Predicting_WCA_using_laser_processing_data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OBzbk-UmqGw6"
      },
      "source": [
        "## Importing various packages and load data with Google Drive."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "PETDpinjr6BE"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder, PolynomialFeatures\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.feature_selection import SelectKBest, f_regression\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aHuqweNOqANM",
        "outputId": "03415d4a-0a47-433c-b6cf-2300f08a7514"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "   Data Source  laser fluence/(J/cm²)  focal length/mm  \\\n",
            "0            1                  0.000            250.0   \n",
            "1            1                  0.290            250.0   \n",
            "2            1                  0.550            250.0   \n",
            "3            1                  0.876            250.0   \n",
            "4            1                  1.190            250.0   \n",
            "\n",
            "   essential WCA without decoration/°  essential WCA with drcoration/°  \\\n",
            "0                                87.0                           114.81   \n",
            "1                                87.0                           114.81   \n",
            "2                                87.0                           114.81   \n",
            "3                                87.0                           114.81   \n",
            "4                                87.0                           114.81   \n",
            "\n",
            "  scanning method  line spacing/µm  scanning speed/(mm/s)  spot size/µm  \\\n",
            "0   perpendicular             20.0                  400.0         140.0   \n",
            "1   perpendicular             20.0                  400.0         140.0   \n",
            "2   perpendicular             20.0                  400.0         140.0   \n",
            "3   perpendicular             20.0                  400.0         140.0   \n",
            "4   perpendicular             20.0                  400.0         140.0   \n",
            "\n",
            "   center wave length/nm  pulse duration/fs  laser frequency/kHz   WCA/°  \\\n",
            "0                    800                100                  1.0  114.81   \n",
            "1                    800                100                  1.0  156.81   \n",
            "2                    800                100                  1.0  158.52   \n",
            "3                    800                100                  1.0  159.63   \n",
            "4                    800                100                  1.0  160.74   \n",
            "\n",
            "         Unnamed: 13 Unnamed: 14  \n",
            "0  spot size是取最大最小平均         NaN  \n",
            "1                NaN         NaN  \n",
            "2                NaN         NaN  \n",
            "3                NaN         NaN  \n",
            "4                NaN         NaN  \n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Now we can read the file by its name.\n",
        "data_path = 'drive/MyDrive/laser_WCA.xlsx'\n",
        "data = pd.read_excel(data_path)\n",
        "\n",
        "print(data.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ac55IhiCovCV"
      },
      "source": [
        "## Preprocessing\n",
        "The data consists of several different types of data:\n",
        "\n",
        "* Data Source\n",
        "* laser fluence/(J/cm²)\n",
        "* focal length/mm\n",
        "*\tessential WCA without decoration/°\n",
        "*\tessential WCA with drcoration/°\n",
        "*\tscanning method\tline spacing/µm\n",
        "*\tscanning speed/(mm/s)\n",
        "* spot size/µm\n",
        "*\tcenter wave length/nm\n",
        "*\tpulse duration/fs\n",
        "*\tlaser frequency/kHz\n",
        "*\tWCA/°\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfYebEHv2ST8",
        "outputId": "2e90fe2e-c8c0-40dc-d9ce-b0de36ffac16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 276 entries, 0 to 275\n",
            "Data columns (total 15 columns):\n",
            " #   Column                              Non-Null Count  Dtype  \n",
            "---  ------                              --------------  -----  \n",
            " 0   Data Source                         276 non-null    int64  \n",
            " 1   laser fluence/(J/cm²)               276 non-null    float64\n",
            " 2   focal length/mm                     268 non-null    float64\n",
            " 3   essential WCA without decoration/°  276 non-null    float64\n",
            " 4   essential WCA with drcoration/°     276 non-null    float64\n",
            " 5   scanning method                     276 non-null    object \n",
            " 6   line spacing/µm                     276 non-null    float64\n",
            " 7   scanning speed/(mm/s)               276 non-null    float64\n",
            " 8   spot size/µm                        276 non-null    float64\n",
            " 9   center wave length/nm               276 non-null    int64  \n",
            " 10  pulse duration/fs                   276 non-null    int64  \n",
            " 11  laser frequency/kHz                 276 non-null    float64\n",
            " 12  WCA/°                               276 non-null    float64\n",
            " 13  Unnamed: 13                         6 non-null      object \n",
            " 14  Unnamed: 14                         1 non-null      object \n",
            "dtypes: float64(9), int64(3), object(3)\n",
            "memory usage: 32.5+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# The basic info of the data.\n",
        "print(data.info())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3ikOQ0a2bjT"
      },
      "source": [
        "### Dealing with the missing data\n",
        "\n",
        "Using interpolation, machine learning prediction and deletion."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MkwY5QAQ2bGR",
        "outputId": "64f0af43-5d72-4bfa-b5e5-2d929436c057"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   laser fluence/(J/cm²)  focal length/mm  essential WCA without decoration/°  \\\n",
            "0                  0.000            250.0                                87.0   \n",
            "1                  0.290            250.0                                87.0   \n",
            "2                  0.550            250.0                                87.0   \n",
            "3                  0.876            250.0                                87.0   \n",
            "4                  1.190            250.0                                87.0   \n",
            "\n",
            "   essential WCA with drcoration/° scanning method  line spacing/µm  \\\n",
            "0                           114.81   perpendicular             20.0   \n",
            "1                           114.81   perpendicular             20.0   \n",
            "2                           114.81   perpendicular             20.0   \n",
            "3                           114.81   perpendicular             20.0   \n",
            "4                           114.81   perpendicular             20.0   \n",
            "\n",
            "   scanning speed/(mm/s)  spot size/µm  center wave length/nm  \\\n",
            "0                  400.0         140.0                    800   \n",
            "1                  400.0         140.0                    800   \n",
            "2                  400.0         140.0                    800   \n",
            "3                  400.0         140.0                    800   \n",
            "4                  400.0         140.0                    800   \n",
            "\n",
            "   pulse duration/fs  laser frequency/kHz   WCA/°  \n",
            "0                100                  1.0  114.81  \n",
            "1                100                  1.0  156.81  \n",
            "2                100                  1.0  158.52  \n",
            "3                100                  1.0  159.63  \n",
            "4                100                  1.0  160.74  \n"
          ]
        }
      ],
      "source": [
        "columns_to_drop = [\n",
        "    \"Data Source\",\n",
        "    \"Unnamed: 13\",\n",
        "    \"Unnamed: 14\"\n",
        "]\n",
        "\n",
        "# Drop the specified columns\n",
        "data = data.drop(columns=columns_to_drop)\n",
        "\n",
        "# Drop rows with any NaN values\n",
        "data.dropna(inplace=True)\n",
        "\n",
        "# Print the first few rows of the modified data\n",
        "print(data.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gvPBqva8Q-_",
        "outputId": "fdb6aaf2-7a9d-4103-9302-390b54eeb732"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(268, 12)\n"
          ]
        }
      ],
      "source": [
        "print(data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "hcjB8bo-4Cst"
      },
      "outputs": [],
      "source": [
        "# Convert the 'scanning method' column to numerical using Label Encoding\n",
        "label_encoder = LabelEncoder()\n",
        "data['scanning method'] = label_encoder.fit_transform(\n",
        "    data['scanning method']\n",
        "    )\n",
        "# Split data into X (features) and y (target)\n",
        "X = data.drop(columns=['WCA/°'])\n",
        "y = data['WCA/°']\n",
        "# Standardize X data\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.1, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrEbYAPMGY6J",
        "outputId": "65f275ea-ba25-42d7-de9e-353fc68e9f49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial Test Mean Absolute Error: 137.09107971191406\n",
            "Final Test Mean Absolute Error: 22.977954864501953\n"
          ]
        }
      ],
      "source": [
        "# Define the model without data-strengthening\n",
        "input_shape = X_train.shape[1]\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Input(shape=(input_shape,)),\n",
        "    tf.keras.layers.Dense(256, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
        "    tf.keras.layers.Dropout(0.3),\n",
        "    tf.keras.layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
        "    tf.keras.layers.Dropout(0.3),\n",
        "    tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
        "    tf.keras.layers.Dropout(0.3),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# Compile with an adjusted learning rate\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.05)\n",
        "model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['mae'])\n",
        "\n",
        "# Evaluate the model before training\n",
        "initial_loss, initial_mae = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"Initial Test Mean Absolute Error:\", initial_mae)\n",
        "\n",
        "# Train with early stopping\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\n",
        "history = model.fit(X_train, y_train, epochs=500, validation_split=0.2, batch_size=32, callbacks=[early_stopping], verbose=0)\n",
        "\n",
        "# Evaluate the model after training\n",
        "final_loss, final_mae = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"Final Test Mean Absolute Error:\", final_mae)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'], label='train_loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kSUcKuzhnBxO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using Data Strengthening to process the data."
      ],
      "metadata": {
        "id": "M4jVjuJyjgUq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Strengthening\n",
        "# Scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Selecting\n",
        "selector = SelectKBest(score_func=f_regression, k='all')  # All features are selected.\n",
        "X_train_selected = selector.fit_transform(X_train_scaled, y_train)\n",
        "X_test_selected = selector.transform(X_test_scaled)\n",
        "\n",
        "# Polynomial Features: Expand the feature set by generating polynomial features (degree 2). This dramatically increases the dimensionality of the dataset and can capture non-linear relationships between features and the target variable.\n",
        "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
        "X_train_poly = poly.fit_transform(X_train_selected)\n",
        "X_test_poly = poly.transform(X_test_selected)\n",
        "\n",
        "# Adding noises：preventing overfitting during training\n",
        "noise_factor = 0.1\n",
        "X_train_noisy = X_train_poly + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=X_train_poly.shape)\n",
        "X_test_noisy = X_test_poly + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=X_test_poly.shape)\n",
        "\n",
        "# The neural network\n",
        "input_shape = X_train_noisy.shape[1]\n",
        "model = tf.keras.Sequential([\n",
        "    layers.Input(shape=(input_shape,)),\n",
        "    layers.Dense(256, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
        "    layers.Dropout(0.3),\n",
        "    layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
        "    layers.Dropout(0.3),\n",
        "    layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
        "    layers.Dropout(0.3),\n",
        "    layers.Dense(1)\n",
        "])\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
        "model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['mae'])\n",
        "\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\n",
        "history = model.fit(X_train_noisy, y_train, epochs=500, validation_split=0.2, batch_size=32, callbacks=[early_stopping], verbose=1)\n",
        "\n",
        "loss, mae = model.evaluate(X_test_noisy, y_test, verbose=0)\n",
        "print(\"Test Mean Absolute Error:\", mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "MEjAEjZbtXJL",
        "outputId": "80f22910-e618-4a5c-a9e8-f93f8ba7462d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'X_train' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-da1d95fa9805>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Scaling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mX_train_scaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mX_test_scaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'], label='train_loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "96qtm_lPm6NW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yet, the significance reduction in MAE could be due to these reasons:\n",
        "* Potential Data Leakage\n",
        "* Overfitting\n",
        "So it is a must to conduct a k-fold cross-validation to get a more reliable assessment."
      ],
      "metadata": {
        "id": "iNkd0Xznmab5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## k-fold cross-validation used to avoid overfitting and provides a more reliable assessment"
      ],
      "metadata": {
        "id": "Zr06voW0kwFm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "\n",
        "# Defining the cross-validation parameters\n",
        "kfold = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "mae_scores = []\n",
        "\n",
        "# Cross-validation\n",
        "for train_index, test_index in kfold.split(X):\n",
        "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
        "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
        "\n",
        "\n",
        "    X_train_scaled = scaler.fit_transform(X_train)\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "    X_train_selected = selector.fit_transform(X_train_scaled, y_train)\n",
        "    X_test_selected = selector.transform(X_test_scaled)\n",
        "\n",
        "    X_train_poly = poly.fit_transform(X_train_selected)\n",
        "    X_test_poly = poly.transform(X_test_selected)\n",
        "\n",
        "    # Training the model.\n",
        "    model = tf.keras.Sequential([\n",
        "        layers.Input(shape=(X_train_poly.shape[1],)),\n",
        "        layers.Dense(256, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
        "        layers.Dropout(0.3),\n",
        "        layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
        "        layers.Dropout(0.3),\n",
        "        layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
        "        layers.Dropout(0.3),\n",
        "        layers.Dense(1)\n",
        "    ])\n",
        "\n",
        "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
        "    model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['mae'])\n",
        "\n",
        "    early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\n",
        "    model.fit(X_train_poly, y_train, epochs=10000, validation_split=0.2, batch_size=32, callbacks=[early_stopping], verbose=0)\n",
        "\n",
        "    # Assessment\n",
        "    predictions = model.predict(X_test_poly)\n",
        "    mae = mean_absolute_error(y_test, predictions)\n",
        "    mae_scores.append(mae)\n",
        "\n",
        "# Results\n",
        "print(f\"Cross-validated Mean Absolute Errors: {mae_scores}\")\n",
        "print(f\"Mean MAE: {np.mean(mae_scores)}\")\n",
        "print(f\"Standard Deviation of MAE: {np.std(mae_scores)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lxmac1CQu2do",
        "outputId": "9a1c91ab-5bcb-4dbe-be3d-7d0bfcbe5b60"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7aea5d3092d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 73ms/step"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7aea5d3092d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
            "Cross-validated Mean Absolute Errors: [0.4411240573282595, 0.5329262735667052, 0.5823105681963541, 0.5706467854061147, 0.5008305896003291]\n",
            "Mean MAE: 0.5255676548195525\n",
            "Standard Deviation of MAE: 0.051085573415055444\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "js2zGIQndxTh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "48254102-5413-4f51-940e-f56f96104d0d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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6dSp0Oh1yc92fJX/84x+xatUqfPLJJ7jzzjsDeu533nkHTU1NePPNN2E2S4HY888/j9mzZ+Mvf/kLDAYDqqur8ZOf/ATnnHMOAGDo0KHK/fPz8/HAAw9gyJAhAICBAwcGtJ5gYHCjEgHA6xtPQCcAT1w9QrWsh5y5yUk2I8lVg6JmQbEcKCVGG6DTCUiOMaKwuglltZYuD27OVjavuZGDG2ZuJHLmZliWNJqC21JE7TBESxkUrZ7bS/PmzcMtt9yCF198ESaTCcuXL8cNN9wAnU6Huro6PProo/jss89QWFgIu92OxsZG5OfnB7zEAwcOIDc3VwlsAGDy5MlwOp04dOgQLrroIixcuBAzZszApZdeiunTp+O6665DZmYmAODee+/FL37xC7z11luYPn06fvrTnypBUHfBmhuVpMaakGQ2wikCR4rrOr+Dl5STUqlmJLuCm9omO6x2Z0d381qlEtxIj50SI2WHurqouKbJpmyPuU9LydtSzNyIooh8V3Az1DV3y2J3wu5Q588BUVgRBGlrSIsvH4b8zp49G6Io4rPPPsPp06exfv16zJs3DwBw//33Y9WqVXjyySexfv165OXlYeTIkbBau+Y/e2+88QY2bdqE888/H++99x4GDRqEzZs3AwAeffRR7Nu3D1dccQW+//57DBs2DKtWreqSdXmLwY1KBEFQCnIPFtWo9rgnXCel+iWbER9lgN41b0mt3i9yFkjOCqXESL92dXAjb0klRhsQbZQSikrmplGaDN6TVTa4g7/BGbHK9TwOThS6IiMjMXfuXCxfvhwrVqzA4MGDMWbMGADAxo0bsXDhQlx99dUYOXIkMjIycPLkSVWed+jQodi1axfq69091DZu3AidTofBgwcr140ePRpLlizBDz/8gBEjRuCdd95RfjZo0CD8+te/xtdff425c+fijTfeUGVtamFwo6IhmXJwU6vK49kdTuV/6/1SzdDpBKX3S7lKXYrlIMkd3MiZm67dCipocQwccGduHE6x2eDQnkg+KZUeZ0JcpAFGvfRXl0XFRKFt3rx5+Oyzz/D6668rWRtAqmP58MMPkZeXh127duGmm25qdbIqkOeMjIzEggULsHfvXqxevRp33XUXfv7znyM9PR0nTpzAkiVLsGnTJpw6dQpff/01jhw5gqFDh6KxsRF33nkn1qxZg1OnTmHjxo3Ytm1bs5qc7oA1NyoamiFtF6iVuTlb1Qi7U4QpQofMOGmrJslsRFmdVbWiYqXmRg5uXCeySmu7NnNztso9U0oWadDDFKGDxe5EVb0NcZHdqxq/K51yZfD6Jkl75GaTHtYGJ3vdEIW4Sy65BElJSTh06BBuuukm5fpnn30WixYtwvnnn4+UlBT89re/RU2NOp8t0dHR+Oqrr3D33Xdj/PjxiI6OxjXXXINnn31W+fnBgwfxn//8B+Xl5cjMzMQdd9yBX/7yl7Db7SgvL8f8+fNRXFyMlJQUzJ07F4899pgqa1MLgxsVyZmbA4W1EEVROTbnr+MexcQ613aUu6hYneBDrrlJcmVJkoNQtOyNlsXEssRoI4pqmlDVaEUfeF+oF27kDF6fZOn3wGyKQGWDjUXFRCFOp9OhoKB18XNOTg6+//77Ztfdcccdzb73ZZuq5db+yJEjWz2+LD09vd0aGqPRiBUrVnj9vFrhtpSKBqbFQidI2RA1GuEpJ6VS3B/qySofB29Zc5PqytyUdXHmpuUxcBmPg0tOubal+ia5ghtXXRJrboiIWmNwo6Ioox45rvlPBwsDr7txz5SKUa6TgxC1gpv2a260CW5aZm54HFzSOnMjtRpgzQ0RLV++HDExMW1+DR8+XOvlaYLbUiobkhGL46X1OFRUi4sGpQb0WO7gxp25UbvXTUW9lBFJ7DbBTWSz63kcXHLK1cCvb7JccyNnbhjcEPV0V155JSZOnNjmz7pb5+CuwuBGZUMy4vD5niIcUKGoWB6Y2VbmRq1hki1rbuSj4JUNNtgcThj0wU/u2RxOFNW4CooT28vc9NzgpsnmQHGNFGz2cW1LxTC4ISKX2NhYxMbGdn7DHoTbUiob4upBEui2lMXuUIpsc4KUuRFFUdneSnIFNYnRRqWXjtoDOttTXNMEpwgY9TqkuGqKZAnRcrDVc7elTrvqbWJNEUorALkXUB1rbogAQLVj0qQttXqaMXOjMrl77NGSuoAyH6crGuAUpf+hp8a4P/CTVay5qbc6YHV1uJUzNzqdgCSzEaW1FpTWWpAeF9nRQ6hCHpiZmRCpnAqTJUSx5uaUR72NfAIvxlVz08CaG+rhjEajcuIoNTUVRqMx4JOqpA1RFFFaWgpBEALeTmNwo7JeCVGIMUWgzmLHibJ6ZdK2r46Xuk9Kef5FlTMsagQ38pZUpEGHKKN7FlZKjAmltZYuq7tR6m3iW8+yUmpuGnvutpRyUirZncGTa254FJx6Op1Oh379+qGwsLDNI9UUWgRBQO/evaHXBzafkcGNynQ6AYMzYrHjVCUOFNb4Hdy0VW8DeNTcNFjhcIrKFpI/KlrU28jcIxi6Jltytp2TUgCPggNAvuvPQp8k95A7FhQTuRmNRvTp0wd2ux0OB7dqQ5nBYAg4sAEY3ATFEFdwc7CoFnP8fAzlpFRy88Z1ciZDFKWtmuQYU6v7eqtlvY0stYtPTMnBTctiYsBdc1Pdk7el2sjcuAuK+Q85EQBlK6Onng6i5lhQHARyUfGhAGZMeU4D92TQ6xDvqkMJdGuqosVEcFlKFzfyczfwa13fk8jMjTJXSj4pBQDRRva5ISJqD4ObIBjiKio+WOj/cfATHqMXWlJrRELLBn5qP7632mvgBwDxruCmpskGh7PnTQZ3OEWcqZB+fzyDGx4FJyJqH4ObIBjsytwUVDeh2o+MQ73FrvQ16ZfSOrhRq0txu5mbLtyWEkWx3blSAJAQ5d6Gq+mBRcVFNU2wOpww6IVmvz/ugmJuSxERtcTgJgjiIg3KjCR/JoTLxcSJ0Qal5sSTWr1u5OAm2dz2tlRXTAavabKj3ip9QLd1WsoYoVOyFD2x1408Dbx3YnSz4nEWFBMRtY/BTZAMdU0IP+hH3c3JMqnGoq2sDeCRuQnwNJOSuWkZ3HThaSk5a5NsNjY7ju5JrjHqicfBlZlSSc0Ly83sc0NE1C4GN0EyJMNVd+NH5uZEWR0AKEM4W/I8Dh6I9mpu5NNSFfWWoNe5dFRvI0s099xGfm2dlALcU8HZ54aIqDUGN0Ei1934k7k54crc9O8kuFFrW6plzU2S2QhBAJxi8LeCCqrbHpjpSa67qazvgZmbNk5KAe6C4iabE3YH284TEXlicBMk8rbUoaJaOH3MfnSWuUlWuhQHVhOj1Ny06HMTodcpAU+wi4o7auAnU4ZncltKIdfcAFBqloiISMLgJkhyks0wRujQYHXgdGWDT/c9Wd5ZzY20bVQeQE2MwykqwULLzA3gUXdTG+TMjWuuVK+OtqXkEQw9cVvKVVDct0VLAGOEDga9VGDMomIiouYY3ARJhF6HQenS6IQDPkwIr26wKRmVtnrcAOoMz6xutEEevipnRjx11XHws67Ar6PgRsnc9LBGflUNVtQ0SYFLy8wN4M7esKiYiKg5BjdB5E9R8QnX/9TT40zNth48eRYU+zseXg6M4iIj2pxc3lXBjZy56XhbSp0C6lAjTwNPizW1eZLMXVTMbSkiIk+aBjfr1q3D7NmzkZWVBUEQ8NFHH3V6H4vFgoceegh9+/aFyWRCTk4OXn/99eAv1g/yGIaDPmRulHqbdrI2gDu4sTlE5X/2vnLX27Q9m0oObkqDGNzYHE4U13oR3ET1zMxNeyelZOxSTETUNk0HZ9bX1yM3NxeLFi3C3LlzvbrPddddh+LiYrz22msYMGAACgsL4XR2z9MiQ11jGA4V+xLcuE5KpbYf3EQa9DAb9ai3OlBRb1X6wPjCfVKq7fumxAa/5qaougmiKNWPtGwk6Ek5Ct7YszI3p13BTXYbW1IAEO3qdcPj4EREzWka3MyaNQuzZs3y+vZffvkl1q5di+PHjyMpKQkAkJOTE6TVBU4+Dn6yvB4NVjuijZ3/dnc0U8pTUowR9RWNqKi3tFt43JH2etzIUuSi5QBPZHVE6XETHwmdR/fdlpRtqR52FFwpJk5q+/2NYc0NEVGbQqrm5pNPPsG4cePw1FNPoVevXhg0aBDuv/9+NDY2tnsfi8WCmpqaZl9dJSXGhJQYE0QROFxc59V9TsrTwDsJWAI9MdVejxuZkrkJ4raUfAy8V2L7W1KAe1uquocdBZdrbtrblmLNDRFR20IquDl+/Dg2bNiAvXv3YtWqVXjuueewcuVK3H777e3eZ+nSpYiPj1e+srOzu3DFHmMYvJgQLoqikrnpNLhxbSf5e2JKvl9STDvBjVxQHMRtKXfmpuPgRg7A6ix2WO3dcwsyGJQGfu0FN6y5ISJqU0gFN06nE4IgYPny5ZgwYQIuv/xyPPvss/jPf/7TbvZmyZIlqK6uVr5Onz7dpWse4kOn4rI6K+osdghC+3UWMjlzU+HnCaJKObhpL3MT496W8vdEVmfOenFSCgDiogwQXLtWPaXupsnmQFGN9PvTt50/C/J8KQY3RETNhVRwk5mZiV69eiE+Pl65bujQoRBFEWfOnGnzPiaTCXFxcc2+upJ8HPyAF5kbOWvTKyEKkYa2h0jKlC7F/m5LNbQ9NLPl49scYtC2g+TMTUc9bgBArxMQF+namuohJ6bOVDZAFKW6mvbqotyZG25LERF5CqngZvLkySgoKEBdnbt+5fDhw9DpdOjdu7eGK2vfEI/p4J1lQLyttwE8JoP7uS3VWebGFKFHXKT04RmsuhtvhmbK5FNdlT0kuMn3OCklCG0XW/MoOBFR2zQNburq6pCXl4e8vDwAwIkTJ5CXl4f8/HwA0pbS/PnzldvfdNNNSE5Oxs0334z9+/dj3bp1eOCBB7Bo0SJERXX+AamFAWkx0OsEVDfaUFzTcZBw3I/gxt/hmeWd1NwAQEqsq9dNEOpuRFH0uqAYAOJ72AgGpZi4g+1Js6uxXx1PSxERNaNpcLN9+3aMHj0ao0ePBgDce++9GD16NB555BEAQGFhoRLoAEBMTAy++eYbVFVVYdy4cZg3bx5mz56Nf/zjH5qs3xumCL0y3ftAJ52KT3p5DBwIfARDZ5kbILhdiqsbbWhwDXzMjG9/IrgssYeNYOjspBQARDNzQ0TUJk373EyZMqXDrZply5a1um7IkCH45ptvgrgq9Q3JjMORkjocLKzF1MFp7d7upKuvSb8OGvjJAtmWarI5lEnS7dXcAEBqEIMbOWuTEmPstL4I8OhS3EMKijs7KQV49LlhzQ0RUTMhVXMTqtwnptrP3DidHsfAvcrc+N9kT85+ROgEpa6mLcpk8CAEN97MlPLkni/VUzI3HTfwA9wFxexQTETUHIObLuDuddP+cfCimiZY7E5E6AT09qIGRa6VabI5fe5QKwdEiWZju8WqQHB73Xjb40aW2INqbpxOEacrpd+fjralYuSj4Ky5ISJqhsFNF5CPgx8rrWu3CZ2ctemTFI2INqZ0t2Q26mGMkG7na5dieYxBR/U2gLugOBgjGHwpJgaAhB5Uc1Nc2wSrK9DtqB5JHufBmhsiouYY3HSBzPhIxEVGwO4Ucay07TEM3nYmlgmCoAQnvtbduHvcdDxwUy5aLvWzl05HzvpwDBxwBzeVPSBzIxcT90qM6jDQjWGfGyKiNjG46QKCICjZm/bqbpSBmT4MwfS3qFg5KdVBMTHgztyU1Qaj5kZu4Nf5SSnAXXPTEzI3+a7gpk8nXarlmptGmwMOZ3C6SBMRhSIGN11kSCd1N7408JMpXYp9DG7KvQxuPE9LqT2CwZcGfkDPOgp+qsJVTNxBvQ3gHr8AsO6GiMgTg5suooxhaGfGlK/bUoAKmZvOam5cwY3F7lT1RI7V7kSJKxvk9bZUlHxaqudsS3V0UgoAjHodInRSQTjrboiI3BjcdJEhHUwHtzucSl8Tf4IbX7sUdzZXShZl1CtdcMtUrLspqm6CKAKmCJ1S19OZBFd9kMXuRJMtvGtMvOlxA0jbnZwvRUTUGoObLjI4XQpuSmotKG/RN+ZMZSPsThGmCB0y4ryrQQE8uxT7VhPjbc0N4FF3o2Kvm7MeAzM7OoruKdYUAb0rSxHu2RsluOmk5gbgfCkiorYwuOkiZlOEUkNxqMXW1Ily95aUTufdhz0AJLka+fl8WsqX4CZG/aJiX+ttAClLoXQpDuO6m+pGm/L6vAlu5LobBjdERG4MbrqQnL052DK4KfV+ppQnv7elXLdP7KTmBghOl2J3cON9lgroGcfB5ZNSKTEmZcupI3KvG3YpJiJyY3DThYZktn0c3JeZUp78OS0liqISHPiSuVGz142vPW5kPeE4uLcnpWTKthRPSxERKRjcdKGhGe1kbnyYKeVJOS3lQ+BRZ7HD5hCb3b8jwZgM7m9w0xOOg7tPSnkX3Li3pVhQTEQkY3DTheTMzaGi2mZN15TgxtfMjSs4qbXYYbF79+EmZ3mijXqvpnErIxiCsC3V28/MTU/YlurspJTMzIJiIqJWGNx0oT5J0Ygy6GGxO5WtqCabQ8lk+FpzExdpcJ8gqvcum+FLvQ0ApJjlmht1AgpRFH2eCC6TC4qrG8M3c+PLSSkAMHO+FBFRKwxuupBeJ2BQRvNOxacrGiCK0lFnuXjXWzqdoGzVeDvc0pd6G0D9o+BVDTY0uvrUZHQwFLItcl+eSh8LqEOJHNx4W3MjZ27quC1FRKRgcNPF5LqbQ66i4uMeM6W87fniKUn5wPc2cyPdrrMGfjK1j4LLWaqUGJNX22Ke4qPk01Lhmbmx2B0oqJZ+f/p00p1YFuOquWlgQTERkYLBTRcb7Apu5DEM/syU8uQ+Du5d8CE3/PO2M7CcTaq3OtBoDTw7cNbHgZme5K206sbwzNycqWyEKEr1UN5m8dyZGwY3REQyBjddrOV0cH+mgXtK9rGRn5K58bLmJsYUAVOE9MdEja0pZRp4om/1NoD7tFS4Zm48p4F7m8VjzQ0RUWsMbrrYEFfm5nRFI2qbbEpw0z/AzI23wY179ILBq9sLguDR60a94CYr3vfgJj7Mj4KfKvetxw0AzpYiImoDg5sulmg2KvOjDhfXBpy58bVLsbdDMz0pRcUq1N34e1IKcGebqhqsEEWxk1uHnvwKKfDr68OpOaXPDWtuiIgUDG40IE8I33GqEiWugMHXBn4ypUuxl0e15QyPtzU3AJAao95x8DN+NvAD3OMX7E4R9SrU/3Q3+a7uxNleHgMHODiTiKgtDG40INfdfLm3CICUfZG3XHzl77aUtzU3gLpdigs8JoL7Ksqgh9FV/xOOx8F97U4M8Cg4EVFbGNxoYKgrc/NjfhUA/09KAX6clvKxzw2gXnBjsTtQ6spU+VNQLAhC2I5gcDpFn3vcACwoJiJqC4MbDcjHwWW+dib25Evmxu5wKt19faq5cW1LlQe4LVVULdXbRBp0SpDiq4QoV91NmB0HL6m1wGJ3Qq8TfNqyk2tuGm2OZiM9iIh6MgY3GuifEgOD3n3Ut7+PM6U8ycFNVaOt0w+3qkYbRBEQBPcoA2/IBcWBnpbyHJjpT8NCwF13E27HweWTUr0SomDQe//XUt6WAtjIj4hIxuBGA8YIHc5JjVG+DyRzI9fOiGLnAyXlOpX4KAMifPgAlXvpBLotdbbS/3obmRzcVIfZ8Ex/tqQAwBShQ4RrvhiPgxMRSRjcaGSoa0I4EFjNjUGvc48l6GRrSt66SvKhmBgAUmNdp6UCPAquHAP3o8eNLFGZDB5emRs5uPHlpBQg1SFFG6WtKXYpJiKSMLjRyBCPupucFN8+0FpK9rLXTaUfPW4Ad0FxTZMdFrv/2YFAuhPLEpTgJrwyN/6clJLJx8G5LUVEJGFwo5EhrsxNRlwkoo0Rndy6Y94WFZfX+35SCpC2seQaoUCKiuWhkP70uJG5t6XCK3Nzys9tKYDzpYiIWgrsU5X8dv45yfjZeX0woV9ywI/lbZfiSj+3pQRBQLLZhKKaJpTVWfwOTtwFxb4PzZS550uFV+Ym31VQ7O00cE8cwUBE1ByDG40Y9Dr86aqRqjyWt12KlaGZPmZuACAl1qgEN/4QRTGgBn6y+Cj36bBwUdNkU2qI+viVuXGNYGDmhogIALelwoJ7W6rjwKNSaeDne48ZpZFfrX8Zk4p6K5psTgBARnzgmZtwauInTwNPiTEq9TO+UBr5seaGiAgAg5uwkOQ6qt3ZtpS75sbk83MEOhlcPimVFmuCKULv12MA7qxTOG1L+XtSSsb5UkREzTG4CQPJXhYUKzU3gWRu/AxuzgYwMNOT3HywutEGZ5h05A3kpBTA+VJERC0xuAkDiV4GNxV+DM2UBTqC4awK9TYAlAGjoijVqoQDeRp4Hz+bOUaz5oaIqBkGN2HA1z43vh4FB4DU2MAyNwUqnJQCAFOEXmlaFy51N8dLpeAmx49iYgCIMbLPDRGRJwY3YUAOVirrrRDFtrdqmmwONFgdzW7vi0C3pQpU2pYCPLsUh37djSiKOFxcCwAYlB7bya3bxm0pIqLmGNyEATlYsTtF1DS1/b93eUvKoBf8OpEjHzcv83NbSo1j4DJ53EQ4ZG7K6qyobLBBENBs3pgvWFBMRNQcg5swEGnQw+zaqmmv7saz3safidxy5qaywQq7w+nTfa12p7L1EsjoBVmiqyC6qjH0MzdHXFmbvknRiDL6d4pMrrlhh2IiIgmDmzCRFNNxr5tA6m0AKSjSCVIhb2eFyy1tPl6OWosdKTEmDMmI6/wOnVDmS9WHfubmkCu4GejnlhTg2aGYwQ0REcDgJmwovW7a2Taq8HOulEyvE5Tn8LXXzZf7igAAlw1Ph17ne9aoJfk4eDh0KT5cXAcAGBxAcOMenMmaGyIigMFN2Ois142yLeVncAO4j4P7UnfjcIr4el8xAGDm8Ay/n9uTXFBcFQYFxYeVzI1/9TaAu0Mxt6WIiCQMbsJEZ8Mz/R2a6Uk5Dl7rfebmx/xKlNVZEBcZgfP6Bz4kFHBPBq8M8YJiz5NSgzMC2ZZinxsiIk8MbsJEp5mbBjUyN74fB/9yr7QlNX1oOowR6vxxSwiTzE1RTRNqm+zQ6wT0S/GvgR/grrlpsDrCpmszEVEgGNyEiSQvt6WSVdmW8i64EUVRCW5mjFBnSwoIn+GZcr1NvxRzQPO2PI/2N9hYd0NExOAmTCR2si2lTs1Nx0XLLe0rqMHZqkZEGfS4aGCq38/bkrwtFepHwQ8Xyc37/K+3AQBThE4p1ObWFBERg5uw4d6WaucouOvYdCA1N75OBpezNlMGp/rdw6UtyrZUiB8FD7QzsUwQBGUkBYuKiYgY3IQN9wiGtj/w3TU3vk8El6Uo86W8y5jIR8BnqrglBbiPgtda7LD52FCwO1EruAHYpZiIyBODmzCRLPe5aSNzI4qiclpKvp1/z+F9zc3RklocLamDQS9g6pA0v5+zLfL4BQCoDtFeN06niCMlUs2NGsGNu5Efa26IiDQNbtatW4fZs2cjKysLgiDgo48+8vq+GzduREREBM4999ygrS+UyB2Km2zOVtOha5rssLtO0cj1Kv6Qj4JX1Fs7PZXzlau3zeQBKYiL9P852xKh1yEuUvowD9UTU2erGtFgdcCo1/k9DdwTuxQTEblpGtzU19cjNzcXL7zwgk/3q6qqwvz58zFt2rQgrSz0mI165ah1y4JfOWtjNuoRafC/9kXe+nI4xU4ncsv1Nmo17mvJfRw8NDM38pZU/1QzIvSB/zWUZ4vVWxncEBH5Ph5aRbNmzcKsWbN8vt/ixYtx0003Qa/X+5TtCWeCICDZbERhdRMq6q3ITnJnA9TocQMABr0OidEGVDbYUFZnRXJM21tcZyobsOdsNXQCMH1YekDP2Z7EaAPyK0K3kd8hFZr3eZIzNywoJiIKwZqbN954A8ePH8cf/vAHr25vsVhQU1PT7CtctdfrpqIu8B43Mm8a+clbUuNzkpTbqy0+xBv5HSlWr94G8JgvxZobIqLQCm6OHDmCBx98EG+//TYiIrxLOi1duhTx8fHKV3Z2dpBXqZ32RjColbkBvAxu9gbnlJSnUG/kd6hIvZNSgHsEAzM3REQhFNw4HA7cdNNNeOyxxzBo0CCv77dkyRJUV1crX6dPnw7iKrXVXq8bNeZKyeTj4KXtzJcqrbVg26kKAMCMINXbAO7hmZ3V/nRHDqeIo6Vy5iawBn4yeXgmC4qJiDSuufFFbW0ttm/fjp07d+LOO+8EADidToiiiIiICHz99de45JJLWt3PZDLBZArO1kh3016XYnUzNx1PBv9mfzFEEcjtHY+shKiAn6898nHwqhA8Cn6qvB5WuxORBh2yEwM/KQV4nJZiQTERUegEN3FxcdizZ0+z61588UV8//33WLlyJfr166fRyroPJXNT13bNTVIXbEvJjfvUnCXVFve2VOhlbuSZUgPTYqFzjU0IFPvcEBG5aRrc1NXV4ejRo8r3J06cQF5eHpKSktCnTx8sWbIEZ8+exZtvvgmdTocRI0Y0u39aWhoiIyNbXd9TJZndfWg8yVs3agQ3qcp8qdbBTXWjDT8cLQMQvCPgslA+Cq5mZ2JZjKvmhttSREQaBzfbt2/H1KlTle/vvfdeAMCCBQuwbNkyFBYWIj8/X6vlhRzltFSLbIYyNFOVmpv2t6W+P1gMu1PEoPQY9E9Vp5akPXIzwlA8Cu4ObtT7PYo28ig4EZFM0+BmypQpEMX2O90uW7asw/s/+uijePTRR9VdVAhLjmnnKHh912xLfbEnuI37PCWG8FFwJbhRqccN4DFbijU3REShc1qKOpfUXs2NisFNsrItZW0WmDZY7Vh7uBRA8OttAHfmJtS2pax2J46X1gNQd1vKzD43REQKBjdhRC4orrXYYbFLH3I2hxM1TdL/5lUJblyPYXU4UdPozhKsPVQKi92J7KQoDMuMC/h5OiPX3DTaHGiyhc4H+snyetidImJMEciKj1TtcdnnhojIjcFNGImLNEDvOn1TWS9lNOTMhiA0n6btr0iDHrGuoZWlHltT8impmcMzIAjqnADqSKwpAvJBo1CaDC437xuYHqPq7xP73BARuTG4CSM6naDUopS7GvnJW1IJUe7AJ1CpLepuLHYHvj9QAiC4XYk96XSCkr0JpUZ+R+SZUipuSQGefW4cnU5s74zTKXZYC0dE1N0xuAkzyS3mS6lZbyNrWVT8w7Fy1FrsSI01YXR2omrP05kEVyZKzlKFAnlg5kCVgxu5oBiQtur8ZbU7Mevv6zH/9a1qLIuISBMh08SPvNNyeKaaPW5kynFw1wgGeZbUjOHpqjWl84ZcVFzdGEqZG6mBn9qZm0iDDjoBcIrS1pTZ5N9f7fyKehwqrsWh4lo02RyINOhVXScRUVdg5ibMKMMz65pnbtTocSNzZ26scDhFfL1fmgI+c3imas/hDfd8qdDI3DTZHDhZLp+UUrcPkCAISkATSFFxSY27jqq9+WFERN0dg5sw0zJzE+xtqW0nK1BRb0V8lAET+yep9hzeiA+x4+DHSuvgFKWMU2qs+vPO3EXF/m9LlXgENCW1TQGviYhICwxuwkxSi+GZwQ1urPjStSU1fWg6DPqu/eMUao38lOZ9abFBOVGmxnFwz4DGM4tDRBRKWHMTZuQuxZXBrLlxPUdpnQX7CqoBdN0pKU9KQXHIBDdSvc2gjOCMppCLihsC6FLcbFuqneGoRETdHYObMNPetpSqNTeuLZV9Z6thd4qINupx4cAU1R7fWwnm0BqeebhI/YGZnlSpufHclmLmhohCFLelwox7W6p5nxs1Mzdynxu7q5/K1CFpmpyqSQyxmpvDJcENbqJVqblpavMyEVEoYXATZpLNUuChHAUPQnAjb33JumJQZlsSolyZmxA4Cl5vseN0RSOA4AU3Ma6am0C6FHtmbnhaiohCFYObMCMHMVWNNjicIiqCUHMTbYxAtFH6IDXqdZg6JE21x/aF3OcmFI6CHy2R6m1SYkyqvheezCpMBvfciiphcENEIYrBTZiRt2pEESioakSTzSldr/IHqnxi6sKBKc2643YlpYlfg63bjwuQOxOr3d/Gk/w++Ju5abDam9XrMHNDRKGKwU2YidDrlA/9o6VStsCo18FsVLcmJjspCoA2p6RkcpG01eFEg7V7TwY/UhzcehvAXXNT52fNTcsC4rI6CxwBzqkiItICg5swlOT60D/qOnqcZDaq3lflkZ8Mx2NXDsfcMb1VfVxfRBv1MLp663T34+CH5GPgQQxuzAHW3MjbUL0ToyC4RjnIhelERKGEwU0Ykms65DoPtbekAGBwRiwWnJ+j2qRxfwiCEDJdipVp4EHqcQME3udGPh2VGR+pFKZza4qIQhGDmzCkBDelcubGoOVygioUjoNXN9pQWC0FDgPSgpm5CazPjbwtlRYbqYyHYFExEYUiBjdhSD6qrWRuVGzg192EwnHwo67+NpnxkYiPCl6g6d6W8rPmxhXIpMaakOYKbkrZyI+IQhA7FIchOXNT3ShlM5KDdPS4OwiF4+CHioJfbwN4Ds4MbFsqLc6kPAZHMBBRKGJwE4aSzM0nTgej5qa7UIZn1nffzM3hLjgGDgTe50aur0mLjURdk/QYJTXsUkxEoYfBTRhqmakJVtO47kDO3FQ1dt/MzeEuOAYOePa5CewoeFqsCXVN0u8na26IKBQxuAlDLYOZsK65cb227nwUvKuCm2i55sZqhyiKPh//l7el0uMilaJknpYiolDEguIw1DK46Qk1N9XdtOamvM6Csjop8BoY5G0pOXMjivC5qaHV7lTqltJiTTwtRUQhjcFNGGo52DK8a27kguLumbk57Grel50UpXQQDpYogx5y2yFf627kwmGjq8N1mhLcNHX70RZERC0xuAlDLbehwrvmxlVQ3E0zN0dcx8AHB3lLCpCaGrpPTPmWuZELh1NjTRAEQcncNNmcfvfNISLSCoObMBRp0DebJSVv3YSj7l5QfKhICm4GdkFwA3jU3fgYkHj2uAGkOVXyNhe3pogo1DC4CVNJrq2pWFMETBHqDs3sTpSj4A1WOLvhkMcjrm2prsjcAP53KZYzN/J2lOdlFhUTUahhcBOm5F434VxvA0Dp+OsUgdputn0iiiIOFcuZm+AWE8v8nS8lZ2fS4tzBTQqLiokoRDG4CVPyCalwD24iDXpEGaTMVFU3KyourbWgutEGnQCck9o1wY1cc1Pnc82Nu4GfTCkqZiM/IgoxDG7ClFxEnBTG9TayxG46gkHO2uQkmxFp6JqtQbPfNTdtbUtJgQ5HMBBRqGFwE6bkzE3LUQzhKN6j7qY7kY+BB7t5nydlBIOfBcWe21KpHJ5JRCGKwU2YmjokDRlxkbh0WLrWSwk6OXPT3Y6DHy7qmplSnsx+jmAoqe1gW4o1N0QUYjh+IUyd1z8Zm383TetldAnlOHh3y9y4etwMyui6zE2MH8MzHU4R5XXuuVIyOYvD01JEFGr8ytycPn0aZ86cUb7funUr7rnnHrzyyiuqLYzIW+75Ut0ncyOKonIMvCu3paJd/Y18OQpeXmeBUwR0ApAc03pbSq7HISIKFX4FNzfddBNWr14NACgqKsKll16KrVu34qGHHsLjjz+u6gKJOpMQ1f0yNwXVTaiz2GHQC8hJNnfZ88b4UXMjbzslx5ig17mHbcpbVJUNNljtThVXSUQUXH4FN3v37sWECRMAAO+//z5GjBiBH374AcuXL8eyZcvUXB9Rp5RGft2oS7Fcb9MvxQxjRNeVtvlTUNzWSSlAChoNeinYKeOJKSIKIX79q2uz2WAySf8Qfvvtt7jyyisBAEOGDEFhYaF6qyPyQkI3PAp+uFguJu66LSnAv4Jid4+b5sGNTicgJYZFxUQUevwKboYPH46XX34Z69evxzfffIOZM2cCAAoKCpCcnKzqAok6I9fcVHejbalDWgU3rpobXwqK5cAlPS6y1c/YyI+IQpFfwc1f/vIX/Otf/8KUKVNw4403Ijc3FwDwySefKNtVRF2lOzbx06KYGPBvtlR721IAkMpGfkQUgvw6Cj5lyhSUlZWhpqYGiYmJyvW33noroqOjVVsckTfkwtei6iY02Rxd1g24PU6niCMlXd/jBvCzoNi1LZXaRuZGOTHFRn5EFEL8ytw0NjbCYrEogc2pU6fw3HPP4dChQ0hLS1N1gUSdyU6KQkqMCVaHE3mnq7ReDk5XNqDJ5oQxQoe+XXhSCnBnbhp8qbmpbbvmxvM6Zm6IKJT4FdzMmTMHb775JgCgqqoKEydOxDPPPIOrrroKL730kqoLJOqMIAiY2D8JALDleIXGqwEOuU5KDUiNaXa0uit41tyIoujVfUo7Cm7imLkhotDjV3Dz448/4sILLwQArFy5Eunp6Th16hTefPNN/OMf/1B1gUTeOK+fK7g5Ua7xStwnpQZ3YWdimZy5cYpAo63z7I0oiu6am7a2pWLkLsUsKCai0OFXcNPQ0IDYWOkf7q+//hpz586FTqfDeeedh1OnTqm6QCJvTOwvndL7Mb9S04Zzoijii71FAIBRveO7/PmjjXoIrmSRN0XFlQ022BxShic1pq3MjaugmEfBiSiE+BXcDBgwAB999BFOnz6Nr776CpdddhkAoKSkBHFxcaoukMgbA9NikGQ2osnmxO4zVZqtY/eZauwrqIFRr8Occ3t1+fMLggCz0fu6GzlrkxhtaLPZYKpHzY2321xERFrzK7h55JFHcP/99yMnJwcTJkzApEmTAEhZnNGjR6u6QCJvCIKACTny1pR2dTfLt0iZy8tHZiDJbNRkDb7Ml3I38Gu9JQW4szk2h9itjtoTEXXEr+Dm2muvRX5+PrZv346vvvpKuX7atGn429/+ptriiHwhFxVvPq5N3U11ow2f7CoAAMw7r68mawB8Ow6unJSKa70lBQDGCJ3SR4hbU0QUKvzqcwMAGRkZyMjIUKaD9+7dmw38SFMT+0l1NztOVcLmcMKg77qZTgDw0c6zaLI5MSg9BuP6JnZ+hyBRRjB40aVY3pZKbeOklCw11oTKBhtKaps0KZImIvKVX//6O51OPP7444iPj0ffvn3Rt29fJCQk4I9//COcTk4PJm0MyYhFfJQBDVYH9p6t7tLnFkVR2ZKaN7EvBKFrj4B7Mptcx8G9qbnpZFvK82c8Dk5EocKv4Oahhx7C888/jz//+c/YuXMndu7ciSeffBL//Oc/8fDDD3v9OOvWrcPs2bORlZUFQRDw0UcfdXj7Dz/8EJdeeilSU1MRFxeHSZMmNdsWo55NpxMwXqO6m+2nKnG4uA5RBj2uHtP1hcSefNmW6qjHjYyN/Igo1PgV3PznP//Bq6++ittuuw2jRo3CqFGjcPvtt+Pf//43li1b5vXj1NfXIzc3Fy+88IJXt1+3bh0uvfRSfP7559ixYwemTp2K2bNnY+fOnf68DApD5ynN/Lq27mb5Zilrc2VuFuIiDV363C1FG72fL+XucdPxthTAzA0RhQ6/am4qKiowZMiQVtcPGTIEFRXe/4951qxZmDVrlte3f+6555p9/+STT+Ljjz/Gp59+2u4pLYvFAovF/Y9yTU2N189HoUeuu9l+shIOp9glHYIr6q34fI/U22beeX2C/nydUWpuvDoK3vm2lBLcsJEfEYUIvzI3ubm5eP7551td//zzz2PUqFEBL8pbTqcTtbW1SEpKavc2S5cuRXx8vPKVnZ3dZeujrjcsKw6xpgjUWuzYX9A1gezKHadhdTgxolccRvVO6JLn7EiMq+amoZOCYlEUPWpuOtiWYiM/IgoxfmVunnrqKVxxxRX49ttvlR43mzZtwunTp/H555+rusCO/PWvf0VdXR2uu+66dm+zZMkS3Hvvvcr3NTU1DHDCmF4nYFxOIlYfKsWWE+UYGeQuwU6niHe25AOQCom7Azlz09m2VJ3Froxo6HBbShnBwOCGiEKDX5mbiy++GIcPH8bVV1+NqqoqVFVVYe7cudi3bx/eeusttdfYpnfeeQePPfYY3n///Q4nkZtMJsTFxTX7ovAmj2LY3AVDNH84Vo6T5Q2IMUXgytysoD+fN+QOxZ0VFMtbUrGmCKVOpy1y4MPghohChd99brKysvDEE080u27Xrl147bXX8MorrwS8sI68++67+MUvfoEPPvgA06dPD+pzUeiZ6Bqiue1kBZxOEbog1t28s1UqJL56dC8lY6I1d+am45obeUsqtYOsDeDesqq12NFodSDK1QGZiKi76touZypYsWIFbr75ZqxYsQJXXHGF1suhbmhEr3hEG/WobrThYFFt0J6npKYJX+8rBgDcNFH7QmKZ2cuaG+WkVAf1NoB0tDzSoGt2HyKi7kzT4Kaurg55eXnIy8sDAJw4cQJ5eXnIz5dqGJYsWYL58+crt3/nnXcwf/58PPPMM5g4cSKKiopQVFSE6uqubdhG3ZtBr8NYV4fgLSeCdyT8/e2nYXeKGNs3EUMzu892p7d9bkq9OCkFSHO75Ntwa4qIQoGmwc327dsxevRo5Rj3vffei9GjR+ORRx4BABQWFiqBDgC88sorsNvtuOOOO5CZmal83X333Zqsn7qv81x1N1uCVHfjcIpYsfU0AGBeN8raAN73uSmu8S5z43mbEgY3RBQCfCoSmDt3boc/r6qq8unJp0yZAlEU2/15y4aAa9as8enxqeeS6262nqyAKIqqj0NYe7gEZ6sakRBtwOUjM1V97EDFeNnnprOhmZ7cjfy4LUVE3Z9PwU18fMfHauPj45ttIxFpZVTvBEQadKiot+JISR0Gpas78HH5ZimjeO2Y3og0dK8CW2W2VGc1N17MlZJxBAMRhRKfgps33ngjWOsgUpUxQocxfRLxw7FybDlermpwc6ayAd8fKgEA3NjNtqSA5jU3HWWtvC0oBtyN/DiCgYhCQcidliLyljyKYbPKQzTf23Yaogicf04yzkmNUfWx1RDtCm6cItBkc7Z7O5+2pWJYc0NEoYPBDYWticoQzYoOa7t8YXM48d42uZC4e3QkbinaY5usvaLiJpsDtU3Sz1K92JZKZSM/IgohDG4obJ2bnQBjhA5ldRYcL6tX5TG/O1CMkloLUmJMuHRYuiqPqTadToDZ1WivvePg8vaSKUKHuMjOd6eZuSGiUMLghsJWpEGPc7MTAKh3JHy5a47UdeN6wxjRff/6KJPB2ykqVupt4kxenSSTt67K6y2wO9rf6iIi6g6677/ORCo4z3UkXI1mfifL6rH+SBkEAbhxQvcrJPZk7uQ4eImXDfxkyWYTdAIgikBFvVWdRRIRBQmDGwprEz2a+QVad7Niq5S1uXhQKrKTogNeWzApx8Hb3Zby/qQUIE1bT+bWFBGFCAY3FNbG9EmEQS+gqKYJ+RUNfj+Oxe7A+9u7dyGxJ3MnXYrdmRvvghvP27KomIi6OwY3FNaijHqM6p0AILC6my/3FqGywYbM+EhMHZyq0uqCR+51097wTPcxcO+2pQDPEQzsUkxE3RuDGwp757mOhG8OoO5G7kh8w/g+iNB3/782cq+buk5qblJ9yNy4RzAwc0NE3Vv3/1eaKEByMz9/MzeHi2ux9WQF9DoB14/PVnNpQRPjZc1Nuk+ZG9dkcI5gIKJujsENhb2xfROh1wk4W9WIM5W+1d002Rx44INdAIDpQ9OQEe99MKAlueamveCm1J+amzhmbogoNDC4obBnNkVgZC9p6Kuv2ZvHPt2HXWeqkRBtwO+vGBaM5QVFR31ubA4nyl3HuX0JbtyN/FhzQ0TdG4Mb6hGUUQw+1N28ty0fK7aehiAA/7hhdLc//u3JfRS8dc2NnLWJ0AlIjDZ6/Zhy5obbUkTU3TG4oR7hPLnuxsshmrvPVOHhj/cBAO67dBAuGtT9T0h5MpvaPwruWUys03XenVgm19yU1FhUm9VFRBQMDG6oRxiXkwidAJwqb0BRdcfbKhX1Vtz29o+w2p2YPjQdt08Z0EWrVE+Mqf2aG18b+Mnk01IWuxM1TW3X8hARdQcMbqhHiI00YHiWq+6mg60ph1PEr1bsxNmqRuQkR+PZ63N9ym50F0pBsbX1tpQ7c+NbcXSkQY9Y15BNNvIjou6MwQ31GBNdc6Y2d1BU/MzXh7DhaBmiDHr86+fjEBdp6KrlqSq6g6Pg7gZ+vmVuADbyI6LQwOCGegxlzlQ7mZuv9hXhxTXHAAB/uXYUBmfEdtna1NbRtlRprX/bUoB7a4qZGyLqzhjcUI8xIScJggAcL61vlXk4XlqH+9+X+tksmtwPV+ZmabFE1XRYUFzj20RwT0ojPwY3RNSNMbihHiM+2oAhGXEAgK0ep6bqLXb88q0dqLXYMaFfEpZcPkSrJarGPVvK0epkkz9DM2XubSkGN0TUfTG4oR5FrruRm/mJoojf/Hc3jpTUIS3WhOdvGg1DCMyO6ky0Uaq5cThFWOzOZj+Ts1b+1Ny450ux5oaIuq/Q/1e8u2H/j27tvBbN/F7bcAKf7S5EhE7ASz8b49dWTXckn5YCmm9NOZwiyurk7sR+bEuxkR8RhQAGN2opPwa8cTnw8gVar4Q6MMHVzO9wcR2+2FOIpV8cBAA8/JNhGNs3SculqUqnE5TsjWdRcUW9FQ6nCEEAUmK8704sS41xN/IjIuquGNyoJToZOLURKN4L1JVqvRpqR5LZiEHpMQCAO1fshMMp4urRvTB/Ul+NV6Y+Zb6UxwgGeUsq2WxEhB/bb4FmbkRRxGe7C7mtRURBxeBGLVEJQOpQ6fKZrZouhTo20ZW9cThFDM2Mw5NXj4QghF6jvs6Y5cyNx/BMfxv4yeSC4qoGGyz21g0CO/PfH8/ijnd+xIMf7vHr+YmIvMHgRk3ZE6Rf8zdruw7q0OQBUnATFxmBl382BlGuICDctHUcvNS1nZTuRzExAMRHGWB0ZXz8OQ7+5d5CAMCGo2VobKN7MhGRGhjcqKnPedKvp5m56c4uG5aBR2cPw7u3TkLfZLPWywkacxuN/EoCaOAHAIIg+N3Ir8nmwIajZQAAq92JzT5MaCci8gWDGzVlT5R+LdgJ2Flw2V3pdAIWTu6HYVlxWi8lqJReNx41N8UBNPCTpfrZ6+aHY2VosrmPpa87zNo0IgoOBjdqSuoPRKcADgtQuEvr1VAPJ5+Wqmsrc+PnthTgf3Dz3YESAECvhCgADG6IKHgY3KhJENzZm9NbtF0L9XhtzZcKpDuxLM2PbSlRFPH9QSm4+c3MwdAJwLHSepytavR7HURE7WFwoza5qJjBDWlMKSj2PC1VE9hpKcBzvpT3x7n3FdSgsLoJUQY9ZgzPwLnZCQCA9czeEFEQMLhRm5y5yd/CbsWkqZYFxaIoKtmWQDI37hEM3mdu5KzNBQNTEGnQ46JBqQCAdUcY3BCR+hjcqC1rNKAzAPUlQOVJrVdDPZjc50YuKK5utMHqkAp6U9XYlvKhkd93B4oBANOGpAGAEtxsOFIGu8PZ7v2IiPzB4EZthkgg61zpMo+Ek4Za9rmR623iowyINPjf20cuRvY2c1NS24RdZ6oBAJe4gptRveIRFxmBmia78jMiIrUwuAkGpaiYzfxIO0pBsavmpqQm8C0pwJ31KauzwOnsfOt1tWtLalTveKTFSfU6EXodLhiYAoCnpohIfQxugkEpKmbmhrTjztxI21JqHAMHgJQYEwQBsDtFVDZYO729fAR82pD0ZtdfNJB1N0QUHAxugkHO3BTvA5pqtF0L9Vhmk1xz03xbKpAGfgBg0OuQFG1s9pjtabI5sP6I1JV42tC0Zj+T6252na5CdYMtoDUREXlicBMMsRlAQl8AInB2u9aroR7KbGx+WkqtbSkAXo9g2HS8HI02BzLiIjG8RUforIQoDEiLgVOEMpaBiEgNDG6CxfNIOJEGWhcUS9tSgZyUknnbpfh715bUJUPT2py8rmxNse6GiFTE4CZY+rBTMWnLXVDsgCiK7m2puMC2pQDP4Kb9Rn6iKLY6At7SRYNcRcVHSiGGYV+ooyV1OFpSp/UyiHocBjfBImduzmwHnI6Ob0sUBHLNjcMpwmJ3qtLAT+buUtx+5uZgUS0KqpsQadBh8oCUNm8zsV8yjBE6FFY3hV0QUNVgxVUvbMRVL2xElReF10SkHgY3wZI2DDDGAtZaoGS/1quhHijaVXMDSHU3JTWu01KqBDedb0vJWZsLBqS021cnyqjHxH5JAIC1YbY19dW+ItRZ7Kiz2PHF3iKtl0PUozC4CRadHug9TrrMrSnSgF4nIMoVVJTUWlBvlTKIam5LlXbQyO87V3+bS1ocAW/JfSQ8vIqKP91VqFz+aOdZDVdC1PMwuAkmpZkf+92QNuSi4hNl9dL3Rr1SixOIzkYwlNVZkHe6CoC7K3F75CPhW46Xo8kWHlu4pbUW/HDMHaxtPVmBAk5AJ+oyDG6CSW7ml89OxaSNGFfdjRzcqJG18XwceaurpdUHSyCKwIhecciI7/g5B6XHICMuEha7E9tOVqiyPq19sbcQThHIzU7AhJwkiCLwv90FWi+LqMdgcBNMvccDEICqU0At99yp68l1N3Jwo8YxcM/Hqbc6lD46ntrrStwWQRBwYZiNYvh0lxTIzB6ViSvPzQIAfJzH4IaoqzC4CabIOCB9uHSZW1OkgZgW21JqFBPLjxvtmjre8sSUxe7AetdIhZZdidsjb02tOxz6dTcFVY3YdrISggD8ZFQWLh+ZiQidgH0FNThaUqv18oh6BAY3wabMmWJRMXU9c8ttqQBHL3hq78TUluMVqLc6kBZrwoiseK8e64IBKRAE4FBxLYqq2++dEwo+2y0VEo/PSUJGfCSSzEYleGP2hqhraBrcrFu3DrNnz0ZWVhYEQcBHH33U6X3WrFmDMWPGwGQyYcCAAVi2bFnQ1xmQ7POkXxnckAbkguKKeqnPSqBDMz2118hPadw3NA06XeuuxG1JNBsxqpcUCIX6IM1PXbU1s3OzlOvmeGxNhWOzQqLuRtPgpr6+Hrm5uXjhhRe8uv2JEydwxRVXYOrUqcjLy8M999yDX/ziF/jqq6+CvNIAyJmbgjzAFtr/I6XQYzY2Pxml1raU9FitG/mJouj1EfCW3FtToRvcnCyrx+4z1dDrBMwakaFcf+mwdEQZ9MivaFBOkQWqpLYJC17five25avyeEThRNPgZtasWfjTn/6Eq6++2qvbv/zyy+jXrx+eeeYZDB06FHfeeSeuvfZa/O1vfwvySgOQmAOY0wCnDSjYqfVqqIcxm1oGN+ptS7U1X+pwcR3OVDbCFKHDBe10JW6PHNxsOFoGh9O/7MaHP57BjL+tUy2A8JV8Iur8c5KREuMOJKONEbhsuBTsqbU19c/vjmLt4VL86bMDaLSGxxF6IrWEVM3Npk2bMH369GbXzZgxA5s2bWr3PhaLBTU1Nc2+upQgsO6GNCMfBZcFY1vKM3Pz3UFpS+r8c5IRZWy7K3F7zs1OQKwpAlUNNuw5W+3zevYX1ODBD/fgUHEtfv1eniY9c+TGfZ5bUjJ5a+p/uwthdzgDep6i6ia8t+00AKC2yY7P9xR2cg+iniWkgpuioiKkpzdPdaenp6OmpgaNjW03yFq6dCni4+OVr+zs7K5YanN95LobnpiirtU6c6PmtlTrzI1yBHyob1tSAGDQ63D+gGQAvm9NNVjtuHPFj7DapaDhRFk9/vbNYZ/XEIhDRbU4VFwLg17AjOEZrX5+4cBUJEYbUFZnwQ/HygN6rpfXHoPV4YRBL9U0rdiq/tbUusOluOIf68NuLAb1DCEV3PhjyZIlqK6uVr5Onz7d9YtQOhVvAVhMSF0o2iO4MUboEB9lUO2xWzbyq6i34sf8SgDeHwFvyd+6m0c/2YfjpfVIjzPh6WtHAQD+vf44dnXh9pS8JXXxoLQ2f58Neh2uGJUJILCtqZLaJiWY+fPcUdDrBGw/VYkjxeodMxdFEU98dgD7Cmrwy7e2Y6frfSUKFSEV3GRkZKC4uLjZdcXFxYiLi0NUVFSb9zGZTIiLi2v21eUycwG9CWgoAyqOd/3zU4/luS2VGmOCIHh3eskbqa6akjLXCAa5K/GwzDhkxrf997Ez8pypnaerUNNk8+o+n+wqwPvbz0AQgOeuH42fjsvGnHOz4BSB36zcrWRzgkkURXfjvtzMdm8359xeAKShmv5um/173XFY7E6M6ZOAuWN6YepgKZB8d5t6/3H74Vg5DrmCpSabE4uWbcPx0vCa2k7hLaSCm0mTJuG7775rdt0333yDSZMmabQiL0WYgKzR0mXW3VAX8jwtpWa9jefjlddbYXc4lXqb6X5mbQAgOyka/VPMcDhF/HC084Z++eUNeOjDPQCAu6YOwKRzpG2tP8wejmSzEYeKa/HC6qN+r8dbe8/W4GR5AyINOkzvYEtubJ9E9EqIQp3Fju9dp8p8UVZnwdubpazNXdMGQhAE3DhB2mr/8MczsNjVqTN6fcMJAMC1Y3tjVO94VDbYsOCNra2O/RN1V5oGN3V1dcjLy0NeXh4A6ah3Xl4e8vOlv7xLlizB/PnzldsvXrwYx48fx29+8xscPHgQL774It5//338+te/1mL5vuGcKdKA55BMNettACAp2gi9ToAoAoXVTUp34Uv8qLfxpGxNdTIl3OZw4lfv7kStxY5xfRPxq2kD3WszG/HYHKk7+Aurj+JAYXAPEsi9baYNSW9V5+RJpxOUYuOP83yfFP7q+hNotDkwqnc8prh+ny4elIqMuEhUNtjw1b7iTh6hcyfK6pXj/HdMHYDXF45H3+RonK5oxKJl21DXxrgNou5G0+Bm+/btGD16NEaPlrIa9957L0aPHo1HHnkEAFBYWKgEOgDQr18/fPbZZ/jmm2+Qm5uLZ555Bq+++ipmzJihyfp9wgnhpIHoZsGNesfAAemDOiXGCEA6AVRnsSMlxqQ04/PXRYPcc6Y6anj3zNeHkXe6CnGREXjuhnMRoW/+z9kVIzNx2bB02J0ifrNyd8AnlNrjdIr4nxdbUrKrRkvBzeqDpahu8G7rDQAq6614c9NJAMCvLhmobDFG6HW4blxvAMC7KhQWL9soZW2mDUlDvxQzUmJMeHPRBCSbjdh7tga3vb2jS7b6iAKhaXAzZcoUiKLY6kvuOrxs2TKsWbOm1X127twJi8WCY8eOYeHChV2+br/IwU3pAaCxStOlUM/hWXOjduZGekwpYJIbyV0yJNXrrsTtOa9/Mox6Hc5UNipjI1paf6QUL689BgB46tpR6J0Y3eo2giDgT1eNQFxkBPacrca/158IaF3t+TG/EgXVTYgxRWDK4M635IZkxGFweiysDie+3Of9Ee7XN55Ag9WBYZlxrQq2rxufDUGQamVOlbf9e+aN6kYbPthxBgCw6IJ+yvV9k8144+bxiDbqsf5IGX77391w+tmLqCuIoojlW07hCx6R77FCquYmpMWkAkn9pctntmm7FuoxPLdI0uPUzdwA7l43J8sbAPh3BLylaGMExuUkAmj71FRZnQW/fm8XAGDexD6YOaL9bElaXCQe/skwAMDfvj2MY0EoipULiS8blo5Ig3e9fXydFF7dYMOyjScBAL+aNrBVYXjvxGhc6CrGDqSw+L1t+WiwOjA4PRbnu+qXZKN6J+DFeWMQoROwaudZ/OWrg34/T7DtOFWJh1btlbYtvSxMp/DC4KYreR4JJ+oCnsFNqsoFxUDzbJDRj67E7Wmv7sbpFHHf+7tQVmfBoPQYJXDpyLVje+OiQamw2p347Up1Mw4Op4jP9hQBaLtxX3uudN120/FyFNd0XqT7xg8nUGuxY3B6LC4b1nYAeeN4qbD4g+1nYPNjC87ucOI/P5wCACy6IKfNk3VTBqfhz9dIR+3/tfY43tgYnGxYoJZvkTKJNocYcE8hCk0MbroSgxvqYtGGYG9LuR9zUv/kDotpfXHhQClI2nSsvNkJoNc2nMDaw6UwRejw/E1jvMqUCIKAJ68eAbNRj+2nKpW6FTVsOV6OsjoLEqINmOxDYJedFI2xfRMhiu7MT3tqmmzK6aW7pg1od9tv2tB0pMQYUVZnUZop+uLr/cU4W9WIJLNRObLelmvH9sYDMwYDAB7/336lv4+v5DIEtVXWW/GZx3ZUKM8qI/8xuOlKcnBzZgfg4IkDCr4IvQ79U8yIMUWgb7JZ9cdP9QhuAjkC3tLQjDikxJjQaHNgx0mpgdzuM1V4yrUV8sjsYRiUHuv14/VOjMaDs4YAAJ766hBOVzSosk75lNSsERkwRvj2z+lVXm5NvfnDSdQ02TEgLQazOtiCM0bocM1YV2GxH8M05QBq3sQ+nQaNt085Bz8/ry9EEbj3vV3YfNy77EhNkw2f7S7E/R/swvgnvsPYP32r2nsh+++PZ2C1OxFpkN6P9Z2cuqPwxOCmK6UOAUzxgK0eKN6r9Wqoh1h1+2R8d9/FzY6FqyXV4wTW1CHqBTc6nYCLXNmbtUdKUWex464VO2FziJg1IgM3Tejj82POm9gXE/olocHqwJIP9wScNbDanfhir2tLapT3W1Kyy0dmQq8TsOdsdbu1QHUWO16VszaXDIC+k2LtG8ZLvy9rD5fibFXbI2nasut0FbafqoRBL+Dn5/Xt9PaCIODRK4dj5vAMWB1O3PLmdhwsan3cXhRFHCmuxb/WHsMNr2zCmMe/wR3v/IiVO86grM6Cinor/rXumNfr7IwoinjHdWLsvksHw6AXkF/RgJPtFKZT+GJw05V0OiB7vHSZR8Kpi8RHG4JSTAxI3YgjdAIm9Etq88RSINyjGMrw8Ed7caq8Ab0SovDnuaP86rSs0wn4yzWjYIrQYcPRMry/PbCOvhuPlqGqwYaUGBMm9k/u/A4tJMeYlO23T9rJ3ry9+RSqGmzon2LGT7wIoPqlmHFe/ySIIvC+D4XFcu3M7FFZyliNzuh1Ap674VyMz0lEbZMdC1/fhoKqRjTZHFh9sAQPf7QXFz61Gpf+bR2WfnEQm49XwO4U0T/VjF9c0E+pl/pg+xmU11k6eTbvbD5egeOl9TAb9bhxYh+M7esqTD/CramehsFNV2PdDYWRPsnR+Pbei/Hv+eNUf+wLXB/8BwprsGrnWegE4O83nIv4aP/nY/VLMeP+y6R6kT/97wCKqv3vuCvXyvxkVGanGZX2yJPCP9lV0CqT1GC149/rpHEtt0/tPGsju9GV1fpg+2k4vCieLq5pwv92SzUqN0/u18mtm4s06PHq/PEYmBaDopomXPn8BuQ+9jVuXrYNb20+hTOVjTBG6HDRoFQ8OnsY1j4wBd/fNwW//8kwLJqcg1G942GxO/GfTad8et72LN8iPc6c0b0QY4rwe1YZhT4GN11N7lTM4IbCRE6KWdWBnLKUGBNG9HLPgrtn+iCMy0kK+HEXXdAPudkJqLXY8dAq/7anmmwOfL1f6gbsTeO+9lw2LAORBh1OlNVjz9nqZj97Z0s+yuut6JMUrQRB3pgxPAMJ0QYUVDd59aH+1qZTsDtFTMhJwsjevjdgjI824D+LJiAjLhJldVZY7E5kxUdi3sQ+eHX+OOQ9cineXDQBCyf3a1b3JQgCfnnROQCANzedRIM1sDrEsjoLvtonbRPK25byrLJNx8rZeLCHYXDT1XqNBQQdUH0aqPa9/TpRTzJtiHTs+bz+Sbhj6gBVHlOvE/D0taNg0Av47mAJPunktFJb1hwqQZ3Fjl4JURidnej3WsymCFw6LAMA8NFO9zqabA68vFbK2twx9RwY9N7/Ux1p0GPuaKmweEUnHYubbA4l27Hoghxflt5MVkIUPlg8CX+8agS+uucibHzwEjxx9UhMH5aOaGP7tV4zR2SgT1I0qhps+GD7Gb+fH5CPwIvIzU7ACFeX7GGZcUiJMaLe6sCOU5xs3pMwuOlqplggfYR0+Qzrbog6ctuUc/Dc9efi3/PH+b3105ZB6bG46xJpFtWjn+zrdNRDS5/ukrZxfjIqM+COzHNcPW8+3V2gbCOt2JqPsjoLeiVE4WpXoOILeZjmdwdLUNJBH52Pdp5FZYMNvROjlCDLX9lJ0fj5eX0xOCPW65oovU7ALRdJzU3/vf643yMynE5RCeTmTXQXm+t0gtLckHU3PQuDGy3IdTf53Joi6kikQY+rRvdCbKT62163TTkHwzLjUNlgw/zXt+Kal37wKsips9iVCei+NO5rz0WDUpEQbUBprQWbj5e7sjbSCaLbp57j8xFzABiYHouxfRPhcIrKOIWWRFHE665C4oXn56gaPPrip2N7I8lsxJnKRnzuOn3mqw1Hy5Bf0YDYyIhWJ9fkWWXrGdz0KAxutMCiYiLNGfQ6vP2LiVg0uR9METr8mF/lVZDz3YFiNNmc6JdixvCsuDZv4wtjhA6Xj5Tqdj7OO4sPdpxBcY0FmfGRuHas71kb2Q2ujsXvbTvdZlfmDUfLcLi4DmajHte5bquFSIMeCyblAAD+tfaYXzVQ77g6El8zpjeijM179FwwQMrc7D1bgzKVTmVR98fgRgt9XMFN0W7Aqm4DKyLyXpLZiEdmD8P630z1OsiRT0nNHpXp15H0tshbU1/sKcJLq48CABZffA5MEd7NqmrLFaMyEWuKQH5FAza10WRPbtr303HZiAtCZswX8yf1RZRBj30FNT6PSyiuacI3B6RM2k0TW/c/So01YVimFIRuYEO/HoPBjRbis4HYTMBpBwp+1Ho1RD1eWlykV0FOdYMNa10nkNTYkpKNz0lCZnwkai12FFQ3IS3WhOsDzKZEGyMwZ7S0xpaFxcdK67D6UCkEAVhwfk5Az6OGRLMR142TslTylpy33t8mHXkf1zex3a7VPBLe8zC40YIg8Eg4UTfUUZAz96Uf8NevD8HmEDE4PRYDfRj/0BmdTlCGaQLALy8+x+sJ4x2ROxZ/va8YFfVW5Xp5wvi0IWnol6L+WA5//OLC/tAJ0riEfQXVnd8B0uBSpZD4vPa7Vst1N+uOlKk6OJW6LwY3Wsk+T/qVRcVE3U5bQc7O/Cq8tVk6Nh1Ib5v2XD2mF3SCNIzUn/ESbRnRKx4je8XD6nDiwx+lwuLqBhtWuoqMF/nYtC+YspOicYWrGFhuXtiZtYdLUFDdhIRoQ4dzt8b1TUK0UY+yOgsOtDEmgtT13LeHsTNf26P3DG60kjNZ+vXY90CV70PuiCj42gpyzEZ9h1Oz/TUkIw6rbp+M/952fqui2EDc4DoWvmJrPkRRxLvb8tFoc2BIRiwmneP72Ihg+qXrWPinuwtxprLzesTlm6V/O68d07vDTJcxQodJrhEZHKQZXB/+eAbPfXsE17+yOaAO4IFicKOVzFyg30WA0wasfUrr1RBRB+QgZ8vvpuH7+6cgO0ndOVqy3OwE1R/7ytwsRBn0OFZaj83HK/CfH04CkLI2ahVEq2VEr3hMHpAMh1PEa66C5/acrWrE6kMlAIAb2ygkbkme48W6m+DZe7YaSz7cA0AqiM+ID85MO28wuNHSJQ9Lv+a9A5SrNxmXiIIjIdoYtCGkwRIbaVC20e7/YBcKqpuQbDbiSh9GOnQleSTDe9tOo6rB2u7t3tuaD6cITOqfjHNSYzp9XLmoePvJyoBHPVBrVQ1W3LZ8Byx2J6YMTsU90wZquh4GN1rKngAMnAGIDmDNUq1XQ0Rh6gZXDc/ZqkYAwLzz+qpSsBwMFw5MwdDMODRYHXh7c9sDNe0OJ95zTXVv6/h3W/qlmNE7MQpWhxOb2zgaT/5zOEXc/W4eTlc0ok9SNJ67/tyAO3cHisGN1i55SPp1z0qgeL+2ayGisDQ6OwGDXae7DHoBP+vgZJHWBEHA4oul2ptlP5xEk83R6jbfHSxBcY0FyWYjZgz3bmyEIAgeR8JZd6Omv397GGsPlyLSoMPLPxuLhGij1kticKO5zFxg2BwAIrDmSa1XQ0RhSBAE3Dw5BwBw7djeSIvt3ltrl4/MRK+EKJTVWfHhj60HDC93dST+6bhsn8ZTyFPCWXejnm/3F+Mf30uNJ5fOHYlhKnTtVgODm+5gyu8ACMCBT4GCnVqvhojC0PXjs/HJnZPx2JUjtF5Kpwx6Hf7vAumY+r/XH1cGigJAfnmDMifK1yPz5w9Ihl4n4HhZPU5XsDt8oE6U1ePX7+cBABZM6uvXkNdgYXDTHaQNAUZdJ13+/glt10JEYUkQBIzqneDXIE4tXD8+G/FRBpwoq8c3+90DNVdsy4coSrU5fZJ9O1kWF2nAmD4JAHgkPFANVjsWv7UDtU12jO2biIeuGKb1kpoJjT/lPcGUBwFBDxz9BsjfrPVqiIg0ZTZFYP6kvgCAl9cehyiKsNqd+MBVSDzPy0Lili7k1lTARFHEb/+7B4eKa5Eaa8KL88Z0u6C5e62mJ0vqD4z+mXT5+z8BfkzGJSIKJ/Mn5cAYoUPe6SpsO1mJr/cXoazOirRYE6YNTffrMeWi4o3HymB3ONVcbo/x+saT+HRXASJ0Al6cN6ZbtkdgcNOdXPQAoDcCJ9cDJ9ZqvRoiIk2lxppw7VipjuNfa4/hHVch8fXjs2HQ+/fxNbJXPBKiDahtsiPvdJVaS+0xNh8vx5OfHwAAPHTFUIzPSdJ4RW1jcNOdJGQDY2+WLjN7Q0SEWy7sD0GQjn//cKwcOsHdt8cfep2ACwawW7E/iqqbcOc7P8LhFDHn3Cws7AYT5dvD4Ka7ufA+ICIKOLMNOPyV1qshItJUvxQzZgxz97KZMjgNvRKiAnpMeWtqLYuKvWa1O3Hb8h0oq7NiSEYsls4d2e3Gd3hicNPdxKYDE2+VLq/+E+DknjAR9Wy/dDX1A/wvJPYk97vZfaaqwxEP5PbH/+3HzvwqxEVG4F8/H4toY4TWS+oQg5vuaPI9gDEWKNoDHPhE69UQEWlqdJ9E3D7lHNw4IRtTBqcF/HgZ8ZEYnB4LUQQ2HGX2pjP/3XEGb7lGYTx3w7nom2zWeEWdY3DTHUUnAZPukC6vfhJwtm4/TkTUk/xm5hAsnTsKepVmFnFKuHfOVjXioY+kSd93TxuIS4b4d0qtqzG46a4m3Q5EJgBlh4A9H2i9GiKisOI5Z0rk4Y12/fmLg2iyOTEhJwl3azzp2xcMbrqryHhg8t3S5TVLAYdN2/UQEYWRCf2SYIrQoaimCUdK6rReTre07WQFPt1VAEEA/nDlMM0nffuCwU13NvGXgDkVqDwJ5C3XejVERGEj0qDHxP7JALg11RanU8Rjn+4DANwwvg+GZ8VrvCLfMLjpzoxm6Wg4AKx9CrA1abseIqIwcpGr7mYtg5tWVu44g71naxBrisB9lw3Sejk+Y3DT3Y29GYjNAmrOAjuWab0aIqKwcbGr7mbriQo02XhwQ1bbZMNTXx0CAPxq2kCkxJg0XpHvGNx0d4ZI4OIHpMvrnwGs9dquh4goTAxIi0FmfCQsdie2nqjQejndxgurj6GszoJ+KWYs6MZdiDvC4CYUjP45kJgD1JcAm1/SejVERGFBEAQeCW/hVHk9Xt9wAgDw+yuGdrtp394KzVX3NHoDMGWJdHntU0DRXm3XQ0QUJpQj4UcY3ADAE58dgNXhxEWDUnHJkMAbJmqFwU2oGHU9MHAG4LAAKxcB1gatV0REFPIuGJACnQAcLq5DYXWj1svR1MajZfh6fzH0OgEPXzG0W8+O6gyDm1AhCMBVLwIx6VJjv69+p/WKiIhCXkK0EaN6JwAA1h/uuaMY7A4nHv90PwDg5+f1xcD0WI1XFBgGN6HEnAJc/S8AArDjDWA/504REQVK3ppafahE45VoZ8XWfBwqrkVCtAH3TA+dTsTtYXATas6Z6u5c/MmdQNVpbddDRBTipg+Vaku+2FuET3cVaLyarlfVYMWz3xwGANx76SAkRBs1XlHgGNyEokt+D2SNAZqqgQ9v5WBNIqIAjOqdgFsu7AcAuP+DXdh1ukrbBXWx5749gsoGGwalx+CmCX20Xo4qGNyEIr0BuPY1wBgD5P8ArPur1isiIgppD84aikuGpMFid+KWN7f3mOLiI8W1eGvzKQDAIz8Zjgh9eIQF4fEqeqKk/sAVz0qX1/4ZyN+s7XqIiEKYXifg7zeci8HpsSipteCWN7ejwWrXellBJYoi/vjZATicIi4dlo4LXD1/wgGDm1CWe710RFx0Av/9BdBYqfWKiIhCVmykAa8uGIdksxF7z9bgvvd3wekUu+z5qxqsWLnjDH7xn+24+OnV+N/u4Nb/rD5UgnWHS2HQC3jo8qFBfa6uxuAm1F3+VyCxH1B9Gvj0HkDsur+IREThJjspGi//fCyMeh2+2FuEv317OKjPV1LbhLc3n8LPX9uCcX/6Fvd/sAvfHijGqfIG3PnOTvz5i4NwBCHAstqd+NP/DgAAFl3QDzkpZtWfQ0sRWi+AAhQZJ9XfvHYZsP8j4Mc3gbELtF4VEVHIGp+ThCfnjsT9H+zCP78/igFpMZhzbi/VHv9MZQO+3FuEr/YVYfupymb/Jx2SEYuZIzJQ22THaxtO4OW1x7C/sAb/uOFcVU8xvbnpJI6X1SMlxoQ7pw5Q7XG7i26RuXnhhReQk5ODyMhITJw4EVu3bu3w9s899xwGDx6MqKgoZGdn49e//jWampq6aLXdUK+xwCUPS5e/fBAoDe7/NIiIwt21Y3tj8cXnAAAeWLkbP+YHtu1/rLQOL6w+itn/3IAL/rIaf/rsALadlAKb3OwEPDhrCFbfPwVf3nMR7pk+CA//ZBj+eeNoRBn0WHe4FFc+vxEHi2rUeGkoq7Pg798dAQA8MGMQYiMNqjxudyKIorb7GO+99x7mz5+Pl19+GRMnTsRzzz2HDz74AIcOHUJaWuu5Fu+88w4WLVqE119/Heeffz4OHz6MhQsX4oYbbsCzzz7b6fPV1NQgPj4e1dXViIuLC8ZL0obTCbx9NXB8DZA+EvjFt9JEcSIi8ovTKeKXb+/AN/uLkRJjwsd3TkavhCiv7y+KIjYcLcNLa47hh2PlyvU6QcoOzRyRgRnDM5DVwWPuL6jBrW9tx5nKRkQb9fjrT3Nx+chMv19TcU0THv5oL77eX4wRveLw8R0XQK8LjTELvnx+ax7cTJw4EePHj8fzzz8PAHA6ncjOzsZdd92FBx98sNXt77zzThw4cADfffedct19992HLVu2YMOGDZ0+X9gGNwBQWwS8dD7QUA5MvA2Y9WetV0REFNLqLXZc+/ImHCiswdDMOKxcPAlmU8cVHQ6niM/3FOLltcewr0DKtuh1AiYPSMGsERm4dFg6UmJMXq+hst6Ku1bsxIaj0niIO6aeg3svHexTULKvoBqvrT+BT3cXwOaQPvbf/+UkTOiX5PVjaM2Xz29Nt6WsVit27NiB6dOnK9fpdDpMnz4dmzZtavM+559/Pnbs2KFsXR0/fhyff/45Lr/88jZvb7FYUFNT0+wrbMVmAFe9JF3e8hJw+Ctt10NEFOLMpgi8umAcUmJMOFBYg3vey2v3BFWTzYHlW07hkmfW4K4VO7GvoAZRBj0Wnp+DtQ9MwZuLJuDGCX18CmwAINFsxLKbx+PWi/oDAF5YfQz/959tqG60dXg/p1PEdweKceMrm3HFPzbgw51nYXOImJCThP8smhBSgY2vNC0oLisrg8PhQHp6erPr09PTcfDgwTbvc9NNN6GsrAwXXHABRFGE3W7H4sWL8bvftT1IcunSpXjsscdUX3u3NWgGMHExsOVl4KPbgNt+kIIeIiLyS6+EKLwyfyxueGUzvtlfjKe/PoTfzhyi/Ly60Ya3N5/CGxtPoqzOAgBIiDZgwaQcLDg/B0nmwAuBI/Q6/O7yoRieFYff/nc31hwqxZznN+CV+eMwqMWQy0arAx/uPIPXNpzA8dJ6AFLm6IqRmfi/C/ohNzsh4PV0d5puSxUUFKBXr1744YcfMGnSJOX63/zmN1i7di22bNnS6j5r1qzBDTfcgD/96U+YOHEijh49irvvvhu33HILHn744Va3t1gssFgsyvc1NTXIzs4Oz20pma0JeHU6ULwHyMwF5n8CRCVovSoiopD2cd5Z3P1uHgDgrz/NxYUDU/D6hhNYviUfdRap4V9WfCRuuag/rh+fjWhjcPIH+wqqceubO3C2qhFmox7PXJeLmSMyUVLbhLc2ncLbm0+hskHK6sSaInDjxD5YcH6OT/VC3VHI1NxYrVZER0dj5cqVuOqqq5TrFyxYgKqqKnz88cet7nPhhRfivPPOw9NPP61c9/bbb+PWW29FXV0ddLqOd9rCuubGU/kx6Xh4QxnQezzw81WAKbRH2BMRae2Zrw/hn98fhUEvQIAAq8MJABiUHoNfXnQOrjw3C4YuGGFQUW/Fne/8qBQqXzgwBVuOVyjr6Z0YhUWT++G68dmI6aRGKFSETM2N0WjE2LFjmxUHO51OfPfdd80yOZ4aGhpaBTB6vR6AVJlOLsnnAPM/BiITgDPbgBU3AtYGrVdFRBTSfj19EGaNyIDNIcLqcGJc30S8tmAcvrz7IlwztneXBDYAkGQ24s1FE/B/F0gDP9cfKYPV4cTYvol4ad4YrH1gKhZd0C9sAhtfaf6q7733XixYsADjxo3DhAkT8Nxzz6G+vh4333wzAGD+/Pno1asXli5dCgCYPXs2nn32WYwePVrZlnr44Ycxe/ZsJcghl4wRUsbmzTnAyfXAez8DblwBRPhWzEZERBKdTsDfrj8X559zGkMz4zAuR7ui3Ai9Dg//ZBjG9k3ExqNluGZsb4zpk6jZeroTzYOb66+/HqWlpXjkkUdQVFSEc889F19++aVSZJyfn98sU/P73/8egiDg97//Pc6ePYvU1FTMnj0bTzzxhFYvoXvrNQaY9wHw1tXAse+AD24GrvuPNFmciIh8FmnQ4+eTcrRehuLykZkB9b4JR5r3uelqPabmpqXja4Dl1wEOCzB8LnDNq4COmS4iIgoNIVNzQ12o/xTg+rcBnQHY9yHwyV1SV2MiIqIww+CmJxl0mTRkU9ADecuBz+/nFHEiIgo7DG56mmFzgKtfBiAA218Dvv49AxwiIgorDG56olHXAVf+Q7q86Xlg9ZParoeIiEhFDG56qjHzgVlPSZfXPQWsf0bb9RAREamEwU1PNvGXwHTX3K3vHgc2v6TteoiIiFTA4Kanu+Ae4OIHpctfPgisfYqnqIiIKKQxuCFgyoPA5Huky6ufAN69CWis0nJFREREfmNwQ4AgAJc+Bsx5AdCbgMNfAP+eChTv13plREREPmNwQ26jfwb831dAfDZQcRx4dRqw979ar4qIiMgnDG6ouazRwK1rpY7GtgZg5SLgy98BDpvWKyMiIvIKgxtqzZwM/OxD4IJfS99vfkGaLF5Xou26iIiIvMDghtqm0wPTHwWuewswxgCnNgL/ugg4vU3rlREREXWIwQ11bNiVwC2rgZRBQG0h8MYsYNurHNlARETdFoMb6lzqIOCW74GhVwJOG/DZfcBHtwO2Rq1XRkRE1AqDG/KOKRa47k2po7GgA3a9A7x2GXBmu9YrIyIiaobBDXlPEKSOxj9fBUQnA0W7pePi7/0MKD2s9eqIiIgAMLghf/SfAizeAJz7MymLc+BT4MWJwCd3ATUFWq+OiIh6OEEUe1ZlaE1NDeLj41FdXY24uDitlxP6Sg5IQzcPfS59HxEJTFwsZXiiEjVdWsCcDqC2CKg+DdQVA/G9gbThgCFS65UREfU4vnx+M7ghdeRvBr59FMjfJH0fGQ9ccK80edwQpenS2mVtAGrOAlX5UgBTfQaocv1anS9loZz25vcR9NLJscxRQMYoIGOk9BWdpM1rICLqIRjcdIDBTRCJInD4S+Dbx4DSA9J1sVnA1CVA7k2APkLbtZUfBU6sA05ukIKw2sLO76eLAOKyAHMaUHkCaChv+3bx2VKwkykHPKOkTI8gqPs6iIh6KAY3HWBw0wWcDmD3e8DqJ6WMCACkDAYuuh/IuRCIywz+GkQRKD8GnFzv+togbS21ZIwFErKlQCQ+23VZ/uoNxGZIDQ3lx6wtBAp3A0V7gKJd0uWqU22vITpFGmfh+eXvaxdFoL4UKDsClB8BaosBh1U6mu+wt3PZ9eW0AXojYE4FYtKAmPTWlyPjGYgRUbfG4KYDDG66kK1Javi3/q9AY6X7+rheQK+x0lfvcUDmuYApJrDnEkVp2KccyJzc0DozozcB2ROkACvnAiB9uDof6o1VQPFeKeCRA5/SA623tAAgJkMKcnqNcQc85hT3z22NUlBWfkTKNJUdlS6XHQUs1YGtsyN6o5SdinF9JZ0DZI8Hek8A4nsF73mJiLzE4KYDDG400FQN/PA8cPAz6UNfdDb/uaADUodKH/i9xwG9xgGpQ6RtLFGUAqO6YtdXiftyrcd1tYVAU1Xzx9UbpQ/nfq5gpte4risGtjUBxfuAgh+BgjygYGfbrx2QskSJOUDlKVemq72/koKUWUoeKGWVIiIBvUHaOtMbW1/WGwCd61d7E1BXCtSXuH4PS1yXSzsPmuJ6SUFh7wnSrxmjgAhjYL8/REQ+YnDTAQY3GrPUAYV5UvO/szukr5qzrW9niJZOW9WVSNsq3tAbgd7jpUAm5wLpcncqZrbWS1mdgp3ur7IjaBXMRCYAKQOB5AHSV8pAKaBJ6h+c4MzW5A506l3BY9Fe4PQWKUATHc1vrzdJGSc5s5M9Qdq+C2eiCFSeBAp3AY0VUp+n6BQp6xadLP1ZlbcviSgoGNx0gMFNN1RT6A50zm4Hzu4ErLXNbxOVKNWHxKRJWztyvYhyXZrrw78bBTPeaKqRPjBrzgIJfaVAJjq5+9S/WOuBsz9Kgc6ZbcDprdKHe0sx6VK2LXUIkDpY+jVtaGieInM6gYpj0vtSsFP6tWi3lIFsj6CT/ox6BjzmFOl7o1n6ufyl07suC82vV770UtZSF+HOvOlc38vZOJ3efdloBiLjpAG33eXPDVEQMLjpAIObEOB0SnUmljog1lXwGmHSelUEuAu1z2yVAp3TW4GS/Wh3K82c2jzgkb/MKd3jg9hukWqbCne5v4r2ANa61rfVG4G0YUBsphTg1ZcBDWUdBz1dSdBJBfKRcYAprv1fzalSpi0mXfo1OgXQsZ8rdX8MbjrA4IZIZZZaafxG6QGg9CBQekj6tSq//ftEJbkCnsHSSTr5clwv9YOephrpGH/FCdevx12XT0o9jdoKzCKigIwRQGau6+tcKShrq9bIYQMaKqRARw546svd39sapVqrdr9Ej8sO6bSh0y59yafdHK7vlcs218+tUnatreJ1bwl6dyY0NsOdHY11ZUYj46WgyBTrvqxVzZUoSvVj1voWX3XSr7YG92VrvfT7J7+/yked2Pyy58/0BmnbNcIk1bRFGF2/mlzXuy5HmKRgF6Lr/fJ4z5x26XvR0fx7p10Kpu0WwGGRXof8vXKd5/fWNtbuseaW1wuCa42uNeuN7tehXDZ53CZKOshhjJHeW2OMlAWUL7f3HjvsruC+tO0/7/L3pljgpnf9fafbxOCmAwxuiLqItd4V6BxqHvRUnkS7mR5jjNQk0TPwSR4g/cOt/MPfJH05rB4fEB4fFJYa6TnkYKa93kTKc8ZK/YmUQCZXqnHSsi+TL0RRCqAsNVIgZ6lpftnz16Zqqa6qthioK5I+jNotYO+A3uTOBJk8skWGaOl9cdjcH9YOq+u9srquc33vsEgflNKLcL+Wjr53WNsuyif16Y1SsGOMlYIgh00KXDxPvnYkKgn47QlVl8TgpgMMbog0Zm2QtoJKDwFlh9wBUMWxwDIQHYlOAZL6AYn9Wv8ak9Y9tsi04LBJ/wOvLWpxCrFI+rW+xCNgqm17u04rEVGuD9+WXzFSkGWMloIwwOP9FTy+F1r8DK7sSpMUgLUKoC2trxP00paeXBOli3DVVOlbXOe6TVtZIb2cHWpxnd4obTXKmv0ZFVpfLzrdwaS8PuWytUVmyPUfBEudK9NV575sb/LiN1+Q6unaqjEzpwLmZOnXfhd5/356wZfP7xD5rwkRhQ1jtCtTMqr59Q6btGUkBztlrkxPxUnpwyEissWHQ4ttAvl7QzSQ0McdvCTmSJkFak1vkDpwx2V5d3unQwpylIxQrTvwaaqWMkh6o/QhrTdJjy9vhXhejnB9eOsi2gk82vle7yqgNkTzdFqwOOytAx5LrfReyQFMdFK3//1ncENE3YPe4N6Oou5JpweiEqQvCk/6iLB4j1kiT0RERGGFwQ0RERGFFQY3REREFFYY3BAREVFYYXBDREREYYXBDREREYUVBjdEREQUVhjcEBERUVhhcENERERhhcENERERhRUGN0RERBRWGNwQERFRWGFwQ0RERGGFwQ0RERGFlQitF9DVRFEEANTU1Gi8EiIiIvKW/Lktf453pMcFN7W1tQCA7OxsjVdCREREvqqtrUV8fHyHtxFEb0KgMOJ0OlFQUIDY2FgIgqDqY9fU1CA7OxunT59GXFycqo/d3fSk1wr0rNfL1xq+etLr5WsNP6Ioora2FllZWdDpOq6q6XGZG51Oh969ewf1OeLi4sL6D5innvRagZ71evlaw1dPer18reGls4yNjAXFREREFFYY3BAREVFYYXCjIpPJhD/84Q8wmUxaLyXoetJrBXrW6+VrDV896fXytfZsPa6gmIiIiMIbMzdEREQUVhjcEBERUVhhcENERERhhcENERERhRUGNyp54YUXkJOTg8jISEycOBFbt27VeklB8eijj0IQhGZfQ4YM0XpZqli3bh1mz56NrKwsCIKAjz76qNnPRVHEI488gszMTERFRWH69Ok4cuSINotVQWevd+HCha3e65kzZ2qz2AAtXboU48ePR2xsLNLS0nDVVVfh0KFDzW7T1NSEO+64A8nJyYiJicE111yD4uJijVbsP29e65QpU1q9t4sXL9Zoxf576aWXMGrUKKV53aRJk/DFF18oPw+X91TW2esNl/dVDQxuVPDee+/h3nvvxR/+8Af8+OOPyM3NxYwZM1BSUqL10oJi+PDhKCwsVL42bNig9ZJUUV9fj9zcXLzwwgtt/vypp57CP/7xD7z88svYsmULzGYzZsyYgaampi5eqTo6e70AMHPmzGbv9YoVK7pwhepZu3Yt7rjjDmzevBnffPMNbDYbLrvsMtTX1yu3+fWvf41PP/0UH3zwAdauXYuCggLMnTtXw1X7x5vXCgC33HJLs/f2qaee0mjF/uvduzf+/Oc/Y8eOHdi+fTsuueQSzJkzB/v27QMQPu+prLPXC4TH+6oKkQI2YcIE8Y477lC+dzgcYlZWlrh06VINVxUcf/jDH8Tc3FytlxF0AMRVq1Yp3zudTjEjI0N8+umnleuqqqpEk8kkrlixQoMVqqvl6xVFUVywYIE4Z84cTdYTbCUlJSIAce3ataIoSu+lwWAQP/jgA+U2Bw4cEAGImzZt0mqZqmj5WkVRFC+++GLx7rvv1m5RQZSYmCi++uqrYf2eepJfryiG9/vqK2ZuAmS1WrFjxw5Mnz5duU6n02H69OnYtGmThisLniNHjiArKwv9+/fHvHnzkJ+fr/WSgu7EiRMoKipq9j7Hx8dj4sSJYfs+A8CaNWuQlpaGwYMH47bbbkN5ebnWS1JFdXU1ACApKQkAsGPHDthstmbv75AhQ9CnT5+Qf39bvlbZ8uXLkZKSghEjRmDJkiVoaGjQYnmqcTgcePfdd1FfX49JkyaF9XsKtH69snB7X/3V4wZnqq2srAwOhwPp6enNrk9PT8fBgwc1WlXwTJw4EcuWLcPgwYNRWFiIxx57DBdeeCH27t2L2NhYrZcXNEVFRQDQ5vss/yzczJw5E3PnzkW/fv1w7Ngx/O53v8OsWbOwadMm6PV6rZfnN6fTiXvuuQeTJ0/GiBEjAEjvr9FoREJCQrPbhvr729ZrBYCbbroJffv2RVZWFnbv3o3f/va3OHToED788EMNV+ufPXv2YNKkSWhqakJMTAxWrVqFYcOGIS8vLyzf0/ZeLxBe72ugGNyQT2bNmqVcHjVqFCZOnIi+ffvi/fffx//93/9puDJS2w033KBcHjlyJEaNGoVzzjkHa9aswbRp0zRcWWDuuOMO7N27N2xqxTrS3mu99dZblcsjR45EZmYmpk2bhmPHjuGcc87p6mUGZPDgwcjLy0N1dTVWrlyJBQsWYO3atVovK2jae73Dhg0Lq/c1UNyWClBKSgr0en2rCvzi4mJkZGRotKquk5CQgEGDBuHo0aNaLyWo5Peyp77PANC/f3+kpKSE9Ht955134n//+x9Wr16N3r17K9dnZGTAarWiqqqq2e1D+f1t77W2ZeLEiQAQku+t0WjEgAEDMHbsWCxduhS5ubn4+9//HpbvKdD+621LKL+vgWJwEyCj0YixY8fiu+++U65zOp347rvvmu2Dhqu6ujocO3YMmZmZWi8lqPr164eMjIxm73NNTQ22bNnSI95nADhz5gzKy8tD8r0WRRF33nknVq1ahe+//x79+vVr9vOxY8fCYDA0e38PHTqE/Pz8kHt/O3utbcnLywOAkHxvW3I6nbBYLGH1nnZEfr1tCaf31WdaVzSHg3fffVc0mUzismXLxP3794u33nqrmJCQIBYVFWm9NNXdd9994po1a8QTJ06IGzduFKdPny6mpKSIJSUlWi8tYLW1teLOnTvFnTt3igDEZ599Vty5c6d46tQpURRF8c9//rOYkJAgfvzxx+Lu3bvFOXPmiP369RMbGxs1Xrl/Onq9tbW14v333y9u2rRJPHHihPjtt9+KY8aMEQcOHCg2NTVpvXSf3XbbbWJ8fLy4Zs0asbCwUPlqaGhQbrN48WKxT58+4vfffy9u375dnDRpkjhp0iQNV+2fzl7r0aNHxccff1zcvn27eOLECfHjjz8W+/fvL1500UUar9x3Dz74oLh27VrxxIkT4u7du8UHH3xQFARB/Prrr0VRDJ/3VNbR6w2n91UNDG5U8s9//lPs06ePaDQaxQkTJoibN2/WeklBcf3114uZmZmi0WgUe/XqJV5//fXi0aNHtV6WKlavXi0CaPW1YMECURSl4+APP/ywmJ6eLppMJnHatGnioUOHtF10ADp6vQ0NDeJll10mpqamigaDQezbt694yy23hGzA3tbrBCC+8cYbym0aGxvF22+/XUxMTBSjo6PFq6++WiwsLNRu0X7q7LXm5+eLF110kZiUlCSaTCZxwIAB4gMPPCBWV1dru3A/LFq0SOzbt69oNBrF1NRUcdq0aUpgI4rh857KOnq94fS+qkEQRVHsujwRERERUXCx5oaIiIjCCoMbIiIiCisMboiIiCisMLghIiKisMLghoiIiMIKgxsiIiIKKwxuiIiIKKwwuCEiIqKwwuCGiHokQRDw0Ucfab0MIgoCBjdE1OUWLlwIQRBafc2cOVPrpRFRGIjQegFE1DPNnDkTb7zxRrPrTCaTRqshonDCzA0RacJkMiEjI6PZV2JiIgBpy+ill17CrFmzEBUVhf79+2PlypXN7r9nzx5ccskliIqKQnJyMm699VbU1dU1u83rr7+O4cOHw2QyITMzE3feeWezn5eVleHqq69GdHQ0Bg4ciE8++UT5WWVlJebNm4fU1FRERUVh4MCBrYIxIuqeGNwQUbf08MMP45prrsGuXbswb9483HDDDThw4AAAoL6+HjNmzEBiYiK2bduGDz74AN9++22z4OWll17CHXfcgVtvvRV79uzBJ598ggEDBjR7jsceewzXXXcddu/ejcsvvxzz5s1DRUWF8vz79+/HF198gQMHDuCll15CSkpK1/0GEJH/tB5LTkQ9z4IFC0S9Xi+azeZmX0888YQoiqIIQFy8eHGz+0ycOFG87bbbRFEUxVdeeUVMTEwU6+rqlJ9/9tlnok6nE4uKikRRFMWsrCzxoYceancNAMTf//73yvd1dXUiAPGLL74QRVEUZ8+eLd58883qvGAi6lKsuSEiTUydOhUvvfRSs+uSkpKUy5MmTWr2s0mTJiEvLw8AcODAAeTm5sJsNis/nzx5MpxOJw4dOgRBEFBQUIBp06Z1uIZRo0Ypl81mM+Li4lBSUgIAuO2223DNNdfgxx9/xGWXXYarrroK559/vl+vlYi6FoMbItKE2WxutU2klqioKK9uZzAYmn0vCAKcTicAYNasWTh16hQ+//xzfPPNN5g2bRruuOMO/PWvf1V9vUSkLtbcEFG3tHnz5lbfDx06FAAwdOhQ7Nq1C/X19crPN27cCJ1Oh8GDByM2NhY5OTn47rvvAlpDamoqFixYgLfffhvPPfccXnnllYAej4i6BjM3RKQJi8WCoqKiZtdFREQoRbsffPABxo0bhwsuuADLly/H1q1b8dprrwEA5s2bhz/84Q9YsGABHn30UZSWluKuu+7Cz3/+c6SnpwMAHn30USxevBhpaWmYNWsWamtrsXHjRtx1111ere+RRx7B2LFjMXz4cFgsFvzvf/9Tgisi6t4Y3BCRJr788ktkZmY2u27w4ME4ePAgAOkk07vvvovbb78dmZmZWLFiBYYNGwYAiI6OxldffYW7774b48ePR3R0NK655ho8++yzymMtWLAATU1N+Nvf/ob7778fKSkpuPbaa71en9FoxJIlS3Dy5ElERUXhwgsvxLvvvqvCKyeiYBNEURS1XgQRkSdBELBq1SpcddVVWi+FiEIQa26IiIgorDC4ISIiorDCmhsi6na4W05EgWDmhoiIiMIKgxsiIiIKKwxuiIiIKKwwuCEiIqKwwuCGiIiIwgqDGyIiIgorDG6IiIgorDC4ISIiorDy//rIL9S/uGx+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['loss'], label='train_loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VH9SI9_vKLNR"
      },
      "source": [
        "## Classification Algorithms"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Now we can read the file by its name.\n",
        "data_path = 'drive/MyDrive/laser_WCA.xlsx'\n",
        "data = pd.read_excel(data_path)\n",
        "# Preprocess the dataset\n",
        "data = data.drop(columns=['Unnamed: 13', 'Unnamed: 14', 'Data Source']).dropna()\n",
        "# Convert the 'scanning method' column to numerical using Label Encoding\n",
        "label_encoder = LabelEncoder()\n",
        "data['scanning method'] = label_encoder.fit_transform(\n",
        "    data['scanning method']\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6n87GUrvsTRK",
        "outputId": "f5e5a099-8b04-40da-d4ae-d89bca34333c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "69ogSTEvKXvM"
      },
      "outputs": [],
      "source": [
        "data['superhydrophobic'] = (data['WCA/°'] > 150).astype(int)\n",
        "\n",
        "X = data.drop(['WCA/°', 'superhydrophobic'], axis=1)\n",
        "y = data['superhydrophobic']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "OqPO-h6lKpp1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "031fde76-46ae-4ede-c47f-d903dac571ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Accuracy: 0.8703703703703703\n",
            "Random Forest Accuracy: 0.8888888888888888\n",
            "SVM Accuracy: 0.5370370370370371\n",
            "KNN Accuracy: 0.8703703703703703\n",
            "Gradient Boosting Accuracy: 0.8333333333333334\n",
            "Decision Tree Accuracy: 0.8333333333333334\n",
            "Naive Bayes Accuracy: 0.5370370370370371\n",
            "AdaBoost Accuracy: 0.8703703703703703\n"
          ]
        }
      ],
      "source": [
        "# Using XGB to classify.\n",
        "from xgboost import XGBClassifier\n",
        "xgb_model = XGBClassifier(random_state=42)\n",
        "xgb_model.fit(X_train, y_train)\n",
        "xgb_pred = xgb_model.predict(X_test)\n",
        "xgb_accuracy = accuracy_score(y_test, xgb_pred)\n",
        "\n",
        "# Using Random Forest to classify.\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf_model = RandomForestClassifier(random_state=42)\n",
        "rf_model.fit(X_train, y_train)\n",
        "rf_pred = rf_model.predict(X_test)\n",
        "rf_accuracy = accuracy_score(y_test, rf_pred)\n",
        "\n",
        "# Using SVM to classify.\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "svm_model = SVC()\n",
        "svm_model.fit(X_train, y_train)\n",
        "svm_predictions = svm_model.predict(X_test)\n",
        "svm_accuracy = accuracy_score(y_test, svm_predictions)\n",
        "\n",
        "# Using KNN to Classify\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model = KNeighborsClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "knn_accuracy = model.score(X_test, y_test)\n",
        "\n",
        "# Using GBM.\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "model = GradientBoostingClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "gbm_accuracy = model.score(X_test, y_test)\n",
        "\n",
        "# Using Decision Trees.\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "dt_accuracy = model.score(X_test, y_test)\n",
        "\n",
        "# Naive Bayes\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "model = GaussianNB()\n",
        "model.fit(X_train, y_train)\n",
        "nb_accuracy = model.score(X_test, y_test)\n",
        "\n",
        "# AdaBoost\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "model = AdaBoostClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "ada_accuracy = model.score(X_test, y_test)\n",
        "\n",
        "# Print the accuracies\n",
        "print(\"XGBoost Accuracy:\", xgb_accuracy)\n",
        "print(\"Random Forest Accuracy:\", rf_accuracy)\n",
        "print(\"SVM Accuracy:\", svm_accuracy)\n",
        "print(\"KNN Accuracy:\", knn_accuracy)\n",
        "print(\"Gradient Boosting Accuracy:\", gbm_accuracy)\n",
        "print(\"Decision Tree Accuracy:\", dt_accuracy)\n",
        "print(\"Naive Bayes Accuracy:\", nb_accuracy)\n",
        "print(\"AdaBoost Accuracy:\", ada_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "GT9gWwpVjk4Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9e75f6f-f30b-4b5b-e057-8a409b16288b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n",
            "Best parameters found:  {'colsample_bytree': 0.6, 'learning_rate': 0.2, 'max_depth': 5, 'n_estimators': 200, 'subsample': 0.8}\n",
            "Best cross-validation accuracy:  0.7756367663344408\n",
            "XGBoost Test set accuracy with best parameters:  0.9074074074074074\n"
          ]
        }
      ],
      "source": [
        "# XGBoost Grid Search Refinement\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# 定义要搜索的超参数范围\n",
        "param_grid = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'learning_rate': [0.01, 0.1, 0.2],\n",
        "    'max_depth': [3, 5, 7],\n",
        "    'subsample': [0.6, 0.8, 1.0],\n",
        "    'colsample_bytree': [0.6, 0.8, 1.0]\n",
        "}\n",
        "\n",
        "# 创建XGBoost分类器\n",
        "xgb = XGBClassifier(random_state=42)\n",
        "\n",
        "# 使用网格搜索进行超参数调整\n",
        "grid_search = GridSearchCV(xgb, param_grid, cv=5, verbose=2, n_jobs=-1)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# 获取最佳参数和最佳准确率\n",
        "best_params = grid_search.best_params_\n",
        "best_accuracy = grid_search.best_score_\n",
        "\n",
        "print(\"Best parameters found: \", best_params)\n",
        "print(\"Best cross-validation accuracy: \", best_accuracy)\n",
        "\n",
        "# 使用最佳参数进行预测\n",
        "best_model = grid_search.best_estimator_\n",
        "y_pred = best_model.predict(X_test)\n",
        "best_xgb_accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "print(\"XGBoost Test set accuracy with best parameters: \", best_xgb_accuracy)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "CmohmZzEh4Rg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "596d5e8d-5fe4-4b01-f15a-247df1965c78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 45 candidates, totalling 225 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters found:  {'base_estimator__max_depth': 1, 'learning_rate': 1, 'n_estimators': 200}\n",
            "Best cross-validation accuracy:  0.7756367663344407\n",
            "Test set accuracy with best parameters:  0.8703703703703703\n"
          ]
        }
      ],
      "source": [
        "# AdaBoost Grid Search Refinement\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# 基分类器\n",
        "base_estimator = DecisionTreeClassifier()\n",
        "\n",
        "# 定义要搜索的超参数范围\n",
        "param_grid = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'learning_rate': [0.01, 0.1, 1],\n",
        "    'base_estimator__max_depth': [1, 2, 3, 4, 5]\n",
        "}\n",
        "\n",
        "# 创建AdaBoost分类器\n",
        "ada = AdaBoostClassifier(base_estimator=base_estimator)\n",
        "\n",
        "# 使用网格搜索进行超参数调整\n",
        "grid_search = GridSearchCV(ada, param_grid, cv=5, verbose=2, n_jobs=-1)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# 获取最佳参数和最佳准确率\n",
        "best_params = grid_search.best_params_\n",
        "best_accuracy = grid_search.best_score_\n",
        "\n",
        "print(\"Best parameters found: \", best_params)\n",
        "print(\"Best cross-validation accuracy: \", best_accuracy)\n",
        "\n",
        "# 使用最佳参数进行预测\n",
        "best_model = grid_search.best_estimator_\n",
        "y_pred = best_model.predict(X_test)\n",
        "best_ada_accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "print(\"Test set accuracy with best parameters: \", best_ada_accuracy)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "FY0EBYGuw_DE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "030bf5c4-612e-46a8-ae9b-1c8f0a9c3f70"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 准确率数据\n",
        "algorithms = ['XGBoost','Best XGBoost', 'Random Forest', 'SVM', 'KNN',\n",
        "             'Gradient Boosting', 'Decision Tree', 'Naive Bayes',\n",
        "              'AdaBoost','Best AdaBoost']\n",
        "accuracies = [xgb_accuracy, best_xgb_accuracy, rf_accuracy, svm_accuracy, knn_accuracy,\n",
        "              gbm_accuracy, dt_accuracy, nb_accuracy,\n",
        "              ada_accuracy, best_ada_accuracy]\n",
        "\n",
        "\n",
        "# 创建条形图\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.barh(algorithms, accuracies, color='skyblue')\n",
        "plt.xlabel('Accuracy')\n",
        "plt.title('Comparison of Classification Algorithms')\n",
        "plt.xlim(0, 1)  # 设置 x 轴范围为 0 到 1\n",
        "plt.grid(axis='x', linestyle='--', alpha=0.7)\n",
        "\n",
        "# 在条形图上显示准确率数值\n",
        "for index, value in enumerate(accuracies):\n",
        "    plt.text(value, index, f\"{value:.4f}\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using different methods to ensure the accuracy and overfitting problems addressed by the algorithms."
      ],
      "metadata": {
        "id": "q7GoQXSo2sZq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix, roc_auc_score, average_precision_score\n",
        "from sklearn.metrics import roc_curve, precision_recall_curve\n",
        "from sklearn.model_selection import cross_val_score, train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# List of models to evaluate\n",
        "models = {\n",
        "    \"XGBoost\": XGBClassifier(use_label_encoder=False, eval_metric='logloss'),\n",
        "    \"Random Forest\": RandomForestClassifier(),\n",
        "    \"SVM\": SVC(probability=True),\n",
        "    \"AdaBoost\": AdaBoostClassifier(),\n",
        "    \"KNN\": KNeighborsClassifier(),\n",
        "    \"Decision Tree\": DecisionTreeClassifier(),\n",
        "    \"Gradient Boosting\": GradientBoostingClassifier(),\n",
        "    \"Naive Bayes\": GaussianNB()\n",
        "}\n",
        "\n",
        "for name, model in models.items():\n",
        "    print(f\"Evaluating {name}...\")\n",
        "\n",
        "    # Cross-validated F1 Score\n",
        "    cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='f1_weighted')\n",
        "    print(f\"Cross-validated F1 Score: {cv_scores.mean()}\")\n",
        "\n",
        "    # Train the model\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Predictions\n",
        "    y_pred = model.predict(X_test)\n",
        "    y_prob = model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "    # Accuracy\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    print(f\"Accuracy: {accuracy}\")\n",
        "\n",
        "    # Precision, Recall, F1 Score\n",
        "    precision = precision_score(y_test, y_pred, average='weighted')\n",
        "    recall = recall_score(y_test, y_pred, average='weighted')\n",
        "    f1 = f1_score(y_test, y_pred, average='weighted')\n",
        "    print(f\"Precision: {precision}\")\n",
        "    print(f\"Recall: {recall}\")\n",
        "    print(f\"F1 Score: {f1}\")\n",
        "\n",
        "    # Confusion Matrix\n",
        "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "    print(f\"Confusion Matrix:\\n{conf_matrix}\")\n",
        "\n",
        "    # ROC Curve and AUC\n",
        "    fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
        "    roc_auc = roc_auc_score(y_test, y_prob)\n",
        "    print(f\"AUC: {roc_auc}\")\n",
        "\n",
        "    plt.figure()\n",
        "    plt.plot(fpr, tpr, label=f'ROC curve (area = {roc_auc:.2f})')\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.title(f'{name} - ROC Curve')\n",
        "    plt.legend(loc='lower right')\n",
        "    plt.show()\n",
        "\n",
        "    # PR Curve and AP\n",
        "    precision_vals, recall_vals, _ = precision_recall_curve(y_test, y_prob)\n",
        "    ap_score = average_precision_score(y_test, y_prob)\n",
        "    print(f\"Average Precision (AP): {ap_score}\")\n",
        "\n",
        "    plt.figure()\n",
        "    plt.plot(recall_vals, precision_vals, label=f'PR curve (AP = {ap_score:.2f})')\n",
        "    plt.xlabel('Recall')\n",
        "    plt.ylabel('Precision')\n",
        "    plt.title(f'{name} - Precision-Recall Curve')\n",
        "    plt.legend(loc='lower left')\n",
        "    plt.show()\n",
        "\n",
        "    print(\"-\" * 50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zPB5MqRRnSwW",
        "outputId": "d08b041f-8460-4ee2-a68d-38f9188b711f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating XGBoost...\n",
            "Cross-validated F1 Score: 0.7508000364221215\n",
            "Accuracy: 0.8703703703703703\n",
            "Precision: 0.8820806100217865\n",
            "Recall: 0.8703703703703703\n",
            "F1 Score: 0.868312757201646\n",
            "Confusion Matrix:\n",
            "[[28  1]\n",
            " [ 6 19]]\n",
            "AUC: 0.9668965517241379\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Precision (AP): 0.9652384174964821\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "Evaluating Random Forest...\n",
            "Cross-validated F1 Score: 0.7597598431489633\n",
            "Accuracy: 0.8888888888888888\n",
            "Precision: 0.8888888888888888\n",
            "Recall: 0.8888888888888888\n",
            "F1 Score: 0.8888888888888888\n",
            "Confusion Matrix:\n",
            "[[26  3]\n",
            " [ 3 22]]\n",
            "AUC: 0.9655172413793103\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Precision (AP): 0.9687190683537247\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "Evaluating SVM...\n",
            "Cross-validated F1 Score: 0.5450261791781128\n",
            "Accuracy: 0.5370370370370371\n",
            "Precision: 0.7685185185185185\n",
            "Recall: 0.5370370370370371\n",
            "F1 Score: 0.43883277216610544\n",
            "Confusion Matrix:\n",
            "[[ 4 25]\n",
            " [ 0 25]]\n",
            "AUC: 0.5558620689655173\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Precision (AP): 0.5672488539295188\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "Evaluating AdaBoost...\n",
            "Cross-validated F1 Score: 0.7438404996513264\n",
            "Accuracy: 0.8703703703703703\n",
            "Precision: 0.8712352462352463\n",
            "Recall: 0.8703703703703703\n",
            "F1 Score: 0.8705041470779343\n",
            "Confusion Matrix:\n",
            "[[25  4]\n",
            " [ 3 22]]\n",
            "AUC: 0.9248275862068965\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Precision (AP): 0.9147114380905402\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "Evaluating KNN...\n",
            "Cross-validated F1 Score: 0.6697968195688281\n",
            "Accuracy: 0.8703703703703703\n",
            "Precision: 0.8712352462352463\n",
            "Recall: 0.8703703703703703\n",
            "F1 Score: 0.8705041470779343\n",
            "Confusion Matrix:\n",
            "[[25  4]\n",
            " [ 3 22]]\n",
            "AUC: 0.94\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Precision (AP): 0.9233448833448834\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "Evaluating Decision Tree...\n",
            "Cross-validated F1 Score: 0.7180153413778271\n",
            "Accuracy: 0.8148148148148148\n",
            "Precision: 0.8148148148148148\n",
            "Recall: 0.8148148148148148\n",
            "F1 Score: 0.8148148148148148\n",
            "Confusion Matrix:\n",
            "[[24  5]\n",
            " [ 5 20]]\n",
            "AUC: 0.8137931034482759\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Precision (AP): 0.7325925925925927\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "Evaluating Gradient Boosting...\n",
            "Cross-validated F1 Score: 0.7165082242564607\n",
            "Accuracy: 0.8333333333333334\n",
            "Precision: 0.8342490842490842\n",
            "Recall: 0.8333333333333334\n",
            "F1 Score: 0.8335053319573442\n",
            "Confusion Matrix:\n",
            "[[24  5]\n",
            " [ 4 21]]\n",
            "AUC: 0.950344827586207\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Precision (AP): 0.953879298370347\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "Evaluating Naive Bayes...\n",
            "Cross-validated F1 Score: 0.5253488467567717\n",
            "Accuracy: 0.5370370370370371\n",
            "Precision: 0.7685185185185185\n",
            "Recall: 0.5370370370370371\n",
            "F1 Score: 0.43883277216610544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loV8d6Bw6o6W"
      },
      "source": [
        "Gradient Boosting Machines (GBM):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OeralamjK0Ds"
      },
      "source": [
        "## Using cGAN to generate coding information of different data.\n",
        "\n",
        "cGAN is more suitable to generate related models around the inverse designation of WCA laser processing tech."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Now we can read the file by its name.\n",
        "data_path = 'drive/MyDrive/laser_WCA.xlsx'\n",
        "data = pd.read_excel(data_path)\n",
        "# Preprocess the dataset\n",
        "data = data.drop(columns=['Unnamed: 13', 'Unnamed: 14', 'Data Source']).dropna()\n",
        "data = pd.get_dummies(data, columns=['scanning method'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2rLGMsO_BTr",
        "outputId": "f7038f21-21b2-4489-8f38-80539fd19d98"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure all data is numeric by converting it to the appropriate type\n",
        "for column in data.columns:\n",
        "    data[column] = pd.to_numeric(data[column], errors='coerce')\n",
        "\n",
        "# Convert DataFrame to PyTorch tensors\n",
        "X = data.drop(columns=['WCA/°']).values.astype(np.float32)\n",
        "y = data['WCA/°'].values.astype(np.float32)\n",
        "\n",
        "X_tensor = torch.tensor(X, dtype=torch.float32)\n",
        "y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1)\n",
        "\n",
        "# Create a dataset and dataloader\n",
        "dataset = TensorDataset(X_tensor, y_tensor)\n",
        "dataloader = DataLoader(dataset, batch_size=32, shuffle=True)"
      ],
      "metadata": {
        "id": "OeXSCzqJoqXE"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "class Generator(nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(Generator, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(input_dim, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm1d(128),\n",
        "            nn.Linear(128, 256),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm1d(256),\n",
        "            nn.Linear(256, output_dim)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)\n",
        "\n",
        "class Discriminator(nn.Module):\n",
        "    def __init__(self, input_dim):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(input_dim, 256),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.BatchNorm1d(256),\n",
        "            nn.Linear(256, 128),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.BatchNorm1d(128),\n",
        "            nn.Linear(128, 1),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)\n",
        "\n",
        "# Use this new generator and discriminator in your training loop"
      ],
      "metadata": {
        "id": "9wjqhHZdpAJ4"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the GAN model\n",
        "input_dim = X.shape[1]\n",
        "output_dim = y_tensor.shape[1]\n",
        "\n",
        "generator = Generator(input_dim, output_dim)\n",
        "discriminator = Discriminator(input_dim + output_dim)\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.BCELoss()\n",
        "# Adjusted learning rates\n",
        "optimizer_G = optim.Adam(generator.parameters(), lr=0.00002)\n",
        "optimizer_D = optim.Adam(discriminator.parameters(), lr=0.00001)\n",
        "\n",
        "# Training loop with label smoothing and noise\n",
        "num_epochs = 1000\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (real_X, real_y) in enumerate(dataloader):\n",
        "        # Train Discriminator\n",
        "        optimizer_D.zero_grad()\n",
        "\n",
        "        real_data = torch.cat((real_X, real_y), 1)\n",
        "        real_labels = torch.FloatTensor(real_X.size(0), 1).uniform_(0.8, 1.0)  # Label smoothing within range [0.8, 1.0]\n",
        "\n",
        "        fake_y = generator(real_X)\n",
        "        fake_data = torch.cat((real_X, fake_y), 1)\n",
        "        fake_labels = torch.FloatTensor(real_X.size(0), 1).uniform_(0.0, 0.2)  # Label smoothing within range [0.0, 0.2]\n",
        "\n",
        "        real_output = discriminator(real_data)\n",
        "        fake_output = discriminator(fake_data)\n",
        "\n",
        "        real_loss = criterion(real_output, real_labels)\n",
        "        fake_loss = criterion(fake_output, fake_labels)\n",
        "        d_loss = real_loss + fake_loss\n",
        "\n",
        "        d_loss.backward()\n",
        "        optimizer_D.step()\n",
        "\n",
        "        # Train Generator\n",
        "        optimizer_G.zero_grad()\n",
        "        fake_y = generator(real_X)\n",
        "        fake_data = torch.cat((real_X, fake_y), 1)\n",
        "        fake_output = discriminator(fake_data)\n",
        "\n",
        "        # Flip labels for generator training (target is real labels for fake output)\n",
        "        g_loss = criterion(fake_output, torch.ones_like(fake_output))\n",
        "\n",
        "        g_loss.backward()\n",
        "        optimizer_G.step()\n",
        "\n",
        "    if epoch % 100 == 0:\n",
        "        print(f\"Epoch [{epoch}/{num_epochs}] | D Loss: {d_loss.item()} | G Loss: {g_loss.item()}\")\n",
        "\n",
        "print(\"GAN training complete.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGJaL396-jSJ",
        "outputId": "467da156-5328-479c-ff94-5d848af4dcc4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [0/1000] | D Loss: 1.5182812213897705 | G Loss: 0.733550488948822\n",
            "Epoch [100/1000] | D Loss: 1.386519432067871 | G Loss: 0.686634361743927\n",
            "Epoch [200/1000] | D Loss: 1.3856616020202637 | G Loss: 0.6924781203269958\n",
            "Epoch [300/1000] | D Loss: 1.3862195014953613 | G Loss: 0.6931745409965515\n",
            "Epoch [400/1000] | D Loss: 1.386972188949585 | G Loss: 0.6937238574028015\n",
            "Epoch [500/1000] | D Loss: 1.3869882822036743 | G Loss: 0.6932299733161926\n",
            "Epoch [600/1000] | D Loss: 1.3864355087280273 | G Loss: 0.6936883330345154\n",
            "Epoch [700/1000] | D Loss: 1.3862979412078857 | G Loss: 0.6934888362884521\n",
            "Epoch [800/1000] | D Loss: 1.3859460353851318 | G Loss: 0.6934176087379456\n",
            "Epoch [900/1000] | D Loss: 1.387260913848877 | G Loss: 0.6934265494346619\n",
            "GAN training complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example input parameters with placeholders (-1)\n",
        "input_params = np.array([0.290, 250.0, -1, 114.81, 20.0, 400.0, 140.0, 800.0, 100, 1.0, 1, 18, 100, 160])\n",
        "\n",
        "def predict_missing_parameters(generator, input_params):\n",
        "    generator.eval()\n",
        "    with torch.no_grad():\n",
        "        # Convert input_params to a tensor and ensure it's the correct shape\n",
        "        input_tensor = torch.tensor(input_params, dtype=torch.float32).unsqueeze(0)\n",
        "\n",
        "        # Predict values for the placeholders\n",
        "        predicted_values = generator(input_tensor).squeeze(0).numpy()\n",
        "\n",
        "        # Replace placeholders in input_params with predicted values\n",
        "        idx = 0\n",
        "        for i in range(len(input_params)):\n",
        "            if input_params[i] == -1:  # Placeholder for missing values\n",
        "                input_params[i] = predicted_values[idx]\n",
        "                idx += 1\n",
        "\n",
        "    return input_params\n",
        "# Predict the missing parameter(s) using the trained generator\n",
        "predicted_params = predict_missing_parameters(generator, input_params)\n",
        "\n",
        "# Output the result without scientific notation\n",
        "np.set_printoptions(suppress=True)  # Suppress scientific notation\n",
        "formatted_params = [f\"{param:.2f}\" for param in predicted_params]\n",
        "\n",
        "print(f\"Predicted Parameters: {formatted_params}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWO5D8oB_6aM",
        "outputId": "1b993766-4def-41e9-a04b-7e6f8abc0f8f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Parameters: ['0.29', '250.00', '-2.30', '114.81', '20.00', '400.00', '140.00', '800.00', '100.00', '1.00', '1.00', '18.00', '100.00', '160.00']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8sA4MxoGsEMq"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}