DAI_ASSIGNMENT_1_Q2_1500to2000_with_shuffle_SVM.ipynb

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    "colab": {
      "name": "DAI_ASSIGNMENT_1_Q2_SVM.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
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      "metadata": {
        "id": "X8Y6BjULYyWH"
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      "source": [
        "Q2. Evaluation Metrics for Bias Detection. ​[50 Marks]\n",
        "Dataset : Fashion MNIST\n",
        "Motive of this question is to use metrics to detect and evaluate the bias in the machine learning model.\n",
        "● AIM: ​Perform a 2-class classification between ​Pullover​ and ​Coat\n",
        "● Training data split:​ Take all the training samples corresponding to Pullover, and only 500\n",
        "samples for Coat.\n",
        "● Testing data split:​ Take all the testing samples corresponding to Pullover and Coat.\n",
        " \n",
        "● Cross-validation:​ Repeat each of the experiments 2 more times by taking different 500 samples of Coat.\n",
        "● Algorithms:​ Perform classification using:\n",
        "○ A linear SVM. (You may use sklearn library)\n",
        "○ A 5 layer neural network with architecture: [ 128 -- 128 -- 128 -- 64 -- 1 ]\n",
        "(These numbers denote the number of nodes in each layer)\n",
        "● Perform a 2 class classification and report the performance as follows:\n",
        "○ Report :\n",
        "■ Testing performance (mean ± std) [5 Marks]\n",
        "■ Comparison of Testing Accuracy and show Confusion matrix for the\n",
        "classification performed: SVM vs Neural Network [5 Marks]\n",
        "○ ROC curve (just 1 ROC for each algorithm with error bars, as shown in below graph) and\n",
        "report EER (Equal Error Rate). [MUST be done from scratch] [15 Marks]\n",
        "○ Draw the Precision-Recall curve. [MUST be done from scratch] [5 Marks]\n",
        "○ Of the two curves stated above, which is more reliable for biased/imbalanced data? Why?\n",
        "Draw inferences from the observed results on why one approach performs worse/better than the other. [20 Marks]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2QS8RWWY13f"
      },
      "source": [
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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_Voj7aDW-Wx"
      },
      "source": [
        "# Pullover = 2, coat = 4"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNxN-SY-ZMUz"
      },
      "source": [
        "# Ref : https://www.tensorflow.org/tutorials/keras/classification\n",
        "# TensorFlow and tf.keras\n",
        "import tensorflow as tf\n",
        "\n",
        "# Helper libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYMmd4xXew4B",
        "outputId": "2d98ea32-f50c-49c4-c76a-7d7a8d5eded8"
      },
      "source": [
        "# For pullover\n",
        "idx = (train_labels == 2).reshape(train_images.shape[0])\n",
        "Xtrain_pullover = train_images[idx]\n",
        "Ytrain_pullover = train_labels[idx]\n",
        "print(Xtrain_pullover.shape)\n",
        "print(Ytrain_pullover.shape)\n",
        "idx_test = (test_labels == 2).reshape(test_images.shape[0])\n",
        "Xtest_pullover = test_images[idx_test]\n",
        "Ytest_pullover = test_labels[idx_test]\n",
        "print(Xtest_pullover.shape)\n",
        "print(Ytest_pullover.shape)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(6000, 28, 28)\n",
            "(6000,)\n",
            "(1000, 28, 28)\n",
            "(1000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBGFHPxdmcrf",
        "outputId": "6b1c397e-a313-4f29-d516-c8b19c905f13"
      },
      "source": [
        "# For coat\n",
        "idx = (train_labels == 4).reshape(train_images.shape[0])\n",
        "Xtrain_coat = train_images[idx]\n",
        "Ytrain_coat = train_labels[idx]\n",
        "print(Xtrain_coat.shape)\n",
        "print(Ytrain_coat.shape)\n",
        "idx_test = (test_labels == 4).reshape(test_images.shape[0])\n",
        "Xtest_coat = test_images[idx_test]\n",
        "Ytest_coat = test_labels[idx_test]\n",
        "print(Xtest_coat.shape)\n",
        "print(Ytest_coat.shape)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(6000, 28, 28)\n",
            "(6000,)\n",
            "(1000, 28, 28)\n",
            "(1000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tFRIjVaOsxZI"
      },
      "source": [
        "Training data split:​ Take all the training samples corresponding to Pullover, and only 500 samples for Coat. ● Testing data split:​ Take all the testing samples corresponding to Pullover and Coat."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VL6zMyXepEHj",
        "outputId": "8eee51c6-c865-4237-fa42-23d7661355f1"
      },
      "source": [
        "# X_train, Y_train, X_test, Y_test\n",
        "# np.array(Xtrain_coat[0:500]).shape\n",
        "X_train = np.concatenate((Xtrain_pullover, Xtrain_coat[1500:2000]))\n",
        "# To take different samples just change teh indices from 500:1000 and then 1500:2000\n",
        "print(X_train.shape) # Expecting 6500\n",
        "Y_train = np.concatenate((Ytrain_pullover, Ytrain_coat[1500:2000]))\n",
        "# To take different samples just change teh indices from 500:1000 and then 1500:2000\n",
        "print(Y_train.shape) # Expecting 6500\n",
        "X_test = np.concatenate((Xtest_pullover, Xtest_coat))\n",
        "print(X_test.shape)\n",
        "Y_test = np.concatenate((Ytest_pullover, Ytest_coat))\n",
        "print(Y_test.shape)\n"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(6500, 28, 28)\n",
            "(6500,)\n",
            "(2000, 28, 28)\n",
            "(2000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vI4qmV_fuePb"
      },
      "source": [
        "Algorithms:​ Perform classification using: ○ A linear SVM. (You may use sklearn library) ○ A 5 layer neural network with architecture: [ 128 -- 128 -- 128 -- 64 -- 1 ] (These numbers denote the number of nodes in each layer) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJBogGf6vqB2"
      },
      "source": [
        "# Linear SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cuP04v5s8Kr",
        "outputId": "747c9a8f-fd80-42f2-98df-40ac30cb063c"
      },
      "source": [
        "# xtrain = X_train.reshape(-1, 784)\n",
        "# xtrain.shape\n",
        "# from sklearn import svm\n",
        "# # Create a classifier: a support vector classifier\n",
        "# classifier = svm.SVC(gamma=0.001)\n",
        "# #fit to the trainin data\n",
        "# classifier.fit(X_train,Y_train)\n",
        "\n",
        "\n",
        "X_train = X_train.reshape(-1, 784)\n",
        "Y_train = Y_train\n",
        "X_test = X_test.reshape(-1, 784)\n",
        "Y_test = Y_test\n",
        "print(X_train.shape)\n",
        "print(Y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(Y_test.shape)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(6500, 784)\n",
            "(6500,)\n",
            "(2000, 784)\n",
            "(2000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gh9oMEaOL4Zn"
      },
      "source": [
        "# RBF KERNEL\n",
        "\n",
        "# # C=1 Default kernel = default (rbf)\n",
        "# from sklearn import svm\n",
        "# # Create a classifier: a support vector classifier\n",
        "# classifier = svm.SVC(gamma=0.001)\n",
        "# #fit to the trainin data\n",
        "# classifier.fit(X_train,Y_train)\n",
        "# Y_pred = classifier.predict(X_test)\n",
        "# accu = classifier.score(X_test, Y_test)\n",
        "# print(\"Testing accuracy:\",accu)\n",
        "# from sklearn.metrics import classification_report, confusion_matrix\n",
        "# print(\"Classification Report: \\n\",classification_report(Y_test, Y_pred))\n",
        "# print(\"Confusion Matrix: \\n\", confusion_matrix(Y_test, Y_pred))\n",
        "\n",
        "# import seaborn as sns\n",
        "\n",
        "# matrix = confusion_matrix(Y_test, Y_pred)\n",
        "# sns.heatmap(matrix, square=True, annot=True, fmt='d', cbar=False,\n",
        "#             xticklabels=classes,\n",
        "#             yticklabels=classes)\n",
        "# plt.xlabel('true label')\n",
        "# plt.ylabel('predicted label');"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "Rq6vpp9A6mNW",
        "outputId": "e12543cc-9a0d-42cf-93c3-bde1ef67ae31"
      },
      "source": [
        "# C=1 kernel = linear\n",
        "from sklearn import svm\n",
        "# Create a classifier: a support vector classifier\n",
        "classifier = svm.SVC(kernel='linear', probability=True)\n",
        "#fit to the trainin data\n",
        "classifier.fit(X_train/255,Y_train)\n",
        "# p = np.array(classifier.decision_function(X_train))\n",
        "# prob = np.exp(p)/np.sum(np.exp(p)) # softmax \n",
        "# Y_score = prob\n",
        "Y_pred = classifier.predict(X_test/255)\n",
        "accu = classifier.score(X_test/255, Y_test)\n",
        "print(\"Testing accuracy:\",accu)\n",
        "print(\"----------------------------------------\")\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve\n",
        "print(\"Classification Report: \\n\",classification_report(Y_test, Y_pred))\n",
        "print(\"Confusion Matrix: \\n\", confusion_matrix(Y_test, Y_pred))\n",
        "print(\"----------------------------------------\")\n",
        "import seaborn as sns\n",
        "\n",
        "matrix = confusion_matrix(Y_test, Y_pred)\n",
        "sns.heatmap(matrix, square=True, annot=True, fmt='d', cbar=False,)\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label');\n",
        "print(\"----------------------------------------\")\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing accuracy: 0.6955\n",
            "----------------------------------------\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           2       0.63      0.97      0.76      1000\n",
            "           4       0.94      0.42      0.58      1000\n",
            "\n",
            "    accuracy                           0.70      2000\n",
            "   macro avg       0.78      0.70      0.67      2000\n",
            "weighted avg       0.78      0.70      0.67      2000\n",
            "\n",
            "Confusion Matrix: \n",
            " [[973  27]\n",
            " [582 418]]\n",
            "----------------------------------------\n",
            "----------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCNK8yik0aGz",
        "outputId": "91e67fc6-d491-4497-e87d-2408c2f9a876"
      },
      "source": [
        "print(\"Mean / STD:\")\n",
        "a_mean=(matrix[0,0]+matrix[1,1])/(matrix[0,0]+matrix[0,1]+matrix[1,0]+matrix[1,1])\n",
        "result=a_mean*100\n",
        "print(result)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean / STD:\n",
            "69.55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_e75za46LMu"
      },
      "source": [
        ""
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MTsjovHOh3Et",
        "outputId": "e35bd4d2-62fa-4d47-c11d-c916428ab947"
      },
      "source": [
        "fpr, tpr, thresholds = roc_curve(Y_test, Y_pred, pos_label=2)\n",
        "plt.plot(tpr, fpr)\n",
        "print(thresholds)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5 4 2]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAm3FsTlUOkN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6929f215-b722-4a5d-9146-988dfa81197a"
      },
      "source": [
        "# tn = 817\n",
        "# fp = 233\n",
        "# fn = 183\n",
        "# tp = 767\n",
        "# print(Y_test)\n",
        "# print(Y_pred)\n",
        "Y_test = np.where(Y_test==2, 0, Y_test)\n",
        "Y_test = np.where(Y_test==4, 1, Y_test) \n",
        "Y_pred = np.where(Y_pred==2, 0, Y_pred)\n",
        "Y_pred = np.where(Y_pred==4, 1, Y_pred)\n",
        "print(Y_test) # IMP\n",
        "print(Y_pred)\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 1 1 1]\n",
            "[0 0 0 ... 0 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZPhrrCSTyBc"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hgony12ZUS9F"
      },
      "source": [
        "# fpr = fp/(fp+tn)\n",
        "# tpr = tp/(tp+fn)\n",
        "# plt.plot(fpr,tpr,linestyle='--')"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mex_VUHkUuaQ"
      },
      "source": [
        "# Didnt work!!!\n",
        "# tns=[]\n",
        "# fps=[]\n",
        "# fns=[]\n",
        "# tps=[]\n",
        "\n",
        "# tn=0\n",
        "# fp=0\n",
        "# fn=0\n",
        "# tp=0\n",
        "# for i in range(len(Y_test)):\n",
        "#   if(Y_test[i]):\n",
        "#     if(Y_test[i]==Y_pred[i]):\n",
        "#       tp=tp+1\n",
        "#       tps.append(tp)\n",
        "#     else:\n",
        "#       fn=fn+1\n",
        "#       fns.append(fn)\n",
        "#   else:\n",
        "#     if(Y_test[i]==Y_pred[i]):\n",
        "#       tn=tn+1\n",
        "#       tns.append(tn)\n",
        "#     else:\n",
        "#       fp=fp+1\n",
        "#       fps.append(fp)\n",
        "\n",
        "# # print(tn)\n",
        "# # print(fp)\n",
        "# # print(fn)\n",
        "# # print(tp)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_n8UY4RIQIC"
      },
      "source": [
        "to code beyond this,I referred. specially the threshold part. hence referred GitHub Repo: The notebook link: https://github.com/tejas2454/Bias-Detection-and-Mitigation-Trusted-AI/blob/master/MNIST%20Bias%20detecton%20ROC%20scratch%20code.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgJDWYqwhCWw",
        "outputId": "287c9485-c775-482b-fabb-8976b5de2498"
      },
      "source": [
        "prb = classifier.predict_proba(X_test/255)\n",
        "print(prb.shape)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2000, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "vXgfbSpcig-R",
        "outputId": "df7c6bbe-862f-43c3-e855-ad24fe9da701"
      },
      "source": [
        "prb\n",
        "plt.plot(prb)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe28968dac8>,\n",
              " <matplotlib.lines.Line2D at 0x7fe28968dbe0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ziQBc9k9N0i"
      },
      "source": [
        "prb=prb[:,0]"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-h_YbkoKVXj"
      },
      "source": [
        ""
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDJWfCK49vi-"
      },
      "source": [
        "roc=np.concatenate((Y_test.astype(int).reshape((2000,1)),Y_pred.reshape((2000,1)),prb.reshape((2000,1))),axis=1)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3PCvfyM_lJW",
        "outputId": "2746c2ea-c1e2-4df2-de04-7d85ffd41127"
      },
      "source": [
        "roc"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.87921581],\n",
              "       [0.        , 0.        , 0.99473185],\n",
              "       [0.        , 0.        , 0.99467601],\n",
              "       ...,\n",
              "       [1.        , 0.        , 0.76770409],\n",
              "       [1.        , 1.        , 0.62072565],\n",
              "       [1.        , 0.        , 0.72978935]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9kZD5LHKAuAQ",
        "outputId": "a34e5d30-737a-42c0-bc7f-58e4bbd8fe08"
      },
      "source": [
        "pullover_2 = []\n",
        "coat_4 = []\n",
        "for i in range(len(roc)):\n",
        "  if (roc[i,0]==1):\n",
        "    coat_4.append(roc[i,:])\n",
        "  else:\n",
        "    pullover_2.append(roc[i,:])\n",
        "\n",
        "print(\"coat_4\")\n",
        "print(coat_4)\n",
        "print('\\n')\n",
        "print(\"pullover_2\")\n",
        "print(pullover_2)\n",
        "\n",
        "thresh=np.unique(np.concatenate((np.asarray(coat_4)[:,-1],np.asarray(pullover_2)[:,-1]),axis=0))\n",
        "thresh_matrix=np.sort(thresh[::-1].T).reshape((len(thresh),1))[::-1]\n",
        "sequence_arr=np.concatenate((np.asarray(coat_4)[:,:],np.asarray(pullover_2)[:,:]),axis=0)\n",
        "\n"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "coat_4\n",
            "[array([1.        , 1.        , 0.66379496]), array([1.        , 0.        , 0.78169668]), array([1.        , 0.        , 0.81203991]), array([1.        , 0.        , 0.74766504]), array([1.        , 0.        , 0.89600706]), array([1.        , 1.        , 0.62302352]), array([1.        , 0.        , 0.96228478]), array([1.        , 0.        , 0.85178121]), array([1.      , 0.      , 0.821578]), array([1.       , 0.       , 0.8629276]), array([1.        , 1.        , 0.60097456]), array([1.        , 0.        , 0.87264641]), array([1.        , 0.        , 0.88094849]), array([1.        , 0.        , 0.94529033]), array([1.        , 0.        , 0.99033563]), array([1.        , 0.        , 0.82152934]), array([1.        , 1.        , 0.56405053]), array([1.        , 0.        , 0.90947152]), array([1.        , 1.        , 0.60026651]), array([1.        , 0.        , 0.87614207]), array([1.        , 0.        , 0.88543639]), array([1.       , 0.       , 0.7843264]), array([1.        , 1.        , 0.46413097]), array([1.        , 1.        , 0.48384952]), array([1.        , 1.        , 0.23358896]), array([1.        , 0.        , 0.99273975]), array([1.       , 0.       , 0.9436624]), array([1.        , 0.        , 0.71574461]), array([1.       , 0.       , 0.8730156]), array([1.        , 1.        , 0.36269705]), array([1.        , 1.        , 0.42228928]), array([1.        , 0.        , 0.83244436]), array([1.        , 1.        , 0.62699631]), array([1.        , 0.        , 0.85661041]), array([1.      , 1.      , 0.104496]), array([1.        , 1.        , 0.64064192]), array([1.        , 0.        , 0.72158902]), array([1.        , 1.        , 0.61666101]), array([1.        , 0.        , 0.69203274]), array([1.        , 0.        , 0.89277248]), array([1.        , 1.        , 0.30783031]), array([1.        , 1.        , 0.23858294]), array([1.        , 1.        , 0.68628261]), array([1.        , 0.        , 0.77178755]), array([1.        , 1.  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array([0.        , 0.        , 0.90430674]), array([0.        , 0.        , 0.93481953]), array([0.       , 0.       , 0.9393213]), array([0.        , 0.        , 0.97708817]), array([0.        , 0.        , 0.91357686]), array([0.        , 0.        , 0.96012499]), array([0.        , 0.        , 0.99136805]), array([0.        , 0.        , 0.90989197]), array([0.        , 0.        , 0.96374334]), array([0.        , 0.        , 0.99518129]), array([0.        , 0.        , 0.98111722]), array([0.        , 0.        , 0.98345387]), array([0.        , 0.        , 0.80090612]), array([0.        , 0.        , 0.96105476]), array([0.        , 0.        , 0.98537639]), array([0.        , 1.        , 0.68613999]), array([0.        , 0.        , 0.99160744]), array([0.        , 0.        , 0.99825024]), array([0.        , 0.        , 0.99903876]), array([0.        , 0.        , 0.98797618]), array([0.        , 0.        , 0.98697278]), array([0.        , 0.        , 0.99764116]), array([0.        , 0.        , 0.99276597]), array([0.        , 0.        , 0.88831691]), array([0.        , 0.        , 0.92274476]), array([0.        , 0.        , 0.99533663]), array([0.        , 0.        , 0.95687936]), array([0.       , 0.       , 0.9573577]), array([0.        , 0.        , 0.99858569]), array([0.        , 0.        , 0.94878688]), array([0.        , 0.        , 0.99564854]), array([0.        , 0.        , 0.92955693]), array([0.        , 0.        , 0.88540035]), array([0.        , 0.        , 0.87856892]), array([0.        , 0.        , 0.95750682]), array([0.       , 0.       , 0.9216337]), array([0.        , 0.        , 0.97255441]), array([0.        , 0.        , 0.98500721]), array([0.        , 1.        , 0.66661589]), array([0.        , 0.        , 0.97974038]), array([0.        , 0.        , 0.99900059]), array([0.        , 0.        , 0.92995976]), array([0.        , 0.        , 0.98897598]), array([0.        , 1.        , 0.57704836]), array([0.       , 0.       , 0.9933431]), array([0.        , 0.        , 0.96920217]), array([0.        , 0.        , 0.94754988]), array([0.        , 0.        , 0.99801537]), array([0.        , 0.        , 0.70693852]), array([0.        , 1.        , 0.42475159]), array([0.        , 0.        , 0.99469663]), array([0.        , 0.        , 0.85907883]), array([0.       , 0.       , 0.9923696]), array([0.        , 0.        , 0.97826374]), array([0.        , 0.        , 0.95271784]), array([0.        , 0.        , 0.99200042]), array([0.        , 0.        , 0.99456707]), array([0.        , 0.        , 0.92877462]), array([0.       , 0.       , 0.9652133]), array([0.        , 0.        , 0.88978509]), array([0.       , 0.       , 0.9930341]), array([0.        , 0.        , 0.75297043]), array([0.        , 0.        , 0.95625774]), array([0.        , 0.        , 0.93984156]), array([0.       , 0.       , 0.8580245]), array([0.        , 0.        , 0.96640811]), array([0.        , 0.        , 0.86735136]), array([0.       , 0.       , 0.9661249]), array([0.        , 0.        , 0.97090947]), array([0.        , 0.        , 0.97999901]), array([0.        , 0.        , 0.99105923]), array([0.        , 0.        , 0.92918211]), array([0.        , 0.        , 0.98050573]), array([0.        , 0.        , 0.95228217]), array([0.        , 0.        , 0.99455575]), array([0.        , 0.        , 0.96762538]), array([0.        , 0.        , 0.97420911]), array([0.       , 0.       , 0.9656696]), array([0.        , 0.        , 0.98282657]), array([0.        , 0.        , 0.97603163]), array([0.        , 0.        , 0.86978977]), array([0.        , 0.        , 0.99649427]), array([0.        , 0.        , 0.99113539]), array([0.        , 0.        , 0.99798739]), array([0.        , 0.        , 0.97761836])]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unDPIOW9A2tl"
      },
      "source": [
        "def tpr_fpr(arr,thresh):\n",
        "  x=[]\n",
        "  for k in range(len(thresh)):\n",
        "    t=thresh[k]\n",
        "    tp=0\n",
        "    fp=0\n",
        "    tn=0\n",
        "    fn=0\n",
        "    for i in range(len(arr)):  \n",
        "      if (arr[i,0]==0 and (arr[i,2]==t or arr[i,2]>t )):\n",
        "        tp+=1\n",
        "      elif (arr[i,0]==1 and (arr[i,2]==t or arr[i,2]>t)):\n",
        "        fp+=1\n",
        "      elif (arr[i,0]==1 and arr[i,2]<t):\n",
        "        tn+=1\n",
        "      else :\n",
        "        fn+=1\n",
        "    x.append(tp/(tp+fn))\n",
        "    x.append(fp/(tn+fp))\n",
        "  return(x)\n",
        "\n",
        "def precision_recall(arr,thresh):\n",
        "  x=[]\n",
        "  for k in range(len(thresh)):\n",
        "    t=thresh[k]\n",
        "    tp=0\n",
        "    fp=0\n",
        "    tn=0\n",
        "    fn=0\n",
        "    for i in range(len(arr)):  \n",
        "      if (arr[i,0]==0 and (arr[i,2]==t or arr[i,2]>t )):\n",
        "        tp+=1\n",
        "      elif (arr[i,0]==1 and (arr[i,2]==t or arr[i,2]>t)):\n",
        "        fp+=1\n",
        "      elif (arr[i,0]==1 and arr[i,2]<t):\n",
        "        tn+=1\n",
        "      else :\n",
        "        fn+=1\n",
        "    x.append(tp/(tp+fp))\n",
        "    x.append(tp/(tp+fn))\n",
        "  return(x)"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVswi7tSEmIy"
      },
      "source": [
        "final_plot_roc=np.asarray(tpr_fpr(sequence_arr,thresh_matrix))\n",
        "roc_ma=final_plot_roc.reshape((int(len(final_plot_roc)/2),2))"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3IjVeNquFRaw",
        "outputId": "630bc9cc-937e-47ac-8b6c-a0388111e501"
      },
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.title('ROC Curve for SVM Training Data')\n",
        "plt.xlabel('True Positive Rate')\n",
        "plt.ylabel('False Positive Rate')\n",
        "plt.plot(roc_ma[:,1],roc_ma[:,0],'r')   \n",
        "plt.show()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CW5mWg5KFbdy"
      },
      "source": [
        "final_plot_pr=np.asarray(precision_recall(sequence_arr,thresh_matrix))\n",
        "pr=final_plot_pr.reshape((int(len(final_plot_pr)/2),2))\n"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Nbht4yD0HMkx",
        "outputId": "551361b4-9f58-49eb-9677-31f158363642"
      },
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.title('Precision Recall Curve for SVM')\n",
        "plt.ylabel('Precision ')\n",
        "plt.xlabel('Recall')\n",
        "plt.plot(pr[:,0],pr[:,1],'r')  \n",
        "plt.show()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Wexz5NvKHVHJ",
        "outputId": "b97661b8-2ab6-4cb1-93d1-8951b3d4d6a5"
      },
      "source": [
        "def fpr_fnr(arr,thresh):\n",
        "  x=[]\n",
        "  for k in range(len(thresh)):\n",
        "    t=thresh[k]\n",
        "    tp=0\n",
        "    fp=0\n",
        "    tn=0\n",
        "    fn=0\n",
        "    for i in range(len(arr)):  \n",
        "      if (arr[i,0]==0 and (arr[i,2]==t or arr[i,2]>t )):\n",
        "        tp+=1\n",
        "      elif (arr[i,0]==1 and (arr[i,2]>=t)):\n",
        "        fp+=1\n",
        "      elif (arr[i,0]==1 and arr[i,2]<t):\n",
        "        tn+=1\n",
        "      else :\n",
        "        fn+=1\n",
        "    x.append(fp/(tn+fp))\n",
        "    x.append(fn/(tp+fn))\n",
        "  return(x)\n",
        "EER=np.asarray(fpr_fnr(sequence_arr,thresh_matrix))\n",
        "plt.title(\"Equal Error Rate | Blue : FPR, Orange : FNR  \")\n",
        "plt.xlabel('Samples')\n",
        "plt.ylabel('Rate')\n",
        "plt.plot(EER.reshape((int(len(EER)/2),2))[:,0])\n",
        "plt.plot(EER.reshape((int(len(EER)/2),2))[:,1])\n",
        "plt.show()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aAJsqiIIw2a"
      },
      "source": [
        ""
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22E85c4qN6N7"
      },
      "source": [
        "# Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfJaQLBfN7xs"
      },
      "source": [
        ""
      ],
      "execution_count": 88,
      "outputs": []
    }
  ]
}