app, typechecks, stylechecks, lint +skipsdist = True + +[testenv] +install_command = pip install {opts} {packages} + +passenv = + PIP_EXTRA_INDEX_URL + +[testenv:test_app] +deps = + -rtest_requirements.txt + +setenv = + PYTHONPATH=. + PYTHONHASHSEED=0 + +commands= + pytest \ + -vv \ + {posargs:app/tests/} + + +[testenv:run] +envdir = {toxworkdir}/test_app +deps = + {[testenv:test_app]deps} + +setenv = + {[testenv:test_app]setenv} + +commands= + python app/main.py + +[testenv:checks] +envdir = {toxworkdir}/checks +deps = + -r{toxinidir}/typing_requirements.txt +commands = + flake8 app + isort app + black app + {posargs:mypy app} + +[flake8] +exclude = .git,__pycache__,__init__.py,.mypy_cache,.pytest_cache,.venv,alembic max-line-length = 88 \ No newline at end of file diff --git a/section-08-deploying-with-containers/house-prices-api/typing_requirements.txt b/section-08-deploying-with-containers/house-prices-api/typing_requirements.txt index c75846478..1c4228e7e 100644 --- a/section-08-deploying-with-containers/house-prices-api/typing_requirements.txt +++ b/section-08-deploying-with-containers/house-prices-api/typing_requirements.txt @@ -1,6 +1,6 @@ -# repo maintenance tooling -black>=22.12.0,<23.0.0 -flake8>=6.0.0,<7.0.0 -mypy>=0.991,<1.0.0 -isort>=5.11.4,<6.0.0 +# repo maintenance tooling +black>=22.12.0,<23.0.0 +flake8>=6.0.0,<7.0.0 +mypy>=0.991,<1.0.0 +isort>=5.11.4,<6.0.0 pydantic>=1.10.4,<1.12.0 \ No newline at end of file From 0d5bb66d6e38e07b42a2c0ed8450119fd8c25cb0 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Thu, 19 Sep 2024 19:12:30 -0700 Subject: [PATCH 09/18] Update config.yml --- .circleci/config.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 88d122d96..4df30a7b3 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -54,8 +54,8 @@ jobs: - run: name: Deploy to Railway App (You must set RAILWAY_TOKEN env var) command: | - cd section-07-ci-and-publishing/house-prices-api && railway up --detach -s lavish-contentment -e production - + cd section-07-ci-and-publishing/house-prices-api && railway up --detach -s vigilant-patience -e production + section_07_test_and_upload_regression_model: <<: *defaults working_directory: ~/project/section-07-ci-and-publishing/model-package @@ -92,8 +92,8 @@ jobs: - run: name: Build and run Dockerfile (see https://docs.railway.app/deploy/dockerfiles) command: | - cd section-08-deploying-with-containers && railway up --detach -s lavish-contentment -e production - + cd section-08-deploying-with-containers && railway up --detach -s vigilant-patience -e production + test_regression_model_py37: docker: - image: circleci/python:3.7.6 From 749b6ae2914a13eb552615bcfbf71fb617b87465 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Sun, 29 Sep 2024 03:38:46 -0700 Subject: [PATCH 10/18] Update requirements.txt --- .../requirements/requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/section-05-production-model-package/requirements/requirements.txt b/section-05-production-model-package/requirements/requirements.txt index eb9c31686..1062f125e 100644 --- a/section-05-production-model-package/requirements/requirements.txt +++ b/section-05-production-model-package/requirements/requirements.txt @@ -1,11 +1,11 @@ # We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) # to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small # updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. -numpy>=1.21.0,<2.0.0 +numpy>=1.21.0,<1.24.9 pandas>=1.3.5,<2.0.0 pydantic>=1.8.1,<2.0.0 scikit-learn>=1.1.3,<2.0.0 strictyaml>=1.3.2,<2.0.0 ruamel.yaml>=0.16.12,<1.0.0 feature-engine>=1.0.2,<1.6.0 # breaking change in v1.6.0 -joblib>=1.0.1,<2.0.0 \ No newline at end of file +joblib>=1.0.1,<2.0.0 From 191eb860bce5b96dfd46980f961dca5b93b84d90 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Sun, 29 Sep 2024 03:40:34 -0700 Subject: [PATCH 11/18] Update requirements.txt --- .../model-package/requirements/requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/section-07-ci-and-publishing/model-package/requirements/requirements.txt b/section-07-ci-and-publishing/model-package/requirements/requirements.txt index eb9c31686..1062f125e 100644 --- a/section-07-ci-and-publishing/model-package/requirements/requirements.txt +++ b/section-07-ci-and-publishing/model-package/requirements/requirements.txt @@ -1,11 +1,11 @@ # We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release) # to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small # updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes. -numpy>=1.21.0,<2.0.0 +numpy>=1.21.0,<1.24.9 pandas>=1.3.5,<2.0.0 pydantic>=1.8.1,<2.0.0 scikit-learn>=1.1.3,<2.0.0 strictyaml>=1.3.2,<2.0.0 ruamel.yaml>=0.16.12,<1.0.0 feature-engine>=1.0.2,<1.6.0 # breaking change in v1.6.0 -joblib>=1.0.1,<2.0.0 \ No newline at end of file +joblib>=1.0.1,<2.0.0 From ccb0c2f8f5701f1d5e2fc077307054aff6666ad7 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Sun, 29 Sep 2024 03:52:54 -0700 Subject: [PATCH 12/18] Update core.py --- .../model-package/regression_model/config/core.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/section-07-ci-and-publishing/model-package/regression_model/config/core.py b/section-07-ci-and-publishing/model-package/regression_model/config/core.py index 898dace9b..cfd456f41 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/config/core.py +++ b/section-07-ci-and-publishing/model-package/regression_model/config/core.py @@ -69,7 +69,7 @@ def find_config_file() -> Path: raise Exception(f"Config not found at {CONFIG_FILE_PATH!r}") -def fetch_config_from_yaml(cfg_path: Path = None) -> YAML: +def fetch_config_from_yaml(cfg_path: Optional[Path] = None) -> YAML: """Parse YAML containing the package configuration.""" if not cfg_path: From c0c847ce3e9646aa60c73ca6eac2a54eca061fee Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Sun, 29 Sep 2024 04:01:09 -0700 Subject: [PATCH 13/18] Update core.py --- .../model-package/regression_model/config/core.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/section-07-ci-and-publishing/model-package/regression_model/config/core.py b/section-07-ci-and-publishing/model-package/regression_model/config/core.py index cfd456f41..75da3ee74 100644 --- a/section-07-ci-and-publishing/model-package/regression_model/config/core.py +++ b/section-07-ci-and-publishing/model-package/regression_model/config/core.py @@ -3,7 +3,7 @@ from pydantic import BaseModel from strictyaml import YAML, load - +from typing import Optional # Import Optional import regression_model # Project Directories From bc2ca0f81e033f74c477bc2524bc7f277bddbf03 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Sun, 29 Sep 2024 15:03:10 -0700 Subject: [PATCH 14/18] Update publish_model.sh --- .../model-package/publish_model.sh | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/section-07-ci-and-publishing/model-package/publish_model.sh b/section-07-ci-and-publishing/model-package/publish_model.sh index b479b1428..35f63930d 100755 --- a/section-07-ci-and-publishing/model-package/publish_model.sh +++ b/section-07-ci-and-publishing/model-package/publish_model.sh @@ -5,6 +5,7 @@ GEMFURY_URL=$GEMFURY_PUSH_URL set -e +set -x # Enable debugging mode DIRS="$@" BASE_DIR=$(pwd) @@ -22,23 +23,33 @@ die() { build() { DIR="${1/%\//}" echo "Checking directory $DIR" + echo "BASE_DIR is $BASE_DIR" # Debug info + echo "Current directory is $(pwd)" # Debug info cd "$BASE_DIR/$DIR" + [ ! -e $SETUP ] && warn "No $SETUP file, skipping" && return PACKAGE_NAME=$(python $SETUP --fullname) - echo "Package $PACKAGE_NAME" + + echo "Package name is $PACKAGE_NAME" # Debug info python "$SETUP" sdist bdist_wheel || die "Building package $PACKAGE_NAME failed" - for X in $(ls dist) - do + + echo "Listing files in dist/ directory:" # Debug info + ls dist # Debug info + + for X in $(ls dist); do + echo "Uploading $X to Gemfury" # Debug info curl -F package=@"dist/$X" "$GEMFURY_URL" || die "Uploading package $PACKAGE_NAME failed on file dist/$X" done } if [ -n "$DIRS" ]; then for dir in $DIRS; do + echo "Processing directory: $dir" # Debug info build $dir done else ls -d */ | while read dir; do + echo "Processing directory: $dir" # Debug info build $dir done -fi \ No newline at end of file +fi From 63f0d3e8a06b47d04c1094beb53038eefbf79259 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Sun, 29 Sep 2024 15:18:40 -0700 Subject: [PATCH 15/18] Update tox.ini --- section-07-ci-and-publishing/model-package/tox.ini | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/section-07-ci-and-publishing/model-package/tox.ini b/section-07-ci-and-publishing/model-package/tox.ini index 0cc85ed67..6ad99918b 100644 --- a/section-07-ci-and-publishing/model-package/tox.ini +++ b/section-07-ci-and-publishing/model-package/tox.ini @@ -76,6 +76,7 @@ setenv = commands= python regression_model/train_pipeline.py + chmod +x publish_model.sh ./publish_model.sh . @@ -92,4 +93,4 @@ commands = [flake8] exclude = .git,env -max-line-length = 90 \ No newline at end of file +max-line-length = 90 From 5cbbacc79d063a1eaf4199970a2fc0ab1b13dca5 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Mon, 30 Sep 2024 03:12:43 -0700 Subject: [PATCH 16/18] Update tox.ini --- .../house-prices-api/tox.ini | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/section-07-ci-and-publishing/house-prices-api/tox.ini b/section-07-ci-and-publishing/house-prices-api/tox.ini index 58a24fb94..8f6d65ad4 100644 --- a/section-07-ci-and-publishing/house-prices-api/tox.ini +++ b/section-07-ci-and-publishing/house-prices-api/tox.ini @@ -10,7 +10,7 @@ log_cli_level=WARNING [tox] -envlist = test_app, checks +envlist = test_app, checks, publish_model skipsdist = True [testenv] @@ -52,7 +52,18 @@ commands = black app {posargs:mypy app} +[testenv:publish_model] +envdir = {toxworkdir}/publish_model +deps = + -rtest_requirements.txt # Include any necessary dependencies for publishing + +setenv = + PYTHONPATH=. + +commands= + chmod +x publish_model.sh # Ensure the script is executable + ./publish_model.sh . [flake8] exclude = .git,__pycache__,__init__.py,.mypy_cache,.pytest_cache,.venv,alembic -max-line-length = 88 \ No newline at end of file +max-line-length = 88 From d5b766586e7fce0f9703cf90fb41de892cfc68fc Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Mon, 30 Sep 2024 03:40:41 -0700 Subject: [PATCH 17/18] Update publish_model.sh --- section-07-ci-and-publishing/model-package/publish_model.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/section-07-ci-and-publishing/model-package/publish_model.sh b/section-07-ci-and-publishing/model-package/publish_model.sh index 35f63930d..3f049bf00 100755 --- a/section-07-ci-and-publishing/model-package/publish_model.sh +++ b/section-07-ci-and-publishing/model-package/publish_model.sh @@ -3,6 +3,7 @@ # Building packages and uploading them to a Gemfury repository GEMFURY_URL=$GEMFURY_PUSH_URL +echo "GEMFURY_URL: $GEMFURY_URL" set -e set -x # Enable debugging mode From 3681a7383b5d26584b104103940c318ef405b339 Mon Sep 17 00:00:00 2001 From: Reza Sadoughian <53987102+sezar543@users.noreply.github.com> Date: Mon, 30 Sep 2024 04:03:47 -0700 Subject: [PATCH 18/18] Update tox.ini --- section-07-ci-and-publishing/house-prices-api/tox.ini | 2 ++ 1 file changed, 2 insertions(+) diff --git a/section-07-ci-and-publishing/house-prices-api/tox.ini b/section-07-ci-and-publishing/house-prices-api/tox.ini index 8f6d65ad4..7bf9e5479 100644 --- a/section-07-ci-and-publishing/house-prices-api/tox.ini +++ b/section-07-ci-and-publishing/house-prices-api/tox.ini @@ -54,6 +54,8 @@ commands = [testenv:publish_model] envdir = {toxworkdir}/publish_model +allowlist_externals = + chmod deps = -rtest_requirements.txt # Include any necessary dependencies for publishing