SurveyResultAnalysis_sandbox-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn import preprocessing\n",
    "from sklearn import cluster\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from pathlib import Path\n",
    "import librosa, librosa.display\n",
    "import IPython.lib.display as ipd\n",
    "\n",
    "\n",
    "# Setup Seaborn\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = pd.read_csv('./surveyresult/results_completed_only_trimmed.csv', index_col=0, encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_mapping = {'A1': 1, 'A2': 2, 'A3': 3, 'A4': 4, 'A5':5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ipaddr</th>\n",
       "      <th>beethovenhammerklav[L001]</th>\n",
       "      <th>islamei[L001]</th>\n",
       "      <th>waldstein3[L001]</th>\n",
       "      <th>albse1[L001]</th>\n",
       "      <th>beethovenlesadieux1[L001]</th>\n",
       "      <th>brahmsopus12[L001]</th>\n",
       "      <th>mond3[L001]</th>\n",
       "      <th>albesp1[L001]</th>\n",
       "      <th>bach847[L001]</th>\n",
       "      <th>brim6[L001]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>::ffff:71.59.32.20</td>\n",
       "      <td>A1</td>\n",
       "      <td>A3</td>\n",
       "      <td>A4</td>\n",
       "      <td>A5</td>\n",
       "      <td>A1</td>\n",
       "      <td>A1</td>\n",
       "      <td>A4</td>\n",
       "      <td>A2</td>\n",
       "      <td>A5</td>\n",
       "      <td>A2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>::ffff:24.6.172.180</td>\n",
       "      <td>A1</td>\n",
       "      <td>A4</td>\n",
       "      <td>A4</td>\n",
       "      <td>A3</td>\n",
       "      <td>A2</td>\n",
       "      <td>A2</td>\n",
       "      <td>A3</td>\n",
       "      <td>A4</td>\n",
       "      <td>A5</td>\n",
       "      <td>A3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>::ffff:73.237.197.48</td>\n",
       "      <td>A1</td>\n",
       "      <td>A3</td>\n",
       "      <td>A4</td>\n",
       "      <td>A4</td>\n",
       "      <td>A3</td>\n",
       "      <td>A2</td>\n",
       "      <td>A5</td>\n",
       "      <td>A2</td>\n",
       "      <td>A3</td>\n",
       "      <td>A4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>::ffff:174.218.0.29</td>\n",
       "      <td>A2</td>\n",
       "      <td>A4</td>\n",
       "      <td>A3</td>\n",
       "      <td>A4</td>\n",
       "      <td>A2</td>\n",
       "      <td>A2</td>\n",
       "      <td>A3</td>\n",
       "      <td>A4</td>\n",
       "      <td>A5</td>\n",
       "      <td>A3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>::ffff:73.237.196.242</td>\n",
       "      <td>A1</td>\n",
       "      <td>A3</td>\n",
       "      <td>A4</td>\n",
       "      <td>A3</td>\n",
       "      <td>A2</td>\n",
       "      <td>A1</td>\n",
       "      <td>A2</td>\n",
       "      <td>A2</td>\n",
       "      <td>A3</td>\n",
       "      <td>A3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ipaddr beethovenhammerklav[L001] islamei[L001]  \\\n",
       "id                                                                  \n",
       "11     ::ffff:71.59.32.20                        A1            A3   \n",
       "12    ::ffff:24.6.172.180                        A1            A4   \n",
       "13   ::ffff:73.237.197.48                        A1            A3   \n",
       "15    ::ffff:174.218.0.29                        A2            A4   \n",
       "16  ::ffff:73.237.196.242                        A1            A3   \n",
       "\n",
       "   waldstein3[L001] albse1[L001] beethovenlesadieux1[L001] brahmsopus12[L001]  \\\n",
       "id                                                                              \n",
       "11               A4           A5                        A1                 A1   \n",
       "12               A4           A3                        A2                 A2   \n",
       "13               A4           A4                        A3                 A2   \n",
       "15               A3           A4                        A2                 A2   \n",
       "16               A4           A3                        A2                 A1   \n",
       "\n",
       "   mond3[L001] albesp1[L001] bach847[L001] brim6[L001]  \n",
       "id                                                      \n",
       "11          A4            A2            A5          A2  \n",
       "12          A3            A4            A5          A3  \n",
       "13          A5            A2            A3          A4  \n",
       "15          A3            A4            A5          A3  \n",
       "16          A2            A2            A3          A3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = results.drop(['ipaddr'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = results.applymap(lambda x: label_mapping[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beethovenhammerklav[L001]    1.571429\n",
       "islamei[L001]                3.678571\n",
       "waldstein3[L001]             3.642857\n",
       "albse1[L001]                 3.821429\n",
       "beethovenlesadieux1[L001]    2.678571\n",
       "brahmsopus12[L001]           1.642857\n",
       "mond3[L001]                  3.464286\n",
       "albesp1[L001]                3.107143\n",
       "bach847[L001]                3.642857\n",
       "brim6[L001]                  3.035714\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame(results.mean().tolist(), columns=['happiness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "selected_files = pd.DataFrame(['beethoven_hammerklavier_3', 'islamei', 'waldstein_3', 'alb_se1', 'beethoven_les_adieux_1', 'brahms_opus1_2', 'mond_3', 'alb_esp1', 'bach_847', 'br_im6'], columns=['file'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "selected_files['file'] = selected_files['file'].apply(lambda x: x + '.mp3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = pd.concat([selected_files, results], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "allr = pd.read_csv('./surveyresult/all.csv',  encoding='latin-1', names=['file', 'happiness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "allr['happiness'] = allr['happiness'].apply(lambda x: label_mapping[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = results.append(allr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results['happiness'] = results['happiness'].round(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results['happiness'] = results.happiness.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results.to_csv('./surveyresult/results.csv', columns=['file', 'happiness'], index=False, encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bpm</th>\n",
       "      <th>zero_crossings</th>\n",
       "      <th>spectral_centroid_avg</th>\n",
       "      <th>spectral_bw_avg</th>\n",
       "      <th>Chroma_A</th>\n",
       "      <th>Chroma_A#</th>\n",
       "      <th>Chroma_B</th>\n",
       "      <th>Chroma_C</th>\n",
       "      <th>Chroma_C#</th>\n",
       "      <th>Chroma_D</th>\n",
       "      <th>...</th>\n",
       "      <th>Chroma_SD_B</th>\n",
       "      <th>Chroma_SD_C</th>\n",
       "      <th>Chroma_SD_C#</th>\n",
       "      <th>Chroma_SD_D</th>\n",
       "      <th>Chroma_SD_D#</th>\n",
       "      <th>Chroma_SD_E</th>\n",
       "      <th>Chroma_SD_F</th>\n",
       "      <th>Chroma_SD_F#</th>\n",
       "      <th>Chroma_SD_G</th>\n",
       "      <th>Chroma_SD_G#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90.666118</td>\n",
       "      <td>725502</td>\n",
       "      <td>476.077863</td>\n",
       "      <td>663.836440</td>\n",
       "      <td>0.311842</td>\n",
       "      <td>0.206367</td>\n",
       "      <td>0.187227</td>\n",
       "      <td>0.267701</td>\n",
       "      <td>0.270091</td>\n",
       "      <td>0.243628</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200215</td>\n",
       "      <td>0.147396</td>\n",
       "      <td>0.195893</td>\n",
       "      <td>0.162455</td>\n",
       "      <td>0.156692</td>\n",
       "      <td>0.177100</td>\n",
       "      <td>0.199258</td>\n",
       "      <td>0.165242</td>\n",
       "      <td>0.114056</td>\n",
       "      <td>0.139364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126.048018</td>\n",
       "      <td>575308</td>\n",
       "      <td>680.998049</td>\n",
       "      <td>780.169102</td>\n",
       "      <td>0.352625</td>\n",
       "      <td>0.343420</td>\n",
       "      <td>0.240992</td>\n",
       "      <td>0.244380</td>\n",
       "      <td>0.250207</td>\n",
       "      <td>0.162909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127714</td>\n",
       "      <td>0.140272</td>\n",
       "      <td>0.168282</td>\n",
       "      <td>0.130241</td>\n",
       "      <td>0.154759</td>\n",
       "      <td>0.125400</td>\n",
       "      <td>0.164967</td>\n",
       "      <td>0.138473</td>\n",
       "      <td>0.152864</td>\n",
       "      <td>0.185501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>105.468750</td>\n",
       "      <td>610616</td>\n",
       "      <td>580.741632</td>\n",
       "      <td>692.782043</td>\n",
       "      <td>0.040398</td>\n",
       "      <td>0.091967</td>\n",
       "      <td>0.236239</td>\n",
       "      <td>0.233721</td>\n",
       "      <td>0.130886</td>\n",
       "      <td>0.171261</td>\n",
       "      <td>...</td>\n",
       "      <td>0.121536</td>\n",
       "      <td>0.132552</td>\n",
       "      <td>0.104888</td>\n",
       "      <td>0.135578</td>\n",
       "      <td>0.123498</td>\n",
       "      <td>0.198634</td>\n",
       "      <td>0.162182</td>\n",
       "      <td>0.099428</td>\n",
       "      <td>0.128543</td>\n",
       "      <td>0.102507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132.512019</td>\n",
       "      <td>317856</td>\n",
       "      <td>643.616803</td>\n",
       "      <td>739.800848</td>\n",
       "      <td>0.261937</td>\n",
       "      <td>0.183222</td>\n",
       "      <td>0.265922</td>\n",
       "      <td>0.421197</td>\n",
       "      <td>0.258747</td>\n",
       "      <td>0.131771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.081097</td>\n",
       "      <td>0.083933</td>\n",
       "      <td>0.060221</td>\n",
       "      <td>0.138567</td>\n",
       "      <td>0.167003</td>\n",
       "      <td>0.109643</td>\n",
       "      <td>0.113225</td>\n",
       "      <td>0.096489</td>\n",
       "      <td>0.100674</td>\n",
       "      <td>0.107103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123.046875</td>\n",
       "      <td>363008</td>\n",
       "      <td>585.345616</td>\n",
       "      <td>724.212805</td>\n",
       "      <td>0.181873</td>\n",
       "      <td>0.281990</td>\n",
       "      <td>0.259810</td>\n",
       "      <td>0.178869</td>\n",
       "      <td>0.133541</td>\n",
       "      <td>0.193422</td>\n",
       "      <td>...</td>\n",
       "      <td>0.169361</td>\n",
       "      <td>0.180378</td>\n",
       "      <td>0.146605</td>\n",
       "      <td>0.137997</td>\n",
       "      <td>0.141499</td>\n",
       "      <td>0.154004</td>\n",
       "      <td>0.170220</td>\n",
       "      <td>0.139611</td>\n",
       "      <td>0.188848</td>\n",
       "      <td>0.154492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>117.453835</td>\n",
       "      <td>314106</td>\n",
       "      <td>582.421503</td>\n",
       "      <td>822.770055</td>\n",
       "      <td>0.072875</td>\n",
       "      <td>0.129332</td>\n",
       "      <td>0.237549</td>\n",
       "      <td>0.328942</td>\n",
       "      <td>0.277713</td>\n",
       "      <td>0.341063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177739</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>0.143668</td>\n",
       "      <td>0.211349</td>\n",
       "      <td>0.201344</td>\n",
       "      <td>0.154176</td>\n",
       "      <td>0.096331</td>\n",
       "      <td>0.116597</td>\n",
       "      <td>0.200906</td>\n",
       "      <td>0.124421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>109.956782</td>\n",
       "      <td>443928</td>\n",
       "      <td>629.878502</td>\n",
       "      <td>683.649064</td>\n",
       "      <td>0.330118</td>\n",
       "      <td>0.159443</td>\n",
       "      <td>0.130828</td>\n",
       "      <td>0.282236</td>\n",
       "      <td>0.298362</td>\n",
       "      <td>0.233987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107528</td>\n",
       "      <td>0.127496</td>\n",
       "      <td>0.139410</td>\n",
       "      <td>0.095967</td>\n",
       "      <td>0.122289</td>\n",
       "      <td>0.127270</td>\n",
       "      <td>0.123494</td>\n",
       "      <td>0.128146</td>\n",
       "      <td>0.083975</td>\n",
       "      <td>0.112199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120.185320</td>\n",
       "      <td>98094</td>\n",
       "      <td>621.574748</td>\n",
       "      <td>881.923976</td>\n",
       "      <td>0.332951</td>\n",
       "      <td>0.259815</td>\n",
       "      <td>0.141876</td>\n",
       "      <td>0.174059</td>\n",
       "      <td>0.235315</td>\n",
       "      <td>0.191668</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175779</td>\n",
       "      <td>0.193223</td>\n",
       "      <td>0.195864</td>\n",
       "      <td>0.210012</td>\n",
       "      <td>0.152202</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>0.167383</td>\n",
       "      <td>0.110378</td>\n",
       "      <td>0.088626</td>\n",
       "      <td>0.167561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>143.554688</td>\n",
       "      <td>210332</td>\n",
       "      <td>780.659917</td>\n",
       "      <td>973.022824</td>\n",
       "      <td>0.205133</td>\n",
       "      <td>0.180064</td>\n",
       "      <td>0.209045</td>\n",
       "      <td>0.212927</td>\n",
       "      <td>0.234177</td>\n",
       "      <td>0.313651</td>\n",
       "      <td>...</td>\n",
       "      <td>0.104775</td>\n",
       "      <td>0.138016</td>\n",
       "      <td>0.117120</td>\n",
       "      <td>0.148757</td>\n",
       "      <td>0.115832</td>\n",
       "      <td>0.095209</td>\n",
       "      <td>0.126109</td>\n",
       "      <td>0.076569</td>\n",
       "      <td>0.122057</td>\n",
       "      <td>0.123116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>95.703125</td>\n",
       "      <td>156344</td>\n",
       "      <td>609.424457</td>\n",
       "      <td>742.435808</td>\n",
       "      <td>0.247062</td>\n",
       "      <td>0.178649</td>\n",
       "      <td>0.198154</td>\n",
       "      <td>0.284025</td>\n",
       "      <td>0.261316</td>\n",
       "      <td>0.221709</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161649</td>\n",
       "      <td>0.106614</td>\n",
       "      <td>0.187461</td>\n",
       "      <td>0.125560</td>\n",
       "      <td>0.149242</td>\n",
       "      <td>0.161210</td>\n",
       "      <td>0.121903</td>\n",
       "      <td>0.171299</td>\n",
       "      <td>0.119107</td>\n",
       "      <td>0.153919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          bpm  zero_crossings  spectral_centroid_avg  spectral_bw_avg  \\\n",
       "0   90.666118          725502             476.077863       663.836440   \n",
       "1  126.048018          575308             680.998049       780.169102   \n",
       "2  105.468750          610616             580.741632       692.782043   \n",
       "3  132.512019          317856             643.616803       739.800848   \n",
       "4  123.046875          363008             585.345616       724.212805   \n",
       "5  117.453835          314106             582.421503       822.770055   \n",
       "6  109.956782          443928             629.878502       683.649064   \n",
       "7  120.185320           98094             621.574748       881.923976   \n",
       "8  143.554688          210332             780.659917       973.022824   \n",
       "9   95.703125          156344             609.424457       742.435808   \n",
       "\n",
       "   Chroma_A  Chroma_A#  Chroma_B  Chroma_C  Chroma_C#  Chroma_D      ...       \\\n",
       "0  0.311842   0.206367  0.187227  0.267701   0.270091  0.243628      ...        \n",
       "1  0.352625   0.343420  0.240992  0.244380   0.250207  0.162909      ...        \n",
       "2  0.040398   0.091967  0.236239  0.233721   0.130886  0.171261      ...        \n",
       "3  0.261937   0.183222  0.265922  0.421197   0.258747  0.131771      ...        \n",
       "4  0.181873   0.281990  0.259810  0.178869   0.133541  0.193422      ...        \n",
       "5  0.072875   0.129332  0.237549  0.328942   0.277713  0.341063      ...        \n",
       "6  0.330118   0.159443  0.130828  0.282236   0.298362  0.233987      ...        \n",
       "7  0.332951   0.259815  0.141876  0.174059   0.235315  0.191668      ...        \n",
       "8  0.205133   0.180064  0.209045  0.212927   0.234177  0.313651      ...        \n",
       "9  0.247062   0.178649  0.198154  0.284025   0.261316  0.221709      ...        \n",
       "\n",
       "   Chroma_SD_B  Chroma_SD_C  Chroma_SD_C#  Chroma_SD_D  Chroma_SD_D#  \\\n",
       "0     0.200215     0.147396      0.195893     0.162455      0.156692   \n",
       "1     0.127714     0.140272      0.168282     0.130241      0.154759   \n",
       "2     0.121536     0.132552      0.104888     0.135578      0.123498   \n",
       "3     0.081097     0.083933      0.060221     0.138567      0.167003   \n",
       "4     0.169361     0.180378      0.146605     0.137997      0.141499   \n",
       "5     0.177739     0.219122      0.143668     0.211349      0.201344   \n",
       "6     0.107528     0.127496      0.139410     0.095967      0.122289   \n",
       "7     0.175779     0.193223      0.195864     0.210012      0.152202   \n",
       "8     0.104775     0.138016      0.117120     0.148757      0.115832   \n",
       "9     0.161649     0.106614      0.187461     0.125560      0.149242   \n",
       "\n",
       "   Chroma_SD_E  Chroma_SD_F  Chroma_SD_F#  Chroma_SD_G  Chroma_SD_G#  \n",
       "0     0.177100     0.199258      0.165242     0.114056      0.139364  \n",
       "1     0.125400     0.164967      0.138473     0.152864      0.185501  \n",
       "2     0.198634     0.162182      0.099428     0.128543      0.102507  \n",
       "3     0.109643     0.113225      0.096489     0.100674      0.107103  \n",
       "4     0.154004     0.170220      0.139611     0.188848      0.154492  \n",
       "5     0.154176     0.096331      0.116597     0.200906      0.124421  \n",
       "6     0.127270     0.123494      0.128146     0.083975      0.112199  \n",
       "7     0.207500     0.167383      0.110378     0.088626      0.167561  \n",
       "8     0.095209     0.126109      0.076569     0.122057      0.123116  \n",
       "9     0.161210     0.121903      0.171299     0.119107      0.153919  \n",
       "\n",
       "[10 rows x 52 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, rando,_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y, X = df.iloc[:, 0].values, df.iloc[:,1:] "
   ]
  }
 ],
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   "name": "python",
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