SpeedDating.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An Experiment on Speed Dating\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.regression.linear_model as regression\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Standardizes preference scores to correct collection variations introduced by experiment\n",
    "From data description : \n",
    "Waves 1-5 and 10-21: You have 100 points to distribute among the following attributes -- \n",
    "give more points to those attributes that are more important in a potential date, \n",
    "and fewer points to those attributes that are less important in a potential date.  Total points must equal 100.\n",
    "Waves 6-9: Please rate the importance of the following attributes in a potential \n",
    "date on a scale of 1-10 (1=not at all important, 10=extremely important): \n",
    "\"\"\"\n",
    "def standardize_pref_ratings(df):    \n",
    "    #print(df.head())\n",
    "    \n",
    "    # TODO: gather list of all preference cols\n",
    "    # pref attributes : pf_o_att, attr_o\n",
    "    pref_attr = df.filter( regex='attr|sinc|intel|fun|amb|shar' ).columns.tolist() \n",
    "    \n",
    "    # locate rows that have been scaled 1-10 (waves 6 - 10)\n",
    "    off = df.loc[ df['wave'].isin( range(6,10 ) ) ]\n",
    "    #print(off.head())\n",
    "    \n",
    "    for index, row in off.iterrows():\n",
    "        row_attr_tot_points = np.sum( [row[pref] for pref in pref_attr ] )\n",
    "        #print(row_attr_tot_points)\n",
    "        for pref in pref_attr:\n",
    "            # scale cell value to relative percentage considering provided preference scores, mult by 100 \n",
    "            df.loc[index, pref] = ( row[pref] / row_attr_tot_points ) * 100        \n",
    "    return df\n",
    "\n",
    "# =========================================================\n",
    "# Below used as test dataframe for development of above\n",
    "# =========================================================\n",
    "\n",
    "d =  {'wave' : pd.Series([ 1, 8, 6, 7 ]) ,\n",
    "    'attr1_2': pd.Series([25, 2, 1, 5 ]) ,\n",
    "    'attr4_3': pd.Series([25, 2, 1, 10 ]) ,\n",
    "    'fun8675309': pd.Series([50, 1, 1, 0 ] )\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "df = standardize_pref_ratings(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def standarize_lookup(df):    \n",
    "    #print(df.head())\n",
    "    \n",
    "    # TODO: gather list of all preference cols\n",
    "    # pref attributes : pf_o_att, attr_o\n",
    "    pref_attr = df.filter( regex='attr_o|inc_o|intel_o|fun_o|amb_o|shar_o' ).columns.tolist() \n",
    "    \n",
    "    # locate rows that have been scaled 1-10 (waves 6 - 10)\n",
    "    #off = df.loc[ df['wave'].isin( range(6,10 ) ) ]\n",
    "    #print(off.head())\n",
    "    \n",
    "    for index, row in df.iterrows():\n",
    "        row_attr_tot_points = np.sum( [row[pref] for pref in pref_attr ] )\n",
    "        #print(row_attr_tot_points)\n",
    "        for pref in pref_attr:\n",
    "            # scale cell value to relative percentage considering provided preference scores, mult by 100 \n",
    "            df.loc[index, pref] = ( row[pref] / row_attr_tot_points ) * 100        \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/speed_dating.csv', encoding=\"ISO-8859-1\")\n",
    "df = df.dropna(axis=1,how=\"all\")\n",
    "df=standardize_pref_ratings(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lookup = pd.DataFrame(index=range(1,553), columns=df.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating lookup table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rayw512/Anaconda/anaconda/lib/python2.7/site-packages/numpy/lib/nanfunctions.py:675: RuntimeWarning: Mean of empty slice\n",
      "  warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
      "/Users/rayw512/Anaconda/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "for x in range(1,553):\n",
    "    lookup.loc[x]['gender']=np.average(df.loc[df[\"iid\"]==x][\"gender\"])\n",
    "    lookup.loc[x]['match']=np.average(df.loc[df[\"iid\"]==x][\"match\"])\n",
    "    lookup.loc[x]['age']=np.average(df.loc[df[\"iid\"]==x][\"age\"])\n",
    "    lookup.loc[x]['career_c']=np.average(df.loc[df[\"iid\"]==x][\"career_c\"])\n",
    "    lookup.loc[x]['field_cd']=np.average(df.loc[df[\"iid\"]==x][\"field_cd\"])\n",
    "    #lookup.loc[x]['income']=np.average(df.loc[df[\"iid\"]==x][\"income\"])\n",
    "    lookup.loc[x]['imprace']=np.average(df.loc[df[\"iid\"]==x][\"imprace\"])\n",
    "    lookup.loc[x]['imprelig']=np.average(df.loc[df[\"iid\"]==x][\"imprelig\"])\n",
    "    lookup.loc[x]['goal']=np.average(df.loc[df[\"iid\"]==x][\"goal\"])\n",
    "    lookup.loc[x]['date']=np.average(df.loc[df[\"iid\"]==x][\"date\"])\n",
    "    lookup.loc[x]['go_out']=np.average(df.loc[df[\"iid\"]==x][\"go_out\"])\n",
    "    lookup.loc[x]['date']=np.average(df.loc[df[\"iid\"]==x][\"date\"])\n",
    "    lookup.loc[x]['sports']=np.average(df.loc[df[\"iid\"]==x][\"sports\"])\n",
    "    lookup.loc[x]['tvsports']=np.average(df.loc[df[\"iid\"]==x][\"tvsports\"])\n",
    "    lookup.loc[x]['exercise']=np.average(df.loc[df[\"iid\"]==x][\"exercise\"])\n",
    "    lookup.loc[x]['museums']=np.average(df.loc[df[\"iid\"]==x][\"museums\"])\n",
    "    lookup.loc[x]['dining']=np.average(df.loc[df[\"iid\"]==x][\"dining\"])\n",
    "    lookup.loc[x]['art']=np.average(df.loc[df[\"iid\"]==x][\"art\"])\n",
    "    lookup.loc[x]['hiking']=np.average(df.loc[df[\"iid\"]==x][\"hiking\"])\n",
    "    lookup.loc[x]['gaming']=np.average(df.loc[df[\"iid\"]==x][\"gaming\"])\n",
    "    lookup.loc[x]['clubbing']=np.average(df.loc[df[\"iid\"]==x][\"clubbing\"])\n",
    "    lookup.loc[x]['reading']=np.average(df.loc[df[\"iid\"]==x][\"reading\"])\n",
    "    lookup.loc[x]['tv']=np.average(df.loc[df[\"iid\"]==x][\"tv\"])\n",
    "    lookup.loc[x]['theater']=np.average(df.loc[df[\"iid\"]==x][\"theater\"])\n",
    "    lookup.loc[x]['movies']=np.average(df.loc[df[\"iid\"]==x][\"movies\"])\n",
    "    lookup.loc[x]['concerts']=np.average(df.loc[df[\"iid\"]==x][\"concerts\"])\n",
    "    lookup.loc[x]['music']=np.average(df.loc[df[\"iid\"]==x][\"music\"])\n",
    "    lookup.loc[x]['shopping']=np.average(df.loc[df[\"iid\"]==x][\"shopping\"])\n",
    "    lookup.loc[x]['yoga']=np.average(df.loc[df[\"iid\"]==x][\"yoga\"])\n",
    "    lookup.loc[x]['attr3_1']=np.average(df.loc[df[\"iid\"]==x][\"attr3_1\"])\n",
    "    lookup.loc[x]['sinc3_1']=np.average(df.loc[df[\"iid\"]==x][\"sinc3_1\"])\n",
    "    lookup.loc[x]['intel3_1']=np.average(df.loc[df[\"iid\"]==x][\"intel3_1\"])\n",
    "    lookup.loc[x]['fun3_1']=np.average(df.loc[df[\"iid\"]==x][\"fun3_1\"])\n",
    "    lookup.loc[x]['amb3_1']=np.average(df.loc[df[\"iid\"]==x][\"amb3_1\"])\n",
    "    lookup.loc[x]['attr5_1']=np.average(df.loc[df[\"iid\"]==x][\"attr5_1\"])\n",
    "    lookup.loc[x]['sinc5_1']=np.average(df.loc[df[\"iid\"]==x][\"sinc5_1\"])\n",
    "    lookup.loc[x]['intel5_1']=np.average(df.loc[df[\"iid\"]==x][\"intel5_1\"])\n",
    "    lookup.loc[x]['fun5_1']=np.average(df.loc[df[\"iid\"]==x][\"fun5_1\"])\n",
    "    lookup.loc[x]['attr_o']=np.nanmean(df.loc[df[\"iid\"]==x][\"attr_o\"])\n",
    "    lookup.loc[x]['sinc_o']=np.nanmean(df.loc[df[\"iid\"]==x][\"sinc_o\"])\n",
    "    lookup.loc[x]['intel_o']=np.nanmean(df.loc[df[\"iid\"]==x][\"intel_o\"])\n",
    "    lookup.loc[x]['fun_o']=np.nanmean(df.loc[df[\"iid\"]==x][\"fun_o\"])\n",
    "    lookup.loc[x]['amb_o']=np.nanmean(df.loc[df[\"iid\"]==x][\"amb_o\"])\n",
    "    lookup.loc[x]['shar_o']=np.nanmean(df.loc[df[\"iid\"]==x][\"shar_o\"])\n",
    "    lookup.loc[x]['expnum']=np.nanmean(df.loc[df[\"iid\"]==x][\"expnum\"])\n",
    "    lookup.loc[x]['dec_o']=np.nanmean(df.loc[df[\"iid\"]==x][\"dec_o\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lookup[\"creative_technical\"]=np.nan\n",
    "#lookup.field_cd=lookup.field_cd.astype(int)\n",
    "for index, row in lookup.iterrows():\n",
    "    rating=0\n",
    "    field=row[\"field_cd\"]\n",
    "    if(field==1):\n",
    "        rating=14\n",
    "    elif(field==2):\n",
    "        rating=18\n",
    "    elif(field==3):\n",
    "        rating=6\n",
    "    elif(field==4):\n",
    "        rating=15\n",
    "    elif(field==5):\n",
    "        rating=17\n",
    "    elif(field==6):\n",
    "        rating=3\n",
    "    elif(field==7):\n",
    "        rating=7\n",
    "    elif(field==8):\n",
    "        rating=13\n",
    "    elif(field==9):\n",
    "        rating=9\n",
    "    elif(field==10):\n",
    "        rating=18              \n",
    "    elif(field==11):\n",
    "        rating=4\n",
    "    elif(field==12):\n",
    "        rating=11\n",
    "    elif(field==13):\n",
    "        rating=12\n",
    "    elif(field==14):\n",
    "        rating=2\n",
    "    elif(field==15):\n",
    "        rating=1\n",
    "    elif(field==16):\n",
    "        rating=8\n",
    "    elif(field==17):\n",
    "        rating=5\n",
    "    elif(field==18):\n",
    "        rating=10\n",
    "    lookup.set_value(index,\"creative_technical\",rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>gender</th>\n",
       "      <th>match</th>\n",
       "      <th>dec_o</th>\n",
       "      <th>attr_o</th>\n",
       "      <th>sinc_o</th>\n",
       "      <th>intel_o</th>\n",
       "      <th>fun_o</th>\n",
       "      <th>amb_o</th>\n",
       "      <th>shar_o</th>\n",
       "      <th>age</th>\n",
       "      <th>field_cd</th>\n",
       "      <th>imprace</th>\n",
       "      <th>imprelig</th>\n",
       "      <th>goal</th>\n",
       "      <th>date</th>\n",
       "      <th>go_out</th>\n",
       "      <th>career_c</th>\n",
       "      <th>sports</th>\n",
       "      <th>tvsports</th>\n",
       "      <th>exercise</th>\n",
       "      <th>dining</th>\n",
       "      <th>museums</th>\n",
       "      <th>art</th>\n",
       "      <th>hiking</th>\n",
       "      <th>gaming</th>\n",
       "      <th>clubbing</th>\n",
       "      <th>reading</th>\n",
       "      <th>tv</th>\n",
       "      <th>theater</th>\n",
       "      <th>movies</th>\n",
       "      <th>concerts</th>\n",
       "      <th>music</th>\n",
       "      <th>shopping</th>\n",
       "      <th>yoga</th>\n",
       "      <th>expnum</th>\n",
       "      <th>attr3_1</th>\n",
       "      <th>sinc3_1</th>\n",
       "      <th>fun3_1</th>\n",
       "      <th>intel3_1</th>\n",
       "      <th>amb3_1</th>\n",
       "      <th>attr5_1</th>\n",
       "      <th>sinc5_1</th>\n",
       "      <th>intel5_1</th>\n",
       "      <th>fun5_1</th>\n",
       "      <th>creative_technical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.250464</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.929936</td>\n",
       "      <td>7.933121</td>\n",
       "      <td>8.257962</td>\n",
       "      <td>7.38535</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>6.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.250464</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.929936</td>\n",
       "      <td>7.933121</td>\n",
       "      <td>8.257962</td>\n",
       "      <td>7.38535</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.111111</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.250464</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.929936</td>\n",
       "      <td>7.933121</td>\n",
       "      <td>8.257962</td>\n",
       "      <td>7.38535</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>7.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.929936</td>\n",
       "      <td>7.933121</td>\n",
       "      <td>8.257962</td>\n",
       "      <td>7.38535</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.929936</td>\n",
       "      <td>7.933121</td>\n",
       "      <td>8.257962</td>\n",
       "      <td>7.38535</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  match  dec_o  attr_o  sinc_o  intel_o  fun_o     amb_o  shar_o  \\\n",
       "1     0.0    0.4    0.5     6.7     7.4      8.0    7.2  8.000000     7.1   \n",
       "2     0.0    0.2    0.6     7.7     7.1      7.9    7.5  7.500000     6.5   \n",
       "3     0.0    0.0    0.5     6.5     7.1      7.3    6.2  7.111111     6.0   \n",
       "4     0.0    0.2    0.6     7.0     7.1      7.7    7.5  7.700000     7.2   \n",
       "5     0.0    0.2    0.3     5.3     7.7      7.6    7.2  7.800000     6.2   \n",
       "\n",
       "    age  field_cd  imprace  imprelig  goal  date  go_out  career_c  sports  \\\n",
       "1  21.0       1.0      2.0       4.0   2.0   7.0     1.0  5.250464     9.0   \n",
       "2  24.0       1.0      2.0       5.0   1.0   5.0     1.0  5.250464     3.0   \n",
       "3  25.0       2.0      8.0       4.0   6.0   3.0     1.0  5.250464     3.0   \n",
       "4  23.0       1.0      1.0       1.0   1.0   5.0     1.0  1.000000     1.0   \n",
       "5  21.0       1.0      8.0       1.0   2.0   4.0     1.0  1.000000     7.0   \n",
       "\n",
       "   tvsports  exercise  dining  museums  art  hiking  gaming  clubbing  \\\n",
       "1       2.0       8.0     9.0      1.0  1.0     5.0     1.0       5.0   \n",
       "2       2.0       7.0    10.0      8.0  6.0     3.0     5.0       8.0   \n",
       "3       8.0       7.0     8.0      5.0  5.0     8.0     4.0       5.0   \n",
       "4       1.0       6.0     7.0      6.0  7.0     7.0     5.0       7.0   \n",
       "5       4.0       7.0     7.0      6.0  8.0     6.0     6.0       8.0   \n",
       "\n",
       "   reading   tv  theater  movies  concerts  music  shopping  yoga  expnum  \\\n",
       "1      6.0  9.0      1.0    10.0      10.0    9.0       8.0   1.0     2.0   \n",
       "2     10.0  1.0      9.0     8.0       7.0    8.0       3.0   1.0     5.0   \n",
       "3      7.0  8.0      7.0     7.0       7.0    5.0       8.0   7.0     2.0   \n",
       "4      7.0  7.0      9.0     7.0       8.0    7.0       1.0   8.0     2.0   \n",
       "5      6.0  8.0      6.0     6.0       3.0    7.0       8.0   3.0    10.0   \n",
       "\n",
       "   attr3_1  sinc3_1  fun3_1  intel3_1  amb3_1   attr5_1   sinc5_1  intel5_1  \\\n",
       "1      6.0      8.0     8.0       8.0     7.0  6.929936  7.933121  8.257962   \n",
       "2      7.0      5.0    10.0       8.0     3.0  6.929936  7.933121  8.257962   \n",
       "3      8.0      9.0     8.0       9.0     8.0  6.929936  7.933121  8.257962   \n",
       "4      7.0      8.0     9.0       7.0     8.0  6.929936  7.933121  8.257962   \n",
       "5      6.0      3.0     6.0      10.0     8.0  6.929936  7.933121  8.257962   \n",
       "\n",
       "    fun5_1  creative_technical  \n",
       "1  7.38535                14.0  \n",
       "2  7.38535                14.0  \n",
       "3  7.38535                18.0  \n",
       "4  7.38535                14.0  \n",
       "5  7.38535                14.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup=lookup.dropna(axis=1,how=\"all\")\n",
    "lookup=lookup.fillna(lookup.mean())\n",
    "lookup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lookup_men=lookup.loc[lookup[\"gender\"]==1]\n",
    "lookup_women=lookup.loc[lookup[\"gender\"]==0]\n",
    "lookup_stand=lookup.copy()\n",
    "lookup_stand=standarize_lookup(lookup_stand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How attributes contribute to match success rate in men and women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',\n",
       "             max_depth=3, max_features=None, max_leaf_nodes=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "             presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "             warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=lookup[\"dec_o\"]\n",
    "x=lookup[[\"attr_o\",\"sinc_o\",\"intel_o\",\"fun_o\",\"amb_o\",\"shar_o\",\"age\",\"field_cd\",\"imprace\",\"imprelig\",\"goal\",\"date\",\"go_out\",\"career_c\",\"sports\",\"tvsports\",\"exercise\",\"dining\",\"museums\",\"art\",\"hiking\",\"gaming\",\"clubbing\",\"reading\",\"tv\",\"theater\",\"movies\",\"concerts\",\"music\",\"shopping\",\"yoga\",\"expnum\",\"attr3_1\",\"sinc3_1\",\"fun3_1\",\"intel3_1\",\"amb3_1\",\"attr5_1\",\"sinc5_1\",\"intel5_1\",\"fun5_1\",\"creative_technical\"]]  \n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)\n",
    "gbr=GradientBoostingRegressor()\n",
    "gbr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First model with all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HGR4e7ncYkiRJWoEGBwdXTbK0nEyYVrBmpb117UW0FoW4XVVdvbXtDQ8Ps2HDhl6FJ0mS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OQ39iuBh0h+ezTkbR3pfQvMJxleLv58fajbOhr6t0T+NfmCyatvgZ1Jfqu3Ik1zIqChbutI71uS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\nZfx/HyKaZq1Fu3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157a4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = gbr.feature_importances_\n",
    "# make importances relative to max importance\n",
    "#feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "#plt.subplot(.5, .5, .5)\n",
    "plt.figure(figsize=(9,9))\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, x[sorted_idx],size=\"small\")\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Uv7Uu376u97oO656Y1/7RmHGqnf4EqqOZK4BDOyy/NdW71xuoXuQ/QvVEa19nx7a1zH9p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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159197d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=lookup[\"dec_o\"]\n",
    "x=lookup[[\"attr_o\",\"sinc_o\",\"intel_o\",\"fun_o\",\"amb_o\",\"shar_o\"]]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)\n",
    "gbr=GradientBoostingRegressor()\n",
    "gbr.fit(x_train,y_train)\n",
    "\n",
    "feature_importance = gbr.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, x[sorted_idx],size=\"small\")\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.title('Attribute Importance for Both Genders')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',\n",
       "             max_depth=3, max_features=None, max_leaf_nodes=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "             presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "             warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=lookup_men[\"dec_o\"]\n",
    "x=lookup_men[[\"attr_o\",\"sinc_o\",\"intel_o\",\"fun_o\",\"amb_o\",\"shar_o\"]]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)\n",
    "gbr_m=GradientBoostingRegressor()\n",
    "gbr_m.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',\n",
       "             max_depth=3, max_features=None, max_leaf_nodes=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "             presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "             warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=lookup_women[\"dec_o\"]\n",
    "x=lookup_women[[\"attr_o\",\"sinc_o\",\"intel_o\",\"fun_o\",\"amb_o\",\"shar_o\"]]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)\n",
    "gbr_w=GradientBoostingRegressor()\n",
    "gbr_w.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How physical attractiveness effects desirability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TTy53NGrBTKgkSZLUsvzzn3DYYdCvH5x1FkSUOyK1YCZUkiRJajluvBEOPBAO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\nP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115988a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[]\n",
    "y=[]\n",
    "y_m=[]\n",
    "y_w=[]\n",
    "for j in range (1,11):\n",
    "    avg=0\n",
    "    for i in range (0,552):\n",
    "        test_person=lookup[[\"attr_o\",\"sinc_o\",\"intel_o\",\"fun_o\",\"amb_o\",\"shar_o\"]].iloc[i]\n",
    "        test_person[\"attr_o\"]=j\n",
    "        avg=avg+gbr.predict(test_person.reshape(1,-1))[0]\n",
    "        #print est.predict(test_person)[0]\n",
    "    y.append(float(avg/552))\n",
    "    x.append(j)\n",
    "for j in range (1,11):\n",
    "    avg=0\n",
    "    for i in range (0,277):\n",
    "        test_person=lookup_men[[\"attr_o\",\"sinc_o\",\"intel_o\",\"fun_o\",\"amb_o\",\"shar_o\"]].iloc[i]\n",
    "        test_person[\"attr_o\"]=j\n",
    "        avg=avg+gbr_m.predict(test_person.reshape(1,-1))[0]\n",
    "        #print est.predict(test_person)[0]\n",
    "    y_m.append(float(avg/277))\n",
    "for j in range (1,11):\n",
    "    avg=0\n",
    "    for i in range (0,274):\n",
    "        test_person=lookup_women[[\"attr_o\",\"sinc_o\",\"intel_o\",\"fun_o\",\"amb_o\",\"shar_o\"]].iloc[i]\n",
    "        test_person[\"attr_o\"]=j\n",
    "        avg=avg+gbr_w.predict(test_person.reshape(1,-1))[0]\n",
    "        #print est.predict(test_person)[0]\n",
    "    y_w.append(float(avg/274))\n",
    "plt.title(\"How Attractiveness Effects Success Rate\")\n",
    "plt.xlabel(\"Attractiveness from 1 to 10\")\n",
    "plt.ylabel(\"Average Match Rate\")\n",
    "plt.plot(x,y,label=\"Both Genders\",color='g')\n",
    "plt.plot(x,y_m,label=\"Men\",color='b')\n",
    "plt.plot(x,y_w,label=\"Women\",color='r')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How attributes contribute to match success in men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KMcBJXWWr2ockaZRMTEwwMTGxUdnU1NSitN13mCb5X8CrgN+dLgLG2u+v7Kh6\nc4/nO3Q8/+n0N6WUm5NcBfwOMFOY7gr8pGOdG9t1dgVmCNNXA4fNuj+SpNGwatUqVq3a+GRp7dq1\nrFy5csHb7usyb5LdgWOBF5RS7kkTfmWebd+vY7t3o7ls/PNZ6l8B7N6xzvbtOlfMs31Jkgai33um\nOwC3AeuT3AX4v2155tH2fkkOSLItcBTwzVku8QKcAjwryWPatt8CnDXzJV5JkhZHX2FaSrkIeB/w\nPeBS4BLgFpqz0+4z1E09/wTw58DVwH7ACzbR9g+AlwIfojmD3RtY3U//JUlaCPOZgPQampm6097X\nft2rq95Tup7/Wcf3b+q33Xa9T9CEsCRJS4Zv2iBJUqUlFabt/dDr2zdy6Hx8ddh9kyRpJn1f5l1I\npZRzgB2H3Q9JkvqxpM5MJUnaHBmmkiRVMkwlSapkmEqSVMkwlSSpkmEqSVIlw1SSpEqGqSRJlQxT\nSZIqGaaSJFUyTCVJqmSYSpJUyTCVJKmSYSpJUiXDVJKkSkvq80wHbx2wdtidkEbE5LA7IC1ZIx6m\nR7YPSYOwbNl2jI2NDbsb0pIz0mG6Zs0axsfHh90NaWSMjY2xfPnyYXdDWnJGOkzHx8dZsWLFsLsh\nSRpxTkCSJKmSYSpJUiXDVJKkSoapJEmVDFNJkioZppIkVTJMJUmqZJhKklTJMJUkqdJIvwPS5KRv\nzK0tj2/5Jy2+kQ7T1atXD7sL0qJbtmw7Lr540kCVFtFIhym8GThw2J2QFtEkGzasZv369YaptIhG\nPEz3BHyje0nSwnICkiRJlQxTSZIqGaaSJFUyTCVJqmSYSpJUyTCVJKmSYSpJUiXDVJKkSoapJEmV\nDFNJkioZppIkVTJMJUmqZJhKklTJMJUkqZJhKklSJcNUkqRKhqkkSZUMU0mSKhmmkiRVGliYJtkn\nyQVJppI8e1DblSRpqRvkmenfAaeVUnYqpXxygNuVJGlJG2SYLge+P8DtSZK0WRhImCb5LPBk4ANJ\nrk9yXZL9OpafmOT17fdHJVmT5OS23tlJdp9DG89J8v0kVyU5Lcmug+i7JEm1thnERkopByb5CnB8\nKWUiybpNrPInwIHAIcCJwNHAi2eqnGQf4ATgGcC3gbcDa4CnzN7MMcBJXWWr2ockaZRMTEwwMTGx\nUdnU1NSitD2QMO2QOdb7UinlawBJPga8dRP1nwt8spRydrvO64FrkuxSSvnlzKu9Gjhsjl2SJG3O\nVq1axarOrOBIAAAMA0lEQVRVG58srV27lpUrVy5428N6aUxnAN4E7LCJ+rsCP5l+Ukq5EbiqLZck\naagWKkxvBO7W8XyXyu1dAfz2vmqS7YGd23JJkoZqocL0u8CfJtkqydOAJ1Vu7xTgWUkek+QuwFuA\ns2a/xCtJ0uIYZJiWju+PAlYA19BMLPpU1YZL+QHwUuBDwM+BvYHVNduUJGlQBjYBqZTylI7vfwQ8\ncoZ6b+p6fibwwDls/xPAJyq7KUnSwPnevJIkVVoyYdreD51+w4fOx1eH3TdJkmYz6NeZzlsp5Rxg\nx2H3Q5Kkfi2ZM1NJkjZXhqkkSZUMU0mSKhmmkiRVMkwlSapkmEqSVMkwlSSpkmEqSVIlw1SSpEqG\nqSRJlQxTSZIqGaaSJFUyTCVJqmSYSpJUyTCVJKnSkvk804WxDlg77E5Ii2hy2B2QtkgjHqZHtg9p\ny7Fs2XaMjY0NuxvSFmWkw3TNmjWMj48PuxublTPOOIMDDjhg2N3YrCy1MRsbG2P58uXD7sYmTUxM\nsGrVqmF3Y7PimC1dKaUMuw8Dl2QFcN55553HihUrht2dzcpBBx3EaaedNuxubFYcs/lx3PrnmPVv\n7dq1rFy5EmBlKWXB7vs5AUmSpEqGqSRJlQxTSZIqjeoEpGUAk5O+TKBfU1NTrF3ry4n64ZjNj+PW\nP8esfx05sGwh2xnVCUiHAv8x7H5IkpaMw0opH12ojY9qmO4M/CFwGbBhuL2RJA3RMmAP4POllKsW\nqpGRDFNJkhaTE5AkSapkmEqSVMkwlSSpkmEqSVKlzSZMk/xVknVJbk5yTpJHbqL+k5Kcl2RDkv9J\ncniPOs9LMtlu87tJnrFwe7D4Bj1mSQ5PcnuS29qvtye5aWH3YvH1M25J7pvkP5Jc3I7LO2ao57F2\nR91NjpnHWs+6z07yhSRXJplK8o0k+/eo57F2R91NjtmgjrXNIkyTPB84BjgKeDjwXeDzSXp+zlSS\nPYD/BL4EPBQ4DvhAkqd31NkP+CjwfuBhwKeBTyXZd8F2ZBEtxJi1poD7djx2X4DuD02/4wbcFbgS\neDNw/gzb9Fjb2CbHrOWxtrEnAF8AngGsAL4CnJ7koR3b9Fjb2CbHrFV/rJVSlvwDOAc4ruN5gMuB\n18xQ/5+AC7rKJoDPdjz/GHBaV52zgX8b9v4u4TE7HLh62Pu2lMata92vAO/oUe6x1v+YeazNbRsX\nAm/oeO6x1v+YDeRYW/Jnpkm2BVbSnDEBUJoR+CLw2BlWe0y7vNPnu+o/dg51NksLOGYAOyS5LMlP\nkozMf7ww73GbC4+1+fFYm30bAXYEru4o9libfRu9xgwGcKwt+TAFxoCtgV92lf+S5nS8l/vOUP/u\nSe66iTozbXNzslBjdjHwZ8BBwGE0x883kuw6iE4vAfMZt7nwWOufx9qm/R2wPXByR5nH2ux6jdlA\njrVRfaN7LYBSyjk0l1kASHI2MAn8Bc09DGkgPNZml+b9x48EDiqlrB92fzYHM43ZoI61zeHMdD1w\nG7BLV/kuwC9mWOcXM9S/rpTy603UmWmbm5OFGrONlFJ+A3wHeMD8u7qkzGfc5sJjrZLH2h2SHAIc\nDzyvlPKVrsUeaz1sYsw2Mt9jbcmHaSnlVuA84KnTZe1176cC35hhtbM767f2b8tnq/P0rjqbpQUc\ns40k2Qr4feDnNf1dKuY5bnPhsVbJY+23dVYBHwQOKaWc0aOKx1qXOYxZd/35HWvDnp01x9lXfwrc\nBLwQ2Ad4H3AVcO92+T8CH+6ovwdwPc0M1QcBLwduAZ7WUeexwK+Bv2nrHE3zCTP7Dnt/l/CYHUnz\ni7knzbT0CeBGYJ9h7++wxq0teyjNyxDOBf69fT7usVY1Zh5rd/4dPbT9nXwZzdnY9OPuHmtVYzaQ\nY23og9PHIL6c5iPVbqb5L+sRHctOBL7cVf8JNP/F3Az8EHhBj20eDPygrXMB8IfD3s+lPGbAO4B1\n7fIrgNOBhwx7P5fAuN1Oc/mp83Gpx9r8x8xj7c7jRvMyou4xuw04wWNt/mM2qGPNj2CTJKnSkr9n\nKknSUmeYSpJUyTCVJKmSYSpJUiXDVJKkSoapJEmVDFNJkioZppIkVTJMJUmqZJhKklTJMNVISXJi\nktuT3NZ+nf5+rwG3ceqgtre59mE2SXZvx/4hw+6LtBj8cHCNos8BLwLSUfar4XRlZu1HPZUyYm+Q\nnWRbmrEfqf2SZuOZqUbRr0spvyqlXNnxKNB8/mGS1yW5NMlNSb6T5ODpFZNsleQDHct/kOSVHcuP\nAg4HntVx1vuEJE9sn9+9o+5D27Ll7fPDk1yT5JlJLqL5aKz7tctemuT7SW5uv/5lPzuc5CtJ3pXk\n2CRXJ/lFkpck2S7JCUmuS/LDJAd0rDPd5wOTfLdt++wkv9e17YOTXJhkQ5J1Sf6ma/m6JG9I8uEk\n19J8CPOl7eLz2za+3NZ9RJIvJPlVkmuTfDXJw7u2d3vb91OT3Jjkf5I8s6vOvklOTzLV7tuZSfbs\nWF41nlLfhv1xOj58DPJB8xFMp86y/P8AFwFPo/kM1xfSfD7iH7TLtwGOovlcw92BVTSf8/rcdvn2\nwMeAzwD3Bu7TrvNEmo926vycxIe2Zcvb54fTfNbk14HHAHsDy4DDgMuBZ7Vt/gnNmfSdPjZwpv2k\n+aipa4HXA/dvv97a9vMlbdm/AlcCy9p1nkjzUWgXAk8Bfg84DbgE2LqtsxL4Tbu9B7TjdSPwwo62\n1wHXAEfQfCbknsAj2m0/qR2je7R1n0zzGZN703ze5vE0H8K8fcf2bgd+TPPZlXsB7wSu69jGrsB6\n4OT253R/YDWwd7u87/H04aP2MfQO+PAxyEcbMre2ATj9OKlddhfgBuDRXeu8H1gzyzbfDZzc1cap\nXXXmGqa3AQ/uWveHwPO7yv4PcNYm9rM7TM/seL5Vu+8f6ijbpQ2qR3X0+XbafxTasnu2YTn9z8Ma\n4Iyutv8J+F7H83XAKV11dm+3PevnQrb9nAIO7Ci7HTi64/l2bdn+7fN/AH5EG/g9ttn3ePrwUfvw\nnqlG0ZeBl3HHPdMb268PoPnD/F9JOu+nbgt8Z/pJkr8CXgwsB+5GE8LfYTBuKaVc2NHWdjRnVh9M\n8oGOelvTnGn244Lpb0optye5CvheR9kv292+T8c6BTino841SS4GxtuiceBTXe2cBbwqSUop0/dF\nz5tLB5PcB3grTZDfh2Y/70Yz1p06+31Tkus6+v1Q4OullNt6bH+Q4ynNmWGqUXRjKWVdj/Id2q8H\nAld0Lfs1QJJDgH+huWR5Ds3Z3WuAR22izdvbr90h3e3mGfr0UuBbXcvuFBabcGvX89KjDBZmrsSN\nm64CwEdozn7/GvgJzbifQ/MPS6de+zLd7+4x7DTI8ZTmzDDVluT7NH+8dy+l/PcMdfajuRz4vumC\nJPfvqnMLzZlOp1/RBOnv0Fy2hOZ+3qxKKVcmuQK4fynlY5vehYELzf3bUwCS3BN4IM1YAUwCj+ta\n5/HA/3SclfZyS/u1e5z2A/6ylPL5tr37AWN99vkC4IVJtu4+O10C46ktlGGqLUYp5YYkbweOTbI1\n8N/ATjRhMVVK+Xea+20vSLI/zb3AFwCP5I7ZqQCXAfsneSBwFU14/gj4KXB0kjfQTK7ZaNbrLI4C\njmsvZZ4B3JVmAs89SinvrNjluXpjkqtpJie9leYfg0+3y44BvtXu00k0YfhXNJfRZ3MlzRnkAUl+\nBmwopVzHHeN7Hs3Y/zPNBLB+vAd4BXBSkn+kGf/HAN8spfyQ4Y+ntkC+NEZblFLKkcCbgdfSnH19\njuay7/Rl4fcBp9LM2D0HuBfNLNhO7wcuBr5NExr7lVJ+AxwC7AN8F/g7mkkvc+nTB2kuS76Y5qzr\nqzSTlXpdqp5xM/MsKzRjcRxwLs0M5We2+0Mp5Ts0s2qfT3Mf82jgDe0/HjO2054x/jXwF8DPuOO+\n60toLvOeB3y4bffKfvpdSrmaZvbx9jRj9W2a8bu1XT6I8ZT6ktmv1EgaVUmeSDNZ657tWaOkefLM\nVNqyZdNVJG2KYSpt2bw0JQ2Al3klSarkmakkSZUMU0mSKhmmkiRVMkwlSapkmEqSVMkwlSSpkmEq\nSVIlw1SSpEr/H8zTf2RighdCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f364e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=lookup[[\"attr_o\",\"sinc_o\",\"intel_o\",\"fun_o\",\"amb_o\",\"shar_o\",'age']]\n",
    "feature_importance = gbr_m.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, x[sorted_idx],size=\"small\")\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.title('Attribute Importance for Men')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How attributes contribute to the match success of women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5ZlUW9Zafs3klSapkmEqSVGlUL/MCzbVy9Wd6epr169cPuxvLimO2MI5b/5b6mI2NjbFq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\nVJKkSoapJEmVDFNJkir9f2XgjbDQc8x7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f371110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=lookup[[\"attr_o\",\"sinc_o\",\"intel_o\",\"fun_o\",\"amb_o\",\"shar_o\",'age']]\n",
    "feature_importance = gbr_w.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, x[sorted_idx],size=\"small\")\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.title('Attribute Importance for Women')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What determines compatibility?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df=df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"attractiveness_diff\"]=np.nan\n",
    "df[\"age_diff\"]=np.nan\n",
    "df[\"attribute_diff\"]=np.nan\n",
    "df.pid=df.pid.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for index, row in df.iterrows():\n",
    "    attractivness_diff=abs(lookup.loc[row[\"iid\"]][\"attr_o\"]-lookup.loc[row[\"pid\"]][\"attr_o\"])\n",
    "    age_diff=abs(row[\"age\"]-row[\"age_o\"])\n",
    "    attribute_diff=np.sqrt((lookup_stand.loc[row[\"pid\"]][\"attr_o\"]-df.loc[row[\"pid\"]][\"pf_o_att\"])**2\n",
    "    +(lookup_stand.loc[row[\"pid\"]][\"sinc_o\"]-df.loc[row[\"pid\"]][\"pf_o_sin\"])**2\n",
    "    +(lookup_stand.loc[row[\"pid\"]][\"intel_o\"]-df.loc[row[\"pid\"]][\"pf_o_int\"])**2\n",
    "    +(lookup_stand.loc[row[\"pid\"]][\"fun_o\"]-df.loc[row[\"pid\"]][\"pf_o_fun\"])**2\n",
    "    +(lookup_stand.loc[row[\"pid\"]][\"amb_o\"]-df.loc[row[\"pid\"]][\"pf_o_amb\"])**2\n",
    "    +(lookup_stand.loc[row[\"pid\"]][\"shar_o\"]-df.loc[row[\"pid\"]][\"pf_o_sha\"])**2)\n",
    "\n",
    "    df.set_value(index,\"attractiveness_diff\",attractivness_diff)\n",
    "    df.set_value(index,\"age_diff\",age_diff)\n",
    "    df.set_value(index,\"attribute_diff\",attribute_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>iid</th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>idg</th>\n",
       "      <th>condtn</th>\n",
       "      <th>wave</th>\n",
       "      <th>round</th>\n",
       "      <th>position</th>\n",
       "      <th>positin1</th>\n",
       "      <th>order</th>\n",
       "      <th>partner</th>\n",
       "      <th>pid</th>\n",
       "      <th>match</th>\n",
       "      <th>int_corr</th>\n",
       "      <th>samerace</th>\n",
       "      <th>age_o</th>\n",
       "      <th>race_o</th>\n",
       "      <th>pf_o_att</th>\n",
       "      <th>pf_o_sin</th>\n",
       "      <th>pf_o_int</th>\n",
       "      <th>pf_o_fun</th>\n",
       "      <th>pf_o_amb</th>\n",
       "      <th>pf_o_sha</th>\n",
       "      <th>dec_o</th>\n",
       "      <th>attr_o</th>\n",
       "      <th>sinc_o</th>\n",
       "      <th>intel_o</th>\n",
       "      <th>fun_o</th>\n",
       "      <th>amb_o</th>\n",
       "      <th>shar_o</th>\n",
       "      <th>like_o</th>\n",
       "      <th>prob_o</th>\n",
       "      <th>met_o</th>\n",
       "      <th>age</th>\n",
       "      <th>field</th>\n",
       "      <th>field_cd</th>\n",
       "      <th>undergra</th>\n",
       "      <th>mn_sat</th>\n",
       "      <th>tuition</th>\n",
       "      <th>race</th>\n",
       "      <th>imprace</th>\n",
       "      <th>imprelig</th>\n",
       "      <th>from</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>income</th>\n",
       "      <th>goal</th>\n",
       "      <th>date</th>\n",
       "      <th>go_out</th>\n",
       "      <th>career</th>\n",
       "      <th>career_c</th>\n",
       "      <th>sports</th>\n",
       "      <th>tvsports</th>\n",
       "      <th>exercise</th>\n",
       "      <th>dining</th>\n",
       "      <th>museums</th>\n",
       "      <th>art</th>\n",
       "      <th>hiking</th>\n",
       "      <th>gaming</th>\n",
       "      <th>clubbing</th>\n",
       "      <th>reading</th>\n",
       "      <th>tv</th>\n",
       "      <th>theater</th>\n",
       "      <th>movies</th>\n",
       "      <th>concerts</th>\n",
       "      <th>music</th>\n",
       "      <th>shopping</th>\n",
       "      <th>yoga</th>\n",
       "      <th>exphappy</th>\n",
       "      <th>expnum</th>\n",
       "      <th>attr1_1</th>\n",
       "      <th>sinc1_1</th>\n",
       "      <th>intel1_1</th>\n",
       "      <th>fun1_1</th>\n",
       "      <th>amb1_1</th>\n",
       "      <th>shar1_1</th>\n",
       "      <th>attr4_1</th>\n",
       "      <th>sinc4_1</th>\n",
       "      <th>intel4_1</th>\n",
       "      <th>fun4_1</th>\n",
       "      <th>amb4_1</th>\n",
       "      <th>shar4_1</th>\n",
       "      <th>attr2_1</th>\n",
       "      <th>sinc2_1</th>\n",
       "      <th>intel2_1</th>\n",
       "      <th>fun2_1</th>\n",
       "      <th>amb2_1</th>\n",
       "      <th>shar2_1</th>\n",
       "      <th>attr3_1</th>\n",
       "      <th>sinc3_1</th>\n",
       "      <th>fun3_1</th>\n",
       "      <th>intel3_1</th>\n",
       "      <th>amb3_1</th>\n",
       "      <th>attr5_1</th>\n",
       "      <th>sinc5_1</th>\n",
       "      <th>intel5_1</th>\n",
       "      <th>fun5_1</th>\n",
       "      <th>amb5_1</th>\n",
       "      <th>dec</th>\n",
       "      <th>attr</th>\n",
       "      <th>sinc</th>\n",
       "      <th>intel</th>\n",
       "      <th>fun</th>\n",
       "      <th>amb</th>\n",
       "      <th>shar</th>\n",
       "      <th>like</th>\n",
       "      <th>prob</th>\n",
       "      <th>met</th>\n",
       "      <th>match_es</th>\n",
       "      <th>attr1_s</th>\n",
       "      <th>sinc1_s</th>\n",
       "      <th>intel1_s</th>\n",
       "      <th>fun1_s</th>\n",
       "      <th>amb1_s</th>\n",
       "      <th>shar1_s</th>\n",
       "      <th>attr3_s</th>\n",
       "      <th>sinc3_s</th>\n",
       "      <th>intel3_s</th>\n",
       "      <th>fun3_s</th>\n",
       "      <th>amb3_s</th>\n",
       "      <th>satis_2</th>\n",
       "      <th>length</th>\n",
       "      <th>numdat_2</th>\n",
       "      <th>attr7_2</th>\n",
       "      <th>sinc7_2</th>\n",
       "      <th>intel7_2</th>\n",
       "      <th>fun7_2</th>\n",
       "      <th>amb7_2</th>\n",
       "      <th>shar7_2</th>\n",
       "      <th>attr1_2</th>\n",
       "      <th>sinc1_2</th>\n",
       "      <th>intel1_2</th>\n",
       "      <th>fun1_2</th>\n",
       "      <th>amb1_2</th>\n",
       "      <th>shar1_2</th>\n",
       "      <th>attr4_2</th>\n",
       "      <th>sinc4_2</th>\n",
       "      <th>intel4_2</th>\n",
       "      <th>fun4_2</th>\n",
       "      <th>amb4_2</th>\n",
       "      <th>shar4_2</th>\n",
       "      <th>attr2_2</th>\n",
       "      <th>sinc2_2</th>\n",
       "      <th>intel2_2</th>\n",
       "      <th>fun2_2</th>\n",
       "      <th>amb2_2</th>\n",
       "      <th>shar2_2</th>\n",
       "      <th>attr3_2</th>\n",
       "      <th>sinc3_2</th>\n",
       "      <th>intel3_2</th>\n",
       "      <th>fun3_2</th>\n",
       "      <th>amb3_2</th>\n",
       "      <th>attr5_2</th>\n",
       "      <th>sinc5_2</th>\n",
       "      <th>intel5_2</th>\n",
       "      <th>fun5_2</th>\n",
       "      <th>amb5_2</th>\n",
       "      <th>you_call</th>\n",
       "      <th>them_cal</th>\n",
       "      <th>date_3</th>\n",
       "      <th>numdat_3</th>\n",
       "      <th>num_in_3</th>\n",
       "      <th>attr1_3</th>\n",
       "      <th>sinc1_3</th>\n",
       "      <th>intel1_3</th>\n",
       "      <th>fun1_3</th>\n",
       "      <th>amb1_3</th>\n",
       "      <th>shar1_3</th>\n",
       "      <th>attr7_3</th>\n",
       "      <th>sinc7_3</th>\n",
       "      <th>intel7_3</th>\n",
       "      <th>fun7_3</th>\n",
       "      <th>amb7_3</th>\n",
       "      <th>shar7_3</th>\n",
       "      <th>attr4_3</th>\n",
       "      <th>sinc4_3</th>\n",
       "      <th>intel4_3</th>\n",
       "      <th>fun4_3</th>\n",
       "      <th>amb4_3</th>\n",
       "      <th>shar4_3</th>\n",
       "      <th>attr2_3</th>\n",
       "      <th>sinc2_3</th>\n",
       "      <th>intel2_3</th>\n",
       "      <th>fun2_3</th>\n",
       "      <th>amb2_3</th>\n",
       "      <th>shar2_3</th>\n",
       "      <th>attr3_3</th>\n",
       "      <th>sinc3_3</th>\n",
       "      <th>intel3_3</th>\n",
       "      <th>fun3_3</th>\n",
       "      <th>amb3_3</th>\n",
       "      <th>attr5_3</th>\n",
       "      <th>sinc5_3</th>\n",
       "      <th>intel5_3</th>\n",
       "      <th>fun5_3</th>\n",
       "      <th>amb5_3</th>\n",
       "      <th>attractiveness_diff</th>\n",
       "      <th>age_diff</th>\n",
       "      <th>attribute_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>9.295775</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Law</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60,521</td>\n",
       "      <td>69,487.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>5.277791</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.999797</td>\n",
       "      <td>12.347121</td>\n",
       "      <td>14.455799</td>\n",
       "      <td>17.931062</td>\n",
       "      <td>10.84266</td>\n",
       "      <td>12.323829</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.941908</td>\n",
       "      <td>7.927232</td>\n",
       "      <td>8.284346</td>\n",
       "      <td>7.426213</td>\n",
       "      <td>7.617611</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.635849</td>\n",
       "      <td>14.4</td>\n",
       "      <td>16.422642</td>\n",
       "      <td>14.092075</td>\n",
       "      <td>9.268868</td>\n",
       "      <td>11.329245</td>\n",
       "      <td>7.082249</td>\n",
       "      <td>7.978959</td>\n",
       "      <td>8.164499</td>\n",
       "      <td>7.612471</td>\n",
       "      <td>7.50153</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.819556</td>\n",
       "      <td>13.529923</td>\n",
       "      <td>15.293851</td>\n",
       "      <td>18.868448</td>\n",
       "      <td>7.286957</td>\n",
       "      <td>12.156028</td>\n",
       "      <td>19.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.89</td>\n",
       "      <td>22.22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>16.67</td>\n",
       "      <td>32.607951</td>\n",
       "      <td>13.502627</td>\n",
       "      <td>13.78387</td>\n",
       "      <td>17.425177</td>\n",
       "      <td>10.274617</td>\n",
       "      <td>12.642221</td>\n",
       "      <td>32.687286</td>\n",
       "      <td>13.158874</td>\n",
       "      <td>13.201295</td>\n",
       "      <td>17.984654</td>\n",
       "      <td>10.83915</td>\n",
       "      <td>12.083368</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.827964</td>\n",
       "      <td>7.394106</td>\n",
       "      <td>7.838702</td>\n",
       "      <td>7.279415</td>\n",
       "      <td>7.332191</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.230615</td>\n",
       "      <td>0.934132</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.330357</td>\n",
       "      <td>15.654266</td>\n",
       "      <td>16.679563</td>\n",
       "      <td>16.418155</td>\n",
       "      <td>7.823909</td>\n",
       "      <td>12.207837</td>\n",
       "      <td>33.600198</td>\n",
       "      <td>12.363095</td>\n",
       "      <td>13.688492</td>\n",
       "      <td>17.12996</td>\n",
       "      <td>10.260417</td>\n",
       "      <td>13.195933</td>\n",
       "      <td>32.697917</td>\n",
       "      <td>12.576885</td>\n",
       "      <td>14.229663</td>\n",
       "      <td>18.135913</td>\n",
       "      <td>10.609127</td>\n",
       "      <td>11.96627</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.81002</td>\n",
       "      <td>7.615079</td>\n",
       "      <td>7.93254</td>\n",
       "      <td>7.155258</td>\n",
       "      <td>7.048611</td>\n",
       "      <td>1.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>64.462203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>9.295775</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Law</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60,521</td>\n",
       "      <td>69,487.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>5.277791</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.999797</td>\n",
       "      <td>12.347121</td>\n",
       "      <td>14.455799</td>\n",
       "      <td>17.931062</td>\n",
       "      <td>10.84266</td>\n",
       "      <td>12.323829</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.941908</td>\n",
       "      <td>7.927232</td>\n",
       "      <td>8.284346</td>\n",
       "      <td>7.426213</td>\n",
       "      <td>7.617611</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.635849</td>\n",
       "      <td>14.4</td>\n",
       "      <td>16.422642</td>\n",
       "      <td>14.092075</td>\n",
       "      <td>9.268868</td>\n",
       "      <td>11.329245</td>\n",
       "      <td>7.082249</td>\n",
       "      <td>7.978959</td>\n",
       "      <td>8.164499</td>\n",
       "      <td>7.612471</td>\n",
       "      <td>7.50153</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.819556</td>\n",
       "      <td>13.529923</td>\n",
       "      <td>15.293851</td>\n",
       "      <td>18.868448</td>\n",
       "      <td>7.286957</td>\n",
       "      <td>12.156028</td>\n",
       "      <td>19.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.89</td>\n",
       "      <td>22.22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>16.67</td>\n",
       "      <td>32.607951</td>\n",
       "      <td>13.502627</td>\n",
       "      <td>13.78387</td>\n",
       "      <td>17.425177</td>\n",
       "      <td>10.274617</td>\n",
       "      <td>12.642221</td>\n",
       "      <td>32.687286</td>\n",
       "      <td>13.158874</td>\n",
       "      <td>13.201295</td>\n",
       "      <td>17.984654</td>\n",
       "      <td>10.83915</td>\n",
       "      <td>12.083368</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.827964</td>\n",
       "      <td>7.394106</td>\n",
       "      <td>7.838702</td>\n",
       "      <td>7.279415</td>\n",
       "      <td>7.332191</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.230615</td>\n",
       "      <td>0.934132</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.330357</td>\n",
       "      <td>15.654266</td>\n",
       "      <td>16.679563</td>\n",
       "      <td>16.418155</td>\n",
       "      <td>7.823909</td>\n",
       "      <td>12.207837</td>\n",
       "      <td>33.600198</td>\n",
       "      <td>12.363095</td>\n",
       "      <td>13.688492</td>\n",
       "      <td>17.12996</td>\n",
       "      <td>10.260417</td>\n",
       "      <td>13.195933</td>\n",
       "      <td>32.697917</td>\n",
       "      <td>12.576885</td>\n",
       "      <td>14.229663</td>\n",
       "      <td>18.135913</td>\n",
       "      <td>10.609127</td>\n",
       "      <td>11.96627</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.81002</td>\n",
       "      <td>7.615079</td>\n",
       "      <td>7.93254</td>\n",
       "      <td>7.155258</td>\n",
       "      <td>7.048611</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.301978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>9.295775</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Law</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60,521</td>\n",
       "      <td>69,487.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>5.277791</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.999797</td>\n",
       "      <td>12.347121</td>\n",
       "      <td>14.455799</td>\n",
       "      <td>17.931062</td>\n",
       "      <td>10.84266</td>\n",
       "      <td>12.323829</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.941908</td>\n",
       "      <td>7.927232</td>\n",
       "      <td>8.284346</td>\n",
       "      <td>7.426213</td>\n",
       "      <td>7.617611</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.207523</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.635849</td>\n",
       "      <td>14.4</td>\n",
       "      <td>16.422642</td>\n",
       "      <td>14.092075</td>\n",
       "      <td>9.268868</td>\n",
       "      <td>11.329245</td>\n",
       "      <td>7.082249</td>\n",
       "      <td>7.978959</td>\n",
       "      <td>8.164499</td>\n",
       "      <td>7.612471</td>\n",
       "      <td>7.50153</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.819556</td>\n",
       "      <td>13.529923</td>\n",
       "      <td>15.293851</td>\n",
       "      <td>18.868448</td>\n",
       "      <td>7.286957</td>\n",
       "      <td>12.156028</td>\n",
       "      <td>19.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.89</td>\n",
       "      <td>22.22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>16.67</td>\n",
       "      <td>32.607951</td>\n",
       "      <td>13.502627</td>\n",
       "      <td>13.78387</td>\n",
       "      <td>17.425177</td>\n",
       "      <td>10.274617</td>\n",
       "      <td>12.642221</td>\n",
       "      <td>32.687286</td>\n",
       "      <td>13.158874</td>\n",
       "      <td>13.201295</td>\n",
       "      <td>17.984654</td>\n",
       "      <td>10.83915</td>\n",
       "      <td>12.083368</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.827964</td>\n",
       "      <td>7.394106</td>\n",
       "      <td>7.838702</td>\n",
       "      <td>7.279415</td>\n",
       "      <td>7.332191</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.230615</td>\n",
       "      <td>0.934132</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.330357</td>\n",
       "      <td>15.654266</td>\n",
       "      <td>16.679563</td>\n",
       "      <td>16.418155</td>\n",
       "      <td>7.823909</td>\n",
       "      <td>12.207837</td>\n",
       "      <td>33.600198</td>\n",
       "      <td>12.363095</td>\n",
       "      <td>13.688492</td>\n",
       "      <td>17.12996</td>\n",
       "      <td>10.260417</td>\n",
       "      <td>13.195933</td>\n",
       "      <td>32.697917</td>\n",
       "      <td>12.576885</td>\n",
       "      <td>14.229663</td>\n",
       "      <td>18.135913</td>\n",
       "      <td>10.609127</td>\n",
       "      <td>11.96627</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.81002</td>\n",
       "      <td>7.615079</td>\n",
       "      <td>7.93254</td>\n",
       "      <td>7.155258</td>\n",
       "      <td>7.048611</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.843133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>9.295775</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Law</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60,521</td>\n",
       "      <td>69,487.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>5.277791</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.999797</td>\n",
       "      <td>12.347121</td>\n",
       "      <td>14.455799</td>\n",
       "      <td>17.931062</td>\n",
       "      <td>10.84266</td>\n",
       "      <td>12.323829</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.941908</td>\n",
       "      <td>7.927232</td>\n",
       "      <td>8.284346</td>\n",
       "      <td>7.426213</td>\n",
       "      <td>7.617611</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.635849</td>\n",
       "      <td>14.4</td>\n",
       "      <td>16.422642</td>\n",
       "      <td>14.092075</td>\n",
       "      <td>9.268868</td>\n",
       "      <td>11.329245</td>\n",
       "      <td>7.082249</td>\n",
       "      <td>7.978959</td>\n",
       "      <td>8.164499</td>\n",
       "      <td>7.612471</td>\n",
       "      <td>7.50153</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.819556</td>\n",
       "      <td>13.529923</td>\n",
       "      <td>15.293851</td>\n",
       "      <td>18.868448</td>\n",
       "      <td>7.286957</td>\n",
       "      <td>12.156028</td>\n",
       "      <td>19.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.89</td>\n",
       "      <td>22.22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>16.67</td>\n",
       "      <td>32.607951</td>\n",
       "      <td>13.502627</td>\n",
       "      <td>13.78387</td>\n",
       "      <td>17.425177</td>\n",
       "      <td>10.274617</td>\n",
       "      <td>12.642221</td>\n",
       "      <td>32.687286</td>\n",
       "      <td>13.158874</td>\n",
       "      <td>13.201295</td>\n",
       "      <td>17.984654</td>\n",
       "      <td>10.83915</td>\n",
       "      <td>12.083368</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.827964</td>\n",
       "      <td>7.394106</td>\n",
       "      <td>7.838702</td>\n",
       "      <td>7.279415</td>\n",
       "      <td>7.332191</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.230615</td>\n",
       "      <td>0.934132</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.330357</td>\n",
       "      <td>15.654266</td>\n",
       "      <td>16.679563</td>\n",
       "      <td>16.418155</td>\n",
       "      <td>7.823909</td>\n",
       "      <td>12.207837</td>\n",
       "      <td>33.600198</td>\n",
       "      <td>12.363095</td>\n",
       "      <td>13.688492</td>\n",
       "      <td>17.12996</td>\n",
       "      <td>10.260417</td>\n",
       "      <td>13.195933</td>\n",
       "      <td>32.697917</td>\n",
       "      <td>12.576885</td>\n",
       "      <td>14.229663</td>\n",
       "      <td>18.135913</td>\n",
       "      <td>10.609127</td>\n",
       "      <td>11.96627</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.81002</td>\n",
       "      <td>7.615079</td>\n",
       "      <td>7.93254</td>\n",
       "      <td>7.155258</td>\n",
       "      <td>7.048611</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.727721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>9.295775</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Law</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60,521</td>\n",
       "      <td>69,487.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>5.277791</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.999797</td>\n",
       "      <td>12.347121</td>\n",
       "      <td>14.455799</td>\n",
       "      <td>17.931062</td>\n",
       "      <td>10.84266</td>\n",
       "      <td>12.323829</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.941908</td>\n",
       "      <td>7.927232</td>\n",
       "      <td>8.284346</td>\n",
       "      <td>7.426213</td>\n",
       "      <td>7.617611</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.635849</td>\n",
       "      <td>14.4</td>\n",
       "      <td>16.422642</td>\n",
       "      <td>14.092075</td>\n",
       "      <td>9.268868</td>\n",
       "      <td>11.329245</td>\n",
       "      <td>7.082249</td>\n",
       "      <td>7.978959</td>\n",
       "      <td>8.164499</td>\n",
       "      <td>7.612471</td>\n",
       "      <td>7.50153</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.819556</td>\n",
       "      <td>13.529923</td>\n",
       "      <td>15.293851</td>\n",
       "      <td>18.868448</td>\n",
       "      <td>7.286957</td>\n",
       "      <td>12.156028</td>\n",
       "      <td>19.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.89</td>\n",
       "      <td>22.22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>16.67</td>\n",
       "      <td>32.607951</td>\n",
       "      <td>13.502627</td>\n",
       "      <td>13.78387</td>\n",
       "      <td>17.425177</td>\n",
       "      <td>10.274617</td>\n",
       "      <td>12.642221</td>\n",
       "      <td>32.687286</td>\n",
       "      <td>13.158874</td>\n",
       "      <td>13.201295</td>\n",
       "      <td>17.984654</td>\n",
       "      <td>10.83915</td>\n",
       "      <td>12.083368</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.827964</td>\n",
       "      <td>7.394106</td>\n",
       "      <td>7.838702</td>\n",
       "      <td>7.279415</td>\n",
       "      <td>7.332191</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.230615</td>\n",
       "      <td>0.934132</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.330357</td>\n",
       "      <td>15.654266</td>\n",
       "      <td>16.679563</td>\n",
       "      <td>16.418155</td>\n",
       "      <td>7.823909</td>\n",
       "      <td>12.207837</td>\n",
       "      <td>33.600198</td>\n",
       "      <td>12.363095</td>\n",
       "      <td>13.688492</td>\n",
       "      <td>17.12996</td>\n",
       "      <td>10.260417</td>\n",
       "      <td>13.195933</td>\n",
       "      <td>32.697917</td>\n",
       "      <td>12.576885</td>\n",
       "      <td>14.229663</td>\n",
       "      <td>18.135913</td>\n",
       "      <td>10.609127</td>\n",
       "      <td>11.96627</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.81002</td>\n",
       "      <td>7.615079</td>\n",
       "      <td>7.93254</td>\n",
       "      <td>7.155258</td>\n",
       "      <td>7.048611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>46.024711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iid   id  gender  idg  condtn  wave  round  position  positin1  order  \\\n",
       "0    1  1.0       0    1       1     1     10         7  9.295775      4   \n",
       "1    1  1.0       0    1       1     1     10         7  9.295775      3   \n",
       "2    1  1.0       0    1       1     1     10         7  9.295775     10   \n",
       "3    1  1.0       0    1       1     1     10         7  9.295775      5   \n",
       "4    1  1.0       0    1       1     1     10         7  9.295775      7   \n",
       "\n",
       "   partner  pid  match  int_corr  samerace  age_o  race_o  pf_o_att  pf_o_sin  \\\n",
       "0        1   11      0      0.14         0   27.0     2.0      35.0      20.0   \n",
       "1        2   12      0      0.54         0   22.0     2.0      60.0       0.0   \n",
       "2        3   13      1      0.16         1   22.0     4.0      19.0      18.0   \n",
       "3        4   14      1      0.61         0   23.0     2.0      30.0       5.0   \n",
       "4        5   15      1      0.21         0   24.0     3.0      30.0      10.0   \n",
       "\n",
       "   pf_o_int  pf_o_fun  pf_o_amb  pf_o_sha  dec_o  attr_o  sinc_o  intel_o  \\\n",
       "0      20.0      20.0       0.0       5.0      0     6.0     8.0      8.0   \n",
       "1       0.0      40.0       0.0       0.0      0     7.0     8.0     10.0   \n",
       "2      19.0      18.0      14.0      12.0      1    10.0    10.0     10.0   \n",
       "3      15.0      40.0       5.0       5.0      1     7.0     8.0      9.0   \n",
       "4      20.0      10.0      10.0      20.0      1     8.0     7.0      9.0   \n",
       "\n",
       "   fun_o  amb_o  shar_o  like_o  prob_o  met_o   age field  field_cd undergra  \\\n",
       "0    8.0    8.0     6.0     7.0     4.0    2.0  21.0   Law       1.0      NaN   \n",
       "1    7.0    7.0     5.0     8.0     4.0    2.0  21.0   Law       1.0      NaN   \n",
       "2   10.0   10.0    10.0    10.0    10.0    1.0  21.0   Law       1.0      NaN   \n",
       "3    8.0    9.0     8.0     7.0     7.0    2.0  21.0   Law       1.0      NaN   \n",
       "4    6.0    9.0     7.0     8.0     6.0    2.0  21.0   Law       1.0      NaN   \n",
       "\n",
       "  mn_sat tuition  race  imprace  imprelig     from zipcode     income  goal  \\\n",
       "0    NaN     NaN   4.0      2.0       4.0  Chicago  60,521  69,487.00   2.0   \n",
       "1    NaN     NaN   4.0      2.0       4.0  Chicago  60,521  69,487.00   2.0   \n",
       "2    NaN     NaN   4.0      2.0       4.0  Chicago  60,521  69,487.00   2.0   \n",
       "3    NaN     NaN   4.0      2.0       4.0  Chicago  60,521  69,487.00   2.0   \n",
       "4    NaN     NaN   4.0      2.0       4.0  Chicago  60,521  69,487.00   2.0   \n",
       "\n",
       "   date  go_out  career  career_c  sports  tvsports  exercise  dining  \\\n",
       "0   7.0     1.0  lawyer  5.277791     9.0       2.0       8.0     9.0   \n",
       "1   7.0     1.0  lawyer  5.277791     9.0       2.0       8.0     9.0   \n",
       "2   7.0     1.0  lawyer  5.277791     9.0       2.0       8.0     9.0   \n",
       "3   7.0     1.0  lawyer  5.277791     9.0       2.0       8.0     9.0   \n",
       "4   7.0     1.0  lawyer  5.277791     9.0       2.0       8.0     9.0   \n",
       "\n",
       "   museums  art  hiking  gaming  clubbing  reading   tv  theater  movies  \\\n",
       "0      1.0  1.0     5.0     1.0       5.0      6.0  9.0      1.0    10.0   \n",
       "1      1.0  1.0     5.0     1.0       5.0      6.0  9.0      1.0    10.0   \n",
       "2      1.0  1.0     5.0     1.0       5.0      6.0  9.0      1.0    10.0   \n",
       "3      1.0  1.0     5.0     1.0       5.0      6.0  9.0      1.0    10.0   \n",
       "4      1.0  1.0     5.0     1.0       5.0      6.0  9.0      1.0    10.0   \n",
       "\n",
       "   concerts  music  shopping  yoga  exphappy  expnum  attr1_1  sinc1_1  \\\n",
       "0      10.0    9.0       8.0   1.0       3.0     2.0     15.0     20.0   \n",
       "1      10.0    9.0       8.0   1.0       3.0     2.0     15.0     20.0   \n",
       "2      10.0    9.0       8.0   1.0       3.0     2.0     15.0     20.0   \n",
       "3      10.0    9.0       8.0   1.0       3.0     2.0     15.0     20.0   \n",
       "4      10.0    9.0       8.0   1.0       3.0     2.0     15.0     20.0   \n",
       "\n",
       "   intel1_1  fun1_1  amb1_1  shar1_1    attr4_1    sinc4_1   intel4_1  \\\n",
       "0      20.0    15.0    15.0     15.0  31.999797  12.347121  14.455799   \n",
       "1      20.0    15.0    15.0     15.0  31.999797  12.347121  14.455799   \n",
       "2      20.0    15.0    15.0     15.0  31.999797  12.347121  14.455799   \n",
       "3      20.0    15.0    15.0     15.0  31.999797  12.347121  14.455799   \n",
       "4      20.0    15.0    15.0     15.0  31.999797  12.347121  14.455799   \n",
       "\n",
       "      fun4_1    amb4_1    shar4_1  attr2_1  sinc2_1  intel2_1  fun2_1  amb2_1  \\\n",
       "0  17.931062  10.84266  12.323829     35.0     20.0      15.0    20.0     5.0   \n",
       "1  17.931062  10.84266  12.323829     35.0     20.0      15.0    20.0     5.0   \n",
       "2  17.931062  10.84266  12.323829     35.0     20.0      15.0    20.0     5.0   \n",
       "3  17.931062  10.84266  12.323829     35.0     20.0      15.0    20.0     5.0   \n",
       "4  17.931062  10.84266  12.323829     35.0     20.0      15.0    20.0     5.0   \n",
       "\n",
       "   shar2_1  attr3_1  sinc3_1  fun3_1  intel3_1  amb3_1   attr5_1   sinc5_1  \\\n",
       "0      5.0      6.0      8.0     8.0       8.0     7.0  6.941908  7.927232   \n",
       "1      5.0      6.0      8.0     8.0       8.0     7.0  6.941908  7.927232   \n",
       "2      5.0      6.0      8.0     8.0       8.0     7.0  6.941908  7.927232   \n",
       "3      5.0      6.0      8.0     8.0       8.0     7.0  6.941908  7.927232   \n",
       "4      5.0      6.0      8.0     8.0       8.0     7.0  6.941908  7.927232   \n",
       "\n",
       "   intel5_1    fun5_1    amb5_1  dec  attr  sinc  intel  fun  amb  shar  like  \\\n",
       "0  8.284346  7.426213  7.617611    1   6.0   9.0    7.0  7.0  6.0   5.0   7.0   \n",
       "1  8.284346  7.426213  7.617611    1   7.0   8.0    7.0  8.0  5.0   6.0   7.0   \n",
       "2  8.284346  7.426213  7.617611    1   5.0   8.0    9.0  8.0  5.0   7.0   7.0   \n",
       "3  8.284346  7.426213  7.617611    1   7.0   6.0    8.0  7.0  6.0   8.0   7.0   \n",
       "4  8.284346  7.426213  7.617611    1   5.0   6.0    7.0  7.0  6.0   6.0   6.0   \n",
       "\n",
       "       prob  met  match_es    attr1_s  sinc1_s   intel1_s     fun1_s  \\\n",
       "0  6.000000  2.0       4.0  22.635849     14.4  16.422642  14.092075   \n",
       "1  5.000000  1.0       4.0  22.635849     14.4  16.422642  14.092075   \n",
       "2  5.207523  1.0       4.0  22.635849     14.4  16.422642  14.092075   \n",
       "3  6.000000  2.0       4.0  22.635849     14.4  16.422642  14.092075   \n",
       "4  6.000000  2.0       4.0  22.635849     14.4  16.422642  14.092075   \n",
       "\n",
       "     amb1_s    shar1_s   attr3_s   sinc3_s  intel3_s    fun3_s   amb3_s  \\\n",
       "0  9.268868  11.329245  7.082249  7.978959  8.164499  7.612471  7.50153   \n",
       "1  9.268868  11.329245  7.082249  7.978959  8.164499  7.612471  7.50153   \n",
       "2  9.268868  11.329245  7.082249  7.978959  8.164499  7.612471  7.50153   \n",
       "3  9.268868  11.329245  7.082249  7.978959  8.164499  7.612471  7.50153   \n",
       "4  9.268868  11.329245  7.082249  7.978959  8.164499  7.612471  7.50153   \n",
       "\n",
       "   satis_2  length  numdat_2    attr7_2    sinc7_2   intel7_2     fun7_2  \\\n",
       "0      6.0     2.0       1.0  32.819556  13.529923  15.293851  18.868448   \n",
       "1      6.0     2.0       1.0  32.819556  13.529923  15.293851  18.868448   \n",
       "2      6.0     2.0       1.0  32.819556  13.529923  15.293851  18.868448   \n",
       "3      6.0     2.0       1.0  32.819556  13.529923  15.293851  18.868448   \n",
       "4      6.0     2.0       1.0  32.819556  13.529923  15.293851  18.868448   \n",
       "\n",
       "     amb7_2    shar7_2  attr1_2  sinc1_2  intel1_2  fun1_2  amb1_2  shar1_2  \\\n",
       "0  7.286957  12.156028    19.44    16.67     13.89   22.22   11.11    16.67   \n",
       "1  7.286957  12.156028    19.44    16.67     13.89   22.22   11.11    16.67   \n",
       "2  7.286957  12.156028    19.44    16.67     13.89   22.22   11.11    16.67   \n",
       "3  7.286957  12.156028    19.44    16.67     13.89   22.22   11.11    16.67   \n",
       "4  7.286957  12.156028    19.44    16.67     13.89   22.22   11.11    16.67   \n",
       "\n",
       "     attr4_2    sinc4_2  intel4_2     fun4_2     amb4_2    shar4_2    attr2_2  \\\n",
       "0  32.607951  13.502627  13.78387  17.425177  10.274617  12.642221  32.687286   \n",
       "1  32.607951  13.502627  13.78387  17.425177  10.274617  12.642221  32.687286   \n",
       "2  32.607951  13.502627  13.78387  17.425177  10.274617  12.642221  32.687286   \n",
       "3  32.607951  13.502627  13.78387  17.425177  10.274617  12.642221  32.687286   \n",
       "4  32.607951  13.502627  13.78387  17.425177  10.274617  12.642221  32.687286   \n",
       "\n",
       "     sinc2_2   intel2_2     fun2_2    amb2_2    shar2_2  attr3_2  sinc3_2  \\\n",
       "0  13.158874  13.201295  17.984654  10.83915  12.083368      6.0      7.0   \n",
       "1  13.158874  13.201295  17.984654  10.83915  12.083368      6.0      7.0   \n",
       "2  13.158874  13.201295  17.984654  10.83915  12.083368      6.0      7.0   \n",
       "3  13.158874  13.201295  17.984654  10.83915  12.083368      6.0      7.0   \n",
       "4  13.158874  13.201295  17.984654  10.83915  12.083368      6.0      7.0   \n",
       "\n",
       "   intel3_2  fun3_2  amb3_2   attr5_2   sinc5_2  intel5_2    fun5_2    amb5_2  \\\n",
       "0       8.0     7.0     6.0  6.827964  7.394106  7.838702  7.279415  7.332191   \n",
       "1       8.0     7.0     6.0  6.827964  7.394106  7.838702  7.279415  7.332191   \n",
       "2       8.0     7.0     6.0  6.827964  7.394106  7.838702  7.279415  7.332191   \n",
       "3       8.0     7.0     6.0  6.827964  7.394106  7.838702  7.279415  7.332191   \n",
       "4       8.0     7.0     6.0  6.827964  7.394106  7.838702  7.279415  7.332191   \n",
       "\n",
       "   you_call  them_cal  date_3  numdat_3  num_in_3  attr1_3  sinc1_3  intel1_3  \\\n",
       "0       1.0       1.0     0.0  1.230615  0.934132     15.0     20.0      20.0   \n",
       "1       1.0       1.0     0.0  1.230615  0.934132     15.0     20.0      20.0   \n",
       "2       1.0       1.0     0.0  1.230615  0.934132     15.0     20.0      20.0   \n",
       "3       1.0       1.0     0.0  1.230615  0.934132     15.0     20.0      20.0   \n",
       "4       1.0       1.0     0.0  1.230615  0.934132     15.0     20.0      20.0   \n",
       "\n",
       "   fun1_3  amb1_3  shar1_3    attr7_3    sinc7_3   intel7_3     fun7_3  \\\n",
       "0    15.0    15.0     15.0  31.330357  15.654266  16.679563  16.418155   \n",
       "1    15.0    15.0     15.0  31.330357  15.654266  16.679563  16.418155   \n",
       "2    15.0    15.0     15.0  31.330357  15.654266  16.679563  16.418155   \n",
       "3    15.0    15.0     15.0  31.330357  15.654266  16.679563  16.418155   \n",
       "4    15.0    15.0     15.0  31.330357  15.654266  16.679563  16.418155   \n",
       "\n",
       "     amb7_3    shar7_3    attr4_3    sinc4_3   intel4_3    fun4_3     amb4_3  \\\n",
       "0  7.823909  12.207837  33.600198  12.363095  13.688492  17.12996  10.260417   \n",
       "1  7.823909  12.207837  33.600198  12.363095  13.688492  17.12996  10.260417   \n",
       "2  7.823909  12.207837  33.600198  12.363095  13.688492  17.12996  10.260417   \n",
       "3  7.823909  12.207837  33.600198  12.363095  13.688492  17.12996  10.260417   \n",
       "4  7.823909  12.207837  33.600198  12.363095  13.688492  17.12996  10.260417   \n",
       "\n",
       "     shar4_3    attr2_3    sinc2_3   intel2_3     fun2_3     amb2_3   shar2_3  \\\n",
       "0  13.195933  32.697917  12.576885  14.229663  18.135913  10.609127  11.96627   \n",
       "1  13.195933  32.697917  12.576885  14.229663  18.135913  10.609127  11.96627   \n",
       "2  13.195933  32.697917  12.576885  14.229663  18.135913  10.609127  11.96627   \n",
       "3  13.195933  32.697917  12.576885  14.229663  18.135913  10.609127  11.96627   \n",
       "4  13.195933  32.697917  12.576885  14.229663  18.135913  10.609127  11.96627   \n",
       "\n",
       "   attr3_3  sinc3_3  intel3_3  fun3_3  amb3_3  attr5_3   sinc5_3  intel5_3  \\\n",
       "0      5.0      7.0       7.0     7.0     7.0  6.81002  7.615079   7.93254   \n",
       "1      5.0      7.0       7.0     7.0     7.0  6.81002  7.615079   7.93254   \n",
       "2      5.0      7.0       7.0     7.0     7.0  6.81002  7.615079   7.93254   \n",
       "3      5.0      7.0       7.0     7.0     7.0  6.81002  7.615079   7.93254   \n",
       "4      5.0      7.0       7.0     7.0     7.0  6.81002  7.615079   7.93254   \n",
       "\n",
       "     fun5_3    amb5_3  attractiveness_diff  age_diff  attribute_diff  \n",
       "0  7.155258  7.048611                  1.1       6.0       64.462203  \n",
       "1  7.155258  7.048611                  0.4       1.0        5.301978  \n",
       "2  7.155258  7.048611                  1.9       1.0       35.843133  \n",
       "3  7.155258  7.048611                  1.5       2.0       16.727721  \n",
       "4  7.155258  7.048611                  1.0       3.0       46.024711  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score: 0.724066395246\n"
     ]
    }
   ],
   "source": [
    "y=df[\"match\"]\n",
    "x=df[[\"imprace\",\"imprelig\",\"date\",\"age_diff\",\"go_out\",\"goal\",\"attribute_diff\",\"attr3_1\",\"sinc3_1\",\"fun3_1\",\"intel3_1\",\"amb3_1\",'shar_o']]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)\n",
    "gb=GradientBoostingClassifier()\n",
    "gb.fit(x_train,y_train)\n",
    "\n",
    "prob=gb.predict_proba(x_test)\n",
    "output=[]\n",
    "for x in prob:\n",
    "    output.append(x[1])\n",
    "print \"AUC Score: \"+ str(roc_auc_score(y_true=y_test,y_score=output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance in compatibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ai4DDgXMlbZRZ/m7gdcDwT7v9H7AjsAWwBDgJnr3C/yVwHbBlWufqdJ3DgNeS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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f268790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df[[\"imprace\",\"imprelig\",\"date\",\"age_diff\",\"go_out\",\"goal\",\"attribute_diff\",\"attr3_1\",\"sinc3_1\",\"fun3_1\",\"intel3_1\",\"amb3_1\",'shar_o']]\n",
    "feature_importance = gb.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, x[sorted_idx],size=\"small\")\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.title('Attribute Importance for a match')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score: 0.733533512441\n"
     ]
    }
   ],
   "source": [
    "y=df[\"match\"]\n",
    "x=df[[\"imprace\",\"imprelig\",\"date\",\"age_diff\",\"go_out\",\"goal\",\"attribute_diff\",\"attr3_1\",\"sinc3_1\",\"fun3_1\",\"intel3_1\",\"amb3_1\",'shar_o',\"attractiveness_diff\"]]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)\n",
    "gb=GradientBoostingClassifier()\n",
    "gb.fit(x_train,y_train)\n",
    "\n",
    "prob=gb.predict_proba(x_test)\n",
    "output2=[]\n",
    "for x in prob:\n",
    "    output2.append(x[1])\n",
    "print \"AUC Score: \"+ str(roc_auc_score(y_true=y_test,y_score=output2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance in compatibility with attractiveness_diff added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YmZlNICcYw4iIO4BTgT8C9wB3A6vT7g5gX+AR4MNkCz6fSsf9FPgB8AtJy4Gr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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11728c610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df[[\"imprace\",\"imprelig\",\"date\",\"age_diff\",\"go_out\",\"goal\",\"attribute_diff\",\"attr3_1\",\"sinc3_1\",\"fun3_1\",\"intel3_1\",\"amb3_1\",'shar_o',\"attractiveness_diff\"]]\n",
    "feature_importance = gb.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, x[sorted_idx],size=\"small\")\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.title('Attribute Importance for Both')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AUC Curve plotted with with and without attractiveness_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nZ/x75x1o3TroaAQlViIiIpItCgrgf/8rV+QczJwJGzbEPuWg5YXMOfKPvH/8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E2aNOGrr77ik08+SVdeukWLFvj5+TFjxgxOnTpFmTJl6Nq1K1WqVCE8PJz58+czZcoU6tevT9Wq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\nMcYYk0f/H8oHqmI71N42AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116bc7090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "from matplotlib.pylab import rcParams\n",
    "\n",
    "rcParams['figure.figsize'] = 10, 8\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_true=y_test,y_score=output)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "false_positive_rate2, true_positive_rate2, thresholds2 = roc_curve(y_true=y_test,y_score=output2)\n",
    "roc_auc2 = auc(false_positive_rate2, true_positive_rate2)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate, true_positive_rate, 'b', label='AUC w/o attractivness_diff = %0.3f'% roc_auc)\n",
    "plt.plot(false_positive_rate2, true_positive_rate2, 'r', label='AUC with attractivness_diff = %0.3f'% roc_auc2)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling the effect of attractivness_diff on match rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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w1ltlVydJkiTVPsOXWmXddeGCC/IU9dtsA1/+Mnz84/maYStWlF2dJEmSVLsM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\nK6yzDtx8M2y/fQ5gt94K739/2VVJktQ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BvffmMWEXXgjrrJPL2GWXwdtvF51SklSE1ky4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\nmZl1gJMvMzMzMzOzDvgJnkkW7nCTSkwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116bf4790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[]\n",
    "y=[]\n",
    "for j in range (0,5):\n",
    "    avg=0\n",
    "    for i in range (0,8377):\n",
    "        test_person=df[[\"imprace\",\"imprelig\",\"date\",\"age_diff\",\"go_out\",\"goal\",\"attribute_diff\",\"attr3_1\",\"sinc3_1\",\"fun3_1\",\"intel3_1\",\"amb3_1\",'shar_o',\"attractiveness_diff\"]].iloc[i]\n",
    "        test_person[\"attractiveness_diff\"]=j\n",
    "        avg=avg+gb.predict_proba(test_person.reshape(1,-1))[0][1]\n",
    "    y.append(float(avg/8377))\n",
    "    x.append(j)\n",
    "plt.title(\"Effect on match probability of attractivness_diff\")\n",
    "plt.xlabel(\"Attractivness Difference\")\n",
    "plt.ylabel(\"Match Probability\")\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling the effect of attribute_diff on match rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QxxFNZxat84GFgQsq2/wYeIh67cnJDSemA4Ma2adjm/narV1v/rDK/Potwy8GJtebtwfw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\nmSRJkiQVZjGTJEmSpML+P1Yl6NYVWTMQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f268f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[]\n",
    "y=[]\n",
    "for j in range (0,5):\n",
    "    avg=0\n",
    "    for i in range (0,8377):\n",
    "        test_person=df[[\"imprace\",\"imprelig\",\"date\",\"age_diff\",\"go_out\",\"goal\",\"attribute_diff\",\"attr3_1\",\"sinc3_1\",\"fun3_1\",\"intel3_1\",\"amb3_1\",'shar_o',\"attractiveness_diff\"]].iloc[i]\n",
    "        test_person[\"attribute_diff\"]=j*10\n",
    "        avg=avg+gb.predict_proba(test_person.reshape(1,-1))[0][1]\n",
    "    y.append(float(avg/8377))\n",
    "    x.append(j*10)\n",
    "plt.title(\"Effect on match probability of attribute_diff\")\n",
    "plt.xlabel(\"Attribute Difference\")\n",
    "plt.ylabel(\"Match Probability\")\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}