SentimentAnalysis-DocumentLevel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#essentials\n",
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import string\n",
    "import sklearn\n",
    "\n",
    "#models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "#from sknn.mlp import Classifier, Layer\n",
    "\n",
    "\n",
    "\n",
    "#visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style='ticks', palette='Set2')\n",
    "sns.despine()\n",
    "import prettyplotlib as ppl\n",
    "import brewer2mpl\n",
    "from prettyplotlib import mpl\n",
    "from pandas.tools.plotting import scatter_matrix, lag_plot\n",
    "\n",
    "%matplotlib inline  \n",
    "\n",
    "#preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler,OneHotEncoder, normalize,FunctionTransformer, scale,StandardScaler\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split, StratifiedKFold\n",
    "from sklearn import metrics\n",
    "from patsy import dmatrices\n",
    "from sklearn.feature_selection import RFE, RFECV\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ACCESS FILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('testsentimentclass.csv',sep =',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>below</th>\n",
       "      <th>awarda</th>\n",
       "      <th>achievement</th>\n",
       "      <th>approve</th>\n",
       "      <th>reduction</th>\n",
       "      <th>increased</th>\n",
       "      <th>growth</th>\n",
       "      <th>exceeded</th>\n",
       "      <th>increasing</th>\n",
       "      <th>...</th>\n",
       "      <th>increasingby</th>\n",
       "      <th>equityincentivesgranted</th>\n",
       "      <th>abovethetotalshareholderreturn</th>\n",
       "      <th>netincomegrowth</th>\n",
       "      <th>profitableyear</th>\n",
       "      <th>increasedbasesalaries</th>\n",
       "      <th>continuedachievement</th>\n",
       "      <th>continuedimprovement</th>\n",
       "      <th>performanceabovethesuperiorlevel</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>317</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>323</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
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       "      <th>4</th>\n",
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       "      <td>8</td>\n",
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       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 114 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   file  below  awarda  achievement  approve  reduction  increased  growth  \\\n",
       "0    24      0       0            0        3          0          8       0   \n",
       "1    37     15       0            0        0          0          0       0   \n",
       "2   317      5       0           45       12          4          0       0   \n",
       "3   323      8       0            0        6          0         28      33   \n",
       "4   335      8       0           21        4          0          0       6   \n",
       "\n",
       "   exceeded  increasing   ...    increasingby  equityincentivesgranted  \\\n",
       "0         0           0   ...               0                        0   \n",
       "1         0           0   ...               0                        0   \n",
       "2         0           0   ...               0                        0   \n",
       "3         0           0   ...               0                        0   \n",
       "4         4           0   ...               0                        0   \n",
       "\n",
       "   abovethetotalshareholderreturn  netincomegrowth  profitableyear  \\\n",
       "0                               0                0               0   \n",
       "1                               0                0               0   \n",
       "2                               0                0               0   \n",
       "3                               0                0               0   \n",
       "4                               0                0               0   \n",
       "\n",
       "   increasedbasesalaries  continuedachievement  continuedimprovement  \\\n",
       "0                      0                     0                     0   \n",
       "1                      0                     0                     0   \n",
       "2                      0                     0                     0   \n",
       "3                      0                     0                     0   \n",
       "4                      0                     0                     0   \n",
       "\n",
       "   performanceabovethesuperiorlevel  actual  \n",
       "0                                 0      -1  \n",
       "1                                 0      -1  \n",
       "2                                 0      -1  \n",
       "3                                 0      -1  \n",
       "4                                 0      -1  \n",
       "\n",
       "[5 rows x 114 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['file',\n",
       " 'below',\n",
       " 'awarda',\n",
       " 'achievement',\n",
       " 'approve',\n",
       " 'reduction',\n",
       " 'increased',\n",
       " 'growth',\n",
       " 'exceeded',\n",
       " 'increasing',\n",
       " 'increase',\n",
       " 'inexcessof',\n",
       " 'strong',\n",
       " 'decrease',\n",
       " 'belowtarget',\n",
       " 'loss',\n",
       " 'successful',\n",
       " 'increaseof',\n",
       " 'increasein',\n",
       " 'outstandingperformance',\n",
       " 'anincrease',\n",
       " 'nobonus',\n",
       " 'accelerate',\n",
       " 'successfulcompletion',\n",
       " 'strongperformance',\n",
       " 'increasedby',\n",
       " 'outperforming',\n",
       " 'salesgrowth',\n",
       " 'revenuegrowth',\n",
       " 'decidednottoincrease',\n",
       " 'improved',\n",
       " 'successfullymanaged',\n",
       " 'noincrease',\n",
       " 'progress',\n",
       " 'outperformed',\n",
       " 'expensereduction',\n",
       " 'topquartile',\n",
       " 'higherthan',\n",
       " 'generated',\n",
       " 'notmet',\n",
       " 'atahighlevel',\n",
       " 'grantedrestrictedstock',\n",
       " 'increasedfrom',\n",
       " 'receivedabasesalaryincrease',\n",
       " 'didnotachieve',\n",
       " 'bottom',\n",
       " 'increaseinrevenues',\n",
       " 'revenueincreased',\n",
       " 'upfrom',\n",
       " 'revenueincrease',\n",
       " 'netincomeincreased',\n",
       " 'milestones',\n",
       " 'receivedasalaryincrease',\n",
       " 'achievedtarget',\n",
       " 'significantprogress',\n",
       " 'anincreaseof',\n",
       " 'grewby',\n",
       " 'stronggrowth',\n",
       " 'achievedrecordannualnetrevenues',\n",
       " 'achievementofouroverallbusinessgoals',\n",
       " 'towardsoursuccess',\n",
       " 'fellshort',\n",
       " 'grantaward',\n",
       " 'approvedadiscretionarybonus',\n",
       " 'oneofthelargest',\n",
       " 'incentivecashbonuses',\n",
       " 'receiveseverancebenefits',\n",
       " 'exceedingourtarget',\n",
       " 'exceptionalperformance',\n",
       " 'demonstratedability',\n",
       " 'continuedtoimprove',\n",
       " 'increasesinthebasesalaries',\n",
       " 'rose',\n",
       " 'highestforanyfiscalyear',\n",
       " 'significantprogressin',\n",
       " 'continuetogrow',\n",
       " 'increaseingrossprofit',\n",
       " 'recordrevenue',\n",
       " 'continuedprogress',\n",
       " 'depositsincreased',\n",
       " 'remainedstrong',\n",
       " 'annualincentiveincreasedto',\n",
       " 'evolution',\n",
       " 'emerginggrowthcompany',\n",
       " 'didnotmeetthethresholdrequirementsof',\n",
       " 'millionofgrowth',\n",
       " 'attainmentofthismeasure',\n",
       " 'abovetargetedperformancelevels',\n",
       " 'reductionintheirbasesalaries',\n",
       " 'toaloss',\n",
       " 'netsalesgrowth',\n",
       " 'decreasefrom',\n",
       " 'raisedthebasesalary',\n",
       " 'increasesinbasesalary',\n",
       " 'bestperformance',\n",
       " 'meaningfulperformance',\n",
       " 'increasebasesalary',\n",
       " 'performedextremelywell',\n",
       " 'rewardindividualperformance',\n",
       " 'achievethecorporategoals',\n",
       " 'exceedingindustryaverage',\n",
       " 'stockvolatility',\n",
       " 'slowdownin',\n",
       " 'slowdown',\n",
       " 'increasingby',\n",
       " 'equityincentivesgranted',\n",
       " 'abovethetotalshareholderreturn',\n",
       " 'netincomegrowth',\n",
       " 'profitableyear',\n",
       " 'increasedbasesalaries',\n",
       " 'continuedachievement',\n",
       " 'continuedimprovement',\n",
       " 'performanceabovethesuperiorlevel',\n",
       " 'actual']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fix arrangement of columns\n",
    "#scale and normalize if needed (but since this is cash, no need)\n",
    "#encode categorical features.. if needed\n",
    "#list all column names\n",
    "cols = list(data.columns.values) #usable\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# #dropping unncessary columns\n",
    "# #convert col[type] to numeric\n",
    "# data = pd.get_dummies(data, prefix = 'type', columns = ['type'])\n",
    "# #arrange columns\n",
    "# data = data[['isFraud','type_CASH_IN','type_CASH_OUT','type_TRANSFER','type_PAYMENT','amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest']]\n",
    "# data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA EXPLORATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>below</th>\n",
       "      <th>awarda</th>\n",
       "      <th>achievement</th>\n",
       "      <th>approve</th>\n",
       "      <th>reduction</th>\n",
       "      <th>increased</th>\n",
       "      <th>growth</th>\n",
       "      <th>exceeded</th>\n",
       "      <th>increasing</th>\n",
       "      <th>...</th>\n",
       "      <th>slowdown</th>\n",
       "      <th>increasingby</th>\n",
       "      <th>equityincentivesgranted</th>\n",
       "      <th>abovethetotalshareholderreturn</th>\n",
       "      <th>netincomegrowth</th>\n",
       "      <th>profitableyear</th>\n",
       "      <th>increasedbasesalaries</th>\n",
       "      <th>continuedachievement</th>\n",
       "      <th>continuedimprovement</th>\n",
       "      <th>performanceabovethesuperiorlevel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>747.000000</td>\n",
       "      <td>7.857143</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>15.714286</td>\n",
       "      <td>8.095238</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>9.904762</td>\n",
       "      <td>12.714286</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.190476</td>\n",
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       "    <tr>\n",
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       "      <td>903.880952</td>\n",
       "      <td>7.095238</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>13.785714</td>\n",
       "      <td>5.452381</td>\n",
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       "      <td>5.428571</td>\n",
       "      <td>8.785714</td>\n",
       "      <td>0.357143</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>792.070866</td>\n",
       "      <td>9.551181</td>\n",
       "      <td>1.173228</td>\n",
       "      <td>22.393701</td>\n",
       "      <td>7.889764</td>\n",
       "      <td>0.653543</td>\n",
       "      <td>13.196850</td>\n",
       "      <td>18.897638</td>\n",
       "      <td>0.677165</td>\n",
       "      <td>0.385827</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.015748</td>\n",
       "      <td>0.015748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031496</td>\n",
       "      <td>0.015748</td>\n",
       "      <td>0.015748</td>\n",
       "      <td>0.062992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 113 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              file     below    awarda  achievement   approve  reduction  \\\n",
       "actual                                                                     \n",
       "-1      747.000000  7.857143  0.523810    15.714286  8.095238   0.476190   \n",
       " 0      903.880952  7.095238  1.166667    13.785714  5.452381   0.547619   \n",
       " 1      792.070866  9.551181  1.173228    22.393701  7.889764   0.653543   \n",
       "\n",
       "        increased     growth  exceeded  increasing  \\\n",
       "actual                                               \n",
       "-1       9.904762  12.714286  0.523810    0.190476   \n",
       " 0       5.428571   8.785714  0.357143    0.380952   \n",
       " 1      13.196850  18.897638  0.677165    0.385827   \n",
       "\n",
       "                      ...                 slowdown  increasingby  \\\n",
       "actual                ...                                          \n",
       "-1                    ...                 0.095238      0.095238   \n",
       " 0                    ...                 0.000000      0.000000   \n",
       " 1                    ...                 0.000000      0.000000   \n",
       "\n",
       "        equityincentivesgranted  abovethetotalshareholderreturn  \\\n",
       "actual                                                            \n",
       "-1                          0.0                        0.000000   \n",
       " 0                          0.0                        0.000000   \n",
       " 1                          0.0                        0.015748   \n",
       "\n",
       "        netincomegrowth  profitableyear  increasedbasesalaries  \\\n",
       "actual                                                           \n",
       "-1             0.000000             0.0               0.000000   \n",
       " 0             0.000000             0.0               0.000000   \n",
       " 1             0.015748             0.0               0.031496   \n",
       "\n",
       "        continuedachievement  continuedimprovement  \\\n",
       "actual                                               \n",
       "-1                  0.000000              0.000000   \n",
       " 0                  0.000000              0.000000   \n",
       " 1                  0.015748              0.015748   \n",
       "\n",
       "        performanceabovethesuperiorlevel  \n",
       "actual                                    \n",
       "-1                              0.000000  \n",
       " 0                              0.000000  \n",
       " 1                              0.062992  \n",
       "\n",
       "[3 rows x 113 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data exploration\n",
    "data.groupby('actual').mean()\n",
    "#on average, Fraud = 1 happen more on CASH_OUT and TRANSFER transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>below</th>\n",
       "      <th>awarda</th>\n",
       "      <th>achievement</th>\n",
       "      <th>approve</th>\n",
       "      <th>reduction</th>\n",
       "      <th>increased</th>\n",
       "      <th>growth</th>\n",
       "      <th>exceeded</th>\n",
       "      <th>increasing</th>\n",
       "      <th>...</th>\n",
       "      <th>increasingby</th>\n",
       "      <th>equityincentivesgranted</th>\n",
       "      <th>abovethetotalshareholderreturn</th>\n",
       "      <th>netincomegrowth</th>\n",
       "      <th>profitableyear</th>\n",
       "      <th>increasedbasesalaries</th>\n",
       "      <th>continuedachievement</th>\n",
       "      <th>continuedimprovement</th>\n",
       "      <th>performanceabovethesuperiorlevel</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>file</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.232203</td>\n",
       "      <td>0.174011</td>\n",
       "      <td>0.088736</td>\n",
       "      <td>0.214715</td>\n",
       "      <td>0.081024</td>\n",
       "      <td>0.134445</td>\n",
       "      <td>0.034663</td>\n",
       "      <td>-0.027373</td>\n",
       "      <td>-0.019382</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.116854</td>\n",
       "      <td>-0.006480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012895</td>\n",
       "      <td>0.014483</td>\n",
       "      <td>0.014642</td>\n",
       "      <td>0.022423</td>\n",
       "      <td>-0.019245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>below</th>\n",
       "      <td>0.232203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.386649</td>\n",
       "      <td>0.297629</td>\n",
       "      <td>0.432599</td>\n",
       "      <td>0.348686</td>\n",
       "      <td>0.461562</td>\n",
       "      <td>0.242704</td>\n",
       "      <td>0.105039</td>\n",
       "      <td>0.103021</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.095316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.034350</td>\n",
       "      <td>0.001934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008872</td>\n",
       "      <td>-0.008872</td>\n",
       "      <td>0.034350</td>\n",
       "      <td>0.045156</td>\n",
       "      <td>0.129151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>awarda</th>\n",
       "      <td>0.174011</td>\n",
       "      <td>0.386649</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.194223</td>\n",
       "      <td>0.232957</td>\n",
       "      <td>0.269266</td>\n",
       "      <td>0.152430</td>\n",
       "      <td>0.122657</td>\n",
       "      <td>0.129067</td>\n",
       "      <td>0.169278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.033328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.033328</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.083844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>achievement</th>\n",
       "      <td>0.088736</td>\n",
       "      <td>0.297629</td>\n",
       "      <td>0.194223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.339350</td>\n",
       "      <td>0.348720</td>\n",
       "      <td>0.163941</td>\n",
       "      <td>0.335144</td>\n",
       "      <td>0.096446</td>\n",
       "      <td>0.046054</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.034618</td>\n",
       "      <td>0.061968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.090943</td>\n",
       "      <td>0.486946</td>\n",
       "      <td>0.158553</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>0.143141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>approve</th>\n",
       "      <td>0.214715</td>\n",
       "      <td>0.432599</td>\n",
       "      <td>0.232957</td>\n",
       "      <td>0.339350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.261688</td>\n",
       "      <td>0.272480</td>\n",
       "      <td>0.198991</td>\n",
       "      <td>0.138512</td>\n",
       "      <td>-0.005555</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.045784</td>\n",
       "      <td>0.033159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.093097</td>\n",
       "      <td>0.007908</td>\n",
       "      <td>0.007908</td>\n",
       "      <td>-0.017344</td>\n",
       "      <td>0.067311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reduction</th>\n",
       "      <td>0.081024</td>\n",
       "      <td>0.348686</td>\n",
       "      <td>0.269266</td>\n",
       "      <td>0.348720</td>\n",
       "      <td>0.261688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.233169</td>\n",
       "      <td>0.291326</td>\n",
       "      <td>0.009223</td>\n",
       "      <td>0.056141</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>0.233526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>0.233526</td>\n",
       "      <td>0.180324</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>0.046630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increased</th>\n",
       "      <td>0.134445</td>\n",
       "      <td>0.461562</td>\n",
       "      <td>0.152430</td>\n",
       "      <td>0.163941</td>\n",
       "      <td>0.272480</td>\n",
       "      <td>0.233169</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.338668</td>\n",
       "      <td>0.162613</td>\n",
       "      <td>0.057730</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026289</td>\n",
       "      <td>0.176999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>0.164993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>growth</th>\n",
       "      <td>0.034663</td>\n",
       "      <td>0.242704</td>\n",
       "      <td>0.122657</td>\n",
       "      <td>0.335144</td>\n",
       "      <td>0.198991</td>\n",
       "      <td>0.291326</td>\n",
       "      <td>0.338668</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>0.195711</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.023068</td>\n",
       "      <td>0.581813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.052816</td>\n",
       "      <td>0.145506</td>\n",
       "      <td>0.135589</td>\n",
       "      <td>0.086009</td>\n",
       "      <td>0.153605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exceeded</th>\n",
       "      <td>-0.027373</td>\n",
       "      <td>0.105039</td>\n",
       "      <td>0.129067</td>\n",
       "      <td>0.096446</td>\n",
       "      <td>0.138512</td>\n",
       "      <td>0.009223</td>\n",
       "      <td>0.162613</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074410</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>0.260406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>0.063733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasing</th>\n",
       "      <td>-0.019382</td>\n",
       "      <td>0.103021</td>\n",
       "      <td>0.169278</td>\n",
       "      <td>0.046054</td>\n",
       "      <td>-0.005555</td>\n",
       "      <td>0.056141</td>\n",
       "      <td>0.057730</td>\n",
       "      <td>0.195711</td>\n",
       "      <td>0.074410</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>0.201622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>0.125159</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>0.052628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increase</th>\n",
       "      <td>0.155540</td>\n",
       "      <td>0.569946</td>\n",
       "      <td>0.247644</td>\n",
       "      <td>0.278733</td>\n",
       "      <td>0.346792</td>\n",
       "      <td>0.239987</td>\n",
       "      <td>0.741204</td>\n",
       "      <td>0.434133</td>\n",
       "      <td>0.237538</td>\n",
       "      <td>0.137166</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066415</td>\n",
       "      <td>0.271202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.060358</td>\n",
       "      <td>-0.031103</td>\n",
       "      <td>-0.011599</td>\n",
       "      <td>-0.021351</td>\n",
       "      <td>0.151581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inexcessof</th>\n",
       "      <td>0.095874</td>\n",
       "      <td>0.187284</td>\n",
       "      <td>0.211178</td>\n",
       "      <td>0.064852</td>\n",
       "      <td>0.147868</td>\n",
       "      <td>0.230038</td>\n",
       "      <td>0.257567</td>\n",
       "      <td>0.151684</td>\n",
       "      <td>0.131846</td>\n",
       "      <td>0.051376</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038291</td>\n",
       "      <td>0.136316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038291</td>\n",
       "      <td>0.078114</td>\n",
       "      <td>-0.038291</td>\n",
       "      <td>-0.038291</td>\n",
       "      <td>-0.090748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strong</th>\n",
       "      <td>0.050713</td>\n",
       "      <td>0.375609</td>\n",
       "      <td>0.183167</td>\n",
       "      <td>0.313877</td>\n",
       "      <td>0.245954</td>\n",
       "      <td>0.234268</td>\n",
       "      <td>0.419351</td>\n",
       "      <td>0.593075</td>\n",
       "      <td>0.354363</td>\n",
       "      <td>0.081969</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019946</td>\n",
       "      <td>0.433364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.048957</td>\n",
       "      <td>0.157752</td>\n",
       "      <td>-0.048957</td>\n",
       "      <td>-0.048957</td>\n",
       "      <td>0.226120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decrease</th>\n",
       "      <td>-0.046904</td>\n",
       "      <td>0.148939</td>\n",
       "      <td>0.003980</td>\n",
       "      <td>0.151981</td>\n",
       "      <td>0.063478</td>\n",
       "      <td>0.031082</td>\n",
       "      <td>0.385088</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.118615</td>\n",
       "      <td>-0.025600</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.030504</td>\n",
       "      <td>0.067729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.030504</td>\n",
       "      <td>0.067729</td>\n",
       "      <td>0.116845</td>\n",
       "      <td>0.116845</td>\n",
       "      <td>0.073628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>belowtarget</th>\n",
       "      <td>0.056037</td>\n",
       "      <td>0.226399</td>\n",
       "      <td>0.166845</td>\n",
       "      <td>-0.011973</td>\n",
       "      <td>-0.009735</td>\n",
       "      <td>0.015260</td>\n",
       "      <td>0.022501</td>\n",
       "      <td>0.063559</td>\n",
       "      <td>-0.056223</td>\n",
       "      <td>0.129531</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018557</td>\n",
       "      <td>-0.018557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018557</td>\n",
       "      <td>-0.018557</td>\n",
       "      <td>-0.018557</td>\n",
       "      <td>-0.018557</td>\n",
       "      <td>0.002316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loss</th>\n",
       "      <td>-0.014513</td>\n",
       "      <td>0.095253</td>\n",
       "      <td>0.172899</td>\n",
       "      <td>0.011419</td>\n",
       "      <td>0.056869</td>\n",
       "      <td>0.109532</td>\n",
       "      <td>0.241731</td>\n",
       "      <td>0.005304</td>\n",
       "      <td>0.017380</td>\n",
       "      <td>0.330661</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.024101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.024101</td>\n",
       "      <td>0.033140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.024101</td>\n",
       "      <td>0.061760</td>\n",
       "      <td>-0.024101</td>\n",
       "      <td>0.033140</td>\n",
       "      <td>0.032512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>successful</th>\n",
       "      <td>0.161704</td>\n",
       "      <td>0.153377</td>\n",
       "      <td>0.080706</td>\n",
       "      <td>0.184995</td>\n",
       "      <td>0.130184</td>\n",
       "      <td>0.106015</td>\n",
       "      <td>0.177916</td>\n",
       "      <td>0.322426</td>\n",
       "      <td>0.354430</td>\n",
       "      <td>0.094765</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.032080</td>\n",
       "      <td>0.173837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.032080</td>\n",
       "      <td>0.132654</td>\n",
       "      <td>-0.032080</td>\n",
       "      <td>0.338571</td>\n",
       "      <td>0.158743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increaseof</th>\n",
       "      <td>-0.081637</td>\n",
       "      <td>0.025539</td>\n",
       "      <td>-0.084602</td>\n",
       "      <td>0.022540</td>\n",
       "      <td>0.025392</td>\n",
       "      <td>0.023537</td>\n",
       "      <td>0.029708</td>\n",
       "      <td>0.235598</td>\n",
       "      <td>0.124249</td>\n",
       "      <td>0.289043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018678</td>\n",
       "      <td>0.316120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018678</td>\n",
       "      <td>-0.018678</td>\n",
       "      <td>-0.018678</td>\n",
       "      <td>-0.018678</td>\n",
       "      <td>0.137662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasein</th>\n",
       "      <td>0.052752</td>\n",
       "      <td>0.238145</td>\n",
       "      <td>0.136027</td>\n",
       "      <td>0.146086</td>\n",
       "      <td>0.043170</td>\n",
       "      <td>0.035590</td>\n",
       "      <td>0.326096</td>\n",
       "      <td>0.286205</td>\n",
       "      <td>0.154265</td>\n",
       "      <td>0.094490</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038074</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038074</td>\n",
       "      <td>0.298398</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038074</td>\n",
       "      <td>-0.038074</td>\n",
       "      <td>0.046044</td>\n",
       "      <td>-0.038074</td>\n",
       "      <td>0.142585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outstandingperformance</th>\n",
       "      <td>0.169490</td>\n",
       "      <td>0.080850</td>\n",
       "      <td>0.022985</td>\n",
       "      <td>-0.040873</td>\n",
       "      <td>-0.039508</td>\n",
       "      <td>0.038050</td>\n",
       "      <td>0.076590</td>\n",
       "      <td>0.098294</td>\n",
       "      <td>0.084007</td>\n",
       "      <td>0.092514</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>0.072968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anincrease</th>\n",
       "      <td>-0.024576</td>\n",
       "      <td>0.094082</td>\n",
       "      <td>0.109822</td>\n",
       "      <td>0.085816</td>\n",
       "      <td>0.075299</td>\n",
       "      <td>0.166123</td>\n",
       "      <td>0.228194</td>\n",
       "      <td>0.235940</td>\n",
       "      <td>0.058969</td>\n",
       "      <td>-0.034820</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029707</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.029707</td>\n",
       "      <td>0.259747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.029707</td>\n",
       "      <td>-0.029707</td>\n",
       "      <td>-0.029707</td>\n",
       "      <td>-0.029707</td>\n",
       "      <td>0.118550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nobonus</th>\n",
       "      <td>0.045112</td>\n",
       "      <td>-0.052012</td>\n",
       "      <td>-0.033398</td>\n",
       "      <td>-0.014336</td>\n",
       "      <td>0.056578</td>\n",
       "      <td>-0.055421</td>\n",
       "      <td>-0.007623</td>\n",
       "      <td>-0.017606</td>\n",
       "      <td>0.021981</td>\n",
       "      <td>0.064449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.009028</td>\n",
       "      <td>-0.009028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.009028</td>\n",
       "      <td>-0.009028</td>\n",
       "      <td>-0.009028</td>\n",
       "      <td>-0.009028</td>\n",
       "      <td>-0.127173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accelerate</th>\n",
       "      <td>0.092105</td>\n",
       "      <td>0.332175</td>\n",
       "      <td>0.223093</td>\n",
       "      <td>0.256364</td>\n",
       "      <td>0.199885</td>\n",
       "      <td>0.363644</td>\n",
       "      <td>0.115576</td>\n",
       "      <td>0.085483</td>\n",
       "      <td>-0.024868</td>\n",
       "      <td>0.041602</td>\n",
       "      <td>...</td>\n",
       "      <td>0.085374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.028325</td>\n",
       "      <td>0.085374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.028325</td>\n",
       "      <td>0.161174</td>\n",
       "      <td>-0.028325</td>\n",
       "      <td>-0.028325</td>\n",
       "      <td>0.007133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>successfulcompletion</th>\n",
       "      <td>0.110722</td>\n",
       "      <td>0.077572</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.235169</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>0.176999</td>\n",
       "      <td>0.026512</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strongperformance</th>\n",
       "      <td>0.124169</td>\n",
       "      <td>0.113483</td>\n",
       "      <td>-0.009020</td>\n",
       "      <td>0.057010</td>\n",
       "      <td>0.126548</td>\n",
       "      <td>0.074502</td>\n",
       "      <td>0.179430</td>\n",
       "      <td>0.331106</td>\n",
       "      <td>0.227350</td>\n",
       "      <td>0.086400</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019024</td>\n",
       "      <td>0.330777</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019024</td>\n",
       "      <td>-0.019024</td>\n",
       "      <td>-0.019024</td>\n",
       "      <td>-0.019024</td>\n",
       "      <td>0.070383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasedby</th>\n",
       "      <td>-0.067477</td>\n",
       "      <td>0.144209</td>\n",
       "      <td>-0.080785</td>\n",
       "      <td>0.014169</td>\n",
       "      <td>-0.013864</td>\n",
       "      <td>0.042630</td>\n",
       "      <td>0.382721</td>\n",
       "      <td>0.164388</td>\n",
       "      <td>0.069783</td>\n",
       "      <td>0.016251</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.017191</td>\n",
       "      <td>0.216118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.017191</td>\n",
       "      <td>-0.017191</td>\n",
       "      <td>-0.017191</td>\n",
       "      <td>-0.017191</td>\n",
       "      <td>0.091073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outperforming</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salesgrowth</th>\n",
       "      <td>0.009872</td>\n",
       "      <td>-0.016885</td>\n",
       "      <td>0.069898</td>\n",
       "      <td>0.024221</td>\n",
       "      <td>0.112129</td>\n",
       "      <td>0.023233</td>\n",
       "      <td>0.066676</td>\n",
       "      <td>0.432648</td>\n",
       "      <td>-0.017455</td>\n",
       "      <td>-0.031970</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010503</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.010503</td>\n",
       "      <td>0.090970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.010503</td>\n",
       "      <td>-0.010503</td>\n",
       "      <td>-0.010503</td>\n",
       "      <td>-0.010503</td>\n",
       "      <td>0.093347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenuegrowth</th>\n",
       "      <td>-0.110005</td>\n",
       "      <td>0.073188</td>\n",
       "      <td>-0.096020</td>\n",
       "      <td>0.275958</td>\n",
       "      <td>0.039961</td>\n",
       "      <td>0.135822</td>\n",
       "      <td>0.052690</td>\n",
       "      <td>0.384622</td>\n",
       "      <td>0.110764</td>\n",
       "      <td>0.163719</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.017789</td>\n",
       "      <td>0.074811</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.017789</td>\n",
       "      <td>0.398911</td>\n",
       "      <td>-0.017789</td>\n",
       "      <td>0.213711</td>\n",
       "      <td>0.148300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decidednottoincrease</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>didnotmeetthethresholdrequirementsof</th>\n",
       "      <td>-0.124477</td>\n",
       "      <td>-0.041289</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>-0.093097</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>-0.052816</td>\n",
       "      <td>0.116017</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.059339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>millionofgrowth</th>\n",
       "      <td>-0.083345</td>\n",
       "      <td>-0.062900</td>\n",
       "      <td>0.107390</td>\n",
       "      <td>0.042650</td>\n",
       "      <td>0.007908</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>-0.003236</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attainmentofthismeasure</th>\n",
       "      <td>-0.083345</td>\n",
       "      <td>-0.062900</td>\n",
       "      <td>0.107390</td>\n",
       "      <td>0.042650</td>\n",
       "      <td>0.007908</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>-0.003236</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abovetargetedperformancelevels</th>\n",
       "      <td>-0.079374</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>-0.034618</td>\n",
       "      <td>0.108913</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>-0.032984</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reductionintheirbasesalaries</th>\n",
       "      <td>-0.078580</td>\n",
       "      <td>-0.041289</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.081285</td>\n",
       "      <td>0.058410</td>\n",
       "      <td>0.180324</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>-0.052816</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.165700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toaloss</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netsalesgrowth</th>\n",
       "      <td>0.008766</td>\n",
       "      <td>0.034350</td>\n",
       "      <td>0.070359</td>\n",
       "      <td>-0.044277</td>\n",
       "      <td>0.020533</td>\n",
       "      <td>0.127123</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>0.195086</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decreasefrom</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raisedthebasesalary</th>\n",
       "      <td>-0.122888</td>\n",
       "      <td>-0.019677</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.042650</td>\n",
       "      <td>0.020533</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>0.095925</td>\n",
       "      <td>0.116017</td>\n",
       "      <td>0.201622</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasesinbasesalary</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bestperformance</th>\n",
       "      <td>-0.053806</td>\n",
       "      <td>0.066767</td>\n",
       "      <td>0.107390</td>\n",
       "      <td>0.129578</td>\n",
       "      <td>0.134164</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>-0.013152</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>0.125159</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meaningfulperformance</th>\n",
       "      <td>-0.052059</td>\n",
       "      <td>0.001934</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.172041</td>\n",
       "      <td>0.073922</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>-0.052816</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasebasesalary</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>performedextremelywell</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rewardindividualperformance</th>\n",
       "      <td>-0.037925</td>\n",
       "      <td>-0.095316</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>-0.063594</td>\n",
       "      <td>-0.055220</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>-0.052816</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.059339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>achievethecorporategoals</th>\n",
       "      <td>-0.036972</td>\n",
       "      <td>-0.030483</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>-0.029969</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>-0.052816</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exceedingindustryaverage</th>\n",
       "      <td>-0.031890</td>\n",
       "      <td>0.153211</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>0.071036</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>0.047819</td>\n",
       "      <td>0.046345</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>0.125159</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stockvolatility</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slowdownin</th>\n",
       "      <td>-0.015691</td>\n",
       "      <td>-0.095316</td>\n",
       "      <td>0.033328</td>\n",
       "      <td>-0.044277</td>\n",
       "      <td>-0.017344</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>-0.032984</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>0.125159</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.165700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slowdown</th>\n",
       "      <td>-0.015691</td>\n",
       "      <td>-0.095316</td>\n",
       "      <td>0.033328</td>\n",
       "      <td>-0.044277</td>\n",
       "      <td>-0.017344</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>-0.032984</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>0.125159</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.165700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasingby</th>\n",
       "      <td>-0.015691</td>\n",
       "      <td>-0.095316</td>\n",
       "      <td>0.033328</td>\n",
       "      <td>-0.044277</td>\n",
       "      <td>-0.017344</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>-0.032984</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>0.125159</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.165700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equityincentivesgranted</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abovethetotalshareholderreturn</th>\n",
       "      <td>-0.116854</td>\n",
       "      <td>0.034350</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>-0.034618</td>\n",
       "      <td>0.045784</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>0.026289</td>\n",
       "      <td>-0.023068</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netincomegrowth</th>\n",
       "      <td>-0.006480</td>\n",
       "      <td>0.001934</td>\n",
       "      <td>0.033328</td>\n",
       "      <td>0.061968</td>\n",
       "      <td>0.033159</td>\n",
       "      <td>0.233526</td>\n",
       "      <td>0.176999</td>\n",
       "      <td>0.581813</td>\n",
       "      <td>0.260406</td>\n",
       "      <td>0.201622</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profitableyear</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasedbasesalaries</th>\n",
       "      <td>0.012895</td>\n",
       "      <td>-0.008872</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.090943</td>\n",
       "      <td>-0.093097</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>-0.052816</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continuedachievement</th>\n",
       "      <td>0.014483</td>\n",
       "      <td>-0.008872</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.486946</td>\n",
       "      <td>0.007908</td>\n",
       "      <td>0.233526</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>0.145506</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>0.125159</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continuedimprovement</th>\n",
       "      <td>0.014642</td>\n",
       "      <td>0.034350</td>\n",
       "      <td>0.033328</td>\n",
       "      <td>0.158553</td>\n",
       "      <td>0.007908</td>\n",
       "      <td>0.180324</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>0.135589</td>\n",
       "      <td>-0.028371</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>performanceabovethesuperiorlevel</th>\n",
       "      <td>0.022423</td>\n",
       "      <td>0.045156</td>\n",
       "      <td>-0.040734</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>-0.017344</td>\n",
       "      <td>-0.032481</td>\n",
       "      <td>-0.016771</td>\n",
       "      <td>0.086009</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>-0.027768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <td>-0.019245</td>\n",
       "      <td>0.129151</td>\n",
       "      <td>0.083844</td>\n",
       "      <td>0.143141</td>\n",
       "      <td>0.067311</td>\n",
       "      <td>0.046630</td>\n",
       "      <td>0.164993</td>\n",
       "      <td>0.153605</td>\n",
       "      <td>0.063733</td>\n",
       "      <td>0.052628</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.165700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.047023</td>\n",
       "      <td>0.047023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.047023</td>\n",
       "      <td>0.047023</td>\n",
       "      <td>0.047023</td>\n",
       "      <td>0.047023</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 114 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          file     below    awarda  \\\n",
       "file                                  1.000000  0.232203  0.174011   \n",
       "below                                 0.232203  1.000000  0.386649   \n",
       "awarda                                0.174011  0.386649  1.000000   \n",
       "achievement                           0.088736  0.297629  0.194223   \n",
       "approve                               0.214715  0.432599  0.232957   \n",
       "reduction                             0.081024  0.348686  0.269266   \n",
       "increased                             0.134445  0.461562  0.152430   \n",
       "growth                                0.034663  0.242704  0.122657   \n",
       "exceeded                             -0.027373  0.105039  0.129067   \n",
       "increasing                           -0.019382  0.103021  0.169278   \n",
       "increase                              0.155540  0.569946  0.247644   \n",
       "inexcessof                            0.095874  0.187284  0.211178   \n",
       "strong                                0.050713  0.375609  0.183167   \n",
       "decrease                             -0.046904  0.148939  0.003980   \n",
       "belowtarget                           0.056037  0.226399  0.166845   \n",
       "loss                                 -0.014513  0.095253  0.172899   \n",
       "successful                            0.161704  0.153377  0.080706   \n",
       "increaseof                           -0.081637  0.025539 -0.084602   \n",
       "increasein                            0.052752  0.238145  0.136027   \n",
       "outstandingperformance                0.169490  0.080850  0.022985   \n",
       "anincrease                           -0.024576  0.094082  0.109822   \n",
       "nobonus                               0.045112 -0.052012 -0.033398   \n",
       "accelerate                            0.092105  0.332175  0.223093   \n",
       "successfulcompletion                  0.110722  0.077572 -0.040734   \n",
       "strongperformance                     0.124169  0.113483 -0.009020   \n",
       "increasedby                          -0.067477  0.144209 -0.080785   \n",
       "outperforming                              NaN       NaN       NaN   \n",
       "salesgrowth                           0.009872 -0.016885  0.069898   \n",
       "revenuegrowth                        -0.110005  0.073188 -0.096020   \n",
       "decidednottoincrease                       NaN       NaN       NaN   \n",
       "...                                        ...       ...       ...   \n",
       "didnotmeetthethresholdrequirementsof -0.124477 -0.041289 -0.040734   \n",
       "millionofgrowth                      -0.083345 -0.062900  0.107390   \n",
       "attainmentofthismeasure              -0.083345 -0.062900  0.107390   \n",
       "abovetargetedperformancelevels       -0.079374  0.012739 -0.040734   \n",
       "reductionintheirbasesalaries         -0.078580 -0.041289 -0.040734   \n",
       "toaloss                                    NaN       NaN       NaN   \n",
       "netsalesgrowth                        0.008766  0.034350  0.070359   \n",
       "decreasefrom                               NaN       NaN       NaN   \n",
       "raisedthebasesalary                  -0.122888 -0.019677 -0.040734   \n",
       "increasesinbasesalary                      NaN       NaN       NaN   \n",
       "bestperformance                      -0.053806  0.066767  0.107390   \n",
       "meaningfulperformance                -0.052059  0.001934 -0.040734   \n",
       "increasebasesalary                         NaN       NaN       NaN   \n",
       "performedextremelywell                     NaN       NaN       NaN   \n",
       "rewardindividualperformance          -0.037925 -0.095316 -0.040734   \n",
       "achievethecorporategoals             -0.036972 -0.030483 -0.040734   \n",
       "exceedingindustryaverage             -0.031890  0.153211 -0.040734   \n",
       "stockvolatility                            NaN       NaN       NaN   \n",
       "slowdownin                           -0.015691 -0.095316  0.033328   \n",
       "slowdown                             -0.015691 -0.095316  0.033328   \n",
       "increasingby                         -0.015691 -0.095316  0.033328   \n",
       "equityincentivesgranted                    NaN       NaN       NaN   \n",
       "abovethetotalshareholderreturn       -0.116854  0.034350 -0.040734   \n",
       "netincomegrowth                      -0.006480  0.001934  0.033328   \n",
       "profitableyear                             NaN       NaN       NaN   \n",
       "increasedbasesalaries                 0.012895 -0.008872 -0.040734   \n",
       "continuedachievement                  0.014483 -0.008872 -0.040734   \n",
       "continuedimprovement                  0.014642  0.034350  0.033328   \n",
       "performanceabovethesuperiorlevel      0.022423  0.045156 -0.040734   \n",
       "actual                               -0.019245  0.129151  0.083844   \n",
       "\n",
       "                                      achievement   approve  reduction  \\\n",
       "file                                     0.088736  0.214715   0.081024   \n",
       "below                                    0.297629  0.432599   0.348686   \n",
       "awarda                                   0.194223  0.232957   0.269266   \n",
       "achievement                              1.000000  0.339350   0.348720   \n",
       "approve                                  0.339350  1.000000   0.261688   \n",
       "reduction                                0.348720  0.261688   1.000000   \n",
       "increased                                0.163941  0.272480   0.233169   \n",
       "growth                                   0.335144  0.198991   0.291326   \n",
       "exceeded                                 0.096446  0.138512   0.009223   \n",
       "increasing                               0.046054 -0.005555   0.056141   \n",
       "increase                                 0.278733  0.346792   0.239987   \n",
       "inexcessof                               0.064852  0.147868   0.230038   \n",
       "strong                                   0.313877  0.245954   0.234268   \n",
       "decrease                                 0.151981  0.063478   0.031082   \n",
       "belowtarget                             -0.011973 -0.009735   0.015260   \n",
       "loss                                     0.011419  0.056869   0.109532   \n",
       "successful                               0.184995  0.130184   0.106015   \n",
       "increaseof                               0.022540  0.025392   0.023537   \n",
       "increasein                               0.146086  0.043170   0.035590   \n",
       "outstandingperformance                  -0.040873 -0.039508   0.038050   \n",
       "anincrease                               0.085816  0.075299   0.166123   \n",
       "nobonus                                 -0.014336  0.056578  -0.055421   \n",
       "accelerate                               0.256364  0.199885   0.363644   \n",
       "successfulcompletion                     0.004016  0.235169  -0.032481   \n",
       "strongperformance                        0.057010  0.126548   0.074502   \n",
       "increasedby                              0.014169 -0.013864   0.042630   \n",
       "outperforming                                 NaN       NaN        NaN   \n",
       "salesgrowth                              0.024221  0.112129   0.023233   \n",
       "revenuegrowth                            0.275958  0.039961   0.135822   \n",
       "decidednottoincrease                          NaN       NaN        NaN   \n",
       "...                                           ...       ...        ...   \n",
       "didnotmeetthethresholdrequirementsof     0.004016 -0.093097  -0.032481   \n",
       "millionofgrowth                          0.042650  0.007908  -0.032481   \n",
       "attainmentofthismeasure                  0.042650  0.007908  -0.032481   \n",
       "abovetargetedperformancelevels          -0.034618  0.108913  -0.032481   \n",
       "reductionintheirbasesalaries             0.081285  0.058410   0.180324   \n",
       "toaloss                                       NaN       NaN        NaN   \n",
       "netsalesgrowth                          -0.044277  0.020533   0.127123   \n",
       "decreasefrom                                  NaN       NaN        NaN   \n",
       "raisedthebasesalary                      0.042650  0.020533  -0.032481   \n",
       "increasesinbasesalary                         NaN       NaN        NaN   \n",
       "bestperformance                          0.129578  0.134164  -0.032481   \n",
       "meaningfulperformance                    0.004016  0.172041   0.073922   \n",
       "increasebasesalary                            NaN       NaN        NaN   \n",
       "performedextremelywell                        NaN       NaN        NaN   \n",
       "rewardindividualperformance             -0.063594 -0.055220  -0.032481   \n",
       "achievethecorporategoals                 0.023333 -0.029969  -0.032481   \n",
       "exceedingindustryaverage                 0.023333  0.071036  -0.032481   \n",
       "stockvolatility                               NaN       NaN        NaN   \n",
       "slowdownin                              -0.044277 -0.017344  -0.032481   \n",
       "slowdown                                -0.044277 -0.017344  -0.032481   \n",
       "increasingby                            -0.044277 -0.017344  -0.032481   \n",
       "equityincentivesgranted                       NaN       NaN        NaN   \n",
       "abovethetotalshareholderreturn          -0.034618  0.045784  -0.032481   \n",
       "netincomegrowth                          0.061968  0.033159   0.233526   \n",
       "profitableyear                                NaN       NaN        NaN   \n",
       "increasedbasesalaries                    0.090943 -0.093097  -0.032481   \n",
       "continuedachievement                     0.486946  0.007908   0.233526   \n",
       "continuedimprovement                     0.158553  0.007908   0.180324   \n",
       "performanceabovethesuperiorlevel         0.023333 -0.017344  -0.032481   \n",
       "actual                                   0.143141  0.067311   0.046630   \n",
       "\n",
       "                                      increased    growth  exceeded  \\\n",
       "file                                   0.134445  0.034663 -0.027373   \n",
       "below                                  0.461562  0.242704  0.105039   \n",
       "awarda                                 0.152430  0.122657  0.129067   \n",
       "achievement                            0.163941  0.335144  0.096446   \n",
       "approve                                0.272480  0.198991  0.138512   \n",
       "reduction                              0.233169  0.291326  0.009223   \n",
       "increased                              1.000000  0.338668  0.162613   \n",
       "growth                                 0.338668  1.000000  0.252304   \n",
       "exceeded                               0.162613  0.252304  1.000000   \n",
       "increasing                             0.057730  0.195711  0.074410   \n",
       "increase                               0.741204  0.434133  0.237538   \n",
       "inexcessof                             0.257567  0.151684  0.131846   \n",
       "strong                                 0.419351  0.593075  0.354363   \n",
       "decrease                               0.385088  0.146552  0.118615   \n",
       "belowtarget                            0.022501  0.063559 -0.056223   \n",
       "loss                                   0.241731  0.005304  0.017380   \n",
       "successful                             0.177916  0.322426  0.354430   \n",
       "increaseof                             0.029708  0.235598  0.124249   \n",
       "increasein                             0.326096  0.286205  0.154265   \n",
       "outstandingperformance                 0.076590  0.098294  0.084007   \n",
       "anincrease                             0.228194  0.235940  0.058969   \n",
       "nobonus                               -0.007623 -0.017606  0.021981   \n",
       "accelerate                             0.115576  0.085483 -0.024868   \n",
       "successfulcompletion                   0.176999  0.026512  0.067888   \n",
       "strongperformance                      0.179430  0.331106  0.227350   \n",
       "increasedby                            0.382721  0.164388  0.069783   \n",
       "outperforming                               NaN       NaN       NaN   \n",
       "salesgrowth                            0.066676  0.432648 -0.017455   \n",
       "revenuegrowth                          0.052690  0.384622  0.110764   \n",
       "decidednottoincrease                        NaN       NaN       NaN   \n",
       "...                                         ...       ...       ...   \n",
       "didnotmeetthethresholdrequirementsof  -0.059831 -0.052816  0.116017   \n",
       "millionofgrowth                       -0.059831 -0.003236 -0.028371   \n",
       "attainmentofthismeasure               -0.059831 -0.003236 -0.028371   \n",
       "abovetargetedperformancelevels        -0.059831 -0.032984 -0.028371   \n",
       "reductionintheirbasesalaries          -0.059831 -0.052816 -0.028371   \n",
       "toaloss                                     NaN       NaN       NaN   \n",
       "netsalesgrowth                        -0.059831  0.195086 -0.028371   \n",
       "decreasefrom                                NaN       NaN       NaN   \n",
       "raisedthebasesalary                   -0.016771  0.095925  0.116017   \n",
       "increasesinbasesalary                       NaN       NaN       NaN   \n",
       "bestperformance                       -0.016771 -0.013152 -0.028371   \n",
       "meaningfulperformance                 -0.059831 -0.052816 -0.028371   \n",
       "increasebasesalary                          NaN       NaN       NaN   \n",
       "performedextremelywell                      NaN       NaN       NaN   \n",
       "rewardindividualperformance           -0.059831 -0.052816 -0.028371   \n",
       "achievethecorporategoals              -0.059831 -0.052816 -0.028371   \n",
       "exceedingindustryaverage               0.047819  0.046345 -0.028371   \n",
       "stockvolatility                             NaN       NaN       NaN   \n",
       "slowdownin                            -0.016771 -0.032984 -0.028371   \n",
       "slowdown                              -0.016771 -0.032984 -0.028371   \n",
       "increasingby                          -0.016771 -0.032984 -0.028371   \n",
       "equityincentivesgranted                     NaN       NaN       NaN   \n",
       "abovethetotalshareholderreturn         0.026289 -0.023068  0.067888   \n",
       "netincomegrowth                        0.176999  0.581813  0.260406   \n",
       "profitableyear                              NaN       NaN       NaN   \n",
       "increasedbasesalaries                 -0.016771 -0.052816 -0.028371   \n",
       "continuedachievement                  -0.016771  0.145506  0.067888   \n",
       "continuedimprovement                  -0.059831  0.135589 -0.028371   \n",
       "performanceabovethesuperiorlevel      -0.016771  0.086009  0.067888   \n",
       "actual                                 0.164993  0.153605  0.063733   \n",
       "\n",
       "                                      increasing    ...     increasingby  \\\n",
       "file                                   -0.019382    ...        -0.015691   \n",
       "below                                   0.103021    ...        -0.095316   \n",
       "awarda                                  0.169278    ...         0.033328   \n",
       "achievement                             0.046054    ...        -0.044277   \n",
       "approve                                -0.005555    ...        -0.017344   \n",
       "reduction                               0.056141    ...        -0.032481   \n",
       "increased                               0.057730    ...        -0.016771   \n",
       "growth                                  0.195711    ...        -0.032984   \n",
       "exceeded                                0.074410    ...        -0.028371   \n",
       "increasing                              1.000000    ...         0.125159   \n",
       "increase                                0.137166    ...        -0.011599   \n",
       "inexcessof                              0.051376    ...        -0.038291   \n",
       "strong                                  0.081969    ...        -0.048957   \n",
       "decrease                               -0.025600    ...        -0.030504   \n",
       "belowtarget                             0.129531    ...        -0.018557   \n",
       "loss                                    0.330661    ...        -0.024101   \n",
       "successful                              0.094765    ...        -0.032080   \n",
       "increaseof                              0.289043    ...        -0.018678   \n",
       "increasein                              0.094490    ...        -0.038074   \n",
       "outstandingperformance                  0.092514    ...        -0.008210   \n",
       "anincrease                             -0.034820    ...        -0.029707   \n",
       "nobonus                                 0.064449    ...        -0.009028   \n",
       "accelerate                              0.041602    ...         0.085374   \n",
       "successfulcompletion                   -0.027768    ...        -0.005291   \n",
       "strongperformance                       0.086400    ...        -0.019024   \n",
       "increasedby                             0.016251    ...        -0.017191   \n",
       "outperforming                                NaN    ...              NaN   \n",
       "salesgrowth                            -0.031970    ...        -0.010503   \n",
       "revenuegrowth                           0.163719    ...        -0.017789   \n",
       "decidednottoincrease                         NaN    ...              NaN   \n",
       "...                                          ...    ...              ...   \n",
       "didnotmeetthethresholdrequirementsof   -0.027768    ...        -0.005291   \n",
       "millionofgrowth                        -0.027768    ...        -0.005291   \n",
       "attainmentofthismeasure                -0.027768    ...        -0.005291   \n",
       "abovetargetedperformancelevels         -0.027768    ...        -0.005291   \n",
       "reductionintheirbasesalaries           -0.027768    ...        -0.005291   \n",
       "toaloss                                      NaN    ...              NaN   \n",
       "netsalesgrowth                         -0.027768    ...        -0.005291   \n",
       "decreasefrom                                 NaN    ...              NaN   \n",
       "raisedthebasesalary                     0.201622    ...        -0.005291   \n",
       "increasesinbasesalary                        NaN    ...              NaN   \n",
       "bestperformance                         0.125159    ...        -0.005291   \n",
       "meaningfulperformance                  -0.027768    ...        -0.005291   \n",
       "increasebasesalary                           NaN    ...              NaN   \n",
       "performedextremelywell                       NaN    ...              NaN   \n",
       "rewardindividualperformance            -0.027768    ...        -0.005291   \n",
       "achievethecorporategoals               -0.027768    ...        -0.005291   \n",
       "exceedingindustryaverage                0.125159    ...        -0.005291   \n",
       "stockvolatility                              NaN    ...              NaN   \n",
       "slowdownin                              0.125159    ...         1.000000   \n",
       "slowdown                                0.125159    ...         1.000000   \n",
       "increasingby                            0.125159    ...         1.000000   \n",
       "equityincentivesgranted                      NaN    ...              NaN   \n",
       "abovethetotalshareholderreturn         -0.027768    ...        -0.005291   \n",
       "netincomegrowth                         0.201622    ...        -0.005291   \n",
       "profitableyear                               NaN    ...              NaN   \n",
       "increasedbasesalaries                  -0.027768    ...        -0.005291   \n",
       "continuedachievement                    0.125159    ...        -0.005291   \n",
       "continuedimprovement                   -0.027768    ...        -0.005291   \n",
       "performanceabovethesuperiorlevel       -0.027768    ...        -0.005291   \n",
       "actual                                  0.052628    ...        -0.165700   \n",
       "\n",
       "                                      equityincentivesgranted  \\\n",
       "file                                                      NaN   \n",
       "below                                                     NaN   \n",
       "awarda                                                    NaN   \n",
       "achievement                                               NaN   \n",
       "approve                                                   NaN   \n",
       "reduction                                                 NaN   \n",
       "increased                                                 NaN   \n",
       "growth                                                    NaN   \n",
       "exceeded                                                  NaN   \n",
       "increasing                                                NaN   \n",
       "increase                                                  NaN   \n",
       "inexcessof                                                NaN   \n",
       "strong                                                    NaN   \n",
       "decrease                                                  NaN   \n",
       "belowtarget                                               NaN   \n",
       "loss                                                      NaN   \n",
       "successful                                                NaN   \n",
       "increaseof                                                NaN   \n",
       "increasein                                                NaN   \n",
       "outstandingperformance                                    NaN   \n",
       "anincrease                                                NaN   \n",
       "nobonus                                                   NaN   \n",
       "accelerate                                                NaN   \n",
       "successfulcompletion                                      NaN   \n",
       "strongperformance                                         NaN   \n",
       "increasedby                                               NaN   \n",
       "outperforming                                             NaN   \n",
       "salesgrowth                                               NaN   \n",
       "revenuegrowth                                             NaN   \n",
       "decidednottoincrease                                      NaN   \n",
       "...                                                       ...   \n",
       "didnotmeetthethresholdrequirementsof                      NaN   \n",
       "millionofgrowth                                           NaN   \n",
       "attainmentofthismeasure                                   NaN   \n",
       "abovetargetedperformancelevels                            NaN   \n",
       "reductionintheirbasesalaries                              NaN   \n",
       "toaloss                                                   NaN   \n",
       "netsalesgrowth                                            NaN   \n",
       "decreasefrom                                              NaN   \n",
       "raisedthebasesalary                                       NaN   \n",
       "increasesinbasesalary                                     NaN   \n",
       "bestperformance                                           NaN   \n",
       "meaningfulperformance                                     NaN   \n",
       "increasebasesalary                                        NaN   \n",
       "performedextremelywell                                    NaN   \n",
       "rewardindividualperformance                               NaN   \n",
       "achievethecorporategoals                                  NaN   \n",
       "exceedingindustryaverage                                  NaN   \n",
       "stockvolatility                                           NaN   \n",
       "slowdownin                                                NaN   \n",
       "slowdown                                                  NaN   \n",
       "increasingby                                              NaN   \n",
       "equityincentivesgranted                                   NaN   \n",
       "abovethetotalshareholderreturn                            NaN   \n",
       "netincomegrowth                                           NaN   \n",
       "profitableyear                                            NaN   \n",
       "increasedbasesalaries                                     NaN   \n",
       "continuedachievement                                      NaN   \n",
       "continuedimprovement                                      NaN   \n",
       "performanceabovethesuperiorlevel                          NaN   \n",
       "actual                                                    NaN   \n",
       "\n",
       "                                      abovethetotalshareholderreturn  \\\n",
       "file                                                       -0.116854   \n",
       "below                                                       0.034350   \n",
       "awarda                                                     -0.040734   \n",
       "achievement                                                -0.034618   \n",
       "approve                                                     0.045784   \n",
       "reduction                                                  -0.032481   \n",
       "increased                                                   0.026289   \n",
       "growth                                                     -0.023068   \n",
       "exceeded                                                    0.067888   \n",
       "increasing                                                 -0.027768   \n",
       "increase                                                    0.066415   \n",
       "inexcessof                                                 -0.038291   \n",
       "strong                                                      0.019946   \n",
       "decrease                                                   -0.030504   \n",
       "belowtarget                                                -0.018557   \n",
       "loss                                                       -0.024101   \n",
       "successful                                                 -0.032080   \n",
       "increaseof                                                 -0.018678   \n",
       "increasein                                                 -0.038074   \n",
       "outstandingperformance                                     -0.008210   \n",
       "anincrease                                                 -0.029707   \n",
       "nobonus                                                    -0.009028   \n",
       "accelerate                                                 -0.028325   \n",
       "successfulcompletion                                       -0.005291   \n",
       "strongperformance                                          -0.019024   \n",
       "increasedby                                                -0.017191   \n",
       "outperforming                                                    NaN   \n",
       "salesgrowth                                                -0.010503   \n",
       "revenuegrowth                                              -0.017789   \n",
       "decidednottoincrease                                             NaN   \n",
       "...                                                              ...   \n",
       "didnotmeetthethresholdrequirementsof                       -0.005291   \n",
       "millionofgrowth                                            -0.005291   \n",
       "attainmentofthismeasure                                    -0.005291   \n",
       "abovetargetedperformancelevels                             -0.005291   \n",
       "reductionintheirbasesalaries                               -0.005291   \n",
       "toaloss                                                          NaN   \n",
       "netsalesgrowth                                             -0.005291   \n",
       "decreasefrom                                                     NaN   \n",
       "raisedthebasesalary                                        -0.005291   \n",
       "increasesinbasesalary                                            NaN   \n",
       "bestperformance                                            -0.005291   \n",
       "meaningfulperformance                                      -0.005291   \n",
       "increasebasesalary                                               NaN   \n",
       "performedextremelywell                                           NaN   \n",
       "rewardindividualperformance                                -0.005291   \n",
       "achievethecorporategoals                                   -0.005291   \n",
       "exceedingindustryaverage                                   -0.005291   \n",
       "stockvolatility                                                  NaN   \n",
       "slowdownin                                                 -0.005291   \n",
       "slowdown                                                   -0.005291   \n",
       "increasingby                                               -0.005291   \n",
       "equityincentivesgranted                                          NaN   \n",
       "abovethetotalshareholderreturn                              1.000000   \n",
       "netincomegrowth                                            -0.005291   \n",
       "profitableyear                                                   NaN   \n",
       "increasedbasesalaries                                      -0.005291   \n",
       "continuedachievement                                       -0.005291   \n",
       "continuedimprovement                                       -0.005291   \n",
       "performanceabovethesuperiorlevel                           -0.005291   \n",
       "actual                                                      0.047023   \n",
       "\n",
       "                                      netincomegrowth  profitableyear  \\\n",
       "file                                        -0.006480             NaN   \n",
       "below                                        0.001934             NaN   \n",
       "awarda                                       0.033328             NaN   \n",
       "achievement                                  0.061968             NaN   \n",
       "approve                                      0.033159             NaN   \n",
       "reduction                                    0.233526             NaN   \n",
       "increased                                    0.176999             NaN   \n",
       "growth                                       0.581813             NaN   \n",
       "exceeded                                     0.260406             NaN   \n",
       "increasing                                   0.201622             NaN   \n",
       "increase                                     0.271202             NaN   \n",
       "inexcessof                                   0.136316             NaN   \n",
       "strong                                       0.433364             NaN   \n",
       "decrease                                     0.067729             NaN   \n",
       "belowtarget                                 -0.018557             NaN   \n",
       "loss                                         0.033140             NaN   \n",
       "successful                                   0.173837             NaN   \n",
       "increaseof                                   0.316120             NaN   \n",
       "increasein                                   0.298398             NaN   \n",
       "outstandingperformance                      -0.008210             NaN   \n",
       "anincrease                                   0.259747             NaN   \n",
       "nobonus                                     -0.009028             NaN   \n",
       "accelerate                                   0.085374             NaN   \n",
       "successfulcompletion                        -0.005291             NaN   \n",
       "strongperformance                            0.330777             NaN   \n",
       "increasedby                                  0.216118             NaN   \n",
       "outperforming                                     NaN             NaN   \n",
       "salesgrowth                                  0.090970             NaN   \n",
       "revenuegrowth                                0.074811             NaN   \n",
       "decidednottoincrease                              NaN             NaN   \n",
       "...                                               ...             ...   \n",
       "didnotmeetthethresholdrequirementsof        -0.005291             NaN   \n",
       "millionofgrowth                             -0.005291             NaN   \n",
       "attainmentofthismeasure                     -0.005291             NaN   \n",
       "abovetargetedperformancelevels              -0.005291             NaN   \n",
       "reductionintheirbasesalaries                -0.005291             NaN   \n",
       "toaloss                                           NaN             NaN   \n",
       "netsalesgrowth                              -0.005291             NaN   \n",
       "decreasefrom                                      NaN             NaN   \n",
       "raisedthebasesalary                         -0.005291             NaN   \n",
       "increasesinbasesalary                             NaN             NaN   \n",
       "bestperformance                             -0.005291             NaN   \n",
       "meaningfulperformance                       -0.005291             NaN   \n",
       "increasebasesalary                                NaN             NaN   \n",
       "performedextremelywell                            NaN             NaN   \n",
       "rewardindividualperformance                 -0.005291             NaN   \n",
       "achievethecorporategoals                    -0.005291             NaN   \n",
       "exceedingindustryaverage                    -0.005291             NaN   \n",
       "stockvolatility                                   NaN             NaN   \n",
       "slowdownin                                  -0.005291             NaN   \n",
       "slowdown                                    -0.005291             NaN   \n",
       "increasingby                                -0.005291             NaN   \n",
       "equityincentivesgranted                           NaN             NaN   \n",
       "abovethetotalshareholderreturn              -0.005291             NaN   \n",
       "netincomegrowth                              1.000000             NaN   \n",
       "profitableyear                                    NaN             NaN   \n",
       "increasedbasesalaries                       -0.005291             NaN   \n",
       "continuedachievement                        -0.005291             NaN   \n",
       "continuedimprovement                        -0.005291             NaN   \n",
       "performanceabovethesuperiorlevel            -0.005291             NaN   \n",
       "actual                                       0.047023             NaN   \n",
       "\n",
       "                                      increasedbasesalaries  \\\n",
       "file                                               0.012895   \n",
       "below                                             -0.008872   \n",
       "awarda                                            -0.040734   \n",
       "achievement                                        0.090943   \n",
       "approve                                           -0.093097   \n",
       "reduction                                         -0.032481   \n",
       "increased                                         -0.016771   \n",
       "growth                                            -0.052816   \n",
       "exceeded                                          -0.028371   \n",
       "increasing                                        -0.027768   \n",
       "increase                                          -0.060358   \n",
       "inexcessof                                        -0.038291   \n",
       "strong                                            -0.048957   \n",
       "decrease                                          -0.030504   \n",
       "belowtarget                                       -0.018557   \n",
       "loss                                              -0.024101   \n",
       "successful                                        -0.032080   \n",
       "increaseof                                        -0.018678   \n",
       "increasein                                        -0.038074   \n",
       "outstandingperformance                            -0.008210   \n",
       "anincrease                                        -0.029707   \n",
       "nobonus                                           -0.009028   \n",
       "accelerate                                        -0.028325   \n",
       "successfulcompletion                              -0.005291   \n",
       "strongperformance                                 -0.019024   \n",
       "increasedby                                       -0.017191   \n",
       "outperforming                                           NaN   \n",
       "salesgrowth                                       -0.010503   \n",
       "revenuegrowth                                     -0.017789   \n",
       "decidednottoincrease                                    NaN   \n",
       "...                                                     ...   \n",
       "didnotmeetthethresholdrequirementsof              -0.005291   \n",
       "millionofgrowth                                   -0.005291   \n",
       "attainmentofthismeasure                           -0.005291   \n",
       "abovetargetedperformancelevels                    -0.005291   \n",
       "reductionintheirbasesalaries                      -0.005291   \n",
       "toaloss                                                 NaN   \n",
       "netsalesgrowth                                    -0.005291   \n",
       "decreasefrom                                            NaN   \n",
       "raisedthebasesalary                               -0.005291   \n",
       "increasesinbasesalary                                   NaN   \n",
       "bestperformance                                   -0.005291   \n",
       "meaningfulperformance                             -0.005291   \n",
       "increasebasesalary                                      NaN   \n",
       "performedextremelywell                                  NaN   \n",
       "rewardindividualperformance                       -0.005291   \n",
       "achievethecorporategoals                          -0.005291   \n",
       "exceedingindustryaverage                          -0.005291   \n",
       "stockvolatility                                         NaN   \n",
       "slowdownin                                        -0.005291   \n",
       "slowdown                                          -0.005291   \n",
       "increasingby                                      -0.005291   \n",
       "equityincentivesgranted                                 NaN   \n",
       "abovethetotalshareholderreturn                    -0.005291   \n",
       "netincomegrowth                                   -0.005291   \n",
       "profitableyear                                          NaN   \n",
       "increasedbasesalaries                              1.000000   \n",
       "continuedachievement                              -0.005291   \n",
       "continuedimprovement                              -0.005291   \n",
       "performanceabovethesuperiorlevel                  -0.005291   \n",
       "actual                                             0.047023   \n",
       "\n",
       "                                      continuedachievement  \\\n",
       "file                                              0.014483   \n",
       "below                                            -0.008872   \n",
       "awarda                                           -0.040734   \n",
       "achievement                                       0.486946   \n",
       "approve                                           0.007908   \n",
       "reduction                                         0.233526   \n",
       "increased                                        -0.016771   \n",
       "growth                                            0.145506   \n",
       "exceeded                                          0.067888   \n",
       "increasing                                        0.125159   \n",
       "increase                                         -0.031103   \n",
       "inexcessof                                        0.078114   \n",
       "strong                                            0.157752   \n",
       "decrease                                          0.067729   \n",
       "belowtarget                                      -0.018557   \n",
       "loss                                              0.061760   \n",
       "successful                                        0.132654   \n",
       "increaseof                                       -0.018678   \n",
       "increasein                                       -0.038074   \n",
       "outstandingperformance                           -0.008210   \n",
       "anincrease                                       -0.029707   \n",
       "nobonus                                          -0.009028   \n",
       "accelerate                                        0.161174   \n",
       "successfulcompletion                             -0.005291   \n",
       "strongperformance                                -0.019024   \n",
       "increasedby                                      -0.017191   \n",
       "outperforming                                          NaN   \n",
       "salesgrowth                                      -0.010503   \n",
       "revenuegrowth                                     0.398911   \n",
       "decidednottoincrease                                   NaN   \n",
       "...                                                    ...   \n",
       "didnotmeetthethresholdrequirementsof             -0.005291   \n",
       "millionofgrowth                                  -0.005291   \n",
       "attainmentofthismeasure                          -0.005291   \n",
       "abovetargetedperformancelevels                   -0.005291   \n",
       "reductionintheirbasesalaries                     -0.005291   \n",
       "toaloss                                                NaN   \n",
       "netsalesgrowth                                   -0.005291   \n",
       "decreasefrom                                           NaN   \n",
       "raisedthebasesalary                              -0.005291   \n",
       "increasesinbasesalary                                  NaN   \n",
       "bestperformance                                  -0.005291   \n",
       "meaningfulperformance                            -0.005291   \n",
       "increasebasesalary                                     NaN   \n",
       "performedextremelywell                                 NaN   \n",
       "rewardindividualperformance                      -0.005291   \n",
       "achievethecorporategoals                         -0.005291   \n",
       "exceedingindustryaverage                         -0.005291   \n",
       "stockvolatility                                        NaN   \n",
       "slowdownin                                       -0.005291   \n",
       "slowdown                                         -0.005291   \n",
       "increasingby                                     -0.005291   \n",
       "equityincentivesgranted                                NaN   \n",
       "abovethetotalshareholderreturn                   -0.005291   \n",
       "netincomegrowth                                  -0.005291   \n",
       "profitableyear                                         NaN   \n",
       "increasedbasesalaries                            -0.005291   \n",
       "continuedachievement                              1.000000   \n",
       "continuedimprovement                             -0.005291   \n",
       "performanceabovethesuperiorlevel                 -0.005291   \n",
       "actual                                            0.047023   \n",
       "\n",
       "                                      continuedimprovement  \\\n",
       "file                                              0.014642   \n",
       "below                                             0.034350   \n",
       "awarda                                            0.033328   \n",
       "achievement                                       0.158553   \n",
       "approve                                           0.007908   \n",
       "reduction                                         0.180324   \n",
       "increased                                        -0.059831   \n",
       "growth                                            0.135589   \n",
       "exceeded                                         -0.028371   \n",
       "increasing                                       -0.027768   \n",
       "increase                                         -0.011599   \n",
       "inexcessof                                       -0.038291   \n",
       "strong                                           -0.048957   \n",
       "decrease                                          0.116845   \n",
       "belowtarget                                      -0.018557   \n",
       "loss                                             -0.024101   \n",
       "successful                                       -0.032080   \n",
       "increaseof                                       -0.018678   \n",
       "increasein                                        0.046044   \n",
       "outstandingperformance                           -0.008210   \n",
       "anincrease                                       -0.029707   \n",
       "nobonus                                          -0.009028   \n",
       "accelerate                                       -0.028325   \n",
       "successfulcompletion                             -0.005291   \n",
       "strongperformance                                -0.019024   \n",
       "increasedby                                      -0.017191   \n",
       "outperforming                                          NaN   \n",
       "salesgrowth                                      -0.010503   \n",
       "revenuegrowth                                    -0.017789   \n",
       "decidednottoincrease                                   NaN   \n",
       "...                                                    ...   \n",
       "didnotmeetthethresholdrequirementsof             -0.005291   \n",
       "millionofgrowth                                  -0.005291   \n",
       "attainmentofthismeasure                          -0.005291   \n",
       "abovetargetedperformancelevels                   -0.005291   \n",
       "reductionintheirbasesalaries                     -0.005291   \n",
       "toaloss                                                NaN   \n",
       "netsalesgrowth                                   -0.005291   \n",
       "decreasefrom                                           NaN   \n",
       "raisedthebasesalary                              -0.005291   \n",
       "increasesinbasesalary                                  NaN   \n",
       "bestperformance                                  -0.005291   \n",
       "meaningfulperformance                            -0.005291   \n",
       "increasebasesalary                                     NaN   \n",
       "performedextremelywell                                 NaN   \n",
       "rewardindividualperformance                      -0.005291   \n",
       "achievethecorporategoals                         -0.005291   \n",
       "exceedingindustryaverage                         -0.005291   \n",
       "stockvolatility                                        NaN   \n",
       "slowdownin                                       -0.005291   \n",
       "slowdown                                         -0.005291   \n",
       "increasingby                                     -0.005291   \n",
       "equityincentivesgranted                                NaN   \n",
       "abovethetotalshareholderreturn                   -0.005291   \n",
       "netincomegrowth                                  -0.005291   \n",
       "profitableyear                                         NaN   \n",
       "increasedbasesalaries                            -0.005291   \n",
       "continuedachievement                             -0.005291   \n",
       "continuedimprovement                              1.000000   \n",
       "performanceabovethesuperiorlevel                 -0.005291   \n",
       "actual                                            0.047023   \n",
       "\n",
       "                                      performanceabovethesuperiorlevel  \\\n",
       "file                                                          0.022423   \n",
       "below                                                         0.045156   \n",
       "awarda                                                       -0.040734   \n",
       "achievement                                                   0.023333   \n",
       "approve                                                      -0.017344   \n",
       "reduction                                                    -0.032481   \n",
       "increased                                                    -0.016771   \n",
       "growth                                                        0.086009   \n",
       "exceeded                                                      0.067888   \n",
       "increasing                                                   -0.027768   \n",
       "increase                                                     -0.021351   \n",
       "inexcessof                                                   -0.038291   \n",
       "strong                                                       -0.048957   \n",
       "decrease                                                      0.116845   \n",
       "belowtarget                                                  -0.018557   \n",
       "loss                                                          0.033140   \n",
       "successful                                                    0.338571   \n",
       "increaseof                                                   -0.018678   \n",
       "increasein                                                   -0.038074   \n",
       "outstandingperformance                                       -0.008210   \n",
       "anincrease                                                   -0.029707   \n",
       "nobonus                                                      -0.009028   \n",
       "accelerate                                                   -0.028325   \n",
       "successfulcompletion                                         -0.005291   \n",
       "strongperformance                                            -0.019024   \n",
       "increasedby                                                  -0.017191   \n",
       "outperforming                                                      NaN   \n",
       "salesgrowth                                                  -0.010503   \n",
       "revenuegrowth                                                 0.213711   \n",
       "decidednottoincrease                                               NaN   \n",
       "...                                                                ...   \n",
       "didnotmeetthethresholdrequirementsof                         -0.005291   \n",
       "millionofgrowth                                              -0.005291   \n",
       "attainmentofthismeasure                                      -0.005291   \n",
       "abovetargetedperformancelevels                               -0.005291   \n",
       "reductionintheirbasesalaries                                 -0.005291   \n",
       "toaloss                                                            NaN   \n",
       "netsalesgrowth                                               -0.005291   \n",
       "decreasefrom                                                       NaN   \n",
       "raisedthebasesalary                                          -0.005291   \n",
       "increasesinbasesalary                                              NaN   \n",
       "bestperformance                                              -0.005291   \n",
       "meaningfulperformance                                        -0.005291   \n",
       "increasebasesalary                                                 NaN   \n",
       "performedextremelywell                                             NaN   \n",
       "rewardindividualperformance                                  -0.005291   \n",
       "achievethecorporategoals                                     -0.005291   \n",
       "exceedingindustryaverage                                     -0.005291   \n",
       "stockvolatility                                                    NaN   \n",
       "slowdownin                                                   -0.005291   \n",
       "slowdown                                                     -0.005291   \n",
       "increasingby                                                 -0.005291   \n",
       "equityincentivesgranted                                            NaN   \n",
       "abovethetotalshareholderreturn                               -0.005291   \n",
       "netincomegrowth                                              -0.005291   \n",
       "profitableyear                                                     NaN   \n",
       "increasedbasesalaries                                        -0.005291   \n",
       "continuedachievement                                         -0.005291   \n",
       "continuedimprovement                                         -0.005291   \n",
       "performanceabovethesuperiorlevel                              1.000000   \n",
       "actual                                                        0.047023   \n",
       "\n",
       "                                        actual  \n",
       "file                                 -0.019245  \n",
       "below                                 0.129151  \n",
       "awarda                                0.083844  \n",
       "achievement                           0.143141  \n",
       "approve                               0.067311  \n",
       "reduction                             0.046630  \n",
       "increased                             0.164993  \n",
       "growth                                0.153605  \n",
       "exceeded                              0.063733  \n",
       "increasing                            0.052628  \n",
       "increase                              0.151581  \n",
       "inexcessof                           -0.090748  \n",
       "strong                                0.226120  \n",
       "decrease                              0.073628  \n",
       "belowtarget                           0.002316  \n",
       "loss                                  0.032512  \n",
       "successful                            0.158743  \n",
       "increaseof                            0.137662  \n",
       "increasein                            0.142585  \n",
       "outstandingperformance                0.072968  \n",
       "anincrease                            0.118550  \n",
       "nobonus                              -0.127173  \n",
       "accelerate                            0.007133  \n",
       "successfulcompletion                  0.047023  \n",
       "strongperformance                     0.070383  \n",
       "increasedby                           0.091073  \n",
       "outperforming                              NaN  \n",
       "salesgrowth                           0.093347  \n",
       "revenuegrowth                         0.148300  \n",
       "decidednottoincrease                       NaN  \n",
       "...                                        ...  \n",
       "didnotmeetthethresholdrequirementsof -0.059339  \n",
       "millionofgrowth                       0.047023  \n",
       "attainmentofthismeasure               0.047023  \n",
       "abovetargetedperformancelevels        0.047023  \n",
       "reductionintheirbasesalaries         -0.165700  \n",
       "toaloss                                    NaN  \n",
       "netsalesgrowth                        0.047023  \n",
       "decreasefrom                               NaN  \n",
       "raisedthebasesalary                   0.047023  \n",
       "increasesinbasesalary                      NaN  \n",
       "bestperformance                       0.047023  \n",
       "meaningfulperformance                 0.047023  \n",
       "increasebasesalary                         NaN  \n",
       "performedextremelywell                     NaN  \n",
       "rewardindividualperformance          -0.059339  \n",
       "achievethecorporategoals              0.047023  \n",
       "exceedingindustryaverage              0.047023  \n",
       "stockvolatility                            NaN  \n",
       "slowdownin                           -0.165700  \n",
       "slowdown                             -0.165700  \n",
       "increasingby                         -0.165700  \n",
       "equityincentivesgranted                    NaN  \n",
       "abovethetotalshareholderreturn        0.047023  \n",
       "netincomegrowth                       0.047023  \n",
       "profitableyear                             NaN  \n",
       "increasedbasesalaries                 0.047023  \n",
       "continuedachievement                  0.047023  \n",
       "continuedimprovement                  0.047023  \n",
       "performanceabovethesuperiorlevel      0.047023  \n",
       "actual                                1.000000  \n",
       "\n",
       "[114 rows x 114 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr(method = 'pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_y = data[['actual']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_x = data.iloc[:,1:113]\n",
    "data_x_log = (data_x+1).apply(np.log)\n",
    "#data_x_scale = normalize(data_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-97-56df4a0c2fd0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter_matrix\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[0mdata_x_log\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[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'scatter_matrix.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mscatter_matrix\u001b[0;34m(frame, alpha, figsize, ax, grid, diagonal, marker, density_kwds, hist_kwds, range_padding, **kwds)\u001b[0m\n\u001b[1;32m    347\u001b[0m     \u001b[0mnaxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m     fig, axes = _subplots(naxes=naxes, figsize=figsize, ax=ax,\n\u001b[0;32m--> 349\u001b[0;31m                           squeeze=False)\n\u001b[0m\u001b[1;32m    350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m     \u001b[0;31m# no gaps between subplots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m_subplots\u001b[0;34m(naxes, sharex, sharey, squeeze, subplot_kw, ax, layout, layout_type, **fig_kw)\u001b[0m\n\u001b[1;32m   3430\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sharex'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3431\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sharey'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3432\u001b[0;31m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3433\u001b[0m         \u001b[0maxarr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36madd_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1003\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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   1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1007\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axes/_subplots.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;31m# _axes_class is set in the subplot_class_factory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axes_class\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigbox\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     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__reduce__\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/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, rect, axisbg, frameon, sharex, sharey, label, xscale, yscale, **kwargs)\u001b[0m\n\u001b[1;32m    506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m         \u001b[0;31m# this call may differ for non-sep axes, e.g., polar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_axis\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    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxisbg\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/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_init_axis\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    566\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXAxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bottom'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 568\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'top'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    569\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mYAxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/spines.pyc\u001b[0m in \u001b[0;36mregister_axis\u001b[0;34m(self, axis)\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcla\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    164\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mcla\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    740\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 742\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_ticks\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    743\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    744\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mreset_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    754\u001b[0m         \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 756\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\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    757\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastNumMajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   1673\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1674\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1675\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mXTick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1677\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\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/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick2line\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    152\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_gridline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m_get_tick2line\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    412\u001b[0m                           \u001b[0mmarkersize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m                           \u001b[0mmarkeredgewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_width\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m                           zorder=self._zorder)\n\u001b[0m\u001b[1;32m    415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_xaxis_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tick2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/lines.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    336\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_color\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMarkerStyle\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    339\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_marker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/markers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, marker, fillstyle)\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0;31m# _recache() in set_marker.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fillstyle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfillstyle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_marker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_fillstyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfillstyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/markers.pyc\u001b[0m in \u001b[0;36mset_marker\u001b[0;34m(self, marker)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recache\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    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_path\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/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/markers.pyc\u001b[0m in \u001b[0;36m_recache\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_recache\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--> 185\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\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    186\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIdentityTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alt_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/path.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, vertices, codes, _interpolation_steps, closed, readonly)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_codes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_interpolation_steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_values\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    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/path.pyc\u001b[0m in \u001b[0;36m_update_values\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m_update_values\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[0m\u001b[1;32m    213\u001b[0m         self._should_simplify = (\n\u001b[1;32m    214\u001b[0m             \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'path.simplify'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAAsCAYAAACNKbiKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngkz+kpQgk78kJegd+1LxEWQGs3QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144e9e290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = pd.tools.plotting.scatter_matrix(pd.DataFrame(data_x_log))\n",
    "plt.tight_layout()\n",
    "plt.savefig('scatter_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#(data_x_nums < 0).any() #check if there are negatives or zeroes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sf6JNT263m0AgPMA88ki9T5G/HdnnWmoC4S4D2LboC0aPHo3H4+Hqq68m541F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O6NC2NgBtAm/JpgMMTChQspLZW5buzYsdTU1NCmTRvy8/Px+Xwcd9xx3Hfffbjdbjp37syXX35J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Mbn\nriehYxrObln0vmIs2G2MHz+epKQkBgwYwLhx48jIMP2LO5D2plLXXxpiiZ2paVpf/R5rRagn0GaF\na7+OK8RyT+0ppdRQTdMGI+d2zlRKKU3TLkDcu/QApun/f42M3POBhfqeWj7wglLqWT1C9dfAyUhF\n3gSMVkpt1ffLpimlhmuatg34tVIqX9O0Zci0fCsSG+wK4O/AQ8AHiEHIMGSmcAkyOp0CdFZK3W7s\n4SEF5iml1NAwsr2hp9uOdrSjHf9JlOt/1yHtaie9TdwJ/Buos7Zrhq3C8YiYm/QrpbYAvwcW68uP\nM4CLlVIuRPkepON5B9hsefTPSFyn75G9r+/09IqQvYSX9d/uRPbFQKz1lmiadi76TEYptRW4BXgW\n6cT+ipjjWy0/7YgXjcuAczVNyzKebwL3EvAldzYygzKGhxUETMgHETDv3av/VVg+36JfFyB7jiAj\n/yr0A8PICPOw/v1zlverI/iMT6Xle+M9AH5uucdPYPZ5JbJhb6AMseKqsby/ad5vQYNzRchMJdQO\n2arH+gj3WtPyEfBkYk2ryvL8Hv39jCXFwwTkrESWiNHf2TB/TrW8SyGyXGhAWa5/sFzvoyEMXdUR\niDfnBt5F9u0OEdCVYXpvwHp2wypzaGwSH/Ctfv0iEnMOpNGqRZbTx6HvqSEGCdaReQ3BejdWJax5\n6EHCyhjwE9CbG9GbQgaEX1vuOxryntb/oTio/+9r+c44MgIy6Jwe8t7hsCXMd4a8obb1eyzXRpn4\nPyR/bkEO/Rvv+yYB/5kXIXuJxnMuy3v6COTXhwTyZgvSZhm+S+v19ypBXIvN0b8/FCLbW/p/w7p6\nl/4/33LPVoJXk/YT0MM8JH9u1rkPIitL/08p9QXBCG3HjnuXWjGZqf03Qt8cXaSUOlPTtCuAXyql\nbtRngVOQznYz4ivQrZR6p7UciPuiK5ElgRv0z1OQinOmhWepUsqmP+sPdx3hcyQekFmowfM1YhW6\nVCllMw4Ht5Bnl/7sFQTveRo8ILPscuBSpdQ7lkPIWHmRjvUj4HR9Np2AzOLnAi9beBrTm8nTmN5C\nZWlCnjMQ61IsegO4FHg7VHex4rHqLJw8yFEIqxFUYzyG3oLyx+AJSTf0c1P5Y/BcD7wYkscrkX3v\nIF2F6M/YG/65UurfEeSxNkxWvRm4GrEA7EOgU6CxvGpCb38HPkc6rUuRwUQKcmSnxXXU+j3QRSl1\nxCJTB6A8VD+tqaPtaIj/qsPXsYKmaaehe+HQJGTMecBFmkT3nQvcpZRaBQxG1sXXtZJqOGL1CTIb\nPFW//gh91BbC01pE4iGEhyjwGMgL+W2O5Xqw/r+B3vTZNPo7GQ3YXP37NUigS+tBs6b0FpanmYgk\nj+GY2qq3/3WeZuVPBDSVPwaeBNA0baPlu7B+FQ1Y0rWWBwPDGz4BWPRmQavKdYS2AMTq+U39ehpy\n3jVa53g/D6kH7b4aY4jjvlPTNO1eZLkOJWFijJAx2chsaZ9SqlDTtFOQynYRragwOs90AkuolyHL\neOg8t+v3WXlaK08kHkJ4iAKPgctCbrndcm0skzTQm34MIvS7U3TDoRORZZ1Qnsb0FpanKTQhzwWW\n+06xfP9fxRMyS2uKp1n5E4GnqfwxcL5+v9UNldWdW7i0R4bcD4SVx4oLwnx3kZ7e7jC/hYW1LdBx\nGfo5TGAMYgkNsgUwFbFiPGYopU4MVw/aERsc98uPmqZdjqyVv6IvPc4B8pVSS/Xf9yqlemqaNgbx\nfO4G5lsbkBbyvAqcjhwXWIvMBPsiS005yF7I1cj+xjUE9ht2Rrh2IKbCSUopdxM8IOvrBs8iYImF\nx/A+0VyeAmQdfwBwH2LZZcDgARm9X440EusJ9kJi5R1g5bDoLZRnXSN6s/JE0pujmTyGPNZ3NPSG\nzvNWGN3Fiseqs2OVx9AbBOfPW5Z7DA7jc2vlscpllLdIvzeXZ3uIPkI924Qr16H3BpW3H6uOGi+i\naVoiMigYgOyBhStD4f4b9zSLpx3BOO47NWiwn/Yc8Lqx9KFp2i7EnVKLfSlqmjaNMCHv29GOdrTj\nR0a7Q2Mdx5vvx+bAGjIGwN6aDg1AL0TTADTxk3cNeuDLCy+8kFNOOYXRo0cDMG7cOBYtarlz/UOH\nDjFhwgQI9pOXHWMew9VTBTBi4cKF5OXlMXjw4GPiCSPLJuRsUi/ghhjwPInoqwzxXBFrnkwkX9KJ\nrd6CfBi287SIJ9Z1p4Hvx4ULF5rnO48FYXjC+n5s62iLnZoZMka3fDQ3ubVgh8azVcCLSItx2mmn\nsWHDBiZMmMC6des44YQT6NGjR2sd2f5HebKzsxk9ejTLli1rwBMl+GPEE9acuTGeVuot7HJHDHjC\nop2ndYhUd6DVDqcjyhPFutKOJtAWO7U84DxN04yzJdbwtk6l1O81TRuObIK3pFNbhzhF7g2kjho1\nioKCggZh4t1uN9OnT2fr1q2sXLmyJZXlG2TGUfYj8BiuC7zo3swjhb1vJayyZFh/iDJPRETiOQa9\ngex3FCOushrVWyt5QvXWztNynibrThTlyaCZXvpbCIOntVazxzWOe+tHMC2k7tI0bZlSyo8cwtyA\nuIt5RtO0fvp972ualow4NH6pFVSp6AeG120OnCO3hok/++yzcblcvPLKK1x2WahBYct5CrZviyVP\nJUBNTQ0ej4dHHnmExYsXs3jxYvr27dvUsy3mAXFqHEOelHBfxkBvKQDvfvwRVVUBv9ix4lmxamXQ\nl7Hi2bVrV1Ak6FjxrFz9TdCXUeYpR3wh8vW3+UybNs3ssKZOncrevXujxWPiu/Vrm76pHVFDm+jU\nLKa8hvmuYdZ/JhIW/R/6fZmIl4OHlVLF4dJqBNcg7rYcAAvffTNsmPiSkhKmT5/OHXfcEeSotQUY\nY+V5Ne+1WPGY+POb/8fsF54+pjQiYJ2+LxmrfYEnlFLT9H2HasSjDACffvppNPX2hC7HOuRQ9TkA\nn9Qd4NX/tzBW+WPyvL1ldbTlCcvzyNsv89Zbb8Wc593NP448HxV8H0seEx9vWtP0Te2IGtpEp4Ys\nC1kdGo9CP9CplMon4NB4DrKRPFP3NNASfIxUmIMAZUVHgsLEGzGZZs2aRXFxMXPmzOHjjz+OlFY4\nGA3ni1aeo4eKYsFzvVLqevTOJk3rQ0JycuNPtQyGLIYF6Z3RTNyCOzVNm6Yb8ZyExR3YmjVroqm3\nO3U5QMKcXAlgd9hRBSqaPNZO2sLjiLY8YXlSsjqzYcOG40Yem90eMx7rYM1mj7oDEINnebQTPh7Q\nJvbUlFJ5WnAMoXR0D/Q6vJqm2ZVSv21Juo2Z9Hvq6oJC3jscDnw+H7NmzWoJhRU7w63r10eJ56WX\nzNXWsDxRxo/BAVL5dxkfNE37KeLaiaqqqmjmzx36/wZlweVyRZMnot6iLE9Enurq6uNKnh+LJ8oI\nx9Nu0q+jrczUQhEVs369EDkR33E7kM3baoC4hISgkPc+n88a2qM1qEf20UoRc14vQHyUeH77W7M/\n34M4cPURJsxPlLAfOfRaixx2r2r89lYjX9O0w5qmuTRNqwI+NX5ITU2NZv5UIw5wfUi+mHpzOp3R\n5DmM+GKsRvRmuluKsjzWsubCkj8pKSnR5HETcBbstfJEWR7D+fRenSNWPFa91WDZK44yduvpb0OC\nq+a2d2gBtNVObSW6m50wZv1j9APazcUkxOhkLuItPQmgY9csVqxYAcC6devIzc0Neuibb77hoYce\naul7G16969G9hnfO7hptns5IZdxJwAt9LPATxLNLEpYI21GGEYH4n/q1uXc3cuTIaOrtQ6Qj24Po\n7LDxg6blRpPHAQxFDJwOY+k8oywPBJc10xXV0KFDo8lThSyj+4CvsKyeRFkeI2JGDqI707qqMZ5W\n6CwO8a4P0rYeaOTeY0GqzjNPKTVWKbWtqQfaEtrE8mMYhDXr1zStP2JSnBjpwTAYhQSGTEbCw0wC\nyO7bi4SK+rDmwnv27GHr1q3WyLzNgQsZnXVAQoJkACnd+vUmobQmmjyJBGaysRr0lCDv30H36JIa\nI54UZJDxa6RBG2v80NjRgVboLRmZsScjch0xfjj5lFM4dPBQtHhcOs+Xumy90KMYR1kea1nbhIR9\n+gnA6NGjWb9+fbR4jMC/SUgHXYpE946FPG4kb/YiqwSNHh1oZd3ZiAwGBiJlYDOBIKjRRAJSDm7U\nNC1JKfW3GHD8z6ItzdSykYJthU3/zgOglNqhlPpHpAQ0TbtR07SNmqZt0P/2IefZXkHCYPxBTxOb\nzRbRXLhXr17ccMMNYTkaQSriSaAeaWwKYsTjQBrMVMQrQiwwROfoigysMhq/vdXYo3OsRBqZWoC7\n77472npboqf/NTL7SAH4YcnH0ebpCcQDo5HwLtkA2z5eGW0ea1nLRMobuz/4Ito8RchSdDXSQacB\nbPl31HkykTwCqetNHu1oZd1xIR20A2kH9rY0gWaiCImecBcwStO0VjktP17RJjq15pr0WxDWXEkp\n9YLucXuo/tcDsXqcoJTqgrjMMvfmIpkLG2ih300vEpywDlnWMELPRJtnA3COUiqFwBJUtHEYaQAu\n0XW4vYn7W4shyLLjPqShqQOor5dYo1HUWwoi03ZklnsUwO/1RZtnI7JEdwjpsEsB/D5vtHmsZe1j\n9MFNDOR5CtnndAOPox9TiQHPdsRi2IE4Bz7L+CHKdWcTAe8y9xC7oypPKaX+rJT6DHgffdbZDkGb\n6NRo2qT/5JD7u4TEiAoL3frxCuBbPbjfdCw6DTUXzs/P52c/+xllZbJVZbO1yNQ3HjkQnqa/b5dw\nPPPmzWPdumBHA+F47r//fhYsWGB+tlg/DrPIcwKAp0aC/l5++eVUVVWRl5fHzTffDMBZZ53FJ598\nAsCUKVPYsiVckGK46aabqKgw7RqykeUTg6dZSzRer5fCwkIKCwvxekMDbIfFIQLRiGuR/UITofmz\nadMmAB566CEWLVrEZ599xogRI5g/f35TPPOQWGD3ILOpPs3hMdCCcnAiEg6lD+IVoyuAq7SCAwcO\nBPEsXryY/HwJpGzkdQt4rGXteizLtqHyWHkGDRpEWVkZNpuN6667LqwpfEg5eAK4G5lJzQOCfEmF\n6m3lypUsWLCAm266yeQxrpvAicj5TjsyADGiFzQ7bzZt2sTYsUFqoLS0lEGDBgWJhwQYjUMiMJjR\nA0Lr2zHiCU3T/HrdmQfMsBwpafNoE52aUiqPYEu+UJN+j6ZpVl38lWaEPdctjpKB15DZUy2yNAA0\nNBe22+0UFZk/M3v27JaI4dPfyU+IhZ2V59lnnyUpKQmfL2DM2Rwei/VjCYHZZpD1Y15eHqmpwdtf\nRUVFjBw5ksLCQpYvXx6xs1m5MsjrRSUys6nT/5plJbZjxw7ufPcF7nz3BXbs2NGcRzohS2l3IB3a\nYOuPofkTFxeHz+djxYoV/PPdJfgyUujfvz99+vRpiseFLKEZeRRkSRuJx0ALyoFb5/FgKQPOjul0\n69YtbHlrJY8hh5eQshZOHoPH6AAa4wkpBx7L/wbWtuF4/H6/mcbs2bND04sEPzK4OahzhK070Hje\n/P/2zjw+yur6/+/JJJM9rGHfwVzBsiruCwKKWm1xqRuK61f7dSkoVr9q60a/ahWsaH/Un1rrRkFK\nS+uCK4ILIiiLEYEbiUBIICEh+57MzO+Pc5+ZJ8NMSMIk/iTP5/XKK888yz333O3ce+6554ROCvx+\nf7iJglVuliVse6CYoMbmIa21y7F+DKJThJ6Bg8LPzAfWaq2XKaWSEUGkkdVDDPADMFBr/TOlVBrw\nf5CZuA+xdLsfCf9epbX+vVKqD9LAMs0743sPHUSCz0VRURENDQ2kpqZSUlKCz+cjLS2NY489lvLy\ncrZu3YrX66Vr167MmTOHjz76iEmTJgHwl7/8hdzcXIuFTcgKp4eh1R1IS0pJxtfoZdKkSSxYsIDJ\nkydTWlrKkCFD2LdvHyAd1+VykZGRwfz58xk8eDD3TgXFtgAAIABJREFU3nsvGRkZXHfddSxbtoxX\nX30VrTUEO2W1KY84V6ybi2+8hmXPvURqamrg7NVxxx3Hhx9+iMvlwu8CfH48Hg9+v5+GhgZSUlKo\nqanB7/fj8/no1q0bJSUlFg3rfw0y4HQdNmwYbreb2NhYGhoacLvdFBQU0Lt3b2praykoKKDe10hc\nWgrHjxzN+vXr6dOnDy6Xi7KyMmJjYxk+fDjr1q2z1EbWHuoGZEV9AfBCQkICw4cPR2vNSSedxDPP\nPMO5555Lfn7+QYIgNjaW448/nnXr1gXqacCAAeTk5JCWlmbVTyWyed+AqLhdGFVaUlISPp8v8Ofx\neKiuriYuLo7GxkZcLlegTXTr1o26ujry8/Pp2rVrwD0ZYOXJa6Vryk72b2NimP7LX/Lmm2/icrmI\nj4/H6/VSW1vLc889x4MPPkhJSQkul4uYmBj69evHI488AsD999/Pvn37iImJIT09nZycHJAJh6UJ\n8CEDaGJMXCw9u3UnOTmZpKQkZsyYwbx58ygtLaVv377k5eUxdepUKioqKCgoYM6cOZx99tk88MAD\nrF69moqKCqqrq/F4PJYBRoAHU3ZxALEJ8cTiorZWNARJSUn87W9/49prr6W2tvYglaDL5aJnz554\nvV6qqqqor6/H7/eTkJBgpVFj/roi/dsPuDzJiSTGekhMTKSiooKqqipcLhdTpkzh2muv5aqrrgIg\nLS0t0I7T09MpKipi4MCBTJw4kTfeeIOTTz6ZL774AlNOcSZbVYiKeNSwY0dTmLWLxMREJkyYwMyZ\nM1m4cCGbNm2iW7du3HrrrVx00UVcffXVXH311Zx99tmMHj2aKVOmoLVm3rx5rFy5kvfee8+azJUh\nRij/QLZOemqtiwGUUj7rt1LqemQl3Ij4JL1Ga50X5v61WutcpdT5wO8MD9XAb7XWXyrZ2PyrrW3/\nVWv9l0j3+ZHRKVZqYRAw6UdURlUmMu2XiF+4zQSNF54Fiky03uMQ4TYHCf1+jnnnHGQfpRwRPiQk\nJ1FbW0tycjIbN25kzJgxJCZKpPsVK1awb98+tm3bxrx581i7di1paWk89thjKKVYsWIF8+fPZ+rU\nqfToEdCYuRG1RjliNp4CcO7My+nbty+rVq0CCHTomJiYgIrmzjvv5I477qCyspJbbrmlSUFUV1ez\nbNkyzj33XOvWUqTT52MCSk6470YK9+UDMGPGDB555BESEhIC3hdmzZrFsCtNUXRJJKarrOZcLhcn\nnngi550nRW07/2PNlOcgHTQB4IUXXmDQoEHk5OTw6KOPctttt1FaWsqECROYNWsWfr+fuC4p9Bh/\nNPv376euro6XX36ZGTNmUFpaysUXX8wrr7xiH/TGAhOBCUhgyBqAgQMH8uyzz+LxePjiiy/48MMP\nKS8vJz4+nvHjxxMTE0PywN643G7S0tLIy8sDYNiwYXg8YrBXUVHBiy++aNGJB/4L6eBgrTxdLqqr\nqxkzZgxut5vRo0fj8/lISUkhNjaWiRMnEhcXR1xcHH379qWkpIRRo2Qx6fV6OeaYYxg1apRdyFrq\n1BLEzL4UwOWO4fPPPyctLY3Y2FgWLFgQEKAPPvggpaWluN1urrnmGhYtWkRdXR2ffvops2bNora2\nltdff51ly5Zx4MABi843iABdD/zBXBPfLY358+eTk5PDgQMH2L9/P8UlJbiSEwJe6IuLixk2bBiF\nhWIAWlZWxj//+U/mzZvHAw88AMhZNwNrdfsgZr8TwFsvk8CnnnqKY445Brfbza233kpaWlpgZZSa\nmhpYYV111VUUFRVRVlbGBRdcwCmnnAKIMDRoQLQzRabt1QP4vD7Kysqora3l4YcfxuVykZKSwtat\nWwOHr10uF8ceeyx9+/YlPT2dqqoqrrrqKoYMGcLgweLPoa4uYH+2HfgPYm6/C3NU5UBuPpMnT+b6\n669n+PDh3Hjjjdx1110cc8wxzJo1i7/+9a9kZmZiR0NDA1OmTOHdd9+lR48evPrqqyxcuNBePznI\nOBU2EoVSaizwOHC21noc8CZwv1JqTJj79ymlRiAaqnO11sciRm//UkolAr8F3tRaTwR+DpxmaEW6\n/6OiMwm1cQSNK5YDxyulypGB1aeUWoUMgn9CNuSt4w7nYKJKa60bgOeAcxHB2N/4i5yGWEAebZ4x\neORRFBUVceDAASZMmMCePXuYOXMmENwbcrlcTJ06lZSUFH71q1/Rs2dP6uvr2bRpE6eeeirffPNN\nYLaIWKMlInsCYzADjd/vJykpifr6esaNG0dJSQlJSUl06dKFlJQUpk2bRlVVFaNGjcLj8bB///7A\nIA3S8Z977rnArJjgnsBWjLAB6D2gPwAvv/wyTz75JGlpaYHBtqGhgdItPwDgSUmhsbIaXC4qKyuZ\nMWNGQOV60kknWck1IgP/ZmAlNsOc9evX06tXL37zm9+wcOFCkpOT6dmzJ/Hx8fTu3ZvGihq6j8sg\nNzc3oApNTU2lV69exMVZk+QA6kz4oA3AJOASEMe8V155JT6fD6/XywMPPEB1dTVJSUmUlZXh9/up\nLSrF7/PRo0cPunfvTlJSErGxsQwZMoTq6mpABnCDVVrrVxFXXHuRAQdPl2QSEhLYs2cPdXV1bNq0\nidraWqqqqqitrSUnJyewqt21axcDBw5kwwbxEzh+/Hi+++47EhISbOyQgAgyP6IVqARwx7qpq6uj\npKQkECrFmtyUl5cTExPD0UcfzaJFi1i8eDFz5sxh7NixDB48mAsvvJBbbrmFl19+OTQ8SjlBQRNQ\n1/Xr1w+v18uBAwdkkHW5SB3Wn++K5UjWd999R48ePWhoaGDv3r28/vrr+P1+HnvsMRYsWAA0UeOV\nIEKt3LQ3QNxkjRw5kscff5ytW7dSUVFBUVERo0ePDmSuZ8+egclLSUkJHo+HcePGsW3btsAen82Z\ndDIyKUw3/70AiWnSfioqKvjtb38bqIvi4mJ73QY0G2VlZSQlJfHVV19RXV3NZZdJgO+f/exn1qsj\ngJMQLcpHVrn1yxANBMiRiOrqau699162bNnC008/TV1dXdi96GOPFe99vXv3ZuTIkdx8883Wo0JE\nswQRjNqQfdD3tNZ7AbTWz2itb0H8Xoa7fxaiCVqplNoELDL5H4GMl3crpf6JaDx+Y2hEuv+jolMI\nNWP9OBdZsoNYP67XWqchlbkOmaWANMZhBDtyaBnFAHHG2/9bSGy2E5C9tR6I4KGqrIL09HRmzpzJ\neeedx549eygqEh/J1szOauggwsnr9RITE0OfPn3Iz8+nsrKSs846y3rlZ8hg6UbUarEAe3fuxufz\nMX78eCZNmoTf72fatGl4PB5iYmKorq4mPT1dVIRGDWiptAAKCgqYPn26/TzOGqSj/BzZXAeguEAE\n0+23386QIUPIysoKCMJVq1bR50yxtYnxxNLrzONweWLx+/3cdsfswCw0RG1kZWIUwfNKAUEzffp0\nLrvsskC+qqurKS4uJjYlkdikBGJiYprsZ4QRaHbEmnI7HWDKlCn07t2bE088kSlTpnDDDTfgcrk4\n66yziIuLw+Vy4U6QLO3cuTOiIYKtHP1Kqd7IYNkFGQjoPv5oGhsb8fl83HXXXdx+++243W6uvPJK\nkpOTKS4uZty4caSlpTFw4EAaGxsDq5iGhgbi4uJCBU0NskJPA4ZjVIRdBvalb9++xMbGkpGRwdKl\nS0lNTWXgwIGccIJEPhk1ahQ33XQTo0ePZtGiRTz22GP4/X5mz57NkiVLGD16NAUFBXZaVmWdbGgy\n5IJJfP7553Lz5JO58MILccW4cCfGE58WXJ336tWLXr16sXbtWj7//HMGDhzI8uXLmTVLvIlZ/0MQ\nMN7web18/fXXdO3alZtvvpmePSVmrX0/t7GxsYnnD5/PR2ZmJj+U7id1/FHEx8fbV7hWQ9lh6igR\nIKm7KGNSU1M5++yzAVBKMX78+CZ9s7Kykrlz5wLQtWtXLr/8cvx+f+AdSwMD/A0xDolDVu4HeUK2\nLG//8Y9/MHr0aO655x4WL17MRRddFHhmwVppulwuXnvtNe655x7r0RnIRLoJf0opeydoxLaKU0rF\nK6WOaua+G1iptZ6gtR6vtR6PGNRt0Vq/g5y9ewNZHGxRSg2NdD+U545GpxBqNGP9iJjDTtVafwjc\niKhEbsOYSwPvA7eCNADgJsTMGUQ9dzeysnvf/E4C+OGb73C5XGzcuDEww/r0Uzl/8+ijj5Kamorf\n72fJkiVs376dxYsXU1hYyFFHHcXIkSP5+uuvGT58OGVlAXuW8cje3j7gecwsfe/O3aSnp9O7d282\nbdqE1+tl9OjRjB07ltraWj744ANGjBhBYWEhxcXFpKam4na7qays5IcffuCNN94gJSXFLnC+QWbP\nnwErAGoPlJH55VeAzBotQWsN6mVlZex5U7wyeGvqKN6wDVesG3eCB39jY0BNZA2ISKc6YMptIEYt\naO0l5efnk5GRgdvtpqGhgYqKCt555x1qampoKKuk8Out9OvXj4qKCvLz88nLy6OwsJCKigr7HiRA\nslJqAmL9tg1Ru7Bu3Tp27NhBUlISmzZt4tVXXwWge/fujBs3TgR/lQjshIQEvF5vYD+otrY2IORs\nZXYqcsB7ian/zQCFazMDasSFCxfy0Ucf4fV6effdd6murqZLly589dVXVFRUkJeXR15eHvn5oubN\nysqipqaG3bsDzjxA9n59SL/1IupIqopK2LFjBz6fj7y8PFatWhVY3ezevZva2lqWLFnCiBEjSE9P\nJysri4KCArKysjj++OPJzs6mf//+9tX6x4iqbjPiAPh7gLrSCmvviJqaGpKSkvA3emmoqCY1YxBA\nQIXbpUsXvv7664Cq8u2337b2bHn0UWv+SKXhZ6kpN0BWngC/+MUv2LBhQ0CNXlNTExBk+/btC0xk\nKioqaGhowOv1EpeSSF1RKXV1dcTHB3woWH05B/Fc4geIiRGBUV5eziWXXELXrl3JzMxk48aNAUHq\n9/upqqoiJSWF1NRU8vLyWLp0KSUlJTz55JMB+gaxiFbnK0R1qwFyt31PeXk5paWlrFixAo/Hw6JF\ni0hMTGTlypWcf/75LF26lO3bt3PgwIFAG87NzeWzzz7j73//O9OmTbPz8wRQYxwa7ydovX2xra2s\nAqaayRaIU4gnTd2Gu78SONvsk6GUmoZspSQopRYBl2utlyJjYRkwMNJ9fmR0VkORF4BlWuv3Tfy0\n/UiDrzT/XwPmaq3HKKW6I/tqY5AZ2LvIBmqjUmousrHqwIEDBz8m9iHHMK4GxhhDkSuRSbffPL9e\na53fzP2LCY5njcAsrfUXSqmjgRcRNa4X+FBrfW+k+x3FcCR0VqEWsH40z3IQleTt5vW7tdaFEZI6\nFI0lwImI0cAG5BzJUESt1w9RV15rPrnEPANRW4S7diMrzQStdV0H0zkK2eweAfwPspIdSvD8jf0a\nZE9pQ8izVcD15nqEoXGa1vpzpZQHWRE+B7xknr9ieLgbyEBUxnHArxCjBWXoXA9cgah4MpDZ/jhD\n9wdEJRPgpRl+Qvk4H/HWYOcnHM9Dw5VZBDobiVw/lxg6xTStD1pJZwcS/NayJthr6FhYZmhZCG0D\n9vppSbkNRVYG88KUG4hxzsYItFrKjz2v9nJ7O+TZzjD/saVzFVCstX5XKeVCtDRnm+erkfNx9jwe\ndt8x/MQjx3xGIIN+pO+bS/uQdBw0RWdRP4YinEPjeOQ80wpks7etsGYJKxDLO5ANWOtEdDFySHMp\ngNZ6lxUeJdy11jrb/A5txB1Bp97cy0ZWqNhDudivDewbQNZJ01tstKzDZX9SSm1EBMbbiErEev4e\nIrAwNK0BKg+xLAMxZZ5M8Ezgv5BNaytP/jC8hOXHoI/tuth2PSASz4eom1A6zdUPwIAw9dFaOtjo\nYKNjYan9RygNezotKTfzTRfbO8Uh31jq/XDtraX8WAgttyZ8hPsfks5mxPJvE+L1Y5/t+X7bd1Y7\niEoftX5rcb/X3PcRn7WEjoOm6BRCTSl1AnLCHzNTOws4TylViujA79Bar0WMFuZw8IDQUhxkYWmu\n30MCkBJCp63oSDoWlgMYi9FICJSb1vpM8/8gV1ta64lmQ3q01npuCJ3/JejlvifGQbThZ7m5f6bW\nepTWeqR5Nlpr/YgtfTfhcRA/BvYBeG04flqJUDrN1U+06GCjg0XHhvZoBwFrt5ByQ2vdm9YjlB8L\noeXWKmitv9Nan6rF+OF6ZGVvjQXrzbU1FkSr7zj4kXDECzWb30e0+Hq0/D72QSyIcrXWWUqpicjK\n4Tza0JgjWVia62mYASCETlv56Ug6FqYDaLEYjYTDKTc7neb4iSYdC9Ns79lXOz8lfrDRgYPNq9uj\nHUQqt2jQsSO03A6Hhj2s1HSC/mCnIT5CD7vvOPhxccTvqSmlLkTO9Lxm209bp8ViB6XUHq31QKXU\nmcgsrg54Xmu9PnKqzdJ5neBe1yZk9jcU8eDeF9kHuhRRtVxGK/XoHUxnO3J+qKV7atMxB7Zpusdh\n0TpozyYCnc3N8HOhofNNM/yE2x+MxE8oH1dgVvU2flq8p3aY/LR4Ty0MnR2I1e6fzbf7sJnJI2bX\nl9l+t2hP7RDtwPo+tNxCy7Qt9RO6p2Yvt3B0mttT69C+Y2XM2VP7cXDECzWIbPlonu0Chuk2RL42\nTkQfjGJWHThw4KAteFg7/h+Bzhkk1AqAaSGmLQINAg6NHwJQSv0Z8cbRC/Cce+65TJw4kTPOOAOA\nK664gsWLF0dIKTLy8/OZMWMGiKFEIzKz3IXMIPu2A51nEEOZQYjBRZ9FixaxfPlyRo0adVh0wvBS\njhwkjnfodGo6m5CV6iDgunag8wxyVm0swQgX7dlHrXKLBfovWrSIPn36NJNCm+m8rbW+7bATPsLQ\nGYXaGsRkd5nN8hEApdRk4HLE48ATWlwstRTLEB9zlwF9TzjhBDIzM5kxYwabN29m5MiRod4hWotl\niIukUiTsCACR6Kxdu5YVK1YE3CaZM5UtgR9RAW1FVCbX9+nThzPOOINVq1ZFi46dl67IgU2Hzk+Y\nThsRSscP0A78WOqoT7BNCJvro1Eqt67ArD59+hxu349EZ3U0Ez1S0BmF2nLgLKWUFbPiOtuzRK31\nTUqpccg5ltYItXGInj4V4NRTT2X79u1hw9G3ESci+vktiPCkOTp1dXXMnTuXbdu2sWbNmtYINRDT\n5j7YQqhECnvfRjoWLyVIAMqA8YlD56dJp42wt+ki+4Mo82NhBHIm8JB9NErlltD8622GRccRbGFw\nxFs/AmitdwN3KKVWafHZeAuyYewCnlNKDTPvvWM8jNyOHAJuDY2ntdYnIS6ZWLNhPQ8//DBLlixh\nyZIlDB162C7RLtdaT0JmaSmEidVkD0c/adIkampqeO2115g+fXroq4fCl8Bs4JFwD6NA53Kt9SSt\n9YWGp0cA/v7Wv9HZ37c7nW++zaSwKDiGthedULRbub35ryb+PNuBzkUAS95cjsvlima7trdpEM8U\nByGK7XqZ6aNXA3zw6WoeeuihgMB68MEH2bNnz+HQCS23ywFWrf28+a9aD4tOe0XW/kmjU6zUjCnv\n1Rh/iQTN+k82Z9ieAqYbj/t/BB7QWheFTy0ijUmIJ/iuAOt/2C4tOnqYbc7S9EYcKO8HBqxZsyYQ\njv6bb77hscceY+HChRQXFzNv3jxmzZpF9+4H+VVtDk8jkZXvwxYm3h72Pgp0LF5KEbPp8QBb+sRQ\n9PpruOsb25XO3/I28f5nq0n2u9uVzuNLXmKYJ63dy21teS5Hv/tue9Gx1GisKdzFXS1NoXV0ViM+\nV8daD6Lc3p7WWu9SSk1SSn2AKbc1ezSD2q9dB8rty11apGj0ECg3Lf4fHdjQKVZqNOPQWGu9DlFJ\ngBzw7AM8ppS6qDUEtNarjeFIe82enjbp5yGup/oCfPvtt03C0Vsxzv74xz9SVFTE/Pnz+eCDD8Kn\nGB6zEeH8KcZB6jP/eJ1/v/VmNOk8rbV+yMw0/4LNEWve7px2p5PUoxsFe/e1O53y4T3ZtHlzh5Tb\n5nai00FtGqQPfmk92LBhQzT5mW0slSGk3KJMp0PLzRFo4dEpVmpa6+XGrN9CGuJR2oJXKRWjtb6m\npWnavBEcd6h3o4Sd4fT61dXVTcLRu91ufD5fIMhhS/HKKwFt60FxQfKHpFG1flNU6BiE5QWgvq6u\ng+jUdwidutqO4aeqqqpD6EQZEelUVlZGkx+rTR90/CbKdH7McnNM+g06y0otFBHN+pVSZ5qzbIfC\ndOBbrbULuAfZHP4E2fRuD9QhBzErkJAW5SAxl6qqqgIv+Xy+JnGmvvzyS373u0MHErjmmoA8r0aC\nEJZbNAA88fFRoWOwETF20ciKMGD4EmU6ecgB2mykbgJRIz3xnmjSqTZpZyH1lGM9iE+IKj9+pH2V\nIIYCAR+JycnJ0aRzACm7atq3TRchdV+JHA4PlFtKSko0+SkH1hp6XoIH95ul00oaIPWTg5TbfiQ6\neXugDuk3dyAH0S9wBFoQnVWohXNojFJqOKJvj4/8aQD2mGy9CQa6rAr/+mEjD/FWH4OoUwtAou5+\n8onEMtu8eTMZGRmBD3Jycti2bZs9AGhL8DXiJDkBmzFKv4EDA3Q2btxI//79ycrKwuv1toXOMYaP\nfchAELCy7D9oUDT5icN460esUwOaib4DB0STTgNybshqBwHfkwOiy89+w08qIqwDnhPGjRsXTTqp\nSPSDBOTsWHu16WSEnyTEA/+L1oMJEyZEkx8PorJPJcTAKhKdNvad/YanWMNXdWs+bgXyCNZ9LDLB\ncWDQmYTaQU6AjVNWu7PUbK31Uy1Mz67CTEMGmU+w7QtEGZZHcTfibLU7iFmyx+PhlFNO4frrr6ek\npCRgwTVo0CCuu+66iAlGwHBkgPFjVjZbn/8nI0aqJnQyc7K5ddGzZGdnt4WONfCPR0KUxFp0jhp1\ndDT5SUcEWwLS1uMtOsND+DlMOmXIYPYDMpD5LToZo0ZGk84ugquzCmS2zq5313DmmWdGk84eJDBo\nFSKs81vzcSuwGVkV1iNGItMB7rzzTs4666xo8vMF4t7Lg9RRYEU4depUMjMzmTBhAjfccEMg3Tb2\nnV0EA5+WEgwmHG1kI8Y1Q4FkrfVH7UTnJ4lOIdQiOQG2OWUNdf7qakGydhVmOVKW7elzrA4xYnEj\nka8LQSL3nnrqqZx++uls3LiR3/3udwedHWqlK7StyMzZjen8/kZvEzpLly5l8AVnULpRs2PHDrZt\n20ZWVhY+X4sds3xm+NmCWJ42hKMTBX6sQfkVQ6e6nej0QqJ3lyJtp74ldLxeL1lZWZSVleH1HnRC\nIxw8SDy5GGTylAHg90adH0ugxSAq1fSWfthKbEYEgBeJFp0A0NDQEG1+RgL3Im2hD2IIBcDKlSsZ\nM2YMGzdu5KWXXuK1115rKw2Q+rE8ibzRmg9biTok0sixmOMWDoLoFEKNQ1s/hhp7tKQlB1SYiICp\nMHrtTw4rp5HRiMwEC5C9qAXWg1ALri1bmm6BuFwtkdEBpAH9kVlmkwM2djrJA3pRlVfAA8teYs6K\nV5j1nxfJz89n27ZtASHXzEB9j/mfCzxqeAtL5zD5eR1ZRcWYummipokinV3Ifs1XSCwuuxFSRDrZ\n2dnMfuslvs7LJjs7NIRYWAxG6n4zMpFqMgJHkZ9xiAq6BBHWK1v6YSvxC0SolSETqUT7wyjyUwKc\ni+xxVSAWkNGmAVI/O4B8rfXvad+xYIMJp+M4Ng6BY/0oaDTWjz7z/swWJLscuFcpFRCA9utQrFmz\nht///vf06NGDRYsW4fF4Ir0aCfYToP+yPwi14IqNjQ1seHu9Xm688Ua8Xi9ud6QwY02sH08w/4cQ\nEnojlI7LFUNCt1RS+6ZTsa+Q4vED+PUrfyI5Xc71PH3B9U32QmywYmJdRlPP8YfkB+CJJ56IyEcI\nfm3+z1ZKzT5cOl6vNyB8hg8fbn90tPl/t/nfxCdSJDoAqX3TSb58Gjt37gxMAtxuN8OHDw9XX90R\nQQCitm2iH4tiufUhGAbmkuZePEz0t133C30YiR+/3092dnaL2rXBKIKx6yBYT9EsM5D66Q7NjwVR\nwHRD43bbPcf60aBTCLUwOGynxsYzSSC2k3FofD7GiWno+++88w6XXnopv/71r0MftRShzl8HYiJE\nN2fBlZ2dzQ3P/5G/3nRPJAEDiPXjq6++CrICPBkxEGi08xJKB7+/yUw2tW96k/8t4KWJ49dIdEIt\n31qBqNKxVlYgAjspKSkqdKoKDrCgYBVVq4pJTu+O3+dj9pgpDBo0CICioPeT/y/KLYoIbdNdsR0p\nicRPVlZWk3porl0bLEBUw2MJGoL1aY7GYfLTUeXmODQOg84q1MI6NVZKnQTcjKgfZ2mtw0Z5Vkqd\nAfxZaz3a9vsiRNedCLB6yb+ZnVPIH/7wB9544w1WrlxJQkICWmt2797N/v37SU5OZuzYsTz66KM8\n9thjfPnll7jdbsaOHcu9995LUlKS5ZUbxJecG1GhDQROByguLmbChAmsWrWKc845J2DBtWnTJt54\n4w3Ky8tJ7JvSmrJ5GlGnTUf2OAKd0qIzbNgwqnIL8HRPo6G4nN3/Xk1jTS1JF00OJOLz+di5c2ek\nFUcuB/vIO4hOOH5cLhf3338/KSkt4inqdFL7pgd4swm1NtF5/vnnySnOpcu4DLoM7hdIv2JfIQu2\nBIVcQ0XVYdGJdrmFQxsdAFt0PtFaP62UGoJNqDVXbiXFufQ/5+SW0ICgR5HZBH1Z9olEA+iwcmsj\nLDo7DvViZ0RnFWqRnBrfZP6OR/y2Pd9MGqHqhRrEsOI4gEmXT8eVV8zChQu5++672bFjBxkZGQwd\nOpQtW7awfPlyLr30Um644QaeffZZCgsLeeutt4iJieG+++7jiSee4KGHHrKn/wLSQS5FZpwvAZ+/\n//773HXXXQc5f33++ed55JFHeOedd9j85t8klgCxAAAX2ElEQVRbWz6bCbr5sdSRASezd999N3sr\ni+lx0mgK12aScc0FFGVmUbIlm/h+PQFZffxh81KGDh0abib9JQd7Mz+ITjh+MjMzeeedd7jssoO0\nluHQLnSslZVN2LSJzm233cbdb71M2Y6cgFCzYF/11iUG/OL+f1Fu4dBGB8AWnc3hHh6q3Eq2ZMMp\nLSETgNWuB2JcZUVynLx06dIOKbc2otly6+zoLIYiIDOz0E1Vl7lnGSrEaK3rERPm1gZAss4PAWI1\nVVVVxaJFi5g5c2ZAxTFp0iQaGhpwu91ceKHYrnz66adcccUVAbXH1VdfzaeffhqORjFiAp0MzAMo\nLy/noYceQmuNx+PhySefZOjQofh8PjweD926dcNb0+a95D4Ax9xyKX6/P0AnLi6OQReeiadrKvj9\nxLjdxCYm0FjZ9FhOcnq3w6ITjp/09HQKCwujyk9b6KT2TSe5V4/DohMXF0dcSiKN1YdXP0PPP71D\nyg2kXT/44INcfvnlzJw583AdAIdiHMCzzz7bsnKrbPMxsEM6Tm6u3Cyr1UMYQzn4kdAphJox6X+B\n4KHqgENjxNTXOptWrZTyIH4Vmzub46ep2b8H6IIYDLgACnbtoaGhgWHDhjFnzhwyMzMBURd+8skn\nHHfccQFHqaGm8F6vt4nX9RAkGF48AHl5eQGHrHPmzAnMNBMSEqivr6ekpAR3YkvOkh+Ek4Ep1o/s\n7TpAZ+bMmeS9L7YeLrcbn9dLY3UtsSlJkdJqMZ3vt21vlp/CwkLS09tkYd6Ezg/bszqETlYz/DQ0\nNNBQUU1s0uHXz6pVqzqEH7ujYTud4uJi5s6d2xYH2kCgjwZs9iPRaVJuUWhvzdGJVG7W3urst15q\nqdWqgw5EZ1E/Wib9lgl0E5N+pZTl0PgF4P8i5XJzM+kVAoOMV/8DiJCsMOn/GqBg9x7S3PEcd9xx\n9OjRg+LiYkpLS3nggQcoLy9n/fr1LF68mNNOO42xY8fy0ksv0a9fP/x+P3/6058YMWIEOTk59png\ns4g645eGl6UAe/fuZfLkyeTm5tKjRw8yMzPJzc3ljDPO4K677qKsrIz4vt0pKCiw7wEdhPz8gAx3\na61XK6W6ISqOj+uKy/khawcXnX8Bubm5dO3aleq8QhoqqvD06sbuf35MQ00tA887lZqSMuoSE2io\nqMJb38DXX39NQUEBgwcPZv/+/QFetNbZAEqp3U3pfM/0886PyI/X6+WOO+4gNze3Sf59Ph+7d4tX\novj4+EPS2ZW1g4vP/0Wr6BQUFFBXWkFDRRV1iQnUl1cekk62zuIX5/08LJ158+axv3gvaaOHN0k3\n9P+h6NRXVLNmzRpGjx7dKn7sZdaS+qmvrGb16tUcc8wxB9F5/PHHKS8vZ+7cuZxyyikBM/lW0OmG\nhIT6OD8/PyIde7n1nTyx2XZtb9MgTsft7bq2oqpZOs21A49RCRcUFJCYGDiJEODH8DQcmFVTUXlQ\ne20JWlJuDg6Gq5WHC3+yMCb9i024mReQ2Ervm2e7gGGtsYBUSj2B7L91aYfsOnDgwEFr4Jj0G3SW\nlVooDjLpB8bZzn3crbVudgNCa303tvMuAEqpp4ArER+AfwU2AAsRdzZrkLM49qiKO22/I127kZVm\ngnXQ0gjoJUgE3PakcxTiUWIE4mboxhak09z1iFAaEehsbIYfe5qR6B3ES5T5aS2dlvITibeW0NmB\nrHTeNd/ttdEJzXcknlpaP/Zyay9+fiw6LWlfodeR6MQjVoojEK8pLclzq+k4CIHf7+8UfxkZGYMz\nMjK+MNcXZWRkvGSuT8zIyHgnIyPjpIyMjLSMjIxfZWRk/KKNNJ7KyMjYl5GR4bdomGuLhj/kff+h\nriP8HpyRkfFFB9Hx28rMuj6xJWmGuw6lEYFORH7ayls0+WkDnRbx01LeItDx29q0304n0v9DPWth\nubUXPz8WnTa1r3B02vp9W+g4f8G/zmIocgISosGKg3YWcJ6JHvtn4A6t9VrE68Ac2m4quwCZWYEc\nG7BmU/ORMBHtgY6kgzkGMb+daFh0HH7ahjrbMZWOKrcjjY6DnziOeKFms3zEWDtalo99EGfGuVrr\nLKXURESNdx4i2FoNrfVuQwOttV9r/d/m+hStddbhc9PxdGzXfhuN1p0OaiUdh5/W0zG//9vipaPK\n7UijEy0aDn48dIY9tZZaPqYgB5rraP7QdbMwenTL8slrrofYng8JeT/sM9u120rX0aM7cODAQfM4\n4oVaC5wZe40z41VIIMEWQyn1EGFCxBvYXdjsjHDd3LPQ92rDeGmwewF/OMJ1c8+ae68137bmOhyN\nQ+UlGry15v3W8BBNflr6Tbh3mstnpP+HetbSvLQHPz8WnWj2nY6k48CgU5j0h5jzzwfWaq2XmWc5\nWutBUaDRxKHxokWL6NOntU5JDkZ+fr7l/9FxYurAgQMHh8ARv1ILg7DOjAGUUpMRn4+JwBNa62/D\nJxEWy5CYWl2BWX369GHAgAGH+KRVWIYcGl0dzUQdOHDg4EhCZxRqkZwZAyRqrW9SSo0DzsYm8FqA\ncUhE3YRDvNdWnIiceXEEmwMHDhxEwBFv/QgBa6c7lFKrjLXTLUAm4qfxOaXUMPPeO0qpJOB24JVW\n0nhaaz0JWenxweero8eA4HKt9SSt9dPRTtiBAwcOjhR0ipWaMeu/GrCc6AUcGpszbE8B040vxz8C\nD2iti8KnFpHGJGSl1hVgy97dzb3eFsw25+pWa61XRztxBw4cODgS0ClWagTN+i00MesHLLP++UiY\njceUUhe1hoDWerXxvdZeK6mntdYPOQLNgQMHDiKjU6zUWmHWf01L0zSeSdZjgoJ2AHaGMenPQfb9\nbgKeAc5EHCxXI77/ioEMZCV6FaCQsDllSOgaL2JNeZXhZwuQbp7VIZOeLSbth5E9vXhE8HuAOGQP\nsdo8S0GCpVpu8msNvSSTT8udeS/bt5Xm/lYgG5lg9Ed88yUhAR29wH7EEGcw8B+k7J9G1MhpwDBg\nG/A1MBFoQKIPh35fi0Q86Ah+ZiCTqQfN+1VI6JOvkECvHpNetaHViEQdrzDPfYhh0yBEA5AK7AN6\n2urvB1MmiUh/LjfP8pC2sM7QTDXpxyH7ssnAx8BkU4bfmnI4CzkbGWd4KDXXtUh7ONbQ3Q50R/yc\nZhn65SYvXyD+Docivii/Ae4LUz/fGT6Hmd+bTR67mnJsRPpXqimn701eKpF27QN6mGf7TF1Z+fkK\nuJWmbToLaRNF5t1I/NSZe9uQEFR1yJ75UFNOPZE2ercp598j7cxlysH+/Y2I16LTzL1CU86hbTLf\n8Hkawa0Ri85rtjL7G9KOZpnf32qtb8FBAJ1lpRaKgxwaWx76lVJnGi/+h8J0pEG5gHuQEO6fIJ2o\nPdAI7EbcBe0EtmutByMNfjHBGG83IYNIbyRC7l7gv5FBoBvwENJ5f4l4VDlDKXU+4nC3JzJQPId0\ntHuBPcDLwFHAm4ZGD+AjZCCxnDrHIMLTY+7PRzqfC7gfKECEwR8xq2RgEVCnte6CGPCchgzMvzT8\nZCAx6h5FnNluRAaBS4C/IELwLeBnpkzOAP4LuAgZSEK/fxSpn47gZ7bJ26PI4PYmIkB2mTpzmTzd\nhwy65wNDkFh+o5HB7EIk9tcbwAcmnz0QIXi9oelChOk2YCVSt/8036cBF5gy9SDCIM6kUW7K+Xut\n9WmI8Pg5kGt4W2DSHgeca+ifYtJ9HIkcHWu+8SGDuVU/0wkK9ljgEZM/e/1MA2YiQu9ZxPgpHkjX\nWp+ODP7K1EsJ8CHSB+qQdl1jyq0b8DuTr0STnzxEML5DsE1fh7SpAuCaZvj5o0l7FRJWahpwsY2f\n8YjQ/w7xPPS0+fvQ0HzW9v3bSN+06MwDhnNwm1xn6uIcINvwb6djldkkJCTW/wJnmHrravqvA4PO\nKtTWIO6wsJv1Gy8g4wnOzJtDQIWJCBCPua6Kak6DqEM6GUj+0sz1u8jA0x3pdFMQS0lr1bAMWUls\n1VqXI4OAHxhpVK89kBnoqUjHrEE6cj+kky4H6hEh9x0y0FoCpwcyaBQiA+BZyKBprSA+NvdvQTq2\nF5kZv2vunw/0U0r9D1KGXZBBdhXS8T82vFjlOwwRLqnIANoPGaDKkAFvHDJ7LUcGafv31mpjVUfw\nY8q2H7DWvJeFrMAGAbcB+019LDf0/wCsMHyuMc8Gmme/Bk4CnjRpJJvfaxB/pVmIAHjZ1F+hKQ8P\nIhD2IX39U8O7tapZDKQopU5ChGmt+cZarQxFBKgfGAn4Tb7SEUExV2v9uclDtamfRYbWeuAYpD/k\nIgLDXj9VyKpjGtKPYhHB0QtBV5OPHESITTZlXYi06yLAa8sPwJ9Nfo4z3x5NsE1PMd/vNC63wvJj\n2kCK+T4ZESw1Nn4akdVXjCkrS7vwtuFnjPk+Fllx/QwRVFbfq6dpmxxqy38+0n5W2+gkW2WmtW4A\nPjPlbnkXijX15sCgMwm1cUijBBlIjldKlSMdaj6A1jpba/1UhO9DYVdhpiGN9ROkA7YHEhDVzlyk\nQ/RSSmUiA5sHafwjkRVCg7lnrQhcBAXiVKTD1JpQOy5kdvuVubZUfwfMuyXIoNaotV5uvq1GVgUf\nIyvDpYiA6IZELx4I/B9kgKsHBiCzy1SkDsqQDr/b0DnV3PfSVC28HxF0pyODy8Umj15k8rEPqdci\nRI31b2CPGUDSbN+fZHj42PDQ7vwopc5DBpsyEBU4slopMnWUopS6GHgVUX0tN+8XAElKqb8hq6Xt\nJt07geORdtBg+Cs15WGVWbnhx1Jl1Rv+v0FWiJeY7z8y+ShEJkh/RQRPisnjncBvkTa11jYge5VS\nMYa2l+D44TFldJGhG4MM8N8hqraPkMH+fVOPnyPC6kZTjx+auh2NBN/9FlkFxyGD9qmIEKxDVvOv\nIgLQZ/JjtWnLuCvZpLWaYJt+BhFORxujrkj8pBl+Rht+MO++ZPjJRQTYWKSvHyA4FljtLQ5I1lp/\nFFI/aQTVqyeZez9HJjkNSL/eZ/ix6BxNsE2DTGpcAKb/WnQcGHQKoWasH+cijRJEPbJea52GzBR/\nE/KJqwXJ2lWY5UhZtqd7lv2IWuUbZGWwS2s9Blkx1COdPpugR3iQgfJaZGAYoJRaZPLYiKilpiAD\naQ+kI1lh7tOQwec9gnuG9v3XaoTnqYiKx42sFN4D/oWsap4BXkQEzS3AXYiATEHK7TtkUHWbNFII\nDiqYfHYBfoGo5Xzmvf9BBuYSk/e+yCCRh6ysUo2wKLd93wuo0E2DwLY3P+NN2nY19/dIXZUgg9MC\n5OjIV4ha6WVEoPbVWl+HrB6HIXuj1h5YgqFh8ec3Zea3leF1Nv78yCpngEm/DlmNdjdp9jJl6DLv\nnoioV39n8ppuyrOEoJreau8lSqknDa0DyErzSkN3nPn/JdLfNiDC6QAS0HIgoqorBjYhQuxjpF2P\nNnVSjwz2m8x7sUjb/TciqGIRgWi1aSs/yYjQtLfpE817W5A2HZYfk+8E8/0KkxZIfXpMuT2HaApS\nTB1ZY0GqyVc8QYHoI9imLToXmHJvQPqenU6qqQ+LzlaCbdp6Xmr4nGKj48CgUwg1Dm39GGrs0RLh\nFFBhIoNZhbF+/OSwchoZRUCN1vpUpPMVm/vnIerTMmRW9ytE8DUClyKz5D2IYNiODJrxJr3HgS+0\n1hOR1cP3iHDMRASGtbm9BUhQSnUlqBa6CumkvYArkNnsLoJCqBvSma9HBoBagkYEFyL7BNvN/cnI\n6rASGKGUmmryMtXkcz6Qr7WeDPzD0GlEBh/rrxpZZSQiar91tu9fNHQsdXNH8FOFDMaDgVilVA/k\nDGM1MrAfhTjOXofsLT2IrECmAb1N3hKRQex9U88jkT5bZ747jaDqMRuZwMQj+4urEUFbhqjOGpHV\nlBsRqHHIgFipte5m0qo07xch+2h7kMG1G6CBGJOvAyadiwx/m4FPTP0sNrSslf+Jhmfs9aOU8iDH\nbHKQFXSdoW216zKkXw1H+udOU28NSLteZ95bjbRpe34+1FofR9M2XWmuzzTlEomfeqStHYfsd241\n339l3kkyfLiQcaW/qefzDT/FiGq5zrQ1jbRpi47V914ESkzfs+jUmLpJsNGpCSmz000+47XW0x0n\n5wejU/h+hIP8P74ALNNav2+e7QKGhczkD5Ves9aPP795Jk/def9h5zs3N5cpU6ZEepyDDHCPIoPe\n+UinqUEGuwFIh3gTcyjcwBoYfYgAXIMMyFcgahfLgisG6aQ3ElS9xSAqJZf5Ps5cl5rv3MggZqnJ\nGs39HYgwHoDMSn3mv2WFVoqU5+nISmIPYkFXY9KpQQRsLMEYZTeb/HdHBuz1BK0EqxEhUGF4tr6v\n6UB+zkMMe65DJh71yP5LEcF9nBjDkzU4FSCCcYihtRZZrfVEhG4lIoSTkMnXNkSoWaq6GIL7aunI\nYDnC5K0EWd01mPetfcYcQ6sWmRhZ6XyDTBBqTL4agBMIDujHmGeZiJCuN+W9DVHP9UEMegYharbQ\n+jkaUUWORFZBu5GJRHfz7QHzrWXskWXuW1ac3Qmixlb+mcgqarVJ22rTPxj6DYfgp8J8Xw78CVlZ\nDUaE+wZkH6wXEn3eMlzqYp6XE7ScLUeMY94w+fAh7cjaAysn2CafN+mMMeWeatLoYsptKNIW/44Y\npn1m0vADC7TW/8EB0ElM+sMgovVjS2E8k0y0fiulZiNqlgSko0Qb65DO8O8IXkVePsT3M1pA48Vm\nnvVvwfcdjVmH8W1H8fPf5s/BTxdPHOL5bw/xfPwhnreUjoXDny0fweisQi2sU2NjBXYzMvuZZduc\ndeDAgQMHPwF0VqEWyanxTebveERd15pgoZsR1UZX2mel9iWi0trcDmk7cODAwRGBzmIoAqKLD91U\ndZl7jeZ3jNa6HjGJPvxgaA4cOHDgoEPRKYSaMel/geCh6oBDY8RrhnU2rdpYGPVFBJsDBw4cOPgJ\nobOoHy2T/tfM7yYm/Uopy6HxC8D/Rcrl5tYQMI6GVxuvJLOqKyrJzc1tdUZ9Ph+7d4uH/8GDB7N/\n/37r0bNa6+xWJ+jAgQMHnQiOST9tM+k33z2EnC/6sfCwORvnwIEDBw7oPCu1UBy2ST+AESgP2e8p\npeIR0/sRyBmUncgZE0KuQ39HunYjK80E56ClAwcOHDSPzirUwpr0RwPGkwCWqtBc7wq9bu5ZhPcc\ngebAgQMHh0BnFWqRTPodOHDgwMFPGJ1GqGmtdyPBEi1vII6XBwcOHDg4wtApTPodOHDgwEHngCPU\n2gcPt+C6re85cODAgYMI6DQm/Q4cOHDg4MiHs1Jz4MCBAwdHDByh5sCBAwcOjhg4Qs2BAwcOHBwx\ncISaAwcOHDg4YuAINQcOHDhwcMSg0xy+jjaUUi5gITAW8fW4Fxhmrm8E/gychvh/LETis3mB/UCR\neS8f8UF5GpCJxHfrCfRHIgpMBBqAvwFVwCzz+1ut9S0dwKYDBw4c/KTgrNTaDntMtveA02zx2RYD\npyDCaR4wHDgaeBToZ/6vA34JnANka61PB8YjDoy/A/4LuAiYhITB+V/gDK31aUBXpdT5HcOmAwcO\nHPx04Ai1tiMQkw3oDaSAxGcDfoYIqnKgG1APnGjei0M88Kebb/OBfkqp1UgE7huBZGRFVq61bgA+\nA+banBrHIis9Bw4cOHBggyPU2o40oMx23aCUssrTFfKsEegKnGTu/Ry4DRFcvYF9wFQgF3gbWdXt\nMUIRoMKkiVLqdiBZa/1R+7DlwIEDBz9dOEKt7bDHZCsH3LaYbD5EmFnPYoALgF6IIOsBrEBWZJh0\n+gLdgeeArUCqUupi2/NSpdSTwBRELenAgQMHDkLgCLW2Yw1wnrkuxKgDTXw2DYxQSnUFDgDxiHHI\ni0CJ1noicB8ivGqQlVgCkARsM/cSEbWkBzgdEWTxWuvpTmw1Bw4cOAgPx/djG2Gzfhxjbu0GjkUM\nPc4D3gBGIqu2BIJ7YOWIFWQN8DxwiUnjG2RFlgJ0AdYje29u4O/APcjeGoAfWKC1/k+7MejAgQMH\nP0E4Qs2BAwcOHBwxcNSPDhw4cODgiIEj1Bw4cODAwREDR6g5cODAgYMjBo5Qc+DAgQMHRwwcoebA\ngQMHDo4YOELNgQMHDhwcMXCEmgMHDhw4OGLgCDUHDhw4cHDE4P8BvK8Wz4dA6NcAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d86750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_x_log.hist(figsize = (5,5),bins = 10, grid = False, log = True) #usable\n",
    "#pd.DataFrame(data_x_scale).hist(figsize = (5,5),bins = 10, grid = False) #too skewed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SPLIT TO X&Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "final_data = pd.concat([data_y,data_x], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['actual',\n",
       " 'below',\n",
       " 'awarda',\n",
       " 'achievement',\n",
       " 'approve',\n",
       " 'reduction',\n",
       " 'increased',\n",
       " 'growth',\n",
       " 'exceeded',\n",
       " 'increasing',\n",
       " 'increase',\n",
       " 'inexcessof',\n",
       " 'strong',\n",
       " 'decrease',\n",
       " 'belowtarget',\n",
       " 'loss',\n",
       " 'successful',\n",
       " 'increaseof',\n",
       " 'increasein',\n",
       " 'outstandingperformance',\n",
       " 'anincrease',\n",
       " 'nobonus',\n",
       " 'accelerate',\n",
       " 'successfulcompletion',\n",
       " 'strongperformance',\n",
       " 'increasedby',\n",
       " 'outperforming',\n",
       " 'salesgrowth',\n",
       " 'revenuegrowth',\n",
       " 'decidednottoincrease',\n",
       " 'improved',\n",
       " 'successfullymanaged',\n",
       " 'noincrease',\n",
       " 'progress',\n",
       " 'outperformed',\n",
       " 'expensereduction',\n",
       " 'topquartile',\n",
       " 'higherthan',\n",
       " 'generated',\n",
       " 'notmet',\n",
       " 'atahighlevel',\n",
       " 'grantedrestrictedstock',\n",
       " 'increasedfrom',\n",
       " 'receivedabasesalaryincrease',\n",
       " 'didnotachieve',\n",
       " 'bottom',\n",
       " 'increaseinrevenues',\n",
       " 'revenueincreased',\n",
       " 'upfrom',\n",
       " 'revenueincrease',\n",
       " 'netincomeincreased',\n",
       " 'milestones',\n",
       " 'receivedasalaryincrease',\n",
       " 'achievedtarget',\n",
       " 'significantprogress',\n",
       " 'anincreaseof',\n",
       " 'grewby',\n",
       " 'stronggrowth',\n",
       " 'achievedrecordannualnetrevenues',\n",
       " 'achievementofouroverallbusinessgoals',\n",
       " 'towardsoursuccess',\n",
       " 'fellshort',\n",
       " 'grantaward',\n",
       " 'approvedadiscretionarybonus',\n",
       " 'oneofthelargest',\n",
       " 'incentivecashbonuses',\n",
       " 'receiveseverancebenefits',\n",
       " 'exceedingourtarget',\n",
       " 'exceptionalperformance',\n",
       " 'demonstratedability',\n",
       " 'continuedtoimprove',\n",
       " 'increasesinthebasesalaries',\n",
       " 'rose',\n",
       " 'highestforanyfiscalyear',\n",
       " 'significantprogressin',\n",
       " 'continuetogrow',\n",
       " 'increaseingrossprofit',\n",
       " 'recordrevenue',\n",
       " 'continuedprogress',\n",
       " 'depositsincreased',\n",
       " 'remainedstrong',\n",
       " 'annualincentiveincreasedto',\n",
       " 'evolution',\n",
       " 'emerginggrowthcompany',\n",
       " 'didnotmeetthethresholdrequirementsof',\n",
       " 'millionofgrowth',\n",
       " 'attainmentofthismeasure',\n",
       " 'abovetargetedperformancelevels',\n",
       " 'reductionintheirbasesalaries',\n",
       " 'toaloss',\n",
       " 'netsalesgrowth',\n",
       " 'decreasefrom',\n",
       " 'raisedthebasesalary',\n",
       " 'increasesinbasesalary',\n",
       " 'bestperformance',\n",
       " 'meaningfulperformance',\n",
       " 'increasebasesalary',\n",
       " 'performedextremelywell',\n",
       " 'rewardindividualperformance',\n",
       " 'achievethecorporategoals',\n",
       " 'exceedingindustryaverage',\n",
       " 'stockvolatility',\n",
       " 'slowdownin',\n",
       " 'slowdown',\n",
       " 'increasingby',\n",
       " 'equityincentivesgranted',\n",
       " 'abovethetotalshareholderreturn',\n",
       " 'netincomegrowth',\n",
       " 'profitableyear',\n",
       " 'increasedbasesalaries',\n",
       " 'continuedachievement',\n",
       " 'continuedimprovement',\n",
       " 'performanceabovethesuperiorlevel']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = list(final_data.columns.values) #usable\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stri = ''\n",
    "for i in cols:\n",
    "    stri = stri +str(i) + '+'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file+below+awarda+achievement+approve+reduction+increased+growth+exceeded+increasing+increase+inexcessof+strong+decrease+belowtarget+loss+successful+increaseof+increasein+outstandingperformance+anincrease+nobonus+accelerate+successfulcompletion+strongperformance+increasedby+outperforming+salesgrowth+revenuegrowth+decidednottoincrease+improved+successfullymanaged+noincrease+progress+outperformed+expensereduction+topquartile+higherthan+generated+notmet+atahighlevel+grantedrestrictedstock+increasedfrom+receivedabasesalaryincrease+didnotachieve+bottom+increaseinrevenues+revenueincreased+upfrom+revenueincrease+netincomeincreased+milestones+receivedasalaryincrease+achievedtarget+significantprogress+anincreaseof+grewby+stronggrowth+achievedrecordannualnetrevenues+achievementofouroverallbusinessgoals+towardsoursuccess+fellshort+grantaward+approvedadiscretionarybonus+oneofthelargest+incentivecashbonuses+receiveseverancebenefits+exceedingourtarget+exceptionalperformance+demonstratedability+continuedtoimprove+increasesinthebasesalaries+rose+highestforanyfiscalyear+significantprogressin+continuetogrow+increaseingrossprofit+recordrevenue+continuedprogress+depositsincreased+remainedstrong+annualincentiveincreasedto+evolution+emerginggrowthcompany+didnotmeetthethresholdrequirementsof+millionofgrowth+attainmentofthismeasure+abovetargetedperformancelevels+reductionintheirbasesalaries+toaloss+netsalesgrowth+decreasefrom+raisedthebasesalary+increasesinbasesalary+bestperformance+meaningfulperformance+increasebasesalary+performedextremelywell+rewardindividualperformance+achievethecorporategoals+exceedingindustryaverage+stockvolatility+slowdownin+slowdown+increasingby+equityincentivesgranted+abovethetotalshareholderreturn+netincomegrowth+profitableyear+increasedbasesalaries+continuedachievement+continuedimprovement+performanceabovethesuperiorlevel+actual+'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'Intercept', u'below', u'awarda', u'achievement', u'approve',\n",
      "       u'reduction', u'increased', u'growth', u'exceeded', u'increasing',\n",
      "       ...\n",
      "       u'slowdown', u'increasingby', u'equityincentivesgranted',\n",
      "       u'abovethetotalshareholderreturn', u'netincomegrowth',\n",
      "       u'profitableyear', u'increasedbasesalaries', u'continuedachievement',\n",
      "       u'continuedimprovement', u'performanceabovethesuperiorlevel'],\n",
      "      dtype='object', length=113)\n"
     ]
    }
   ],
   "source": [
    "#S1.1\n",
    "y_log,x_log = dmatrices('actual~below+awarda+achievement+approve+reduction+increased+growth+exceeded+increasing+increase+inexcessof+strong+decrease+belowtarget+loss+successful+increaseof+increasein+outstandingperformance+anincrease+nobonus+accelerate+successfulcompletion+strongperformance+increasedby+outperforming+salesgrowth+revenuegrowth+decidednottoincrease+improved+successfullymanaged+noincrease+progress+outperformed+expensereduction+topquartile+higherthan+generated+notmet+atahighlevel+grantedrestrictedstock+increasedfrom+receivedabasesalaryincrease+didnotachieve+bottom+increaseinrevenues+revenueincreased+upfrom+revenueincrease+netincomeincreased+milestones+receivedasalaryincrease+achievedtarget+significantprogress+anincreaseof+grewby+stronggrowth+achievedrecordannualnetrevenues+achievementofouroverallbusinessgoals+towardsoursuccess+fellshort+grantaward+approvedadiscretionarybonus+oneofthelargest+incentivecashbonuses+receiveseverancebenefits+exceedingourtarget+exceptionalperformance+demonstratedability+continuedtoimprove+increasesinthebasesalaries+rose+highestforanyfiscalyear+significantprogressin+continuetogrow+increaseingrossprofit+recordrevenue+continuedprogress+depositsincreased+remainedstrong+annualincentiveincreasedto+evolution+emerginggrowthcompany+didnotmeetthethresholdrequirementsof+millionofgrowth+attainmentofthismeasure+abovetargetedperformancelevels+reductionintheirbasesalaries+toaloss+netsalesgrowth+decreasefrom+raisedthebasesalary+increasesinbasesalary+bestperformance+meaningfulperformance+increasebasesalary+performedextremelywell+rewardindividualperformance+achievethecorporategoals+exceedingindustryaverage+stockvolatility+slowdownin+slowdown+increasingby+equityincentivesgranted+abovethetotalshareholderreturn+netincomegrowth+profitableyear+increasedbasesalaries+continuedachievement+continuedimprovement+performanceabovethesuperiorlevel',final_data,return_type = 'dataframe')\n",
    "\n",
    "\n",
    "print x_log.columns\n",
    "#flatten y into array\n",
    "y_log = np.ravel(y_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
       "       -1., -1., -1., -1., -1., -1., -1., -1.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#SPLIT\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_log,y_log,test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# svc = SVC(kernel = 'linear')\n",
    "# rfecv = RFECV(estimator = svc, step = 1, cv = StratifiedKFold(3), scoring = 'accuracy')\n",
    "# rfecv.fit(x_train, y_train)\n",
    "# plt.figure()\n",
    "# plt.plot(range(1, len(rfecv.grid_scores_) +1), rfecv.grid_scores_)\n",
    "# plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOG REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create logR model and fit with x and y\n",
    "logr = LogisticRegression() \n",
    "logr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted_logr = logr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prob_logr = logr.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "Accuracy: 0.838709677419\n",
      "ROC AUC Score: 0.587179487179\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.00      0.00      0.00        15\n",
      "        1.0       0.84      1.00      0.91        78\n",
      "\n",
      "avg / total       0.70      0.84      0.77        93\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>1.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  1.0  All\n",
       "Actual             \n",
       "0.0         15   15\n",
       "1.0         78   78\n",
       "All         93   93"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print 'Logistic Regression'\n",
    "print 'Accuracy: ' + str(metrics.accuracy_score(y_test,predicted_logr))\n",
    "print 'ROC AUC Score: ' + str(metrics.roc_auc_score(y_test,prob_logr[:,1]))\n",
    "print metrics.classification_report(y_test, predicted_logr)\n",
    "pd.crosstab(y_test,predicted_logr, rownames = ['Actual'],colnames = ['Predicted'], margins = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra = ExtraTreesClassifier()\n",
    "extra.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "featurenames = x_train.columns\n",
    "featureimportance = extra.feature_importances_\n",
    "feature_importance_list = pd.DataFrame(featureimportance, index = featurenames, columns = ['importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>approve</th>\n",
       "      <td>0.087592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>achievement</th>\n",
       "      <td>0.080485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strong</th>\n",
       "      <td>0.070003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increase</th>\n",
       "      <td>0.068069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>below</th>\n",
       "      <td>0.062606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inexcessof</th>\n",
       "      <td>0.050372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increased</th>\n",
       "      <td>0.048924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>growth</th>\n",
       "      <td>0.043662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loss</th>\n",
       "      <td>0.033271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accelerate</th>\n",
       "      <td>0.032568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reduction</th>\n",
       "      <td>0.031397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>awarda</th>\n",
       "      <td>0.030845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasein</th>\n",
       "      <td>0.027663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exceeded</th>\n",
       "      <td>0.027047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>progress</th>\n",
       "      <td>0.023310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decrease</th>\n",
       "      <td>0.023270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>successful</th>\n",
       "      <td>0.021113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasing</th>\n",
       "      <td>0.020876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>belowtarget</th>\n",
       "      <td>0.015250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strongperformance</th>\n",
       "      <td>0.013363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increasedby</th>\n",
       "      <td>0.012050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reductionintheirbasesalaries</th>\n",
       "      <td>0.011781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenuegrowth</th>\n",
       "      <td>0.011585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nobonus</th>\n",
       "      <td>0.009905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improved</th>\n",
       "      <td>0.009749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>higherthan</th>\n",
       "      <td>0.009108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anincrease</th>\n",
       "      <td>0.008848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rewardindividualperformance</th>\n",
       "      <td>0.008566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grantaward</th>\n",
       "      <td>0.008164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>milestones</th>\n",
       "      <td>0.007783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>performedextremelywell</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bestperformance</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netincomegrowth</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>evolution</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remainedstrong</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outperforming</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decidednottoincrease</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outperformed</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grantedrestrictedstock</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>receivedabasesalaryincrease</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>didnotachieve</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>increaseinrevenues</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenueincreased</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upfrom</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenueincrease</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netincomeincreased</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>achievedtarget</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>achievedrecordannualnetrevenues</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>achievementofouroverallbusinessgoals</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>towardsoursuccess</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fellshort</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>approvedadiscretionarybonus</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exceedingourtarget</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>demonstratedability</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continuedtoimprove</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continuetogrow</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continuedprogress</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depositsincreased</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>performanceabovethesuperiorlevel</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      importance\n",
       "approve                                 0.087592\n",
       "achievement                             0.080485\n",
       "strong                                  0.070003\n",
       "increase                                0.068069\n",
       "below                                   0.062606\n",
       "inexcessof                              0.050372\n",
       "increased                               0.048924\n",
       "growth                                  0.043662\n",
       "loss                                    0.033271\n",
       "accelerate                              0.032568\n",
       "reduction                               0.031397\n",
       "awarda                                  0.030845\n",
       "increasein                              0.027663\n",
       "exceeded                                0.027047\n",
       "progress                                0.023310\n",
       "decrease                                0.023270\n",
       "successful                              0.021113\n",
       "increasing                              0.020876\n",
       "belowtarget                             0.015250\n",
       "strongperformance                       0.013363\n",
       "increasedby                             0.012050\n",
       "reductionintheirbasesalaries            0.011781\n",
       "revenuegrowth                           0.011585\n",
       "nobonus                                 0.009905\n",
       "improved                                0.009749\n",
       "higherthan                              0.009108\n",
       "anincrease                              0.008848\n",
       "rewardindividualperformance             0.008566\n",
       "grantaward                              0.008164\n",
       "milestones                              0.007783\n",
       "...                                          ...\n",
       "performedextremelywell                  0.000000\n",
       "bestperformance                         0.000000\n",
       "netincomegrowth                         0.000000\n",
       "Intercept                               0.000000\n",
       "evolution                               0.000000\n",
       "remainedstrong                          0.000000\n",
       "outperforming                           0.000000\n",
       "decidednottoincrease                    0.000000\n",
       "outperformed                            0.000000\n",
       "grantedrestrictedstock                  0.000000\n",
       "receivedabasesalaryincrease             0.000000\n",
       "didnotachieve                           0.000000\n",
       "increaseinrevenues                      0.000000\n",
       "revenueincreased                        0.000000\n",
       "upfrom                                  0.000000\n",
       "revenueincrease                         0.000000\n",
       "netincomeincreased                      0.000000\n",
       "achievedtarget                          0.000000\n",
       "achievedrecordannualnetrevenues         0.000000\n",
       "achievementofouroverallbusinessgoals    0.000000\n",
       "towardsoursuccess                       0.000000\n",
       "fellshort                               0.000000\n",
       "approvedadiscretionarybonus             0.000000\n",
       "exceedingourtarget                      0.000000\n",
       "demonstratedability                     0.000000\n",
       "continuedtoimprove                      0.000000\n",
       "continuetogrow                          0.000000\n",
       "continuedprogress                       0.000000\n",
       "depositsincreased                       0.000000\n",
       "performanceabovethesuperiorlevel        0.000000\n",
       "\n",
       "[113 rows x 1 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_list.sort_values(by = ['importance'], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted_etc = extra.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prob_etc = extra.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Accuracy: 0.631578947368\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "multiclass format is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-131-542d0f0f2ed6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Random Forest'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Accuracy: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredicted_etc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0;34m'ROC AUC Score: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprob_etc\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[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted_etc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrosstab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredicted_etc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrownames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Actual'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Predicted'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmargins\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/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/metrics/ranking.pyc\u001b[0m in \u001b[0;36mroc_auc_score\u001b[0;34m(y_true, y_score, average, sample_weight)\u001b[0m\n\u001b[1;32m    258\u001b[0m     return _average_binary_score(\n\u001b[1;32m    259\u001b[0m         \u001b[0m_binary_roc_auc_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m         sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/metrics/base.pyc\u001b[0m in \u001b[0;36m_average_binary_score\u001b[0;34m(binary_metric, y_true, y_score, average, sample_weight)\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0my_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multilabel-indicator\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0} format is not supported\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\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     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: multiclass format is not supported"
     ]
    }
   ],
   "source": [
    "print 'Random Forest'\n",
    "print 'Accuracy: ' + str(metrics.accuracy_score(y_test,predicted_etc))\n",
    "print 'ROC AUC Score: ' + str(metrics.roc_auc_score(y_test,prob_etc[:,1]))\n",
    "print metrics.classification_report(y_test, predicted_etc)\n",
    "pd.crosstab(y_test,predicted_etc, rownames = ['Actual'],colnames = ['Predicted'], margins = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NEURAL NETWORK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(activation = 'logistic', solver= 'lbfgs', hidden_layer_sizes = 7, max_iter = 100, verbose = True, early_stopping = True)\n",
    "#nnmpl = Classifier(layers = [Layer(\"Sigmoid\", units = 100),Layer(\"Softmax\")],learning_rate = 0.001, n_iter = 25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mlp.fit(x_train,y_train)\n",
    "predicted_mlp = mlp.predict(x_test)\n",
    "prob_mlp = mlp.predict_proba(x_test)\n",
    "\n",
    "#nnmpl.fit(x_train,y_train)\n",
    "#predicted_nnmpl = nnmpl.predict(x_test)\n",
    "#proba_nnmpl = nnmpl.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network\n",
      "Accuracy: 0.578947368421\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.33      0.25      0.29         4\n",
      "        0.0       0.00      0.00      0.00         8\n",
      "        1.0       0.68      0.81      0.74        26\n",
      "\n",
      "avg / total       0.50      0.58      0.53        38\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>-1.0</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>-1.0</th>\n",
       "      <td>1</td>\n",
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       "      <th>0.0</th>\n",
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       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  -1.0  0.0  1.0  All\n",
       "Actual                        \n",
       "-1.0          1    1    2    4\n",
       "0.0           0    0    8    8\n",
       "1.0           2    3   21   26\n",
       "All           3    4   31   38"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print 'Neural Network'\n",
    "print 'Accuracy: ' + str(metrics.accuracy_score(y_test,predicted_mlp))\n",
    "#print 'ROC AUC Score: ' +str(metrics.roc_auc_score(y_test,prob_mlp[:,1]))\n",
    "print metrics.classification_report(y_test, predicted_mlp)\n",
    "pd.crosstab(y_test,predicted_mlp, rownames = ['Actual'],colnames = ['Predicted'], margins = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new = pd.DataFrame(y_test, columns =[''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "     0\n",
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       "2  1.0\n",
       "3  1.0\n",
       "4  1.0"
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     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 152,
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   "source": [
    "new['Predicted'] = predicted_mlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
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       "     0  Predicted\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      0  Predicted\n",
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       "2   1.0       -1.0\n",
       "3   1.0        0.0\n",
       "4   1.0        1.0\n",
       "5   1.0        1.0\n",
       "6  -1.0        1.0\n",
       "7   1.0        1.0\n",
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       "9  -1.0       -1.0\n",
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       "15  1.0        1.0\n",
       "16  0.0        1.0\n",
       "17  1.0        1.0\n",
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       "19  1.0        1.0\n",
       "20  0.0        1.0\n",
       "21  1.0        1.0\n",
       "22  1.0       -1.0\n",
       "23  1.0        1.0\n",
       "24  1.0        1.0\n",
       "25  1.0        0.0\n",
       "26  1.0        1.0\n",
       "27  0.0        1.0\n",
       "28  1.0        1.0\n",
       "29  0.0        1.0\n",
       "30 -1.0        0.0\n",
       "31  0.0        1.0\n",
       "32  1.0        1.0\n",
       "33  0.0        1.0\n",
       "34  0.0        1.0\n",
       "35  0.0        1.0\n",
       "36  1.0        1.0\n",
       "37  1.0        1.0"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.rename(columns={'0':'Actual'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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