model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>age</th>\n",
       "      <th>emotion</th>\n",
       "      <th>gender</th>\n",
       "      <th>industry</th>\n",
       "      <th>revisit</th>\n",
       "      <th>store</th>\n",
       "      <th>time_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>surprise</td>\n",
       "      <td>f</td>\n",
       "      <td>cos</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>surprise</td>\n",
       "      <td>f</td>\n",
       "      <td>cos</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>surprise</td>\n",
       "      <td>f</td>\n",
       "      <td>cos</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>neutral</td>\n",
       "      <td>f</td>\n",
       "      <td>cos</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>surprise</td>\n",
       "      <td>f</td>\n",
       "      <td>cos</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   emotion gender industry  revisit  store  time_spent\n",
       "0    6  surprise      f      cos        0      1          25\n",
       "1   15  surprise      f      cos        0      1          24\n",
       "2   20  surprise      f      cos        0      1          28\n",
       "3   19   neutral      f      cos        1      1          27\n",
       "4   17  surprise      f      cos        0      1          28"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_age = pd.DataFrame(df[\"age\"])\n",
    "plot_age = d_age.plot.hist(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_time = pd.DataFrame(df[\"time_spent\"])\n",
    "plot_time = d_time.plot.hist(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emo_plt = dict(pd.value_counts(df['emotion']))\n",
    "fig_emo, ax_emo = plt.subplots()\n",
    "ax_emo.pie(emo_plt.values(), labels=emo_plt.keys(),autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax_emo.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_plt = dict(pd.value_counts(df['gender']))\n",
    "fig_gen, ax_gen = plt.subplots()\n",
    "ax_gen.pie(gender_plt.values(), labels=gender_plt.keys(),autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax_gen.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_plt = dict(pd.value_counts(df['revisit']))\n",
    "fig_rev, ax_rev = plt.subplots()\n",
    "ax_rev.pie(rev_plt.values(), labels=rev_plt.keys(),autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax_rev.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "app      167\n",
      "elec     142\n",
      "toys      93\n",
      "jewel     63\n",
      "cos       55\n",
      "auto      37\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data = df['industry']\n",
    "values = array(data)\n",
    "\n",
    "print pd.value_counts(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_enc_industry = LabelEncoder()\n",
    "int_enc_industry = label_enc_industry.fit_transform(values)\n",
    "int_enc_industry = int_enc_industry.reshape(len(int_enc_industry), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_enc_industry = OneHotEncoder(sparse=False)\n",
    "onehot_enc_industry = onehot_enc_industry.fit_transform(int_enc_industry)\n",
    "\n",
    "rev_map_ind = lambda row: label_enc_industry.inverse_transform(argmax(row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(df.industry.unique().shape[0]):\n",
    "    col_name = label_enc_industry.inverse_transform(i)\n",
    "    col_data = onehot_enc_industry[:,i]\n",
    "    df[col_name] = col_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder_emotion = LabelEncoder()\n",
    "df[\"enc_emotion\"] = label_encoder_emotion.fit_transform(df.emotion)\n",
    "label_encoder_gender = LabelEncoder()\n",
    "df[\"enc_gender\"]= label_encoder_emotion.fit_transform(df.gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_X = ['age', 'revisit', 'time_spent', 'enc_emotion', 'enc_gender']\n",
    "cols_y = ['app', 'auto', 'cos', 'elec', 'jewel', 'toys']\n",
    "X = df[cols_X].values\n",
    "y = df[cols_y].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier as clf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_clf = clf(class_weight='balanced', random_state=69)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=69)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Entries for Industries\n",
    "###### app      167\n",
    "###### elec     142\n",
    "###### toys      93\n",
    "###### jewel     63\n",
    "###### cos       55\n",
    "###### auto      37"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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O3CzppMYuzMysOqW0tPYDNomIeQCSfg08B/ymMQszM6tOKV29N/lquLUBXmuccszMalfbCdO/I41hzQOmSBqXPR5O2oNoZtbkauseFvYQTgH+XjT96cYrx8ysdrWdMP3HpizEzKwUdQ7ES1oH+DWwHtC+MD0i+jRiXWZm1SplIP5a4BrSdbRGALcDtzZiTWZmNSoltDpGxDiAiJgWEaeQrvpgZtbkSjlO63NJAqZJOgz4H7BS45ZlZla9UkLrWKAz8GPS2FZX4KDGLMrMrCalnDA9Ifv1U768EKCZWUXUdnDpnWTX0KpORHy/USoyM6tFbS2ti5usimXcwpVXYPaJwytdhtVDtFqu0iVYI6nt4NIHm7IQM7NS+NpYZpYrDi0zy5WSQ0tSu8YsxMysFKVcuXSQpBeBqdnjjSVd1OiVmZlVo5SW1u+BXYCZABHxPD6Nx8wqpJTQahURb1aZtqgxijEzq0spp/G8LWkQEJJaA0cDrzZuWWZm1SulpXU4MBZYE/gAGJxNMzNrcqWce/ghsHcT1GJmVqdSrlz6B6o5BzEiDm2UiszMalHKmNYDRb+3B0YCbzdOOWZmtSule3hb8WNJNwD3N1pFZma1WJrTeNYCvlnuQszMSlHKmNZHfDmm1QqYBfysMYsyM6tJraGVXRt+Y9J14QEWR0SNFwY0M2tstXYPs4C6MyIWZT8OLDOrqFLGtJ6RNKDRKzEzK0Ft14hvExELgS2AQyRNA+aSbtoaEeEgM7MmV9uY1jPAAGC3JqrFzKxOtYWWIN1VuolqMTOrU22h1VPS2JpmRsT5jVCPmVmtagut1qQ7S6uJajEzq1NtofVeRJzRZJWYmZWgtkMe3MIys2VObaG1bZNVYWZWohpDKyJmNWUhZmal8M1azSxXHFpmlisOLTPLFYeWmeWKQ8vMcsWhZWa54tAys1xxaJlZrji0zCxXHFpmlisOLTPLFYeWmeWKQ8vMcsWhZWa54tAys1xxaJlZrji0zCxXHFpmlisOLTPLldpuIWYtQM/Ot7Pe+l2XPL7h9i1468257Lrjw9z0py3YcefVANjn+49x5DH92GLLlRq0vT2+9ygTn53J4M17cMsdWy6Z/ujDH3DayZNZvBg6dW7DxVcOYu11ujRoW83RRzPnsde2NwMw/f25tGotuvfsCMBLz3/IehuvxKKFi+n9rR5ccN136dBxuQZvc/Kz7/K9wddx6W27scvu32rw+hrKodXCdejQmkcn7PCVaW+9OZdVV+vA+ee8tCS0yuWoY/sxf95CrvvjtK9M/+lPJnLD7UPp2295/njFVM47+yUuuXKzsm67OVihe0fum3wwAOed9hidOrflsOMHA9Cn87lL5h016q/ccPkkDh3bsPdw0aLFnHniw2y1w9oNK7yM3D20am2wYTeW79qWhx98v6zr3Wrrb9C5SzV//SU+/WQBAJ98soCVV+5Q1u22NJsNXYM3/vtRg9dzzUUT2ekHfemxUscyVFUebmm1cPPnL2KrzcYBsGavTtxw2xZL5h134nqcefqLbL3tyjU+/6Lfvcyfb33za9M336InZ503oOQ6Lrx0IHt//zHat29Nl+WXY9wj29XjVVixhQsX8/A/pjFsx6+3jg7f606mvTLza9MPHbsZu/9ww69Me+9/n/KPO1/h9odG8fyzf2+0euuryUNL0iLgxWzb/wEOiIh59VzHVcD5EfGSpJMj4syieU9GxJCyFt2MVdc9LNj8Oz0BeOqJ6TU+/+hj+3H0sf0aXMdlF73CrXdsyaaDunPR717m5yf+iwsvG9Tg9bYkn81fyPD+VwEwaOga7D2m/9eWuey2kSWv77Rj7ufks7ehdetlq0NWiZbW/IjoDyDpJuAw4Pz6rCAiDi56eDJwZtE8B1YZjT1hPc4/+yXatFG188vR0pox/TOmvPgxmw7qDsDI3ddgj10fW/qiW6j2HdosGdOqSX1aWi9MfI8j974LgFkz5vHQvdNo06YVO+7Wt3xFL4VKdw8fBzYCkDQWOCibflVEXCCpE3A7sDrQGvhlRNwm6RHgeGB3oIOkycCUiBglaU5EdJZ0G3BdRNybrf9a4B7gLuAsYBjQDrgkIq5oklebQ1tvtzJnnvEi7783v9r55WhpdVuhLZ98soD/Tv2U3ut24ZEHP6BP3+UbtE6rXn1aWk+9fuSS348dfQ/b7tK74oEFFQwtSW2AEcA/JW0CHAhsBgiYIOlRYG3g3YjYOXtO1+J1RMTPJB1VaLlVcSuwF3CvpLbAtsDhwBhgdkQMlNQOeELSfRHxeuO80vwbe8J67Lfn+LKsa+ftHmTqq58yd85CNuh9N7+/bCDbbL8Kv7t4IKP3fYJWraBbt7b8/nJ3Da16ioim3eCXY1qQWlrHkcKke0Scmi3zS2A68E9gHKm19beIeDyb/whwfERMLLSsitZfaGm1B6YCvYEdgT2zltifSa27wjhaV+BHEXFflToPBQ4FWH2Njps8/8p3y/xOWGOa32GdSpdg9bS6znwuIjata7mKjmkVSKp2wCQiXs1aYTsBv8laRGeUspGI+CwLtx1ILa5bCpsDjo6IcXU8/0rgSoD+A1Zs2mQ3sxotK7sFHgN2k9QxG8caCTwuaVVgXkTcCPwWqG5kd4Gkmg77vZXU7RxKarGR/Xt44TmS+mTbNLMcqPRAPAARMSkbKH8mm3RVRPxL0g7AuZIWAwtI3ciqrgRekDQpIkZVmXcfcD1wd0R8UVg30AuYlLXwpgO7lfUFmVmjafIxrTzqP2DFeOiJ4ZUuw+rBY1r5U+qY1rLSPTQzK4lDy8xyxaFlZrni0DKzXHFomVmuOLTMLFccWmaWKw4tM8sVh5aZ5YpDy8xyxaFlZrni0DKzXHFomVmuOLTMLFccWmaWKw4tM8sVh5aZ5YpDy8xyxaFlZrni0DKzXHFomVmuOLTMLFccWmaWKw4tM8sVh5aZ5YpDy8xyxaFlZrni0DKzXHFomVmuOLTMLFccWmaWKw4tM8sVh5aZ5YpDy8xyxaFlZrni0DKzXHFomVmuOLTMLFccWmaWKw4tM8sVh5aZ5YpDy8xyxaFlZrni0DKzXHFomVmuOLTMLFccWmaWKw4tM8sVh5aZ5YoiotI1LPMkTQferHQdjaAHMKPSRVi9NOfP7JsR0bOuhRxaLZikiRGxaaXrsNL5M3P30MxyxqFlZrni0GrZrqx0AVZvLf4z85iWmeWKW1pmlisOLTPLFYeWmeWKQ8vMcsWhZXWSpOzfVSStWul6rGaFz6o5895DK4mk3YBjgNnAy8BFEfFOZauyYpIU2Rda0nbA8sAE4P2IWFTR4srILS2rk6QNgbHALsAzwNak8LJlSFFg/QQ4HdgMeAgYVMm6ys2hZaVYBPwN2APYGdg7Ij6VtH5ly7KqJPUBtoqI7wBvAG+RWluF+bnvPjq0rEaS1pO0O/AFMBQ4AvhhRLwmaQTwB0krV7RIW0JSd+Bd4AVJ1wK7ASMiYrGkAyR1jWYwHuTQstp8Bzg2Iv4LPAhMBYZJ2hf4LXBmRLxfyQItkTQYOAlYCKwM9AbGRMRCSfsBxwFdKlhi2Xgg3pYoDORKal0YuJV0M/BURFwk6WDgm8CKwF8j4r7iwV9rGlkXTxGxuGjaWqQ/LAeTuoTnAB8BrYFvA6Mi4t8VKLfsHFpWGAfZOCL+JGlTYCtgWkTcle2FGh4RJxQtv1xELKhUvS1dlb2E3YHPI2KOpB8AW0fEUZLWJbW4vgE8GxHN5iKW7h4apP8HH0rqArwNtAOOlHQxsADYSdL+RcsvrECNLZ6SjYDbs8ebAJcDv5D0LeBpYHlJ60bE1Ih4PCL+3JwCCxxaBkTEy8ATpMDaLSLOBL5H6loMBroBB0jqnC3v5nkFRPICcJSkYcBk4OfAh8CdpJ0l6wDnSWpbsUIbWZtKF2CVIakjsH1E/FXSZqQ9hNsA/5TUPiIulHQkqYvxGfDfiJhTwZJbNEkdImJ+9nAGcCBwGdA/Is6V9AIpsD4HvgV0JH2mzY7HtFqwbLf4pqRQOiQi/iVpAPAAcEpEXFpleQ+6V4Ck9qS9f/eS9gpuGBGnSroa2JwUXJ9LagN0ArpHxGuVq7hxObRaoKK9hH2Bh4G3ImJw0fwBpAMSj4+ICytVp4GkHhExQ9JQ4FHgv6TQ+jybfw1p7+DgiPisgqU2GY9ptTBFgdUKeI/0l3qupH8WlomIScB6wEsVKrPFywbd1wB+lY0lvgT8FViF1DoGICIOBKYAj1Wk0ApwS6sFKQqs4aQB9vcj4sps3kPAXOBXpGN8RkbELHcJK0vS8sAGQKeIuF/SNsBdwL4R8TdJgyPiaUkrRcSHla22abil1YJkgbUj8DvgceAMSZdIWjEitgHmkE60PS8iZhWeU7mKW6bi8wMj4hNgY+BUSTtGxEPAfsCfJJ0HXC1p9ZYSWOC9hy1G1h3sAhwG7E066PA90hHuv5d0dETsI6lbRHzsFlZlVDlwdF9gdkRcJmkB8NNs/t2SticdBLxbS7tEkEOrmSv6ErSPiNmSxpBOwzmDNDbSkRRe70g6PSI+BrewKqUosI4knZKzZzb9KknzgOOyMxLulvRES/yc3D1sxorGsDYDHpe0YUTMJP2x+gJYgTSwOw74S9FxQFYh2QD8usAPSZcBmiZppKQfA/8EbgDGSOrUEgMLPBDf7GXdiO+T9hKuBOwQES9KOoc0VtILOCoi7q9clS1bdV3x7PMZTLpK7Iqkiy6+HRGnFbrwFSh1meDQasayM//vBQ7M9jCdCowm+wtO6h4ujIhnKldly1ZlDGsIqfU7mXQmwgDgoYiYJukwYKOIOKKljzd6TKt5mwlMJF3Bkog4I+t6jAO+ExFPVrC2Fq0QPEWBdTxpB8l00uc2Hrgp0hVix5DGt0aDxxs9ptWMFHaVS+qqdJXKT0h7DL9ftNi1wDvAXwsnQFtFLGkwKF39dQdgaESMIJ383A9YX9I6pCPeD2wu18NqKLe0mpFs0P27pJtQfCTpadLVLG+RtDowj3RzijHA0aTz1HwSdBPLxhkPkvQ8qSv4INAZ2BIYFxF/UbqO2a4RcZKk4wqn7ZhbWrlXfCCi0iV3Twb2J90155CI+A9pt/k7pC/GwaRjtIYAi7+2QmtU2cG9vwaeJP3R2Ic0dnUzMEhS4c45zwGtla4i68Aq4oH4HJPUk3TzgluyK1duSbr2VTtSa2vfiHhdUq+IeCN7zhDgetJBie5uNCFJK5IuK7NrRNyTnVv4W+A60vX39yF1E6eQLhO0a0RMqVS9yyqHVo4p3UB1F1IX41pgIHAxaSD3e9mR7duTjoI/LJu+CtCmuV3NMi8k7Uw6t3PziPhE0k3AoxFxpaQVgLVIh6E858+oeh7TyiF9eeOJe0hXFx0G7J+d7nEHaeB9FUk7AKcCJ0TE9Ozp/6tEzZZExN8lLQaekzQO6ADcmM37iHQzikkVLHGZ55ZWzmTXwDoYuA94LLv42whgBPBSRFwu6TRSi6obcHVEjGvpx/Ysa7KB9vuAlSPiQ6WrxbaI62E1lEMrZyRtRbpw31TSDQ7WBs4Ftgfakm7WeW22J9FfhGVY9sfmt6Q76LSYqzQ0lEMrhyRtQbpN/WbAD0hHUY8k7SHsDZwGXA0QRffGs2WPpF2BX5DOTgi3huvm0Mqp7K/0OcCQ7KjprYANgUNJd4V+sKIFWskkdQ7fNKRkDq0ck7QTcBEwsHDRvqIrO3gMy5ol7z3MsYi4N9sT9bKkvhHxUSGoHFjWXLml1Qxkx/7MjYhHKl2LWWNzaDUj7hJaS+DQMrNc8QnTZpYrDi0zyxWHlpnlikPLGkzSIkmTJf1b0p8kdWzAuoZJ+lv2+/ck/ayWZbtJOmIptnFadnnjkqZXWeZaSbvXY1u9JPkSQGXk0LJymB8R/SNiA9KtyQ4rnpndFqve/9ci4u6IOKuWRboB9Q4tyzeHlpXb40DvrIXxH0mXki61soak4ZKekjQpa5F1hnQ1T0kvSxpP0fXsJY2WdHH2+zck3Snp+exnCHAWsE7Wyjs3W+6nkp6V9IKk04vW9X+SXpH0ANC3rhch6ZBsPc9L+kuV1uN2kh6X9KqkXbLlW0s6t2jbP2roG2nVc2hZ2UhqQ7pEzovZpL7A9RHxbWAucAopHb6OAAACK0lEQVSwXUQMIN0laKyk9sAfgO8CQ0m3zqrO70kXy9uYdHniKcDPgGlZK++nkoYD6wKDgP7AJpK2lLQJ6U433yaF4sASXs4dETEw295/SNfVL+hFuiX9zsDl2WsYQ7qF/cBs/Yco3cLNysyn8Vg5dJA0Ofv9ceCPwKrAmxHxdDZ9MLAe8ER2Wfu2wFOku868HhFTASTdSDrpu6ptSHddJrsA4uzsSp/Fhmc//8oedyaFWBfgzoiYl23j7hJe0waSfkXqgnYm3Xat4Pbs6hlTJb2WvYbhwEZF411ds22/WsK2rB4cWlYO8yOif/GELJjmFk8C7o+Ifaos1x8o1xHOAn4TEVdU2cYxS7GNa0nX0X9e0mjS1WELqq4rsm0fHRHF4YakXvXcrtXB3UNrKk8D35HUG0BSR0l9SLd9X0vp/n6Qbu5QnQeBw7Pntpa0PPApqRVVMI50a67CWNlqklYCHgNGSuogqQupK1qXLsB7kpYDRlWZt4ekVlnNawOvZNs+PFseSX0kdSphO1ZPbmlZk4iI6VmL5RZJ7bLJp0TEq5IOBf4uaQbpzsobVLOKnwBXKt1teRFweEQ8JemJ7JCCf2TjWt8CnspaenOA/SJikqTbSDcAeZPUha3Lz4EJ2fIv8tVwfAV4lHQrtsMi4jNJV5HGuiYpbXw66U5JVmY+99DMcsXdQzPLFYeWmeWKQ8vMcsWhZWa54tAys1xxaJlZrji0zCxXHFpmliv/Dzou6l4CxRyIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_map = dict()\n",
    "for i in range(len(cols_y)):\n",
    "    ind_clf.fit(X_train,y_train[:,i])\n",
    "    ind_name = label_enc_industry.inverse_transform(i)\n",
    "    y_test_p = y_test[:,i]\n",
    "    y_pred_p = ind_clf.predict(X_test)\n",
    "    cm = confusion_matrix(y_test_p, y_pred_p)\n",
    "    accuracy = accuracy_score(y_test_p,y_pred_p)\n",
    "    score_map[ind_name] = cm.ravel()+tuple([accuracy])\n",
    "    \n",
    "    plt.clf()\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Wistia)\n",
    "    classNames = ['Negative','Positive']\n",
    "    plt.title('Confusion Matrix - %s'%ind_name)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    tick_marks = np.arange(len(classNames))\n",
    "    plt.xticks(tick_marks, classNames, rotation=45)\n",
    "    plt.yticks(tick_marks, classNames)\n",
    "    s = [['TN','FP'], ['FN', 'TP']]\n",
    "\n",
    "    for i in range(2):\n",
    "        for j in range(2):\n",
    "            plt.text(j,i, str(s[i][j])+\" = \"+str(cm[i][j]))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cos': array([156.86956522,   6.86956522,  18.86956522,   4.86956522]), 'elec': array([123.73369565,  15.73369565,  34.73369565,  12.73369565]), 'auto': array([168.91304348,   2.91304348,  14.91304348,   0.91304348]), 'app': array([126.83695652,  14.83695652,  16.83695652,  28.83695652]), 'jewel': array([157.85869565,   6.85869565,  20.85869565,   1.85869565]), 'toys': array([136.8423913,  11.8423913,  18.8423913,  19.8423913])}\n"
     ]
    }
   ],
   "source": [
    "print score_map"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}