02-Classification/trees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision trees are a supervised, probabilistic, machine learning classifier that are often used as decision support tools. Like any other classifier, they are capable of predicting the label of a sample, and the way they do this is by examining the probabilistic outcomes of your samples' features.   \n",
    "\n",
    "Decision trees are one of the oldest and most used machine learning algorithms, perhaps even pre-dating machine learning. They're very popular and have been around for decades. Following through with sequential cause-and-effect decisions comes very naturally.  \n",
    "Decision trees are a good tool to use when you want backing evidence to support a decision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train the [UCI's wheat-seeds dataset](https://archive.ics.uci.edu/ml/datasets/seeds) with decision trees and see the decision boundary plots produced by it.  \n",
    "You can find the dataset in the above link or in this Github dataset folder.  \n",
    "\n",
    "You can refer [to the related \"Decision Trees\" article](https://mashimo.wordpress.com/2020/01/05/decision-trees/) for more details about the method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wheat Seed prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "# Load up the wheat dataset into dataframe 'df'\n",
    "#\n",
    "df = pd.read_csv(\"../datasets/wheat.data\", index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>area</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>compactness</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>asymmetry</th>\n",
       "      <th>groove</th>\n",
       "      <th>wheat_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>15.26</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.8710</td>\n",
       "      <td>5.763</td>\n",
       "      <td>3.312</td>\n",
       "      <td>2.221</td>\n",
       "      <td>5.220</td>\n",
       "      <td>kama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>14.88</td>\n",
       "      <td>14.57</td>\n",
       "      <td>0.8811</td>\n",
       "      <td>5.554</td>\n",
       "      <td>3.333</td>\n",
       "      <td>1.018</td>\n",
       "      <td>4.956</td>\n",
       "      <td>kama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>14.29</td>\n",
       "      <td>14.09</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>5.291</td>\n",
       "      <td>3.337</td>\n",
       "      <td>2.699</td>\n",
       "      <td>4.825</td>\n",
       "      <td>kama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13.84</td>\n",
       "      <td>13.94</td>\n",
       "      <td>0.8955</td>\n",
       "      <td>5.324</td>\n",
       "      <td>3.379</td>\n",
       "      <td>2.259</td>\n",
       "      <td>4.805</td>\n",
       "      <td>kama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>16.14</td>\n",
       "      <td>14.99</td>\n",
       "      <td>0.9034</td>\n",
       "      <td>5.658</td>\n",
       "      <td>3.562</td>\n",
       "      <td>1.355</td>\n",
       "      <td>5.175</td>\n",
       "      <td>kama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area  perimeter  compactness  length  width  asymmetry  groove wheat_type\n",
       "id                                                                            \n",
       "0   15.26      14.84       0.8710   5.763  3.312      2.221   5.220       kama\n",
       "1   14.88      14.57       0.8811   5.554  3.333      1.018   4.956       kama\n",
       "2   14.29      14.09       0.9050   5.291  3.337      2.699   4.825       kama\n",
       "3   13.84      13.94       0.8955   5.324  3.379      2.259   4.805       kama\n",
       "4   16.14      14.99       0.9034   5.658  3.562      1.355   5.175       kama"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we want to do is to predict the wheat type based on the wheat seed characteristics.  \n",
    "To simplify the boundary plot, we consider only two of the characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we keep groove and perimeter, plus of course the target: wheat type\n",
    "wheatSimpler = df.drop(columns = ['compactness', 'width', 'length', 'area', 'asymmetry'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 210 entries, 0 to 209\n",
      "Data columns (total 3 columns):\n",
      "perimeter     210 non-null float64\n",
      "groove        206 non-null float64\n",
      "wheat_type    210 non-null object\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 6.6+ KB\n"
     ]
    }
   ],
   "source": [
    "wheatSimpler.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     perimeter  groove wheat_type\n",
      "id                               \n",
      "7        14.10     NaN   canadian\n",
      "60       12.86     NaN   canadian\n",
      "135      14.66     NaN   canadian\n",
      "201      13.32     NaN   canadian\n"
     ]
    }
   ],
   "source": [
    "# An easy way to show which rows have nans in them\n",
    "\n",
    "print (wheatSimpler[pd.isnull(wheatSimpler).any(axis=1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "# Only a few rows. Go ahead and drop any row with a nan\n",
    "#\n",
    "wheatSimpler.dropna(axis=0, inplace=True)\n",
    "\n",
    "# \n",
    "# INFO: In the future, you might try setting the nan values to the\n",
    "# mean value of that column, groove; the mean should only be calculated for\n",
    "# the specific class rather than across all classes, in this case for canadian type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Copy the labels out of the dataframe into variable 'labels' , then remove\n",
    "# them from the dataframe. Encode the labels:\n",
    "# canadian:0, kama:1, and rosa:2\n",
    "#\n",
    "labels = wheatSimpler.wheat_type.copy() # copy “y” values out\n",
    "wheatSimpler.drop(['wheat_type'], axis=1, inplace=True) # drop output column \n",
    "\n",
    "labels = labels.map({'canadian':0, 'kama':1, 'rosa':2}) # encoding\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split dataset into training and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(wheatSimpler, labels, test_size=0.3, \n",
    "                                                    random_state=7)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# We use the SkLearn module tree\n",
    "#\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=2, splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "# note that I force the tree depth to a maximum of two (for simplification)\n",
    "#\n",
    "\n",
    "model = tree.DecisionTreeClassifier(max_depth=2, random_state=2)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the tree using the sklearn.tree internal feature, which gives an idea of how the features are considered in the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(167.4, 181.2, 'groove <= 5.576\\nentropy = 0.666\\nsamples = 144\\nvalue = [51, 47, 46]'),\n",
       " Text(83.7, 108.72, 'perimeter <= 13.54\\nentropy = 0.51\\nsamples = 96\\nvalue = [49, 46, 1]'),\n",
       " Text(41.85, 36.23999999999998, 'entropy = 0.045\\nsamples = 43\\nvalue = [42, 1, 0]'),\n",
       " Text(125.55000000000001, 36.23999999999998, 'entropy = 0.261\\nsamples = 53\\nvalue = [7, 45, 1]'),\n",
       " Text(251.10000000000002, 108.72, 'perimeter <= 13.95\\nentropy = 0.119\\nsamples = 48\\nvalue = [2, 1, 45]'),\n",
       " Text(209.25, 36.23999999999998, 'entropy = 0.0\\nsamples = 1\\nvalue = [1, 0, 0]'),\n",
       " Text(292.95, 36.23999999999998, 'entropy = 0.082\\nsamples = 47\\nvalue = [1, 1, 45]')]"
      ]
     },
     "execution_count": 13,
     "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": [
    "tree.plot_tree(model, feature_names=X_train.columns, filled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the tree consists of a series of splitting rules, starting at the top of the tree with a question (answer is Yes on the left and No on the right) and goes on with \"branches\"to finally ends in \"leaves\" or \"terminal nodes\" after several splits (in this case two, since I have limited it above using *max_depth*).  \n",
    "\n",
    "Decision trees are simple and very easy to interpret, you just need to look at the questions.  \n",
    "The first question is: \"*has this seed a groove less or equal than 5.576?*\"  \n",
    "This question in fact splits the predictor space in two.   \n",
    "Let's plot the decision boundaries to see how they work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = np.meshgrid(wheatSimpler.perimeter, wheatSimpler.groove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = model.predict(np.c_[x.ravel(), y.ravel()])\n",
    "Z = Z.reshape(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(wheatSimpler.perimeter, wheatSimpler.groove, c = labels)\n",
    "\n",
    "ax.hlines(y=5.6, xmin=12, xmax=17.5, linestyle='-', label=\"1st question\")\n",
    "ax.vlines(x=13.5, ymin=3.5, ymax=5.6, linestyle='--', label=\"2nd question\")\n",
    "ax.vlines(x=14, ymin=5.6, ymax=6.9, linestyle=':', label=\"3rd question\")\n",
    "\n",
    "ax.legend(loc='best')\n",
    "fig.suptitle(\"Predictor space\")\n",
    "ax.set_xlabel(\"Seed Perimeter\")\n",
    "ax.set_ylabel(\"Seed Groove\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the predictor space has been segmented in a number of simple regions, four to be precise and a wheat type is predicted according to its position in one of the regions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Another example: poisonous mushrooms  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use decision trees to peruse [The Mushroom Data Set](https://archive.ics.uci.edu/ml/datasets/mushroom), drawn from the Audubon Society Field Guide to North American Mushrooms (1981). The data set details mushrooms described in terms of many physical characteristics, such as cap size  and stalk length, along with a classification of poisonous or edible.  \n",
    "You can find the dataset in the above UCI link or in the Dataset folder here in github.  \n",
    "\n",
    "As a standard disclaimer, if you eat a random mushroom you find, you are doing so at your own risk.\n",
    "\n",
    "## Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "# Load up the mushroom dataset into dataframe 'X'\n",
    "# Header information is on the dataset's website at the UCI ML Repo\n",
    "#\n",
    "colNames=['label', 'cap-shape','cap-surface','cap-color','bruises','odor',\n",
    "          'gill-attachment','gill-spacing','gill-size','gill-color','stalk-shape',\n",
    "          'stalk-root','stalk-surface-above-ring','stalk-surface-below-ring',\n",
    "          'stalk-color-above-ring','stalk-color-below-ring','veil-type',\n",
    "          'veil-color','ring-number','ring-type','spore-print-color','population',\n",
    "          'habitat']\n",
    "X = pd.read_csv(\"../datasets/agaricus-lepiota.data\", header=None, na_values='?',\n",
    "                names=colNames) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
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       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
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       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
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       "      <td>4</td>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
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       "      <td>a</td>\n",
       "      <td>g</td>\n",
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       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  label 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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
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       "    <tr>\n",
       "      <td>freq</td>\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",
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       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label 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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-process the data\n",
    "### remove missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5644, 23)\n"
     ]
    }
   ],
   "source": [
    "# \n",
    "# drop any row with a nan\n",
    "#\n",
    "X.dropna(axis=0, inplace=True) \n",
    "print (X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### separate label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# : Copy the labels out of the dset into variable 'y' then Remove\n",
    "# them from X. \n",
    "\n",
    "y = X[X.columns[0]].copy()\n",
    "X.drop(X.columns[0], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode the dataset using dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Encode labels poisonous / edible\n",
    "y = y.map({'p':0, 'e':1})\n",
    "\n",
    "# Encode the rest \n",
    "#\n",
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data inot test and training sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, \n",
    "                                                    random_state=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Create a DT classifier. No need to set any parameters, let;s get the ful depth\n",
    "#\n",
    "\n",
    "model = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "# train the classifier on the training data / labels:\n",
    "#\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(193.15384615384616, 199.32, 'spore-print-color_h <= 0.5\\nentropy = 0.469\\nsamples = 3950\\nvalue = [1487, 2463]'),\n",
       " Text(167.4, 163.07999999999998, 'gill-size_n <= 0.5\\nentropy = 0.235\\nsamples = 2851\\nvalue = [388, 2463]'),\n",
       " Text(103.01538461538462, 126.83999999999999, 'ring-number_o <= 0.5\\nentropy = 0.064\\nsamples = 2372\\nvalue = [78, 2294]'),\n",
       " Text(77.26153846153846, 90.6, 'habitat_p <= 0.5\\nentropy = 0.401\\nsamples = 108\\nvalue = [78, 30]'),\n",
       " Text(51.50769230769231, 54.359999999999985, 'stalk-color-above-ring_n <= 0.5\\nentropy = 0.093\\nsamples = 82\\nvalue = [78, 4]'),\n",
       " Text(25.753846153846155, 18.119999999999976, 'entropy = 0.0\\nsamples = 78\\nvalue = [78, 0]'),\n",
       " Text(77.26153846153846, 18.119999999999976, 'entropy = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
       " Text(103.01538461538462, 54.359999999999985, 'entropy = 0.0\\nsamples = 26\\nvalue = [0, 26]'),\n",
       " Text(128.76923076923077, 90.6, 'entropy = 0.0\\nsamples = 2264\\nvalue = [0, 2264]'),\n",
       " Text(231.7846153846154, 126.83999999999999, 'odor_n <= 0.5\\nentropy = 0.457\\nsamples = 479\\nvalue = [310, 169]'),\n",
       " Text(180.27692307692308, 90.6, 'stalk-shape_t <= 0.5\\nentropy = 0.296\\nsamples = 365\\nvalue = [299, 66]'),\n",
       " Text(154.52307692307693, 54.359999999999985, 'entropy = 0.0\\nsamples = 299\\nvalue = [299, 0]'),\n",
       " Text(206.03076923076924, 54.359999999999985, 'entropy = 0.0\\nsamples = 66\\nvalue = [0, 66]'),\n",
       " Text(283.2923076923077, 90.6, 'population_c <= 0.5\\nentropy = 0.174\\nsamples = 114\\nvalue = [11, 103]'),\n",
       " Text(257.53846153846155, 54.359999999999985, 'entropy = 0.0\\nsamples = 103\\nvalue = [0, 103]'),\n",
       " Text(309.04615384615386, 54.359999999999985, 'entropy = 0.0\\nsamples = 11\\nvalue = [11, 0]'),\n",
       " Text(218.90769230769232, 163.07999999999998, 'entropy = 0.0\\nsamples = 1099\\nvalue = [1099, 0]')]"
      ]
     },
     "execution_count": 29,
     "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": [
    "tree.plot_tree(model, feature_names=X_train.columns, filled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RESULT:\n",
    "top two features you should consider when deciding if a mushroom is eadible or not:\n",
    "**Odor** and **Gill Size**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High-Dimensionality Score:  100.0\n"
     ]
    }
   ],
   "source": [
    "# score the classifier on the testing data / labels:\n",
    "# Returns the mean accuracy on the given test data and labels.\n",
    "\n",
    "score = model.score(X_test, y_test)\n",
    "\n",
    "print (\"High-Dimensionality Score: \", round((score*100), 3))"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}