2017-10-24 - 12 Decision Trees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Decison Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "\n",
    "input_file = \"C:/Users/kohleggermichael/OneDrive/Teaching/[WCIS] CRM und Information Mining II/Block-1/PastHires.csv\"\n",
    "df = pd.read_csv(input_file, header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 5,
     "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": {
    "deletable": true,
    "editable": true
   },
   "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": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Years Experience',\n",
       " 'Employed?',\n",
       " 'Previous employers',\n",
       " 'Level of Education',\n",
       " 'Top-tier school',\n",
       " 'Interned']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(df.columns[:6])\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now actually construct the decision tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y = df[\"Hired\"]\n",
    "X = df[features]\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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zczPa2toQCARyPkDSaDQCABobG5Ou093dDQBaN5DapdLS0gIg/fnoqby8HEIIeL1eWCwW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\n8O3btyMQCKC+vh6yLOPee+/NqWR+odsRVSomH2SISCSC1tZWTExMoK6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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Ensemble learning: using a random forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We'll use a random forest of 10 decision trees to predict employment of specific candidate profiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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