classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#import seaborn\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier \n",
    "from sklearn import svm\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error\n",
    "from sklearn.cross_validation import cross_val_score, KFold, train_test_split\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import os.path\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe data ###\n",
    "\n",
    "None Variable: 2\n",
    "Quasar: 3\n",
    "Be Stars: 4\n",
    "Cepheid: 5\n",
    "RR Lyrae: 6\n",
    "Eclipsing Binaries: 7\n",
    "MicroLensing: 8\n",
    "Long Periodic Variable: 9\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path = path = os.getcwd()\n",
    "path = path + '/MACHO_ts/'\n",
    "\n",
    "for idx,j in enumerate(os.listdir(path)):\n",
    "# for index i,j in os.listdir(path):\n",
    "    if j.endswith('.csv'):\n",
    "        if idx == 0:\n",
    "            data = pd.read_csv(path+j,index_col=0)\n",
    "            data['Class'] = idx + np.ones(len(data))\n",
    "\n",
    "        else:        \n",
    "            data2 = pd.read_csv(path+j,index_col=0)\n",
    "            data2['Class'] = idx + np.ones(len(data2))\n",
    "            data = pd.concat([data, data2])\n",
    "\n",
    "data = data.drop('Automean', 1)\n",
    "data.to_csv('MACHO_ts.csv') \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>AndersonDarling</th>\n",
       "      <th>Autocor_length</th>\n",
       "      <th>Beyond1Std</th>\n",
       "      <th>CAR_sigma</th>\n",
       "      <th>CAR_tau</th>\n",
       "      <th>CAR_tmean</th>\n",
       "      <th>Color</th>\n",
       "      <th>Con</th>\n",
       "      <th>Eta_color</th>\n",
       "      <th>...</th>\n",
       "      <th>Rcs</th>\n",
       "      <th>Skew</th>\n",
       "      <th>SlottedA_length</th>\n",
       "      <th>SmallKurtosis</th>\n",
       "      <th>Std</th>\n",
       "      <th>StetsonJ</th>\n",
       "      <th>StetsonK</th>\n",
       "      <th>StetsonK_AC</th>\n",
       "      <th>StetsonL</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80.6708.5218</th>\n",
       "      <td> 0.12700</td>\n",
       "      <td> 1</td>\n",
       "      <td>  44</td>\n",
       "      <td> 0.278136</td>\n",
       "      <td>-0.013622</td>\n",
       "      <td>  37.158804</td>\n",
       "      <td>-0.195121</td>\n",
       "      <td>-0.356996</td>\n",
       "      <td> 0.004307</td>\n",
       "      <td> 4478.196740</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.275917</td>\n",
       "      <td> 0.512233</td>\n",
       "      <td>   0.026157</td>\n",
       "      <td> 1.389718</td>\n",
       "      <td> 0.063573</td>\n",
       "      <td> 0.439091</td>\n",
       "      <td> 0.763764</td>\n",
       "      <td> 0.816338</td>\n",
       "      <td> 0.274887</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.2742.1409</th>\n",
       "      <td> 0.16525</td>\n",
       "      <td> 1</td>\n",
       "      <td>   1</td>\n",
       "      <td> 0.185185</td>\n",
       "      <td>-0.053792</td>\n",
       "      <td>   3.563116</td>\n",
       "      <td>-1.814895</td>\n",
       "      <td>-0.520699</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td>   16.673054</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.207167</td>\n",
       "      <td>-1.929378</td>\n",
       "      <td>   1.024734</td>\n",
       "      <td> 7.272569</td>\n",
       "      <td> 0.076998</td>\n",
       "      <td> 0.006110</td>\n",
       "      <td> 0.673988</td>\n",
       "      <td> 0.721044</td>\n",
       "      <td> 0.003726</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53.3970.353</th>\n",
       "      <td> 0.14600</td>\n",
       "      <td> 1</td>\n",
       "      <td>  19</td>\n",
       "      <td> 0.291367</td>\n",
       "      <td>-0.003886</td>\n",
       "      <td> 490.231454</td>\n",
       "      <td>-0.010910</td>\n",
       "      <td>-0.435427</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 2628.279811</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.300025</td>\n",
       "      <td> 0.387355</td>\n",
       "      <td>  15.233322</td>\n",
       "      <td> 0.318671</td>\n",
       "      <td> 0.072600</td>\n",
       "      <td> 0.399034</td>\n",
       "      <td> 0.769279</td>\n",
       "      <td> 0.765331</td>\n",
       "      <td> 0.280160</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.8863.660</th>\n",
       "      <td> 0.13200</td>\n",
       "      <td> 1</td>\n",
       "      <td> 146</td>\n",
       "      <td> 0.316737</td>\n",
       "      <td>-0.020506</td>\n",
       "      <td>  23.280406</td>\n",
       "      <td>-0.291598</td>\n",
       "      <td>-0.260269</td>\n",
       "      <td> 0.001062</td>\n",
       "      <td> 2390.816592</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.372110</td>\n",
       "      <td>-0.547138</td>\n",
       "      <td> 306.215875</td>\n",
       "      <td>-0.031463</td>\n",
       "      <td> 0.072473</td>\n",
       "      <td> 0.721305</td>\n",
       "      <td> 0.814942</td>\n",
       "      <td> 0.743765</td>\n",
       "      <td> 0.606026</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.8751.968</th>\n",
       "      <td> 0.48125</td>\n",
       "      <td> 1</td>\n",
       "      <td>  41</td>\n",
       "      <td> 0.251832</td>\n",
       "      <td>-0.016820</td>\n",
       "      <td> 216.659786</td>\n",
       "      <td>-0.022690</td>\n",
       "      <td> 0.386472</td>\n",
       "      <td> 0.003670</td>\n",
       "      <td>  577.076785</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.283297</td>\n",
       "      <td> 0.464646</td>\n",
       "      <td>   0.319352</td>\n",
       "      <td> 1.554634</td>\n",
       "      <td> 0.245696</td>\n",
       "      <td> 0.599099</td>\n",
       "      <td> 0.778154</td>\n",
       "      <td> 0.718825</td>\n",
       "      <td> 0.443962</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Amplitude  AndersonDarling  Autocor_length  Beyond1Std  \\\n",
       "80.6708.5218    0.12700                1              44    0.278136   \n",
       "48.2742.1409    0.16525                1               1    0.185185   \n",
       "53.3970.353     0.14600                1              19    0.291367   \n",
       "14.8863.660     0.13200                1             146    0.316737   \n",
       "11.8751.968     0.48125                1              41    0.251832   \n",
       "\n",
       "              CAR_sigma     CAR_tau  CAR_tmean     Color       Con  \\\n",
       "80.6708.5218  -0.013622   37.158804  -0.195121 -0.356996  0.004307   \n",
       "48.2742.1409  -0.053792    3.563116  -1.814895 -0.520699  0.000000   \n",
       "53.3970.353   -0.003886  490.231454  -0.010910 -0.435427  0.000000   \n",
       "14.8863.660   -0.020506   23.280406  -0.291598 -0.260269  0.001062   \n",
       "11.8751.968   -0.016820  216.659786  -0.022690  0.386472  0.003670   \n",
       "\n",
       "                Eta_color  ...         Rcs      Skew  SlottedA_length  \\\n",
       "80.6708.5218  4478.196740  ...    0.275917  0.512233         0.026157   \n",
       "48.2742.1409    16.673054  ...    0.207167 -1.929378         1.024734   \n",
       "53.3970.353   2628.279811  ...    0.300025  0.387355        15.233322   \n",
       "14.8863.660   2390.816592  ...    0.372110 -0.547138       306.215875   \n",
       "11.8751.968    577.076785  ...    0.283297  0.464646         0.319352   \n",
       "\n",
       "              SmallKurtosis       Std  StetsonJ  StetsonK  StetsonK_AC  \\\n",
       "80.6708.5218       1.389718  0.063573  0.439091  0.763764     0.816338   \n",
       "48.2742.1409       7.272569  0.076998  0.006110  0.673988     0.721044   \n",
       "53.3970.353        0.318671  0.072600  0.399034  0.769279     0.765331   \n",
       "14.8863.660       -0.031463  0.072473  0.721305  0.814942     0.743765   \n",
       "11.8751.968        1.554634  0.245696  0.599099  0.778154     0.718825   \n",
       "\n",
       "              StetsonL  Class  \n",
       "80.6708.5218  0.274887      1  \n",
       "48.2742.1409  0.003726      1  \n",
       "53.3970.353   0.280160      1  \n",
       "14.8863.660   0.606026      1  \n",
       "11.8751.968   0.443962      1  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('/Users/isadoranun/Desktop/Features/time-series-feats/MACHO_ts.csv',index_col=0)\n",
    "# data = data.replace([np.inf, -np.inf], np.nan)\n",
    "# data = data.dropna()\n",
    "# data.to_csv('MACHO_ts.csv') \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>AndersonDarling</th>\n",
       "      <th>Autocor_length</th>\n",
       "      <th>Beyond1Std</th>\n",
       "      <th>CAR_mean</th>\n",
       "      <th>CAR_sigma</th>\n",
       "      <th>CAR_tau</th>\n",
       "      <th>Color</th>\n",
       "      <th>Con</th>\n",
       "      <th>Eta_color</th>\n",
       "      <th>...</th>\n",
       "      <th>Q31_color</th>\n",
       "      <th>Rcs</th>\n",
       "      <th>Skew</th>\n",
       "      <th>SlottedA_length</th>\n",
       "      <th>SmallKurtosis</th>\n",
       "      <th>Std</th>\n",
       "      <th>StetsonJ</th>\n",
       "      <th>StetsonK</th>\n",
       "      <th>StetsonK_AC</th>\n",
       "      <th>StetsonL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6.059000e+03</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6.059000e+03</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td>    6059.000000</td>\n",
       "      <td>...</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "      <td> 6059.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>    0.519397</td>\n",
       "      <td>    0.998226</td>\n",
       "      <td>    4.352038</td>\n",
       "      <td>    0.240903</td>\n",
       "      <td>-2.277358e+02</td>\n",
       "      <td>   -0.079005</td>\n",
       "      <td> 6.225808e+05</td>\n",
       "      <td>    0.203994</td>\n",
       "      <td>    0.002906</td>\n",
       "      <td>    7512.680054</td>\n",
       "      <td>...</td>\n",
       "      <td>    0.258388</td>\n",
       "      <td>    0.070828</td>\n",
       "      <td>   -0.364556</td>\n",
       "      <td>    3.870990</td>\n",
       "      <td>    4.662114</td>\n",
       "      <td>    0.253624</td>\n",
       "      <td>    2.480975</td>\n",
       "      <td>    0.697318</td>\n",
       "      <td>    0.676395</td>\n",
       "      <td>    1.586759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>    0.333183</td>\n",
       "      <td>    0.025027</td>\n",
       "      <td>   17.382213</td>\n",
       "      <td>    0.069366</td>\n",
       "      <td> 8.350208e+02</td>\n",
       "      <td>    1.392570</td>\n",
       "      <td> 2.277782e+07</td>\n",
       "      <td>    0.527061</td>\n",
       "      <td>    0.008048</td>\n",
       "      <td>   42221.064803</td>\n",
       "      <td>...</td>\n",
       "      <td>    0.185111</td>\n",
       "      <td>    0.062739</td>\n",
       "      <td>    0.794843</td>\n",
       "      <td>   28.957735</td>\n",
       "      <td>    4.734108</td>\n",
       "      <td>    0.161759</td>\n",
       "      <td>    8.731419</td>\n",
       "      <td>    0.089044</td>\n",
       "      <td>    0.093987</td>\n",
       "      <td>    6.327304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>    0.020250</td>\n",
       "      <td>    0.341722</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    0.044944</td>\n",
       "      <td>-1.091337e+04</td>\n",
       "      <td>  -12.148201</td>\n",
       "      <td> 4.728890e-04</td>\n",
       "      <td>   -1.022921</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>       1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>    0.009000</td>\n",
       "      <td>    0.019402</td>\n",
       "      <td>   -6.136411</td>\n",
       "      <td>    0.011111</td>\n",
       "      <td>   -1.515619</td>\n",
       "      <td>    0.011412</td>\n",
       "      <td>   -0.390739</td>\n",
       "      <td>    0.300758</td>\n",
       "      <td>    0.250155</td>\n",
       "      <td>   -0.168368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>    0.259000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    0.197246</td>\n",
       "      <td>-1.024065e+02</td>\n",
       "      <td>   -0.217989</td>\n",
       "      <td> 4.820299e-02</td>\n",
       "      <td>   -0.165038</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    1385.617661</td>\n",
       "      <td>...</td>\n",
       "      <td>    0.114000</td>\n",
       "      <td>    0.038934</td>\n",
       "      <td>   -0.740190</td>\n",
       "      <td>    0.109699</td>\n",
       "      <td>    2.091797</td>\n",
       "      <td>    0.129221</td>\n",
       "      <td>    0.112678</td>\n",
       "      <td>    0.655451</td>\n",
       "      <td>    0.620188</td>\n",
       "      <td>    0.048919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>    0.462500</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    0.222017</td>\n",
       "      <td>-3.731868e+00</td>\n",
       "      <td>   -0.028763</td>\n",
       "      <td> 1.157814e+00</td>\n",
       "      <td>    0.091590</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    3713.568491</td>\n",
       "      <td>...</td>\n",
       "      <td>    0.210000</td>\n",
       "      <td>    0.049829</td>\n",
       "      <td>   -0.420020</td>\n",
       "      <td>    0.123739</td>\n",
       "      <td>    4.213186</td>\n",
       "      <td>    0.228810</td>\n",
       "      <td>    0.229614</td>\n",
       "      <td>    0.727856</td>\n",
       "      <td>    0.663038</td>\n",
       "      <td>    0.100733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>    0.715500</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    0.263002</td>\n",
       "      <td>-2.668921e-01</td>\n",
       "      <td>    0.018142</td>\n",
       "      <td> 1.809315e+01</td>\n",
       "      <td>    0.405055</td>\n",
       "      <td>    0.001699</td>\n",
       "      <td>    7600.263528</td>\n",
       "      <td>...</td>\n",
       "      <td>    0.370000</td>\n",
       "      <td>    0.070573</td>\n",
       "      <td>   -0.009542</td>\n",
       "      <td>    0.203970</td>\n",
       "      <td>    6.442585</td>\n",
       "      <td>    0.346826</td>\n",
       "      <td>    1.125956</td>\n",
       "      <td>    0.758480</td>\n",
       "      <td>    0.726113</td>\n",
       "      <td>    0.486254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>    3.144000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>  323.000000</td>\n",
       "      <td>    0.788321</td>\n",
       "      <td>-3.600000e-09</td>\n",
       "      <td>   25.646470</td>\n",
       "      <td> 1.379351e+09</td>\n",
       "      <td>    3.263769</td>\n",
       "      <td>    0.143713</td>\n",
       "      <td> 1807743.974000</td>\n",
       "      <td>...</td>\n",
       "      <td>    1.469000</td>\n",
       "      <td>    0.462523</td>\n",
       "      <td>   10.406876</td>\n",
       "      <td>  527.874240</td>\n",
       "      <td>  172.925347</td>\n",
       "      <td>    1.718985</td>\n",
       "      <td>  162.784782</td>\n",
       "      <td>    0.888030</td>\n",
       "      <td>    0.952755</td>\n",
       "      <td>  106.699281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Amplitude  AndersonDarling  Autocor_length   Beyond1Std  \\\n",
       "count  6059.000000      6059.000000     6059.000000  6059.000000   \n",
       "mean      0.519397         0.998226        4.352038     0.240903   \n",
       "std       0.333183         0.025027       17.382213     0.069366   \n",
       "min       0.020250         0.341722        1.000000     0.044944   \n",
       "25%       0.259000         1.000000        1.000000     0.197246   \n",
       "50%       0.462500         1.000000        1.000000     0.222017   \n",
       "75%       0.715500         1.000000        1.000000     0.263002   \n",
       "max       3.144000         1.000000      323.000000     0.788321   \n",
       "\n",
       "           CAR_mean    CAR_sigma       CAR_tau        Color          Con  \\\n",
       "count  6.059000e+03  6059.000000  6.059000e+03  6059.000000  6059.000000   \n",
       "mean  -2.277358e+02    -0.079005  6.225808e+05     0.203994     0.002906   \n",
       "std    8.350208e+02     1.392570  2.277782e+07     0.527061     0.008048   \n",
       "min   -1.091337e+04   -12.148201  4.728890e-04    -1.022921     0.000000   \n",
       "25%   -1.024065e+02    -0.217989  4.820299e-02    -0.165038     0.000000   \n",
       "50%   -3.731868e+00    -0.028763  1.157814e+00     0.091590     0.000000   \n",
       "75%   -2.668921e-01     0.018142  1.809315e+01     0.405055     0.001699   \n",
       "max   -3.600000e-09    25.646470  1.379351e+09     3.263769     0.143713   \n",
       "\n",
       "            Eta_color     ...         Q31_color          Rcs         Skew  \\\n",
       "count     6059.000000     ...       6059.000000  6059.000000  6059.000000   \n",
       "mean      7512.680054     ...          0.258388     0.070828    -0.364556   \n",
       "std      42221.064803     ...          0.185111     0.062739     0.794843   \n",
       "min          1.000000     ...          0.009000     0.019402    -6.136411   \n",
       "25%       1385.617661     ...          0.114000     0.038934    -0.740190   \n",
       "50%       3713.568491     ...          0.210000     0.049829    -0.420020   \n",
       "75%       7600.263528     ...          0.370000     0.070573    -0.009542   \n",
       "max    1807743.974000     ...          1.469000     0.462523    10.406876   \n",
       "\n",
       "       SlottedA_length  SmallKurtosis          Std     StetsonJ     StetsonK  \\\n",
       "count      6059.000000    6059.000000  6059.000000  6059.000000  6059.000000   \n",
       "mean          3.870990       4.662114     0.253624     2.480975     0.697318   \n",
       "std          28.957735       4.734108     0.161759     8.731419     0.089044   \n",
       "min           0.011111      -1.515619     0.011412    -0.390739     0.300758   \n",
       "25%           0.109699       2.091797     0.129221     0.112678     0.655451   \n",
       "50%           0.123739       4.213186     0.228810     0.229614     0.727856   \n",
       "75%           0.203970       6.442585     0.346826     1.125956     0.758480   \n",
       "max         527.874240     172.925347     1.718985   162.784782     0.888030   \n",
       "\n",
       "       StetsonK_AC     StetsonL  \n",
       "count  6059.000000  6059.000000  \n",
       "mean      0.676395     1.586759  \n",
       "std       0.093987     6.327304  \n",
       "min       0.250155    -0.168368  \n",
       "25%       0.620188     0.048919  \n",
       "50%       0.663038     0.100733  \n",
       "75%       0.726113     0.486254  \n",
       "max       0.952755   106.699281  \n",
       "\n",
       "[8 rows x 64 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = data.columns[:-1]\n",
    "data[features].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size: 6059\n"
     ]
    }
   ],
   "source": [
    "print 'Data size:',len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of features: 64 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>  127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>  101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>  255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>  361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>  580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 3963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>   59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>  613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count\n",
       "Class       \n",
       "1        127\n",
       "2        101\n",
       "3        255\n",
       "4        361\n",
       "5        580\n",
       "6       3963\n",
       "7         59\n",
       "8        613"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print \"number of features: %i \" %(len(data.columns[:-1]))\n",
    "\n",
    "counts = pd.DataFrame(data.dropna().groupby(by='Class').CAR_sigma.count())\n",
    "counts.columns = ['count']\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of features: 64 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>  127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>  101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>  255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>  365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>  580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 3966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>   59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>  610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count\n",
       "Class       \n",
       "1        127\n",
       "2        101\n",
       "3        255\n",
       "4        365\n",
       "5        580\n",
       "6       3966\n",
       "7         59\n",
       "8        610"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print \"number of features: %i \" %(len(data.columns[:-1]))\n",
    "\n",
    "counts = pd.DataFrame(data.dropna().groupby(by='Class').CAR_sigma.count())\n",
    "counts.columns = ['count']\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x107867810>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102067b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = data['Class']\n",
    "plt.scatter(data.iloc[:,4], data.iloc[:,10], c=y, s=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest classification ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Xr, Xt, yr, yt = train_test_split(data[features], data['Class'], train_size=5000, test_size=1059, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix: \n",
      "\n",
      "[[ 25   0   0   1   0   0   2   1]\n",
      " [  0  17   1   0   0   1   0   0]\n",
      " [  0   1  47   1   0   0   0   3]\n",
      " [  0   0   1  72   1   0   0   0]\n",
      " [  0   0   0   0  86   0   0   0]\n",
      " [  0   0   0   3   3 677   0   1]\n",
      " [  2   0   0   0   0   1   6   0]\n",
      " [  0   0   6   0   0   0   2  98]]\n",
      "\n",
      " ========================================= \n",
      " \n",
      "score of accuracy 0.970727\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=1000)\n",
    "clf.fit(Xr, yr)\n",
    "\n",
    "ypred = clf.predict(Xt)\n",
    "print 'confusion matrix: \\n' \n",
    "print (confusion_matrix(yt, ypred))\n",
    "print '\\n ========================================= \\n '\n",
    "print 'score of accuracy %f' % (clf.score(Xt, yt))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 25   0   0   1   0   0   2   1]\n",
      " [  0  17   1   0   0   1   0   0]\n",
      " [  0   1  47   1   0   0   0   3]\n",
      " [  0   0   1  72   1   0   0   0]\n",
      " [  0   0   0   0  86   0   0   0]\n",
      " [  0   0   0   3   3 677   0   1]\n",
      " [  2   0   0   0   0   1   6   0]\n",
      " [  0   0   6   0   0   0   2  98]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.86  0.    0.    0.03  0.    0.    0.07  0.03]\n",
      " [ 0.    0.89  0.05  0.    0.    0.05  0.    0.  ]\n",
      " [ 0.    0.02  0.9   0.02  0.    0.    0.    0.06]\n",
      " [ 0.    0.    0.01  0.97  0.01  0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.    1.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.    0.    0.99  0.    0.  ]\n",
      " [ 0.22  0.    0.    0.    0.    0.11  0.67  0.  ]\n",
      " [ 0.    0.    0.06  0.    0.    0.    0.02  0.92]]\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107a3f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10afb9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Be','Cepheid','Eb','LPV','Microlensing','Non_variables','Quasars','RRL']\n",
    "\n",
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(labels))\n",
    "    plt.xticks(tick_marks, labels, rotation=45)\n",
    "    plt.yticks(tick_marks, labels)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "\n",
    "# Compute confusion matrix\n",
    "cm = confusion_matrix(yt, ypred)\n",
    "np.set_printoptions(precision=2)\n",
    "print('Confusion matrix, without normalization')\n",
    "print(cm)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm)\n",
    "\n",
    "# Normalize the confusion matrix by row (i.e by the number of samples\n",
    "# in each class)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "print('Normalized confusion matrix')\n",
    "print(cm_normalized)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')\n",
    "plt.savefig(\"confusion_matrix.png\",dpi=300, bbox_inches='tight') \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### scores = cross_val_score(clf, Xt, yt, cv=2) print(\"Accuracy: %0.2f (+/- %0.2f)\"       % (scores.mean(), scores.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_estimator(estimator, X, y):\n",
    "    estimator.fit(X, y)\n",
    "    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n",
    "    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n",
    "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 50),\n",
    "                         np.linspace(y_min, y_max, 50))\n",
    "    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.figure(figsize=[8,10])\n",
    "    plt.pcolormesh(xx, yy, Z, alpha=0.15,cmap = 'hsv')\n",
    "\n",
    "    # Plot also the training points\n",
    "    plt.scatter(X[:, 0], X[:, 1], c = y, s=50)\n",
    "    plt.axis('tight')\n",
    "    plt.axis('off')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
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      "\u001b[0;32m<ipython-input-11-9a3a7126659c>\u001b[0m in \u001b[0;36mplot_estimator\u001b[0;34m(estimator, X, y)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_estimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mx_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m.1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0my_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m.1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     xx, yy = np.meshgrid(np.linspace(x_min, x_max, 50),\n",
      "\u001b[0;32m/Users/isadoranun/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1676\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1677\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1678\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1680\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/isadoranun/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1683\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1684\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1685\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1687\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/isadoranun/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1048\u001b[0m         \u001b[0;34m\"\"\" return the cached item, item represents a label indexer \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1049\u001b[0m         \u001b[0mcache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1050\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1051\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type"
     ]
    }
   ],
   "source": [
    "plot_estimator(clf, data[features], data['Class'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pruning for $ \\textit{max_depths} $ for decision tree ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10be0bb50>]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "cd70QBQAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b407310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "kfold = KFold(Xr.shape[0], n_folds=10)\n",
    "accs = []\n",
    "max_depths = range(1, 20)\n",
    "for max_depth in max_depths:\n",
    "    k_accs = []\n",
    "    for train, test in kfold:\n",
    "        Xtrain, Xtest, ytrain, ytest = Xr[train], Xr[test], yr[train], yr[test]\n",
    "        clf = DecisionTreeClassifier(max_depth=max_depth)\n",
    "        clf.fit(Xtrain, ytrain)\n",
    "        ypred = clf.predict(Xtest)\n",
    "        k_accs.append(accuracy_score(ytest, ypred))\n",
    "    accs.append(np.mean(k_accs))\n",
    "# plot the accuracies as a function of max_depth\n",
    "plt.plot(max_depths, accs, linewidth=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting to tune $ \\textit{number of trees} $ ###\n",
    "\n",
    "Boosting builds lots of small trees. Unlike Random Forests, each tree tries to make up for the deficiencies in the current ensemble. With Boosting, the number of trees is a tuning parameter. Using the same train/test split, we plot the variance of MSE against the number of trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "800\n",
      "900\n",
      "1000\n",
      "1100\n",
      "1200\n",
      "1300\n",
      "1400\n",
      "1500\n",
      "1600\n",
      "1700\n",
      "1800\n",
      "1900\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10b2b02d0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "pYYp5e6PAW/kPd2R67e/mfUDerr77MxxN+W8p6G0cT2hQPIxup5FeeHk7AFU4fezVoNIoSTGAQnV\n",
       "pZY48KCZzTGzL2We6+vuL2fuvwz0zdzvT7iuWbrGhXX0+uU/vwZd13xfM7MFZnZDTveLrmeJ8pKz\n",
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       "fte853VdM9x9rWcA17O5C1XXsx2Z5OzbgN+5++2Zp2P//azVIPJhEqOZdSMkMd6ZcJ1Szcx6mFnP\n",
       "zP1tgSOAhYTrdlbmsLMIy86Qef5UM+tmZoOBYYQBN9lSh66fu78EvG1m+2cSaj+f856Gl/mgy/os\n",
       "4XcUdD2LKpKcHf/vZ9KzCiqYjXAUYQbCCkISYuJ1SvON0EUwP3NblL1mwI7Ag8AzwP1Ar5z3/Efm\n",
       "+i4Fjkz6Z0j6BtxMWHnhfcKY3BfKuX7AWMKH4wrgqqR/rhRdz7MJA7lPAwsyH159dT1LupYHA62Z\n",
       "v+95mdvEavx+KtlQRETKVqvdWSIikgIKIiIiUjYFERERKZuCiIiIlE1BREREyqYgIiIiZVMQESnC\n",
       "zC4zsyYz+4yZfafA6zuY2VeSqJtIGiiIiBQ3HngCmAA8WuD13sA5hd5oZnFvPy2SOAURkQLM7HIz\n",
       "WwDsB8wEvgj8j5ldlHfoD4GPZzZQutzMJpjZY2Z2B7DIzDqZ2Y/NbHZmZdp/yznHt3Ken5J5blsz\n",
       "u8fM5mc2Bjq5Oj+xSHn0TUmkAHf/tpn9ibB20HlAs7sfXODQC4A9PKyOjJk1EZbh3sPd/54JGm+6\n",
       "+3gz2waYYWb3A8OBoZnnOwF3ZFZV7gOscfdjMuVtH/OPKlIRBRGRto0lrOO0O2GTn0IKbaA0293/\n",
       "nrl/BDDazE7MPN6esNjdEcARZjYv8/y2wFBgBvATM/shcLe7z6j8xxCJj4KISB4z2xv4LWEZ7FeB\n",
       "HuFpm0vYKe5uwr4M/wNMK1DEO3mPJ7v7A3nnOBK4zN1/VeD8Y4BjgP8ys4fc/dLKfiKR+GhMRCSP\n",
       "uy/IdE894+67Aw8DR7j7vu6+2t33cfcxmQCwHuhZpLhpwDnZQXYzG25mPTLPn51Zlh8zG2BmfTJL\n",
       "ob/n7n8ArgD2je8nFamcWiIiBZhZH+D1zMOR7r600HHu/pqZ/dXMFgJTM7fcpbGvBwYBczP7M6wF\n",
       "PuPuD5jZ7sDM8DTrCOMvQ4Efm1krYYl0TR+WVNNS8CIiUjZ1Z4mISNkUREREpGwKIiIiUjYFERER\n",
       "KZuCiIiIlE1BREREyqYgIiIiZVMQERGRsv1/dINfXE9ybFoAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a5b2c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_trees = range(100, 2000, 100)\n",
    "mses = []\n",
    "for num_tree in num_trees:\n",
    "    print (num_tree)\n",
    "    reg = RandomForestClassifier(n_estimators=num_tree)\n",
    "    reg.fit(Xr, yr)\n",
    "    ypred = reg.predict(Xt)\n",
    "    mses.append(mean_squared_error(yt, ypred))\n",
    "plt.plot(num_trees, mses)\n",
    "plt.xlabel(\"#-trees\")\n",
    "plt.ylabel(\"MSE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tuning for $ \\textit{max_features} $ ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-d101fb7305c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnum_feat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnum_feats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_feat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moob_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mypred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mmses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mypred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/isadoranun/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    277\u001b[0m                 \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m                 verbose=self.verbose)\n\u001b[0;32m--> 279\u001b[0;31m             for i in range(n_jobs))\n\u001b[0m\u001b[1;32m    280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m         \u001b[0;31m# Reduce\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/isadoranun/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    651\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 653\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpre_dispatch\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"all\"\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/isadoranun/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch\u001b[0;34m(self, func, args, kwargs)\u001b[0m\n\u001b[1;32m    398\u001b[0m         \"\"\"\n\u001b[1;32m    399\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pool\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateApply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_verbosity_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/isadoranun/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, func, args, kwargs)\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/isadoranun/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[0;34m(trees, forest, X, y, sample_weight, verbose)\u001b[0m\n\u001b[1;32m     87\u001b[0m             tree.fit(X, y,\n\u001b[1;32m     88\u001b[0m                      \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m                      check_input=False)\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m             \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_counts\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/isadoranun/anaconda/lib/python2.7/site-packages/sklearn/tree/tree.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_mask, X_argsorted, check_input, sample_weight)\u001b[0m\n\u001b[1;32m    265\u001b[0m                                            max_leaf_nodes)\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "oob_scores = []\n",
    "mses = []\n",
    "num_feats = range(1, 65)\n",
    "for num_feat in num_feats:\n",
    "    clf = RandomForestClassifier(n_estimators=500, max_features=num_feat, oob_score=True)\n",
    "    clf.fit(Xr, yr)\n",
    "    ypred = clf.predict(Xt)\n",
    "    mses.append(mean_squared_error(yt, ypred))\n",
    "    oob_scores.append(clf.oob_score_)\n",
    "plt.plot(num_feats, mses, color='b', linewidth=2.5);\n",
    "plt.plot(num_feats, oob_scores, color='r', linewidth=2.5);\n",
    "plt.xlabel(\"features\")\n",
    "plt.ylabel(\"MSE (blue); OOB (red)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### standard scale data to center around 0 ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def stand_features(data, feature_names = 'all'):\n",
    "    if feature_names == 'all': \n",
    "        column_names = data.columns\n",
    "        data = pd.DataFrame(StandardScaler().fit_transform(data), columns=column_names)\n",
    "    else:\n",
    "        data[feature_names] = pd.DataFrame(StandardScaler().fit_transform(data), columns=feature_names)\n",
    "    return data       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'columns'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-f299c776d488>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewData\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstand_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mnewData\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-56-9f3c4a89cb56>\u001b[0m in \u001b[0;36mstand_features\u001b[0;34m(data, feature_names)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstand_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'all'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfeature_names\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'all'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m         \u001b[0mcolumn_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumn_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'columns'"
     ]
    }
   ],
   "source": [
    "newData = stand_features(train)\n",
    "newData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.97177798683\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=500)\n",
    "clf.fit(Xr, yr)\n",
    "\n",
    "print clf.score(Xt, yt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### random forest for feature importance ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Features used at the top of the tree are used contribute to the final prediction decision of a larger fraction of the input samples. The expected fraction of the samples they contribute to can thus be used as an estimate of the relative importance of the features. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/isadoranun/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:1282: UserWarning: findfont: Font family [u'normal'] not found. Falling back to Bitstream Vera Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102092ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look up the specific algorithm\n",
    "importances = clf.feature_importances_\n",
    "sorted_idx = np.argsort(importances)\n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 13}\n",
    "plt.rc('font', **font)\n",
    "plt.figure(figsize = [10,22])\n",
    "padding = (np.arange(len(features)))*1.2+10\n",
    "plt.barh(padding, importances[sorted_idx], align='center')\n",
    "plt.yticks(padding, features[sorted_idx])\n",
    "plt.xlabel(\"Relative Importance\")\n",
    "# plt.title(\"Variable Importance\")\n",
    "plt.savefig('importance.png',dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM for classification ###\n",
    "\n",
    "    1. Linear Kernal: no real transformation on the data\n",
    "        Parameters: C (the ratio of how much emphasis we want to put on minimizing the error or maximizing the margin. Large C: Lower bias, higher variance, pro to overfitting)\n",
    "    2. Polynomial Kernal: of degre k on n original features yields O(n^k) expanded features\n",
    "        Parameters: C, degree(of the polynomial), gamma(coefficient of the polynomial)\n",
    "    3. RBF Kernal: normal, yield infinitely many expanded features\n",
    "        Parameters: gamma(1/gamma is the variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute 'linearSVC'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-02d854a67da1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinearSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'score of accuracy %f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'linearSVC'"
     ]
    }
   ],
   "source": [
    "clf = svm.linearSVC()\n",
    "clf.fit(Xr, yr)\n",
    "print 'score of accuracy %f' % (clf.score(Xt, yt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### deep learning ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import theano\n",
    "import theano.tensor as T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "theano.__version__\n",
    "print theano.config.device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>AndersonDarling</th>\n",
       "      <th>B_R</th>\n",
       "      <th>Beyond1Std</th>\n",
       "      <th>Bmean</th>\n",
       "      <th>CAR_sigma</th>\n",
       "      <th>CAR_tau</th>\n",
       "      <th>CAR_tmean</th>\n",
       "      <th>Con</th>\n",
       "      <th>Eta_B_R</th>\n",
       "      <th>...</th>\n",
       "      <th>Std</th>\n",
       "      <th>StetsonJ</th>\n",
       "      <th>StetsonK</th>\n",
       "      <th>StetsonK_AC</th>\n",
       "      <th>StetsonL</th>\n",
       "      <th>autocor</th>\n",
       "      <th>automean</th>\n",
       "      <th>meanvariance</th>\n",
       "      <th>Psi_CS</th>\n",
       "      <th>Psi_eta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.3319.23</th>\n",
       "      <td> 0.42450</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0.249543</td>\n",
       "      <td> 0.196115</td>\n",
       "      <td>-4.938551</td>\n",
       "      <td>-0.165672</td>\n",
       "      <td>   1.206547</td>\n",
       "      <td>-4.093127</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 6818.615959</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.199041</td>\n",
       "      <td> 0.187266</td>\n",
       "      <td> 0.637712</td>\n",
       "      <td> 0.457122</td>\n",
       "      <td> 0.100231</td>\n",
       "      <td>  2</td>\n",
       "      <td>-4.938551</td>\n",
       "      <td>-0.040304</td>\n",
       "      <td> 0.085552</td>\n",
       "      <td> 1.836131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3319.11</th>\n",
       "      <td> 0.06800</td>\n",
       "      <td> 1</td>\n",
       "      <td>-0.409863</td>\n",
       "      <td> 0.264388</td>\n",
       "      <td>-7.432300</td>\n",
       "      <td>-0.028999</td>\n",
       "      <td>   3.137702</td>\n",
       "      <td>-2.368708</td>\n",
       "      <td> 0.004505</td>\n",
       "      <td> 3426.229184</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.035677</td>\n",
       "      <td> 0.435276</td>\n",
       "      <td> 0.716623</td>\n",
       "      <td> 0.875010</td>\n",
       "      <td> 0.196524</td>\n",
       "      <td> 13</td>\n",
       "      <td>-7.432300</td>\n",
       "      <td>-0.004800</td>\n",
       "      <td> 0.151780</td>\n",
       "      <td> 1.644436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3319.42</th>\n",
       "      <td> 0.73575</td>\n",
       "      <td> 1</td>\n",
       "      <td>-0.278173</td>\n",
       "      <td> 0.229023</td>\n",
       "      <td>-4.117237</td>\n",
       "      <td> 0.226868</td>\n",
       "      <td>   2.019573</td>\n",
       "      <td>-2.038667</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 2511.253694</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.360103</td>\n",
       "      <td> 0.213843</td>\n",
       "      <td> 0.707300</td>\n",
       "      <td> 0.482501</td>\n",
       "      <td> 0.136990</td>\n",
       "      <td>  2</td>\n",
       "      <td>-4.117237</td>\n",
       "      <td>-0.087462</td>\n",
       "      <td> 0.059685</td>\n",
       "      <td> 1.871534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3319.33</th>\n",
       "      <td> 0.84100</td>\n",
       "      <td> 1</td>\n",
       "      <td>-0.281362</td>\n",
       "      <td> 0.232558</td>\n",
       "      <td>-4.056352</td>\n",
       "      <td> 0.019112</td>\n",
       "      <td> 251.327957</td>\n",
       "      <td>-0.016140</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td>  632.585112</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.440170</td>\n",
       "      <td> 0.325442</td>\n",
       "      <td> 0.727188</td>\n",
       "      <td> 0.464172</td>\n",
       "      <td> 0.213371</td>\n",
       "      <td>  2</td>\n",
       "      <td>-4.056352</td>\n",
       "      <td>-0.108514</td>\n",
       "      <td> 0.058776</td>\n",
       "      <td> 1.958520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3319.34</th>\n",
       "      <td> 0.35600</td>\n",
       "      <td> 1</td>\n",
       "      <td>-0.720231</td>\n",
       "      <td> 0.205993</td>\n",
       "      <td>-4.844879</td>\n",
       "      <td>-0.154069</td>\n",
       "      <td>   0.974836</td>\n",
       "      <td>-4.969945</td>\n",
       "      <td> 0.000938</td>\n",
       "      <td> 5246.245871</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.179034</td>\n",
       "      <td> 0.181015</td>\n",
       "      <td> 0.674830</td>\n",
       "      <td> 0.407155</td>\n",
       "      <td> 0.086707</td>\n",
       "      <td>  2</td>\n",
       "      <td>-4.844879</td>\n",
       "      <td>-0.036953</td>\n",
       "      <td> 0.047025</td>\n",
       "      <td> 1.992605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Amplitude  AndersonDarling       B_R  Beyond1Std     Bmean  \\\n",
       "1.3319.23    0.42450                1  0.249543    0.196115 -4.938551   \n",
       "1.3319.11    0.06800                1 -0.409863    0.264388 -7.432300   \n",
       "1.3319.42    0.73575                1 -0.278173    0.229023 -4.117237   \n",
       "1.3319.33    0.84100                1 -0.281362    0.232558 -4.056352   \n",
       "1.3319.34    0.35600                1 -0.720231    0.205993 -4.844879   \n",
       "\n",
       "           CAR_sigma     CAR_tau  CAR_tmean       Con      Eta_B_R  \\\n",
       "1.3319.23  -0.165672    1.206547  -4.093127  0.000000  6818.615959   \n",
       "1.3319.11  -0.028999    3.137702  -2.368708  0.004505  3426.229184   \n",
       "1.3319.42   0.226868    2.019573  -2.038667  0.000000  2511.253694   \n",
       "1.3319.33   0.019112  251.327957  -0.016140  0.000000   632.585112   \n",
       "1.3319.34  -0.154069    0.974836  -4.969945  0.000938  5246.245871   \n",
       "\n",
       "              ...            Std  StetsonJ  StetsonK  StetsonK_AC  StetsonL  \\\n",
       "1.3319.23     ...       0.199041  0.187266  0.637712     0.457122  0.100231   \n",
       "1.3319.11     ...       0.035677  0.435276  0.716623     0.875010  0.196524   \n",
       "1.3319.42     ...       0.360103  0.213843  0.707300     0.482501  0.136990   \n",
       "1.3319.33     ...       0.440170  0.325442  0.727188     0.464172  0.213371   \n",
       "1.3319.34     ...       0.179034  0.181015  0.674830     0.407155  0.086707   \n",
       "\n",
       "           autocor  automean  meanvariance    Psi_CS   Psi_eta  \n",
       "1.3319.23        2 -4.938551     -0.040304  0.085552  1.836131  \n",
       "1.3319.11       13 -7.432300     -0.004800  0.151780  1.644436  \n",
       "1.3319.42        2 -4.117237     -0.087462  0.059685  1.871534  \n",
       "1.3319.33        2 -4.056352     -0.108514  0.058776  1.958520  \n",
       "1.3319.34        2 -4.844879     -0.036953  0.047025  1.992605  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.reset_option('display.max_rows')\n",
    "\n",
    "path ='/Users/isadoranun/Desktop/Features/F_1/'\n",
    "\n",
    "\n",
    "data1 = pd.read_csv('F_1_3319.csv',index_col=0)\n",
    "data2 = pd.read_csv('F_1_3319_2.csv',index_col=0)\n",
    "\n",
    "data1.Rcs = data2.Rcs\n",
    "\n",
    "features = data2.columns[1:-1]\n",
    "data2 = data2[features]\n",
    "\n",
    "concatenate = pd.merge(data1,data2, left_index=True, right_index=True, how='outer')\n",
    "concatenate.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.to_csv('yelp_academic_dataset_user.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>AndersonDarling</th>\n",
       "      <th>Autocor_length</th>\n",
       "      <th>Beyond1Std</th>\n",
       "      <th>CAR_sigma</th>\n",
       "      <th>CAR_tau</th>\n",
       "      <th>CAR_tmean</th>\n",
       "      <th>Color</th>\n",
       "      <th>Con</th>\n",
       "      <th>Eta_color</th>\n",
       "      <th>...</th>\n",
       "      <th>Rcs</th>\n",
       "      <th>Skew</th>\n",
       "      <th>SlottedA_length</th>\n",
       "      <th>SmallKurtosis</th>\n",
       "      <th>Std</th>\n",
       "      <th>StetsonJ</th>\n",
       "      <th>StetsonK</th>\n",
       "      <th>StetsonK_AC</th>\n",
       "      <th>StetsonL</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80.6708.5218</th>\n",
       "      <td> 0.12700</td>\n",
       "      <td> 1</td>\n",
       "      <td>  44</td>\n",
       "      <td> 0.278136</td>\n",
       "      <td>-0.013622</td>\n",
       "      <td>  37.158804</td>\n",
       "      <td>-0.195121</td>\n",
       "      <td>-0.356996</td>\n",
       "      <td> 0.004307</td>\n",
       "      <td> 4478.196740</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.275917</td>\n",
       "      <td> 0.512233</td>\n",
       "      <td>   0.026157</td>\n",
       "      <td> 1.389718</td>\n",
       "      <td> 0.063573</td>\n",
       "      <td> 0.439091</td>\n",
       "      <td> 0.763764</td>\n",
       "      <td> 0.816338</td>\n",
       "      <td> 0.274887</td>\n",
       "      <td> 1</td>\n",
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       "    <tr>\n",
       "      <th>48.2742.1409</th>\n",
       "      <td> 0.16525</td>\n",
       "      <td> 1</td>\n",
       "      <td>   1</td>\n",
       "      <td> 0.185185</td>\n",
       "      <td>-0.053792</td>\n",
       "      <td>   3.563116</td>\n",
       "      <td>-1.814895</td>\n",
       "      <td>-0.520699</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td>   16.673054</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.207167</td>\n",
       "      <td>-1.929378</td>\n",
       "      <td>   1.024734</td>\n",
       "      <td> 7.272569</td>\n",
       "      <td> 0.076998</td>\n",
       "      <td> 0.006110</td>\n",
       "      <td> 0.673988</td>\n",
       "      <td> 0.721044</td>\n",
       "      <td> 0.003726</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53.3970.353</th>\n",
       "      <td> 0.14600</td>\n",
       "      <td> 1</td>\n",
       "      <td>  19</td>\n",
       "      <td> 0.291367</td>\n",
       "      <td>-0.003886</td>\n",
       "      <td> 490.231454</td>\n",
       "      <td>-0.010910</td>\n",
       "      <td>-0.435427</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 2628.279811</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.300025</td>\n",
       "      <td> 0.387355</td>\n",
       "      <td>  15.233322</td>\n",
       "      <td> 0.318671</td>\n",
       "      <td> 0.072600</td>\n",
       "      <td> 0.399034</td>\n",
       "      <td> 0.769279</td>\n",
       "      <td> 0.765331</td>\n",
       "      <td> 0.280160</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.8863.660</th>\n",
       "      <td> 0.13200</td>\n",
       "      <td> 1</td>\n",
       "      <td> 146</td>\n",
       "      <td> 0.316737</td>\n",
       "      <td>-0.020506</td>\n",
       "      <td>  23.280406</td>\n",
       "      <td>-0.291598</td>\n",
       "      <td>-0.260269</td>\n",
       "      <td> 0.001062</td>\n",
       "      <td> 2390.816592</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.372110</td>\n",
       "      <td>-0.547138</td>\n",
       "      <td> 306.215875</td>\n",
       "      <td>-0.031463</td>\n",
       "      <td> 0.072473</td>\n",
       "      <td> 0.721305</td>\n",
       "      <td> 0.814942</td>\n",
       "      <td> 0.743765</td>\n",
       "      <td> 0.606026</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.8751.968</th>\n",
       "      <td> 0.48125</td>\n",
       "      <td> 1</td>\n",
       "      <td>  41</td>\n",
       "      <td> 0.251832</td>\n",
       "      <td>-0.016820</td>\n",
       "      <td> 216.659786</td>\n",
       "      <td>-0.022690</td>\n",
       "      <td> 0.386472</td>\n",
       "      <td> 0.003670</td>\n",
       "      <td>  577.076785</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.283297</td>\n",
       "      <td> 0.464646</td>\n",
       "      <td>   0.319352</td>\n",
       "      <td> 1.554634</td>\n",
       "      <td> 0.245696</td>\n",
       "      <td> 0.599099</td>\n",
       "      <td> 0.778154</td>\n",
       "      <td> 0.718825</td>\n",
       "      <td> 0.443962</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Amplitude  AndersonDarling  Autocor_length  Beyond1Std  \\\n",
       "80.6708.5218    0.12700                1              44    0.278136   \n",
       "48.2742.1409    0.16525                1               1    0.185185   \n",
       "53.3970.353     0.14600                1              19    0.291367   \n",
       "14.8863.660     0.13200                1             146    0.316737   \n",
       "11.8751.968     0.48125                1              41    0.251832   \n",
       "\n",
       "              CAR_sigma     CAR_tau  CAR_tmean     Color       Con  \\\n",
       "80.6708.5218  -0.013622   37.158804  -0.195121 -0.356996  0.004307   \n",
       "48.2742.1409  -0.053792    3.563116  -1.814895 -0.520699  0.000000   \n",
       "53.3970.353   -0.003886  490.231454  -0.010910 -0.435427  0.000000   \n",
       "14.8863.660   -0.020506   23.280406  -0.291598 -0.260269  0.001062   \n",
       "11.8751.968   -0.016820  216.659786  -0.022690  0.386472  0.003670   \n",
       "\n",
       "                Eta_color     ...            Rcs      Skew  SlottedA_length  \\\n",
       "80.6708.5218  4478.196740     ...       0.275917  0.512233         0.026157   \n",
       "48.2742.1409    16.673054     ...       0.207167 -1.929378         1.024734   \n",
       "53.3970.353   2628.279811     ...       0.300025  0.387355        15.233322   \n",
       "14.8863.660   2390.816592     ...       0.372110 -0.547138       306.215875   \n",
       "11.8751.968    577.076785     ...       0.283297  0.464646         0.319352   \n",
       "\n",
       "              SmallKurtosis       Std  StetsonJ  StetsonK  StetsonK_AC  \\\n",
       "80.6708.5218       1.389718  0.063573  0.439091  0.763764     0.816338   \n",
       "48.2742.1409       7.272569  0.076998  0.006110  0.673988     0.721044   \n",
       "53.3970.353        0.318671  0.072600  0.399034  0.769279     0.765331   \n",
       "14.8863.660       -0.031463  0.072473  0.721305  0.814942     0.743765   \n",
       "11.8751.968        1.554634  0.245696  0.599099  0.778154     0.718825   \n",
       "\n",
       "              StetsonL  Class  \n",
       "80.6708.5218  0.274887      1  \n",
       "48.2742.1409  0.003726      1  \n",
       "53.3970.353   0.280160      1  \n",
       "14.8863.660   0.606026      1  \n",
       "11.8751.968   0.443962      1  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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