{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='https://www.udemy.com/user/joseportilla/'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "<img src=\"mushroom.jpg\" width=\"400\" height=\"400\">\n",
    "\n",
    "### Mushroom Hunting: Edible or Poisonous?\n",
    "\n",
    "Data Source: https://archive.ics.uci.edu/ml/datasets/Mushroom\n",
    "\n",
    "\n",
    "This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like ``leaflets three, let it be'' for Poisonous Oak and Ivy.\n",
    "\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s\n",
    "2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s\n",
    "3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y\n",
    "4. bruises?: bruises=t,no=f\n",
    "5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s\n",
    "6. gill-attachment: attached=a,descending=d,free=f,notched=n\n",
    "7. gill-spacing: close=c,crowded=w,distant=d\n",
    "8. gill-size: broad=b,narrow=n\n",
    "9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y\n",
    "10. stalk-shape: enlarging=e,tapering=t\n",
    "11. stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?\n",
    "12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
    "13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
    "14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
    "15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
    "16. veil-type: partial=p,universal=u\n",
    "17. veil-color: brown=n,orange=o,white=w,yellow=y\n",
    "18. ring-number: none=n,one=o,two=t\n",
    "19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z\n",
    "20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y\n",
    "21. population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y\n",
    "22. habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goal\n",
    "\n",
    "**THIS IS IMPORTANT, THIS IS NOT OUR TYPICAL PREDICTIVE MODEL!**\n",
    "\n",
    "Our general goal here is to see if we can harness the power of machine learning and boosting to help create not just a predictive model, but a general guideline for features people should look out for when picking mushrooms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../DATA/mushrooms.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<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",
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       "      <td>w</td>\n",
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       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
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       "      <td>e</td>\n",
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       "      <td>t</td>\n",
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       "      <td>o</td>\n",
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       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
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       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
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       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
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       "      <td>w</td>\n",
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       "      <td>o</td>\n",
       "      <td>p</td>\n",
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       "    <tr>\n",
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       "</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": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='count'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>count</th>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>...</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>b</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>w</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4208</td>\n",
       "      <td>3656</td>\n",
       "      <td>3244</td>\n",
       "      <td>2284</td>\n",
       "      <td>4748</td>\n",
       "      <td>3528</td>\n",
       "      <td>7914</td>\n",
       "      <td>6812</td>\n",
       "      <td>5612</td>\n",
       "      <td>1728</td>\n",
       "      <td>...</td>\n",
       "      <td>4936</td>\n",
       "      <td>4464</td>\n",
       "      <td>4384</td>\n",
       "      <td>8124</td>\n",
       "      <td>7924</td>\n",
       "      <td>7488</td>\n",
       "      <td>3968</td>\n",
       "      <td>2388</td>\n",
       "      <td>4040</td>\n",
       "      <td>3148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
       "count   8124      8124        8124      8124    8124  8124            8124   \n",
       "unique     2         6           4        10       2     9               2   \n",
       "top        e         x           y         n       f     n               f   \n",
       "freq    4208      3656        3244      2284    4748  3528            7914   \n",
       "\n",
       "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "count          8124      8124       8124  ...                     8124   \n",
       "unique            2         2         12  ...                        4   \n",
       "top               c         b          b  ...                        s   \n",
       "freq           6812      5612       1728  ...                     4936   \n",
       "\n",
       "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "count                    8124                   8124      8124       8124   \n",
       "unique                      9                      9         1          4   \n",
       "top                         w                      w         p          w   \n",
       "freq                     4464                   4384      8124       7924   \n",
       "\n",
       "       ring-number ring-type spore-print-color population habitat  \n",
       "count         8124      8124              8124       8124    8124  \n",
       "unique           3         5                 9          6       7  \n",
       "top              o         p                 w          v       d  \n",
       "freq          7488      3968              2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>e</td>\n",
       "      <td>4208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape</th>\n",
       "      <td>8124</td>\n",
       "      <td>6</td>\n",
       "      <td>x</td>\n",
       "      <td>3656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-surface</th>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>y</td>\n",
       "      <td>3244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-color</th>\n",
       "      <td>8124</td>\n",
       "      <td>10</td>\n",
       "      <td>n</td>\n",
       "      <td>2284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bruises</th>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>f</td>\n",
       "      <td>4748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor</th>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>n</td>\n",
       "      <td>3528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-attachment</th>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>f</td>\n",
       "      <td>7914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-spacing</th>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "      <td>6812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-size</th>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>5612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-color</th>\n",
       "      <td>8124</td>\n",
       "      <td>12</td>\n",
       "      <td>b</td>\n",
       "      <td>1728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-shape</th>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>4608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-root</th>\n",
       "      <td>8124</td>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>3776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-above-ring</th>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>s</td>\n",
       "      <td>5176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>s</td>\n",
       "      <td>4936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>w</td>\n",
       "      <td>4464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>w</td>\n",
       "      <td>4384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-type</th>\n",
       "      <td>8124</td>\n",
       "      <td>1</td>\n",
       "      <td>p</td>\n",
       "      <td>8124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-color</th>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>w</td>\n",
       "      <td>7924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-number</th>\n",
       "      <td>8124</td>\n",
       "      <td>3</td>\n",
       "      <td>o</td>\n",
       "      <td>7488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-type</th>\n",
       "      <td>8124</td>\n",
       "      <td>5</td>\n",
       "      <td>p</td>\n",
       "      <td>3968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spore-print-color</th>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>w</td>\n",
       "      <td>2388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>8124</td>\n",
       "      <td>6</td>\n",
       "      <td>v</td>\n",
       "      <td>4040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat</th>\n",
       "      <td>8124</td>\n",
       "      <td>7</td>\n",
       "      <td>d</td>\n",
       "      <td>3148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique top  freq\n",
       "class                     8124      2   e  4208\n",
       "cap-shape                 8124      6   x  3656\n",
       "cap-surface               8124      4   y  3244\n",
       "cap-color                 8124     10   n  2284\n",
       "bruises                   8124      2   f  4748\n",
       "odor                      8124      9   n  3528\n",
       "gill-attachment           8124      2   f  7914\n",
       "gill-spacing              8124      2   c  6812\n",
       "gill-size                 8124      2   b  5612\n",
       "gill-color                8124     12   b  1728\n",
       "stalk-shape               8124      2   t  4608\n",
       "stalk-root                8124      5   b  3776\n",
       "stalk-surface-above-ring  8124      4   s  5176\n",
       "stalk-surface-below-ring  8124      4   s  4936\n",
       "stalk-color-above-ring    8124      9   w  4464\n",
       "stalk-color-below-ring    8124      9   w  4384\n",
       "veil-type                 8124      1   p  8124\n",
       "veil-color                8124      4   w  7924\n",
       "ring-number               8124      3   o  7488\n",
       "ring-type                 8124      5   p  3968\n",
       "spore-print-color         8124      9   w  2388\n",
       "population                8124      6   v  4040\n",
       "habitat                   8124      7   d  3148"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6),dpi=200)\n",
    "sns.barplot(data=df.describe().transpose().reset_index().sort_values('unique'),x='index',y='unique')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('class',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AdaBoostClassifier(n_estimators=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(n_estimators=1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,plot_confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['p', 'e', 'p', ..., 'p', 'p', 'e'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           e       0.96      0.81      0.88       655\n",
      "           p       0.81      0.96      0.88       564\n",
      "\n",
      "    accuracy                           0.88      1219\n",
      "   macro avg       0.88      0.88      0.88      1219\n",
      "weighted avg       0.89      0.88      0.88      1219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'odor_n'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='odor', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='odor',hue='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing performance as more weak learners are added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rates = []\n",
    "\n",
    "for n in range(1,96):\n",
    "    \n",
    "    model = AdaBoostClassifier(n_estimators=n)\n",
    "    model.fit(X_train,y_train)\n",
    "    preds = model.predict(X_test)\n",
    "    err = 1 - accuracy_score(y_test,preds)\n",
    "    \n",
    "    error_rates.append(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x289c33b1f70>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,96),error_rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(n_estimators=95)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.01052632, 0.        ,\n",
       "       0.        , 0.01052632, 0.        , 0.        , 0.        ,\n",
       "       0.01052632, 0.        , 0.05263158, 0.03157895, 0.03157895,\n",
       "       0.        , 0.        , 0.06315789, 0.02105263, 0.        ,\n",
       "       0.        , 0.        , 0.09473684, 0.09473684, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.01052632, 0.01052632, 0.        , 0.        , 0.        ,\n",
       "       0.06315789, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.03157895, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.06315789, 0.        , 0.        ,\n",
       "       0.01052632, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.01052632, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.05263158, 0.        , 0.16842105, 0.        , 0.10526316,\n",
       "       0.        , 0.        , 0.04210526, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.01052632])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "feats = pd.DataFrame(index=X.columns,data=model.feature_importances_,columns=['Importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cap-shape_c</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape_f</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape_k</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape_s</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape_x</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_l</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_m</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_p</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_u</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_w</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Importance\n",
       "cap-shape_c    0.000000\n",
       "cap-shape_f    0.000000\n",
       "cap-shape_k    0.000000\n",
       "cap-shape_s    0.000000\n",
       "cap-shape_x    0.000000\n",
       "...                 ...\n",
       "habitat_l      0.000000\n",
       "habitat_m      0.000000\n",
       "habitat_p      0.000000\n",
       "habitat_u      0.000000\n",
       "habitat_w      0.010526\n",
       "\n",
       "[95 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_feats = feats[feats['Importance']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cap-color_c</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-color_n</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-color_w</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bruises_t</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor_c</th>\n",
       "      <td>0.031579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor_f</th>\n",
       "      <td>0.031579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor_n</th>\n",
       "      <td>0.063158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor_p</th>\n",
       "      <td>0.021053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-spacing_w</th>\n",
       "      <td>0.094737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-size_n</th>\n",
       "      <td>0.094737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-shape_t</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-root_b</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-above-ring_k</th>\n",
       "      <td>0.063158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-below-ring_y</th>\n",
       "      <td>0.031579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-below-ring_n</th>\n",
       "      <td>0.063158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-below-ring_w</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-number_t</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spore-print-color_r</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spore-print-color_w</th>\n",
       "      <td>0.168421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_c</th>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_v</th>\n",
       "      <td>0.042105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_w</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Importance\n",
       "cap-color_c                   0.010526\n",
       "cap-color_n                   0.010526\n",
       "cap-color_w                   0.010526\n",
       "bruises_t                     0.052632\n",
       "odor_c                        0.031579\n",
       "odor_f                        0.031579\n",
       "odor_n                        0.063158\n",
       "odor_p                        0.021053\n",
       "gill-spacing_w                0.094737\n",
       "gill-size_n                   0.094737\n",
       "stalk-shape_t                 0.010526\n",
       "stalk-root_b                  0.010526\n",
       "stalk-surface-above-ring_k    0.063158\n",
       "stalk-surface-below-ring_y    0.031579\n",
       "stalk-color-below-ring_n      0.063158\n",
       "stalk-color-below-ring_w      0.010526\n",
       "ring-number_t                 0.010526\n",
       "spore-print-color_r           0.052632\n",
       "spore-print-color_w           0.168421\n",
       "population_c                  0.105263\n",
       "population_v                  0.042105\n",
       "habitat_w                     0.010526"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_feats = imp_feats.sort_values(\"Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6),dpi=200)\n",
    "sns.barplot(data=imp_feats.sort_values('Importance'),x=imp_feats.sort_values('Importance').index,y='Importance')\n",
    "\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='habitat', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='habitat',hue='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting to see how the importance of the features shift as more are allowed to be added in! But remember these are all weak learner stumps, and feature importance is available for all the tree methods!"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}