W07_confusion_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_actual =    [1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0]\n",
    "y_predicted = [1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_actual = np.array(y_actual)\n",
    "y_predicted = np.array(y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "col_0  0  1\n",
      "row_0      \n",
      "0      5  2\n",
      "1      1  4\n"
     ]
    }
   ],
   "source": [
    "conf_matrix = pd.crosstab(y_actual, y_predicted)\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 2],\n",
       "       [1, 4]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix.values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(actual, predicted, classes, title='Confusion Matrix', normalize=False, figsize=(8, 8),\n",
    "                           dpi=72, cmap=plt.cm.binary):\n",
    "    import pandas as pd\n",
    "    \n",
    "    if not normalize:\n",
    "        conf_matrix = pd.crosstab(actual, predicted)  # confusion_matrix(actual, predicted)\n",
    "    else:\n",
    "        conf_matrix = pd.crosstab(actual, predicted).apply(lambda r: r / r.sum(), axis=1)\n",
    "        \n",
    "    #classes = ['c0', 'c1']\n",
    "    #classes = ['c{}'.format(i) for i in range(n_classes)]\n",
    "    fig = plt.figure(figsize=figsize, dpi=dpi)\n",
    "    plt.imshow(conf_matrix, interpolation='nearest', cmap=cmap)  # , cmap=plt.cm.Greens # plt.cm.viridis\n",
    "    plt.title(title, size=12)\n",
    "    plt.colorbar(fraction=0.041, pad=0.05)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    \n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = 0.5\n",
    "    for i in range(conf_matrix.shape[0]):\n",
    "        for j in range(conf_matrix.shape[1]):\n",
    "            plt.text(j, i, format(conf_matrix[i][j], fmt), \n",
    "                     ha=\"center\", va=\"center\", color=\"white\" if conf_matrix[i][j] > thresh else \"black\")  #horizontalalignment\n",
    "    \n",
    "    '''\n",
    "    thresh = conf_matrix.max() / 2.\n",
    "    for i, j in itertools.product(range(conf_matrix.shape[0]), range(conf_matrix.shape[1])):\n",
    "        plt.text(j, i, format(conf_matrix[i, j], 'd'),\n",
    "        horizontalalignment=\"center\", color=\"white\" if conf_matrix[i, j] > thresh else \"black\")\n",
    "    '''\n",
    "    plt.ylabel('Actual')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.grid(False)\n",
    "    plt.tight_layout()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_name = ['c0', 'c1']\n",
    "#class_name = ['c{}'.format(i) for i in range(n_classes)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_confusion_matrix(y_actual, y_predicted,classes=class_name, figsize=(5,5), cmap = plt.cm.binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_confusion_matrix(y_actual, y_predicted, classes=class_name, figsize=(5,5), cmap = plt.cm.binary, normalize=True)"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}