DAI_ASSIGNMENT_1_Q2_0to500_SVM.ipynb

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    "colab": {
      "name": "DAI_ASSIGNMENT_1_Q2_SVM.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
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      "cell_type": "markdown",
      "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": 38,
      "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": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYMmd4xXew4B",
        "outputId": "6cc36918-5f7e-4c58-dcb0-3dd2924f458f"
      },
      "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": 40,
      "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": "17140ff1-c23b-4220-9b52-28d060c31b46"
      },
      "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": 41,
      "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": "03234bce-7271-485c-cbc1-15d038173cba"
      },
      "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[0:500]))\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[0:500]))\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": 42,
      "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": "203d9744-7807-49e7-ffdd-a9f02687bef2"
      },
      "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": 43,
      "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": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "Rq6vpp9A6mNW",
        "outputId": "dbc6ddf0-4999-4fd1-b0fd-4602836fb038"
      },
      "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": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing accuracy: 0.713\n",
            "----------------------------------------\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           2       0.64      0.97      0.77      1000\n",
            "           4       0.94      0.46      0.61      1000\n",
            "\n",
            "    accuracy                           0.71      2000\n",
            "   macro avg       0.79      0.71      0.69      2000\n",
            "weighted avg       0.79      0.71      0.69      2000\n",
            "\n",
            "Confusion Matrix: \n",
            " [[971  29]\n",
            " [545 455]]\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": "386c07ea-ebc9-4e4c-f79b-39452e853928"
      },
      "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": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean / STD:\n",
            "71.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_e75za46LMu"
      },
      "source": [
        ""
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MTsjovHOh3Et",
        "outputId": "da923f48-b40a-42d2-a5be-00bf5dc8e9b7"
      },
      "source": [
        "fpr, tpr, thresholds = roc_curve(Y_test, Y_pred, pos_label=2)\n",
        "plt.plot(tpr, fpr)\n",
        "print(thresholds)"
      ],
      "execution_count": 47,
      "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": "daa9632a-41dc-4478-e573-958fbda6bd84"
      },
      "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": 48,
      "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": 49,
      "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": 50,
      "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": "5d4d1310-6d12-4866-b408-2205b51fc9bc"
      },
      "source": [
        "prb = classifier.predict_proba(X_test/255)\n",
        "print(prb.shape)"
      ],
      "execution_count": 51,
      "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": "d1e0bf41-a6ae-4379-b0d5-354c48a2ba98"
      },
      "source": [
        "prb\n",
        "plt.plot(prb)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe287e2bf28>,\n",
              " <matplotlib.lines.Line2D at 0x7fe287e2b978>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "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": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-h_YbkoKVXj"
      },
      "source": [
        ""
      ],
      "execution_count": 53,
      "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": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3PCvfyM_lJW",
        "outputId": "c5c087f2-2d7a-4ee0-9b75-8dc83b44623c"
      },
      "source": [
        "roc"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.94356987],\n",
              "       [0.        , 0.        , 0.99742742],\n",
              "       [0.        , 0.        , 0.99560372],\n",
              "       ...,\n",
              "       [1.        , 0.        , 0.96473941],\n",
              "       [1.        , 1.        , 0.3597238 ],\n",
              "       [1.        , 0.        , 0.92042549]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9kZD5LHKAuAQ",
        "outputId": "6f1af437-f367-4e9d-cda9-6fb6eef46410"
      },
      "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": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "coat_4\n",
            "[array([1.        , 1.        , 0.21980924]), array([1.        , 0.        , 0.77066734]), array([1.        , 1.        , 0.43046017]), array([1.        , 0.        , 0.98476694]), array([1.        , 0.        , 0.95300185]), array([1.        , 1.        , 0.53815336]), array([1.        , 0.        , 0.96332442]), array([1.        , 0.        , 0.90907302]), array([1.        , 0.        , 0.76480036]), array([1.        , 0.        , 0.69739318]), array([1.        , 1.        , 0.19087377]), array([1.        , 1.        , 0.56331731]), array([1.      , 0.      , 0.917126]), array([1.        , 0.        , 0.85356469]), array([1.        , 0.        , 0.99456328]), array([1.        , 0.        , 0.88328229]), array([1.        , 1.        , 0.29400971]), array([1.       , 0.       , 0.8901543]), array([1.        , 1.        , 0.20876079]), array([1.        , 0.        , 0.72333619]), array([1.        , 0.        , 0.86079546]), array([1.        , 1.        , 0.52073251]), 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0.97483658]), array([0.        , 0.        , 0.99689912]), array([0.        , 0.        , 0.99653934]), array([0.       , 0.       , 0.8010785]), array([0.        , 1.        , 0.36481585]), array([0.        , 0.        , 0.99692316]), array([0.        , 0.        , 0.90349361]), array([0.        , 0.        , 0.99549122]), array([0.        , 0.        , 0.98197633]), array([0.        , 0.        , 0.96679272]), array([0.        , 0.        , 0.99333819]), array([0.       , 0.       , 0.9931873]), array([0.        , 0.        , 0.96301266]), array([0.        , 0.        , 0.99230911]), array([0.        , 0.        , 0.80539173]), array([0.        , 0.        , 0.98792398]), array([0.        , 1.        , 0.62743972]), array([0.        , 0.        , 0.90095535]), array([0.        , 0.        , 0.90482218]), array([0.       , 0.       , 0.8862086]), array([0.        , 0.        , 0.97048245]), array([0.        , 0.        , 0.97211674]), array([0.        , 0.        , 0.98281012]), array([0.        , 0.        , 0.93939994]), array([0.        , 0.        , 0.96096392]), array([0.        , 0.        , 0.98546983]), array([0.        , 0.        , 0.87041721]), array([0.        , 0.        , 0.97845968]), array([0.        , 0.        , 0.94259042]), array([0.        , 0.        , 0.98535984]), array([0.        , 0.        , 0.98989974]), array([0.        , 0.        , 0.95298375]), array([0.        , 0.        , 0.95442063]), array([0.        , 0.        , 0.98601977]), array([0.        , 0.        , 0.97036993]), array([0.       , 0.       , 0.8981748]), array([0.        , 0.        , 0.97635861]), array([0.        , 0.        , 0.96905814]), array([0.        , 0.        , 0.99844091]), array([0.        , 0.        , 0.97252408])]\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": 57,
      "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": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3IjVeNquFRaw",
        "outputId": "0d7ce1d4-29cd-4192-d31f-00fdff4c8c33"
      },
      "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": 59,
      "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": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Nbht4yD0HMkx",
        "outputId": "47c4847f-a083-42e4-b34a-4b52159a1fc8"
      },
      "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": 63,
      "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": "2f60ae91-4f8a-4f0f-ce38-169095cd53b5"
      },
      "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": 62,
      "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": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22E85c4qN6N7"
      },
      "source": [
        "# Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfJaQLBfN7xs"
      },
      "source": [
        ""
      ],
      "execution_count": 62,
      "outputs": []
    }
  ]
}