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": 3,
   "metadata": {
    "collapsed": false
   },
   "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": 4,
   "metadata": {
    "collapsed": false
   },
   "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": {},
   "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
   },
   "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": {},
   "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
   },
   "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": {},
   "source": [
    "Now actually construct the decision tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "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 pyplot2 installed for this to work.\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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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EQF0Q8HV/XJ8+fdxzzz1nL94wiZdeeiml2NB25ZVXmgKC6wi3SZcuXSxLKfTP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5ffhPU3awnO14UmAesW82bJtBleZvzc0vxtikyar56NvWgEjCCsLVS8lZLwMfwAa1vLSpoFgXxK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\nxh4vp1Do5ANhUC6nadFtrr322kCiiIquH1dRLCaBMNYGomRmD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Cfyo+3kx7tHdcpll8OJ\nkONk98TJjogTrxwnDw3348cPJx4iTt6Cte+9e/c68dzQNk6fPu369evnxADSSfRfJ66wTtxm3aZN\nm5yolLSOGA87EXr0XsYu8Ufc69eve4whqwJ5Q9e+ZNenR5Ni/KlzEVdprSNuvk5sgwrqSQwTvca8\nZPeq4Jo/kR0rt2PHDiduwO7o0aNu165djvvi6Pz589qePHDjLseWrV271ok7e+y1rAovXbqka1xN\ne7Lz4sTWKvbWJF4Ca/GOUzzEg81JzB1tQ4RlJx51Wi6qG3fz5k0ncWm0rgiUWgfMwRk+hY/43rlz\npxN7pdhxZFHI/wO8XExp1z8NL6XhSd+/vDg42Qnzp6m+Jc6Ok52sVHWtkiFgCBgCWSPA7kcB1SrI\nyNtuQXvRE9mNiZ46eTMsOC91Il5Jzt8rLtElHyySzNHJzk2pZjIrTxJk0nYiuXQcwkQ5QtDhQZRE\neRRkmG89eAncPcFXcSTB95ykg0jsP+6+aspKCTK+rTTrn5aXfJtJ3ybIJKFj1wwBQ6AZEcjMRsZv\nQ/lYJ/48+h1VN1Ee9ciJ1is+RtXAB0oy6I266Ba3UY/zckaeSX12d3drxOKkOlzDPgQbiyTybtlJ\ndVrxWj14qX///iEUnqfCgv8PsI8hAGNvURIfpVn/tLyUZj555aU0c7c6hoAh0JoIZC7ItCYMlY0a\nYQrjUOw3iERLpmvSKKQl7IQI9jZlypS0t8TWO3nypNoZ4dXDeFo1t1Ds5NqkkJgxhCQQNaPalWHg\nXErAioMkC14iGSkB/whiSXuV9B83JiszBAwBQ6A3ETBBpgq0SZvAp1pC6KhViKFvH3BQbGmqHYrd\n12AEMCiuhbLgJXJa8YHEHquW4di9hoAhYAj0OgKZei31+uitQ0PAEDAEDAFDwBBoawRMkGnr5bfJ\nGwKGgCFgCBgCrY2ACTKtvX42ekPAEDAEDAFDoK0RMEGmrZffJm8IGAKGgCFgCLQ2Aj2MffF84TNn\nzpzWnpmNviwClXhalW0spgJpHiTuj3lTxWCTt6JoGom8zc3mYwgYAs2NQB+C2xQPkfDsP3/+LC62\n85whMGLEiKCzs7Nus5KAcwFJHy02Sd0gboqGSbhJCojiOFFNMTgbhCFgCOQegVhBJveztgkaAoaA\nIWAIGAKGQC4QMBuZXCyjTcIQMAQMAUPAEGhPBEyQac91t1kbAoaAIWAIGAK5QMAEmVwso03CEDAE\nDAFDwBBoTwT+A307sLn1SYrlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image  \n",
    "from sklearn.externals.six import StringIO  \n",
    "import pydot \n",
    "\n",
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(clf, out_file=dot_data,  \n",
    "                         feature_names=features)  \n",
    "graph = pydot.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": 9,
   "metadata": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
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 },
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}