mushroom-report-v3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(1337)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataSet Size : (8124, 23)\n"
     ]
    }
   ],
   "source": [
    "dataset = \"./data/mushrooms.csv\"\n",
    "\n",
    "data = pd.read_csv(dataset)\n",
    "\n",
    "print(\"DataSet Size : {}\".format(data.shape))"
   ]
  },
  {
   "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>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color   ...   stalk-surface-below-ring  \\\n",
       "0            c         n          k   ...                          s   \n",
       "1            c         b          k   ...                          s   \n",
       "2            c         b          n   ...                          s   \n",
       "3            c         n          n   ...                          s   \n",
       "4            w         b          k   ...                          s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] poison : 3916 edible : 4208\n"
     ]
    }
   ],
   "source": [
    "p = data.loc[data['class'] == 'p']\n",
    "e = data.loc[data['class'] == 'e']\n",
    "\n",
    "print(\"[*] poison : {} edible : {}\".format(len(p), len(e)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_bar(p, e, name):\n",
    "    classes = ['poison', 'edible']\n",
    "    counts = [len(p), len(e)]\n",
    "\n",
    "    fig = plt.figure(figsize=(5, 5))\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    rects = plt.bar(np.arange(2), counts, align='center', width=.75)\n",
    "    plt.xticks(np.arange(2), classes)\n",
    "\n",
    "    for i, rect in enumerate(rects):\n",
    "        ax.text(rect.get_x() + rect.get_width() / 2.0, 0.95 * rect.get_height(), str(counts[i]) + \" ({:.1f}%)\".format(counts[i] / 8124 * 100), ha='center')\n",
    "\n",
    "    plt.xlabel(name)\n",
    "    plt.ylabel('Counts')\n",
    "\n",
    "draw_bar(p, e, \"Classes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stalk-root    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = data.isin(['?']).any()\n",
    "res.loc[res == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2480\n"
     ]
    }
   ],
   "source": [
    "print(len(data.loc[data['stalk-root'] == '?']))\n",
    "data.drop('stalk-shape', axis=1, inplace=True)  # drop stalk-shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 5\n",
    "top_p = p.sample(n)\n",
    "top_e = e.sample(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4487</th>\n",
       "      <td>p</td>\n",
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       "      <td>y</td>\n",
       "      <td>g</td>\n",
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       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>p</td>\n",
       "      <td>...</td>\n",
       "      <td>k</td>\n",
       "      <td>p</td>\n",
       "      <td>p</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>l</td>\n",
       "      <td>h</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5575</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>b</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>h</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>h</td>\n",
       "      <td>v</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6462</th>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>k</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7065</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>m</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5981</th>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>h</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "4487     p         x           y         g       f    f               f   \n",
       "5575     p         x           s         b       t    f               f   \n",
       "6462     p         f           y         e       f    s               f   \n",
       "7065     p         x           y         n       f    m               a   \n",
       "5981     p         f           s         w       t    f               f   \n",
       "\n",
       "     gill-spacing gill-size gill-color   ...   stalk-surface-below-ring  \\\n",
       "4487            c         b          p   ...                          k   \n",
       "5575            c         b          h   ...                          s   \n",
       "6462            c         n          b   ...                          k   \n",
       "7065            c         b          w   ...                          y   \n",
       "5981            c         b          w   ...                          s   \n",
       "\n",
       "     stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "4487                      p                      p         p          w   \n",
       "5575                      w                      w         p          w   \n",
       "6462                      w                      p         p          w   \n",
       "7065                      c                      c         p          w   \n",
       "5981                      w                      w         p          w   \n",
       "\n",
       "     ring-number ring-type spore-print-color population habitat  \n",
       "4487           o         l                 h          v       d  \n",
       "5575           o         p                 h          v       g  \n",
       "6462           o         e                 w          v       p  \n",
       "7065           n         n                 w          c       d  \n",
       "5981           o         p                 h          s       u  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7744</th>\n",
       "      <td>e</td>\n",
       "      <td>k</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3517</th>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>u</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>g</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>p</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>p</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "2523     e         x           y         n       t    n               f   \n",
       "7744     e         k           f         w       f    n               f   \n",
       "1849     e         x           f         n       t    n               f   \n",
       "3517     e         f           y         n       t    n               f   \n",
       "3260     e         f           y         g       t    n               f   \n",
       "\n",
       "     gill-spacing gill-size gill-color   ...   stalk-surface-below-ring  \\\n",
       "2523            c         b          n   ...                          s   \n",
       "7744            w         b          w   ...                          s   \n",
       "1849            c         b          w   ...                          s   \n",
       "3517            c         b          u   ...                          s   \n",
       "3260            c         b          p   ...                          s   \n",
       "\n",
       "     stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "2523                      g                      w         p          w   \n",
       "7744                      w                      w         p          w   \n",
       "1849                      g                      w         p          w   \n",
       "3517                      g                      w         p          w   \n",
       "3260                      g                      p         p          w   \n",
       "\n",
       "     ring-number ring-type spore-print-color population habitat  \n",
       "2523           o         p                 k          y       d  \n",
       "7744           t         p                 w          n       g  \n",
       "1849           o         p                 k          v       d  \n",
       "3517           o         p                 k          y       d  \n",
       "3260           o         p                 k          y       d  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = data['class']\n",
    "data = data.drop(['class'], axis=1)\n",
    "train_x = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "Ellipsis",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: an integer is required",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3077\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3078\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: Ellipsis",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: an integer is required",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-067a50fb57e2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mencoder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mce\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolynomial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPolynomialEncoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m...\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mencoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    518\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m             \u001b[1;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 520\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    521\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    522\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\category_encoders\\polynomial.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[0;32m    131\u001b[0m             \u001b[0mhandle_unknown\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandle_unknown\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m         )\n\u001b[1;32m--> 133\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mordinal_encoder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mordinal_encoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    134\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m         \u001b[1;31m# drop all output columns with 0 variance.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\category_encoders\\ordinal.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m             \u001b[0mcols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m             \u001b[0mimpute_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimpute_missing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m             \u001b[0mhandle_unknown\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandle_unknown\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m         )\n\u001b[0;32m    134\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmapping\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcategories\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\category_encoders\\ordinal.py\u001b[0m in \u001b[0;36mordinal_encoding\u001b[1;34m(X_in, mapping, cols, impute_missing, handle_unknown)\u001b[0m\n\u001b[0;32m    267\u001b[0m             \u001b[0mmapping_out\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    268\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 269\u001b[1;33m                 \u001b[0mcategories\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    270\u001b[0m                 \u001b[0mcategories_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcategories\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    271\u001b[0m                 \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'_tmp'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcategories_dict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2686\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2688\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2689\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2690\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2693\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2694\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2695\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2696\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2697\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   2487\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2488\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2489\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2490\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2491\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   4113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4114\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4115\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4116\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\programdata\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3078\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3082\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: Ellipsis"
     ]
    }
   ],
   "source": [
    "import category_encoders as ce\n",
    "\n",
    "train_x = np.asarray(train_x)\n",
    "train_y = np.asarray(train_y)\n",
    "\n",
    "encoder = ce.polynomial.PolynomialEncoder(cols=[...])\n",
    "encoder.fit_transform(train_x, train_y, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data State\n",
    "train_y = data['class']\n",
    "data = data.drop(['class'], axis=1)\n",
    "train_x = data\n",
    "\n",
    "print(train_y.shape, train_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Normalize\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler, MaxAbsScaler, MinMaxScaler \n",
    "\n",
    "scalers = {\"Standard\": StandardScaler(), \"Robust\": RobustScaler(),\n",
    "           \"MaxAbs\": MaxAbsScaler(), \"MinMax\": MinMaxScaler()}\n",
    "\n",
    "x_scaled = []\n",
    "for _, v in scalers.items():\n",
    "    x_scaled.append(v.fit_transform(train_x))\n",
    "\n",
    "x_scaled.append(train_x)  # not nomalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Engineering\n",
    "\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. PCA\n",
    "for idx, x in enumerate(x_scaled):\n",
    "    pca = PCA()\n",
    "    pca.fit_transform(x)\n",
    "    \n",
    "    plt.figure(figsize=(6, 4))\n",
    "    plt.bar(range(21), pca.explained_variance_, alpha=0.5,\n",
    "            align='center', label='individual explained variance')\n",
    "    \n",
    "    plt.title('%s Scaler' % (list(scalers.keys())[idx] if not idx == 4 else \"None\"))\n",
    "    plt.ylabel('Explained variance ratio')\n",
    "    plt.xlabel('Principal Components')\n",
    "    plt.legend(loc='best')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1-1. PCA visualize with k-mean cluserting (only 2 features)\n",
    "pca = PCA(n_components=2)\n",
    "x = pca.fit_transform(data.values)\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.scatter(x[:,0], x[:,1])  # by class\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=2, random_state=42)\n",
    "X_clustered = kmeans.fit_predict(data.values)\n",
    "\n",
    "LABEL_COLOR_MAP = {0 : 'r', 1 : 'y'}\n",
    "label_color = [LABEL_COLOR_MAP[l] for l in X_clustered]\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.scatter(x[:,0], x[:,1], c=label_color)  # by class\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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