LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "digits=load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score,precision_score,f1_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=digits.data\n",
    "y=digits.target\n",
    "y_=np.zeros((y.shape[0],10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(y.shape[0]):\n",
    "    y_[i][y[i]]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LogisticRegression(solver='saga',\n",
    "                        max_iter=10000,\n",
    "                        C=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=50, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=10000,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='saga', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics: Accuracy, Precision, Recall , F1 Score, Confusion matrix \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.968013468013468"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.968013468013468"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.968013468013468"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_pred,Y_test,average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.968013468013468"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_pred,Y_test,average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[55  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 52  2  0  0  0  0  0  1  0]\n",
      " [ 0  0 51  0  0  0  0  0  1  0]\n",
      " [ 0  0  1 52  0  1  0  0  2  0]\n",
      " [ 0  1  0  0 63  0  0  0  0  0]\n",
      " [ 0  1  0  0  0 69  1  0  0  2]\n",
      " [ 0  0  0  0  0  1 56  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 61  0  1]\n",
      " [ 0  1  0  0  0  1  0  0 50  0]\n",
      " [ 0  0  0  0  0  0  0  0  2 66]]\n"
     ]
    }
   ],
   "source": [
    "cf_matrix = confusion_matrix(Y_test, y_pred)\n",
    "print(cf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16458edd208>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(cf_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}