Logistic_Regression_Iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(a) Iris data set will be used in this problem. Load the Iris data set from sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) List target names and feature names of Iris. Select any two features and plot the data based on the\n",
    "two selected features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(data.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(data.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text yticklabel objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "X = data.data[:, :2]\n",
    "y = data.target\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "\n",
    "plt.figure(2, figsize=(8, 6))\n",
    "plt.clf()\n",
    "\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,edgecolor='k')\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) Select a target name in target names. Generate the target (label) Y by setting the label of samples with\n",
    "selected target name to 1 and the labels of the remaining samples to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modified data set: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "data = load_iris()\n",
    "import matplotlib.pyplot as plt\n",
    "#import numpy as np\n",
    "X = data.data[:, :2]\n",
    "y = data.target\n",
    "for i in range(len(Y)):\n",
    "    if Y[i] == 0 :\n",
    "        Y[i]=1\n",
    "    else:\n",
    "        Y[i] =0\n",
    "print(\"Modified data set:\",Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(d) Split your data using model selection.train test split to 80% training data and 20% test data. Build\n",
    "a sklearn.linear model.LogisticRegression model and train with the training data set. Test your trained\n",
    "model on the test data set and report the estimated probabilities of the samples in the test data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1 3.5]\n",
      " [4.9 3. ]\n",
      " [4.7 3.2]\n",
      " [4.6 3.1]\n",
      " [5.  3.6]\n",
      " [5.4 3.9]\n",
      " [4.6 3.4]\n",
      " [5.  3.4]\n",
      " [4.4 2.9]\n",
      " [4.9 3.1]\n",
      " [5.4 3.7]\n",
      " [4.8 3.4]\n",
      " [4.8 3. ]\n",
      " [4.3 3. ]\n",
      " [5.8 4. ]\n",
      " [5.7 4.4]\n",
      " [5.4 3.9]\n",
      " [5.1 3.5]\n",
      " [5.7 3.8]\n",
      " [5.1 3.8]\n",
      " [5.4 3.4]\n",
      " [5.1 3.7]\n",
      " [4.6 3.6]\n",
      " [5.1 3.3]\n",
      " [4.8 3.4]\n",
      " [5.  3. ]\n",
      " [5.  3.4]\n",
      " [5.2 3.5]\n",
      " [5.2 3.4]\n",
      " [4.7 3.2]\n",
      " [4.8 3.1]\n",
      " [5.4 3.4]\n",
      " [5.2 4.1]\n",
      " [5.5 4.2]\n",
      " [4.9 3.1]\n",
      " [5.  3.2]\n",
      " [5.5 3.5]\n",
      " [4.9 3.6]\n",
      " [4.4 3. ]\n",
      " [5.1 3.4]\n",
      " [5.  3.5]\n",
      " [4.5 2.3]\n",
      " [4.4 3.2]\n",
      " [5.  3.5]\n",
      " [5.1 3.8]\n",
      " [4.8 3. ]\n",
      " [5.1 3.8]\n",
      " [4.6 3.2]\n",
      " [5.3 3.7]\n",
      " [5.  3.3]\n",
      " [7.  3.2]\n",
      " [6.4 3.2]\n",
      " [6.9 3.1]\n",
      " [5.5 2.3]\n",
      " [6.5 2.8]\n",
      " [5.7 2.8]\n",
      " [6.3 3.3]\n",
      " [4.9 2.4]\n",
      " [6.6 2.9]\n",
      " [5.2 2.7]\n",
      " [5.  2. ]\n",
      " [5.9 3. ]\n",
      " [6.  2.2]\n",
      " [6.1 2.9]\n",
      " [5.6 2.9]\n",
      " [6.7 3.1]\n",
      " [5.6 3. ]\n",
      " [5.8 2.7]\n",
      " [6.2 2.2]\n",
      " [5.6 2.5]\n",
      " [5.9 3.2]\n",
      " [6.1 2.8]\n",
      " [6.3 2.5]\n",
      " [6.1 2.8]\n",
      " [6.4 2.9]\n",
      " [6.6 3. ]\n",
      " [6.8 2.8]\n",
      " [6.7 3. ]\n",
      " [6.  2.9]\n",
      " [5.7 2.6]\n",
      " [5.5 2.4]\n",
      " [5.5 2.4]\n",
      " [5.8 2.7]\n",
      " [6.  2.7]\n",
      " [5.4 3. ]\n",
      " [6.  3.4]\n",
      " [6.7 3.1]\n",
      " [6.3 2.3]\n",
      " [5.6 3. ]\n",
      " [5.5 2.5]\n",
      " [5.5 2.6]\n",
      " [6.1 3. ]\n",
      " [5.8 2.6]\n",
      " [5.  2.3]\n",
      " [5.6 2.7]\n",
      " [5.7 3. ]\n",
      " [5.7 2.9]\n",
      " [6.2 2.9]\n",
      " [5.1 2.5]\n",
      " [5.7 2.8]\n",
      " [6.3 3.3]\n",
      " [5.8 2.7]\n",
      " [7.1 3. ]\n",
      " [6.3 2.9]\n",
      " [6.5 3. ]\n",
      " [7.6 3. ]\n",
      " [4.9 2.5]\n",
      " [7.3 2.9]\n",
      " [6.7 2.5]\n",
      " [7.2 3.6]\n",
      " [6.5 3.2]\n",
      " [6.4 2.7]\n",
      " [6.8 3. ]\n",
      " [5.7 2.5]\n",
      " [5.8 2.8]\n",
      " [6.4 3.2]\n",
      " [6.5 3. ]\n",
      " [7.7 3.8]\n",
      " [7.7 2.6]\n",
      " [6.  2.2]\n",
      " [6.9 3.2]\n",
      " [5.6 2.8]\n",
      " [7.7 2.8]\n",
      " [6.3 2.7]\n",
      " [6.7 3.3]\n",
      " [7.2 3.2]\n",
      " [6.2 2.8]\n",
      " [6.1 3. ]\n",
      " [6.4 2.8]\n",
      " [7.2 3. ]\n",
      " [7.4 2.8]\n",
      " [7.9 3.8]\n",
      " [6.4 2.8]\n",
      " [6.3 2.8]\n",
      " [6.1 2.6]\n",
      " [7.7 3. ]\n",
      " [6.3 3.4]\n",
      " [6.4 3.1]\n",
      " [6.  3. ]\n",
      " [6.9 3.1]\n",
      " [6.7 3.1]\n",
      " [6.9 3.1]\n",
      " [5.8 2.7]\n",
      " [6.8 3.2]\n",
      " [6.7 3.3]\n",
      " [6.7 3. ]\n",
      " [6.3 2.5]\n",
      " [6.5 3. ]\n",
      " [6.2 3.4]\n",
      " [5.9 3. ]]\n",
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0]\n"
     ]
    }
   ],
   "source": [
    "#Y = Y.values.reshape(-1,)\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test= train_test_split(X,Y,test_size = 1/5,random_state=0)\n",
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(max_iter=500,random_state=0, solver='lbfgs',multi_class='multinomial').fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 1 0 1 1 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print( classification_report(Y_test, predictions) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print( accuracy_score(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(e) There are two k-fold functions provided in the model selection module: model selection.KFold, and\n",
    "model selection.StratifiedKFold. Use both functions to split your training data to 4 folds. Explain the\n",
    "difference between these two methods when splitting the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [ 38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55\n",
      "  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73\n",
      "  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91\n",
      "  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109\n",
      " 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127\n",
      " 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145\n",
      " 146 147 148 149] TEST: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37]\n",
      "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91\n",
      "  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109\n",
      " 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127\n",
      " 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145\n",
      " 146 147 148 149] TEST: [38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61\n",
      " 62 63 64 65 66 67 68 69 70 71 72 73 74 75]\n",
      "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75 113 114 115 116 117 118 119 120 121 122 123 124 125 126\n",
      " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144\n",
      " 145 146 147 148 149] TEST: [ 76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93\n",
      "  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111\n",
      " 112]\n",
      "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112] TEST: [113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130\n",
      " 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148\n",
      " 149]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=4)\n",
    "for train_index, test_index in kf.split(X):\n",
    "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    Y_train, Y_test = Y[train_index], Y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [ 13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30\n",
      "  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48\n",
      "  49  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91\n",
      "  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109\n",
      " 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127\n",
      " 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145\n",
      " 146 147 148 149] TEST: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 50 51 52 53 54 55 56 57 58 59 60\n",
      " 61 62 63 64 65 66 67 68 69 70 71 72 73 74]\n",
      "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  26  27  28  29  30\n",
      "  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48\n",
      "  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66\n",
      "  67  68  69  70  71  72  73  74 100 101 102 103 104 105 106 107 108 109\n",
      " 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127\n",
      " 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145\n",
      " 146 147 148 149] TEST: [13 14 15 16 17 18 19 20 21 22 23 24 25 75 76 77 78 79 80 81 82 83 84 85\n",
      " 86 87 88 89 90 91 92 93 94 95 96 97 98 99]\n",
      "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  38  39  40  41  42  43  44  45  46  47\n",
      "  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65\n",
      "  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83\n",
      "  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 125 126\n",
      " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144\n",
      " 145 146 147 148 149] TEST: [ 26  27  28  29  30  31  32  33  34  35  36  37 100 101 102 103 104 105\n",
      " 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123\n",
      " 124]\n",
      "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65\n",
      "  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83\n",
      "  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 101\n",
      " 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119\n",
      " 120 121 122 123 124] TEST: [ 38  39  40  41  42  43  44  45  46  47  48  49 125 126 127 128 129 130\n",
      " 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148\n",
      " 149]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "skf = StratifiedKFold(n_splits=4)\n",
    "for train_index, test_index in skf.split(X, Y):\n",
    "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    Y_train, Y_test = Y[train_index], Y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Difference between the two corss validation methods:\n",
    "#In K-fold cross validation method, the k subsets are made randomly.\n",
    "#However, in stratified k-fold cross validation the data is rearranged once at the start, and then divided into the number of desired splits.\n",
    "#The test data is always one of the splits, the train data is the rest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(f)Use model selection.cross val score to generate the cross validation score by 4 folds with the model in\n",
    "(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         1.         1.         0.97297297]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "print(cross_val_score(model, X, Y, cv=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reference for the work:\n",
    "#https://scikit-learn.org/stable/model_selection.html\n",
    "#https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html"
   ]
  }
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