decisionTree/myDecisonTree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "手写决策树，以及构建所给数据的决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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>Idx</th>\n",
       "      <th>color</th>\n",
       "      <th>root</th>\n",
       "      <th>knocks</th>\n",
       "      <th>texture</th>\n",
       "      <th>navel</th>\n",
       "      <th>touch</th>\n",
       "      <th>density</th>\n",
       "      <th>sugar_ratio</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.460</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>black</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>black</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.264</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.318</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>light_white</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.215</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.237</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.149</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.211</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>stiff</td>\n",
       "      <td>clear</td>\n",
       "      <td>distinct</td>\n",
       "      <td>even</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>light_white</td>\n",
       "      <td>stiff</td>\n",
       "      <td>clear</td>\n",
       "      <td>blur</td>\n",
       "      <td>even</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>light_white</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>blur</td>\n",
       "      <td>even</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>light_white</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>light_white</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>blur</td>\n",
       "      <td>even</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Idx        color            root          knocks      texture  \\\n",
       "0     1   dark_green         curl_up  little_heavily     distinct   \n",
       "1     2        black         curl_up         heavily     distinct   \n",
       "2     3        black         curl_up  little_heavily     distinct   \n",
       "3     4   dark_green         curl_up         heavily     distinct   \n",
       "4     5  light_white         curl_up  little_heavily     distinct   \n",
       "5     6   dark_green  little_curl_up  little_heavily     distinct   \n",
       "6     7        black  little_curl_up  little_heavily  little_blur   \n",
       "7     8        black  little_curl_up  little_heavily     distinct   \n",
       "8     9        black  little_curl_up         heavily  little_blur   \n",
       "9    10   dark_green           stiff           clear     distinct   \n",
       "10   11  light_white           stiff           clear         blur   \n",
       "11   12  light_white         curl_up  little_heavily         blur   \n",
       "12   13   dark_green  little_curl_up  little_heavily  little_blur   \n",
       "13   14  light_white  little_curl_up         heavily  little_blur   \n",
       "14   15        black  little_curl_up  little_heavily     distinct   \n",
       "15   16  light_white         curl_up  little_heavily         blur   \n",
       "16   17   dark_green         curl_up         heavily  little_blur   \n",
       "\n",
       "             navel        touch  density  sugar_ratio  label  \n",
       "0          sinking  hard_smooth    0.697        0.460      1  \n",
       "1          sinking  hard_smooth    0.774        0.376      1  \n",
       "2          sinking  hard_smooth    0.634        0.264      1  \n",
       "3          sinking  hard_smooth    0.608        0.318      1  \n",
       "4          sinking  hard_smooth    0.556        0.215      1  \n",
       "5   little_sinking   soft_stick    0.403        0.237      1  \n",
       "6   little_sinking   soft_stick    0.481        0.149      1  \n",
       "7   little_sinking  hard_smooth    0.437        0.211      1  \n",
       "8   little_sinking  hard_smooth    0.666        0.091      0  \n",
       "9             even   soft_stick    0.243        0.267      0  \n",
       "10            even  hard_smooth    0.245        0.057      0  \n",
       "11            even   soft_stick    0.343        0.099      0  \n",
       "12         sinking  hard_smooth    0.639        0.161      0  \n",
       "13         sinking  hard_smooth    0.657        0.198      0  \n",
       "14  little_sinking   soft_stick    0.360        0.370      0  \n",
       "15            even  hard_smooth    0.593        0.042      0  \n",
       "16  little_sinking  hard_smooth    0.719        0.103      0  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入数据\n",
    "import pandas as pd\n",
    "data = pd.read_csv('watermelon_3a.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "信息熵函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def calcShannonEnt(dataSet):\n",
    "    '''\n",
    "    给定数据集\n",
    "    '''\n",
    "    numEntries = len(dataSet)   #总数据数D\n",
    "    labelCounts = {}\n",
    "    \n",
    "    for featVec in dataSet:\n",
    "        currentLabel = featVec[-1]\n",
    "        if currentLabel not in labelCounts:\n",
    "            labelCounts[currentLabel] = 0\n",
    "        labelCounts[currentLabel] += 1 \n",
    "    \n",
    "    shannonEnt = 0.0\n",
    "    for key in labelCounts:\n",
    "        pro = float(labelCounts[key])/numEntries\n",
    "        shannonEnt -= pro * np.log2(pro)\n",
    "    return shannonEnt\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提取数据的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "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>color</th>\n",
       "      <th>root</th>\n",
       "      <th>knocks</th>\n",
       "      <th>texture</th>\n",
       "      <th>navel</th>\n",
       "      <th>touch</th>\n",
       "      <th>density</th>\n",
       "      <th>sugar_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dark_green</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>black</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>black</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dark_green</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>light_white</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dark_green</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dark_green</td>\n",
       "      <td>stiff</td>\n",
       "      <td>clear</td>\n",
       "      <td>distinct</td>\n",
       "      <td>even</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>light_white</td>\n",
       "      <td>stiff</td>\n",
       "      <td>clear</td>\n",
       "      <td>blur</td>\n",
       "      <td>even</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>light_white</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>blur</td>\n",
       "      <td>even</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dark_green</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>light_white</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>light_white</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>blur</td>\n",
       "      <td>even</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>dark_green</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          color            root          knocks      texture           navel  \\\n",
       "0    dark_green         curl_up  little_heavily     distinct         sinking   \n",
       "1         black         curl_up         heavily     distinct         sinking   \n",
       "2         black         curl_up  little_heavily     distinct         sinking   \n",
       "3    dark_green         curl_up         heavily     distinct         sinking   \n",
       "4   light_white         curl_up  little_heavily     distinct         sinking   \n",
       "5    dark_green  little_curl_up  little_heavily     distinct  little_sinking   \n",
       "6         black  little_curl_up  little_heavily  little_blur  little_sinking   \n",
       "7         black  little_curl_up  little_heavily     distinct  little_sinking   \n",
       "8         black  little_curl_up         heavily  little_blur  little_sinking   \n",
       "9    dark_green           stiff           clear     distinct            even   \n",
       "10  light_white           stiff           clear         blur            even   \n",
       "11  light_white         curl_up  little_heavily         blur            even   \n",
       "12   dark_green  little_curl_up  little_heavily  little_blur         sinking   \n",
       "13  light_white  little_curl_up         heavily  little_blur         sinking   \n",
       "14        black  little_curl_up  little_heavily     distinct  little_sinking   \n",
       "15  light_white         curl_up  little_heavily         blur            even   \n",
       "16   dark_green         curl_up         heavily  little_blur  little_sinking   \n",
       "\n",
       "          touch  density  sugar_ratio  \n",
       "0   hard_smooth    0.697        0.460  \n",
       "1   hard_smooth    0.774        0.376  \n",
       "2   hard_smooth    0.634        0.264  \n",
       "3   hard_smooth    0.608        0.318  \n",
       "4   hard_smooth    0.556        0.215  \n",
       "5    soft_stick    0.403        0.237  \n",
       "6    soft_stick    0.481        0.149  \n",
       "7   hard_smooth    0.437        0.211  \n",
       "8   hard_smooth    0.666        0.091  \n",
       "9    soft_stick    0.243        0.267  \n",
       "10  hard_smooth    0.245        0.057  \n",
       "11   soft_stick    0.343        0.099  \n",
       "12  hard_smooth    0.639        0.161  \n",
       "13  hard_smooth    0.657        0.198  \n",
       "14   soft_stick    0.360        0.370  \n",
       "15  hard_smooth    0.593        0.042  \n",
       "16  hard_smooth    0.719        0.103  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.loc[:,'color':'sugar_ratio']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据值提取数据函数\n",
    "def splitDataSet(dataSet,axis,value):\n",
    "    retDataSet = []\n",
    "    for featVec in dataSet:\n",
    "        if featVec[axis] == value:\n",
    "            reducedFeatVec = featVec[:axis]\n",
    "            reducedFeatVec1 = list(reducedFeatVec)\n",
    "            reducedFeatVec1.extend(featVec[axis+1:])\n",
    "            retDataSet.append(reducedFeatVec1)\n",
    "    return retDataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitDataSet(data,0,'black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "#二分处理连续数据\n",
    "\n",
    "def splitContinuousDataSet(dataSet,axis,value,direction):\n",
    "    '''\n",
    "    dataSet___待划分数据\n",
    "    axis___划分数据集特征的列号，如：0，1...\n",
    "    value___需要返回特征的值，属性值对应字符串\n",
    "    direction___表示划分方式，大于t值则划分为0，否则小于等于\n",
    "    '''\n",
    "    retDataSet = []\n",
    "    for featVec in dataSet:\n",
    "        if direction == 0:\n",
    "            if featVec[axis] > value:\n",
    "                reducedFeatVec = featVec[:axis]\n",
    "                reducedFeatVec = list(reducedFeatVec)\n",
    "                reducedFeatVec.extend(featVec[axis+1:])\n",
    "                retDataSet.append(reducedFeatVec)\n",
    "        else:\n",
    "            if featVec[axis] <= value:\n",
    "                reducedFeatVec = featVec[:axis]\n",
    "                reducedFeatVec = list(reducedFeatVec)\n",
    "                reducedFeatVec.extend(featVec[axis+1:])\n",
    "                retDataSet.append(reducedFeatVec) \n",
    "    return retDataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "#选择最好属性划分\n",
    "\n",
    "def chooseBestFeatureToSplit(dataSet,labels):\n",
    "    '''\n",
    "    dataSet__带划分的数据集\n",
    "    labels__标签\n",
    "    '''\n",
    "    bestFeature = -1       #初始化\n",
    "    bestInfoGain = 0.0\n",
    "    baseEntropy = calcShannonEnt(dataSet)\n",
    "    numFeatures = len(dataSet) - 1\n",
    "    \n",
    "    for i in range(numFeatures):\n",
    "        featList = [example[i] for example in dataSet]\n",
    "        if type(featList[0]).__name__ == 'float' or type(featList[0]).__name__ == 'int':\n",
    "            sortFeatList = sorted(featList)\n",
    "            splitList = []\n",
    "            for j in range(len(sortFeatList)-1):\n",
    "                splitList.append((sortFeatList[j]+sortFeatList[j+1])/2.0)\n",
    "                \n",
    "            bestSplitEntropy = 10000\n",
    "            slen = len(splitList)\n",
    "            for j in range(slen):\n",
    "                value = splitList[j]\n",
    "                newEntropy = 0.0\n",
    "                subDataSet0 = splitContinuousDataSet(dataSet,i,value,0)\n",
    "                subDataSet1 = splitContinuousDataSet(dataSet,i,value,1)\n",
    "                \n",
    "                #计算条件熵\n",
    "                prob0 = len(subDataSet0)/len(dataSet)\n",
    "                newEntropy += prob0 * calcShannonEnt(subDataSet0)\n",
    "                \n",
    "                prob1 = len(subDataSet1)/len(dataSet)\n",
    "                newEntropy += prob1 * calcShannonEnt(subDataSet1)\n",
    "                \n",
    "                if newEntropy < bestSplitEntropy:\n",
    "                    bestSplitEntropy = newEntropy\n",
    "                    bestSplit = j\n",
    "                bestSplitDict = {}\n",
    "                bestSplitDict[labels[i]] = bestSplitDict[bestSplit]\n",
    "                \n",
    "                infoGain = baseEntropy - bestSplitEntropy\n",
    "        else:\n",
    "            uniqueVals = set(featList)\n",
    "            newEntropy = 0.0\n",
    "            \n",
    "            for value in uniqueVals:\n",
    "                subDataSet = splitDataSet(dataSet,i,value)\n",
    "                prob = len(subDataSet)/float(len(dataSet))\n",
    "                \n",
    "                newEntropy += prob *calcShannonEnt(subDataSet)\n",
    "            infoGain = baseEntropy - newEntropy\n",
    "        if infoGain > bestInfoGain:\n",
    "            bestInfoGain = infoGain\n",
    "            bestFeature = i\n",
    "        return bestFeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 若特征已经划分完，节点下的样本还没有统一取值，则需要进行投票\n",
    "def majorityCnt(classList):\n",
    "    classCount = {}\n",
    "    for vote in classList:\n",
    "        # 统计频数，对象：频数——字典\n",
    "        # \n",
    "        if vote not in classCount.key():\n",
    "            classCount[vote] = 0\n",
    "        classCount[vote] += 1\n",
    "    return max(classCount)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "#递归生成决策树\n",
    "\n",
    "def createTree(dataSet,labels):\n",
    "    '''\n",
    "        递归的几个出口：\n",
    "        标签完成相同，则直接返回该类标签\n",
    "        使用完了所有特征，仍然不能将数据划分成仅包含唯一类别的分组\n",
    "        则使用majorityCnt函数挑选次数出现最多的类别作为返回值\n",
    "    '''\n",
    "    \n",
    "    classList = [example[-1] for example in dataSet]\n",
    "    if classList.count(classList[0]) == len(classList):\n",
    "        return classList[0]\n",
    "    \n",
    "    if len(dataSet) == 1:\n",
    "        return majorityCnt(classList)\n",
    "    \n",
    "    bestFeat = chooseBestFeatureToSplit(dataSet,labels)\n",
    "    bestFeatLabel = labels[bestFeat]\n",
    "    myTree = {bestFeatLabel:{}}\n",
    "\n",
    "    del(labels[bestFeatLabel])\n",
    "    \n",
    "    featValues = [example[bestFeat] for example in dataSet]\n",
    "    uniqueVals = set(featValues)\n",
    "    for value in uniqueVals:\n",
    "        subLabels = labels[:]       #copy all of labels, so trees don't mess up existing labels\n",
    "        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)\n",
    "    return myTree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('watermelon_3a.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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>Idx</th>\n",
       "      <th>color</th>\n",
       "      <th>root</th>\n",
       "      <th>knocks</th>\n",
       "      <th>texture</th>\n",
       "      <th>navel</th>\n",
       "      <th>touch</th>\n",
       "      <th>density</th>\n",
       "      <th>sugar_ratio</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.460</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>black</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>black</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.264</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.318</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>light_white</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.215</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.237</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.149</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.211</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>stiff</td>\n",
       "      <td>clear</td>\n",
       "      <td>distinct</td>\n",
       "      <td>even</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>light_white</td>\n",
       "      <td>stiff</td>\n",
       "      <td>clear</td>\n",
       "      <td>blur</td>\n",
       "      <td>even</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>light_white</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>blur</td>\n",
       "      <td>even</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>light_white</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>black</td>\n",
       "      <td>little_curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>distinct</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>soft_stick</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>light_white</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>little_heavily</td>\n",
       "      <td>blur</td>\n",
       "      <td>even</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>dark_green</td>\n",
       "      <td>curl_up</td>\n",
       "      <td>heavily</td>\n",
       "      <td>little_blur</td>\n",
       "      <td>little_sinking</td>\n",
       "      <td>hard_smooth</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Idx        color            root          knocks      texture  \\\n",
       "0     1   dark_green         curl_up  little_heavily     distinct   \n",
       "1     2        black         curl_up         heavily     distinct   \n",
       "2     3        black         curl_up  little_heavily     distinct   \n",
       "3     4   dark_green         curl_up         heavily     distinct   \n",
       "4     5  light_white         curl_up  little_heavily     distinct   \n",
       "5     6   dark_green  little_curl_up  little_heavily     distinct   \n",
       "6     7        black  little_curl_up  little_heavily  little_blur   \n",
       "7     8        black  little_curl_up  little_heavily     distinct   \n",
       "8     9        black  little_curl_up         heavily  little_blur   \n",
       "9    10   dark_green           stiff           clear     distinct   \n",
       "10   11  light_white           stiff           clear         blur   \n",
       "11   12  light_white         curl_up  little_heavily         blur   \n",
       "12   13   dark_green  little_curl_up  little_heavily  little_blur   \n",
       "13   14  light_white  little_curl_up         heavily  little_blur   \n",
       "14   15        black  little_curl_up  little_heavily     distinct   \n",
       "15   16  light_white         curl_up  little_heavily         blur   \n",
       "16   17   dark_green         curl_up         heavily  little_blur   \n",
       "\n",
       "             navel        touch  density  sugar_ratio  label  \n",
       "0          sinking  hard_smooth    0.697        0.460      1  \n",
       "1          sinking  hard_smooth    0.774        0.376      1  \n",
       "2          sinking  hard_smooth    0.634        0.264      1  \n",
       "3          sinking  hard_smooth    0.608        0.318      1  \n",
       "4          sinking  hard_smooth    0.556        0.215      1  \n",
       "5   little_sinking   soft_stick    0.403        0.237      1  \n",
       "6   little_sinking   soft_stick    0.481        0.149      1  \n",
       "7   little_sinking  hard_smooth    0.437        0.211      1  \n",
       "8   little_sinking  hard_smooth    0.666        0.091      0  \n",
       "9             even   soft_stick    0.243        0.267      0  \n",
       "10            even  hard_smooth    0.245        0.057      0  \n",
       "11            even   soft_stick    0.343        0.099      0  \n",
       "12         sinking  hard_smooth    0.639        0.161      0  \n",
       "13         sinking  hard_smooth    0.657        0.198      0  \n",
       "14  little_sinking   soft_stick    0.360        0.370      0  \n",
       "15            even  hard_smooth    0.593        0.042      0  \n",
       "16  little_sinking  hard_smooth    0.719        0.103      0  "
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataSet = data.loc[:,'color':'sugar_ratio']\n",
    "labels = data.loc[:,'label']\n",
    "dataSet = dataSet.values.tolist()\n",
    "dataSet\n",
    "labels = labels.values.tolist()\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "myTree = createTree(dataSet,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: {'black': {1: {'little_curl_up': {1: {'heavily': 0.091,\n",
       "      'little_heavily': {1: {'distinct': {0: {0.21100000000000002: 0.21100000000000002,\n",
       "          0.37: 0.37}},\n",
       "        'little_blur': 0.149}}}},\n",
       "    'curl_up': {1: {'heavily': 0.376, 'little_heavily': 0.264}}}},\n",
       "  'light_white': {1: {'little_curl_up': 0.198,\n",
       "    'curl_up': {0: {0.215: 0.215, 0.099: 0.099, 0.042: 0.042}},\n",
       "    'stiff': 0.057}},\n",
       "  'dark_green': {1: {'little_curl_up': {0: {0.237: 0.237, 0.161: 0.161}},\n",
       "    'curl_up': {1: {'heavily': {1: {'distinct': 0.318,\n",
       "        'little_blur': 0.10300000000000001}},\n",
       "      'little_heavily': 0.46}},\n",
       "    'stiff': 0.267}}}}"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "decisionNode = dict(boxstyle=\"sawtooth\", fc=\"0.8\")\n",
    "leafNode = dict(boxstyle=\"round4\", fc=\"0.8\")\n",
    "arrow_args = dict(arrowstyle=\"<-\")\n",
    "\n",
    "def getNumLeafs(myTree):\n",
    "    numLeafs = 0\n",
    "    \n",
    "    firstSides = list(myTree.keys())\n",
    "    firstStr = firstSides[0]\n",
    "    secondDict = myTree[firstStr]\n",
    "    for key in secondDict.keys():\n",
    "        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes\n",
    "            numLeafs += getNumLeafs(secondDict[key])\n",
    "        else:   numLeafs +=1\n",
    "    return numLeafs\n",
    "\n",
    "\n",
    "def getTreeDepth(myTree):\n",
    "    maxDepth = 0\n",
    "    firstSides = list(myTree.keys())\n",
    "    firstStr = firstSides[0]\n",
    "    secondDict = myTree[firstStr]\n",
    "    for key in secondDict.keys():\n",
    "        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes\n",
    "            thisDepth = 1 + getTreeDepth(secondDict[key])\n",
    "        else:   thisDepth = 1\n",
    "        if thisDepth > maxDepth: maxDepth = thisDepth\n",
    "    return maxDepth\n",
    "\n",
    "def plotNode(nodeTxt, centerPt, parentPt, nodeType):\n",
    "    createPlot.ax1.annotate(nodeTxt, xy=parentPt,  xycoords='axes fraction',\n",
    "             xytext=centerPt, textcoords='axes fraction',\n",
    "             va=\"center\", ha=\"center\", bbox=nodeType, arrowprops=arrow_args )\n",
    "\n",
    "def plotMidText(cntrPt, parentPt, txtString):\n",
    "    xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0]\n",
    "    yMid = (parentPt[1]-cntrPt[1])/2.0 + cntrPt[1]\n",
    "    createPlot.ax1.text(xMid, yMid, txtString, va=\"center\", ha=\"center\", rotation=30)\n",
    "\n",
    "def plotTree(myTree, parentPt, nodeTxt):#if the first key tells you what feat was split on\n",
    "    numLeafs = getNumLeafs(myTree)  #this determines the x width of this tree\n",
    "    depth = getTreeDepth(myTree)\n",
    "    firstSides = list(myTree.keys())\n",
    "    firstStr = firstSides[0]     #the text label for this node should be this\n",
    "    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff)\n",
    "    plotMidText(cntrPt, parentPt, nodeTxt)\n",
    "    plotNode(firstStr, cntrPt, parentPt, decisionNode)\n",
    "    secondDict = myTree[firstStr]\n",
    "    plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD\n",
    "    for key in secondDict.keys():\n",
    "        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes   \n",
    "            plotTree(secondDict[key],cntrPt,str(key))        #recursion\n",
    "        else:   #it's a leaf node print the leaf node\n",
    "            plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW\n",
    "            plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)\n",
    "            plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))\n",
    "    plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD\n",
    "def createPlot(inTree):\n",
    "    fig = plt.figure(1, facecolor='white')\n",
    "    fig.clf()\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)    #no ticks\n",
    "    #createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropses \n",
    "    plotTree.totalW = float(getNumLeafs(inTree))\n",
    "    plotTree.totalD = float(getTreeDepth(inTree))\n",
    "    plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0;\n",
    "    plotTree(inTree, (0.5,1.0), '')\n",
    "    plt.show()    \n",
    "def retrieveTree(i):\n",
    "    listOfTrees =[{1: {'black': {1: {'little_curl_up': {1: {'heavily': 0.091,\n",
    "      'little_heavily': {1: {'distinct': {0: {0.21100000000000002: 0.21100000000000002,\n",
    "          0.37: 0.37}},\n",
    "        'little_blur': 0.149}}}},\n",
    "    'curl_up': {1: {'heavily': 0.376, 'little_heavily': 0.264}}}},\n",
    "  'light_white': {1: {'little_curl_up': 0.198,\n",
    "    'curl_up': {0: {0.215: 0.215, 0.099: 0.099, 0.042: 0.042}},\n",
    "    'stiff': 0.057}},\n",
    "  'dark_green': {1: {'little_curl_up': {0: {0.237: 0.237, 0.161: 0.161}},\n",
    "    'curl_up': {1: {'heavily': {1: {'distinct': 0.318,\n",
    "        'little_blur': 0.10300000000000001}},\n",
    "      'little_heavily': 0.46}},\n",
    "    'stiff': 0.267}}}}\n",
    "                  ]\n",
    "    return listOfTrees[i]\n",
    "\n",
    "def classify(inputTree,featLabels,testVec):\n",
    "    firstSides = list(inputTree.keys())\n",
    "    firstStr = firstSides[0]\n",
    "    \n",
    "    secondDict = inputTree[firstStr]\n",
    "    featIndex = featLabels.index(firstStr)\n",
    "    key = testVec[featIndex]\n",
    "    valueOfFeat = secondDict[key]\n",
    "    if isinstance(valueOfFeat, dict): \n",
    "        classLabel = classify(valueOfFeat, featLabels, testVec)\n",
    "    else: classLabel = valueOfFeat\n",
    "    return classLabel\n",
    "\n",
    "def storeTree(inputTree,filename):\n",
    "    import pickle\n",
    "    fw = open(filename,'w')\n",
    "    pickle.dump(inputTree,fw)\n",
    "    fw.close()\n",
    "    \n",
    "def grabTree(filename):\n",
    "    import pickle\n",
    "    fr = open(filename)\n",
    "    return pickle.load(fr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "createPlot(myTree)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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