Log Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits= load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "for index, (image, label) in enumerate(zip(digits.data[0:5], digits.target[0:5])):\n",
    " plt.subplot(1, 5, index + 1)\n",
    " plt.imshow(np.reshape(image, (8,8)), cmap=plt.cm.gray)\n",
    " plt.title('Training: %i\\n' % label, fontsize = 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# all parameters not specified are set to their defaults\n",
    "logisticRegr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duanj\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a NumPy Array\n",
    "# Predict for One Observation (image)\n",
    "logisticRegr.predict(x_test[0].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 8, 2, 6, 6, 7, 1, 9, 8, 5])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr.predict(x_test[0:10])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logisticRegr.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9511111111111111\n"
     ]
    }
   ],
   "source": [
    "# Use score method to get accuracy of model\n",
    "score = logisticRegr.score(x_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”)\n",
    "# on a set of test data for which the true values are known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[37  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 40  0  0  0  0  0  0  2  1]\n",
      " [ 0  1 40  3  0  0  0  0  0  0]\n",
      " [ 0  0  0 43  0  0  0  0  1  1]\n",
      " [ 0  0  0  0 37  0  0  1  0  0]\n",
      " [ 0  0  0  0  0 46  0  0  0  2]\n",
      " [ 0  1  0  0  0  0 51  0  0  0]\n",
      " [ 0  0  0  1  1  0  0 46  0  0]\n",
      " [ 0  3  1  0  0  0  0  0 43  1]\n",
      " [ 0  0  0  0  0  1  0  0  1 45]]\n"
     ]
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(y_test, predictions)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(score)\n",
    "plt.title(all_sample_title, size = 15);"
   ]
  }
 ],
 "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
}