DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decison Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we'll load some fake data on past hires I made up. Note how we use pandas to convert a csv file into a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "\n",
    "input_file = \"PastHires.csv\"\n",
    "df = pd.read_csv(input_file, header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Years Experience</th>\n",
       "      <th>Employed?</th>\n",
       "      <th>Previous employers</th>\n",
       "      <th>Level of Education</th>\n",
       "      <th>Top-tier school</th>\n",
       "      <th>Interned</th>\n",
       "      <th>Hired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Y</td>\n",
       "      <td>4</td>\n",
       "      <td>BS</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>BS</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "      <td>6</td>\n",
       "      <td>BS</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>MS</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Years Experience Employed?  Previous employers Level of Education  \\\n",
       "0                10         Y                   4                 BS   \n",
       "1                 0         N                   0                 BS   \n",
       "2                 7         N                   6                 BS   \n",
       "3                 2         Y                   1                 MS   \n",
       "4                20         N                   2                PhD   \n",
       "\n",
       "  Top-tier school Interned Hired  \n",
       "0               N        N     Y  \n",
       "1               Y        Y     Y  \n",
       "2               N        N     N  \n",
       "3               Y        N     Y  \n",
       "4               Y        N     N  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scikit-learn needs everything to be numerical for decision trees to work. So, we'll map Y,N to 1,0 and levels of education to some scale of 0-2. In the real world, you'd need to think about how to deal with unexpected or missing data! By using map(), we know we'll get NaN for unexpected values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Years Experience</th>\n",
       "      <th>Employed?</th>\n",
       "      <th>Previous employers</th>\n",
       "      <th>Level of Education</th>\n",
       "      <th>Top-tier school</th>\n",
       "      <th>Interned</th>\n",
       "      <th>Hired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Years Experience  Employed?  Previous employers  Level of Education  \\\n",
       "0                10          1                   4                   0   \n",
       "1                 0          0                   0                   0   \n",
       "2                 7          0                   6                   0   \n",
       "3                 2          1                   1                   1   \n",
       "4                20          0                   2                   2   \n",
       "\n",
       "   Top-tier school  Interned  Hired  \n",
       "0                0         0      1  \n",
       "1                1         1      1  \n",
       "2                0         0      0  \n",
       "3                1         0      1  \n",
       "4                1         0      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'Y': 1, 'N': 0}\n",
    "df['Hired'] = df['Hired'].map(d)\n",
    "df['Employed?'] = df['Employed?'].map(d)\n",
    "df['Top-tier school'] = df['Top-tier school'].map(d)\n",
    "df['Interned'] = df['Interned'].map(d)\n",
    "d = {'BS': 0, 'MS': 1, 'PhD': 2}\n",
    "df['Level of Education'] = df['Level of Education'].map(d)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to separate the features from the target column that we're trying to bulid a decision tree for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Years Experience',\n",
       " 'Employed?',\n",
       " 'Previous employers',\n",
       " 'Level of Education',\n",
       " 'Top-tier school',\n",
       " 'Interned']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(df.columns[:6])\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now actually construct the decision tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"Hired\"]\n",
    "X = df[features]\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and display it. Note you need to have pydotplus installed for this to work. (!pip install pydotplus)\n",
    "\n",
    "To read this decision tree, each condition branches left for \"true\" and right for \"false\". When you end up at a value, the value array represents how many samples exist in each target value. So value = [0. 5.] mean there are 0 \"no hires\" and 5 \"hires\" by the tim we get to that point. value = [3. 0.] means 3 no-hires and 0 hires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image  \n",
    "from sklearn.externals.six import StringIO  \n",
    "import pydotplus\n",
    "\n",
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(clf, out_file=dot_data,  \n",
    "                         feature_names=features)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble learning: using a random forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use a random forest of 10 decision trees to predict employment of specific candidate profiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "[0]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=10)\n",
    "clf = clf.fit(X, y)\n",
    "\n",
    "#Predict employment of an employed 10-year veteran\n",
    "print (clf.predict([[10, 1, 4, 0, 0, 0]]))\n",
    "#...and an unemployed 10-year veteran\n",
    "print (clf.predict([[10, 0, 4, 0, 0, 0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modify the test data to create an alternate universe where everyone I hire everyone I normally wouldn't have, and vice versa. Compare the resulting decision tree to the one from the original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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