{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 11b - Classwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red</td>\n",
       "      <td>3.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blue</td>\n",
       "      <td>6.0</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>8.2</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>red</td>\n",
       "      <td>5.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>blue</td>\n",
       "      <td>6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x1   x2   x3  y\n",
       "0   red  3.0  yes  0\n",
       "1  blue  6.0   no  1\n",
       "2  blue  8.2  yes  0\n",
       "3   red  1.0   no  0\n",
       "4   red  5.0  yes  1\n",
       "5  blue  6.0  yes  0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"x1\": [\"red\",\"blue\",\"blue\",\"red\",\"red\",\"blue\"], \\\n",
    "                   \"x2\": [3, 6, 8.2, 1, 5,6], \\\n",
    "                    \"x3\": [\"yes\", \"no\", \"yes\",\"no\", \"yes\",\"yes\"], \\\n",
    "                     \"y\": [0,1, 0,0, 1, 0]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/megan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>y</th>\n",
       "      <th>y_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red</td>\n",
       "      <td>3.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blue</td>\n",
       "      <td>6.0</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>8.2</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>red</td>\n",
       "      <td>5.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>blue</td>\n",
       "      <td>6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x1   x2   x3  y y_predicted\n",
       "0   red  3.0  yes  0           0\n",
       "1  blue  6.0   no  1           0\n",
       "2  blue  8.2  yes  0           0\n",
       "3   red  1.0   no  0           1\n",
       "4   red  5.0  yes  1           1\n",
       "5  blue  6.0  yes  0           1"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"y_predicted\"] = [0,0,0,1,1,1]\n",
    "df[\"y_predicted\"] = df[\"y_predicted\"].astype(\"category\", categories = [0,1])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "Predicted  0  1\n",
       "Actual         \n",
       "0          2  2\n",
       "1          1  1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix = pd.crosstab(df[\"y\"], df[\"y_predicted\"], \\\n",
    "                        rownames = [\"Actual\"], colnames = [\"Predicted\"], \\\n",
    "                               dropna = False)\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_pos = confusion_matrix[0][0]\n",
    "false_pos = confusion_matrix[1][0]\n",
    "false_neg = confusion_matrix[0][1]\n",
    "true_neg = confusion_matrix[1][1]\n",
    "sensitivity = true_pos/(true_pos + false_neg)\n",
    "specificity = true_neg/(true_neg + false_pos)\n",
    "precision = true_pos/(true_pos + false_pos)\n",
    "accuracy = (true_pos + true_neg)/(true_pos + false_pos + false_neg + true_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of true positives: 2\n",
      "# of false positives: 2\n",
      "# of false negatives: 1\n",
      "# of true negatives: 1\n",
      "Sensitivity: 0.6666666666666666\n",
      "Specificity: 0.3333333333333333\n",
      "Precision: 0.5\n",
      "Accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "print(\"# of true positives:\",true_pos)\n",
    "print(\"# of false positives:\",false_pos)\n",
    "print(\"# of false negatives:\",false_neg)\n",
    "print(\"# of true negatives:\",true_neg)\n",
    "print(\"Sensitivity:\",sensitivity)\n",
    "print(\"Specificity:\",specificity)\n",
    "print(\"Precision:\",precision)\n",
    "print(\"Accuracy:\",accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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