04-feature-engineering-and-modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import time\n",
    "\n",
    "# music api imports\n",
    "import spotipy\n",
    "import spotipy.util as util\n",
    "import billboard\n",
    "import pygn\n",
    "import pylast\n",
    "\n",
    "import requests\n",
    "import re\n",
    "from bs4 import BeautifulSoup\n",
    "from time import sleep\n",
    "import pickle\n",
    "import cPickle as pk\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import cross_validation, datasets, linear_model, grid_search, metrics, ensemble, tree\n",
    "from sklearn import pipeline, manifold, preprocessing, feature_selection, decomposition\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler, MinMaxScaler, MultiLabelBinarizer\n",
    "from sklearn.learning_curve import learning_curve\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "# Classifiers\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Prepreprocess Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_songs_2 = pd.read_pickle('data/master_songs_2.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>artist</th>\n",
       "      <th>explicit</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>energy</th>\n",
       "      <th>liveness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>...</th>\n",
       "      <th>artist_origin_1</th>\n",
       "      <th>artist_origin_2</th>\n",
       "      <th>artist_type_1</th>\n",
       "      <th>artist_type_2</th>\n",
       "      <th>genre_1</th>\n",
       "      <th>genre_2</th>\n",
       "      <th>genre_3</th>\n",
       "      <th>gnid</th>\n",
       "      <th>mood_1</th>\n",
       "      <th>mood_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nirvana</td>\n",
       "      <td>False</td>\n",
       "      <td>3FUsMXBxA4V7eUwQ7B0HQO</td>\n",
       "      <td>48</td>\n",
       "      <td>Love Buzz</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.169</td>\n",
       "      <td>138.153</td>\n",
       "      <td>0.0354</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>...</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male Group</td>\n",
       "      <td>Alternative &amp; Punk</td>\n",
       "      <td>Alternative</td>\n",
       "      <td>Grunge</td>\n",
       "      <td>5007273-06983AE0CA94A42592A86F999A523B41</td>\n",
       "      <td>Brooding</td>\n",
       "      <td>Alienated / Brooding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Presidents Of The United States Of America</td>\n",
       "      <td>False</td>\n",
       "      <td>2JdzB67NvIa90K4gEZPLeX</td>\n",
       "      <td>58</td>\n",
       "      <td>Lump</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.165</td>\n",
       "      <td>142.726</td>\n",
       "      <td>0.0386</td>\n",
       "      <td>0.004680</td>\n",
       "      <td>...</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male Group</td>\n",
       "      <td>Alternative &amp; Punk</td>\n",
       "      <td>Alternative</td>\n",
       "      <td>Alternative Pop</td>\n",
       "      <td>7176919-C893DAE82E18906A0C45F2D6651DF095</td>\n",
       "      <td>Aggressive</td>\n",
       "      <td>Aggressive Power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stanton Warriors</td>\n",
       "      <td>False</td>\n",
       "      <td>0zXLMZUCYZCx8Bq4jCr174</td>\n",
       "      <td>30</td>\n",
       "      <td>So Sweet</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.118</td>\n",
       "      <td>127.997</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>...</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male Duo</td>\n",
       "      <td>Electronica</td>\n",
       "      <td>Techno</td>\n",
       "      <td>Breakbeat</td>\n",
       "      <td>542968754-3A3DE9DCC8AACF9DE2F055187073DCA2</td>\n",
       "      <td>Energizing</td>\n",
       "      <td>Abstract Beat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Robyn</td>\n",
       "      <td>False</td>\n",
       "      <td>01NJd6s7Kyn6NSsPp503sh</td>\n",
       "      <td>28</td>\n",
       "      <td>Be Mine! - Live At The Cherrytree House</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.114</td>\n",
       "      <td>116.599</td>\n",
       "      <td>0.0451</td>\n",
       "      <td>0.955000</td>\n",
       "      <td>...</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Electronica</td>\n",
       "      <td>Electronica Mainstream</td>\n",
       "      <td>Pop Electronica</td>\n",
       "      <td>176433014-A2FF98AC415031B8A67464F3DED3BC22</td>\n",
       "      <td>Romantic</td>\n",
       "      <td>Heartfelt Passion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pearl Jam</td>\n",
       "      <td>False</td>\n",
       "      <td>6qgpO647NPBUvBkm56vV7d</td>\n",
       "      <td>5</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.179</td>\n",
       "      <td>76.020</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>...</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male Group</td>\n",
       "      <td>Alternative &amp; Punk</td>\n",
       "      <td>Alternative</td>\n",
       "      <td>Grunge</td>\n",
       "      <td>17527306-1F01011247D0A98978073E66BF4D5597</td>\n",
       "      <td>Brooding</td>\n",
       "      <td>Evocative / Intriguing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           artist explicit  \\\n",
       "0                                         Nirvana    False   \n",
       "1  The Presidents Of The United States Of America    False   \n",
       "2                                Stanton Warriors    False   \n",
       "4                                           Robyn    False   \n",
       "5                                       Pearl Jam    False   \n",
       "\n",
       "                       id  popularity  \\\n",
       "0  3FUsMXBxA4V7eUwQ7B0HQO          48   \n",
       "1  2JdzB67NvIa90K4gEZPLeX          58   \n",
       "2  0zXLMZUCYZCx8Bq4jCr174          30   \n",
       "4  01NJd6s7Kyn6NSsPp503sh          28   \n",
       "5  6qgpO647NPBUvBkm56vV7d           5   \n",
       "\n",
       "                                     title  energy  liveness    tempo  \\\n",
       "0                                Love Buzz   0.885     0.169  138.153   \n",
       "1                                     Lump   0.873     0.165  142.726   \n",
       "2                                 So Sweet   0.936     0.118  127.997   \n",
       "4  Be Mine! - Live At The Cherrytree House   0.249     0.114  116.599   \n",
       "5                                    Black   0.548     0.179   76.020   \n",
       "\n",
       "   speechiness  acousticness           ...            artist_origin_1  \\\n",
       "0       0.0354      0.000032           ...              North America   \n",
       "1       0.0386      0.004680           ...              North America   \n",
       "2       0.0425      0.000531           ...             Western Europe   \n",
       "4       0.0451      0.955000           ...                Scandinavia   \n",
       "5       0.0287      0.294000           ...              North America   \n",
       "\n",
       "  artist_origin_2  artist_type_1  artist_type_2             genre_1  \\\n",
       "0   United States           Male     Male Group  Alternative & Punk   \n",
       "1   United States           Male     Male Group  Alternative & Punk   \n",
       "2  United Kingdom           Male       Male Duo         Electronica   \n",
       "4          Sweden         Female         Female         Electronica   \n",
       "5   United States           Male     Male Group  Alternative & Punk   \n",
       "\n",
       "                  genre_2          genre_3  \\\n",
       "0             Alternative           Grunge   \n",
       "1             Alternative  Alternative Pop   \n",
       "2                  Techno        Breakbeat   \n",
       "4  Electronica Mainstream  Pop Electronica   \n",
       "5             Alternative           Grunge   \n",
       "\n",
       "                                         gnid      mood_1  \\\n",
       "0    5007273-06983AE0CA94A42592A86F999A523B41    Brooding   \n",
       "1    7176919-C893DAE82E18906A0C45F2D6651DF095  Aggressive   \n",
       "2  542968754-3A3DE9DCC8AACF9DE2F055187073DCA2  Energizing   \n",
       "4  176433014-A2FF98AC415031B8A67464F3DED3BC22    Romantic   \n",
       "5   17527306-1F01011247D0A98978073E66BF4D5597    Brooding   \n",
       "\n",
       "                   mood_2  \n",
       "0    Alienated / Brooding  \n",
       "1        Aggressive Power  \n",
       "2           Abstract Beat  \n",
       "4       Heartfelt Passion  \n",
       "5  Evocative / Intriguing  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_songs_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mood_map = {'Peaceful': (-2,2), 'Easygoing': (-1,2), 'Tender': (-2,1), 'Romantic': (-1,1), \n",
    "        'Upbeat': (0,2), 'Empowering': (0,1),\n",
    "        'Lively': (1,2), 'Excited': (2,2), 'Stirring': (1,1), 'Rowdy': (2,1),\n",
    "        'Sentimental': (-2,0), 'Sophisticated': (-1,0),\n",
    "        'Sensual': (0,0), \n",
    "        'Fiery': (1,0), 'Energizing': (2,0),\n",
    "        'Melancholy': (-2,-1), 'Cool': (-1,-1), 'Somber': (-2,-2), 'Gritty': (-1,-2),\n",
    "        'Yearning': (0,-1), 'Serious': (0,-2),\n",
    "        'Urgent': (1,-1), 'Defiant': (2,-1), 'Brooding': (1,-2), 'Aggressive': (2,-2)}\n",
    "        # removing 'Other': (0,0)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reverse_mood_map = {v: k for k, v in mood_map.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "moods = list(master_songs_2['mood_1'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([          u'artist',         u'explicit',               u'id',\n",
       "             u'popularity',            u'title',           u'energy',\n",
       "               u'liveness',            u'tempo',      u'speechiness',\n",
       "           u'acousticness', u'instrumentalness',   u'time_signature',\n",
       "           u'danceability',              u'key',      u'duration_ms',\n",
       "               u'loudness',          u'valence',             u'mode',\n",
       "           u'artist_era_1',  u'artist_origin_1',  u'artist_origin_2',\n",
       "          u'artist_type_1',    u'artist_type_2',          u'genre_1',\n",
       "                u'genre_2',          u'genre_3',             u'gnid',\n",
       "                 u'mood_1',           u'mood_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_songs_2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dummies = ['explicit', 'time_signature', 'key', 'artist_era_1', 'artist_origin_1', 'artist_type_2', 'genre_1']\n",
    "drop = ['artist', 'id', 'title', 'artist_origin_2', 'artist_type_1', 'genre_2', 'genre_3', 'mood_1', 'mood_2']\n",
    "gn_drop = ['artist_era_1', 'artist_origin_1', 'artist_type_2', 'genre_1', 'gnid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# discontinued use; master_songs_3 is new feature vector\n",
    "numerical = ['explicit', 'popularity', 'energy', 'liveness',  'tempo', 'speechiness', 'acousticness', 'instrumentalness', 'danceability', 'duration_ms', 'loudness', 'valence', 'mode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# New step - removing mood 'other' from the database - 21059 records\n",
    "master_songs_3 = master_songs_2[master_songs_2['mood_1'] != 'Other'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([          u'artist',         u'explicit',               u'id',\n",
       "             u'popularity',            u'title',           u'energy',\n",
       "               u'liveness',            u'tempo',      u'speechiness',\n",
       "           u'acousticness', u'instrumentalness',   u'time_signature',\n",
       "           u'danceability',              u'key',      u'duration_ms',\n",
       "               u'loudness',          u'valence',             u'mode',\n",
       "           u'artist_era_1',  u'artist_origin_1',  u'artist_origin_2',\n",
       "          u'artist_type_1',    u'artist_type_2',          u'genre_1',\n",
       "                u'genre_2',          u'genre_3',             u'gnid',\n",
       "                 u'mood_1',           u'mood_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_songs_3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# ultimately dropped\n",
    "keys = pd.get_dummies(master_songs_3['key'], drop_first=True, prefix='key').reset_index().drop('index', axis=1)\n",
    "ts = pd.get_dummies(master_songs_3['time_signature'], drop_first=True, prefix='time_sig').reset_index().drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "explicit = pd.get_dummies(master_songs_3['explicit'], drop_first=True, prefix='explicit').reset_index().drop('index', axis=1)\n",
    "artist_era = pd.get_dummies(master_songs_3['artist_era_1'], drop_first=True, prefix='artist_era').reset_index().drop('index', axis=1)\n",
    "artist_origin = pd.get_dummies(master_songs_3['artist_origin_1'], drop_first=True, prefix='artist_origin').reset_index().drop('index', axis=1)\n",
    "artist_type = pd.get_dummies(master_songs_3['artist_type_2'], drop_first=True, prefix='artist_type').reset_index().drop('index', axis=1)\n",
    "genre = pd.get_dummies(master_songs_3['genre_1'], drop_first=True, prefix='genre').reset_index().drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_songs_target = pd.DataFrame(master_songs_3['mood_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_songs_3.to_pickle('data/master_songs_3.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "master_songs_3 = pd.read_pickle('data/master_songs_3.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "master_songs_4 = master_songs_3.drop(drop + gn_drop + ['key', 'time_signature', 'explicit', 'mode'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(master_songs_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data/scaler.pkl', 'wb') as f:\n",
    "    pk.dump(scaler,f,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaled_data = scaler.transform(master_songs_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_songs_scaled = pd.DataFrame(scaled_data, columns = master_songs_4.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "master_songs_plus = pd.concat([master_songs_scaled, explicit, keys, ts, genre, master_songs_3['mode'].reset_index().drop('index', axis=1), \n",
    "                              artist_era, artist_origin, artist_type], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>energy</th>\n",
       "      <th>liveness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>loudness</th>\n",
       "      <th>...</th>\n",
       "      <th>artist_origin_Southern Africa</th>\n",
       "      <th>artist_origin_West Africa</th>\n",
       "      <th>artist_origin_Western Europe</th>\n",
       "      <th>artist_type_Female Duo</th>\n",
       "      <th>artist_type_Female Group</th>\n",
       "      <th>artist_type_Male</th>\n",
       "      <th>artist_type_Male Duo</th>\n",
       "      <th>artist_type_Male Group</th>\n",
       "      <th>artist_type_Mixed Duo</th>\n",
       "      <th>artist_type_Mixed Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.542671</td>\n",
       "      <td>1.027451</td>\n",
       "      <td>-0.089725</td>\n",
       "      <td>0.634236</td>\n",
       "      <td>-0.479225</td>\n",
       "      <td>-0.918312</td>\n",
       "      <td>-0.034706</td>\n",
       "      <td>-0.338594</td>\n",
       "      <td>-0.293056</td>\n",
       "      <td>0.368074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.122133</td>\n",
       "      <td>0.985486</td>\n",
       "      <td>-0.116530</td>\n",
       "      <td>0.788555</td>\n",
       "      <td>-0.441018</td>\n",
       "      <td>-0.905551</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>-0.179941</td>\n",
       "      <td>-0.782808</td>\n",
       "      <td>0.895697</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.500362</td>\n",
       "      <td>1.205807</td>\n",
       "      <td>-0.431483</td>\n",
       "      <td>0.291516</td>\n",
       "      <td>-0.394452</td>\n",
       "      <td>-0.916943</td>\n",
       "      <td>1.145299</td>\n",
       "      <td>1.001753</td>\n",
       "      <td>-0.376475</td>\n",
       "      <td>0.542307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.616254</td>\n",
       "      <td>-1.196742</td>\n",
       "      <td>-0.458287</td>\n",
       "      <td>-0.093116</td>\n",
       "      <td>-0.363408</td>\n",
       "      <td>1.703642</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.006067</td>\n",
       "      <td>0.051520</td>\n",
       "      <td>0.160328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.949018</td>\n",
       "      <td>-0.151091</td>\n",
       "      <td>-0.022714</td>\n",
       "      <td>-1.462477</td>\n",
       "      <td>-0.559223</td>\n",
       "      <td>-0.111196</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>-0.852850</td>\n",
       "      <td>0.484950</td>\n",
       "      <td>-0.164949</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.643649</td>\n",
       "      <td>1.265258</td>\n",
       "      <td>1.116476</td>\n",
       "      <td>1.775443</td>\n",
       "      <td>-0.156848</td>\n",
       "      <td>-0.644940</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>-0.086937</td>\n",
       "      <td>-0.432017</td>\n",
       "      <td>0.907450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.006241</td>\n",
       "      <td>-0.511299</td>\n",
       "      <td>-0.464988</td>\n",
       "      <td>-0.313913</td>\n",
       "      <td>-0.304903</td>\n",
       "      <td>-0.760529</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.930633</td>\n",
       "      <td>-0.221802</td>\n",
       "      <td>-0.024069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.006241</td>\n",
       "      <td>0.272065</td>\n",
       "      <td>-0.498494</td>\n",
       "      <td>-0.653697</td>\n",
       "      <td>-0.325200</td>\n",
       "      <td>-0.661139</td>\n",
       "      <td>1.815352</td>\n",
       "      <td>0.553147</td>\n",
       "      <td>0.554751</td>\n",
       "      <td>0.225130</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.890348</td>\n",
       "      <td>-0.025193</td>\n",
       "      <td>-0.768549</td>\n",
       "      <td>-0.418153</td>\n",
       "      <td>-0.464898</td>\n",
       "      <td>-0.900335</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>1.652779</td>\n",
       "      <td>-0.412244</td>\n",
       "      <td>0.365850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.194993</td>\n",
       "      <td>-1.389086</td>\n",
       "      <td>3.622695</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>-0.492359</td>\n",
       "      <td>1.404372</td>\n",
       "      <td>2.091082</td>\n",
       "      <td>-0.464423</td>\n",
       "      <td>1.183785</td>\n",
       "      <td>-0.778814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.384469</td>\n",
       "      <td>-0.665174</td>\n",
       "      <td>-0.590299</td>\n",
       "      <td>1.031320</td>\n",
       "      <td>-0.396840</td>\n",
       "      <td>-0.616385</td>\n",
       "      <td>1.853895</td>\n",
       "      <td>1.269823</td>\n",
       "      <td>-0.245364</td>\n",
       "      <td>0.147622</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.542671</td>\n",
       "      <td>-1.270182</td>\n",
       "      <td>-0.203644</td>\n",
       "      <td>1.912653</td>\n",
       "      <td>-0.087596</td>\n",
       "      <td>-0.061775</td>\n",
       "      <td>1.845001</td>\n",
       "      <td>0.334315</td>\n",
       "      <td>-0.385311</td>\n",
       "      <td>-0.997837</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.079101</td>\n",
       "      <td>-0.598728</td>\n",
       "      <td>-0.498494</td>\n",
       "      <td>-0.217535</td>\n",
       "      <td>-0.349080</td>\n",
       "      <td>1.536160</td>\n",
       "      <td>1.658216</td>\n",
       "      <td>0.547676</td>\n",
       "      <td>-0.246895</td>\n",
       "      <td>0.190823</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.832402</td>\n",
       "      <td>-0.123114</td>\n",
       "      <td>-0.525298</td>\n",
       "      <td>0.868025</td>\n",
       "      <td>-0.499523</td>\n",
       "      <td>1.006261</td>\n",
       "      <td>-0.590109</td>\n",
       "      <td>-0.360477</td>\n",
       "      <td>-0.564847</td>\n",
       "      <td>-0.204497</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.527757</td>\n",
       "      <td>-0.193057</td>\n",
       "      <td>1.833496</td>\n",
       "      <td>0.257737</td>\n",
       "      <td>-0.568775</td>\n",
       "      <td>-0.610894</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.285078</td>\n",
       "      <td>-0.122944</td>\n",
       "      <td>0.219730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.527757</td>\n",
       "      <td>0.111196</td>\n",
       "      <td>-0.653960</td>\n",
       "      <td>-0.922615</td>\n",
       "      <td>-0.547283</td>\n",
       "      <td>-0.256713</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.766509</td>\n",
       "      <td>-0.292735</td>\n",
       "      <td>0.610285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.094738</td>\n",
       "      <td>-1.259691</td>\n",
       "      <td>-0.498494</td>\n",
       "      <td>-0.685890</td>\n",
       "      <td>-0.544895</td>\n",
       "      <td>1.799738</td>\n",
       "      <td>1.254998</td>\n",
       "      <td>-0.442540</td>\n",
       "      <td>0.086514</td>\n",
       "      <td>-0.670653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.384469</td>\n",
       "      <td>-2.065740</td>\n",
       "      <td>4.085073</td>\n",
       "      <td>-4.027814</td>\n",
       "      <td>0.138050</td>\n",
       "      <td>-0.610894</td>\n",
       "      <td>1.990278</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>20.919825</td>\n",
       "      <td>-2.019728</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.905985</td>\n",
       "      <td>-1.196742</td>\n",
       "      <td>-0.887829</td>\n",
       "      <td>0.369941</td>\n",
       "      <td>-0.413556</td>\n",
       "      <td>1.777773</td>\n",
       "      <td>-0.462829</td>\n",
       "      <td>-1.957953</td>\n",
       "      <td>0.512385</td>\n",
       "      <td>-1.732887</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.210631</td>\n",
       "      <td>-2.025918</td>\n",
       "      <td>-0.337667</td>\n",
       "      <td>0.650367</td>\n",
       "      <td>-0.423108</td>\n",
       "      <td>1.711878</td>\n",
       "      <td>1.933946</td>\n",
       "      <td>-2.105665</td>\n",
       "      <td>1.007058</td>\n",
       "      <td>-3.358638</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.658563</td>\n",
       "      <td>0.453917</td>\n",
       "      <td>-0.676744</td>\n",
       "      <td>1.777164</td>\n",
       "      <td>-0.198637</td>\n",
       "      <td>-0.328098</td>\n",
       "      <td>-0.265964</td>\n",
       "      <td>0.585972</td>\n",
       "      <td>-0.130854</td>\n",
       "      <td>0.304384</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.426778</td>\n",
       "      <td>0.502878</td>\n",
       "      <td>-0.518597</td>\n",
       "      <td>-0.651976</td>\n",
       "      <td>-0.543701</td>\n",
       "      <td>-0.852232</td>\n",
       "      <td>-0.614709</td>\n",
       "      <td>0.126424</td>\n",
       "      <td>0.081703</td>\n",
       "      <td>0.673498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.152684</td>\n",
       "      <td>-2.005635</td>\n",
       "      <td>-0.738394</td>\n",
       "      <td>-0.869500</td>\n",
       "      <td>-0.445794</td>\n",
       "      <td>1.709133</td>\n",
       "      <td>2.257113</td>\n",
       "      <td>-1.974365</td>\n",
       "      <td>-0.518365</td>\n",
       "      <td>-3.524136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.500362</td>\n",
       "      <td>-1.259691</td>\n",
       "      <td>-0.744425</td>\n",
       "      <td>0.395385</td>\n",
       "      <td>-0.375348</td>\n",
       "      <td>1.626765</td>\n",
       "      <td>1.299471</td>\n",
       "      <td>-0.568368</td>\n",
       "      <td>-0.698076</td>\n",
       "      <td>-1.478446</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.790093</td>\n",
       "      <td>0.943520</td>\n",
       "      <td>0.091205</td>\n",
       "      <td>-0.131889</td>\n",
       "      <td>0.005535</td>\n",
       "      <td>1.009006</td>\n",
       "      <td>-0.581719</td>\n",
       "      <td>-0.212766</td>\n",
       "      <td>-0.555950</td>\n",
       "      <td>0.251813</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.107219</td>\n",
       "      <td>-0.679162</td>\n",
       "      <td>-0.650609</td>\n",
       "      <td>-0.819860</td>\n",
       "      <td>2.966636</td>\n",
       "      <td>1.047445</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>-0.147116</td>\n",
       "      <td>-0.265439</td>\n",
       "      <td>0.030567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.890348</td>\n",
       "      <td>1.044937</td>\n",
       "      <td>0.948948</td>\n",
       "      <td>-0.415791</td>\n",
       "      <td>-0.467286</td>\n",
       "      <td>-0.912800</td>\n",
       "      <td>-0.615795</td>\n",
       "      <td>0.263195</td>\n",
       "      <td>-0.210018</td>\n",
       "      <td>0.836931</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.732147</td>\n",
       "      <td>0.013275</td>\n",
       "      <td>-1.021181</td>\n",
       "      <td>-0.317456</td>\n",
       "      <td>-0.181921</td>\n",
       "      <td>-0.861018</td>\n",
       "      <td>-0.613855</td>\n",
       "      <td>-0.015817</td>\n",
       "      <td>0.197629</td>\n",
       "      <td>-0.323140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.107219</td>\n",
       "      <td>-1.553452</td>\n",
       "      <td>-0.610402</td>\n",
       "      <td>0.739894</td>\n",
       "      <td>-0.478031</td>\n",
       "      <td>1.434573</td>\n",
       "      <td>-0.615806</td>\n",
       "      <td>-0.683255</td>\n",
       "      <td>-0.206629</td>\n",
       "      <td>0.114269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.194993</td>\n",
       "      <td>0.691724</td>\n",
       "      <td>1.176786</td>\n",
       "      <td>0.699298</td>\n",
       "      <td>-0.418332</td>\n",
       "      <td>-0.303388</td>\n",
       "      <td>-0.615225</td>\n",
       "      <td>0.060775</td>\n",
       "      <td>-0.316618</td>\n",
       "      <td>0.570578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>21029</th>\n",
       "      <td>1.122133</td>\n",
       "      <td>0.114693</td>\n",
       "      <td>-0.946799</td>\n",
       "      <td>0.687115</td>\n",
       "      <td>0.034191</td>\n",
       "      <td>-0.650705</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.263195</td>\n",
       "      <td>-0.679356</td>\n",
       "      <td>0.189870</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21030</th>\n",
       "      <td>1.527757</td>\n",
       "      <td>0.558832</td>\n",
       "      <td>-0.942108</td>\n",
       "      <td>-0.638511</td>\n",
       "      <td>0.060459</td>\n",
       "      <td>-0.868431</td>\n",
       "      <td>-0.614626</td>\n",
       "      <td>0.635209</td>\n",
       "      <td>-0.113260</td>\n",
       "      <td>0.152387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21031</th>\n",
       "      <td>0.832402</td>\n",
       "      <td>-0.563756</td>\n",
       "      <td>-0.903241</td>\n",
       "      <td>-1.006945</td>\n",
       "      <td>-0.089984</td>\n",
       "      <td>-0.832738</td>\n",
       "      <td>-0.615254</td>\n",
       "      <td>1.417535</td>\n",
       "      <td>0.012628</td>\n",
       "      <td>-0.281845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21032</th>\n",
       "      <td>0.542671</td>\n",
       "      <td>-1.266685</td>\n",
       "      <td>-0.485092</td>\n",
       "      <td>-0.757937</td>\n",
       "      <td>-0.546089</td>\n",
       "      <td>1.360442</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>-0.929441</td>\n",
       "      <td>-0.097470</td>\n",
       "      <td>0.158105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21033</th>\n",
       "      <td>-2.006964</td>\n",
       "      <td>0.530855</td>\n",
       "      <td>-0.590299</td>\n",
       "      <td>0.291989</td>\n",
       "      <td>-0.346692</td>\n",
       "      <td>0.149635</td>\n",
       "      <td>2.005102</td>\n",
       "      <td>0.875925</td>\n",
       "      <td>0.334144</td>\n",
       "      <td>0.489735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21034</th>\n",
       "      <td>0.484724</td>\n",
       "      <td>-1.507989</td>\n",
       "      <td>-0.597000</td>\n",
       "      <td>-0.667667</td>\n",
       "      <td>0.483132</td>\n",
       "      <td>1.753062</td>\n",
       "      <td>1.981383</td>\n",
       "      <td>0.093600</td>\n",
       "      <td>0.086381</td>\n",
       "      <td>-0.287404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21035</th>\n",
       "      <td>1.295972</td>\n",
       "      <td>0.030761</td>\n",
       "      <td>-0.364471</td>\n",
       "      <td>-0.032441</td>\n",
       "      <td>3.814370</td>\n",
       "      <td>-0.135906</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>1.198702</td>\n",
       "      <td>0.232526</td>\n",
       "      <td>0.227353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21036</th>\n",
       "      <td>0.658563</td>\n",
       "      <td>1.167338</td>\n",
       "      <td>-0.930046</td>\n",
       "      <td>2.352493</td>\n",
       "      <td>-0.255949</td>\n",
       "      <td>-0.918378</td>\n",
       "      <td>0.922936</td>\n",
       "      <td>-1.148274</td>\n",
       "      <td>-0.007871</td>\n",
       "      <td>0.710663</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21037</th>\n",
       "      <td>-1.311609</td>\n",
       "      <td>-0.595230</td>\n",
       "      <td>0.198423</td>\n",
       "      <td>0.017401</td>\n",
       "      <td>-0.378930</td>\n",
       "      <td>0.915656</td>\n",
       "      <td>-0.578161</td>\n",
       "      <td>0.257724</td>\n",
       "      <td>0.208559</td>\n",
       "      <td>-1.042308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21038</th>\n",
       "      <td>0.600617</td>\n",
       "      <td>0.695221</td>\n",
       "      <td>1.237097</td>\n",
       "      <td>-0.146771</td>\n",
       "      <td>-0.393258</td>\n",
       "      <td>0.086487</td>\n",
       "      <td>-0.111792</td>\n",
       "      <td>0.235840</td>\n",
       "      <td>-0.534035</td>\n",
       "      <td>0.168111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21039</th>\n",
       "      <td>-0.268577</td>\n",
       "      <td>0.838605</td>\n",
       "      <td>-0.721641</td>\n",
       "      <td>1.425974</td>\n",
       "      <td>0.351793</td>\n",
       "      <td>-0.914447</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>-1.432756</td>\n",
       "      <td>-0.638201</td>\n",
       "      <td>0.238789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21040</th>\n",
       "      <td>-1.659287</td>\n",
       "      <td>-1.326137</td>\n",
       "      <td>-0.498494</td>\n",
       "      <td>-0.452270</td>\n",
       "      <td>-0.437436</td>\n",
       "      <td>1.717370</td>\n",
       "      <td>-0.615719</td>\n",
       "      <td>-0.836438</td>\n",
       "      <td>-0.342521</td>\n",
       "      <td>-0.589175</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21041</th>\n",
       "      <td>0.832402</td>\n",
       "      <td>-1.549955</td>\n",
       "      <td>-0.471689</td>\n",
       "      <td>-1.880348</td>\n",
       "      <td>-0.509075</td>\n",
       "      <td>1.618528</td>\n",
       "      <td>2.147414</td>\n",
       "      <td>0.006067</td>\n",
       "      <td>-0.253032</td>\n",
       "      <td>-1.387121</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21042</th>\n",
       "      <td>-0.790093</td>\n",
       "      <td>0.995977</td>\n",
       "      <td>0.587088</td>\n",
       "      <td>0.386173</td>\n",
       "      <td>-0.320425</td>\n",
       "      <td>-0.281423</td>\n",
       "      <td>1.216455</td>\n",
       "      <td>-0.841908</td>\n",
       "      <td>-0.406597</td>\n",
       "      <td>0.395392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21043</th>\n",
       "      <td>-0.558308</td>\n",
       "      <td>0.583312</td>\n",
       "      <td>-0.371172</td>\n",
       "      <td>-1.107845</td>\n",
       "      <td>1.008489</td>\n",
       "      <td>0.907419</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.591443</td>\n",
       "      <td>-0.275359</td>\n",
       "      <td>0.688904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21044</th>\n",
       "      <td>0.079101</td>\n",
       "      <td>1.006469</td>\n",
       "      <td>1.766485</td>\n",
       "      <td>0.224700</td>\n",
       "      <td>1.545785</td>\n",
       "      <td>-0.613640</td>\n",
       "      <td>-0.593430</td>\n",
       "      <td>1.778608</td>\n",
       "      <td>-0.324013</td>\n",
       "      <td>0.871555</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21045</th>\n",
       "      <td>0.832402</td>\n",
       "      <td>-0.214040</td>\n",
       "      <td>-0.511896</td>\n",
       "      <td>0.762503</td>\n",
       "      <td>-0.437436</td>\n",
       "      <td>-0.651529</td>\n",
       "      <td>-0.592926</td>\n",
       "      <td>0.082658</td>\n",
       "      <td>-0.188714</td>\n",
       "      <td>0.349015</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21046</th>\n",
       "      <td>0.600617</td>\n",
       "      <td>-0.756100</td>\n",
       "      <td>-0.451586</td>\n",
       "      <td>-1.200712</td>\n",
       "      <td>-0.426690</td>\n",
       "      <td>1.135304</td>\n",
       "      <td>-0.615810</td>\n",
       "      <td>-0.475364</td>\n",
       "      <td>-0.140362</td>\n",
       "      <td>0.329320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21047</th>\n",
       "      <td>0.310886</td>\n",
       "      <td>0.793142</td>\n",
       "      <td>-0.574216</td>\n",
       "      <td>0.621345</td>\n",
       "      <td>-0.419526</td>\n",
       "      <td>-0.339080</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.296019</td>\n",
       "      <td>-0.268768</td>\n",
       "      <td>0.529601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21048</th>\n",
       "      <td>0.542671</td>\n",
       "      <td>0.744182</td>\n",
       "      <td>-0.982315</td>\n",
       "      <td>0.022159</td>\n",
       "      <td>-0.504299</td>\n",
       "      <td>-0.909176</td>\n",
       "      <td>-0.610715</td>\n",
       "      <td>0.306961</td>\n",
       "      <td>-0.032728</td>\n",
       "      <td>0.879179</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21049</th>\n",
       "      <td>0.774456</td>\n",
       "      <td>0.485392</td>\n",
       "      <td>-0.785302</td>\n",
       "      <td>-1.258721</td>\n",
       "      <td>-0.531761</td>\n",
       "      <td>-0.677063</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.181132</td>\n",
       "      <td>-0.162005</td>\n",
       "      <td>0.794365</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21050</th>\n",
       "      <td>0.194993</td>\n",
       "      <td>0.856091</td>\n",
       "      <td>-0.966902</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>-0.181921</td>\n",
       "      <td>-0.916309</td>\n",
       "      <td>0.537507</td>\n",
       "      <td>1.559776</td>\n",
       "      <td>0.869083</td>\n",
       "      <td>0.446852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21051</th>\n",
       "      <td>0.137047</td>\n",
       "      <td>-0.350429</td>\n",
       "      <td>-0.290759</td>\n",
       "      <td>1.830584</td>\n",
       "      <td>-0.234457</td>\n",
       "      <td>0.039812</td>\n",
       "      <td>-0.604608</td>\n",
       "      <td>0.235840</td>\n",
       "      <td>-0.439038</td>\n",
       "      <td>-0.120319</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21052</th>\n",
       "      <td>1.353918</td>\n",
       "      <td>0.212613</td>\n",
       "      <td>-0.284058</td>\n",
       "      <td>0.700749</td>\n",
       "      <td>-0.537731</td>\n",
       "      <td>-0.882434</td>\n",
       "      <td>-0.615781</td>\n",
       "      <td>-0.327653</td>\n",
       "      <td>0.463966</td>\n",
       "      <td>-0.099989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21053</th>\n",
       "      <td>-0.500362</td>\n",
       "      <td>-1.661864</td>\n",
       "      <td>-0.478390</td>\n",
       "      <td>-0.491077</td>\n",
       "      <td>-0.470868</td>\n",
       "      <td>1.775027</td>\n",
       "      <td>-0.610448</td>\n",
       "      <td>0.531264</td>\n",
       "      <td>-0.940895</td>\n",
       "      <td>-1.222259</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21054</th>\n",
       "      <td>-0.732147</td>\n",
       "      <td>0.254579</td>\n",
       "      <td>-0.485092</td>\n",
       "      <td>0.360256</td>\n",
       "      <td>-0.563999</td>\n",
       "      <td>-0.001372</td>\n",
       "      <td>-0.615757</td>\n",
       "      <td>0.443731</td>\n",
       "      <td>-0.261421</td>\n",
       "      <td>0.429858</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21055</th>\n",
       "      <td>-0.848039</td>\n",
       "      <td>0.422443</td>\n",
       "      <td>0.915443</td>\n",
       "      <td>0.452516</td>\n",
       "      <td>-0.390870</td>\n",
       "      <td>-0.916311</td>\n",
       "      <td>1.237209</td>\n",
       "      <td>0.274136</td>\n",
       "      <td>0.431605</td>\n",
       "      <td>0.126816</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21056</th>\n",
       "      <td>0.021154</td>\n",
       "      <td>-1.760834</td>\n",
       "      <td>-0.478390</td>\n",
       "      <td>0.053036</td>\n",
       "      <td>-0.426690</td>\n",
       "      <td>1.769536</td>\n",
       "      <td>2.185957</td>\n",
       "      <td>-1.569526</td>\n",
       "      <td>0.887870</td>\n",
       "      <td>-1.491311</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21057</th>\n",
       "      <td>-1.427502</td>\n",
       "      <td>1.139361</td>\n",
       "      <td>-0.531999</td>\n",
       "      <td>0.114487</td>\n",
       "      <td>-0.204607</td>\n",
       "      <td>-0.804459</td>\n",
       "      <td>2.117766</td>\n",
       "      <td>-0.486306</td>\n",
       "      <td>0.399570</td>\n",
       "      <td>0.578202</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21058</th>\n",
       "      <td>0.890348</td>\n",
       "      <td>-0.137103</td>\n",
       "      <td>0.158216</td>\n",
       "      <td>-0.921333</td>\n",
       "      <td>-0.499523</td>\n",
       "      <td>-0.330844</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.706330</td>\n",
       "      <td>-0.065247</td>\n",
       "      <td>-0.451631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21059 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       popularity    energy  liveness     tempo  speechiness  acousticness  \\\n",
       "0        0.542671  1.027451 -0.089725  0.634236    -0.479225     -0.918312   \n",
       "1        1.122133  0.985486 -0.116530  0.788555    -0.441018     -0.905551   \n",
       "2       -0.500362  1.205807 -0.431483  0.291516    -0.394452     -0.916943   \n",
       "3       -0.616254 -1.196742 -0.458287 -0.093116    -0.363408      1.703642   \n",
       "4       -1.949018 -0.151091 -0.022714 -1.462477    -0.559223     -0.111196   \n",
       "5        1.643649  1.265258  1.116476  1.775443    -0.156848     -0.644940   \n",
       "6        1.006241 -0.511299 -0.464988 -0.313913    -0.304903     -0.760529   \n",
       "7        1.006241  0.272065 -0.498494 -0.653697    -0.325200     -0.661139   \n",
       "8        0.890348 -0.025193 -0.768549 -0.418153    -0.464898     -0.900335   \n",
       "9        0.194993 -1.389086  3.622695  0.156433    -0.492359      1.404372   \n",
       "10      -0.384469 -0.665174 -0.590299  1.031320    -0.396840     -0.616385   \n",
       "11       0.542671 -1.270182 -0.203644  1.912653    -0.087596     -0.061775   \n",
       "12       0.079101 -0.598728 -0.498494 -0.217535    -0.349080      1.536160   \n",
       "13       0.832402 -0.123114 -0.525298  0.868025    -0.499523      1.006261   \n",
       "14       1.527757 -0.193057  1.833496  0.257737    -0.568775     -0.610894   \n",
       "15       1.527757  0.111196 -0.653960 -0.922615    -0.547283     -0.256713   \n",
       "16      -0.094738 -1.259691 -0.498494 -0.685890    -0.544895      1.799738   \n",
       "17      -0.384469 -2.065740  4.085073 -4.027814     0.138050     -0.610894   \n",
       "18      -0.905985 -1.196742 -0.887829  0.369941    -0.413556      1.777773   \n",
       "19      -0.210631 -2.025918 -0.337667  0.650367    -0.423108      1.711878   \n",
       "20       0.658563  0.453917 -0.676744  1.777164    -0.198637     -0.328098   \n",
       "21       0.426778  0.502878 -0.518597 -0.651976    -0.543701     -0.852232   \n",
       "22      -0.152684 -2.005635 -0.738394 -0.869500    -0.445794      1.709133   \n",
       "23      -0.500362 -1.259691 -0.744425  0.395385    -0.375348      1.626765   \n",
       "24      -0.790093  0.943520  0.091205 -0.131889     0.005535      1.009006   \n",
       "25       2.107219 -0.679162 -0.650609 -0.819860     2.966636      1.047445   \n",
       "26       0.890348  1.044937  0.948948 -0.415791    -0.467286     -0.912800   \n",
       "27      -0.732147  0.013275 -1.021181 -0.317456    -0.181921     -0.861018   \n",
       "28       2.107219 -1.553452 -0.610402  0.739894    -0.478031      1.434573   \n",
       "29       0.194993  0.691724  1.176786  0.699298    -0.418332     -0.303388   \n",
       "...           ...       ...       ...       ...          ...           ...   \n",
       "21029    1.122133  0.114693 -0.946799  0.687115     0.034191     -0.650705   \n",
       "21030    1.527757  0.558832 -0.942108 -0.638511     0.060459     -0.868431   \n",
       "21031    0.832402 -0.563756 -0.903241 -1.006945    -0.089984     -0.832738   \n",
       "21032    0.542671 -1.266685 -0.485092 -0.757937    -0.546089      1.360442   \n",
       "21033   -2.006964  0.530855 -0.590299  0.291989    -0.346692      0.149635   \n",
       "21034    0.484724 -1.507989 -0.597000 -0.667667     0.483132      1.753062   \n",
       "21035    1.295972  0.030761 -0.364471 -0.032441     3.814370     -0.135906   \n",
       "21036    0.658563  1.167338 -0.930046  2.352493    -0.255949     -0.918378   \n",
       "21037   -1.311609 -0.595230  0.198423  0.017401    -0.378930      0.915656   \n",
       "21038    0.600617  0.695221  1.237097 -0.146771    -0.393258      0.086487   \n",
       "21039   -0.268577  0.838605 -0.721641  1.425974     0.351793     -0.914447   \n",
       "21040   -1.659287 -1.326137 -0.498494 -0.452270    -0.437436      1.717370   \n",
       "21041    0.832402 -1.549955 -0.471689 -1.880348    -0.509075      1.618528   \n",
       "21042   -0.790093  0.995977  0.587088  0.386173    -0.320425     -0.281423   \n",
       "21043   -0.558308  0.583312 -0.371172 -1.107845     1.008489      0.907419   \n",
       "21044    0.079101  1.006469  1.766485  0.224700     1.545785     -0.613640   \n",
       "21045    0.832402 -0.214040 -0.511896  0.762503    -0.437436     -0.651529   \n",
       "21046    0.600617 -0.756100 -0.451586 -1.200712    -0.426690      1.135304   \n",
       "21047    0.310886  0.793142 -0.574216  0.621345    -0.419526     -0.339080   \n",
       "21048    0.542671  0.744182 -0.982315  0.022159    -0.504299     -0.909176   \n",
       "21049    0.774456  0.485392 -0.785302 -1.258721    -0.531761     -0.677063   \n",
       "21050    0.194993  0.856091 -0.966902  0.156500    -0.181921     -0.916309   \n",
       "21051    0.137047 -0.350429 -0.290759  1.830584    -0.234457      0.039812   \n",
       "21052    1.353918  0.212613 -0.284058  0.700749    -0.537731     -0.882434   \n",
       "21053   -0.500362 -1.661864 -0.478390 -0.491077    -0.470868      1.775027   \n",
       "21054   -0.732147  0.254579 -0.485092  0.360256    -0.563999     -0.001372   \n",
       "21055   -0.848039  0.422443  0.915443  0.452516    -0.390870     -0.916311   \n",
       "21056    0.021154 -1.760834 -0.478390  0.053036    -0.426690      1.769536   \n",
       "21057   -1.427502  1.139361 -0.531999  0.114487    -0.204607     -0.804459   \n",
       "21058    0.890348 -0.137103  0.158216 -0.921333    -0.499523     -0.330844   \n",
       "\n",
       "       instrumentalness  danceability  duration_ms  loudness  \\\n",
       "0             -0.034706     -0.338594    -0.293056  0.368074   \n",
       "1             -0.615815     -0.179941    -0.782808  0.895697   \n",
       "2              1.145299      1.001753    -0.376475  0.542307   \n",
       "3             -0.615815      0.006067     0.051520  0.160328   \n",
       "4             -0.615815     -0.852850     0.484950 -0.164949   \n",
       "5             -0.615815     -0.086937    -0.432017  0.907450   \n",
       "6             -0.615815      0.930633    -0.221802 -0.024069   \n",
       "7              1.815352      0.553147     0.554751  0.225130   \n",
       "8             -0.615815      1.652779    -0.412244  0.365850   \n",
       "9              2.091082     -0.464423     1.183785 -0.778814   \n",
       "10             1.853895      1.269823    -0.245364  0.147622   \n",
       "11             1.845001      0.334315    -0.385311 -0.997837   \n",
       "12             1.658216      0.547676    -0.246895  0.190823   \n",
       "13            -0.590109     -0.360477    -0.564847 -0.204497   \n",
       "14            -0.615815      0.285078    -0.122944  0.219730   \n",
       "15            -0.615815      0.766509    -0.292735  0.610285   \n",
       "16             1.254998     -0.442540     0.086514 -0.670653   \n",
       "17             1.990278      0.000774    20.919825 -2.019728   \n",
       "18            -0.462829     -1.957953     0.512385 -1.732887   \n",
       "19             1.933946     -2.105665     1.007058 -3.358638   \n",
       "20            -0.265964      0.585972    -0.130854  0.304384   \n",
       "21            -0.614709      0.126424     0.081703  0.673498   \n",
       "22             2.257113     -1.974365    -0.518365 -3.524136   \n",
       "23             1.299471     -0.568368    -0.698076 -1.478446   \n",
       "24            -0.581719     -0.212766    -0.555950  0.251813   \n",
       "25            -0.615815     -0.147116    -0.265439  0.030567   \n",
       "26            -0.615795      0.263195    -0.210018  0.836931   \n",
       "27            -0.613855     -0.015817     0.197629 -0.323140   \n",
       "28            -0.615806     -0.683255    -0.206629  0.114269   \n",
       "29            -0.615225      0.060775    -0.316618  0.570578   \n",
       "...                 ...           ...          ...       ...   \n",
       "21029         -0.615815      0.263195    -0.679356  0.189870   \n",
       "21030         -0.614626      0.635209    -0.113260  0.152387   \n",
       "21031         -0.615254      1.417535     0.012628 -0.281845   \n",
       "21032         -0.615815     -0.929441    -0.097470  0.158105   \n",
       "21033          2.005102      0.875925     0.334144  0.489735   \n",
       "21034          1.981383      0.093600     0.086381 -0.287404   \n",
       "21035         -0.615815      1.198702     0.232526  0.227353   \n",
       "21036          0.922936     -1.148274    -0.007871  0.710663   \n",
       "21037         -0.578161      0.257724     0.208559 -1.042308   \n",
       "21038         -0.111792      0.235840    -0.534035  0.168111   \n",
       "21039         -0.615815     -1.432756    -0.638201  0.238789   \n",
       "21040         -0.615719     -0.836438    -0.342521 -0.589175   \n",
       "21041          2.147414      0.006067    -0.253032 -1.387121   \n",
       "21042          1.216455     -0.841908    -0.406597  0.395392   \n",
       "21043         -0.615815      0.591443    -0.275359  0.688904   \n",
       "21044         -0.593430      1.778608    -0.324013  0.871555   \n",
       "21045         -0.592926      0.082658    -0.188714  0.349015   \n",
       "21046         -0.615810     -0.475364    -0.140362  0.329320   \n",
       "21047         -0.615815      0.296019    -0.268768  0.529601   \n",
       "21048         -0.610715      0.306961    -0.032728  0.879179   \n",
       "21049         -0.615815      0.181132    -0.162005  0.794365   \n",
       "21050          0.537507      1.559776     0.869083  0.446852   \n",
       "21051         -0.604608      0.235840    -0.439038 -0.120319   \n",
       "21052         -0.615781     -0.327653     0.463966 -0.099989   \n",
       "21053         -0.610448      0.531264    -0.940895 -1.222259   \n",
       "21054         -0.615757      0.443731    -0.261421  0.429858   \n",
       "21055          1.237209      0.274136     0.431605  0.126816   \n",
       "21056          2.185957     -1.569526     0.887870 -1.491311   \n",
       "21057          2.117766     -0.486306     0.399570  0.578202   \n",
       "21058         -0.615815      0.706330    -0.065247 -0.451631   \n",
       "\n",
       "                ...             artist_origin_Southern Africa  \\\n",
       "0               ...                                       0.0   \n",
       "1               ...                                       0.0   \n",
       "2               ...                                       0.0   \n",
       "3               ...                                       0.0   \n",
       "4               ...                                       0.0   \n",
       "5               ...                                       0.0   \n",
       "6               ...                                       0.0   \n",
       "7               ...                                       0.0   \n",
       "8               ...                                       0.0   \n",
       "9               ...                                       0.0   \n",
       "10              ...                                       0.0   \n",
       "11              ...                                       0.0   \n",
       "12              ...                                       0.0   \n",
       "13              ...                                       0.0   \n",
       "14              ...                                       0.0   \n",
       "15              ...                                       0.0   \n",
       "16              ...                                       0.0   \n",
       "17              ...                                       0.0   \n",
       "18              ...                                       0.0   \n",
       "19              ...                                       0.0   \n",
       "20              ...                                       0.0   \n",
       "21              ...                                       0.0   \n",
       "22              ...                                       0.0   \n",
       "23              ...                                       0.0   \n",
       "24              ...                                       0.0   \n",
       "25              ...                                       0.0   \n",
       "26              ...                                       0.0   \n",
       "27              ...                                       0.0   \n",
       "28              ...                                       0.0   \n",
       "29              ...                                       0.0   \n",
       "...             ...                                       ...   \n",
       "21029           ...                                       0.0   \n",
       "21030           ...                                       0.0   \n",
       "21031           ...                                       0.0   \n",
       "21032           ...                                       0.0   \n",
       "21033           ...                                       0.0   \n",
       "21034           ...                                       0.0   \n",
       "21035           ...                                       0.0   \n",
       "21036           ...                                       0.0   \n",
       "21037           ...                                       0.0   \n",
       "21038           ...                                       0.0   \n",
       "21039           ...                                       0.0   \n",
       "21040           ...                                       0.0   \n",
       "21041           ...                                       0.0   \n",
       "21042           ...                                       0.0   \n",
       "21043           ...                                       0.0   \n",
       "21044           ...                                       0.0   \n",
       "21045           ...                                       0.0   \n",
       "21046           ...                                       0.0   \n",
       "21047           ...                                       0.0   \n",
       "21048           ...                                       0.0   \n",
       "21049           ...                                       0.0   \n",
       "21050           ...                                       0.0   \n",
       "21051           ...                                       0.0   \n",
       "21052           ...                                       0.0   \n",
       "21053           ...                                       0.0   \n",
       "21054           ...                                       0.0   \n",
       "21055           ...                                       0.0   \n",
       "21056           ...                                       0.0   \n",
       "21057           ...                                       0.0   \n",
       "21058           ...                                       0.0   \n",
       "\n",
       "       artist_origin_West Africa  artist_origin_Western Europe  \\\n",
       "0                            0.0                           0.0   \n",
       "1                            0.0                           0.0   \n",
       "2                            0.0                           1.0   \n",
       "3                            0.0                           0.0   \n",
       "4                            0.0                           0.0   \n",
       "5                            0.0                           0.0   \n",
       "6                            0.0                           0.0   \n",
       "7                            0.0                           0.0   \n",
       "8                            0.0                           0.0   \n",
       "9                            0.0                           0.0   \n",
       "10                           0.0                           0.0   \n",
       "11                           0.0                           0.0   \n",
       "12                           0.0                           1.0   \n",
       "13                           0.0                           0.0   \n",
       "14                           0.0                           0.0   \n",
       "15                           0.0                           0.0   \n",
       "16                           0.0                           0.0   \n",
       "17                           0.0                           0.0   \n",
       "18                           0.0                           1.0   \n",
       "19                           0.0                           1.0   \n",
       "20                           0.0                           0.0   \n",
       "21                           0.0                           0.0   \n",
       "22                           0.0                           0.0   \n",
       "23                           0.0                           1.0   \n",
       "24                           0.0                           1.0   \n",
       "25                           0.0                           0.0   \n",
       "26                           0.0                           0.0   \n",
       "27                           0.0                           1.0   \n",
       "28                           0.0                           0.0   \n",
       "29                           0.0                           0.0   \n",
       "...                          ...                           ...   \n",
       "21029                        0.0                           0.0   \n",
       "21030                        0.0                           0.0   \n",
       "21031                        0.0                           0.0   \n",
       "21032                        0.0                           0.0   \n",
       "21033                        0.0                           0.0   \n",
       "21034                        0.0                           0.0   \n",
       "21035                        0.0                           0.0   \n",
       "21036                        0.0                           0.0   \n",
       "21037                        0.0                           0.0   \n",
       "21038                        0.0                           0.0   \n",
       "21039                        0.0                           0.0   \n",
       "21040                        0.0                           0.0   \n",
       "21041                        0.0                           0.0   \n",
       "21042                        0.0                           0.0   \n",
       "21043                        0.0                           0.0   \n",
       "21044                        0.0                           0.0   \n",
       "21045                        0.0                           1.0   \n",
       "21046                        0.0                           0.0   \n",
       "21047                        0.0                           0.0   \n",
       "21048                        0.0                           0.0   \n",
       "21049                        0.0                           0.0   \n",
       "21050                        0.0                           1.0   \n",
       "21051                        0.0                           0.0   \n",
       "21052                        0.0                           1.0   \n",
       "21053                        0.0                           1.0   \n",
       "21054                        0.0                           0.0   \n",
       "21055                        0.0                           1.0   \n",
       "21056                        0.0                           0.0   \n",
       "21057                        0.0                           0.0   \n",
       "21058                        0.0                           1.0   \n",
       "\n",
       "       artist_type_Female Duo  artist_type_Female Group  artist_type_Male  \\\n",
       "0                         0.0                       0.0               0.0   \n",
       "1                         0.0                       0.0               0.0   \n",
       "2                         0.0                       0.0               0.0   \n",
       "3                         0.0                       0.0               0.0   \n",
       "4                         0.0                       0.0               0.0   \n",
       "5                         0.0                       0.0               1.0   \n",
       "6                         0.0                       0.0               1.0   \n",
       "7                         0.0                       0.0               1.0   \n",
       "8                         0.0                       0.0               0.0   \n",
       "9                         0.0                       0.0               1.0   \n",
       "10                        0.0                       0.0               1.0   \n",
       "11                        0.0                       0.0               1.0   \n",
       "12                        0.0                       0.0               1.0   \n",
       "13                        0.0                       0.0               0.0   \n",
       "14                        0.0                       0.0               0.0   \n",
       "15                        0.0                       0.0               1.0   \n",
       "16                        0.0                       0.0               0.0   \n",
       "17                        0.0                       0.0               1.0   \n",
       "18                        0.0                       0.0               0.0   \n",
       "19                        0.0                       0.0               1.0   \n",
       "20                        0.0                       0.0               0.0   \n",
       "21                        0.0                       0.0               1.0   \n",
       "22                        0.0                       0.0               1.0   \n",
       "23                        0.0                       0.0               0.0   \n",
       "24                        0.0                       0.0               0.0   \n",
       "25                        0.0                       0.0               0.0   \n",
       "26                        0.0                       0.0               0.0   \n",
       "27                        0.0                       0.0               1.0   \n",
       "28                        0.0                       0.0               0.0   \n",
       "29                        0.0                       0.0               0.0   \n",
       "...                       ...                       ...               ...   \n",
       "21029                     0.0                       0.0               0.0   \n",
       "21030                     0.0                       0.0               1.0   \n",
       "21031                     0.0                       0.0               0.0   \n",
       "21032                     0.0                       0.0               0.0   \n",
       "21033                     0.0                       0.0               0.0   \n",
       "21034                     0.0                       0.0               1.0   \n",
       "21035                     0.0                       0.0               0.0   \n",
       "21036                     0.0                       0.0               0.0   \n",
       "21037                     0.0                       0.0               1.0   \n",
       "21038                     0.0                       0.0               1.0   \n",
       "21039                     0.0                       0.0               0.0   \n",
       "21040                     0.0                       0.0               0.0   \n",
       "21041                     0.0                       0.0               0.0   \n",
       "21042                     0.0                       0.0               0.0   \n",
       "21043                     0.0                       0.0               1.0   \n",
       "21044                     0.0                       0.0               0.0   \n",
       "21045                     0.0                       0.0               0.0   \n",
       "21046                     0.0                       0.0               0.0   \n",
       "21047                     0.0                       0.0               0.0   \n",
       "21048                     0.0                       0.0               0.0   \n",
       "21049                     0.0                       0.0               1.0   \n",
       "21050                     0.0                       0.0               0.0   \n",
       "21051                     0.0                       0.0               1.0   \n",
       "21052                     0.0                       0.0               0.0   \n",
       "21053                     0.0                       0.0               0.0   \n",
       "21054                     0.0                       0.0               1.0   \n",
       "21055                     0.0                       0.0               0.0   \n",
       "21056                     0.0                       0.0               1.0   \n",
       "21057                     0.0                       0.0               1.0   \n",
       "21058                     0.0                       0.0               0.0   \n",
       "\n",
       "       artist_type_Male Duo  artist_type_Male Group  artist_type_Mixed Duo  \\\n",
       "0                       0.0                     1.0                    0.0   \n",
       "1                       0.0                     1.0                    0.0   \n",
       "2                       1.0                     0.0                    0.0   \n",
       "3                       0.0                     0.0                    0.0   \n",
       "4                       0.0                     1.0                    0.0   \n",
       "5                       0.0                     0.0                    0.0   \n",
       "6                       0.0                     0.0                    0.0   \n",
       "7                       0.0                     0.0                    0.0   \n",
       "8                       1.0                     0.0                    0.0   \n",
       "9                       0.0                     0.0                    0.0   \n",
       "10                      0.0                     0.0                    0.0   \n",
       "11                      0.0                     0.0                    0.0   \n",
       "12                      0.0                     0.0                    0.0   \n",
       "13                      0.0                     0.0                    0.0   \n",
       "14                      0.0                     1.0                    0.0   \n",
       "15                      0.0                     0.0                    0.0   \n",
       "16                      0.0                     1.0                    0.0   \n",
       "17                      0.0                     0.0                    0.0   \n",
       "18                      0.0                     0.0                    0.0   \n",
       "19                      0.0                     0.0                    0.0   \n",
       "20                      0.0                     1.0                    0.0   \n",
       "21                      0.0                     0.0                    0.0   \n",
       "22                      0.0                     0.0                    0.0   \n",
       "23                      0.0                     1.0                    0.0   \n",
       "24                      0.0                     0.0                    0.0   \n",
       "25                      0.0                     1.0                    0.0   \n",
       "26                      0.0                     1.0                    0.0   \n",
       "27                      0.0                     0.0                    0.0   \n",
       "28                      1.0                     0.0                    0.0   \n",
       "29                      0.0                     1.0                    0.0   \n",
       "...                     ...                     ...                    ...   \n",
       "21029                   0.0                     1.0                    0.0   \n",
       "21030                   0.0                     0.0                    0.0   \n",
       "21031                   0.0                     0.0                    0.0   \n",
       "21032                   0.0                     0.0                    1.0   \n",
       "21033                   0.0                     1.0                    0.0   \n",
       "21034                   0.0                     0.0                    0.0   \n",
       "21035                   1.0                     0.0                    0.0   \n",
       "21036                   0.0                     1.0                    0.0   \n",
       "21037                   0.0                     0.0                    0.0   \n",
       "21038                   0.0                     0.0                    0.0   \n",
       "21039                   0.0                     1.0                    0.0   \n",
       "21040                   0.0                     0.0                    0.0   \n",
       "21041                   0.0                     0.0                    0.0   \n",
       "21042                   0.0                     1.0                    0.0   \n",
       "21043                   0.0                     0.0                    0.0   \n",
       "21044                   0.0                     1.0                    0.0   \n",
       "21045                   0.0                     1.0                    0.0   \n",
       "21046                   0.0                     0.0                    0.0   \n",
       "21047                   0.0                     1.0                    0.0   \n",
       "21048                   0.0                     0.0                    0.0   \n",
       "21049                   0.0                     0.0                    0.0   \n",
       "21050                   1.0                     0.0                    0.0   \n",
       "21051                   0.0                     0.0                    0.0   \n",
       "21052                   0.0                     1.0                    0.0   \n",
       "21053                   0.0                     0.0                    0.0   \n",
       "21054                   0.0                     0.0                    0.0   \n",
       "21055                   1.0                     0.0                    0.0   \n",
       "21056                   0.0                     0.0                    0.0   \n",
       "21057                   0.0                     0.0                    0.0   \n",
       "21058                   0.0                     1.0                    0.0   \n",
       "\n",
       "       artist_type_Mixed Group  \n",
       "0                          0.0  \n",
       "1                          0.0  \n",
       "2                          0.0  \n",
       "3                          0.0  \n",
       "4                          0.0  \n",
       "5                          0.0  \n",
       "6                          0.0  \n",
       "7                          0.0  \n",
       "8                          0.0  \n",
       "9                          0.0  \n",
       "10                         0.0  \n",
       "11                         0.0  \n",
       "12                         0.0  \n",
       "13                         0.0  \n",
       "14                         0.0  \n",
       "15                         0.0  \n",
       "16                         0.0  \n",
       "17                         0.0  \n",
       "18                         1.0  \n",
       "19                         0.0  \n",
       "20                         0.0  \n",
       "21                         0.0  \n",
       "22                         0.0  \n",
       "23                         0.0  \n",
       "24                         1.0  \n",
       "25                         0.0  \n",
       "26                         0.0  \n",
       "27                         0.0  \n",
       "28                         0.0  \n",
       "29                         0.0  \n",
       "...                        ...  \n",
       "21029                      0.0  \n",
       "21030                      0.0  \n",
       "21031                      0.0  \n",
       "21032                      0.0  \n",
       "21033                      0.0  \n",
       "21034                      0.0  \n",
       "21035                      0.0  \n",
       "21036                      0.0  \n",
       "21037                      0.0  \n",
       "21038                      0.0  \n",
       "21039                      0.0  \n",
       "21040                      0.0  \n",
       "21041                      0.0  \n",
       "21042                      0.0  \n",
       "21043                      0.0  \n",
       "21044                      0.0  \n",
       "21045                      0.0  \n",
       "21046                      0.0  \n",
       "21047                      0.0  \n",
       "21048                      1.0  \n",
       "21049                      0.0  \n",
       "21050                      0.0  \n",
       "21051                      0.0  \n",
       "21052                      0.0  \n",
       "21053                      0.0  \n",
       "21054                      0.0  \n",
       "21055                      0.0  \n",
       "21056                      0.0  \n",
       "21057                      0.0  \n",
       "21058                      0.0  \n",
       "\n",
       "[21059 rows x 69 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_songs_plus # 69 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                        u'popularity',\n",
       "                                   u'energy',\n",
       "                                 u'liveness',\n",
       "                                    u'tempo',\n",
       "                              u'speechiness',\n",
       "                             u'acousticness',\n",
       "                         u'instrumentalness',\n",
       "                             u'danceability',\n",
       "                              u'duration_ms',\n",
       "                                 u'loudness',\n",
       "                                  u'valence',\n",
       "                            u'explicit_True',\n",
       "                                  u'key_1.0',\n",
       "                                  u'key_2.0',\n",
       "                                  u'key_3.0',\n",
       "                                  u'key_4.0',\n",
       "                                  u'key_5.0',\n",
       "                                  u'key_6.0',\n",
       "                                  u'key_7.0',\n",
       "                                  u'key_8.0',\n",
       "                                  u'key_9.0',\n",
       "                                 u'key_10.0',\n",
       "                                 u'key_11.0',\n",
       "                             u'time_sig_3.0',\n",
       "                             u'time_sig_4.0',\n",
       "                             u'time_sig_5.0',\n",
       "                          u'genre_Classical',\n",
       "                        u'genre_Electronica',\n",
       "                               u'genre_Jazz',\n",
       "                              u'genre_Other',\n",
       "                                u'genre_Pop',\n",
       "                               u'genre_Rock',\n",
       "                         u'genre_Soundtrack',\n",
       "                        u'genre_Traditional',\n",
       "                              u'genre_Urban',\n",
       "                                     u'mode',\n",
       "                         u'artist_era_1940s',\n",
       "                         u'artist_era_1950s',\n",
       "                         u'artist_era_1960s',\n",
       "                         u'artist_era_1970s',\n",
       "                         u'artist_era_1980s',\n",
       "                         u'artist_era_1990s',\n",
       "                         u'artist_era_2000s',\n",
       "                         u'artist_era_2010s',\n",
       "                 u'artist_era_Early 20th C.',\n",
       "                   u'artist_era_Pre-20th C.',\n",
       "          u'artist_origin_Australia/Oceania',\n",
       "                  u'artist_origin_Caribbean',\n",
       "            u'artist_origin_Central America',\n",
       "               u'artist_origin_Central Asia',\n",
       "        u'artist_origin_East/Central Africa',\n",
       "             u'artist_origin_Eastern Europe',\n",
       "       u'artist_origin_Mediterranean Europe',\n",
       "                u'artist_origin_Middle East',\n",
       "               u'artist_origin_North Africa',\n",
       "              u'artist_origin_North America',\n",
       "                u'artist_origin_Scandinavia',\n",
       "              u'artist_origin_South America',\n",
       "             u'artist_origin_Southeast Asia',\n",
       "            u'artist_origin_Southern Africa',\n",
       "                u'artist_origin_West Africa',\n",
       "             u'artist_origin_Western Europe',\n",
       "                   u'artist_type_Female Duo',\n",
       "                 u'artist_type_Female Group',\n",
       "                         u'artist_type_Male',\n",
       "                     u'artist_type_Male Duo',\n",
       "                   u'artist_type_Male Group',\n",
       "                    u'artist_type_Mixed Duo',\n",
       "                  u'artist_type_Mixed Group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_songs_plus.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_mood_map(x):\n",
    "    return mood_map[x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "master_songs_target['mood_map'] = master_songs_target['mood_1'].apply(get_mood_map)\n",
    "master_songs_target['mood_x'] = master_songs_target['mood_map'].apply(lambda x: x[0])\n",
    "master_songs_target['mood_y'] = master_songs_target['mood_map'].apply(lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21059"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(master_songs_target) # 21059 songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, -1)     1684\n",
       "(0, 1)      1644\n",
       "(0, 0)      1552\n",
       "(2, 2)      1351\n",
       "(2, 0)      1328\n",
       "(-1, -1)    1285\n",
       "(2, -2)     1245\n",
       "(1, -2)     1195\n",
       "(0, -1)     1129\n",
       "(1, -1)      851\n",
       "(-2, -1)     771\n",
       "(-2, 0)      748\n",
       "(-2, 1)      687\n",
       "(-1, -2)     609\n",
       "(0, -2)      606\n",
       "(2, 1)       587\n",
       "(-1, 0)      548\n",
       "(0, 2)       534\n",
       "(-1, 1)      528\n",
       "(-2, 2)      463\n",
       "(1, 0)       388\n",
       "(-1, 2)      369\n",
       "(-2, -2)     350\n",
       "(1, 2)       324\n",
       "(1, 1)       283\n",
       "Name: mood_map, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_songs_target['mood_map'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>mood_1</th>\n",
       "      <th>mood_map</th>\n",
       "      <th>mood_x</th>\n",
       "      <th>mood_y</th>\n",
       "      <th>simple_x</th>\n",
       "      <th>simple_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brooding</td>\n",
       "      <td>(1, -2)</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aggressive</td>\n",
       "      <td>(2, -2)</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Energizing</td>\n",
       "      <td>(2, 0)</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Romantic</td>\n",
       "      <td>(-1, 1)</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brooding</td>\n",
       "      <td>(1, -2)</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Yearning</td>\n",
       "      <td>(0, -1)</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brooding</td>\n",
       "      <td>(1, -2)</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Excited</td>\n",
       "      <td>(2, 2)</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Energizing</td>\n",
       "      <td>(2, 0)</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Empowering</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mood_1 mood_map  mood_x  mood_y  simple_x  simple_y\n",
       "0     Brooding  (1, -2)       1      -2         1        -1\n",
       "1   Aggressive  (2, -2)       2      -2         1        -1\n",
       "2   Energizing   (2, 0)       2       0         1         0\n",
       "4     Romantic  (-1, 1)      -1       1        -1         1\n",
       "5     Brooding  (1, -2)       1      -2         1        -1\n",
       "6     Yearning  (0, -1)       0      -1         0        -1\n",
       "8     Brooding  (1, -2)       1      -2         1        -1\n",
       "9      Excited   (2, 2)       2       2         1         1\n",
       "10  Energizing   (2, 0)       2       0         1         0\n",
       "11  Empowering   (0, 1)       0       1         0         1"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_songs_target.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Generates 5 columns of random noise\n",
    "def add_noise(df):\n",
    "    for i in xrange(0,6):\n",
    "        col_name = 'rand_' + str(i)\n",
    "        df[col_name] = 1\n",
    "        df[col_name] = df[col_name].map(lambda x: x * np.random.randn())\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Adding noise\n",
    "master_songs_5 = add_noise(master_songs_plus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>popularity</th>\n",
       "      <th>energy</th>\n",
       "      <th>liveness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>loudness</th>\n",
       "      <th>...</th>\n",
       "      <th>artist_type_Male Duo</th>\n",
       "      <th>artist_type_Male Group</th>\n",
       "      <th>artist_type_Mixed Duo</th>\n",
       "      <th>artist_type_Mixed Group</th>\n",
       "      <th>rand_0</th>\n",
       "      <th>rand_1</th>\n",
       "      <th>rand_2</th>\n",
       "      <th>rand_3</th>\n",
       "      <th>rand_4</th>\n",
       "      <th>rand_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21054</th>\n",
       "      <td>-0.732147</td>\n",
       "      <td>0.254579</td>\n",
       "      <td>-0.485092</td>\n",
       "      <td>0.360256</td>\n",
       "      <td>-0.563999</td>\n",
       "      <td>-0.001372</td>\n",
       "      <td>-0.615757</td>\n",
       "      <td>0.443731</td>\n",
       "      <td>-0.261421</td>\n",
       "      <td>0.429858</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.602650</td>\n",
       "      <td>0.366128</td>\n",
       "      <td>-1.464839</td>\n",
       "      <td>1.141419</td>\n",
       "      <td>-0.747184</td>\n",
       "      <td>0.187230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21055</th>\n",
       "      <td>-0.848039</td>\n",
       "      <td>0.422443</td>\n",
       "      <td>0.915443</td>\n",
       "      <td>0.452516</td>\n",
       "      <td>-0.390870</td>\n",
       "      <td>-0.916311</td>\n",
       "      <td>1.237209</td>\n",
       "      <td>0.274136</td>\n",
       "      <td>0.431605</td>\n",
       "      <td>0.126816</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.875359</td>\n",
       "      <td>-0.251586</td>\n",
       "      <td>-0.629040</td>\n",
       "      <td>0.494993</td>\n",
       "      <td>0.628675</td>\n",
       "      <td>0.102791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21056</th>\n",
       "      <td>0.021154</td>\n",
       "      <td>-1.760834</td>\n",
       "      <td>-0.478390</td>\n",
       "      <td>0.053036</td>\n",
       "      <td>-0.426690</td>\n",
       "      <td>1.769536</td>\n",
       "      <td>2.185957</td>\n",
       "      <td>-1.569526</td>\n",
       "      <td>0.887870</td>\n",
       "      <td>-1.491311</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777270</td>\n",
       "      <td>-0.870546</td>\n",
       "      <td>0.103313</td>\n",
       "      <td>1.095108</td>\n",
       "      <td>0.323276</td>\n",
       "      <td>-0.383638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21057</th>\n",
       "      <td>-1.427502</td>\n",
       "      <td>1.139361</td>\n",
       "      <td>-0.531999</td>\n",
       "      <td>0.114487</td>\n",
       "      <td>-0.204607</td>\n",
       "      <td>-0.804459</td>\n",
       "      <td>2.117766</td>\n",
       "      <td>-0.486306</td>\n",
       "      <td>0.399570</td>\n",
       "      <td>0.578202</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.994235</td>\n",
       "      <td>-0.364150</td>\n",
       "      <td>1.971204</td>\n",
       "      <td>-2.076161</td>\n",
       "      <td>-0.036597</td>\n",
       "      <td>-0.357296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21058</th>\n",
       "      <td>0.890348</td>\n",
       "      <td>-0.137103</td>\n",
       "      <td>0.158216</td>\n",
       "      <td>-0.921333</td>\n",
       "      <td>-0.499523</td>\n",
       "      <td>-0.330844</td>\n",
       "      <td>-0.615815</td>\n",
       "      <td>0.706330</td>\n",
       "      <td>-0.065247</td>\n",
       "      <td>-0.451631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.873393</td>\n",
       "      <td>-0.696063</td>\n",
       "      <td>0.455278</td>\n",
       "      <td>-0.364388</td>\n",
       "      <td>1.487188</td>\n",
       "      <td>0.293009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       popularity    energy  liveness     tempo  speechiness  acousticness  \\\n",
       "21054   -0.732147  0.254579 -0.485092  0.360256    -0.563999     -0.001372   \n",
       "21055   -0.848039  0.422443  0.915443  0.452516    -0.390870     -0.916311   \n",
       "21056    0.021154 -1.760834 -0.478390  0.053036    -0.426690      1.769536   \n",
       "21057   -1.427502  1.139361 -0.531999  0.114487    -0.204607     -0.804459   \n",
       "21058    0.890348 -0.137103  0.158216 -0.921333    -0.499523     -0.330844   \n",
       "\n",
       "       instrumentalness  danceability  duration_ms  loudness    ...     \\\n",
       "21054         -0.615757      0.443731    -0.261421  0.429858    ...      \n",
       "21055          1.237209      0.274136     0.431605  0.126816    ...      \n",
       "21056          2.185957     -1.569526     0.887870 -1.491311    ...      \n",
       "21057          2.117766     -0.486306     0.399570  0.578202    ...      \n",
       "21058         -0.615815      0.706330    -0.065247 -0.451631    ...      \n",
       "\n",
       "       artist_type_Male Duo  artist_type_Male Group  artist_type_Mixed Duo  \\\n",
       "21054                   0.0                     0.0                    0.0   \n",
       "21055                   1.0                     0.0                    0.0   \n",
       "21056                   0.0                     0.0                    0.0   \n",
       "21057                   0.0                     0.0                    0.0   \n",
       "21058                   0.0                     1.0                    0.0   \n",
       "\n",
       "       artist_type_Mixed Group    rand_0    rand_1    rand_2    rand_3  \\\n",
       "21054                      0.0  0.602650  0.366128 -1.464839  1.141419   \n",
       "21055                      0.0  0.875359 -0.251586 -0.629040  0.494993   \n",
       "21056                      0.0  0.777270 -0.870546  0.103313  1.095108   \n",
       "21057                      0.0  0.994235 -0.364150  1.971204 -2.076161   \n",
       "21058                      0.0 -0.873393 -0.696063  0.455278 -0.364388   \n",
       "\n",
       "         rand_4    rand_5  \n",
       "21054 -0.747184  0.187230  \n",
       "21055  0.628675  0.102791  \n",
       "21056  0.323276 -0.383638  \n",
       "21057 -0.036597 -0.357296  \n",
       "21058  1.487188  0.293009  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_songs_5.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactions w/Random Noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 1, 1, 0],\n",
       "       [0, 0, 0, ..., 1, 0, 1],\n",
       "       [0, 0, 0, ..., 0, 1, 1]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = PolynomialFeatures(2, interaction_only=True)\n",
    "poly.fit(master_songs_5)\n",
    "poly.powers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "poly_X = poly.transform(master_songs_5)\n",
    "y = master_songs_target['mood_1']\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(poly_X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      " Aggressive       0.56      0.53      0.54       380\n",
      "   Brooding       0.29      0.26      0.28       364\n",
      "       Cool       0.36      0.56      0.44       377\n",
      "    Defiant       0.40      0.56      0.47       496\n",
      "  Easygoing       0.45      0.15      0.23       100\n",
      " Empowering       0.34      0.54      0.42       517\n",
      " Energizing       0.42      0.45      0.43       405\n",
      "    Excited       0.35      0.42      0.38       412\n",
      "      Fiery       0.41      0.06      0.11       111\n",
      "     Gritty       0.34      0.15      0.21       186\n",
      "     Lively       0.41      0.20      0.27        88\n",
      " Melancholy       0.36      0.42      0.39       227\n",
      "   Peaceful       0.29      0.23      0.26       134\n",
      "   Romantic       0.42      0.06      0.11       154\n",
      "      Rowdy       0.46      0.15      0.22       170\n",
      "    Sensual       0.36      0.47      0.41       464\n",
      "Sentimental       0.45      0.48      0.46       238\n",
      "    Serious       0.38      0.55      0.45       181\n",
      "     Somber       0.35      0.12      0.18       105\n",
      "Sophisticated       0.56      0.09      0.16       166\n",
      "   Stirring       0.00      0.00      0.00        97\n",
      "     Tender       0.44      0.60      0.50       217\n",
      "     Upbeat       0.48      0.27      0.35       163\n",
      "     Urgent       0.26      0.04      0.07       266\n",
      "   Yearning       0.22      0.25      0.24       300\n",
      "\n",
      "avg / total       0.38      0.38      0.35      6318\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = {}\n",
    "models['RFC'] = RandomForestClassifier(n_estimators=100, max_features=15)\n",
    "\n",
    "for name, model in models.iteritems(): \n",
    "    model.fit(X_train, y_train)\n",
    "    print name\n",
    "    print metrics.classification_report(y_test, model.predict(X_test))\n",
    "    print '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('energy^1xacousticness^1', 0.0048602177029389943),\n",
       " ('acousticness^1', 0.0045432862399522852),\n",
       " ('acousticness^1xtime_sig_4.0^1', 0.0043748432107968173),\n",
       " ('danceability^1', 0.004366634224924908),\n",
       " ('energy^1', 0.0043296826771661075),\n",
       " ('speechiness^1', 0.0042328719106960941),\n",
       " ('energy^1xtime_sig_4.0^1', 0.0040760695409215938),\n",
       " ('acousticness^1xinstrumentalness^1', 0.0040094047089015098),\n",
       " ('speechiness^1xinstrumentalness^1', 0.0039269404432085197),\n",
       " ('acousticness^1xloudness^1', 0.0038500468563264028),\n",
       " ('loudness^1', 0.0038293213288209688),\n",
       " ('energy^1xdanceability^1', 0.0037675629306306167),\n",
       " ('energy^1xloudness^1', 0.0037632866811671307),\n",
       " ('danceability^1xtime_sig_4.0^1', 0.0037496883752949801),\n",
       " ('acousticness^1xdanceability^1', 0.0037443620714312847),\n",
       " ('danceability^1xloudness^1', 0.0037056932578593605),\n",
       " ('loudness^1xtime_sig_4.0^1', 0.0036233783683302561),\n",
       " ('energy^1xinstrumentalness^1', 0.0035759646807439456),\n",
       " ('energy^1xmode^1', 0.0035625938555439263),\n",
       " ('speechiness^1xdanceability^1', 0.0035558611048640532),\n",
       " ('speechiness^1xacousticness^1', 0.0035284438045343597),\n",
       " ('energy^1xartist_origin_North America^1', 0.0035080225987646897),\n",
       " ('energy^1xvalence^1', 0.0034699467539028362),\n",
       " ('energy^1xspeechiness^1', 0.0034313782295506729),\n",
       " ('speechiness^1xtime_sig_4.0^1', 0.0033396213094112372),\n",
       " ('instrumentalness^1xdanceability^1', 0.0033350719072492764),\n",
       " ('speechiness^1xloudness^1', 0.0033293443797872526),\n",
       " ('instrumentalness^1xloudness^1', 0.0033107818786237962),\n",
       " ('valence^1', 0.003304394387469229),\n",
       " ('loudness^1xvalence^1', 0.0032721403096748975),\n",
       " ('acousticness^1xvalence^1', 0.0032627821059972003),\n",
       " ('speechiness^1xvalence^1', 0.0032198620687809666),\n",
       " ('acousticness^1xduration_ms^1', 0.0032001452796148995),\n",
       " ('valence^1xtime_sig_4.0^1', 0.0031898272354491252),\n",
       " ('energy^1xduration_ms^1', 0.0031717689834935142),\n",
       " ('danceability^1xvalence^1', 0.0031628845837026213),\n",
       " ('instrumentalness^1xtime_sig_4.0^1', 0.0031004656958708203),\n",
       " ('duration_ms^1', 0.0030719703986847929),\n",
       " ('danceability^1xduration_ms^1', 0.0030583145912925418),\n",
       " ('acousticness^1xmode^1', 0.0030354417830510696),\n",
       " ('speechiness^1xduration_ms^1', 0.003019082963814302),\n",
       " ('duration_ms^1xloudness^1', 0.0030183698833728062),\n",
       " ('acousticness^1xartist_origin_North America^1', 0.0030044223669174648),\n",
       " ('instrumentalness^1', 0.0029955907269494765),\n",
       " ('energy^1xliveness^1', 0.0029932573383827728),\n",
       " ('instrumentalness^1xvalence^1', 0.0029825797769848696),\n",
       " ('loudness^1xartist_origin_North America^1', 0.0029753313095592067),\n",
       " ('energy^1xtempo^1', 0.0029636385458280106),\n",
       " ('popularity^1xloudness^1', 0.0029342211222805458),\n",
       " ('tempo^1xloudness^1', 0.002901872486857855),\n",
       " ('liveness^1xdanceability^1', 0.0028929081021095778),\n",
       " ('liveness^1xloudness^1', 0.0028641857248728878),\n",
       " ('duration_ms^1xvalence^1', 0.0028459885003258105),\n",
       " ('speechiness^1xartist_origin_North America^1', 0.0028352447242154838),\n",
       " ('tempo^1xtime_sig_4.0^1', 0.0028250886728263371),\n",
       " ('tempo^1xspeechiness^1', 0.0028250710730797764),\n",
       " ('popularity^1xenergy^1', 0.0027948073224536097),\n",
       " ('tempo^1', 0.0027928051752962677),\n",
       " ('tempo^1xdanceability^1', 0.0027912240213280792),\n",
       " ('speechiness^1xmode^1', 0.0027793190324536554),\n",
       " ('duration_ms^1xtime_sig_4.0^1', 0.0027791745854323746),\n",
       " ('popularity^1xdanceability^1', 0.0027709720824065425),\n",
       " ('danceability^1xartist_origin_North America^1', 0.0027694944922761304),\n",
       " ('instrumentalness^1xmode^1', 0.0027622346098811422),\n",
       " ('instrumentalness^1xduration_ms^1', 0.0027577545414824776),\n",
       " ('popularity^1xvalence^1', 0.0027529593185991546),\n",
       " ('tempo^1xacousticness^1', 0.0027374972353941592),\n",
       " ('liveness^1xacousticness^1', 0.002723339352112472),\n",
       " ('loudness^1xmode^1', 0.0027104048214549713),\n",
       " ('tempo^1xinstrumentalness^1', 0.0026975502671954437),\n",
       " ('liveness^1xduration_ms^1', 0.0026764088337754431),\n",
       " ('popularity^1xduration_ms^1', 0.0026683742929531238),\n",
       " ('liveness^1xspeechiness^1', 0.0026639468432016139),\n",
       " ('popularity^1xspeechiness^1', 0.002653956537806959),\n",
       " ('popularity^1', 0.0026514567008805124),\n",
       " ('liveness^1xvalence^1', 0.0026460581494112301),\n",
       " ('liveness^1xinstrumentalness^1', 0.0026460033986809408),\n",
       " ('tempo^1xvalence^1', 0.0026345937201529691),\n",
       " ('tempo^1xduration_ms^1', 0.0026198887308453989),\n",
       " ('valence^1xmode^1', 0.0026025038598635236),\n",
       " ('energy^1xrand_2^1', 0.0025944292111014663),\n",
       " ('acousticness^1xrand_2^1', 0.002584597035355947),\n",
       " ('duration_ms^1xrand_0^1', 0.0025843314312798209),\n",
       " ('energy^1xrand_1^1', 0.0025830022847887755),\n",
       " ('valence^1xrand_1^1', 0.0025692150854599899),\n",
       " ('instrumentalness^1xrand_0^1', 0.0025689350435385938),\n",
       " ('popularity^1xinstrumentalness^1', 0.0025604218460404072),\n",
       " ('energy^1xrand_5^1', 0.0025536887401387386),\n",
       " ('speechiness^1xrand_4^1', 0.0025534252472446343),\n",
       " ('loudness^1xrand_4^1', 0.002544133679179824),\n",
       " ('danceability^1xrand_2^1', 0.0025433814120005773),\n",
       " ('speechiness^1xrand_0^1', 0.002539982762598628),\n",
       " ('danceability^1xrand_1^1', 0.0025386541845789155),\n",
       " ('liveness^1xtime_sig_4.0^1', 0.0025276761579559113),\n",
       " ('popularity^1xacousticness^1', 0.0025224991905055281),\n",
       " ('duration_ms^1xrand_2^1', 0.002520504555365823),\n",
       " ('liveness^1', 0.0025199644330941399),\n",
       " ('liveness^1xtempo^1', 0.0025185581286020571),\n",
       " ('danceability^1xmode^1', 0.0025159238846277953),\n",
       " ('liveness^1xrand_3^1', 0.0025113818707624236),\n",
       " ('valence^1xrand_5^1', 0.0025106844593457138),\n",
       " ('danceability^1xrand_0^1', 0.0025029999096072577),\n",
       " ('tempo^1xrand_0^1', 0.0025007567321498072),\n",
       " ('danceability^1xrand_4^1', 0.0024997782274836138),\n",
       " ('rand_2^1xrand_5^1', 0.002499558829521931),\n",
       " ('energy^1xrand_3^1', 0.00249828860637075),\n",
       " ('instrumentalness^1xrand_3^1', 0.0024948674647123316),\n",
       " ('duration_ms^1xrand_1^1', 0.0024946127576686741),\n",
       " ('loudness^1xrand_3^1', 0.0024940913538454412),\n",
       " ('popularity^1xrand_1^1', 0.0024907736439797111),\n",
       " ('valence^1xrand_0^1', 0.0024859844782424739),\n",
       " ('danceability^1xrand_5^1', 0.0024846926609989064),\n",
       " ('tempo^1xrand_4^1', 0.0024827926934461683),\n",
       " ('loudness^1xrand_0^1', 0.0024806507384549062),\n",
       " ('rand_1^1xrand_4^1', 0.0024796254264579255),\n",
       " ('tempo^1xrand_3^1', 0.0024736399240737751),\n",
       " ('rand_3^1xrand_5^1', 0.0024730999172057917),\n",
       " ('acousticness^1xrand_1^1', 0.0024706411790472766),\n",
       " ('popularity^1xtempo^1', 0.0024684665649976297),\n",
       " ('rand_2^1xrand_3^1', 0.0024684297416322177),\n",
       " ('instrumentalness^1xrand_2^1', 0.0024677826627712233),\n",
       " ('duration_ms^1xrand_3^1', 0.0024658153474643916),\n",
       " ('rand_4^1xrand_5^1', 0.0024652813993987395),\n",
       " ('duration_ms^1xrand_5^1', 0.0024615794733732961),\n",
       " ('valence^1xartist_origin_North America^1', 0.0024581494642868136),\n",
       " ('energy^1xrand_4^1', 0.0024569117861993638),\n",
       " ('acousticness^1xrand_3^1', 0.0024567898870166372),\n",
       " ('acousticness^1xrand_0^1', 0.0024546971065122366),\n",
       " ('popularity^1xliveness^1', 0.0024542432219689147),\n",
       " ('liveness^1xrand_0^1', 0.0024524839222106356),\n",
       " ('liveness^1xrand_4^1', 0.0024505507493035325),\n",
       " ('acousticness^1xrand_4^1', 0.0024494673054404137),\n",
       " ('rand_1^1xrand_2^1', 0.0024485294476553295),\n",
       " ('valence^1xrand_2^1', 0.0024444882358156551),\n",
       " ('speechiness^1xrand_3^1', 0.0024433376380164633),\n",
       " ('acousticness^1xartist_type_Male^1', 0.0024409284481576479),\n",
       " ('acousticness^1xrand_5^1', 0.0024400918454162645),\n",
       " ('rand_1^1xrand_5^1', 0.0024365690275307736),\n",
       " ('valence^1xrand_4^1', 0.0024354618748323215),\n",
       " ('rand_0^1xrand_2^1', 0.0024341302077780136),\n",
       " ('popularity^1xrand_2^1', 0.0024332296036806136),\n",
       " ('valence^1xrand_3^1', 0.0024331069798755032),\n",
       " ('loudness^1xrand_1^1', 0.002424183198318065),\n",
       " ('loudness^1xrand_5^1', 0.0024240284573866931),\n",
       " ('instrumentalness^1xrand_1^1', 0.0024194124448012415),\n",
       " ('loudness^1xrand_2^1', 0.0024163628391957906),\n",
       " ('speechiness^1xrand_1^1', 0.002411472036839655),\n",
       " ('tempo^1xrand_5^1', 0.0024050473501953805),\n",
       " ('rand_0^1', 0.0024036574997369957),\n",
       " ('rand_2^1xrand_4^1', 0.0023998520926974565),\n",
       " ('danceability^1xrand_3^1', 0.0023994544613518284),\n",
       " ('popularity^1xrand_5^1', 0.0023983879972993157),\n",
       " ('instrumentalness^1xrand_4^1', 0.0023957495976292685),\n",
       " ('tempo^1xrand_1^1', 0.002395384223221515),\n",
       " ('speechiness^1xrand_5^1', 0.002395306486707504),\n",
       " ('duration_ms^1xrand_4^1', 0.0023923182171571417),\n",
       " ('energy^1xrand_0^1', 0.0023920764749961227),\n",
       " ('speechiness^1xrand_2^1', 0.0023894671352914076),\n",
       " ('rand_3^1', 0.002386363533113276),\n",
       " ('popularity^1xrand_0^1', 0.0023844427829115629),\n",
       " ('tempo^1xrand_2^1', 0.0023841100572263726),\n",
       " ('duration_ms^1xmode^1', 0.0023803323831434429),\n",
       " ('rand_0^1xrand_1^1', 0.0023789422736287106),\n",
       " ('rand_1^1', 0.002376173986301985),\n",
       " ('rand_4^1', 0.0023755118498702274),\n",
       " ('instrumentalness^1xrand_5^1', 0.0023658627576106251),\n",
       " ('popularity^1xrand_3^1', 0.0023611105220085159),\n",
       " ('liveness^1xrand_2^1', 0.0023528556315216757),\n",
       " ('rand_0^1xrand_3^1', 0.0023520746434721679),\n",
       " ('rand_0^1xrand_4^1', 0.0023509054798729443),\n",
       " ('rand_2^1', 0.0023425539808603351),\n",
       " ('rand_1^1xrand_3^1', 0.0023417028673037316),\n",
       " ('time_sig_4.0^1xrand_1^1', 0.0023389261740375646),\n",
       " ('rand_5^1', 0.0023351861797288333),\n",
       " ('rand_3^1xrand_4^1', 0.0023192729309274981),\n",
       " ('liveness^1xrand_5^1', 0.0023178828834979383),\n",
       " ('time_sig_4.0^1xrand_2^1', 0.0023096090208984976),\n",
       " ('popularity^1xtime_sig_4.0^1', 0.0023053160616017128),\n",
       " ('popularity^1xrand_4^1', 0.0022757393553453405),\n",
       " ('liveness^1xrand_1^1', 0.0022726784453646759),\n",
       " ('rand_0^1xrand_5^1', 0.0022654775391380746),\n",
       " ('time_sig_4.0^1xrand_3^1', 0.0022622773312109459),\n",
       " ('duration_ms^1xartist_origin_North America^1', 0.0022462146652132507),\n",
       " ('instrumentalness^1xartist_origin_North America^1', 0.0022399696448885849),\n",
       " ('tempo^1xartist_origin_North America^1', 0.0022336495109474682),\n",
       " ('time_sig_4.0^1xrand_4^1', 0.0021848185535943958),\n",
       " ('acousticness^1xartist_era_2010s^1', 0.0021827917479262555),\n",
       " ('danceability^1xartist_type_Male^1', 0.0021765290078870872),\n",
       " ('time_sig_4.0^1xrand_0^1', 0.0021607181341094951),\n",
       " ('energy^1xartist_type_Male^1', 0.0021598663029665046),\n",
       " ('energy^1xartist_era_2010s^1', 0.0021522541581694152),\n",
       " ('time_sig_4.0^1xrand_5^1', 0.0021516559821119306),\n",
       " ('liveness^1xmode^1', 0.0020970029807619518),\n",
       " ('popularity^1xmode^1', 0.002085020195628447),\n",
       " ('liveness^1xartist_origin_North America^1', 0.0020720343737312521),\n",
       " ('mode^1xrand_2^1', 0.0020658870321101654),\n",
       " ('energy^1xartist_type_Male Group^1', 0.0020653492165430064),\n",
       " ('mode^1xrand_0^1', 0.0020603343986524305),\n",
       " ('tempo^1xmode^1', 0.0020243950675490202),\n",
       " ('artist_origin_North America^1xrand_2^1', 0.0020116570116867688),\n",
       " ('mode^1xrand_1^1', 0.002005249555908003),\n",
       " ('mode^1xrand_5^1', 0.0019904865812167914),\n",
       " ('artist_origin_North America^1xrand_5^1', 0.001978524817718172),\n",
       " ('artist_origin_North America^1xrand_4^1', 0.0019729888530927546),\n",
       " ('artist_origin_North America^1xrand_3^1', 0.001965306111342185),\n",
       " ('loudness^1xartist_type_Male^1', 0.0019551306400831018),\n",
       " ('artist_origin_North America^1xrand_0^1', 0.0019521408609659634),\n",
       " ('loudness^1xartist_era_2010s^1', 0.0019301748694430967),\n",
       " ('speechiness^1xartist_type_Male^1', 0.0019121621767458851),\n",
       " ('valence^1xartist_type_Male^1', 0.0018959893511810253),\n",
       " ('popularity^1xartist_origin_North America^1', 0.0018920308482455938),\n",
       " ('mode^1xrand_3^1', 0.0018854695424506988),\n",
       " ('mode^1xrand_4^1', 0.001877216202672037),\n",
       " ('artist_origin_North America^1xrand_1^1', 0.0018713537030029702),\n",
       " ('danceability^1xartist_era_2010s^1', 0.0018680773356547554),\n",
       " ('acousticness^1xartist_type_Male Group^1', 0.0018601824756961647),\n",
       " ('speechiness^1xartist_era_2010s^1', 0.0018098271758276378),\n",
       " ('tempo^1xartist_type_Male^1', 0.0017670105950449572),\n",
       " ('acousticness^1xgenre_Rock^1', 0.0017379669530242265),\n",
       " ('duration_ms^1xartist_type_Male^1', 0.0016834845876457652),\n",
       " ('tempo^1xartist_era_2010s^1', 0.001675004954564368),\n",
       " ('valence^1xartist_era_2010s^1', 0.0016526690367442904),\n",
       " ('instrumentalness^1xartist_type_Male^1', 0.0016435879769212034),\n",
       " ('artist_type_Male^1xrand_3^1', 0.0016424605065968828),\n",
       " ('popularity^1xartist_type_Male^1', 0.0016350668873430657),\n",
       " ('liveness^1xartist_type_Male^1', 0.0016135316318811735),\n",
       " ('duration_ms^1xartist_era_2010s^1', 0.0016114735924722984),\n",
       " ('instrumentalness^1xartist_era_2010s^1', 0.0015830278890593785),\n",
       " ('artist_type_Male^1xrand_5^1', 0.0015826100601977597),\n",
       " ('artist_type_Male^1xrand_0^1', 0.0015821113051176852),\n",
       " ('energy^1xgenre_Rock^1', 0.0015819874941590299),\n",
       " ('acousticness^1xartist_era_2000s^1', 0.0015594324691087917),\n",
       " ('loudness^1xartist_type_Male Group^1', 0.0015532021899392636),\n",
       " ('valence^1xartist_type_Male Group^1', 0.0015384111283955053),\n",
       " ('artist_type_Male^1xrand_1^1', 0.0015309408875873465),\n",
       " ('artist_era_2010s^1xrand_0^1', 0.0014989930533872648),\n",
       " ('danceability^1xartist_type_Male Group^1', 0.00146508501935813),\n",
       " ('artist_type_Male^1xrand_4^1', 0.0014648876379972447),\n",
       " ('artist_type_Male^1xrand_2^1', 0.0014636164999070412),\n",
       " ('artist_era_2010s^1xrand_5^1', 0.0014591571206121607),\n",
       " ('artist_era_2010s^1xrand_3^1', 0.0014492086782820935),\n",
       " ('artist_era_2010s^1xrand_1^1', 0.0014427348613351462),\n",
       " ('artist_era_2010s^1xrand_4^1', 0.0014351876338696756),\n",
       " ('popularity^1xartist_era_2010s^1', 0.0014315390441272775),\n",
       " ('liveness^1xartist_era_2010s^1', 0.0014277671180854832),\n",
       " ('tempo^1xgenre_Electronica^1', 0.0014110542346099982),\n",
       " ('artist_era_2010s^1xrand_2^1', 0.0014078906188321682),\n",
       " ('instrumentalness^1xartist_type_Male Group^1', 0.0013828691773884521),\n",
       " ('danceability^1xartist_era_2000s^1', 0.0013635554422241785),\n",
       " ('energy^1xartist_era_2000s^1', 0.0013561324036570612),\n",
       " ('speechiness^1xartist_type_Male Group^1', 0.0013508018653381748),\n",
       " ('loudness^1xgenre_Electronica^1', 0.0012973697896700187),\n",
       " ('loudness^1xartist_era_2000s^1', 0.0012670971364881272),\n",
       " ('energy^1xgenre_Electronica^1', 0.0012651634830087887),\n",
       " ('speechiness^1xgenre_Classical^1', 0.0012568532974190946),\n",
       " ('acousticness^1xartist_origin_Western Europe^1', 0.001255415085551684),\n",
       " ('loudness^1xgenre_Rock^1', 0.0012248398217921657),\n",
       " ('danceability^1xgenre_Electronica^1', 0.0012216019485075098),\n",
       " ('acousticness^1xgenre_Electronica^1', 0.0011841789340029303),\n",
       " ('danceability^1xartist_origin_Western Europe^1', 0.0011741471920246052),\n",
       " ('speechiness^1xartist_era_2000s^1', 0.0011537795390822243),\n",
       " ('valence^1xartist_era_2000s^1', 0.0011428667179547661),\n",
       " ('duration_ms^1xartist_era_2000s^1', 0.0011335588645051827),\n",
       " ('artist_type_Male Group^1xrand_0^1', 0.0011250799961186476),\n",
       " ('tempo^1xartist_era_2000s^1', 0.0011178460421235662),\n",
       " ('liveness^1xartist_type_Male Group^1', 0.0011120010029415928),\n",
       " ('speechiness^1xgenre_Rock^1', 0.0011097388237053338),\n",
       " ('duration_ms^1xartist_type_Male Group^1', 0.0011065441046910717),\n",
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       " ('valence^1xgenre_Rock^1', 0.0010971037523704505),\n",
       " ('artist_era_2000s^1xrand_1^1', 0.0010908286126980566),\n",
       " ('danceability^1xgenre_Rock^1', 0.0010879779745750062),\n",
       " ('liveness^1xgenre_Electronica^1', 0.0010849735650552657),\n",
       " ('artist_era_2000s^1xrand_5^1', 0.0010680134013770167),\n",
       " ('popularity^1xartist_era_2000s^1', 0.0010552394126507737),\n",
       " ('loudness^1xgenre_Urban^1', 0.0010549603450169416),\n",
       " ('instrumentalness^1xartist_era_2000s^1', 0.001052506591925437),\n",
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       " ('energy^1xartist_origin_Western Europe^1', 0.0010434316582821493),\n",
       " ('artist_type_Male Group^1xrand_4^1', 0.0010423422997586156),\n",
       " ('energy^1xgenre_Classical^1', 0.0010408793877211217),\n",
       " ('liveness^1xartist_era_2000s^1', 0.0010378948768402336),\n",
       " ('artist_type_Male Group^1xrand_3^1', 0.0010377963499574906),\n",
       " ('artist_type_Male Group^1xrand_5^1', 0.0010286414028166486),\n",
       " ('popularity^1xartist_type_Male Group^1', 0.0010236670680065119),\n",
       " ('danceability^1xgenre_Classical^1', 0.0010233694389065835),\n",
       " ('acousticness^1xgenre_Urban^1', 0.0010067821708985164),\n",
       " ('artist_era_2000s^1xrand_2^1', 0.0010037956003685117),\n",
       " ('artist_era_2000s^1xrand_3^1', 0.00099714355763217224),\n",
       " ('artist_era_2000s^1xrand_4^1', 0.00099691541827450457),\n",
       " ('tempo^1xartist_origin_Western Europe^1', 0.00099642203431543294),\n",
       " ('valence^1xartist_origin_Western Europe^1', 0.00098654900579597565),\n",
       " ('speechiness^1xartist_origin_Western Europe^1', 0.00096977754185204458),\n",
       " ('artist_era_2000s^1xrand_0^1', 0.00096680207206828147),\n",
       " ('speechiness^1xgenre_Electronica^1', 0.00096355443712158289),\n",
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       " ('instrumentalness^1xartist_origin_Western Europe^1', 0.00095015487118237855),\n",
       " ('artist_type_Male Group^1xrand_1^1', 0.00094450058009896737),\n",
       " ('genre_Urban^1', 0.00094219323530781416),\n",
       " ('artist_type_Male Group^1xrand_2^1', 0.00093894346331900873),\n",
       " ('instrumentalness^1xgenre_Rock^1', 0.00093595744489978776),\n",
       " ('duration_ms^1xartist_origin_Western Europe^1', 0.0009339568287593746),\n",
       " ('artist_type_Male Group^1', 0.00093097632604131236),\n",
       " ('artist_origin_Western Europe^1xrand_3^1', 0.00092374953527491767),\n",
       " ('liveness^1xartist_origin_Western Europe^1', 0.00091142305228408523),\n",
       " ('artist_origin_Western Europe^1xrand_4^1', 0.00090974855492782368),\n",
       " ('danceability^1xgenre_Urban^1', 0.00090417069135314112),\n",
       " ('artist_origin_Western Europe^1xrand_5^1', 0.00089773832450294007),\n",
       " ('artist_origin_Western Europe^1xrand_1^1', 0.00089527148711741205),\n",
       " ('genre_Electronica^1xrand_1^1', 0.00089422741840193152),\n",
       " ('genre_Electronica^1', 0.00088450370149669637),\n",
       " ('liveness^1xgenre_Urban^1', 0.00087565594217440287),\n",
       " ('valence^1xgenre_Electronica^1', 0.00087410164506415862),\n",
       " ('genre_Classical^1', 0.00085840524337883219),\n",
       " ('artist_origin_Western Europe^1xrand_2^1', 0.00085590907051250455),\n",
       " ('artist_origin_Western Europe^1xrand_0^1', 0.00085241154509608434),\n",
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       " ('popularity^1xgenre_Electronica^1', 0.00080987975177347464),\n",
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       " ('genre_Rock^1xrand_3^1', 0.00079893729900958619),\n",
       " ('popularity^1xgenre_Rock^1', 0.00079489745195793801),\n",
       " ('genre_Classical^1xrand_3^1', 0.00079469323454906579),\n",
       " ('genre_Electronica^1xrand_5^1', 0.00079437125030762542),\n",
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       " ('instrumentalness^1xgenre_Urban^1', 0.00077329969440287924),\n",
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       " ('instrumentalness^1xgenre_Electronica^1', 0.00076717011719545534),\n",
       " ('instrumentalness^1xgenre_Classical^1', 0.00075782756024575005),\n",
       " ('energy^1xartist_era_1990s^1', 0.00075617998388249105),\n",
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       " ('genre_Urban^1xrand_0^1', 0.00075254238928982133),\n",
       " ('duration_ms^1xgenre_Classical^1', 0.00075043224097266108),\n",
       " ('liveness^1xgenre_Rock^1', 0.00074395482991679307),\n",
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       " ('speechiness^1xartist_era_1990s^1', 0.00072876994577544265),\n",
       " ('speechiness^1xkey_7.0^1', 0.00072389289525529184),\n",
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       " ('genre_Urban^1xrand_4^1', 0.0007072367721503792),\n",
       " ('genre_Electronica^1xrand_3^1', 0.00070482080021888789),\n",
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       " ('genre_Rock^1xrand_5^1', 0.0006735222078897941),\n",
       " ('genre_Rock^1xrand_0^1', 0.00067138261621282934),\n",
       " ('acousticness^1xgenre_Pop^1', 0.0006690480245049418),\n",
       " ('tempo^1xkey_2.0^1', 0.00066218301958685698),\n",
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       " ('time_sig_4.0^1xmode^1', 0.00065689657075427383),\n",
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       " ('danceability^1xartist_era_1990s^1', 0.00064587828578984167),\n",
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       " ('danceability^1xkey_2.0^1', 0.00063178811051721971),\n",
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       " ('loudness^1xartist_era_1990s^1', 0.00062000843301008581),\n",
       " ('speechiness^1xgenre_Traditional^1', 0.00061999171974683599),\n",
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       " ('key_2.0^1xrand_4^1', 0.00061772570614197536),\n",
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       " ('valence^1xkey_9.0^1', 0.00059547564081907319),\n",
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       " ('key_7.0^1xrand_1^1', 0.00059423910410259697),\n",
       " ('key_7.0^1xrand_5^1', 0.00059229158390938178),\n",
       " ('key_7.0^1xrand_3^1', 0.0005914389911462404),\n",
       " ('duration_ms^1xartist_era_1990s^1', 0.00058988189209444291),\n",
       " ('time_sig_4.0^1xartist_type_Male^1', 0.00058922284179981467),\n",
       " ('key_7.0^1xrand_0^1', 0.0005874395282419731),\n",
       " ('valence^1xartist_era_1990s^1', 0.0005868269361732775),\n",
       " ('danceability^1xkey_7.0^1', 0.0005855287754099385),\n",
       " ('loudness^1xgenre_Pop^1', 0.00058162544935082272),\n",
       " ('genre_Electronica^1xrand_4^1', 0.00057983886707604253),\n",
       " ('liveness^1xkey_9.0^1', 0.00057973495345703938),\n",
       " ('key_7.0^1xrand_2^1', 0.00057955380244168332),\n",
       " ('artist_type_Male^1', 0.00057850575661606948),\n",
       " ('tempo^1xartist_era_1990s^1', 0.00057849896421855753),\n",
       " ('speechiness^1xexplicit_True^1', 0.00057761637960338787),\n",
       " ('key_2.0^1xrand_5^1', 0.00057667637727036526),\n",
       " ('energy^1xkey_2.0^1', 0.00057608922938835933),\n",
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       " ('time_sig_4.0^1xgenre_Rock^1', 0.00057442717946445769),\n",
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       " ('genre_Urban^1xrand_1^1', 0.00057336707543402738),\n",
       " ('danceability^1xgenre_Pop^1', 0.00057299146769385903),\n",
       " ('acousticness^1xkey_9.0^1', 0.00057222284154306478),\n",
       " ('tempo^1xgenre_Pop^1', 0.00057211569699077238),\n",
       " ('speechiness^1xkey_1.0^1', 0.00057199940578556683),\n",
       " ('instrumentalness^1xartist_era_1990s^1', 0.00057153724911782641),\n",
       " ('instrumentalness^1xgenre_Pop^1', 0.00056898202129742931),\n",
       " ('popularity^1xgenre_Pop^1', 0.00056896019059518197),\n",
       " ('instrumentalness^1xkey_2.0^1', 0.00056694180701659424),\n",
       " ('acousticness^1xkey_4.0^1', 0.000566303768501386),\n",
       " ('danceability^1xkey_1.0^1', 0.00056570752557804434),\n",
       " ('key_9.0^1xrand_0^1', 0.00056509466016518265),\n",
       " ('duration_ms^1xkey_9.0^1', 0.00056159644639852593),\n",
       " ('artist_era_1990s^1xrand_0^1', 0.00055980505215367019),\n",
       " ('valence^1xkey_2.0^1', 0.00055739226368633479),\n",
       " ('danceability^1xexplicit_True^1', 0.0005559808621194819),\n",
       " ('key_2.0^1xrand_2^1', 0.00055468261612825998),\n",
       " ('valence^1xgenre_Traditional^1', 0.00055095051436380472),\n",
       " ('acousticness^1xgenre_Traditional^1', 0.00055091328959767135),\n",
       " ('acousticness^1xkey_5.0^1', 0.00054919902899356362),\n",
       " ('acousticness^1xartist_era_1970s^1', 0.00054878287478724215),\n",
       " ('artist_era_1990s^1xrand_1^1', 0.00054865979652536461),\n",
       " ('acousticness^1xkey_1.0^1', 0.00054706949224447062),\n",
       " ('danceability^1xartist_type_Mixed Group^1', 0.00054684667780619683),\n",
       " ('artist_era_2010s^1xartist_origin_North America^1', 0.00054650334227871302),\n",
       " ('genre_Pop^1xrand_0^1', 0.00054641923637367403),\n",
       " ('duration_ms^1xgenre_Traditional^1', 0.00054618994731291126),\n",
       " ('genre_Urban^1xrand_5^1', 0.00054395239526163494),\n",
       " ('key_9.0^1xrand_3^1', 0.00054382263115443103),\n",
       " ('key_9.0^1xrand_2^1', 0.00054361546119686087),\n",
       " ('duration_ms^1xkey_2.0^1', 0.00053979582287444494),\n",
       " ('liveness^1xkey_2.0^1', 0.00053970138231003244),\n",
       " ('genre_Pop^1xrand_2^1', 0.00053906284765652026),\n",
       " ('energy^1xkey_5.0^1', 0.00053883110905726152),\n",
       " ('valence^1xkey_1.0^1', 0.00053826688110196886),\n",
       " ('acousticness^1xtime_sig_3.0^1', 0.00053775771191002814),\n",
       " ('mode^1xartist_type_Male^1', 0.00053631744963200971),\n",
       " ('instrumentalness^1xkey_9.0^1', 0.00053580111480514773),\n",
       " ('key_9.0^1xrand_5^1', 0.00053300824398546602),\n",
       " ('artist_origin_North America^1', 0.00053289223382293582),\n",
       " ('genre_Pop^1xrand_5^1', 0.00053021356023753819),\n",
       " ('popularity^1xkey_9.0^1', 0.00052817913177425377),\n",
       " ('tempo^1xartist_era_1970s^1', 0.00052814930462256621),\n",
       " ('mode^1xartist_era_2010s^1', 0.00052606799741122226),\n",
       " ('artist_era_1990s^1xrand_2^1', 0.0005260367914155851),\n",
       " ('instrumentalness^1xgenre_Traditional^1', 0.000524744440944032),\n",
       " ('artist_origin_North America^1xartist_type_Male^1', 0.00052396738538608044),\n",
       " ('key_1.0^1xrand_5^1', 0.00052204201497213758),\n",
       " ('key_9.0^1xrand_1^1', 0.00052186885180918817),\n",
       " ('loudness^1xgenre_Traditional^1', 0.00051941885950163751),\n",
       " ('genre_Pop^1xrand_3^1', 0.00051841678657729544),\n",
       " ('loudness^1xartist_era_1970s^1', 0.00051755578827429583),\n",
       " ('energy^1xkey_9.0^1', 0.00051731401448347643),\n",
       " ('energy^1xgenre_Jazz^1', 0.00051488951840948844),\n",
       " ('valence^1xkey_5.0^1', 0.00051373158404420267),\n",
       " ('duration_ms^1xgenre_Jazz^1', 0.00051363251925425632),\n",
       " ('artist_era_1990s^1xrand_4^1', 0.00051346249472293418),\n",
       " ('key_1.0^1xrand_0^1', 0.0005133058549288941),\n",
       " ('genre_Pop^1xrand_4^1', 0.00051235694916197596),\n",
       " ('key_2.0^1xrand_0^1', 0.00051225655007808247),\n",
       " ('popularity^1xkey_5.0^1', 0.00051207365772041843),\n",
       " ('popularity^1xkey_2.0^1', 0.00051104039893188713),\n",
       " ('danceability^1xkey_5.0^1', 0.00050873953149417964),\n",
       " ('tempo^1xkey_5.0^1', 0.00050646476890604244),\n",
       " ('energy^1xtime_sig_3.0^1', 0.00050551068834908707),\n",
       " ('popularity^1xartist_era_1990s^1', 0.00050504704173284971),\n",
       " ('key_5.0^1xrand_1^1', 0.00050334221097216557),\n",
       " ('duration_ms^1xkey_4.0^1', 0.00050275341554654822),\n",
       " ('key_1.0^1xrand_4^1', 0.00050253705147241027),\n",
       " ('loudness^1xkey_1.0^1', 0.00050190326484073175),\n",
       " ('genre_Pop^1xrand_1^1', 0.00050188747005592215),\n",
       " ('key_4.0^1xrand_5^1', 0.00050087924577855482),\n",
       " ('loudness^1xtime_sig_3.0^1', 0.0005008722026968854),\n",
       " ('key_9.0^1xrand_4^1', 0.00050081565440483712),\n",
       " ('duration_ms^1xkey_1.0^1', 0.00050075937022450744),\n",
       " ('tempo^1xkey_1.0^1', 0.00050049233271348356),\n",
       " ('key_1.0^1xrand_2^1', 0.0005000405090562352),\n",
       " ('time_sig_4.0^1xgenre_Electronica^1', 0.00049978604824715248),\n",
       " ('artist_origin_North America^1xartist_type_Male Group^1',\n",
       "  0.00049826889790446666),\n",
       " ('liveness^1xkey_1.0^1', 0.00049766813579964664),\n",
       " ('loudness^1xkey_5.0^1', 0.0004967628700016445),\n",
       " ('acousticness^1xkey_11.0^1', 0.00049624708867325093),\n",
       " ('speechiness^1xartist_type_Mixed Group^1', 0.00049577301713056631),\n",
       " ('valence^1xartist_type_Mixed Group^1', 0.00049450848063137915),\n",
       " ('artist_era_2000s^1', 0.00049253972965386745),\n",
       " ('tempo^1xgenre_Classical^1', 0.0004923850759580386),\n",
       " ('artist_era_2010s^1xartist_type_Male^1', 0.00049192697337207885),\n",
       " ('artist_era_1990s^1xrand_3^1', 0.00049121240550912214),\n",
       " ('liveness^1xkey_5.0^1', 0.00049041719575247753),\n",
       " ('key_4.0^1xrand_3^1', 0.00048635540424422588),\n",
       " ('duration_ms^1xkey_11.0^1', 0.0004850657136100619),\n",
       " ('genre_Classical^1xrand_1^1', 0.00048451585255796263),\n",
       " ('tempo^1xkey_9.0^1', 0.00048432240662267631),\n",
       " ('duration_ms^1xkey_5.0^1', 0.00048396490182863188),\n",
       " ('danceability^1xartist_era_1970s^1', 0.0004828740027322896),\n",
       " ('instrumentalness^1xexplicit_True^1', 0.00048236562620186641),\n",
       " ('speechiness^1xkey_4.0^1', 0.0004822484464130683),\n",
       " ('genre_Urban^1xartist_origin_North America^1', 0.00048136202688468571),\n",
       " ('acousticness^1xartist_type_Mixed Group^1', 0.00048118499700325551),\n",
       " ('time_sig_4.0^1xartist_type_Male Group^1', 0.00048060098590456201),\n",
       " ('key_1.0^1xrand_3^1', 0.00047774751825882945),\n",
       " ('key_2.0^1xrand_3^1', 0.00047743818638784315),\n",
       " ('key_4.0^1xrand_4^1', 0.00047688340012931401),\n",
       " ('key_1.0^1xrand_1^1', 0.00047687840637757902),\n",
       " ('popularity^1xartist_type_Mixed Group^1', 0.00047624263901390975),\n",
       " ('danceability^1xkey_6.0^1', 0.00047572771264962503),\n",
       " ('liveness^1xgenre_Jazz^1', 0.00047562233372868497),\n",
       " ('popularity^1xexplicit_True^1', 0.000475474485654437),\n",
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       " ('time_sig_4.0^1xartist_era_2000s^1', 0.00047489529548509443),\n",
       " ('key_5.0^1xrand_2^1', 0.0004741717521863655),\n",
       " ('popularity^1xkey_1.0^1', 0.0004735533546812467),\n",
       " ('artist_era_1990s^1xrand_5^1', 0.00047265434531004112),\n",
       " ('acousticness^1xexplicit_True^1', 0.0004718324310524253),\n",
       " ('key_11.0^1xrand_3^1', 0.00047174149673693329),\n",
       " ('key_5.0^1xrand_5^1', 0.00047106956571513073),\n",
       " ('loudness^1xgenre_Jazz^1', 0.00047045807479642691),\n",
       " ('liveness^1xkey_4.0^1', 0.00047044243170965116),\n",
       " ('key_4.0^1xrand_0^1', 0.00047035191004841606),\n",
       " ('instrumentalness^1xkey_1.0^1', 0.00046667178022746162),\n",
       " ('genre_Rock^1xartist_origin_North America^1', 0.00046651722606900169),\n",
       " ('energy^1xkey_4.0^1', 0.00046561330837127836),\n",
       " ('instrumentalness^1xkey_5.0^1', 0.00046550984367514604),\n",
       " ('speechiness^1xkey_11.0^1', 0.00046200995874073273),\n",
       " ('popularity^1xartist_era_1970s^1', 0.00046162143970072218),\n",
       " ('liveness^1xkey_11.0^1', 0.0004612426360524073),\n",
       " ('danceability^1xkey_4.0^1', 0.00046057468497013378),\n",
       " ('liveness^1xgenre_Traditional^1', 0.00046049217344608091),\n",
       " ('loudness^1xkey_11.0^1', 0.00046013123640299848),\n",
       " ('danceability^1xtime_sig_3.0^1', 0.0004577580645023207),\n",
       " ('loudness^1xartist_type_Mixed Group^1', 0.00045665137718648872),\n",
       " ('key_11.0^1xrand_4^1', 0.00045549406471349322),\n",
       " ('valence^1xartist_era_1970s^1', 0.00045448462926626909),\n",
       " ('tempo^1xkey_11.0^1', 0.0004540442647330332),\n",
       " ('key_5.0^1xrand_0^1', 0.00045339154593803028),\n",
       " ('key_11.0^1xrand_5^1', 0.00045320595943531021),\n",
       " ('tempo^1xkey_4.0^1', 0.00045306431347048794),\n",
       " ('key_5.0^1xrand_4^1', 0.00045292625426729784),\n",
       " ('key_11.0^1xrand_1^1', 0.00045273411112745776),\n",
       " ('artist_type_Mixed Group^1xrand_5^1', 0.00045156370375513483),\n",
       " ('danceability^1xkey_8.0^1', 0.00045091323728281508),\n",
       " ('duration_ms^1xartist_type_Mixed Group^1', 0.00045074796043637218),\n",
       " ('genre_Classical^1xrand_5^1', 0.00044892612100076875),\n",
       " ('valence^1xkey_4.0^1', 0.0004488971655960194),\n",
       " ('genre_Jazz^1', 0.00044758227490007641),\n",
       " ('artist_type_Mixed Group^1xrand_3^1', 0.00044388864211332505),\n",
       " ('artist_origin_Western Europe^1', 0.00044372810799820874),\n",
       " ('tempo^1xgenre_Traditional^1', 0.0004432540557825862),\n",
       " ('artist_era_2000s^1xartist_origin_North America^1', 0.00044280034856775162),\n",
       " ('artist_era_1970s^1xrand_4^1', 0.00044181971623570289),\n",
       " ('key_11.0^1xrand_0^1', 0.00044037217638007343),\n",
       " ('genre_Electronica^1xartist_type_Male^1', 0.00044013026526305686),\n",
       " ('instrumentalness^1xkey_4.0^1', 0.00043945228184752059),\n",
       " ('key_4.0^1xrand_1^1', 0.00043934340429544345),\n",
       " ('speechiness^1xkey_5.0^1', 0.00043906821026047237),\n",
       " ('danceability^1xkey_11.0^1', 0.00043890398708151167),\n",
       " ('acousticness^1xkey_8.0^1', 0.00043864437856422014),\n",
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       " ('popularity^1xgenre_Traditional^1', 0.00043789584042987781),\n",
       " ('popularity^1xkey_11.0^1', 0.00043776909557445391),\n",
       " ('genre_Traditional^1', 0.00043660660359662177),\n",
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       " ('artist_era_1970s^1xrand_3^1', 0.00043506152343062549),\n",
       " ('valence^1xkey_11.0^1', 0.00043484748610709341),\n",
       " ('genre_Urban^1xmode^1', 0.00043415732103015971),\n",
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       " ('energy^1xartist_type_Mixed Group^1', 0.00043362903424440163),\n",
       " ('popularity^1xkey_4.0^1', 0.00043258808791415271),\n",
       " ('artist_era_1970s^1xrand_1^1', 0.00043186842444940213),\n",
       " ('speechiness^1xgenre_Jazz^1', 0.00043181808326137131),\n",
       " ('instrumentalness^1xartist_type_Mixed Group^1', 0.00042921626497289931),\n",
       " ('liveness^1xexplicit_True^1', 0.00042845136945426069),\n",
       " ('genre_Rock^1xmode^1', 0.00042764876631810937),\n",
       " ('energy^1xartist_era_1970s^1', 0.00042757498993785696),\n",
       " ('valence^1xkey_10.0^1', 0.00042736566627221814),\n",
       " ('genre_Jazz^1xartist_origin_North America^1', 0.00042676456464210287),\n",
       " ('key_4.0^1xrand_2^1', 0.0004257250557257503),\n",
       " ('energy^1xkey_8.0^1', 0.00042430572153674532),\n",
       " ('instrumentalness^1xtime_sig_3.0^1', 0.00042209159714744486),\n",
       " ('liveness^1xartist_era_1970s^1', 0.00042124546201656552),\n",
       " ('acousticness^1xkey_6.0^1', 0.00042116449647730916),\n",
       " ('tempo^1xartist_type_Mixed Group^1', 0.00042103863400430049),\n",
       " ('explicit_True^1xartist_origin_North America^1', 0.00042101631043599291),\n",
       " ('time_sig_4.0^1xartist_origin_Western Europe^1', 0.00042031726859538093),\n",
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       " ('energy^1xgenre_Traditional^1', 0.00041921041436289067),\n",
       " ('energy^1xexplicit_True^1', 0.0004191938973994475),\n",
       " ('artist_era_1970s^1xrand_5^1', 0.00041892597493531402),\n",
       " ('valence^1xgenre_Jazz^1', 0.00041881366954119601),\n",
       " ('mode^1xartist_era_2000s^1', 0.00041854604993177979),\n",
       " ('genre_Traditional^1xrand_0^1', 0.00041792951654882221),\n",
       " ('key_6.0^1xrand_2^1', 0.00041765857509611765),\n",
       " ('time_sig_3.0^1xrand_0^1', 0.00041744146653196421),\n",
       " ('artist_type_Mixed Group^1xrand_4^1', 0.00041702927212389419),\n",
       " ('liveness^1xartist_type_Mixed Group^1', 0.00041561048703599494),\n",
       " ('genre_Traditional^1xrand_1^1', 0.00041519436983297297),\n",
       " ('instrumentalness^1xkey_11.0^1', 0.00041415450539847542),\n",
       " ('artist_era_1970s^1xrand_0^1', 0.0004138181421912786),\n",
       " ('genre_Urban^1xartist_type_Male^1', 0.00040850376634303824),\n",
       " ('time_sig_3.0^1xrand_2^1', 0.00040844991742679319),\n",
       " ('danceability^1xgenre_Traditional^1', 0.00040806753872476507),\n",
       " ('duration_ms^1xtime_sig_3.0^1', 0.00040726933914734013),\n",
       " ('valence^1xexplicit_True^1', 0.0004069593193558891),\n",
       " ('speechiness^1xtime_sig_3.0^1', 0.00040675111069074195),\n",
       " ('liveness^1xtime_sig_3.0^1', 0.00040566721246690617),\n",
       " ('speechiness^1xkey_8.0^1', 0.00040465802234275239),\n",
       " ('artist_era_1970s^1xrand_2^1', 0.00040446321105470717),\n",
       " ('energy^1xkey_11.0^1', 0.00040439241636276935),\n",
       " ('valence^1xtime_sig_3.0^1', 0.00040405900853484738),\n",
       " ('time_sig_3.0^1xrand_4^1', 0.00040402584288387974),\n",
       " ('duration_ms^1xkey_6.0^1', 0.00040352787432219173),\n",
       " ('artist_type_Mixed Group^1xrand_0^1', 0.000402547086269738),\n",
       " ('danceability^1xkey_10.0^1', 0.0004019645981798302),\n",
       " ('artist_type_Mixed Group^1xrand_2^1', 0.0004012392057537296),\n",
       " ('time_sig_3.0^1xrand_1^1', 0.00040067984267410956),\n",
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       " ('instrumentalness^1xartist_era_1970s^1', 0.00039953009063333362),\n",
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       " ('tempo^1xkey_8.0^1', 0.00039800956721783721),\n",
       " ('key_8.0^1xrand_2^1', 0.00039501271882222167),\n",
       " ('time_sig_3.0^1xrand_3^1', 0.00039413303878677399),\n",
       " ('valence^1xkey_6.0^1', 0.00039284288441750903),\n",
       " ('key_9.0^1', 0.00039195356805655856),\n",
       " ('genre_Classical^1xrand_0^1', 0.00038956374139568096),\n",
       " ('artist_type_Mixed Group^1xrand_1^1', 0.0003894836084574804),\n",
       " ('key_8.0^1xrand_0^1', 0.00038946856599601814),\n",
       " ('key_8.0^1xrand_4^1', 0.00038928570821269851),\n",
       " ('key_10.0^1xrand_0^1', 0.00038801158829780756),\n",
       " ('tempo^1xkey_6.0^1', 0.00038716635759803125),\n",
       " ('key_6.0^1xrand_5^1', 0.00038708847274302821),\n",
       " ('key_10.0^1xrand_5^1', 0.00038648698743636733),\n",
       " ('acousticness^1xgenre_Other^1', 0.00038611000210548771),\n",
       " ('tempo^1xkey_10.0^1', 0.00038459665161457503),\n",
       " ('mode^1xartist_origin_Western Europe^1', 0.00038086221824038956),\n",
       " ('explicit_True^1xrand_5^1', 0.00037972346714331177),\n",
       " ('popularity^1xkey_6.0^1', 0.00037950086334267443),\n",
       " ('danceability^1xgenre_Jazz^1', 0.00037910634811999007),\n",
       " ('key_6.0^1xrand_1^1', 0.00037861334493596328),\n",
       " ('instrumentalness^1xkey_10.0^1', 0.0003786133099705144),\n",
       " ('key_2.0^1xtime_sig_4.0^1', 0.00037816259165347055),\n",
       " ('explicit_True^1', 0.00037801740787166241),\n",
       " ('valence^1xkey_8.0^1', 0.00037770295104878394),\n",
       " ('speechiness^1xkey_6.0^1', 0.00037725960366732648),\n",
       " ('speechiness^1xkey_10.0^1', 0.00037683925696138892),\n",
       " ('acousticness^1xkey_10.0^1', 0.00037683097414113292),\n",
       " ('loudness^1xexplicit_True^1', 0.00037600963741094478),\n",
       " ('liveness^1xartist_era_1960s^1', 0.00037447517575611396),\n",
       " ('key_8.0^1xrand_1^1', 0.00037440301856965475),\n",
       " ('valence^1xartist_era_1980s^1', 0.00037195753465447864),\n",
       " ('energy^1xkey_10.0^1', 0.00037151320627241513),\n",
       " ('key_8.0^1xrand_3^1', 0.00037116384176660833),\n",
       " ('key_6.0^1xrand_3^1', 0.00037036012101074995),\n",
       " ('artist_era_1980s^1xrand_4^1', 0.00037015225434638364),\n",
       " ('key_11.0^1xrand_2^1', 0.00037014310320852546),\n",
       " ('popularity^1xkey_8.0^1', 0.00036970554790012529),\n",
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       " ('key_7.0^1', 0.00035837907666245512),\n",
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       " ('time_sig_4.0^1xgenre_Classical^1', 0.00034209830623094966),\n",
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       " ('duration_ms^1xartist_origin_Scandinavia^1', 0.00018575009572673919),\n",
       " ('key_3.0^1xrand_4^1', 0.00018565632897117585),\n",
       " ('popularity^1xartist_origin_Scandinavia^1', 0.00018561417563642079),\n",
       " ('valence^1xkey_3.0^1', 0.00018505077446699102),\n",
       " ('energy^1xkey_3.0^1', 0.00018484203949064838),\n",
       " ('speechiness^1xartist_origin_Australia/Oceania^1', 0.00018457263426340043),\n",
       " ('instrumentalness^1xkey_3.0^1', 0.00018423707340623583),\n",
       " ('time_sig_4.0^1xartist_era_1960s^1', 0.00018399235657291099),\n",
       " ('artist_origin_Scandinavia^1xrand_1^1', 0.00018165128886244935),\n",
       " ('genre_Electronica^1xartist_era_2000s^1', 0.00018065424966830199),\n",
       " ('artist_era_1970s^1xartist_type_Male Group^1', 0.00018058861255218148),\n",
       " ('artist_era_1970s^1xartist_origin_North America^1', 0.00018038644692636066),\n",
       " ('acousticness^1xartist_origin_Australia/Oceania^1', 0.00017985340126944351),\n",
       " ('genre_Electronica^1xartist_origin_Western Europe^1',\n",
       "  0.00017924131996905037),\n",
       " ('tempo^1xkey_3.0^1', 0.00017866562335829242),\n",
       " ('time_sig_4.0^1xgenre_Other^1', 0.00017833791667946149),\n",
       " ('artist_era_1950s^1xrand_0^1', 0.00017713810862537757),\n",
       " ('duration_ms^1xartist_type_Mixed Duo^1', 0.00017656567003869632),\n",
       " ('artist_era_1950s^1xrand_1^1', 0.00017570176806809405),\n",
       " ('key_4.0^1xartist_type_Male^1', 0.00017521201849148582),\n",
       " ('time_sig_3.0^1xartist_origin_North America^1', 0.00017485214875269535),\n",
       " ('liveness^1xartist_origin_Scandinavia^1', 0.00017386231973510476),\n",
       " ('acousticness^1xartist_type_Mixed Duo^1', 0.00017302601336746627),\n",
       " ('liveness^1xartist_era_1950s^1', 0.00017276665711393659),\n",
       " ('explicit_True^1xartist_era_2010s^1', 0.00017160683241775255),\n",
       " ('danceability^1xartist_era_Pre-20th C.^1', 0.00017120955001996839),\n",
       " ('artist_origin_North America^1xartist_type_Mixed Group^1',\n",
       "  0.00017027200715943321),\n",
       " ('danceability^1xartist_origin_Australia/Oceania^1', 0.00016974660663814979),\n",
       " ('energy^1xartist_origin_Australia/Oceania^1', 0.00016879877084176691),\n",
       " ('danceability^1xkey_3.0^1', 0.00016830829223062272),\n",
       " ('artist_era_1950s^1xrand_3^1', 0.00016824753594166968),\n",
       " ('liveness^1xkey_3.0^1', 0.00016784794485413194),\n",
       " ('loudness^1xartist_era_Pre-20th C.^1', 0.00016704915718244073),\n",
       " ('time_sig_3.0^1xmode^1', 0.00016612155824720376),\n",
       " ('key_10.0^1xartist_origin_North America^1', 0.00016473056730805579),\n",
       " ('key_4.0^1xartist_era_2010s^1', 0.00016471205179416953),\n",
       " ('valence^1xartist_origin_Caribbean^1', 0.00016397109386593229),\n",
       " ('artist_origin_Australia/Oceania^1xrand_2^1', 0.0001637940129877527),\n",
       " ('key_7.0^1xartist_era_2000s^1', 0.00016296029731095862),\n",
       " ('artist_origin_Australia/Oceania^1xrand_1^1', 0.00016155690954788193),\n",
       " ('loudness^1xartist_origin_Australia/Oceania^1', 0.0001612630536691459),\n",
       " ('artist_era_1950s^1xrand_2^1', 0.00016070860587252334),\n",
       " ('key_10.0^1xmode^1', 0.00016023760831409206),\n",
       " ('key_9.0^1xartist_era_2010s^1', 0.00015825049198491887),\n",
       " ('key_1.0^1xartist_era_2010s^1', 0.00015824535049375983),\n",
       " ('mode^1xartist_era_1960s^1', 0.00015822679455741476),\n",
       " ('genre_Other^1xartist_type_Male^1', 0.00015766554715624913),\n",
       " ('key_3.0^1xtime_sig_4.0^1', 0.00015760057923851214),\n",
       " ('genre_Pop^1xartist_era_2010s^1', 0.00015753528882989727),\n",
       " ('artist_type_Male Duo^1', 0.00015746354945748179),\n",
       " ('liveness^1xartist_origin_Australia/Oceania^1', 0.000157370012572208),\n",
       " ('key_6.0^1xmode^1', 0.00015430512957328877),\n",
       " ('instrumentalness^1xartist_type_Mixed Duo^1', 0.00015416577194346146),\n",
       " ('popularity^1xartist_origin_Australia/Oceania^1', 0.00015358729169924449),\n",
       " ('key_11.0^1xartist_era_2010s^1', 0.00015335398036803909),\n",
       " ('time_sig_4.0^1xartist_era_1950s^1', 0.00015332994123177515),\n",
       " ('key_1.0^1xartist_type_Male Group^1', 0.00015235738995207033),\n",
       " ('instrumentalness^1xartist_origin_Australia/Oceania^1',\n",
       "  0.00015216023955747719),\n",
       " ('key_3.0^1', 0.00015182506642562411),\n",
       " ('artist_origin_Australia/Oceania^1xrand_0^1', 0.00015169298763640046),\n",
       " ('artist_type_Mixed Duo^1xrand_2^1', 0.00015146657836274447),\n",
       " ('valence^1xgenre_Soundtrack^1', 0.00015119427226114739),\n",
       " ('artist_origin_Australia/Oceania^1xrand_5^1', 0.00015070722125195676),\n",
       " ('artist_origin_Australia/Oceania^1xrand_3^1', 0.00015060027141726592),\n",
       " ('key_6.0^1xartist_era_2010s^1', 0.00015007639333565771),\n",
       " ('loudness^1xartist_type_Mixed Duo^1', 0.00014972128897171557),\n",
       " ('duration_ms^1xartist_origin_Caribbean^1', 0.0001496039516533387),\n",
       " ('valence^1xartist_type_Mixed Duo^1', 0.00014945388993531632),\n",
       " ('artist_type_Mixed Duo^1xrand_3^1', 0.00014790295495765507),\n",
       " ('acousticness^1xartist_origin_Caribbean^1', 0.00014781097822827378),\n",
       " ('time_sig_4.0^1xartist_type_Male Duo^1', 0.00014776832885804043),\n",
       " ('key_6.0^1xartist_type_Male^1', 0.00014668005553520171),\n",
       " ('artist_era_1980s^1xartist_origin_North America^1', 0.00014657779198950997),\n",
       " ('key_2.0^1xartist_type_Male Group^1', 0.00014583827007519847),\n",
       " ('artist_era_2000s^1xartist_type_Mixed Group^1', 0.0001451474367150495),\n",
       " ('genre_Classical^1xartist_era_Pre-20th C.^1', 0.00014506560120882892),\n",
       " ('genre_Rock^1xartist_origin_Western Europe^1', 0.0001446054115360684),\n",
       " ('key_4.0^1xartist_type_Male Group^1', 0.00014399265737408937),\n",
       " ('popularity^1xartist_origin_Caribbean^1', 0.00014371145827657872),\n",
       " ('time_sig_4.0^1xartist_era_Pre-20th C.^1', 0.00014337896389060161),\n",
       " ('danceability^1xartist_type_Mixed Duo^1', 0.00014250446575577794),\n",
       " ('acousticness^1xartist_origin_Eastern Europe^1', 0.00014226265179616526),\n",
       " ('danceability^1xartist_origin_Caribbean^1', 0.00014155847579670045),\n",
       " ('artist_era_Pre-20th C.^1xrand_4^1', 0.00014151489192774588),\n",
       " ('valence^1xartist_origin_Australia/Oceania^1', 0.00014109720832515083),\n",
       " ('key_9.0^1xartist_type_Male Group^1', 0.00014034258235559431),\n",
       " ...]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_feature_names = ['x'.join(['{}^{}'.format(pair[0],pair[1]) for pair in tuple if pair[1]!=0]) for tuple in [zip(master_songs_plus.columns,p) for p in poly.powers_]]\n",
    "output_df = pd.DataFrame(poly_X, columns = target_feature_names)\n",
    "importance = sorted(zip(output_df.columns, models['RFC'].feature_importances_), key = lambda tup: np.abs(tup[1]), reverse=True)\n",
    "\n",
    "importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Baseline Case - Feature Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = master_songs_5, master_songs_target['mood_1']\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.3, random_state=1010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      " Aggressive       0.58      0.50      0.54       395\n",
      "   Brooding       0.32      0.33      0.33       359\n",
      "       Cool       0.38      0.56      0.45       379\n",
      "    Defiant       0.44      0.53      0.48       490\n",
      "  Easygoing       0.31      0.21      0.25       110\n",
      " Empowering       0.35      0.52      0.42       477\n",
      " Energizing       0.46      0.51      0.48       419\n",
      "    Excited       0.34      0.46      0.39       384\n",
      "      Fiery       0.45      0.11      0.18       122\n",
      "     Gritty       0.33      0.13      0.19       187\n",
      "     Lively       0.43      0.22      0.29       104\n",
      " Melancholy       0.34      0.41      0.37       235\n",
      "   Peaceful       0.34      0.29      0.31       145\n",
      "   Romantic       0.38      0.09      0.14       175\n",
      "      Rowdy       0.32      0.12      0.17       173\n",
      "    Sensual       0.42      0.53      0.47       500\n",
      "Sentimental       0.42      0.46      0.44       225\n",
      "    Serious       0.49      0.60      0.54       177\n",
      "     Somber       0.37      0.22      0.27       102\n",
      "Sophisticated       0.43      0.19      0.27       161\n",
      "   Stirring       0.40      0.03      0.05        73\n",
      "     Tender       0.47      0.65      0.54       212\n",
      "     Upbeat       0.41      0.24      0.30       164\n",
      "     Urgent       0.25      0.10      0.14       233\n",
      "   Yearning       0.31      0.30      0.30       317\n",
      "\n",
      "avg / total       0.39      0.40      0.38      6318\n",
      "\n",
      "\n",
      "\n",
      "(u'acousticness', 0.06722005790019342)\n",
      "(u'energy', 0.066239169837704628)\n",
      "(u'danceability', 0.061148529886167181)\n",
      "(u'speechiness', 0.05648986690282623)\n",
      "(u'loudness', 0.054822276031685976)\n",
      "(u'valence', 0.052412295560923486)\n",
      "(u'duration_ms', 0.047269578428298022)\n",
      "(u'instrumentalness', 0.046091022312388479)\n",
      "(u'tempo', 0.04284757667750539)\n",
      "(u'liveness', 0.039064555780065284)\n",
      "('popularity', 0.038081963380719813)\n",
      "('rand_0', 0.037955961411040816)\n",
      "('rand_1', 0.037803433830834003)\n",
      "('rand_2', 0.037773005696123046)\n",
      "('rand_3', 0.037119625468611799)\n",
      "('rand_4', 0.037001410172305606)\n",
      "('rand_5', 0.036963241543879338)\n",
      "('genre_Classical', 0.012207819045551904)\n",
      "('genre_Electronica', 0.011982590546392596)\n",
      "(u'mode', 0.0096312533522023899)\n",
      "('genre_Urban', 0.0092842335641397291)\n",
      "('genre_Rock', 0.009181669976726042)\n",
      "('artist_era_2010s', 0.0081420731345745821)\n",
      "('artist_type_Male', 0.0076332046636510839)\n",
      "('artist_type_Male Group', 0.0075759818266136914)\n",
      "('artist_origin_North America', 0.0075239325612579053)\n",
      "('artist_era_2000s', 0.0065800679879407711)\n",
      "('artist_origin_Western Europe', 0.0065421067935881524)\n",
      "('genre_Jazz', 0.0055357189436613932)\n",
      "('genre_Traditional', 0.0052398187623866862)\n",
      "('key_7.0', 0.0048606682057288049)\n",
      "('key_2.0', 0.0047111506859995531)\n",
      "('key_9.0', 0.004670854605371655)\n",
      "('artist_era_1990s', 0.0045322613604060766)\n",
      "('genre_Pop', 0.0044426972994896993)\n",
      "('artist_era_1970s', 0.0043156976672259024)\n",
      "('key_1.0', 0.004167244658427759)\n",
      "('explicit_True', 0.0041558768699307214)\n",
      "('key_5.0', 0.0040606978698281646)\n",
      "('key_4.0', 0.0040461920511275715)\n",
      "('artist_type_Mixed Group', 0.0037659206781785791)\n",
      "('key_11.0', 0.003718779433319672)\n",
      "('key_6.0', 0.0035205629571092773)\n",
      "('key_8.0', 0.0033964192292317342)\n",
      "('key_10.0', 0.0033097392287980533)\n",
      "('artist_era_1960s', 0.0032776522731646929)\n",
      "('time_sig_4.0', 0.003222121223137339)\n",
      "('artist_era_1980s', 0.0029228611333983047)\n",
      "('time_sig_3.0', 0.0027521715331490487)\n",
      "('genre_Other', 0.0027244771225482436)\n",
      "('artist_type_Male Duo', 0.0024060702725727501)\n",
      "('artist_era_1950s', 0.0022829013211028647)\n",
      "('key_3.0', 0.0019602349599825725)\n",
      "('artist_origin_Scandinavia', 0.0017058777841388109)\n",
      "('artist_era_Pre-20th C.', 0.0015402607385753048)\n",
      "('artist_origin_Australia/Oceania', 0.0014332813188609134)\n",
      "('artist_type_Mixed Duo', 0.0013860538405718031)\n",
      "('artist_origin_Caribbean', 0.0011898996500120124)\n",
      "('genre_Soundtrack', 0.001186861431349362)\n",
      "('artist_origin_Mediterranean Europe', 0.00088415379538609882)\n",
      "('time_sig_5.0', 0.00088401165878281539)\n",
      "('artist_type_Female Group', 0.00078612009600436653)\n",
      "('artist_origin_Eastern Europe', 0.00058716437769814169)\n",
      "('artist_type_Female Duo', 0.00051891004124237131)\n",
      "('artist_era_1940s', 0.00030975065502146829)\n",
      "('artist_era_Early 20th C.', 0.00027027676467018439)\n",
      "('artist_origin_South America', 0.00024526886150263766)\n",
      "('artist_origin_West Africa', 0.00017794900376681984)\n",
      "('artist_origin_Southeast Asia', 8.9957107320811586e-05)\n",
      "('artist_origin_Middle East', 8.8350337174931522e-05)\n",
      "('artist_origin_Southern Africa', 6.6732208256866705e-05)\n",
      "('artist_origin_East/Central Africa', 2.5208085122027536e-05)\n",
      "('artist_origin_Central America', 1.5377336501470126e-05)\n",
      "('artist_origin_Central Asia', 1.2189970780617871e-05)\n",
      "('artist_origin_North Africa', 9.0503180717428127e-06)\n",
      "0.397435897436\n"
     ]
    }
   ],
   "source": [
    "models = {}\n",
    "models['RFC'] = RandomForestClassifier(n_estimators=100, max_features=15)\n",
    "\n",
    "for name, model in models.iteritems(): \n",
    "    model.fit(X_train, y_train)\n",
    "    print name\n",
    "    print metrics.classification_report(y_test, model.predict(X_test))\n",
    "    print '\\n'\n",
    "    a = X.columns.values\n",
    "    ftr_imp = list(zip(a, model.feature_importances_))\n",
    "    ftr_imp = sorted(ftr_imp, key=(lambda x: x[1]), reverse = True)\n",
    "\n",
    "    for i in ftr_imp:\n",
    "        print(i)\n",
    "    print(model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Throwing out features, including interactions between features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "master_songs_6 = pd.concat([master_songs_scaled, ts, master_songs_3['mode'].reset_index().drop('index', axis=1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "master_songs_6 = add_noise(master_songs_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 1, 1, 0],\n",
       "       [0, 0, 0, ..., 1, 0, 1],\n",
       "       [0, 0, 0, ..., 0, 1, 1]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = PolynomialFeatures(2, interaction_only=True)\n",
    "poly.fit(master_songs_6)\n",
    "poly.powers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "poly_X = poly.transform(master_songs_6)\n",
    "y = master_songs_target['mood_1']\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(poly_X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      " Aggressive       0.48      0.50      0.49       399\n",
      "   Brooding       0.28      0.33      0.30       331\n",
      "       Cool       0.32      0.48      0.38       374\n",
      "    Defiant       0.44      0.48      0.46       538\n",
      "  Easygoing       0.24      0.14      0.18       124\n",
      " Empowering       0.34      0.55      0.42       492\n",
      " Energizing       0.43      0.48      0.45       412\n",
      "    Excited       0.29      0.35      0.32       383\n",
      "      Fiery       0.09      0.01      0.02       118\n",
      "     Gritty       0.19      0.07      0.10       188\n",
      "     Lively       0.41      0.11      0.17       117\n",
      " Melancholy       0.30      0.35      0.32       231\n",
      "   Peaceful       0.29      0.31      0.30       139\n",
      "   Romantic       0.19      0.05      0.07       152\n",
      "      Rowdy       0.33      0.05      0.08       174\n",
      "    Sensual       0.30      0.46      0.36       443\n",
      "Sentimental       0.29      0.37      0.33       225\n",
      "    Serious       0.42      0.53      0.47       175\n",
      "     Somber       0.24      0.06      0.10       110\n",
      "Sophisticated       0.37      0.09      0.15       185\n",
      "   Stirring       0.25      0.01      0.02        77\n",
      "     Tender       0.41      0.60      0.48       194\n",
      "     Upbeat       0.34      0.13      0.19       163\n",
      "     Urgent       0.18      0.04      0.07       263\n",
      "   Yearning       0.22      0.22      0.22       311\n",
      "\n",
      "avg / total       0.32      0.34      0.31      6318\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = {}\n",
    "models['RFC'] = RandomForestClassifier(n_estimators=100, max_features=15)\n",
    "\n",
    "for name, model in models.iteritems(): \n",
    "    model.fit(X_train, y_train)\n",
    "    print name\n",
    "    print metrics.classification_report(y_test, model.predict(X_test))\n",
    "    print '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('acousticness^1', 0.014593663851062049),\n",
       " ('energy^1xacousticness^1', 0.011642693461937897),\n",
       " ('energy^1', 0.011558538936623824),\n",
       " ('danceability^1', 0.010620805647821141),\n",
       " ('acousticness^1xloudness^1', 0.010374703757407476),\n",
       " ('acousticness^1xtime_sig_4.0^1', 0.010263464353430417),\n",
       " ('danceability^1xtime_sig_4.0^1', 0.0099311455054263992),\n",
       " ('acousticness^1xdanceability^1', 0.0095354211260768665),\n",
       " ('energy^1xtime_sig_4.0^1', 0.0095247702089951546),\n",
       " ('speechiness^1', 0.0091765278602200153),\n",
       " ('acousticness^1xinstrumentalness^1', 0.0090270781793514807),\n",
       " ('energy^1xdanceability^1', 0.0088624638585784905),\n",
       " ('speechiness^1xinstrumentalness^1', 0.0086572549523515913),\n",
       " ('speechiness^1xacousticness^1', 0.0083437749483305911),\n",
       " ('loudness^1', 0.0081576998050401032),\n",
       " ('speechiness^1xdanceability^1', 0.008112615142640266),\n",
       " ('energy^1xinstrumentalness^1', 0.0080802528302981926),\n",
       " ('energy^1xloudness^1', 0.0079949476143137306),\n",
       " ('danceability^1xloudness^1', 0.0077950005109799882),\n",
       " ('instrumentalness^1xdanceability^1', 0.0077133148466332797),\n",
       " ('speechiness^1xtime_sig_4.0^1', 0.0076449417331791525),\n",
       " ('energy^1xspeechiness^1', 0.0075303302684689268),\n",
       " ('instrumentalness^1', 0.0073169831794609582),\n",
       " ('acousticness^1xmode^1', 0.0070344696870447646),\n",
       " ('speechiness^1xloudness^1', 0.0070284437986815086),\n",
       " ('valence^1', 0.0069067936119175432),\n",
       " ('danceability^1xvalence^1', 0.006783444702932323),\n",
       " ('speechiness^1xvalence^1', 0.0067673704491381078),\n",
       " ('energy^1xvalence^1', 0.0065948350685999567),\n",
       " ('acousticness^1xvalence^1', 0.006578550790077132),\n",
       " ('instrumentalness^1xloudness^1', 0.0065519909380668739),\n",
       " ('acousticness^1xduration_ms^1', 0.0065139952705386547),\n",
       " ('loudness^1xtime_sig_4.0^1', 0.0064574933082278407),\n",
       " ('loudness^1xvalence^1', 0.006427500732676155),\n",
       " ('speechiness^1xmode^1', 0.0064076704193125836),\n",
       " ('instrumentalness^1xvalence^1', 0.0063000667526011498),\n",
       " ('danceability^1xduration_ms^1', 0.0062454979425503347),\n",
       " ('valence^1xtime_sig_4.0^1', 0.0061799747977617104),\n",
       " ('duration_ms^1xvalence^1', 0.0061789811033320825),\n",
       " ('energy^1xduration_ms^1', 0.0058897993139356694),\n",
       " ('instrumentalness^1xtime_sig_4.0^1', 0.005874853043545721),\n",
       " ('tempo^1xdanceability^1', 0.0058056863018194852),\n",
       " ('duration_ms^1xloudness^1', 0.0057276381234307727),\n",
       " ('duration_ms^1', 0.0056703976036025332),\n",
       " ('energy^1xmode^1', 0.0055399560874077047),\n",
       " ('instrumentalness^1xduration_ms^1', 0.0055113528267197263),\n",
       " ('instrumentalness^1xmode^1', 0.0055018449648477656),\n",
       " ('speechiness^1xduration_ms^1', 0.0053522320226548504),\n",
       " ('tempo^1xvalence^1', 0.0052036865142618648),\n",
       " ('tempo^1xspeechiness^1', 0.005193046197134594),\n",
       " ('popularity^1xspeechiness^1', 0.0051886150561387722),\n",
       " ('tempo^1xduration_ms^1', 0.005168445867299456),\n",
       " ('tempo^1xacousticness^1', 0.005160805283353739),\n",
       " ('popularity^1xloudness^1', 0.0051607306205739721),\n",
       " ('duration_ms^1xtime_sig_4.0^1', 0.005154146959483916),\n",
       " ('popularity^1xdanceability^1', 0.005146582201701457),\n",
       " ('liveness^1xloudness^1', 0.0051029285189004205),\n",
       " ('liveness^1xdanceability^1', 0.0051017308445223718),\n",
       " ('popularity^1xacousticness^1', 0.0050840146349268077),\n",
       " ('popularity^1xvalence^1', 0.0050761999098543056),\n",
       " ('tempo^1xloudness^1', 0.0050466332483470322),\n",
       " ('liveness^1xacousticness^1', 0.0050271246833767051),\n",
       " ('liveness^1xduration_ms^1', 0.0049722744287804066),\n",
       " ('popularity^1xtempo^1', 0.004967011101304231),\n",
       " ('tempo^1', 0.0049594495971260789),\n",
       " ('energy^1xliveness^1', 0.0049547299685192489),\n",
       " ('liveness^1xspeechiness^1', 0.0049294772488177032),\n",
       " ('energy^1xtempo^1', 0.0049242695827711076),\n",
       " ('popularity^1xinstrumentalness^1', 0.0049168363327482612),\n",
       " ('popularity^1xduration_ms^1', 0.0049148167662353796),\n",
       " ('tempo^1xinstrumentalness^1', 0.0049119079328371123),\n",
       " ('tempo^1xrand_1^1', 0.0048968053731532754),\n",
       " ('tempo^1xrand_5^1', 0.0048692717807806655),\n",
       " ('tempo^1xtime_sig_4.0^1', 0.0048315447602846427),\n",
       " ('valence^1xmode^1', 0.0048249211882569476),\n",
       " ('danceability^1xmode^1', 0.0048022855186121028),\n",
       " ('popularity^1xenergy^1', 0.0047973912954681593),\n",
       " ('liveness^1xrand_1^1', 0.00476426597092136),\n",
       " ('tempo^1xrand_2^1', 0.0047523411814673509),\n",
       " ('rand_3^1xrand_4^1', 0.0047488125715926879),\n",
       " ('popularity^1xrand_2^1', 0.0047244943206888488),\n",
       " ('rand_1^1xrand_2^1', 0.0047184279814012206),\n",
       " ('rand_3^1xrand_5^1', 0.0047166308879849784),\n",
       " ('tempo^1xrand_0^1', 0.0047164543737437861),\n",
       " ('duration_ms^1xrand_1^1', 0.0046942527793275325),\n",
       " ('duration_ms^1xrand_0^1', 0.0046910991761392757),\n",
       " ('liveness^1xtempo^1', 0.0046836916516403374),\n",
       " ('rand_1^1xrand_5^1', 0.0046769465303481417),\n",
       " ('loudness^1xmode^1', 0.0046760483882339375),\n",
       " ('duration_ms^1xrand_3^1', 0.0046729629701849941),\n",
       " ('liveness^1xvalence^1', 0.0046686177959986445),\n",
       " ('popularity^1xrand_5^1', 0.0046509079107154919),\n",
       " ('popularity^1xrand_3^1', 0.0046437853929688685),\n",
       " ('duration_ms^1xrand_5^1', 0.0046391691854113879),\n",
       " ('rand_4^1xrand_5^1', 0.0046363249986554143),\n",
       " ('rand_2^1xrand_3^1', 0.0046130651626707705),\n",
       " ('rand_0^1xrand_4^1', 0.0046000853271786109),\n",
       " ('liveness^1xinstrumentalness^1', 0.0045978888183133611),\n",
       " ('rand_0^1xrand_5^1', 0.0045964604843103662),\n",
       " ('liveness^1xrand_5^1', 0.0045863431577063676),\n",
       " ('duration_ms^1xrand_2^1', 0.0045857361879768511),\n",
       " ('valence^1xrand_2^1', 0.0045789658935972369),\n",
       " ('valence^1xrand_3^1', 0.0045788545966342995),\n",
       " ('popularity^1xrand_0^1', 0.0045773173602054514),\n",
       " ('danceability^1xrand_1^1', 0.0045731976080542695),\n",
       " ('tempo^1xrand_4^1', 0.0045699720644904237),\n",
       " ('valence^1xrand_5^1', 0.0045625960130809976),\n",
       " ('popularity^1xrand_1^1', 0.0045550823285498586),\n",
       " ('rand_1^1xrand_4^1', 0.0045455373256291683),\n",
       " ('popularity^1xliveness^1', 0.0045453781882079354),\n",
       " ('danceability^1xrand_4^1', 0.0045451760921316553),\n",
       " ('tempo^1xrand_3^1', 0.0045402587601405722),\n",
       " ('rand_0^1xrand_3^1', 0.0045382528312542178),\n",
       " ('popularity^1xrand_4^1', 0.0045330815894708033),\n",
       " ('rand_2^1xrand_4^1', 0.0045282312946151766),\n",
       " ('liveness^1', 0.0045223910820215022),\n",
       " ('valence^1xrand_0^1', 0.0045197225840060921),\n",
       " ('liveness^1xrand_3^1', 0.0045122214017630485),\n",
       " ('liveness^1xrand_4^1', 0.0045062090830661318),\n",
       " ('duration_ms^1xrand_4^1', 0.0044978586949144033),\n",
       " ('valence^1xrand_1^1', 0.0044891366878847686),\n",
       " ('rand_1^1xrand_3^1', 0.0044675431118605634),\n",
       " ('danceability^1xrand_5^1', 0.0044651805926899206),\n",
       " ('loudness^1xrand_2^1', 0.0044644492894383376),\n",
       " ('liveness^1xrand_0^1', 0.0044596149718003327),\n",
       " ('rand_0^1xrand_2^1', 0.0044530727916056098),\n",
       " ('rand_2^1xrand_5^1', 0.0044466490769824903),\n",
       " ('danceability^1xrand_3^1', 0.0044443583830768114),\n",
       " ('speechiness^1xrand_5^1', 0.0044319843144294772),\n",
       " ('rand_0^1xrand_1^1', 0.0044299972151064767),\n",
       " ('liveness^1xrand_2^1', 0.0044007330914779099),\n",
       " ('danceability^1xrand_0^1', 0.0043958602944620404),\n",
       " ('popularity^1', 0.0043822685209984023),\n",
       " ('speechiness^1xrand_2^1', 0.0043818410638843015),\n",
       " ('acousticness^1xrand_5^1', 0.0043769006823013209),\n",
       " ('instrumentalness^1xrand_0^1', 0.0043707966530698106),\n",
       " ('speechiness^1xrand_0^1', 0.0043704162260024028),\n",
       " ('instrumentalness^1xrand_4^1', 0.0043701623908008694),\n",
       " ('valence^1xrand_4^1', 0.004367506321449415),\n",
       " ('energy^1xrand_2^1', 0.0043598668886391471),\n",
       " ('speechiness^1xrand_4^1', 0.0043547188702111293),\n",
       " ('loudness^1xrand_5^1', 0.0043493133022133043),\n",
       " ('energy^1xrand_0^1', 0.0043429956789310907),\n",
       " ('danceability^1xrand_2^1', 0.0043347197108055714),\n",
       " ('energy^1xrand_3^1', 0.0043215296674960603),\n",
       " ('loudness^1xrand_1^1', 0.0043192580890040093),\n",
       " ('loudness^1xrand_0^1', 0.0043163277731590221),\n",
       " ('energy^1xrand_4^1', 0.0043141848760788973),\n",
       " ('speechiness^1xrand_3^1', 0.0043071377858086689),\n",
       " ('acousticness^1xrand_3^1', 0.0043022374359522808),\n",
       " ('loudness^1xrand_4^1', 0.004298573409770641),\n",
       " ('acousticness^1xrand_1^1', 0.0042788627343666114),\n",
       " ('energy^1xrand_1^1', 0.0042767346042754143),\n",
       " ('instrumentalness^1xrand_3^1', 0.0042592356560267027),\n",
       " ('rand_5^1', 0.0042562469212555883),\n",
       " ('loudness^1xrand_3^1', 0.0042508457323228747),\n",
       " ('speechiness^1xrand_1^1', 0.0042287582692627371),\n",
       " ('instrumentalness^1xrand_5^1', 0.0042110129965867932),\n",
       " ('instrumentalness^1xrand_2^1', 0.0042050290491810697),\n",
       " ('rand_2^1', 0.0041653119836605945),\n",
       " ('acousticness^1xrand_4^1', 0.0041488850450986112),\n",
       " ('instrumentalness^1xrand_1^1', 0.0041487443442803883),\n",
       " ('liveness^1xtime_sig_4.0^1', 0.0041445348241841862),\n",
       " ('acousticness^1xrand_2^1', 0.0041416426205150665),\n",
       " ('energy^1xrand_5^1', 0.004123786743855217),\n",
       " ('acousticness^1xrand_0^1', 0.0041182824467822449),\n",
       " ('duration_ms^1xmode^1', 0.0041052004000677366),\n",
       " ('rand_3^1', 0.0040593288146241416),\n",
       " ('rand_1^1', 0.0040442223396331724),\n",
       " ('time_sig_4.0^1xrand_4^1', 0.0040342656063252109),\n",
       " ('rand_4^1', 0.0040281840350553464),\n",
       " ('time_sig_4.0^1xrand_1^1', 0.0038427452203226254),\n",
       " ('time_sig_4.0^1xrand_2^1', 0.0038420239037236232),\n",
       " ('rand_0^1', 0.003838196835991296),\n",
       " ('popularity^1xtime_sig_4.0^1', 0.0038249695299287069),\n",
       " ('time_sig_4.0^1xrand_0^1', 0.0038064322413655198),\n",
       " ('time_sig_4.0^1xrand_3^1', 0.0037905207557296081),\n",
       " ('time_sig_4.0^1xrand_5^1', 0.0037601258895452773),\n",
       " ('tempo^1xmode^1', 0.0035084166900194646),\n",
       " ('popularity^1xmode^1', 0.0034044205329722761),\n",
       " ('liveness^1xmode^1', 0.0033492324779428213),\n",
       " ('mode^1xrand_5^1', 0.0033194408270513531),\n",
       " ('mode^1xrand_2^1', 0.0031913275246704732),\n",
       " ('mode^1xrand_1^1', 0.0031875990336155064),\n",
       " ('mode^1xrand_4^1', 0.0031722469837532764),\n",
       " ('mode^1xrand_3^1', 0.0031528134858112757),\n",
       " ('mode^1xrand_0^1', 0.0030799618197274114),\n",
       " ('mode^1', 0.00087241024501917824),\n",
       " ('acousticness^1xtime_sig_3.0^1', 0.00080800585483878048),\n",
       " ('time_sig_4.0^1xmode^1', 0.00066181050506160808),\n",
       " ('loudness^1xtime_sig_3.0^1', 0.00060881304903170669),\n",
       " ('speechiness^1xtime_sig_3.0^1', 0.00059135279525591446),\n",
       " ('instrumentalness^1xtime_sig_3.0^1', 0.00058462923491363545),\n",
       " ('duration_ms^1xtime_sig_3.0^1', 0.00057440461293255588),\n",
       " ('energy^1xtime_sig_3.0^1', 0.00054581450327932895),\n",
       " ('popularity^1xtime_sig_3.0^1', 0.00053881348850315846),\n",
       " ('danceability^1xtime_sig_3.0^1', 0.00053498384440244027),\n",
       " ('time_sig_3.0^1xrand_5^1', 0.00052707757489548954),\n",
       " ('time_sig_3.0^1xrand_3^1', 0.00052219916197789767),\n",
       " ('time_sig_3.0^1xrand_1^1', 0.00050674478809974958),\n",
       " ('time_sig_3.0^1xrand_2^1', 0.00050131246696447714),\n",
       " ('liveness^1xtime_sig_3.0^1', 0.00049441087488353653),\n",
       " ('tempo^1xtime_sig_3.0^1', 0.00048789209234152903),\n",
       " ('time_sig_3.0^1xrand_0^1', 0.00045210824801470486),\n",
       " ('valence^1xtime_sig_3.0^1', 0.00043019578839942384),\n",
       " ('time_sig_3.0^1xrand_4^1', 0.00042391541925175546),\n",
       " ('time_sig_4.0^1', 0.00021119265547586398),\n",
       " ('time_sig_3.0^1', 0.00020783239912732356),\n",
       " ('time_sig_3.0^1xmode^1', 0.00012915507478695142),\n",
       " ('time_sig_5.0^1xrand_0^1', 0.00012209944121368923),\n",
       " ('tempo^1xtime_sig_5.0^1', 0.00011559105923283269),\n",
       " ('valence^1xtime_sig_5.0^1', 0.0001001444863827136),\n",
       " ('time_sig_5.0^1xrand_1^1', 9.942895691082827e-05),\n",
       " ('loudness^1xtime_sig_5.0^1', 9.8775900495159941e-05),\n",
       " ('time_sig_5.0^1xrand_2^1', 9.4379009783354545e-05),\n",
       " ('time_sig_5.0^1xrand_3^1', 9.4268411922501859e-05),\n",
       " ('acousticness^1xtime_sig_5.0^1', 8.8100967771391277e-05),\n",
       " ('instrumentalness^1xtime_sig_5.0^1', 8.7998144255794017e-05),\n",
       " ('time_sig_5.0^1xrand_5^1', 8.7376801690692806e-05),\n",
       " ('energy^1xtime_sig_5.0^1', 8.6991254555792048e-05),\n",
       " ('liveness^1xtime_sig_5.0^1', 8.093926847342141e-05),\n",
       " ('speechiness^1xtime_sig_5.0^1', 7.8695489531331559e-05),\n",
       " ('time_sig_5.0^1', 7.6522414708395876e-05),\n",
       " ('popularity^1xtime_sig_5.0^1', 7.6338251497892214e-05),\n",
       " ('danceability^1xtime_sig_5.0^1', 7.3248365196421417e-05),\n",
       " ('time_sig_5.0^1xrand_4^1', 6.9876618581005584e-05),\n",
       " ('duration_ms^1xtime_sig_5.0^1', 6.8933531243276026e-05),\n",
       " ('time_sig_5.0^1xmode^1', 3.4259209139703872e-05),\n",
       " ('', 0.0),\n",
       " ('time_sig_3.0^1xtime_sig_4.0^1', 0.0),\n",
       " ('time_sig_3.0^1xtime_sig_5.0^1', 0.0),\n",
       " ('time_sig_4.0^1xtime_sig_5.0^1', 0.0)]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_feature_names = ['x'.join(['{}^{}'.format(pair[0],pair[1]) for pair in tuple if pair[1]!=0]) for tuple in [zip(master_songs_6.columns,p) for p in poly.powers_]]\n",
    "output_df = pd.DataFrame(poly_X, columns = target_feature_names)\n",
    "importance = sorted(zip(output_df.columns, models['RFC'].feature_importances_), key = lambda tup: np.abs(tup[1]), reverse=True)\n",
    "\n",
    "importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Case - Predicting 25 Moods Individually using 11 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X, y = master_songs_scaled, master_songs_target['mood_1']\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.3, random_state=1010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      " Aggressive       0.55      0.50      0.53       395\n",
      "   Brooding       0.31      0.33      0.32       359\n",
      "       Cool       0.37      0.51      0.43       379\n",
      "    Defiant       0.43      0.51      0.46       490\n",
      "  Easygoing       0.32      0.22      0.26       110\n",
      " Empowering       0.35      0.50      0.41       477\n",
      " Energizing       0.42      0.48      0.45       419\n",
      "    Excited       0.28      0.35      0.31       384\n",
      "      Fiery       0.57      0.11      0.18       122\n",
      "     Gritty       0.34      0.11      0.17       187\n",
      "     Lively       0.31      0.15      0.21       104\n",
      " Melancholy       0.36      0.36      0.36       235\n",
      "   Peaceful       0.31      0.24      0.27       145\n",
      "   Romantic       0.38      0.12      0.18       175\n",
      "      Rowdy       0.27      0.09      0.14       173\n",
      "    Sensual       0.39      0.50      0.44       500\n",
      "Sentimental       0.32      0.39      0.35       225\n",
      "    Serious       0.41      0.51      0.45       177\n",
      "     Somber       0.22      0.11      0.14       102\n",
      "Sophisticated       0.35      0.16      0.21       161\n",
      "   Stirring       0.20      0.03      0.05        73\n",
      "     Tender       0.40      0.59      0.48       212\n",
      "     Upbeat       0.30      0.18      0.23       164\n",
      "     Urgent       0.31      0.15      0.20       233\n",
      "   Yearning       0.29      0.32      0.31       317\n",
      "\n",
      "avg / total       0.36      0.37      0.35      6318\n",
      "\n",
      "\n",
      "\n",
      "XGB\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      " Aggressive       0.51      0.49      0.50       395\n",
      "   Brooding       0.28      0.31      0.29       359\n",
      "       Cool       0.34      0.46      0.39       379\n",
      "    Defiant       0.40      0.47      0.43       490\n",
      "  Easygoing       0.23      0.17      0.20       110\n",
      " Empowering       0.33      0.48      0.39       477\n",
      " Energizing       0.41      0.41      0.41       419\n",
      "    Excited       0.25      0.37      0.30       384\n",
      "      Fiery       0.27      0.05      0.08       122\n",
      "     Gritty       0.16      0.05      0.08       187\n",
      "     Lively       0.29      0.21      0.25       104\n",
      " Melancholy       0.31      0.35      0.33       235\n",
      "   Peaceful       0.31      0.26      0.28       145\n",
      "   Romantic       0.31      0.10      0.15       175\n",
      "      Rowdy       0.19      0.06      0.09       173\n",
      "    Sensual       0.34      0.46      0.39       500\n",
      "Sentimental       0.26      0.28      0.27       225\n",
      "    Serious       0.43      0.51      0.47       177\n",
      "     Somber       0.23      0.10      0.14       102\n",
      "Sophisticated       0.15      0.05      0.07       161\n",
      "   Stirring       0.14      0.03      0.05        73\n",
      "     Tender       0.40      0.57      0.47       212\n",
      "     Upbeat       0.27      0.15      0.19       164\n",
      "     Urgent       0.12      0.03      0.05       233\n",
      "   Yearning       0.25      0.26      0.26       317\n",
      "\n",
      "avg / total       0.31      0.33      0.31      6318\n",
      "\n",
      "\n",
      "\n",
      "GBC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      " Aggressive       0.52      0.47      0.49       395\n",
      "   Brooding       0.25      0.29      0.27       359\n",
      "       Cool       0.36      0.42      0.39       379\n",
      "    Defiant       0.40      0.45      0.42       490\n",
      "  Easygoing       0.20      0.16      0.18       110\n",
      " Empowering       0.32      0.42      0.36       477\n",
      " Energizing       0.40      0.39      0.39       419\n",
      "    Excited       0.25      0.36      0.30       384\n",
      "      Fiery       0.13      0.04      0.06       122\n",
      "     Gritty       0.16      0.08      0.11       187\n",
      "     Lively       0.31      0.19      0.24       104\n",
      " Melancholy       0.28      0.31      0.30       235\n",
      "   Peaceful       0.32      0.25      0.28       145\n",
      "   Romantic       0.23      0.10      0.14       175\n",
      "      Rowdy       0.14      0.06      0.08       173\n",
      "    Sensual       0.36      0.46      0.40       500\n",
      "Sentimental       0.26      0.29      0.27       225\n",
      "    Serious       0.36      0.40      0.38       177\n",
      "     Somber       0.25      0.15      0.19       102\n",
      "Sophisticated       0.13      0.08      0.10       161\n",
      "   Stirring       0.08      0.04      0.05        73\n",
      "     Tender       0.39      0.54      0.45       212\n",
      "     Upbeat       0.27      0.16      0.20       164\n",
      "     Urgent       0.15      0.08      0.10       233\n",
      "   Yearning       0.25      0.29      0.27       317\n",
      "\n",
      "avg / total       0.30      0.32      0.30      6318\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = {}\n",
    "\n",
    "models['RFC'] = RandomForestClassifier(n_estimators=300)\n",
    "models['XGB'] = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05)\n",
    "models['GBC'] = GradientBoostingClassifier()\n",
    "\n",
    "# Lesser performing models\n",
    "# models['LogR'] = LogisticRegression(penalty = 'l1')\n",
    "# models['KNN'] = KNeighborsClassifier()\n",
    "# models['GNB'] = GaussianNB()\n",
    "# models['SVC'] = SVC(C=1, gamma=0.1)\n",
    "# models['DTC'] = DecisionTreeClassifier()\n",
    "# models['ABC'] = AdaBoostClassifier()\n",
    "# models['ETC'] = ExtraTreesClassifier()\n",
    "\n",
    "for name, model in models.iteritems(): \n",
    "    model.fit(X_train, y_train)\n",
    "    print name\n",
    "    print metrics.classification_report(y_test, model.predict(X_test))\n",
    "    print '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a feature importances comparison chart\n",
    "feature_importances = pd.DataFrame()\n",
    "\n",
    "for name, model in models.iteritems():\n",
    "    df = pd.DataFrame(data = model.feature_importances_, index = X_test.columns, columns = [name]).transpose()\n",
    "    feature_importances = feature_importances.append(df)\n",
    "\n",
    "feature_importances = feature_importances.transpose()\n",
    "feature_importances['average'] = (feature_importances['RFC'] + feature_importances['XGB'] + feature_importances['GBC']) / 3\n",
    "feature_importances = feature_importances.sort_values('average', ascending = False).drop('average', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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as2aNnnzyST399NN65JFH9Nhjj0mS5s2bp7i4OH366adXrOmf7d8pq8uHDRhVK7q7BHgY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\nRx5v0f47EgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25f10d5190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(10,8))\n",
    "sns.set(rc={'axes.facecolor':'black', 'figure.facecolor':'black', 'axes.grid' : False, 'text.color': 'white', \n",
    "             'xtick.color': 'white', 'ytick.color': 'white', 'axes.labelcolor': 'white', 'axes.edgecolor': 'white'} )\n",
    "\n",
    "sns.heatmap(feature_importances, cmap = 'BuPu')\n",
    "fig.savefig('feature_importances.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "confuse = metrics.confusion_matrix(y_test,models['RFC'].predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mood_list = sorted(moods)\n",
    "mood_list.remove('Other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "confuse_df = pd.DataFrame(confuse, index = mood_list, columns = mood_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(confuse_df, annot=True, fmt='g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.12333333,  0.02666667,  0.08333333, ...,  0.00333333,\n",
       "         0.08666667,  0.03      ],\n",
       "       [ 0.00333333,  0.02333333,  0.00333333, ...,  0.01666667,\n",
       "         0.        ,  0.02333333],\n",
       "       [ 0.04      ,  0.21666667,  0.03333333, ...,  0.02666667,\n",
       "         0.01666667,  0.20666667],\n",
       "       ..., \n",
       "       [ 0.00333333,  0.09333333,  0.02666667, ...,  0.03333333,\n",
       "         0.09666667,  0.33333333],\n",
       "       [ 0.15      ,  0.05      ,  0.01      , ...,  0.07      ,\n",
       "         0.11      ,  0.05333333],\n",
       "       [ 0.00333333,  0.01333333,  0.32      , ...,  0.01      ,\n",
       "         0.04666667,  0.01333333]])"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Pickle Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data/models.pkl', 'wb') as f:\n",
    "    pk.dump(models,f,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data/models.pkl', 'rb') as f:\n",
    "    models = pk.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_songs_scaled.to_pickle('data/master_songs_scaled.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_songs_target.to_pickle('data/master_songs_target.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "master_songs_scaled = pd.read_pickle('data/master_songs_scaled.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "master_songs_target = pd.read_pickle('data/master_songs_target.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Arousal Scale (mood_x) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = [-2,-1,0,1,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With Energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.60      0.74      0.66       919\n",
      "         -1       0.51      0.33      0.40      1012\n",
      "          0       0.52      0.61      0.56      1635\n",
      "          1       0.52      0.15      0.23       891\n",
      "          2       0.66      0.82      0.73      1861\n",
      "\n",
      "avg / total       0.57      0.58      0.55      6318\n",
      "\n",
      "\n",
      "\n",
      "XGB\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.57      0.75      0.65       919\n",
      "         -1       0.45      0.28      0.34      1012\n",
      "          0       0.48      0.57      0.52      1635\n",
      "          1       0.42      0.09      0.15       891\n",
      "          2       0.64      0.81      0.71      1861\n",
      "\n",
      "avg / total       0.53      0.55      0.51      6318\n",
      "\n",
      "\n",
      "\n",
      "GBC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.58      0.75      0.66       919\n",
      "         -1       0.44      0.28      0.34      1012\n",
      "          0       0.48      0.56      0.52      1635\n",
      "          1       0.43      0.12      0.19       891\n",
      "          2       0.65      0.80      0.72      1861\n",
      "\n",
      "avg / total       0.53      0.55      0.52      6318\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X, y = master_songs_scaled, master_songs_target['mood_x']\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.3, random_state=1010)\n",
    "\n",
    "models_x1 = {}\n",
    "\n",
    "models_x1['RFC'] = RandomForestClassifier(n_estimators=300)\n",
    "models_x1['XGB'] = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05)\n",
    "models_x1['GBC'] = GradientBoostingClassifier()\n",
    "\n",
    "for name, model in models_x1.iteritems(): \n",
    "    model.fit(X_train, y_train)\n",
    "    print name\n",
    "    print metrics.classification_report(y_test, model.predict(X_test))\n",
    "    print '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a feature importances comparison chart\n",
    "feature_importances_x1 = pd.DataFrame()\n",
    "\n",
    "for name, model in models_x1.iteritems():\n",
    "    df = pd.DataFrame(data = model.feature_importances_, index = X_test.columns, columns = [name]).transpose()\n",
    "    feature_importances_x1 = feature_importances_x1.append(df)\n",
    "\n",
    "feature_importances_x1 = feature_importances_x1.transpose()\n",
    "feature_importances_x1['average'] = (feature_importances_x1['RFC'] + feature_importances_x1['XGB'] + feature_importances_x1['GBC']) / 3\n",
    "feature_importances_x1 = feature_importances_x1.sort_values('average', ascending = False).drop('average', axis=1)\n",
    "\n",
    "confuse_x1 = metrics.confusion_matrix(y_test,models_x1['RFC'].predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aPuiyNGgzNRwcthEbH0fcmpTnjS9bmcpeDjqGayNPzBwsaL99kCl9R/tgqo3y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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddc5925410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(feature_importances_x1, cmap = 'RdPu', annot=True, fmt='g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L3+aNrf1yNTbNmArA/QcPGBm0xGiKccSJk1y5eo3tWzaQfO8enXv0Ji39AQmJiU9MaG4M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\nr391jmxHbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddc5690710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confuse_x1, cmap = 'RdPu', annot=True, fmt='g', xticklabels=labels, yticklabels=labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('data/models_x1.pkl', 'wb') as f:\n",
    "    pk.dump(models_x1,f,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data/models_x1.pkl', 'rb') as f:\n",
    "    models_x1 = pk.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Without Energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.59      0.73      0.66       919\n",
      "         -1       0.49      0.31      0.38      1012\n",
      "          0       0.51      0.60      0.55      1635\n",
      "          1       0.49      0.14      0.22       891\n",
      "          2       0.65      0.82      0.72      1861\n",
      "\n",
      "avg / total       0.56      0.57      0.54      6318\n",
      "\n",
      "\n",
      "\n",
      "XGB\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.56      0.75      0.64       919\n",
      "         -1       0.47      0.27      0.34      1012\n",
      "          0       0.48      0.58      0.53      1635\n",
      "          1       0.40      0.08      0.13       891\n",
      "          2       0.63      0.81      0.71      1861\n",
      "\n",
      "avg / total       0.52      0.55      0.51      6318\n",
      "\n",
      "\n",
      "\n",
      "GBC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.57      0.74      0.65       919\n",
      "         -1       0.44      0.27      0.33      1012\n",
      "          0       0.48      0.57      0.52      1635\n",
      "          1       0.35      0.10      0.15       891\n",
      "          2       0.63      0.80      0.71      1861\n",
      "\n",
      "avg / total       0.52      0.55      0.51      6318\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X, y = master_songs_scaled.drop('energy', axis=1), master_songs_target['mood_x']\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.3, random_state=1010)\n",
    "\n",
    "models_x2 = {}\n",
    "\n",
    "models_x2['RFC'] = RandomForestClassifier(n_estimators=300)\n",
    "models_x2['XGB'] = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05)\n",
    "models_x2['GBC'] = GradientBoostingClassifier()\n",
    "\n",
    "for name, model in models_x2.iteritems(): \n",
    "    model.fit(X_train, y_train)\n",
    "    print name\n",
    "    print metrics.classification_report(y_test, model.predict(X_test))\n",
    "    print '\\n'\n",
    "    \n",
    "# Create a feature importances comparison chart\n",
    "feature_importances_x2 = pd.DataFrame()\n",
    "\n",
    "for name, model in models_x2.iteritems():\n",
    "    df = pd.DataFrame(data = model.feature_importances_, index = X_test.columns, columns = [name]).transpose()\n",
    "    feature_importances_x2 = feature_importances_x2.append(df)\n",
    "\n",
    "feature_importances_x2 = feature_importances_x2.transpose()\n",
    "feature_importances_x2['average'] = (feature_importances_x2['RFC'] + feature_importances_x2['XGB'] + feature_importances_x2['GBC']) / 3\n",
    "feature_importances_x2 = feature_importances_x2.sort_values('average', ascending = False).drop('average', axis=1)\n",
    "\n",
    "confuse_x2 = metrics.confusion_matrix(y_test,models_x2['RFC'].predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q6XRotVri4uJwd3d/bDmKgiokAl7yKOomlGoMGcZ/TqTw1LT2K1vUTVB4AlTP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\nMBtUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddc5815590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(feature_importances_x2, cmap = 'RdPu', annot=True, fmt='g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nDheuXKFqRfX94GQnZNVqLl66DMDp385TtnQpShUvzrnHc+EPMzKIuHKVki8V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\nqIQUZCGEUAkpyEIIoRL/B3Favp/K/MCBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddc57bde90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confuse_x2, cmap = 'RdPu', annot=True, fmt='g', xticklabels=labels, yticklabels=labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save and Load Pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data/models_x2.pkl', 'wb') as f:\n",
    "    pk.dump(models_x2,f,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data/models_x2.pkl', 'rb') as f:\n",
    "    models_x2 = pk.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Positivity (mood_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With Valence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.52      0.42      0.47      1220\n",
      "         -1       0.45      0.59      0.51      1654\n",
      "          0       0.48      0.54      0.51      1427\n",
      "          1       0.47      0.44      0.45      1110\n",
      "          2       0.47      0.27      0.34       907\n",
      "\n",
      "avg / total       0.48      0.47      0.47      6318\n",
      "\n",
      "\n",
      "\n",
      "XGB\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.49      0.35      0.41      1220\n",
      "         -1       0.41      0.55      0.47      1654\n",
      "          0       0.44      0.51      0.48      1427\n",
      "          1       0.43      0.45      0.44      1110\n",
      "          2       0.40      0.20      0.27       907\n",
      "\n",
      "avg / total       0.44      0.43      0.42      6318\n",
      "\n",
      "\n",
      "\n",
      "GBC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.48      0.34      0.40      1220\n",
      "         -1       0.41      0.54      0.47      1654\n",
      "          0       0.44      0.52      0.47      1427\n",
      "          1       0.42      0.43      0.43      1110\n",
      "          2       0.40      0.20      0.27       907\n",
      "\n",
      "avg / total       0.43      0.43      0.42      6318\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X, y = master_songs_scaled, master_songs_target['mood_y']\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.3, random_state=1010)\n",
    "\n",
    "models_y1 = {}\n",
    "\n",
    "models_y1['RFC'] = RandomForestClassifier(n_estimators=300)\n",
    "models_y1['XGB'] = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05)\n",
    "models_y1['GBC'] = GradientBoostingClassifier()\n",
    "\n",
    "for name, model in models_y1.iteritems(): \n",
    "    model.fit(X_train, y_train)\n",
    "    print name\n",
    "    print metrics.classification_report(y_test, model.predict(X_test))\n",
    "    print '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a feature importances comparison chart\n",
    "feature_importances_y1 = pd.DataFrame()\n",
    "\n",
    "for name, model in models_y1.iteritems():\n",
    "    df = pd.DataFrame(data = model.feature_importances_, index = X_test.columns, columns = [name]).transpose()\n",
    "    feature_importances_y1 = feature_importances_y1.append(df)\n",
    "\n",
    "feature_importances_y1 = feature_importances_y1.transpose()\n",
    "feature_importances_y1['average'] = (feature_importances_y1['RFC'] + feature_importances_y1['XGB'] + feature_importances_y1['GBC']) / 3\n",
    "feature_importances_y1 = feature_importances_y1.sort_values('average', ascending = False).drop('average', axis=1)\n",
    "\n",
    "confuse_y1 = metrics.confusion_matrix(y_test,models_y1['RFC'].predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xt4+/LJKCkpCQkMhnSC4+iX9EqbKeuT2EfM135y/m9hD+X+ArOOT2ECRegvxhP0kKSkJC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\nZHJEfwD71vFso4ExK3+kqX8Qr3XoQbugugyOnYUgQP/Gbe0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f35/8/Hzi4+Mxm83cDkEQ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VxGbl6edlub9+zFs1tX7Wf9sqXobxnfYICKjY1FpXqWZNTMzIzY2NgXlqlUKqKjo4mIiCAjI4Ph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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddc57d1450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(feature_importances_y1, cmap = 'RdPu', annot=True, fmt='g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mk8HAwi9DKFLY2zxt7OBBBDd6GQCvQoVISEwkNj6e1DQjlSpWAOCLsaPyfeEF8CxUiPjE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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddc54f1610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confuse_y1, cmap = 'RdPu', annot=True, fmt='g', xticklabels=labels, yticklabels=labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data/models_y1.pkl', 'wb') as f:\n",
    "    pk.dump(models_y1,f,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data/models_y1.pkl', 'rb') as f:\n",
    "    models_y1 = pk.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Without Danceability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.49      0.40      0.44      1220\n",
      "         -1       0.44      0.61      0.51      1654\n",
      "          0       0.46      0.46      0.46      1427\n",
      "          1       0.45      0.43      0.44      1110\n",
      "          2       0.43      0.25      0.31       907\n",
      "\n",
      "avg / total       0.45      0.45      0.44      6318\n",
      "\n",
      "\n",
      "\n",
      "XGB\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.48      0.34      0.40      1220\n",
      "         -1       0.40      0.57      0.47      1654\n",
      "          0       0.41      0.45      0.43      1427\n",
      "          1       0.42      0.41      0.42      1110\n",
      "          2       0.37      0.19      0.25       907\n",
      "\n",
      "avg / total       0.42      0.41      0.41      6318\n",
      "\n",
      "\n",
      "\n",
      "GBC\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -2       0.48      0.33      0.39      1220\n",
      "         -1       0.40      0.57      0.47      1654\n",
      "          0       0.41      0.45      0.43      1427\n",
      "          1       0.42      0.41      0.41      1110\n",
      "          2       0.37      0.19      0.25       907\n",
      "\n",
      "avg / total       0.42      0.41      0.40      6318\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X, y = master_songs_scaled.drop('danceability', axis=1), master_songs_target['mood_y']\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.3, random_state=1010)\n",
    "\n",
    "models_y2 = {}\n",
    "\n",
    "models_y2['RFC'] = RandomForestClassifier(n_estimators=300)\n",
    "models_y2['XGB'] = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05)\n",
    "models_y2['GBC'] = GradientBoostingClassifier()\n",
    "\n",
    "for name, model in models_y2.iteritems(): \n",
    "    model.fit(X_train, y_train)\n",
    "    print name\n",
    "    print metrics.classification_report(y_test, model.predict(X_test))\n",
    "    print '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a feature importances comparison chart\n",
    "feature_importances_y2 = pd.DataFrame()\n",
    "\n",
    "for name, model in models_y2.iteritems():\n",
    "    df = pd.DataFrame(data = model.feature_importances_, index = X_test.columns, columns = [name]).transpose()\n",
    "    feature_importances_y2 = feature_importances_y2.append(df)\n",
    "\n",
    "feature_importances_y2 = feature_importances_y2.transpose()\n",
    "feature_importances_y2['average'] = (feature_importances_y2['RFC'] + feature_importances_y2['XGB'] + feature_importances_y2['GBC']) / 3\n",
    "feature_importances_y2 = feature_importances_y2.sort_values('average', ascending = False).drop('average', axis=1)\n",
    "\n",
    "confuse_y2 = metrics.confusion_matrix(y_test,models_y2['RFC'].predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U6FyVvsdGggiHxpjDUK0Wd8W1ticqeyt6HxjOrR03CFh8opi19HxEnrpL9IVI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\nq+lXH935cLJ2XEPTrz6Kii55jWMJRF7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cctCefrcy+Z9vQ0yX34tSGfVImfkIro4Ijhps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hNPjzbs+PFejM/ioFZjZlYhxkVFpF+/ro3xr7/RzsODxO++Z/U3ccC9GOfgoLYncMJkUtOuAXDw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddc5977090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(feature_importances_y2, cmap = 'RdPu', annot=True, fmt='g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i1LAshjLFcA6oQNmVr+A14zm0xXIKlJKeU4yNVT0wlHEl/Y9INaPnSavVYjT+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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddc55fcd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confuse_y2, cmap = 'RdPu', annot=True, fmt='g', xticklabels=labels, yticklabels=labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Attempting to remap confusion matrix and classification report\n",
    "X, y = master_songs_scaled, master_songs_target['mood_1']\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.3, random_state=1010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "array_x = models_x1['RFC'].predict(X_test)\n",
    "x_axis = pd.DataFrame(array_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "array_y = models_y1['RFC'].predict(X_test)\n",
    "y_axis = pd.DataFrame(array_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "array = models['RFC'].predict(X_test)\n",
    "results = pd.DataFrame(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Energizing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sentimental</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Excited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Empowering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cool</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "0   Energizing\n",
       "1  Sentimental\n",
       "2      Excited\n",
       "3   Empowering\n",
       "4         Cool"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_mood(x):\n",
    "    try: \n",
    "        return reverse_mood_map[x]\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "results2 = pd.concat([x_axis, y_axis], axis=1)\n",
    "results2.columns = ['pred_mood_x', 'pred_mood_y']\n",
    "results2['pred_mood_map'] = zip(results2['pred_mood_x'],results2['pred_mood_y'])\n",
    "results2['pred_mood'] = results2['pred_mood_map'].apply(find_mood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def diagonalsum(m):\n",
    "    return sum(m[i][i] for i in xrange(len(m)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "confuse = metrics.confusion_matrix(y_test,results)\n",
    "final_confuse = metrics.confusion_matrix(y_test, np.array(results2['pred_mood']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 Mood Classification\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      " Aggressive       0.55      0.50      0.53       395\n",
      "   Brooding       0.32      0.36      0.34       359\n",
      "       Cool       0.38      0.53      0.44       379\n",
      "    Defiant       0.44      0.50      0.47       490\n",
      "  Easygoing       0.36      0.23      0.28       110\n",
      " Empowering       0.36      0.52      0.42       477\n",
      " Energizing       0.43      0.49      0.46       419\n",
      "    Excited       0.27      0.35      0.30       384\n",
      "      Fiery       0.56      0.11      0.19       122\n",
      "     Gritty       0.33      0.12      0.17       187\n",
      "     Lively       0.25      0.15      0.19       104\n",
      " Melancholy       0.35      0.34      0.35       235\n",
      "   Peaceful       0.36      0.26      0.30       145\n",
      "   Romantic       0.42      0.14      0.21       175\n",
      "      Rowdy       0.25      0.10      0.14       173\n",
      "    Sensual       0.38      0.49      0.43       500\n",
      "Sentimental       0.34      0.40      0.37       225\n",
      "    Serious       0.39      0.51      0.44       177\n",
      "     Somber       0.38      0.17      0.23       102\n",
      "Sophisticated       0.32      0.14      0.20       161\n",
      "   Stirring       0.25      0.03      0.05        73\n",
      "     Tender       0.38      0.59      0.46       212\n",
      "     Upbeat       0.29      0.16      0.20       164\n",
      "     Urgent       0.33      0.14      0.20       233\n",
      "   Yearning       0.30      0.33      0.31       317\n",
      "\n",
      "avg / total       0.37      0.37      0.36      6318\n",
      "\n",
      "x * y Mood Classification\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      " Aggressive       0.47      0.55      0.51       395\n",
      "   Brooding       0.48      0.13      0.21       359\n",
      "       Cool       0.47      0.31      0.37       379\n",
      "    Defiant       0.28      0.62      0.38       490\n",
      "  Easygoing       0.25      0.24      0.24       110\n",
      " Empowering       0.40      0.37      0.39       477\n",
      " Energizing       0.41      0.50      0.45       419\n",
      "    Excited       0.38      0.24      0.29       384\n",
      "      Fiery       0.30      0.10      0.15       122\n",
      "     Gritty       0.45      0.05      0.09       187\n",
      "     Lively       0.35      0.12      0.17       104\n",
      " Melancholy       0.42      0.19      0.26       235\n",
      "   Peaceful       0.36      0.14      0.20       145\n",
      "   Romantic       0.20      0.07      0.10       175\n",
      "      Rowdy       0.26      0.03      0.05       173\n",
      "    Sensual       0.37      0.40      0.38       500\n",
      "Sentimental       0.29      0.37      0.32       225\n",
      "    Serious       0.24      0.32      0.27       177\n",
      "     Somber       0.21      0.36      0.26       102\n",
      "Sophisticated       0.17      0.25      0.21       161\n",
      "   Stirring       0.20      0.01      0.03        73\n",
      "     Tender       0.29      0.69      0.40       212\n",
      "     Upbeat       0.30      0.16      0.21       164\n",
      "     Urgent       0.30      0.09      0.14       233\n",
      "   Yearning       0.21      0.43      0.28       317\n",
      "\n",
      "avg / total       0.35      0.32      0.30      6318\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print \"25 Mood Classification\"\n",
    "print metrics.classification_report(y_test, results)\n",
    "print \"x * y Mood Classification\"\n",
    "print metrics.classification_report(y_test, np.array(results2['pred_mood']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mood_list = sorted(moods)\n",
    "mood_list.remove('Other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "confuse_df = pd.DataFrame(confuse, index = mood_list, columns = mood_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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4wiO3cCT6Ozr4+RQv9/FpzL0Hv77QWvIeSyOxtLRk6dK5REXFPrHczMyMCRNHkZycarTa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/8sosyZFoA589SUHBVBvmOpQFZW1m/OTNStW0f7Lam6mNAWJmQo0a1bV6a9MIUe3Xtz5coV5fVN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\negAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddb466c410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(confuse_df, annot=True, fmt='g', cmap = 'magma', cbar=None, vmin=0, vmax=300)\n",
    "plt.axis('off')\n",
    "plt.savefig('confuse_df.png', bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "final_confuse_df = pd.DataFrame(final_confuse, index = mood_list, columns = mood_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4k4iIQ7nbpk4bz4rla9i5cy8DB/Vh1KhBTJw4w6w5Th6+xKR+q174vUmLA9m19kih1pOF\npZnsCX42kFwrlCU+Ljn369ffcMW+hB2nfrto9hyvVS7P61VdiArNfnj93ZSHOJS25/rVW5QoZcfV\nS/Fmz5DX1q3bc//fza0iN27c1KTu242qcDzyIk8eZ/DkcQZBo7dhbVuUsLR0fDvX1iRDfk2bNiE4\neDcAFy5coFSpktjZ2fHo0SOz1o2JPs6JX04DcP/eQ2ztbLC0tMRg0O9ByCkpKdSsWQMAR0cHkpNv\na1bbaDTStm1Xxo0bkbtt5869pKWl0a1bB81y5IiKOsTPPx8D4N69e9ja2mqewWg00qpVWyZMGKd5\n7bxUaAsVMoAafaJCBtCvT2IOn+DE8d+BnLnTmrsXH+BU2oGrV25QslQJLl28pkkW0Hfe/DH42Ym4\n7GOcJJq1eI/5s7IX1RvXhWiWBaBZs8aEh0fy+PFjHj9+zJAh2twR9iJVq1bBw8NDk5PmL6LXsUV+\nKsydemYwGo34te3GuHHDc7cNGzoeo9EIwO3kFGrVqmn2HC87tKv4RlmKl7Dh/G/XC7We3AprZjv2\nLeLLbycwbeI3udv6D2rP98u1OWMybnp35k7agMFgwGQyMWfyepasG82PsXOp/V5Vdv5w6O//iBlE\nR0eybt1qRo0ao0k9ZzdHrO2K8sUP/Vi8dyi1fSpjTEvXpPZLMzk7k5z87ITD7dspODs7a1LbaHwC\nQK8+7QndF01WVhYfDuxG8J4VrFg1m1IOJTTJkWPLlh24u7tx/vyvRESEMG7cZM1qm0wm0tOfHwtp\naWma1X+RnBeeAQP6ERKyT/P6L2oTvejdFqpkUKFPVMiQQ68+yZk7e/ZuR9j+w0waP591PwRx9Jft\nvFevFhs37NYsi57zZo7gfV+y5NtPmTrpGyq6laeJb1227g5i6YqJlNDwVlh3dzfs7GzZuXMTERH7\naNy4oWa185s/fzZjxozX7WStnscW+akwd+qV4UXzZU4Wg8HAoMF92bRxh9lzvO5RnrnrP2TZ3pG8\n09Ajd3vXQY3YsjzqL37z/40sLM2sfcuR9O/+GYuXTwDAysqSt+t6EnvklNlr+3Wuz2/HLhJ/M/ss\npsFgYNKc3gzrsQC/98ZxMvYigQP0eR9CgwaNCQjoyPr12twyZDAYKOFgy8TAVcwaspEJXxXuPeWF\nQesXoVZtGhHYI4BxY75g86Y9TJ2ykIC2H/L76Qt8OmmIplkCAzvz55/XqVbNC19fP5YsCdK0vor8\n/f3o07c3w4fpd+ZdFSq0hQoZxPP06pNWrRsS2MOf8WPnMHveOHp2G029dzoQe/Q3BnzUWbMcKsyb\nAS0/pm/gFJZ8+ykGA/xx6Tqd/Mdw8fw1ho/prlkOg8GAo6MD7dp1pX//Qaxc+c3f/5IZ9OjRjaNH\nY7l+/XpuLr3pnUGFuVOFDDkMBgPfr/mKyIhooqJizFrrxpVkVswJYVyPFXw+ZB2TlnTH0tICSysL\n3qpbiV+PXC70mnIrrJm8+VZlbt++R2L8bc79fgVLS0scHEvgWfMNfjtxXpMMDZvXooJ7GRq1rE25\n8g5kpGdiX9KOU8ezB9LRqN9p0+F9TbLk8PKqRVJSMnFxcZw6dRorKyucnJxISUkxa907yQ/5/edr\nACT8eYe0h08o6WjH/TuPMJnMWvql4uPjnzuL6OJSnoQEbd7T16Tp+4waM4COAYNITU3j8KFfcr/3\nU8hB5i+cpEmOHO+//x6hoQcAOH36DC4u+pxdzc+k0+Bo3tyXCZ+Oo2WLNqSmpuqSQRUqtIUKGcTz\n9OqTxk3rMXJMPzq2G0pqaho13qzC8X+/tSDq4M907NRSsyx6zptvvlWFlNt3SYi/zbkz2cc4T5+a\nOBqTfdL84IHjjJnQW7M8SUlJHDnyMwBXr17j4cNUTY4t8mvduhWvv/4abdu2oUIFV4xGIzdu3CQy\nsvCvDL2MnscW+akwd6qQIa/vVn7JxQuX+eKLhWavdTvxPhG7fgUg/s8UUm49oIxLSSpWKsvZE3+a\npaZcsTSTuu/XZODQjgCULlMq+70Ydx7wj9oenDtzRZMMYwcspavvZwS2mMq2dQf5et5ObsXfoVJV\nFwBqelXi+pVETbLk8PFpwJgxIwEoW7Ysdna2mkz8v0RcoLZPZQBKONhiY1eU+3ey32+g18m80NBw\nOnZsD4CXlxdxcfGa3IJpb2/HtBmj6NppGA8eZE+y368Pws3dFYD6Dd7h3NnCP4v1Vy5f/oO6dbM/\ndMLNrSIPH+oz+ec/s6vHmV57e3vmzJ2FX9sAHjx4oHn9/PQ8261CW6iQIT+9r0DonUGvPrG3t2Pq\n5x/TrdPHPPz33Hkr8TZVqrwGQO3anlz544ZmefScN997vyYDh3YCoHQZB2ztrNm+JYwmzd4F4K1a\nVfnjknZtERp6gCZNsm9/dXR01OzYIr/AwF7Uq+eDt3cjVq5czYwZszRdVIJ+xxb5qTB3qpAhr27d\nOvDkSTozZy7QpF7zjm8TODT706Idy9rjWKY4yfH3qVHbnUtn4sxSU65Ymsn61XuYt2QM2/YGUaxY\nUSaNXQJAmbKOXLui7Qfm5DV1zCqmLRpARnom9+89YvLw5ZrWX7ZsOd999y0HD4ZjbW3N0KHa3JaQ\nkviAg7tOsSz8Y0wmWDRuJz1GN+Xtxh44lLFn3raPOHPsGt9O26tJHoDY2FhOnDjJ4cNRZGVlMXTo\niL//pULQrkNLHBxLsXrtfAwGMJngh3XBrFozl7S0xzxKfczQwVM0yZJj+fLVrFz5FQcO7MXS0pLB\ng7W7XcXL6y3mzp2Gm1sFMjIyadeuLeHhUfj6NqJcuTLs2bOJ2NjjTJo0XZM8Xbp0wsnJkc1bfsh9\nb3TvXv2IizPPi8CLeHl5ERQ0F3d3NzIyMujQoR3t23fi/n1tP0FZhbZQIQOo0ScqZAD9+iSgfXMc\nHUuyas0cMAAmGD92DouWTiEjPZO7d+8zYug0s2bIS895c93qH5m/ZCzb9y6kWLGiTBz7JUeif2PR\n1+Pp2rMVqalpjBw8R7M8CQmJbN8ezJEjkZhMJkaM0ObzG1Sk17FFfirMnXpmqOVVM/fYIjMjg/bt\n/ShbpjTGJ0ZCw3ZgMpk4d+4CIz+eaLYM0T+d4vMVffFp/RZWVpbMHr2ZrKynOJW158aV5L//A/8P\nDCa97vX6H1PRQd9nIgHYG8rqHQGA8/e26R2B+iWG6h0BgOj7S/SOQCm76npHAODB42t6R8DCopje\nEQDIytTnETZ5mVBjajeg/9U1VajSJypQYVw42L+pdwQA7j3S9g6RF3Euoc1jSv5K4oOf9Y4AgMmU\npXcETKZMvSMAauynqrCyctA7Al7FO+kdAYCf7y596ffkVlghhBBCCCGEEAUiC0shhBBCCCGEEAUi\nC0shhBBCCCGEEAUiC0shhBBCCCGEEAUiC0shhBBCCCGEEAUiC0shhBBCCCGEEAUiC0shhBBCCCGE\nEAVipXeA/xXx92P0jsBTU4beEQCY7TFF7wj8647+z5kCMBj034Xup13QOwIA1Up11DsCF+7t1DuC\nyEee3SheRIVxUcrKXe8IANx5elrvCCTci9Y7gjJsbfQfF48UeC40qLGfqvIszYzMO3pHIMlwTe8I\nf0uuWAohhBBCCCGEKBBZWAohhBBCCCGEKBBZWAohhBBCCCGEKBBZWAohhBBCCCGEKBBZWAohhBBC\nCCGEKBBZWGrAx6c+CYnXCAsPIfzATyxcNE+XHEFB84iJOUR09EHq1Klj9nqlq5Tho/3D8Or2du62\nOj3fZeypSVhZZ3+aarkaznRd04uu3/ek65peDDs8Gpd/uBZ6lmK2RRi5qguTtvfmsx/7UbPRGziW\nL8GnW3sxeWcfJmzpSYnSdoVe929zFSvGxYu/07NnoOa1Aaytrdm4cT0REaHExETRunUrzTMULVaE\nfceD+KBrAxasGs6q4Ims3jWJHYe+4LOgfprlUGU/BZgzZxaHY6I4GnuYgIAPdMmg9XyhagZVcqiQ\nAcDT05NLl84xePAg3TLo3RZFixUh4vjXtOvSiFpvV2XT3pmsD/6clZsnU8rBXvM8KvSJCnOWnhmm\nzxhHeMRmIqO20davGQCDB/fizr0z2NhYa5oF9N9HcsjYfEbPtiharAiRx5fTvksTnF2c+GHXF2z6\ncRaLvxuHlZVlodbS/1kJr4iog9F07dpTt/oNGtSncuU38Pb2wcPDg1WrVuDt7WO2elbWVjSb1JJr\nR6/kbvP0r4mtox0Pkx7kbrt1NpFNvdcCULR4Mdp/1YX4f8UVeh6frl7EX7rNllkHKFW2OJN29OHy\niZscWHOcY3vO0qzvO7Qe/D6bpocVeu2/MmXKp6Sk6PcR1n5+bTh+/ARBQQupWLEioaEhhIT8pGmG\nwWMDuHc3FZPJxOh+S3K3T1/8IdvWRWqaRe/9FKBhQx+q16hGfe+GODg4cPLXYwQH79I0g9bzhaoZ\nVMmhQgYAGxsbFi9eSHh4hOa1c6jQFsPHdubu3YcA9Bvsx+iBi4i/mcywTzrTpZcv3365Q7MsKvSJ\nCnOWnhkaNHiXatUr06xJFxwcShJzdBd2draUKetEfPwtTTI8n0f/fQRkbOald1uMGNuVe/+es0Z9\n2oM1K35k/56jjJ3Uk07dfdm4Zl+h1ZIrlhoxGPR9Dk/Tpk0IDt4NwIULFyhVqiR2dua7Qpf5JJOt\nH/7Ao+TU3G0Xw84T/eXLFwp1+9Xj+JpYs+R5mPKI4o42ANg52PAg5RGrxu/h2J6z2d+//YjipWzM\nUvtlqlatgoeHh+YLuby2bt1OUNBCANzcKnLjxk1N679WuTyvV3UhKvS357a7v+FM8RK2nPntqqZ5\n9N5PAaKiDtG5UzcA7t27h62treYZtJ4vVM2gSg4VMgAYjUZatWpLQkKC5rVz6N0Wr1d2oVJVVyJD\nTwAwon8Q8TeTAXAu70hifIpmWUCNPlFhztIzQ3T0MXp2H/7v2g+wtbFhz4/hfD5toWYZ8tJ7H8kh\nY/MZPduiUmVXKlWtQEToL2CAuu+/yYF9xwAI33+M+o1qFWo9WVhqpHqNauzYsZnIg6E0bdpY8/rO\nzs4kJyfnfn37dgrOzs7mK2iCrIys5zZlPM546Y9bFrXkNe83uBxx0SxxYnedoXSFUiyIHcGUnX35\nYWooGcZMIHsx4dvvXY7sOGWW2i8zf/5sxowZr8RiJjo6knXrVjNq1BhN646b3p25kzb8Rxv0HNiS\nDcv3a5oF9N9PcxiNRgAGDOhHSEjhnUn8b2k+XyiaQZUcKmQAMJlMpKena143L73bYuL0vsyctJq8\nU1aDJrUI+3kpTqVLsmtrlGZZQI0+Af3nLL0zGI1PAOjTtzP79x/k0aM0Tevnpfc+kkPG5jN6tsXE\n6f2ZOem73OMsW1trMjOzj89Tku9RppxDodaTW2E1cOnSH3w+bSbbtu3k9ddf40DEPqpU9iQrK+vv\nftVsVFjM5FW1WTX+iLpktr/v3aEmt2/eY2639VSsUY6PFn7AlBbLMRgMDPm6PWeir3I25prZ6ufX\no0c3jh6N5fr164D+/dGgQWPeeqsm69evwcvrHU1q+nWuz2/HLhJ/8zbwrA2srCypXbcqM8Z9r0mO\nHKrtp/7+fvTp25sWzVvrUj8vvcenKhlAjRwqZFCFlm0R0LkhJ4+dz71CmVM7OuI3fOsO45N/9mTQ\nyPYsW6TdrbAqUWHO0jNDm7ZN6dGzAx/49dW89l+R+UKNsamHdp0bc/LYOeJuJr/w++YYG7Kw1EBC\nQgLbtu0E4OrVayQm3sLV1YXr129oliE+Pv65M1YuLuX1uz3B9J+b3mhUhV83HjdbyarvunEq8jIA\nN87eolS54gAMXBxA/OXb7Fyg7Vnm1q1b8frrr9G2bRsqVHDFaDRy48ZNIiO1zeHlVYukpGTi4uI4\ndeo0VlZWODk5kZJi/tu5GjavRQX3MjRqWZtyLo6kGzNIjEvBwsLAqZN/mL1+firspzmaN/dlwqfj\naNmiDampqX//C4VMhflChQyq5FAhgyr0bIvGzetQwb0cTVq+Q3kXJ54Y00lLM7Jv91EA9v14lBHj\numiSRTV6z1l6Z2jarD5jxg4iwL8vqamPcrebXnC8Y24yXzxPhbGpl8bN36GiezmatqxLufKOZKRn\n8ujRY4pLuk1pAAAgAElEQVQWtSI9PRPn8k4kJRbu53zIrbAa6NatM6NGjwCgXLlylC1bhri4eE0z\nhIaG07FjewC8vLyIi4snLU2jWzXynxB5wQkS55ouJJ0335vcb129Q+U6FQEoXaEkTx6l8377mmQ+\nyWRnkLaLOYDAwF7Uq+eDt3cjVq5czYwZszRfVAL4+DRgzJiRAJQtWxY7O1tNFpUAYwcspavvZwS2\nmMr2dQf5Zv5Ofo4+y5teb3Dh9+uaZMhLhf0UwN7enjlzZ+HXNoAHDx78/S+Yga7zhUIZVMmhQob8\n9LoKomdbfDxgAR18x9OpxQQ2rQ1j6fytDBndEY8a7gDUqlOFK5cL/8Pn/lt69YkKc5aeGeztizNj\nxjg6dfiIBw+eX7jo0SUyXzyjwtjMT8u2GDFgLu18x9ChxVg2rwtlybxNxET9i1b+9QFo6e9N1IGT\nhVpTrlhqYPfuvWzY8D3+/m0pUqQIQwaP0Pz2utjYWE6cOMnhw1FkZWUxdOgIs9YrV8OZxuObU9Kl\nJFmZWXg0r861I1d4zfsN7JyK02lFd+J/vUnUggMAFCtu/ZfvwSyoA2uP89GiACbv7IOFpQUrP9lD\n54lNKVLUkkk7+oDJRNzFZL7/NMRsGVS0bNlyvvvuWw4eDMfa2pqhQz/WOxKly5Xk+tVEzeuqsJ8C\ndOnSCScnRzZv+QGDwYDJZKJ3r37ExWl3wKr1fKFqBlVyqJABsg9Sg4Lm4u7uRkZGBh06tKN9+07c\nv39fswyqtEXOseGnI75ietBAMjKyeGJMZ8ygRZrmUKFPVJiz9MzQoWNrHJ0cWLvuy9zahw79TMOG\n71G2bGl2Bq/k559/5bN/zjd7FlBnH5Gx+YwKbZGzoF00ewMLlo2mW+8WxN1IZvvGA4Vbx2TS40L9\n/x5LC30+SSqvpybzLbz+b8z2mKJ3BP51R7/3p+a16fZcvSMAT/UOAEC1Uh31jsCFezv1jgCAyZSp\ndwRML7rnXAiRq5JDW70jAHDl7h69I2B40a1EryhbG3e9I/Do8TW9IyhDlbGpwmvqaw4t9I4AwNU7\nL5+z5FZYIYQQQgghhBAFIgtLIYQQQgghhBAFIgtLIYQQQgghhBAFIgtLIYQQQgghhBAFIgtLIYQQ\nQgghhBAFIgtLIYQQQgghhBAFIgtLIYQQQgghhBAFIs+x/C/1d5mmdwRWJ87WOwIARYuU1jsCjx6v\n0zsCAFaWvnpHwMKimN4RALCytNc7gjLSM27rHUEI8TcsLGz0jgDA06eP9Y6AhaGI3hGUeVa3Cs9N\nVOGZieJ5lpYl9I5AUSv9MwCkGa++9HtyxVIIIYQQQgghRIHIwlIIIYQQQgghRIHIwlIIIYQQQggh\nRIHIwlIIIYQQQgghRIHIwlIIIYQQQgghRIHIwtJMitoUYciKTozd0osJwX3xbFiJKu+6MX5nH8Zu\n6cXw77tiY6/9p3kWK1aMixd/p2fPQE3r1qjhwZmz0Xw0sBcAlpaWfP/9Yg5F72ZvyAZKlCj8TxQ1\nGtMZPWo5vXsF0a3rbKIOniYx8S59egfRq+d8xoxeQUZGFgBff7WHwG5zCOw2h2XLQgo9y8sEBc0j\nJuYQ0dEHqVOnjmZ18woM7MyJE0eIjY2iZUttP+W2hqcHZ88eYeDA3gDUrVuHAxHB7Nu/lV271uPo\n6PBK5QA1xoQqOVTIAODp6cmlS+cYPHiQbhlUaQtVcujdJ7a2tmzduo6wsB+JitqPr28TXXKo0B8+\nPvVJSLxGWHgI4Qd+YuGiebrkAP3HBcCcObM4HBPF0djDBAR8oEsGFcYFqNEferaFp2c1zp//hUGD\n+gHg6upCeHgwERG72bBhBVZWVprkqFGjKqfPRPLRRz0A8K7/LmEHthCybwNbt60o1GNwWViaiXeX\nWiRcTmF+57V8/eEWun7eks6fNWf1qF3M77yWP47fpGFP7Xf2KVM+JSXljqY1bWysWbBgGhER0bnb\n+vUPJCk5BZ8G/mzb+iPe9d8t9LqRkad4s+ZrrFk7hqAFHzFnzlaWLNlNYPfGrF03lopuZdixI4a4\nuBQuX47nh43jWb/hE3YFHyU5+X6h58mvQYP6VK78Bt7ePgwYMJDFixeavWZ+Dg4OTJ48gfr1m+Lv\n3wl//7aa1baxsWHBghnPjYvhIz6kb59htGzRiZ+PnaBf/+6vTA5QY0yokkOFDJA9PhYvXkh4eIQu\n9UGdtlAlhwp90rt3d86fv4ivrx9duvRi4cI5mmdQpT8Aog5G49usNc2atmLUyE90yaDCuGjY0Ifq\nNapR37shrVv5sXDRfM0zqDIuVOgPPdvCxsaGRYtmc+BAVO62qVMnsHTpdzRp4s+VK9fo21eLYxxr\n5i/4jMiImNxts2dPZOCHn9C6ZXd+/vkk/QcU3sUmWViaSeqdNIo7ZD8jq7iDLakpaaTeScPeyQ4A\n21LWpN5J0zRT1apV8PDwICTkJ03rGo1P8PfvRWJCUu62Nm2asWnjTgBWr97ETyEHCr1uq1Zv069f\ncwASEu7g7OzA8V8u0rjxPwBo3Ogtjh49h6urEwsWfgTA/ftpWFpaULy4+Z9v1rRpE4KDdwNw4cIF\nSpUqiZ2dndnr5tWsWWPCwyN5/PgxSUlJDBnysWa1jUYj/n7dSUi8lbutR/dBXL9+EwBXl/LExSW8\nMjlAjTGhSg4VMkD2+GjVqi0JCdqMgRdRpS1UyaFCn6SkpODk5AiAo6MDycnaP7tWlf4AMBj0f/aj\nCuMiKuoQnTt1A+DevXvY2tpqnkGVcaFCf+jZFkajkTZtOpOY59iiYUNv9uzZB8CePftp2rShBjme\nEODfl8TEZ8fgt2/foXSZ7PmrVKmShXrBSRaWZvLL7jM4VSjJF4eHMW57b7Z8HsrmafsZuqoL06OG\nUOVdN2I2/6ZppvnzZzNmzHjNXwBMJhPp6enPbXN3r0DLlo3Zv38za9YsoWRJ8z30tXvgXMaPW8WE\nTzuT9jidIkUsAXB0sud2niuTs77YzAf+0xg0uA02NkXNlieHs7MzycnJuV/fvp2Cs7Oz2evm5e7u\nhp2dLTt3biIiYh+NG5t/ksvxonEB4OvbiNO/R1OmbGk2/rD9lckBaowJVXKokAFePj60pEpbqJJD\nhT7ZsmUH7u5unD//KxERIYwbN1nzDKr0B0D1GtXYsWMzkQdDadq0sS4ZVBgXkL2gABgwoB8hIfs0\nr6/KuFChP/Rsixf9++3sbMnMzAQgKSmZ8uXL6ZJj/LgZbN6ynJO/hfH++2+zbu22QqsnC0szqduu\nJik37zOx/lLmdVpL9y9aEzijFUv7bmJKw6+5dOw6jfsW/u2fL9OjRzeOHo3l+vXrgP5nFw0GA+fP\nX6ZFiy6cPXuRceOHma3Whh/G8dXXQxj3yUowmXK35/lfAD6d2IW9IZ+zcuV+4uJSzJbnZfToE4PB\ngKOjA+3adaV//0GsXPmN5hnyCws7SM03G3Dx4mU+GTf8lc6h936aQ4UcKmRQhSptoUoOPQQGdubP\nP69TrZoXvr5+LFkSpHck3frj0qU/+HzaTNq370K/vh+x4rtvsLS01CWLKvz9/ejTtzfDh2l3F9DL\nvMr7aX4qtYWeWYIWTqNzpw+pXcuXI0eOM3BQr0L727KwNJMq71Tk94N/ABB3PolS5eypUL0cV07G\nAXAu+gqv1SyvWZ7WrVvh7+9HTMxB+vfvy6RJEzS9OpXfrcRkDh/+GYCwsCiqV69S6DXOnrlOYuJd\nADw8KpCV9RRbO2vS0zMASLp1lzJlS3Hr1l3OnPkTAHt7G2p7vcHvv18r9Dz5xcfHP3fmzMWlvOa3\njCQlJXHkSHY/XL16jYcPU3FyctI0Q17+/i1z/3/njr28X++dVyqHCmNClRwqZFCFKm2hSg4VvP/+\ne4SGZr+F4/TpM7i4aH9FSJX+SEhIYNu27Le2XL16jcTEW7i6umieQxXNm/sy4dNxtGrZltTUVM3r\nqzIuVKBaWzx8mErRotl3xLm6lic+PlGXHG++WY1fjmXfNRkZGYNX7TcL7W/LwtJMkq7d4Y06FQBw\nci3Jk7R07ielUr5yaQBe+4crt65q9yE6gYG9qFfPB2/vRqxcuZoZM2YRGRn1979oJvtDI2nRIvt2\nmdq1a3Lx4pVCr3H8+EW+Xx0GwO3bD0hLe0K9etXZv/8kAKFhv1K/vicpKQ/5fNoPPH36lKysp5w5\nc53XXjP/7QmhoeF07NgeAC8vL+Li4klL0/Z9t6GhB2jSJPsEg6OjI3Z2tqSkaH+1NsfkKWN4s2Z1\nAN59tzYXL/7xSuVQYUyokkOFDPnpdYZZlbZQJUdeevXJ5ct/ULdu9gknN7eKPHyo/QJClf7o1q0z\no0aPAKBcuXKULVuGuLh4zXPkpde4sLe3Z87cWfi1DeDBgwe6ZFBlXOT1qs+dOQ4ciKJ9ez8A2rXz\nY//+wv98kf/GrcQkqlatBECdOm/xx+Vrhfa3tfmc21fQwXUn6LvQn0+29cbC0sDacXvIeJJJ7/l+\nZKZn8ejeY1aP3qV3TE3UqvUmc+ZMwc3dlYyMTNq3a03v3sMJWjCNPn268jA1lQH9RxV63S5dGzJ5\n8lp69phHenom//wsEE9PdyaMX8XWLdG4uDgREFAPS0sLfH29COw2F4BGjWri4VGh0PPkFxsby4kT\nJzl8OIqsrCyGDh1h9pr5JSQksn17MEeORGIymRgxYoxmtWvVqsmcuZ/h7laBjMwM2rVvy6CBY1my\nZDYZGZkYHxvp29f8t6CqkgPUGBOq5FAhA2QfjAQFzcXd3Y2MjAw6dGhH+/aduH/f/J8cnUOVtlAl\nhwp9snz5alau/IoDB/ZiaWnJ4MHa3/KoSn/s3r2XDRu+x9+/LUWKFGHI4BFkZWVpnkOFcdGlSyec\nnBzZvOUHDAYDJpOJ3r36ERcXp1kGVcaFCv2hZ1t4eb3FvHmf4+ZWkYyMTDp08Kdnz4GsXv0VH33U\nmz//vMnatZvMnqNWLU9mzZmEm1v2MXhA+1YMHz6Jr5fNJj09g7t37jNo4LhCq2cwmfK/00y8SH+X\naXpHYHXibL0jAFC0SGm9I/Do8Tq9IwBgZantcx9fxMJC++ehvoiVZeE/i/R/VXqG9p8QKYT4v2Nh\nYf5P//5vPH36WO8IWBiK6B2Bp6YMvSMAYED/9+GZkENz1Vhamu9DJv9bRa30zwCQZrz60u/JrbBC\nCCGEEEIIIQpEFpZCCCGEEEIIIQpEFpZCCCGEEEIIIQpEFpZCCCGEEEIIIQpEFpZCCCGEEEIIIQpE\nFpZCCCGEEEIIIQpEFpZCCCGEEEIIIQpEnmP5XzIo8IwnVajw3ETT03S9IwBQqngNvSNwP+2y3hEA\nMCnwDDKT6aneEUQ+Kjwfz4T2D2t/IRVebg36P6MP1NhXLS3t9I4AgKWF/jnSM5L0jqAMFY5xnj59\noncEQI19RIXnvIIac5YKYxMgKyv1pd+TK5ZCCCGEEEIIIQpEFpZCCCGEEEIIIQpEFpZCCCGEEEII\nIQpEFpZCCCGEEEIIIQpEFpZCCCGEEEIIIQpEFpZCCCGEEEIIIQpEFpYaCQqaR0zMIaKjD1KnTp1X\nNkPfvj0JDw8hLGwv4eEh3L0br0uOOXNmcTgmiqOxhwkI+EDT2lOnj2Rf+BrCItbRum1jLC0t+fa7\nLwiLWMeOXcuwL1Fc0zyBgZ05ceIIsbFRtGzpq2ltAGtrazZuXE9ERCgxMVG0bt1K8ww5PD09uXTp\nHIMHD9Itgyo59M7g41OfhMRrhIWHEH7gJxYumqdLDpXGp57zFqjTFiq8lgF8/fVCDh3aR1jYbqpU\neUPT2jU8PTh79jADB/YGYPmKBRw/Eca+/VvYt38LLVo01jSP3vOFSjn0fk0F2UdyqDJngRp9osXY\ntDLLXxXPadCgPpUrv4G3tw8eHh6sWrUCb2+fVy4DwOrV61i9et2/M3nTsWM7zTM0bOhD9RrVqO/d\nEAcHB07+eozg4F2a1Pau/zYe1SrRsllvSjmUIOrwJsqVK83t5DsMHDCRnr3bUe99L0L3RWuSx8HB\ngcmTJ1Cnjjf29vZMnTqJffvCNKmdw8+vDcePnyAoaCEVK1YkNDSEkJCfNM0AYGNjw+LFCwkPj9C8\ntmo5VMgAEHUwmq5de+qaQZXxqee8lUOFtlDltczfvw0lStjj49OS1193Z+HCOQQEdNWkto2NNQsW\nTCci4vBz2ydPmsW+fdrvs6rMFyrkUOE1VfaRZ1SYs0CNPtFqbMrCUgNNmzYhOHg3ABcuXKBUqZLY\n2dnx6NGjVypDfpMnT6BHj36a142KOsTPPx8D4N69e9ja2mpWO+bwcU4cPw3A/XsPsbWzoUUrH2bP\n/AaAdWt2apYFoFmzxoSHR/L48WMeP37MkCEfa1ofYOvW7bn/7+ZWkRs3bmqeAcBoNNKqVVsmTBin\nS32VcqiQAcBgMOhaH9QZn3rOWzlUaAtVXsuqVKnEL7+cBODq1T9xc6uoWW2j8Qn+fj34ZNxQzWr+\nFVXmCxVyqPCaKvvIMyrMWaBGn2g1NuVWWA04OzuTnJyc+/Xt2yk4Ozu/chnyqlPHixs3bjyXSUtG\noxGAAQP6ERKyT+PaTwDo2bsdofsP4+bmgm/z+uzau4LlK2dRoqS9Zlnc3d2ws7Nl585NRETso3Hj\nhprVzi86OpJ161YzatQYXeqbTCbS09N1qa1aDhUyAFSvUY0dOzYTeTCUpk21vbUvP73HJ+g7b+Wl\nZ1uo8lr2++9nad68CQaDgapVK/P6626ULu2kSe2X7Z+DB/dh377NrFm7FAeHUppk+as8WlMhhwqv\nqbKP/Ce9528V+kSrsSlXLHWgwll4vTP079+HNWs26JrB39+PPn1706J5a81rt2rTiO49P6DDB4MJ\nP7ieixeuMm/OckaPHcDosf2ZOmWRJjkMBgOOjg60a9eV119/jQMHQqhUqYYmtfNr0KAxb71Vk/Xr\n1+Dl9Y4uGYQ6Ll36g8+nzWTbtp3ZYzNiH1Uqe5KVlaVLHlXGp57zVg5V2gL0ey3bv/8A9erVJTJy\nL6dOneH8+Yu6vq5uWL+NlDt3+f30OcaMHcI//zmGUaOm6JbnVaXSa2reTHpQaR9Rac4CffpEq7Ep\nVyw1EB8f/9yZCReX8iQkJLxyGfJq2LABR47E6la/eXNfJnw6jlYt25Kamqpp7SZN6zFqTD86thtK\namoat26lcCTmBAARB47gUa2SZlmSkpI4cuRnAK5evcbDh6k4OWl7RtHLqxaurq4AnDp1GisrK80z\nCPUkJCSwbVv2reFXr14jMfEWrq4umudQaXzqOW+BGm2h0mvZ1Klf0KhRa0aM+AQHBweSk2/rkgMg\nKuoIv58+B8DePaHU8KymW5ZXmQqvqbKPPKPCnAVq9IlWY1MWlhoIDQ2nY8f2AHh5eREXF09aWtor\nlyGHs3M5Hj58qNuVB3t7e+bMnYVf2wAePHigcW07pk4fSddOI3j4IPvAMDwshmbN6wPwj1rVuXzp\nmmZ5QkMP0KRJ9u0Qjo6O2NnZkpKSoll9AB+fBowZMxKAsmXL6pIhP72v6OdQIYdeGbp168yo0SMA\nKFeuHGXLliEuTvtPkVZlfOo5b+VQoS1UeS2rWdOT5cuXANCiRVNOnvxN8wx5bdy0nNdey34Pm4/P\n+5w9c16XHCrMWaBfDhVeU2UfeUaFOQvU6BOtxqbcCquB2NhYTpw4yeHDUWRlZTF06IhXMkOO8uWd\ndXtvJUCXLp1wcnJk85YfMBgMmEwmevfqR1xcnNlrt+vQAkfHUqxaMze39pCPJjNj1lh69Aog9WEa\nQwZpd/tSQkIi27cHc+RIJCaTiREjtH//wbJly/nuu285eDAca2trhg7V/sMOIHuyDwqai7u7GxkZ\nGXTo0I727Ttx//79Vy6HChl2797Lhg3f4+/fliJFijBk8AhdTkapMj71nLdyqNAWqryWnT59BoPB\nwJEj4Tx+bKRnzw81q12r1pvMmfsZ7m6uZGRm0q59G77+ehXrN3zDo0ePefToER8OGKVZHhXmC1Vy\nqPCaKvvIMyrMWaBGn2g1Ng0mk8lklr/8/xmDoYjeEZRhYVFM7wiYnur/QQEApYrr+94JgPtpl/WO\nAIDJlKF3BEymp3pHEPlYKDB3mtDn7oj/oMLLrSJXlFTYVy0t7fSOAIClhf450jOS9I6gDBWOcZ4+\nfaJ3BECNfeTp08d6RwDUmLNUGJsAWVkvfyuG3AorhBBCCCGEEKJAZGEphBBCCCGEEKJAZGEphBBC\nCCGEEKJAZGEphBBCCCGEEKJAZGEphBBCCCGEEKJAZGEphBBCCCGEEKJAZGEphBBCCCGEEKJArPQO\n8L9ChWfHqPCsK4DMzLt6R+B1hzZ6RwDg2v0wvSNwrlkfvSMAUC10ud4RMBjUOFemwvOuVPFUgeeb\nWlmV0jsCAJmZ9/SOgIXBWu8IAJhM+j+brnLJVnpHACDu8Um9Iygxd6ryLE0LCxu9IyjzHEsLg/7H\nviaDGq+nFhb6P5PZv+QQvSP8Lf1nEiGEEEIIIYQQ/9NkYSmEEEIIIYQQokBkYSmEEEIIIYQQokBk\nYSmEEEIIIYQQokBkYSmEEEIIIYQQokBkYamBvn17Eh4eQljYXsLDQ7h7N17T+p6e1Th7LpaBg/oA\nUKGCCyE/bSEsfAd7QzZTpkxpTfPMmTOLwzFRHI09TEDAB5rWLlqsCBHHv6Zdl0bUersqm/bOZH3w\n56zcPJlSDvaaZgEIDOzMiRNHiI2NomVLX7PXK+pentdWf0bJtg0AKD+pHxXmDKfC3BG4fzOBsiO6\nZv9cJVfcFn+C2+KxOHZrYfZcoO+4ALC2tmbjxvVERIQSExNF69b6fGJkUNA8YmIOER19kDp16uiS\nAcDT05NLl84xePAg3TLo2RaentU4d+4Ygwb1zd02bNiHPHoUh42N9p8aqcK4sLW1ZevWdYSF/UhU\n1H58fZvokgP0HZ9FixVh/y8L+aBLAywtLZi3bCibQ6ezcvtEittrMzamz/iE8IiNRERtwc+/GS6u\n5dj14ypC9q0lePdKSpdxNHuGGjU8OHM2mo8G9gLA0tKS779fzKHo3ewN2UCJEtq9puo9f6s2X2Rn\n0mcfUemYU+85y9OzGufP/8KgQf0AcHV1ITw8mIiI3WzYsAIrK/M+nKNJYG2mBffN/W/9tUlYWBgY\nuawjs0M/4rPtvbG1L9xP/pWFpQZWr15Hs2at8fVtw7RpM1m79gfNatvY2LBg4UwiIg7lbps6bTwr\nlq/Bt1l7du/+iVGjtJt0Gjb0oXqNatT3bkjrVn4sXDRfs9oAw8d25u7dhwD0G+zH6IGL6BHwT349\nfpEuvcy/sMvLwcGByZMnUL9+U/z9O+Hv39as9QzFilB2SEfSfr2Quy1h5ipujl/CzXGLMV68zv2Q\nGADKjehK4qIfuD5iPkXdnDEUMe/kp/e4APDza8Px4ydo0qQ5Xbv2IChoruYZGjSoT+XKb+Dt7cOA\nAQNZvHih5hkge95YvHgh4eERutQHfdvCxsaGhQu/ICIiKndb9+6dKFOmNHFxCZrlyKHKuOjduzvn\nz1/E19ePLl16sXDhHF1y6D0+h4xtx727qQB06tWElNsP6NJ8Cj/tPMrb9aqZvX79Bu9SrfobNGvS\njQ4BHzJ77kSm/PNjVq3cTOuWvdiz5wAjRvQzawYbG2sWLJhGRER07rZ+/QNJSk7Bp4E/27b+iHf9\nd82aIS8952/V5oucTHrsI6odc+o5Z9nY2LBo0WwOHHg2LqZOncDSpd/RpIk/V65co2/f7mbNEPHD\nST4LWM1nAavZPCeCg5t+w7f329y//YgJzZcTs/N3qtdzL9SasrDU2OTJE5gxQ7uBbTQa8WvbjcSE\nW7nbhg0dz86dewG4nZyCg6ODZnmiog7RuVM3AO7du4etra1mtV+v7EKlqq5Ehp4AYET/IOJvJgPg\nXN6RxPgUzbIANGvWmPDwSB4/fkxSUhJDhnxs1nqm9EziJn9DZsr9//heEdeyWNjZ8OTyDSxLFsfC\nuhjpV+IASJyzBlNGplmz6Tkucmzdup2goOwDdje3ity4cVPzDE2bNiE4eDcAFy5coFSpktjZaf/8\nWqPRSKtWbUlI0OegCPRtC6PRSNu2XUnIM2/u3LmXzz6bpUn9/FQZFykpKTg5ZV8Jc3R0IDn5tuYZ\nQN/x+Xrl8lSq4kJU6K8YDAYat6jNnm3ZJ+S2rY/kYOivZs9wOPoYPbtnv17cu/cAWxsbRo6Yyq7g\n/QDcTr6Dg2NJs2YwGp/g79+LxIRnz55s06YZmzbuBGD16k38FHLArBny0nP+Vm2+yMmkxz6i2jGn\nnnOW0WikTZvOJCY+a4uGDb3Zs2cfAHv27Kdp04aa5en0SWO2zj/I2y2qcWjbvwAIX3+CE6EXC7WO\nLCw1VKeOFzdu3CA5OVmzmiaTifT09Oe2GY1GAAwGA4MG92XTxh2a5clbf8CAfoSE7NOs7sTpfZk5\naTUGw7NtDZrUIuznpTiVLsmurVEv/2UzcHd3w87Olp07NxERsY/Gjc08wZhML10gOgQ04t6u7H9/\nEWcnslLTKDemBxWCRlIqoJF5c/2bXuMiv+joSNatW82oUWM0r+3s7Pzc/HD7dgrOzs6a53jRvKE1\nPdviRf/+tLQ0TWq/iCrjYsuWHbi7u3H+/K9ERIQwbtxkzTOAvuNz/Oc9mD15PYZ/v5C4upXBp1kt\n1gRPZv63w7Avoc1JMaPxCQB9+nYidH8UT55kt4fBYOCjgYFs2bzHrPVf1Afu7hVo2bIx+/dvZs2a\nJZQsWcKsGV5Ej/lbtfkC9NtHVDvm1HPOelFb2NnZkpmZfRyWlJRM+fLlNMnyRi0Xbt+8x/3bjyjr\nVorazaoyLbgvI7/thG0J60KtJQtLDfXv34c1azboHQPI3sG/X/MVkRHRREXFaF7f39+PPn17M3yY\nea/S5Qjo3JCTx87nXqHMOSiIjvgN37rDuHI5nkEj22uSJYfBYMDR0YF27brSv/8gVq78RtP6uSwt\nsIR9U7EAACAASURBVPasxOPTl3M3FSnnSPKy7cR9+hUlmr9H0YraTH5aj4sXadCgMQEBHVm/fo1u\nGXIY8p4FecVJWzyjV1sEBnbmzz+vU62aF76+fixZEqRLDr34d67Pr79cJP7ms6seBoOBK5fi6R0w\ng0sXbjJwVIBmedq0bUqPnu0ZM3p6bpYVK+dx8GAs0Yd+1ixHDoPBwPnzl2nRogtnz15k3PhhmmdQ\naf4Wz+h1zKnynKXlPN6sRx0iN/6aW/fmxWQ+C1jNjQtJdBjlU6i1ZGGpoYYNG3DkSKzeMQD4buWX\nXLxwmS++0P69Os2b+zLh03G0atmW1NRUTWo2bl6HZq3fZev+2XTp6cvQMR1p6V8v9/v7fjxKnbrV\nNcmSIykpiSNHsl/8r169xsOHqTg5OWmaAcD2rSoYL/yZ+3XmvYek/5nA00ePMaVnYDzzB0Xdy5s9\nhx7jIi8vr1q4uroCcOrUaaysrDTvj/j4+OeuRLm4lNf1dlQ9qdoWJpNJ85qqtMX7779HaGj27Y2n\nT5/BxUX7q6Z6aujrRZNWb7Np3zQ69mzMoNEB3E15yC9HzgEQE3GKyh6ummRp2qw+Y8Z+RLsPBpCa\n+giAZd/O4tKlq8yd/bUmGfK7lZjM4cPZr2lhYVFUr15Fs9oqzN8vosd8oSK9jjlVm7MePkylaNGi\nALi6lic+PlGTup71X+f8sRsA3L31kLNHrwHwW8QlKnqULdRasrDUiLNzOR4+fEhWVpbeUejWrQNP\nnqQzc+YCzWvb29szZ+4s/NoG8ODBA83qfjxgAR18x9OpxQQ2rQ1j6fytDBndEY8a2W9arlWnClcu\nx2mWByA09ABNmmTf/uro6IidnS0pKRq9zzPPmbJiVd15cuXZvz3z1h0sbKyxsLMBg4FilSqQfjPp\nRX+l0Og1LvLy8WnAmDEjAShbtqy2/fFvoaHhdOyYfeXcy8uLuLh43W+p0uvqmCptkf/fr0d7qNIW\nly//Qd267wDZ72N7+FD7E0D5/R/27jygqmr////zACJDqIADimKWpkmW5G0wBOdZSEBzHlBz1jLL\nNLU0LUNFy8rKMafMIUVTQiZFwKgcupqzXUsDTCRRQA+T5/sHYejVe+/vB2ev1Yf34y85Gu9Xa6+9\n9l5777OXkdtj8osf0qfTTPp2eYut6/bySfh29u45jH+HJwDwfqIB589Zf8Lv4uLMnLmv0TtkNNev\nF2+DF/oEkJefT9i8j61e/372RO+lc+e2ADz5ZDPOnPmXYbV1GL9Bj/HibqozqDzn1G3MiotLIDg4\nAICgoAD27LH+95Bdaz2AOSePW0W3ADgSd5YnOzwCwMNP1CHtXPl+79S6r3oUt9Wu7WHodytLNPdp\nxvz5s/HyqkthQQHBwQHUrFEdc56Z6JhtWCwWTp48zcsvvWFInj59euPu7samzV9gMpmwWCwMGTyM\n1FTjJnUlY+y0iR8zJ3wUBQVF5JnzmTz6fcMyAKSnX+KrryI4cGAvFouFiROt+52Qyg3rUmNkMHY1\n3aCwCJdWT5D29grsXF24mXZn38xYtg3Pd8aCxULuwRPk/2LdJXJ06BeffrqMFSs+Y9++WBwcHBg3\nzvjHcVNSUjh06DBJSQkUFRUxbtxEwzNA8eQlPHw+9et7UVBQQEhIEMHBvbl27d9f/GQtKtvCx+fx\n2+NmQUEhQUE9iI1NoGPHNtSqVYNdu74kJeUg06fPMSSPLv1i2bLVrFz5MXFxu7G1tWXMGDWPrOvQ\nP0usWxZF2NKxhAxoS27OTaaOs/5XGkJ6dcPNvRpr1hUfs0wmE551Pbh2LZvd36zFYrFw6tQ5Xn3F\nev2zefPHCAubiVd9TwoKCgkO6saQIRMIXzSboUP7kp2Tw4jhk6xW/24qx2/dxoviTGr2Ed3OOVWO\nWT4+j7Ngwdt4edWjoKCQkJBABg0axerVHzNy5BB+/fU31q790uo5XGu5cC0j9/bPkctTmPBxCO0H\nPMnNnHw+HPdVudYzWeQ+/f/E1vYB1RGwtTH+LYD3Ulh4VXUEGrh2Vx0BgF+uxaiOwMkOQ1VHAKBJ\n9DLVEe64E6uSxXJLdQRRip1dNdURACgszFIdARsbNWvq3e3WrZuqI9DYrZfqCACk3jysOgIFRWqf\njADIL7DukzH/K1tb4186dDcdxgqASnbWXw/1vynSYKwAMJkqqY5AYNWxqiMAsC3z/hdJ5FFYIYQQ\nQgghhBBlIhNLIYQQQgghhBBlIhNLIYQQQgghhBBlIhNLIYQQQgghhBBlIhNLIYQQQgghhBBlIhNL\nIYQQQgghhBBlIhNLIYQQQgghhBBlIutY/o8q23uqjqDNGk9uLs1UR6CzQ4DqCABszHhXdQRM6LF2\nY+7EPqojUHP596ojAJBz81+qIwihNZNJ/XVtx8p1VUcA4Ib5guoIohQbm8qqI3DrVp7qCEJDrg94\nq44AwB/ZP97379SP7EIIIYQQQggh/tZkYimEEEIIIYQQokxkYimEEEIIIYQQokxkYimEEEIIIYQQ\nokxkYimEEEIIIYQQokxkYmlFTb0bc+JEEqNGDQFg2fJFHDwUQ9SezUTt2Uznzm0NzePt7c3ZsycZ\nM2a0oXUB3nr7Jb6JWU10/Bq692jLsy192BW1koivP2P9l4txqfKAVetXdqrEy6v6MP2rIbz19TAe\na/0QAJ1HPMPa32Zi71jJqvXvR+U2KREWNo+k5AS+TUmiZ8/nrV7PLrAv9i+/SeVJs7B5vAVUdcV+\n/DTsJ0yn0pBxYGMLgO1zban8yizsJ87A5vF/WC3PnLmvERu/kfiEzQQEdqCOZy12fL2KyKi1ROxc\nSfUablarfT869Ivw8AUkJ+8nMXEfLVq0qLAZdMmhQwZQ3zcdHBzYuHE98fHRJCcn0K1bV0PrN236\nCEd/iufFkQMAaNSoAVHRXxAZtYElH87FZDL2Ld3SL/6iQ1uEhg4iNjaSmJjdxMZGcvVqmpIcOrQF\n6NEvdMmhcpvMmvMyUbFriIlfR7cebbG1teWzFe8SE7+ObTs+LddzcLty+03iDo6ODixaNIf4+KQ7\nPp8xfR5RUfEK8jiyZMliYmONr+3bqgWNmzxE146hVHOtwr7EjWRkZDJy+HTO/+siL78SytDQED78\nYI3VMvj39SHt7BU2z4ujWs0HmL5tKBHv76dKdWf+SM+2Wt3/ROU2KdG6tT+PNm1CK9/WuLq6cvjI\n90RE7LBaPZuGTbDxqEP++2+DkzOVX5vLrTPHKUyM4dY/D2LXvRe2z/pT9M+D2LXtSt68aWAyYT9+\nGvnHf4SiwnLN08rvaZo8+jAd2vXD1bUqSd9uZ39CCqtWbmJHxB5GjOzPxInDeHPmwnKt+5/o0C/8\n/FrRsOHD+Pr607hxY1atWo6vr3+Fy6BLDh0ygB59MyCgOwcPHiI8fDH16tUjOjqSyMhvDKnt6OjA\nwvA32RuffPuzOe+8zoKwpcTFJfHalLGE9OrO1i27DMkj/eIvurTF6tXrWL163Z+ZfOnVK8jwDLq0\nhQ79QpccKreJb6t/0LjJQ3TpMIRqrlVISPqSWrWqcyXjD0aNeINBQ4Jo+ZwP0VGJ5VJP7lhaidmc\nR2DAQNIv/a46CgBms5muXXuQnp5ueO3kpEMMGzwFgGtZ2Tg5O3D16nXcq7sCULVaFTIzs6yaITsz\nlwfcHAFwdnXkemYuByNPsuU9dQONym1SIiFhPy/07gdAVlYWTk5OVq1369wp8ld/WPzDzRuY7Ctj\n07AJt44dAaDopyPYNn4Mk3t1LL+nwa0iKCrEknoBmwcfLvc8SYnfM2jASwBkZV3HydGRlyfOYkfE\nHgCuZPyBq1vVcq/7n+jQL9q3b0dExE4ATp8+TbVqVXF2dq5wGXTJoUMG0KNvbtnyFeHhiwHw8qrH\nxYu/GVbbbM6j5/PDuHTprzWlH364PocOHQUgLi6RDh38DMsj/eIvurRFaTNmTGXu3DDD6+rSFjr0\nC11yqNwmyUkHCR30GlByDu5I567+bNkcCcC6NdvLbVIJMrG0GovFQn5+/r99PmbMUKKiNrFm7Ue4\nulZTnscoZnPxYr+DhgQRsyeJ6a8vZN0X4Xz7w1c827I5GzfstGr9lB3HqV63GotSJjJzeyhfzIom\n70aBVWv+N6q3SQmz2QzAiBHDiIyMsn7BguJ2t322NUUnfsRkX7l4AgmQcx2qVMWS8Tum2vXAyRns\nK2PToCG4WGeCV9I3h4b2JnpPAnl5xdvEZDIxclR/Nm8y5u5DCR36hYeHBxkZGbd/vnIlEw8PjwqX\nQZccOmQAPfpmicTEvaxbt5pJkyYbVvNe///HfzpNly7FX2vp0MGfGjXcDcsj/eIvurRFiRYtfLh4\n8eIdmYyiS1vo0C90yaF6m5Q+B4/ek4SXVx06dmrFjt3LWbZyHlWqupRbLZlYGmjD+q1MnzGPLl36\ncPToCd5807gDog66dmtN/4GBvP5qGO8tmMKgfq/Q8qkQUr79kREjX7Bqbd+QZlz5LYtXnl3CO73W\nMPS97lat93cTGBjA0NAhTBj/kiH1bB57Ertn/CnYuhbu+E7Sn3++eYOCnRuxf/EV7PuP5Fb6b1jz\nm0vde7Rn4KBgJr8ypziFycTylQvYty+FxP3fWbHy34PR3xvTNQPokUOHDKr5+bWlZ89erF9vva9Q\n/C/eeOM9Qnp1Z9futZhMJqXbRvrFX1S3xfDhQ1mzZoPSDCVUt4X4dyq2SdfubRgw6HlenzwPGxsT\nZ06f5/nuL3Lq5M+88urwcqsjE0sDJSQc4KdjJwHYvSuapt5NFCcyTtv2LXl58jB6B48nJ+cGTR9r\nxMEfjgGQsO87nmj+qFXrP/K0F0f3ngPg4onfcfUofXXGYtXauuvUqSNTp02ha5ce5OTkWL2eTZNm\nVOoYQN6nCyDPjMVsBtvir3ubqrliuVb8WPStfx4k/4M55H/+IdjYcOuPK1bJ075DKya/OpKg50eQ\nk5MLwKefzePs2fPMf2+pVWrqLi0t7Y6rqXXq1Db8MSIdMuiSQ4cMuvDxaY6npycAR48ew87ODnd3\n4+4S3i0t9RK9e42kR/fBHPzhR3791bhHc6Vf/EW3tmjd2o8DB1KU1NatLYT6bdKufUsmTR5Gr6Bx\n5OTc4PffMzmQfAiA+LgDNG7yULnVkomlgTZ+uYwHH6wHgL//c5w4fkpJDqOvlLi4ODPr7Zfo1/sl\nsq8XT1x+v3SFRo0eBODJJ735188XrZrh9/N/0LBFcdtXr1sVc05eqb9VfzVP1RVFFxcXwubPI6BH\nT65fv279gpUdqBTYl7xl4WC+CcCtMz9h2/wpAGyfeIpbJ4/efmEPtnbgUhWbOl5YLp4v9zguLs7M\nmfsavUNGc/3PvvlCnwDy8vMJm/dxudf7/0pVv4iOjqVXr2AAfHx8SE1N48aNGxUugy45dMhwN1V9\n09/fj8mTXwagZs2aODs7kZmZqSQLwBvTJ9K5cxsABg3uxTeRxn1vX/rFX3RqCw+PWmRnZ1NUVKSk\nvk5tUUKXu6YVsX+6uDgza87L9O098fY5eGxMMh06tQLgieaPcu7sL+VWT94KayXNmz9G2Py3qO/l\nSUFhIUHB3Vm6dBXrN3xCbu5NcnNzeXHEJMPy+Pj4EB4+n/r1vSgoKCAkJIjg4N5cu3bN6rV7BnfC\nza0qq9aEFc/hLPD6q2G8/9FMCvILuXr1GhPHzbZqhri1Bxn5fk9mbB+Kja0NK1/bxfMv+fFY64ep\nWsOZ1zcO5OzBi3w5N9aqOUpTuU1K9OnTG3d3NzZt/gKTyYTFYmHI4GGkpqZapZ7tk89icn4A+6ET\nbveFgg2fUanfCGyfa4fljysUfZ8IFgtFR76n8qS3wHKLgq1rwFL+d5ZDenXDzb0aa9a9DxQfdDzr\nenDtWja7v1mLxWLh1KlzvPrnI7JG0KFfpKSkcOjQYZKSEigqKmLcuImG1dYpgy45dMgAevTNTz9d\nxooVn7FvXywODg6MG2fM4/sAzZt78+57b+DlVYeCgkKCgroyY0YYixbPYtr0CRxIPkh0dIJheaRf\n/EWXtgCoXdtDyXcrS+jSFjr0C11yqNwmQSGdcXOrxqo182+f540dOYO5815l4OCe5GTfYOzomeVW\nz2SxWOFs7f+gyvaeqiOQX3D5v/8jA7i5NFMdgc4OAaojALAx413VETBpcMcVIHdiH9URqLn8e9UR\nAMi5+S/VEYTQmsmk/oEpx8p1VUcA4Ib5guoIohQbm8qqI3DrVt5//0eiwnF9wFt1BAD+yP7xvn+n\nfmQXQgghhBBCCPG3JhNLIYQQQgghhBBlIhNLIYQQQgghhBBlIhNLIYQQQgghhBBlIhNLIYQQQggh\nhBBlIhNLIYQQQgghhBBlIhNLIYQQQgghhBBlYqc6wN9FNacGqiOQcf0P1REAuJpzXHUE6lbvqToC\nAKYM9WtImmzsVUcAIHBNHdURGODWX3UEAD5Lnas6ghB6kyW0tSJrN/7FYilSHUGUokPfBLBYClRH\nwFaTtvhP5I6lEEIIIYQQQogykYmlEEIIIYQQQogykYmlEEIIIYQQQogykYmlEEIIIYQQQogykYml\nEEIIIYQQQogykYmlEEIIIYQQQogykYmllTg42LNs1Wy27fqQ3dGf0qFTS2rXqcHm7YvZtutDNm1b\nRPXq1QzPVblyZc6c+YlBg9Qsy+Dg4MDGjeuJj48mOTmBbt26Wr1mrUdqMCV+PC0H/gOAqh4ujNow\nmNEbh9D/g2Bs7Ip3gw4T/Bm7JZSxW0JpN7aV1XMBhIXNIyk5gW9TkujZ83lDapYWGjqI2NhIYmJ2\nExsbydWraYbWb9e7OZ8mvsRH8eN5qkNjbGxtmLqsL0tix/He9hE4VXGweob+7/Xgte2hvLxpMDUb\nuFGttgsvfTGQV7YMYeKGgbi4O1k9Q2nh4QtITt5PYuI+WrRoYWhtHXN4e3tz9uxJxowZrSyDDm2h\nQwbQY3uAurGzadNHOPpTPC+OHABAo0YNiIr+gsioDSz5cC4mk/FLUOmwTfr3f4FDhw6QkpJAly4d\nlWTQZR8B9edaurSF6r6p+hynhIpz3+K6lVm+ei4Ru5YSGbOCjp18qV2nJtu/XsqO3Z+wbNVc7Oxs\ny7WmrGNpJZ26tOLI4VN88tFGPOvWYvP2xfzw/THWrt7Brp37GDo8iNHj+zF31ieG5po5cxqZmerW\nwwwI6M7Bg4cID19MvXr1iI6OJDLyG6vVq+Rgx/NvduHcgX/d/qzTpDYkr/2en/acovPktjzVuzmn\nE36mVqMaLO29GpMJXo0dx/ebj5BzJddq2Vq39ufRpk1o5dsaV1dXDh/5noiIHVardy+rV69j9ep1\nAPj5+dKrV5BhtV2qOTJwSgfG+H+A4wOVGTKtI7W8XMm6ksN7I7+k66CnaNbyQb7bc8pqGZ7o3BjH\nB+xZELQa93rV6PN2F3L+uEHihkMc3n2S1oP/QYdRz7H93VirZSjNz68VDRs+jK+vP40bN2bVquX4\n+vobUlvHHI6OjixZspjY2HjDa5fQoS10yAB6bA9QN3Y6OjqwMPxN9sYn3/5szjuvsyBsKXFxSbw2\nZSwhvbqzdcsuq2f5K5P6beLq6sqMGVNp0cIXFxcXZs2aTlRUjKEZdNlHSqg819KlLXTomyrPcUoz\n+ty3ROeurThy+CRLP9yAZ91abI34kO9TjrJy+RZ27dzLGzNH039QIGtXby+3mnLH0kp2RsTzyUcb\nAfCsW4u01MtMnRzOrp37AMi8koWrq4uhmR55pBGNGzc2pDPfz5YtXxEevhgAL696XLz4m1XrFeYV\nsnLYF1y/nHP7s4eeeZATcWcAOBl3hka+D5GVdo0NE78CwLGaI7eKbpGXY93FmhMS9vNC734AZGVl\n4eRk7J2xu82YMZW5c8MMq+fTphGH954l72YBWRk5fPDKdlp2eZT4zUcA+GbdD1adVALUbODOLz8W\nX8HMvJiFW91qbJweyeHdJwHIzryBczVHq2YorX37dkRE7ATg9OnTVKtWFWdnZ8Pq65bDbDbTtWsP\n0tPTDa9dQoe20CED6LE9QN3YaTbn0fP5YVy6dPn2Zw8/XJ9Dh44CEBeXSIcOfoZk+SuT+m3SoUNb\nYmP3cvPmTS5fvszYsS8ZnkGXfQTUn2vp0hY69M3SjD7HKc3oc98SO7bHsfTDDQDUretBaurvtGzl\nQ1TkfgD2fJNE6zZPlWtNmVha2ddRS/l42UxmTvsAszkfAJPJROiLwWzbauwVvYUL32Py5NeVPKpz\nt8TEvaxbt5pJkyZbtY7FAkX5RXd8Zu9UiVuFtwDIyczFpeYDt/8uYGYnXvlmNHEfJVJgLrRqNige\neAFGjBhGZGSU1evdT4sWPly8eJGMjAzDanp4ueLgbM/sLwYTvnsUzf0fppaXK093asKCr0cybXlf\nnKta91HY1FO/07TNw5hMUOshd6rXq0ZlJ3sATCZoM/Qpvo84ZtUMpXl4eNyxDa5cycTDw8Ow+rrl\nsFgs5OfnG163NB3aQocMoMf2KKFi7LzX///xn07TpUtbADp08KdGDXdDsvynTEarX98LZ2cntm//\nkvj4KNq2bW14Bl32EVB/rqVLW+jQN0uoOMe5F6POfe+2e88yli6bxcxp7+Ps5EhhYfF58ZUrf1Cr\nVvVyrSUTSysL6DKWIf2nsXT5W0DxpPLjz2aSmHCQ5MQjhuUYOLAf336bwoULF27nUMnPry09e/Zi\n/fo1SnNwVzt8PSeahR2X0mbkc1SrU9WQCIGBAQwNHcKE8cZf5S0xfPhQ1qzZYGhNkwlcXB2ZNWAd\nC8Zu4dWPe2MymbhwJoPXApbx66nf6fdKW6tmOLHvZ375MZVXtg6l7bBnuHTuCiZTcbbQJUGcSjrP\nmQO/WDXDf6J6Py2hSw4d6NAWOmTQgQ5j5xtvvEdIr+7s2r0Wk8lUIbeNyWTCzc2VoKC+DB8+mpUr\njf2Kz/0yqaDbuZYuGVQbPtz4c5x7UXXu273zSAb1e41Pls++47zXGn1DvmNpJc2eeIQrGVdJT8vg\nxE/nsLWxwc2tKrPeGc+5cxdYvMDYTtWtW1caNHiQHj26U7euJ2azmYsXf2Pv3gRDc/j4NOfy5QxS\nU1M5evQYdnZ2uLu7k5mZaViGvJx8bO1tKcovomotF67/nk2VWi641HiA1J/SMWfn8cuhi9R7og5Z\nadesmqVTp45MnTaFLp27k5OT89//Aytp3dqPCRNeMbTm1YwcTnz3KxaLhUu//sGN7DxMJhNHk4u/\nD3sw7gyDplr/JRBfL9x3+89vJ44nO/MGQxY/z6WfM4n8YL/V65eWlpZ2x5XlOnVqK3mMSJccOtCh\nLXTIoBtdxs601Ev07jUSgPbtW+HhUUNZFlUuX77MgQPfAXD+/C9kZ+cYflzXZR/R4VxLl7bQiYpz\nnNJUnfs+/kRjMjKukp52mRPHz2Fra0tuTi729pXIzy+gdu0aXLpUvndx5Y6llbR87gnGjO8LQI0a\nrjg94Eibdk9TkF/AovmfG56nf//BtGzpj69vG1auXM3cufMMn1QC+Pv7MXnyywDUrFkTZ2cnQw8+\nAOcO/ItmXR4FoFmXRzm9/2cecHcmaE634rtVNiY8H6tNxnnr5nJxcSFs/jwCevTk+vXrVq31n3h4\n1CI7O5uioqL//o/L0aH4MzT3bwiAi6sTDs72xG46zNMdGwPQqHldfjtn3cdWPB+tyaCFgQA0bfMw\nF46l81TPxyjMLyLyfWMnlQDR0bH06hUMgI+PD6mpady4caPC5ihN1VV3HdpChwx3U3kXRJexE+CN\n6RPp3LkNAIMG9+KbSHUvKlG3j8TRrl3x469ubm5Kjuu67CM6nGvp0halqRwvVJ3jlKbq3Lflcz6M\nHV/8ZuIaNdxwdnYkYd8PBDzfDoAege2Ij00p15pyx9JK1qzaweIPpxIR+REOle2Z9uoiXnplEPaV\n7dn29RIsFgunT/3CG1MWq45qqE8/XcaKFZ+xb18sDg4OjBtn3UeYPL096PFGJ6p5VuVWYRHNujzK\nxknb6bPweZ7t14KrqVkc+uqfWG5Z+CnqJGO3DgPgZPxZLp26/F9+e9n06dMbd3c3Nm3+ApPJhMVi\nYcjgYaSmplq17t1q1/ZQ8r2DzEvZ7N9xjCWx48ACH722gx/3n2PKJ33oMvApbubkMX/MZqtmSD1Z\nvI1f/3o4BeZCVk3Yxouf9MLO3o5JmwdjscClsxl8OcOYlzCkpKRw6NBhkpISKCoqYty4iYbU1TWH\nj48P4eHzqV/fi4KCAkJCgggO7s21a9Z9kqA0HdpChwygx/YAdWNn8+bevPveG3h51aGgoJCgoK7M\nmBHGosWzmDZ9AgeSDxIdbfRTQOq3SXr6Jb76KoIDB/ZisViYONHY74+BPvuIDnRpCx36Jqg7xynN\n6HPfEp+v2sb7H01nZ+SnVHawZ8or8/nnj6f4eNksBg/tycWLl/jyi93lWtNksVgs5fob/4+qVc2Y\ndQ3/k4zrh1RH+NMt1QF49cHpqiMAsPD8XNURMNnYq44AQDuXcaoj8PADat+sW+KzVPX9QgidmVD/\nvS9Hh3qqIwBww3xBdQRsbCqrjsCtW9Z9E/v/ymRSf8/FYrH+ywP/LnTomwAWS4HqCLi7NFcdAYCM\na9/d9+/kUVghhBBCCCGEEGUiE0shhBBCCCGEEGUiE0shhBBCCCGEEGUiE0shhBBCCCGEEGUiE0sh\nhBBCCCGEEGUiE0shhBBCCCGEEGUiE0shhBBCCCGEEGUi61j+j2xMGqwVaLJVnUAb9pWqq44AQF7+\nJdURtFgPDsDJsb7qCNzM+111BABsbRxVR6Cw8KrqCOIuFuRwqxNbW2fVEbRxq+iG6gja7B86rJuo\ny5qeOtDlHEeH/qnPuW/6ff9O7lgKIYQQQgghhCgTmVgKIYQQQgghhCgTmVgKIYQQQgghhCgTmVgK\nIYQQQgghhCgTmVgKIYQQQgghhCgTmVgaJCxsHknJCXybkkTPns8ry1G5cmXOnPmJQYP6V8gMixQx\n6wAAIABJREFUTZs25viJREaOGgyAra0tn3++hP2JO9kduYEqVVwMzePt7c3ZsycZM2a0oXVLU9k3\n58ydQmz8JvYmbKVHQAcAxowZzB9Zx3F0dDA0i5OTE1u2rCMm5msSEvbQsWM7Q+t7ezfhxMkURo0e\nCkDdunWI/GYzMbHb2B25iRo1jH0bnC5jlg45dMgAeowX4eELSE7eT2LiPlq0aFFhMwAsXbqY/fuj\niInZSaNGD1fYDCD7SInQ0EHExkYSE7Ob2NhIrl5NU5JDl31EhxzSN6Gpd2NOnDjAqFFD7vi8Y8c2\n3DSnlmstu3L9beKeWrf259GmTWjl2xpXV1cOH/meiIgdSrLMnDmNzMw/lNRWncHR0YFFi2YTH594\n+7Nhw/tzOSOToUMnEhraF99WT/NNZJxBeRxZsmQxsbHxhtS7F5V908/vaZo82pAO7frg6lqV5G93\n4OzsRI2a7qSlGb9syJAhAzh16gwzZ87Bw6MWsbG7eOyxpwyp7ejoyKLF7xAfv//2Z7Nmv87yZWvY\nvn03o0YPZdKk0bzxxlxD8ugyZumQQ4cMoMd44efXioYNH8bX15/GjRuzatVyfH39K1wGgMDA7lSp\n4oK/fxcaNKjP4sVh9OzZt8JlANlHSlu9eh2rV68DwM/Pl169ggzPoMs+okMO6Zt/nl8smnvHuS+A\nvb09r742nrS08l02T+5YGiAhYT8v9O4HQFZWFk5OTkpyPPJIIxo3bkxk5DdK6qvOYDbnERg4mEvp\nl29/1r17B77cuB2A1au/NGxSWZzHTNeuPUhPv/96QNamsm8mJn7PoAET/qx9HSdHR3Z9Hcvbsxcb\nlqG0zMxM3N3dAHBzcyUj44phtc1mMwE9+nEp/a8J9fhxr7N9+24ArmRk4urmalgeXcYsHXLokAH0\nGC/at29HRMROAE6fPk21alVxdjZ2LUgdMgA0avQQP/xwGIDz53/Fy6tehcwAso/cz4wZU5k7N8zw\nurrsIzrkkL5ZXDswYADpl+68YP/61Il88slqCvILyrWeTCwNYjabARgxYhiRkVFKMixc+B6TJ7+O\nyaRusVmVGSwWC/n5+Xd8Vr9+Xbp0acuePZtYs+ZDqlatojSPCir7ptlcvAj00NAX2LNnH7m56hbp\n3rx5G/Xre3Hq1BHi4yOZMmWGYbXv1RdKtovJZGL0mFC+3LjNsDyl66scs3TJoUMGHcYLDw8PMjIy\nbv985UomHh4eFS4DwE8/naBTp3aYTCYeeaQhDRp4Ub26e4XLUEL2kTu1aOHDxYsX7+irRtFlH9El\nR0Xvm/eq3ajRQzRr1pSI7bvL/XxcHoU1UGBgAENDh9C5UzfDaw8c2I9vv03hwoULAEomdjpkuJvJ\nZOLUqXO8++4HvP76BKa8Pp7pb7yrOpbhVPbN7j3aM3BQCM8HhBpeu7T+/V/g118v0L17CM2aebN8\n+cc8+2wbpZlMJhOfr/mYvfGJJCQkG15fZb/QLYcOGXSjyxiuwp49cbRs+Qx79+7m6NHjnDp1xvAs\nOmQoTfaRvwwfPpQ1azaojgHosZ+C2hzSN+80f/4sJk2abpXfLRNLg3Tq1JGp06bQpXN3cnJyDK/f\nrVtXGjR4kB49ulO3ridms5mLF39j796ECpXhbr9fyiAp6TsAYmISmDFzkrIsqqjsm+07tGLyq6Pp\nGRhKTk7u7c8tFkNjAPDcc88SHV38KPSxY8epU8f4K6t3W7HyA86cPse77xr/eLDqMUunHDpk0EFa\nWtoddxzq1Klt+KNdOmQoMWvWXxchT506bOjj8zplANlH7ta6tR8TJryipLYu+4guOaRv3ql27Vo8\n8sjDfL7mY0wmEx61a7IneiudO/Uql98vj8IawMXFhbD58wjo0ZPr168rydC//2BatvTH17cNK1eu\nZu7ceYZP6HTIcLc90Xvp3LktAE8+2YwzZ/6lJIeqK3kq+6aLywPMnTuF3iEjuX79zsFeRXOcO/cz\nzzxT/LIeL696ZGerPQD16xdCXl4+77yzyPDaOoxZuuTQIcPdVI0X0dGx9OoVDICPjw+pqWncuGHs\n4+s6ZABo1sybZcs+BKBz5/YcPvxjhcwAso/czcOjFtnZ2RQVFSmpr8s+okMO6Zv/Xjs9/Xe8vX1p\n0zqQ1v4BXEq/XG6TSpA7lobo06c37u5ubNr8BSaTCYvFwpDBw0hNLd9X/Ir/rHnzxwgLm4lXfU8K\nCgoJDurGkCETCF80m6FD+5Kdk8OI4cbdsfTx8SE8fD7163tRUFBASEgQwcG9uXbtmmEZVPbNkF7d\ncHN3Ze26D27X3r//O1q3fpaaNauzPWIl3313hLfeXGj1LADLlq1m5cqPiYvbja2tLWPGvGRIXYDm\nPs2YP382Xl51KSwoIDg4gJo1qmPOMxMdsw2LxcLJk6d5+aU3DMmjy5ilQw4dMoAe40VKSgqHDh0m\nKSmBoqIixo2baFhtnTJA8VMNJpOJAwdiuXnTzKBBL1bIDCD7yN1q1/ZQ8t3KErrsIzrkkL4JzZs3\nI2z+W9T3qktBYQE9g7rT54XhXLtWPNG2lPMjYiZLef/G/6NsTPaqI4DJVnUCbdhXMnZNv/vJyy/f\n1zT//2FCj+9PODnWVx2Bm3nGL1NyL7Y2jqojUFh4VXUEcRcLcrjVia2t8W/K1NWtInUvTiuhy/5h\nY1NZdQRu3cpTHUEbupzj6NA/9Tn3vf8jzfIorBBCCCGEEEKIMpGJpRBCCCGEEEKIMpGJpRBCCCGE\nEEKIMpGJpRBCCCGEEEKIMpGJpRBCCCGEEEKIMpGJpRBCCCGEEEKIMpGJpRBCCCGEEEKIMrFTHeDv\nQof1a0yWItURALCxdVIdgcKibNURAD3WFLK3q6I6AgC55l9UR9BGQeFN1RG0WaOvqChXdQRMJk2u\noWqwbLQOa/SBHuv0OdrXUh0BgPzC66ojYLmVrzoCFkuB6ggAVHF8SHUEsnJPqo4A6DNeaEGDfcTJ\nvobqCP+VJkdbIYQQQgghhBB/VzKxFEIIIYQQQghRJjKxFEIIIYQQQghRJjKxFEIIIYQQQghRJjKx\nFEIIIYQQQghRJjKxNEh4+AKSk/eTmLiPFi1aKMkQFjaPpOQEvk1JomfP55VkAFi6dDH790cRE7OT\nRo0ernAZmno35sSJA4waNQSAZ55pQVx8BFF7trBjx3rc3FytnmHO3NeIjd9IfMJmAgI7UMezFju+\nXkVk1Foidq6keg03q2co4eDgwMaN64mPjyY5OYFu3boaVlvHHADe3t6cPXuSMWNGK8ugw34K6ttC\nl36hwzEkNHQQsbGRxMTsJjY2kqtX05TkUNkWuoydOhxHSvj7tyL90i/ExEYSG/cNi99fYFjtu6ka\nL2bPmcSe2LXE7N1A94B2fPTJ2ySlbGXH7hXs2L2CDh1bGZpHh/ECoH//Fzh06AApKQl06dKxwmYA\ntefgRvZPWW7EAH5+rWjY8GF8ff1p3Lgxq1Ytx9fX39AMrVv782jTJrTybY2rqyuHj3xPRMQOQzMA\nBAZ2p0oVF/z9u9CgQX0WLw6jZ8++FSaDo6MjixbNJT4+8fZnEya+SOjQ8Vy48BtvTJ/EsOEDWLjg\nI6tlaOX3NE0efZgO7frh6lqVpG+3sz8hhVUrN7EjYg8jRvZn4sRhvDlzodUylBYQ0J2DBw8RHr6Y\nevXqER0dSWTkN4bU1jGHo6MjS5YsJjY23vDaJXTYT0GPttChX+hwDAFYvXodq1ev+zOTL716BRme\nQWVb6DJ26nAcuVvCvkT69h1kWL17UTVe+Pr9g8ZNHqJzh8FUc63C/uTNJOz7jtlvvk9MdJKhWUCf\n8cLV1ZUZM6bSooUvLi4uzJo1naiomAqXAdSegxvdP2ViaYD27dsREbETgNOnT1OtWlWcnZ3JzTVu\nfbeEhP189933AGRlZeHkpGYtykaNHuKHHw4DcP78r3h51atQGcxmM4EBA3htyvjbnw0c8NeVVc86\ntUlK/s6qGZISv+fgD/8EICvrOk6Ojrw8cRZ5ecVrNF3J+IMnnnjUqhlK27Llq9t/9vKqx8WLvxlW\nW8ccZrOZrl17MHXqFCX1QY/9FPRoCx36hQ7HkLvNmDGVgQOHGV5XZVvoMnbqcBy5m8lkMrTevaga\nL5ITD3Loh2MAXMvKxsnZEVtbW2Vtost40aFDW2Jj93Lz5k1u3rzJ2LEvGVpflwyg9hzc6P4pj8Ia\nwMPDg4yMjNs/X7mSiYeHh+E5zGYzACNGDCMyMsrw+gA//XSCTp3aYTKZeOSRhjRo4EX16u4VJoPF\nYiE//98X2e3YsQ3HfkqkRs3qbPziq3v8l+XLbC5enHxoaG+i9yTcPjEymUyMHNWfzZt2WT3D3RIT\n97Ju3WomTZpseG2dctyvjxhJh/0U9GiLEir7hS7HkBItWvhw8eLFOzIZRXVb6DB26nIcKe3Rpk3Y\ntm0Te/dF0759W0Nrl1A5XpT0i8FDg4mOSqSoqIgXR/UjYtdylq96j2quVQzLonofKVG/vhfOzk5s\n3/4l8fFRtG3bukJmKKHyHNzI/il3LBVQeWUvMDCAoaFD6Nypm5L6e/bE0bLlM+zdu5ujR49z6tQZ\nw9tDhwx3i4nZR7PH/Jj7zhu8NmUCC+Z/aPWa3Xu0Z+CgYJ4PGA4U98vlKxewb18KifuNvdoN4OfX\nlscfb8b69Wvw8XnK8Pq65VBJx31ENZ36heptMXz4UNas2aA0QwkVbaHb2FlCxXEE4OzZn3l79jts\n3bqdBg0eJC4+ikYNvSkqKjKkvi66dm9D/4E9CXl+FM2f9OZqZhbHj5/lpUmhTJs+ltdffU9JLlXj\nhclkws3NlaCgvsX9Ii6Shx5qWuEylKbyHNyo/il3LA2QlpZ2x9WiOnVqk56ebniOTp06MnXaFLp2\n6UFOTo7h9UvMmvUubdp0Y+LE13B1dSUj40qFzFAiMLDL7T9v37ab51pa/6S1fYdWTH51JEHPjyAn\np/jxmE8/m8fZs+eZ/95Sq9cvzcenOZ6engAcPXoMOzs73N2NvzumSw5d6LSPqKRDv9DlGFKidWs/\nDhxIUVJbdVvoNHaWpuI4UiI9PZ2tW7cDcP78L1y69DuennUMq6+Ddu2fY9LkEfQOGkNOzg2S9v/A\n8eNnAfgmch+PNm1oWBbV+0iJy5cvc+BA8YWW8+d/ITs7x/CxU4cMJVSegxvZP2ViaYDo6Fh69QoG\nwMfHh9TUNG7cuGFoBhcXF8LmzyOgR0+uX79uaO3SmjXzZtmy4quonTu35/DhHytkhtJmzJzMY82K\nv5fz9NNPcubMz1at5+LizJy5r9E7ZDTXrxcPbi/0CSAvP5+weR9btfa9+Pv7MXnyywDUrFkTZ2cn\nMjMzK2yO0lRdadZtHwF1baFDv9DhGFLCw6MW2dnZyu5GqWwL3cbO0ow+jpTWr98LTHplIgC1atWi\nZs0apKaqeWNwCSPHCxcXZ2bPnUTf3uNv94vP14fjVb/4glQrv6c4eeKcYXl0GS+io+No16740VM3\nNzdFY6f6DKD2HNzo/imPwhogJSWFQ4cOk5SUQFFREePGTTQ8Q58+vXF3d2PT5i8wmUxYLBaGDB5G\namqqoTmOHTuOyWTiwIFYbt40M2jQi4bWV52hefNmhM1/i/pedSkoLCAouAejR73Khx++R0FBIeab\nZkJDJ1g1Q0ivbri5V2PNuveB4gOwZ10Prl3LZvc3a7FYLJw6dY5XX5lj1RwlPv10GStWfMa+fbE4\nODgwbpyaL9frksPHx4fw8PnUr+9FQUEBISFBBAf35tq1a4Zl0GE/BT3aQod+ocMxpETt2h5KvltZ\nQmVb6DJ26nAcKW3nzt1s2PA5gYE9qFSpEmPHTFRy4UHVeBEU0gVXt2qsXrsQkwksFvhiXQSr1szn\nxo2b5ObcZNyYmVbNUJou40V6+iW++iqCAwf2YrFYmDjR+O+n65AB1J6DG90/TRaLxVJuv+3/MJOp\nkuoImNDjO042tmreKKsjWxtH1RGwtzPupQD/Sa75F9URtGGx3FIdAVtbZ9URACgqUvfm0hImkx4P\n5+jQL2xsKquOAMCtW3mqI/CA40OqIwCQX6juKaIShYXGXay5n1uWAtURAKjmbNxb0e8nK/ek6giA\nPuOFDiy31L9IrqpzE9URALiac/S+f6fH0VYIIYQQQgghxN+WTCyFEEIIIYQQQpSJTCyFEEIIIYQQ\nQpSJTCyFEEIIIYQQQpSJTCyFEEIIIYQQQpSJTCyFEEIIIYQQQpSJTCyFEEIIIYQQQpSJrGP5P9Jh\nHUtdmEx2qiNos6annZ2r6ggUFF5RHeFPGlynshi/KPc9mWxVJ8BiKVQdAYBKdm6qI1BQ+IfqCEJD\nNhqsQwxg0WT9RtV0GbN0WANYh/V/AezsqqmOoMWatwAWi/ocJtSfWwAU3bpx37/T4ExQCCGEEEII\nIcTfmUwshRBCCCGEEEKUiUwshRBCCCGEEEKUiUwshRBCCCGEEEKUiUwshRBCCCGEEEKUiUwshRBC\nCCGEEEKUiUwsDRIevoDk5P0kJu6jRYsWFTYDQOXKlTlz5icGDeqvpL6/fyvSL/1CTGwksXHfsPj9\nBYbWb+rdmBMnkhg1aggAy5Yv4uChGKL2bCZqz2Y6d25rWBYHBwc2blxPfHw0yckJdOvW1bDad1Pd\nLwDCwuaRlJzAtylJ9Oz5vLIcOrSFyvHC27sJJ06mMGr0UADq1q1D5DebiYndxu7ITdSoUd3gPN6c\nPXuSMWNGG1pXxxw6ZNDhWObk5MSWLeuIifmahIQ9dOzYTkkO0GO80CGHDv0CYOnSxezfH0VMzE4a\nNXpYSQbV4/fJk98zenTo7c/Gj3+R3NxUHB2NXeJHl/1Uh3Mto8591S9IWAH4+bWiYcOH8fX1p3Hj\nxqxatRxfX/8Kl6HEzJnTyMxUu65cwr5E+vYdZHhdR0cHFi2aQ3x80h2fz5g+j6ioeMPzBAR05+DB\nQ4SHL6ZevXpER0cSGfmN4TlAfb9o3dqfR5s2oZVva1xdXTl85HsiInYoyaK6LVSOF46Ojixa/A7x\n8ftvfzZr9ussX7aG7dt3M2r0UCZNGs0bb8w1LM+SJYuJjTV+/9Qthw4ZdDmWDRkygFOnzjBz5hw8\nPGoRG7uLxx57yvAcoH680CGHLv0iMLA7Vaq44O/fhQYN6rN4cRg9e/Y1NIPq8Xvx4neJj0+4/dmA\nAb2pUaM6qanphmQoTZf9VJdzLSPOfeWOpQHat29HRMROAE6fPk21alVxdjZ2AV4dMgA88kgjGjdu\nrGzyUsJkMimpazbnERgwkPRLvyupf7ctW74iPHwxAF5e9bh48TclOXToFwkJ+3mhdz8AsrKycHJy\nUpJDh7ZQOV6YzWYCevTjUvpf+8j4ca+zfftuAK5kZOLq5mpIlpI8Xbv2ID3d+JMi3XLokEGXY1lm\nZibu7m4AuLm5kpFxxfAMoMd4oUMOXfpFo0YP8cMPhwE4f/5XvLzqGZ5B9fjdo0df0kuN39u37+at\nt+YZUv9uuuynupxrGXHuKxNLA3h4eJCRkXH75ytXMvHw8KhwGQAWLnyPyZNfVzaxK/Fo0yZs27aJ\nvfuiad/euEdPLRYL+fn5//b5mDFDiYraxJq1H+HqWs2wPCUSE/eybt1qJk2abHht0KdfmM1mAEaM\nGEZkZJSSDDq0hcrx4l77SMl2MZlMjB4TypcbtxmS5X55VNAhhw4ZdDmWbd68jfr1vTh16gjx8ZFM\nmTLD8Aygx3ihQw5d+sVPP52gU6d2mEwmHnmkIQ0aeFG9uruhGXQbv2/cuGFI7XvRZT8tofpcy4hz\nX3kUVgHVBwBVGQYO7Me336Zw4cIFZRkAzp79mbdnv8PWrdtp0OBB4uKjaNTQm6KiIiV5NqzfSuYf\nV/np2EkmvzqWN9+czKRJMw3N4OfXlscfb8b69Wvw8TH2MRFd+kWJwMAAhoYOoXOnbobX1q0tSuiQ\nw2Qy8fmaj9kbn0hCQrLqOEITqvpm//4v8OuvF+jePYRmzbxZvvxjnn22jaEZdBkvdMlRmqoMe/bE\n0bLlM+zdu5ujR49z6tQZ5e2hur5KOuynpak81zLq3FcmlgZIS0u742pRnTq1DX+USIcM3bp1pUGD\nB+nRozt163piNpu5ePE39u5N+G//ablKT09n69btAJw//wuXLv2Op2cdLly4aGiOEgkJB27/efeu\naD5YYtwjIz4+zbl8OYPU1FSOHj2GnZ0d7u7uZGZmGpZBl34B0KlTR6ZOm0KXzt3JyckxvL4ubaHD\neHG3FSs/4Mzpc7z77mKlOYRauvTN5557lujoOACOHTtOnTrG3x3TZbzQIYcu/QJg1qx3b//51KnD\nhj9+qVNblGaxWAyvqcN+Cnqcaxl17iuPwhogOjqWXr2CAfDx8SE1Nc3wRwN0yNC//2BatvTH17cN\nK1euZu7ceUomD/36vcCkVyYCUKtWLWrWrEFqaprhOUps/HIZDz5Y/D0Mf//nOHH8lGG1/f39mDz5\nZQBq1qyJs7OToQMd6NMvXFxcCJs/j4AePbl+/brh9UGfttBhvCitX78Q8vLyeeedRcoygD5X/nXI\noSqDLn3z3LmfeeaZ4jsOXl71yM42/kKULuOFDjl06RfNmnmzbNmHAHTu3J7Dh380PIMubXH3GKFi\nzNBhPwU9zrWMOveVO5YGSElJ4dChwyQlJVBUVMS4cRMrZAZd7Ny5mw0bPicwsAeVKlVi7JiJhj0G\n27z5Y4TNf4v6Xp4UFBYSFNydpUtXsX7DJ+Tm3iQ3N5cXR0wyJAvAp58uY8WKz9i3LxYHBwfGjXvJ\nsNq66dOnN+7ubmza/AUmkwmLxcKQwcNITU1VHc1wKseL5j7NmD9/Nl5edSksKCA4OICaNapjzjMT\nHbMNi8XCyZOnefmlNwzJ4+PjQ3j4fOrX96KgoICQkCCCg3tz7do1Q+rrlEOHDLocy5YtW83KlR8T\nF7cbW1tbxoypuGOnDnTpF8eOHcdkMnHgQCw3b5oZNOhFwzOobAsfn8dvj98FBYUEBfUgNjaBjh3b\nUKtWDXbt+pKUlINMnz7HkDy67Kc6nGsZde5rsqi4N/03ZDJVUh1BGyaT+usRJtRfsQewszPu7ZT3\nU1Co5i1n/06DByAsar4n+29MtqoTYLEUqo4AQCU7N9URKChUvxSD0I+NjbFr6t2PxVKgOoIWdBmz\nbG2Nf5vs3YqKclVHAMDOzviXCd7t1q081REAsFjU5zCh/twCoOjW/e+Aa3AmKIQQQgghhBDi70wm\nlkIIIYQQQgghykQmlkIIIYQQQgghykQmlkIIIYQQQgghykQmlkIIIYQQQgghykQmlkIIIYQQQggh\nykQmlkIIIYQQQgghykT9goR/E1qsm2jSIAOarDVl0uOaSEFBhuoIODnWVx0BgNybv6iOoMd+iib7\niCZ0WEOykVuQ6ggAnLu6Q3UEbdZBu6XB2o12GqxXCND4ga6qI3Diuvq+qcM6gcX02Ed0UNulheoI\nXLwapzqCNipVqq46wn+lx9m5EEIIIYQQQoi/LZlYCiGEEEIIIYQoE5lYCiGEEEIIIYQoE5lYCiGE\nEEIIIYQoE5lYCiGEEEIIIYQoE5lYGiQsbB5JyQl8m5JEz57PG17fwcGBjRvXEx8fTXJyAt26qXsL\nnbe3N2fPnmTMmNFK6uvUFqr6xZy5U4iN38TehK30COgAwJgxg/kj6ziOjg6G5ShNdb8A9ftpCR3a\nIjx8AcnJ+0lM3EeLFmreDKg6g33lSsT8sISeffyZ9+EYdu5fwJqIN1kT8Sb+7ZsblkOXMcvfvxXp\nl34hJjaS2LhvWPz+AiU5VPaLpt6NOXHiAKNGDQHgmWdaEBcfQdSeLezYsR43N1erZ2jx3CPsO/0+\nKyJeY8WO13h9Xj8A+o/swKFLy3BwtLd6BgBv7yacOvUDo0cPA8DTsw6xsRHEx+9kw4bl2NkZt/BA\naOggYmMjiYnZTWxsJFevphlWG/Rqi78yGX8cqexgz8crp7Fp53y271lMu45P8/Sz3mzdvZCNEe+x\ncsMsXFycDMtToqIfU4vHraTb49ay5Ys4eCiGqD2bidqzmc6d25ZbLVluxACtW/vzaNMmtPJtjaur\nK4ePfE9EhLGv9g4I6M7Bg4cID19MvXr1iI6OJDLyG0MzADg6OrJkyWJiY+MNr11Cl7ZQ1S/8/J6m\nyaMN6dCuD66uVUn+dgfOzk7UqOlOWtrvVq9/Lzr0Cx32U9CjLfz8WtGw4cP4+vrTuHFjVq1ajq+v\nf4XLMO7VELKuZgNgsUD421+QEHvE0Aygz5gFkLAvkb59BympDWr7haOjI4sWzSU+PvH2ZxMmvkjo\n0PFcuPAbb0yfxLDhA1i44COrZ/kh+TSvDfvk9s89XmiJW3UXLqdftXptKG6L999/j7i4hNufzZo1\nlY8+WkFExC7mzJlOaOgAli9fY0ie1avXsXr1OgD8/Hzp1cu4JYZ0a4uSTCqOIx27PMM/j5xh2Udf\nUcezBhu2zSP7ei4TRr7Hr+fTGfdyHwYM7canH241LFNFP6Y6OjqwaNEc4uOT7vh8xvR5REWVf5vI\nHUsDJCTs54XexVcUs7KycHIy/mrNli1fER6+GAAvr3pcvPib4RkAzGYzXbv2ID09XUl90KctVPWL\nxMTvGTRgwp91r+Pk6Miur2N5e/ZiQ+rfiw79Qof9FPRoi/bt2xERsROA06dPU61aVZydjV3zT3WG\nBg1r81AjT/ZFH8ZkMmEyqVtKWJcxC8CkeD1llf3CbDYTGDCA9Et/XYAbOGA0Fy4Ubw/POrVJTTVm\nv717M8TtOsRH7243pDYUt0X37i9wqVRbtG7ty65dUQDs2rWH9u1bG5antBkzpjJ3bphh9XRsC1XH\nkV0RiSz76CsAPOvWJD01gz/+uIZ79WoAVK32AH/8cd3QTBX9mGo25xEYMPCOccua5I6lQcxmMwAj\nRgwjMjJKWY7ExL14etYhIEDNguEWi4X8/Hwlte+mui1AXb8wm4sXoh4a+gJ79uwjN/et64K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puWw4QMkDu4hIdPpkoVb7KysujYsT0dOnTm8mV1b06bN28+Cxd+wfbtcTg7OzN4sJ7HdkzYFia0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\nLGs6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddc41c6190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(final_confuse_df, annot=True, fmt='g', cmap = 'magma', cbar=None, vmin=0, vmax=300)\n",
    "plt.axis('off')\n",
    "plt.savefig('final_confuse_df.png', bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "results.columns = ['pred_mood']\n",
    "results['pred_mood_map'] = results['pred_mood'].apply(get_mood_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>pred_mood_x</th>\n",
       "      <th>pred_mood_y</th>\n",
       "      <th>pred_mood_map</th>\n",
       "      <th>pred_mood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>(2, 0)</td>\n",
       "      <td>Energizing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>(-2, 0)</td>\n",
       "      <td>Sentimental</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>(2, -1)</td>\n",
       "      <td>Defiant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>Empowering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>(-1, -1)</td>\n",
       "      <td>Cool</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pred_mood_x  pred_mood_y pred_mood_map    pred_mood\n",
       "0            2            0        (2, 0)   Energizing\n",
       "1           -2            0       (-2, 0)  Sentimental\n",
       "2            2           -1       (2, -1)      Defiant\n",
       "3            0            1        (0, 1)   Empowering\n",
       "4           -1           -1      (-1, -1)         Cool"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# sns.heatmap(results['pred_mood_map'], cmap='PuBu', annot=True, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prototypes for Production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JgzZeBgBBmIbjxyNx+rQFy5aZkJkZ5zHvPu5GUFVVHYu/aCg7QRONldOnz6Ow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\nLHIiIoNjkRMRGRyLnIjI4FjkREQG9//3bBNedO1lDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7417be2090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using 25 mood model\n",
    "n = results['pred_mood_map'].value_counts()\n",
    "s = results['pred_mood_map'].value_counts().sum()\n",
    "\n",
    "fig, axes = plt.subplots()\n",
    "for coord, value in n.iteritems():\n",
    "    axes.scatter(coord[0], coord[1], marker='h', s = value, alpha=0.5)\n",
    "    axes.set_label(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aggressive',\n",
       " 'Brooding',\n",
       " 'Cool',\n",
       " 'Defiant',\n",
       " 'Easygoing',\n",
       " 'Empowering',\n",
       " 'Energizing',\n",
       " 'Excited',\n",
       " 'Fiery',\n",
       " 'Gritty',\n",
       " 'Lively',\n",
       " 'Melancholy',\n",
       " 'Peaceful',\n",
       " 'Romantic',\n",
       " 'Rowdy',\n",
       " 'Sensual',\n",
       " 'Sentimental',\n",
       " 'Serious',\n",
       " 'Somber',\n",
       " 'Sophisticated',\n",
       " 'Stirring',\n",
       " 'Tender',\n",
       " 'Upbeat',\n",
       " 'Urgent',\n",
       " 'Yearning']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mood_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vv46Gooz8MVFVK8LCKpCRMQXR0ZFjkHDsDac7HSpyTdPw+uuvo7q6Gna7Ha++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\nwbHIiYgExyInIhIci5yISHD/D+Ne5UqcM2muAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74176ce210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using model_x * model_y\n",
    "n = results2['pred_mood_map'].value_counts()\n",
    "s = results2['pred_mood_map'].value_counts().sum()\n",
    "\n",
    "colors = np.random.rand(len(n))\n",
    "\n",
    "fig, axes = plt.subplots()\n",
    "\n",
    "for coord, value in n.iteritems():\n",
    "    axes.scatter(coord[0], coord[1], marker='h', s = value, alpha=0.5)\n",
    "    axes.set_label(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggressive\n",
      "Brooding\n",
      "Cool\n",
      "Defiant\n",
      "Easygoing\n",
      "Empowering\n",
      "Energizing\n",
      "Excited\n",
      "Fiery\n",
      "Gritty\n",
      "Lively\n",
      "Melancholy\n",
      "Peaceful\n",
      "Romantic\n",
      "Rowdy\n",
      "Sensual\n",
      "Sentimental\n",
      "Serious\n",
      "Somber\n",
      "Sophisticated\n",
      "Stirring\n",
      "Tender\n",
      "Upbeat\n",
      "Urgent\n",
      "Yearning\n"
     ]
    }
   ],
   "source": [
    "for i in mood_list:\n",
    "    print i"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}