Grid Search - Breast Cancer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data import and pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "os.chdir('C:\\\\Users\\\\rohan\\\\Documents\\\\Analytics\\\\Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "data = pd.read_csv('breast-cancer-wisconsin.csv',header=None)\n",
    "\n",
    "#set column names\n",
    "data.columns = ['Sample Code Number','Clump Thickness','Uniformity of Cell Size',\n",
    "                                                        'Uniformity of Cell Shape','Marginal Adhesion','Single Epithelial Cell Size',\n",
    "                                                        'Bare Nuclei','Bland Chromatin','Normal Nucleoli','Mitoses','Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Uniformity of Cell Size</th>\n",
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       "      <th>Marginal Adhesion</th>\n",
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      "text/plain": [
       "   Sample Code Number  Clump Thickness  Uniformity of Cell Size  \\\n",
       "0             1000025                5                        1   \n",
       "1             1002945                5                        4   \n",
       "2             1015425                3                        1   \n",
       "3             1016277                6                        8   \n",
       "4             1017023                4                        1   \n",
       "5             1017122                8                       10   \n",
       "6             1018099                1                        1   \n",
       "7             1018561                2                        1   \n",
       "8             1033078                2                        1   \n",
       "9             1033078                4                        2   \n",
       "\n",
       "   Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "0                         1                  1                            2   \n",
       "1                         4                  5                            7   \n",
       "2                         1                  1                            2   \n",
       "3                         8                  1                            3   \n",
       "4                         1                  3                            2   \n",
       "5                        10                  8                            7   \n",
       "6                         1                  1                            2   \n",
       "7                         2                  1                            2   \n",
       "8                         1                  1                            2   \n",
       "9                         1                  1                            2   \n",
       "\n",
       "  Bare Nuclei  Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "0           1                3                1        1      2  \n",
       "1          10                3                2        1      2  \n",
       "2           2                3                1        1      2  \n",
       "3           4                3                7        1      2  \n",
       "4           1                3                1        1      2  \n",
       "5          10                9                7        1      4  \n",
       "6          10                3                1        1      2  \n",
       "7           1                3                1        1      2  \n",
       "8           1                1                1        5      2  \n",
       "9           1                2                1        1      2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view top rows\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
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      "text/plain": [
       "   Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
       "0                5                        1                         1   \n",
       "1                5                        4                         4   \n",
       "2                3                        1                         1   \n",
       "3                6                        8                         8   \n",
       "4                4                        1                         1   \n",
       "\n",
       "   Marginal Adhesion  Single Epithelial Cell Size Bare Nuclei  \\\n",
       "0                  1                            2           1   \n",
       "1                  5                            7          10   \n",
       "2                  1                            2           2   \n",
       "3                  1                            3           4   \n",
       "4                  3                            2           1   \n",
       "\n",
       "   Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "0                3                1        1      0  \n",
       "1                3                2        1      0  \n",
       "2                3                1        1      0  \n",
       "3                3                7        1      0  \n",
       "4                3                1        1      0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data cleaning\n",
    "data = data.drop(['Sample Code Number'],axis=1) #Drop 1st column\n",
    "data = data[data['Bare Nuclei'] != '?'] #Remove rows with missing data\n",
    "data['Class'] = np.where(data['Class'] ==2,0,1) #Change the Class representation\n",
    "data.head() #View top 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    444\n",
       "1    239\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Distribution of Class\n",
    "data['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data into attributes and class\n",
    "X = data.drop(['Class'],axis=1)\n",
    "y = data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#perform training and test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Dummy Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y actual : \n",
      "0    103\n",
      "1     68\n",
      "Name: Class, dtype: int64\n",
      "y predicted : \n",
      "0    171\n",
      "dtype: int64\n",
      "Accuracy Score : 0.6023391812865497\n",
      "Precision Score : 0.0\n",
      "Recall Score : 0.0\n",
      "F1 Score : 0.0\n",
      "Confusion Matrix : \n",
      "[[103   0]\n",
      " [ 68   0]]\n"
     ]
    }
   ],
   "source": [
    "#Dummy Classifier\n",
    "from sklearn.dummy import DummyClassifier\n",
    "clf = DummyClassifier(strategy= 'most_frequent').fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "#Distribution of y test\n",
    "print('y actual : \\n' +  str(y_test.value_counts()))\n",
    "\n",
    "#Distribution of y predicted\n",
    "print('y predicted : \\n' + str(pd.Series(y_pred).value_counts()))\n",
    "\n",
    "# Model Evaluation metrics \n",
    "from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score\n",
    "print('Accuracy Score : ' + str(accuracy_score(y_test,y_pred)))\n",
    "print('Precision Score : ' + str(precision_score(y_test,y_pred)))\n",
    "print('Recall Score : ' + str(recall_score(y_test,y_pred)))\n",
    "print('F1 Score : ' + str(f1_score(y_test,y_pred)))\n",
    "\n",
    "#Dummy Classifier Confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print('Confusion Matrix : \\n' + str(confusion_matrix(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.9473684210526315\n",
      "Precision Score : 0.9836065573770492\n",
      "Recall Score : 0.8823529411764706\n",
      "F1 Score : 0.9302325581395349\n",
      "Confusion Matrix : \n",
      "[[102   1]\n",
      " [  8  60]]\n"
     ]
    }
   ],
   "source": [
    "#Logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression().fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Model Evaluation metrics \n",
    "from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score\n",
    "print('Accuracy Score : ' + str(accuracy_score(y_test,y_pred)))\n",
    "print('Precision Score : ' + str(precision_score(y_test,y_pred)))\n",
    "print('Recall Score : ' + str(recall_score(y_test,y_pred)))\n",
    "print('F1 Score : ' + str(f1_score(y_test,y_pred)))\n",
    "\n",
    "#Dummy Classifier Confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print('Confusion Matrix : \\n' + str(confusion_matrix(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Logistic Regression + Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=0.009, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l1', random_state=None, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False)\n",
      "Accuracy Score : 0.9122807017543859\n",
      "Precision Score : 0.8732394366197183\n",
      "Recall Score : 0.9117647058823529\n",
      "F1 Score : 0.8920863309352517\n",
      "Confusion Matrix : \n",
      "[[94  9]\n",
      " [ 6 62]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "clf = LogisticRegression()\n",
    "grid_values = {'penalty': ['l1', 'l2'],'C':[0.001,.009,0.01,.09,1,5,10,25]}\n",
    "grid_clf_acc = GridSearchCV(clf, param_grid = grid_values,scoring = 'recall')\n",
    "grid_clf_acc.fit(X_train, y_train)\n",
    "\n",
    "print(grid_clf_acc.best_estimator_)\n",
    "\n",
    "y_pred_acc = grid_clf_acc.predict(X_test)\n",
    "\n",
    "# Model Evaluation metrics \n",
    "from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score\n",
    "print('Accuracy Score : ' + str(accuracy_score(y_test,y_pred_acc)))\n",
    "print('Precision Score : ' + str(precision_score(y_test,y_pred_acc)))\n",
    "print('Recall Score : ' + str(recall_score(y_test,y_pred_acc)))\n",
    "print('F1 Score : ' + str(f1_score(y_test,y_pred_acc)))\n",
    "\n",
    "#Dummy Classifier Confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print('Confusion Matrix : \\n' + str(confusion_matrix(y_test,y_pred_acc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function for confusion matrix plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to plot intuitive confusion matrix\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, y_pred_acc)\n",
    "np.set_printoptions(precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[94  9]\n",
      " [ 6 62]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Fla2pSvqZpNmS7i47ljIsWbKE22+7lU988lj+OeU2BgwcyBnfPa3ssKyTdokt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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "class_names = [0,1]\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
    "                      title='Confusion matrix - Logistic Regression + Grid Search')\n",
    "a = plt.gcf()\n",
    "a.set_size_inches(8,4)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}