release-year-prediction-on-msd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['year_prediction.csv']\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt # data visualisation\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"../input/year_prediction.csv\")\n",
    "data = data.rename(index=str, columns={\"label\":\"year\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot a histogram to understand how evenly spread the data is by viewing number of songs we have for a given year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of songs')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nsongs = {}\n",
    "for y in range(1922,2012):\n",
    "    nsongs[y] = len(data[data.year==y])\n",
    "yrs = range(1922,2011)\n",
    "values = [nsongs[y] for y in yrs]\n",
    "plt.bar(yrs, values, align='center')\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of songs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate input attributes and output into different dataframes\n",
    "X = data.iloc[:,1:]\n",
    "Y = data.iloc[:,0]\n",
    "\n",
    "\n",
    "\n",
    "# Train set\n",
    "X_train = X.iloc[0:463715,:]\n",
    "y_train = Y.iloc[0:463715]\n",
    "\n",
    "# Validation set\n",
    "X_test = X.iloc[463715:,:]\n",
    "y_test = Y.iloc[463715:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of the features takes a wide range of different values and distributions.\n",
    "\n",
    "We apply MinMax scaling to our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Fit on training set only.\n",
    "scaler.fit(X_train)\n",
    "# Apply transform to both the train set and the test set.\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "X_train = pd.DataFrame(X_train_scaled,columns=X_train.columns)\n",
    "X_test = pd.DataFrame(X_test_scaled,columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimbreAvg1</th>\n",
       "      <th>TimbreAvg2</th>\n",
       "      <th>TimbreAvg3</th>\n",
       "      <th>TimbreAvg4</th>\n",
       "      <th>TimbreAvg5</th>\n",
       "      <th>TimbreAvg6</th>\n",
       "      <th>TimbreAvg7</th>\n",
       "      <th>TimbreAvg8</th>\n",
       "      <th>TimbreAvg9</th>\n",
       "      <th>TimbreAvg10</th>\n",
       "      <th>TimbreAvg11</th>\n",
       "      <th>TimbreAvg12</th>\n",
       "      <th>TimbreCovariance1</th>\n",
       "      <th>TimbreCovariance2</th>\n",
       "      <th>TimbreCovariance3</th>\n",
       "      <th>TimbreCovariance4</th>\n",
       "      <th>TimbreCovariance5</th>\n",
       "      <th>TimbreCovariance6</th>\n",
       "      <th>TimbreCovariance7</th>\n",
       "      <th>TimbreCovariance8</th>\n",
       "      <th>TimbreCovariance9</th>\n",
       "      <th>TimbreCovariance10</th>\n",
       "      <th>TimbreCovariance11</th>\n",
       "      <th>TimbreCovariance12</th>\n",
       "      <th>TimbreCovariance13</th>\n",
       "      <th>TimbreCovariance14</th>\n",
       "      <th>TimbreCovariance15</th>\n",
       "      <th>TimbreCovariance16</th>\n",
       "      <th>TimbreCovariance17</th>\n",
       "      <th>TimbreCovariance18</th>\n",
       "      <th>TimbreCovariance19</th>\n",
       "      <th>TimbreCovariance20</th>\n",
       "      <th>TimbreCovariance21</th>\n",
       "      <th>TimbreCovariance22</th>\n",
       "      <th>TimbreCovariance23</th>\n",
       "      <th>TimbreCovariance24</th>\n",
       "      <th>TimbreCovariance25</th>\n",
       "      <th>TimbreCovariance26</th>\n",
       "      <th>TimbreCovariance27</th>\n",
       "      <th>TimbreCovariance28</th>\n",
       "      <th>...</th>\n",
       "      <th>TimbreCovariance39</th>\n",
       "      <th>TimbreCovariance40</th>\n",
       "      <th>TimbreCovariance41</th>\n",
       "      <th>TimbreCovariance42</th>\n",
       "      <th>TimbreCovariance43</th>\n",
       "      <th>TimbreCovariance44</th>\n",
       "      <th>TimbreCovariance45</th>\n",
       "      <th>TimbreCovariance46</th>\n",
       "      <th>TimbreCovariance47</th>\n",
       "      <th>TimbreCovariance48</th>\n",
       "      <th>TimbreCovariance49</th>\n",
       "      <th>TimbreCovariance50</th>\n",
       "      <th>TimbreCovariance51</th>\n",
       "      <th>TimbreCovariance52</th>\n",
       "      <th>TimbreCovariance53</th>\n",
       "      <th>TimbreCovariance54</th>\n",
       "      <th>TimbreCovariance55</th>\n",
       "      <th>TimbreCovariance56</th>\n",
       "      <th>TimbreCovariance57</th>\n",
       "      <th>TimbreCovariance58</th>\n",
       "      <th>TimbreCovariance59</th>\n",
       "      <th>TimbreCovariance60</th>\n",
       "      <th>TimbreCovariance61</th>\n",
       "      <th>TimbreCovariance62</th>\n",
       "      <th>TimbreCovariance63</th>\n",
       "      <th>TimbreCovariance64</th>\n",
       "      <th>TimbreCovariance65</th>\n",
       "      <th>TimbreCovariance66</th>\n",
       "      <th>TimbreCovariance67</th>\n",
       "      <th>TimbreCovariance68</th>\n",
       "      <th>TimbreCovariance69</th>\n",
       "      <th>TimbreCovariance70</th>\n",
       "      <th>TimbreCovariance71</th>\n",
       "      <th>TimbreCovariance72</th>\n",
       "      <th>TimbreCovariance73</th>\n",
       "      <th>TimbreCovariance74</th>\n",
       "      <th>TimbreCovariance75</th>\n",
       "      <th>TimbreCovariance76</th>\n",
       "      <th>TimbreCovariance77</th>\n",
       "      <th>TimbreCovariance78</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "      <td>463715.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.691393</td>\n",
       "      <td>0.469181</td>\n",
       "      <td>0.496357</td>\n",
       "      <td>0.350035</td>\n",
       "      <td>0.395117</td>\n",
       "      <td>0.358260</td>\n",
       "      <td>0.515310</td>\n",
       "      <td>0.397887</td>\n",
       "      <td>0.477290</td>\n",
       "      <td>0.426607</td>\n",
       "      <td>0.691819</td>\n",
       "      <td>0.530809</td>\n",
       "      <td>0.061015</td>\n",
       "      <td>0.036932</td>\n",
       "      <td>0.052829</td>\n",
       "      <td>0.046941</td>\n",
       "      <td>0.045245</td>\n",
       "      <td>0.051828</td>\n",
       "      <td>0.054789</td>\n",
       "      <td>0.053319</td>\n",
       "      <td>0.040334</td>\n",
       "      <td>0.083562</td>\n",
       "      <td>0.041914</td>\n",
       "      <td>0.029258</td>\n",
       "      <td>0.588071</td>\n",
       "      <td>0.354463</td>\n",
       "      <td>0.451344</td>\n",
       "      <td>0.555480</td>\n",
       "      <td>0.508205</td>\n",
       "      <td>0.473871</td>\n",
       "      <td>0.676466</td>\n",
       "      <td>0.443812</td>\n",
       "      <td>0.369822</td>\n",
       "      <td>0.404615</td>\n",
       "      <td>0.353309</td>\n",
       "      <td>0.424043</td>\n",
       "      <td>0.371572</td>\n",
       "      <td>0.562552</td>\n",
       "      <td>0.605844</td>\n",
       "      <td>0.486739</td>\n",
       "      <td>...</td>\n",
       "      <td>0.615812</td>\n",
       "      <td>0.421879</td>\n",
       "      <td>0.573337</td>\n",
       "      <td>0.431647</td>\n",
       "      <td>0.744069</td>\n",
       "      <td>0.400336</td>\n",
       "      <td>0.372550</td>\n",
       "      <td>0.266772</td>\n",
       "      <td>0.362311</td>\n",
       "      <td>0.301800</td>\n",
       "      <td>0.512831</td>\n",
       "      <td>0.537379</td>\n",
       "      <td>0.417522</td>\n",
       "      <td>0.477130</td>\n",
       "      <td>0.719306</td>\n",
       "      <td>0.530612</td>\n",
       "      <td>0.369416</td>\n",
       "      <td>0.353011</td>\n",
       "      <td>0.269319</td>\n",
       "      <td>0.554657</td>\n",
       "      <td>0.639893</td>\n",
       "      <td>0.595036</td>\n",
       "      <td>0.795590</td>\n",
       "      <td>0.521606</td>\n",
       "      <td>0.512374</td>\n",
       "      <td>0.488463</td>\n",
       "      <td>0.494068</td>\n",
       "      <td>0.376795</td>\n",
       "      <td>0.631798</td>\n",
       "      <td>0.563245</td>\n",
       "      <td>0.354631</td>\n",
       "      <td>0.487984</td>\n",
       "      <td>0.368845</td>\n",
       "      <td>0.657332</td>\n",
       "      <td>0.568014</td>\n",
       "      <td>0.466481</td>\n",
       "      <td>0.600205</td>\n",
       "      <td>0.343815</td>\n",
       "      <td>0.503517</td>\n",
       "      <td>0.347715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.100947</td>\n",
       "      <td>0.071570</td>\n",
       "      <td>0.056527</td>\n",
       "      <td>0.036814</td>\n",
       "      <td>0.051474</td>\n",
       "      <td>0.063671</td>\n",
       "      <td>0.040431</td>\n",
       "      <td>0.044802</td>\n",
       "      <td>0.038783</td>\n",
       "      <td>0.063964</td>\n",
       "      <td>0.043463</td>\n",
       "      <td>0.045744</td>\n",
       "      <td>0.040440</td>\n",
       "      <td>0.026595</td>\n",
       "      <td>0.034290</td>\n",
       "      <td>0.034337</td>\n",
       "      <td>0.023954</td>\n",
       "      <td>0.034274</td>\n",
       "      <td>0.029750</td>\n",
       "      <td>0.032271</td>\n",
       "      <td>0.022275</td>\n",
       "      <td>0.044558</td>\n",
       "      <td>0.027650</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>0.024969</td>\n",
       "      <td>0.018895</td>\n",
       "      <td>0.020548</td>\n",
       "      <td>0.028204</td>\n",
       "      <td>0.024609</td>\n",
       "      <td>0.020223</td>\n",
       "      <td>0.017601</td>\n",
       "      <td>0.020666</td>\n",
       "      <td>0.016301</td>\n",
       "      <td>0.013634</td>\n",
       "      <td>0.015131</td>\n",
       "      <td>0.025654</td>\n",
       "      <td>0.018390</td>\n",
       "      <td>0.024946</td>\n",
       "      <td>0.016436</td>\n",
       "      <td>0.012650</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025309</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.012536</td>\n",
       "      <td>0.016366</td>\n",
       "      <td>0.025507</td>\n",
       "      <td>0.023793</td>\n",
       "      <td>0.022147</td>\n",
       "      <td>0.015102</td>\n",
       "      <td>0.020012</td>\n",
       "      <td>0.017587</td>\n",
       "      <td>0.015973</td>\n",
       "      <td>0.023356</td>\n",
       "      <td>0.026971</td>\n",
       "      <td>0.014372</td>\n",
       "      <td>0.021669</td>\n",
       "      <td>0.017463</td>\n",
       "      <td>0.030666</td>\n",
       "      <td>0.015855</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0.021925</td>\n",
       "      <td>0.022821</td>\n",
       "      <td>0.019606</td>\n",
       "      <td>0.015579</td>\n",
       "      <td>0.018304</td>\n",
       "      <td>0.024269</td>\n",
       "      <td>0.018045</td>\n",
       "      <td>0.013502</td>\n",
       "      <td>0.026496</td>\n",
       "      <td>0.015304</td>\n",
       "      <td>0.014604</td>\n",
       "      <td>0.025093</td>\n",
       "      <td>0.019768</td>\n",
       "      <td>0.024255</td>\n",
       "      <td>0.019886</td>\n",
       "      <td>0.026819</td>\n",
       "      <td>0.016738</td>\n",
       "      <td>0.024350</td>\n",
       "      <td>0.019045</td>\n",
       "      <td>0.012446</td>\n",
       "      <td>0.023989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.634471</td>\n",
       "      <td>0.431166</td>\n",
       "      <td>0.464150</td>\n",
       "      <td>0.328296</td>\n",
       "      <td>0.363307</td>\n",
       "      <td>0.314100</td>\n",
       "      <td>0.492040</td>\n",
       "      <td>0.371622</td>\n",
       "      <td>0.455227</td>\n",
       "      <td>0.384236</td>\n",
       "      <td>0.666902</td>\n",
       "      <td>0.502762</td>\n",
       "      <td>0.032903</td>\n",
       "      <td>0.019860</td>\n",
       "      <td>0.029653</td>\n",
       "      <td>0.024999</td>\n",
       "      <td>0.029936</td>\n",
       "      <td>0.028599</td>\n",
       "      <td>0.034962</td>\n",
       "      <td>0.032599</td>\n",
       "      <td>0.026155</td>\n",
       "      <td>0.053510</td>\n",
       "      <td>0.023966</td>\n",
       "      <td>0.019620</td>\n",
       "      <td>0.576851</td>\n",
       "      <td>0.345704</td>\n",
       "      <td>0.443649</td>\n",
       "      <td>0.543756</td>\n",
       "      <td>0.496901</td>\n",
       "      <td>0.463525</td>\n",
       "      <td>0.668716</td>\n",
       "      <td>0.435032</td>\n",
       "      <td>0.362304</td>\n",
       "      <td>0.398372</td>\n",
       "      <td>0.347068</td>\n",
       "      <td>0.410214</td>\n",
       "      <td>0.363964</td>\n",
       "      <td>0.550613</td>\n",
       "      <td>0.599561</td>\n",
       "      <td>0.481041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.603614</td>\n",
       "      <td>0.412993</td>\n",
       "      <td>0.567825</td>\n",
       "      <td>0.424660</td>\n",
       "      <td>0.735812</td>\n",
       "      <td>0.388816</td>\n",
       "      <td>0.362327</td>\n",
       "      <td>0.260170</td>\n",
       "      <td>0.352446</td>\n",
       "      <td>0.293710</td>\n",
       "      <td>0.505172</td>\n",
       "      <td>0.526958</td>\n",
       "      <td>0.404470</td>\n",
       "      <td>0.471317</td>\n",
       "      <td>0.710898</td>\n",
       "      <td>0.523496</td>\n",
       "      <td>0.354213</td>\n",
       "      <td>0.346601</td>\n",
       "      <td>0.262169</td>\n",
       "      <td>0.545554</td>\n",
       "      <td>0.631992</td>\n",
       "      <td>0.586874</td>\n",
       "      <td>0.789989</td>\n",
       "      <td>0.512644</td>\n",
       "      <td>0.501426</td>\n",
       "      <td>0.481987</td>\n",
       "      <td>0.487710</td>\n",
       "      <td>0.365284</td>\n",
       "      <td>0.627335</td>\n",
       "      <td>0.556783</td>\n",
       "      <td>0.340913</td>\n",
       "      <td>0.480545</td>\n",
       "      <td>0.356429</td>\n",
       "      <td>0.648426</td>\n",
       "      <td>0.556221</td>\n",
       "      <td>0.459283</td>\n",
       "      <td>0.589716</td>\n",
       "      <td>0.333782</td>\n",
       "      <td>0.498175</td>\n",
       "      <td>0.336685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.705958</td>\n",
       "      <td>0.479041</td>\n",
       "      <td>0.499274</td>\n",
       "      <td>0.345928</td>\n",
       "      <td>0.396288</td>\n",
       "      <td>0.350109</td>\n",
       "      <td>0.516244</td>\n",
       "      <td>0.398215</td>\n",
       "      <td>0.477664</td>\n",
       "      <td>0.425654</td>\n",
       "      <td>0.692281</td>\n",
       "      <td>0.529513</td>\n",
       "      <td>0.052644</td>\n",
       "      <td>0.030402</td>\n",
       "      <td>0.045217</td>\n",
       "      <td>0.038357</td>\n",
       "      <td>0.040531</td>\n",
       "      <td>0.043547</td>\n",
       "      <td>0.048959</td>\n",
       "      <td>0.045778</td>\n",
       "      <td>0.035742</td>\n",
       "      <td>0.074719</td>\n",
       "      <td>0.034995</td>\n",
       "      <td>0.026409</td>\n",
       "      <td>0.584910</td>\n",
       "      <td>0.352183</td>\n",
       "      <td>0.451626</td>\n",
       "      <td>0.556034</td>\n",
       "      <td>0.507122</td>\n",
       "      <td>0.471686</td>\n",
       "      <td>0.676283</td>\n",
       "      <td>0.443550</td>\n",
       "      <td>0.369430</td>\n",
       "      <td>0.403198</td>\n",
       "      <td>0.354251</td>\n",
       "      <td>0.420130</td>\n",
       "      <td>0.370622</td>\n",
       "      <td>0.560477</td>\n",
       "      <td>0.607698</td>\n",
       "      <td>0.486623</td>\n",
       "      <td>...</td>\n",
       "      <td>0.615471</td>\n",
       "      <td>0.421269</td>\n",
       "      <td>0.572661</td>\n",
       "      <td>0.431431</td>\n",
       "      <td>0.747703</td>\n",
       "      <td>0.397364</td>\n",
       "      <td>0.371202</td>\n",
       "      <td>0.265863</td>\n",
       "      <td>0.361004</td>\n",
       "      <td>0.301399</td>\n",
       "      <td>0.511876</td>\n",
       "      <td>0.538022</td>\n",
       "      <td>0.415502</td>\n",
       "      <td>0.478171</td>\n",
       "      <td>0.720191</td>\n",
       "      <td>0.531227</td>\n",
       "      <td>0.369155</td>\n",
       "      <td>0.351679</td>\n",
       "      <td>0.267990</td>\n",
       "      <td>0.554698</td>\n",
       "      <td>0.642040</td>\n",
       "      <td>0.595653</td>\n",
       "      <td>0.796303</td>\n",
       "      <td>0.521472</td>\n",
       "      <td>0.514188</td>\n",
       "      <td>0.490602</td>\n",
       "      <td>0.492406</td>\n",
       "      <td>0.378191</td>\n",
       "      <td>0.634349</td>\n",
       "      <td>0.563230</td>\n",
       "      <td>0.349484</td>\n",
       "      <td>0.490259</td>\n",
       "      <td>0.366313</td>\n",
       "      <td>0.656434</td>\n",
       "      <td>0.568816</td>\n",
       "      <td>0.466181</td>\n",
       "      <td>0.600938</td>\n",
       "      <td>0.341908</td>\n",
       "      <td>0.502698</td>\n",
       "      <td>0.346333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.765257</td>\n",
       "      <td>0.517551</td>\n",
       "      <td>0.530164</td>\n",
       "      <td>0.367222</td>\n",
       "      <td>0.427238</td>\n",
       "      <td>0.393690</td>\n",
       "      <td>0.539991</td>\n",
       "      <td>0.424327</td>\n",
       "      <td>0.500152</td>\n",
       "      <td>0.468346</td>\n",
       "      <td>0.717497</td>\n",
       "      <td>0.557309</td>\n",
       "      <td>0.078641</td>\n",
       "      <td>0.046080</td>\n",
       "      <td>0.066969</td>\n",
       "      <td>0.058735</td>\n",
       "      <td>0.054802</td>\n",
       "      <td>0.065766</td>\n",
       "      <td>0.067635</td>\n",
       "      <td>0.064731</td>\n",
       "      <td>0.048880</td>\n",
       "      <td>0.102909</td>\n",
       "      <td>0.051911</td>\n",
       "      <td>0.035122</td>\n",
       "      <td>0.597456</td>\n",
       "      <td>0.360806</td>\n",
       "      <td>0.459407</td>\n",
       "      <td>0.567574</td>\n",
       "      <td>0.518682</td>\n",
       "      <td>0.481778</td>\n",
       "      <td>0.684181</td>\n",
       "      <td>0.452904</td>\n",
       "      <td>0.376703</td>\n",
       "      <td>0.408807</td>\n",
       "      <td>0.360523</td>\n",
       "      <td>0.434532</td>\n",
       "      <td>0.377622</td>\n",
       "      <td>0.572979</td>\n",
       "      <td>0.614005</td>\n",
       "      <td>0.492403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.627763</td>\n",
       "      <td>0.429982</td>\n",
       "      <td>0.578105</td>\n",
       "      <td>0.438619</td>\n",
       "      <td>0.756769</td>\n",
       "      <td>0.408971</td>\n",
       "      <td>0.381147</td>\n",
       "      <td>0.271646</td>\n",
       "      <td>0.370888</td>\n",
       "      <td>0.308952</td>\n",
       "      <td>0.519270</td>\n",
       "      <td>0.548485</td>\n",
       "      <td>0.428473</td>\n",
       "      <td>0.483641</td>\n",
       "      <td>0.729664</td>\n",
       "      <td>0.537933</td>\n",
       "      <td>0.384313</td>\n",
       "      <td>0.357536</td>\n",
       "      <td>0.274986</td>\n",
       "      <td>0.563966</td>\n",
       "      <td>0.650647</td>\n",
       "      <td>0.603455</td>\n",
       "      <td>0.802214</td>\n",
       "      <td>0.530068</td>\n",
       "      <td>0.524763</td>\n",
       "      <td>0.497455</td>\n",
       "      <td>0.498366</td>\n",
       "      <td>0.390441</td>\n",
       "      <td>0.639235</td>\n",
       "      <td>0.570163</td>\n",
       "      <td>0.362847</td>\n",
       "      <td>0.497755</td>\n",
       "      <td>0.378403</td>\n",
       "      <td>0.665694</td>\n",
       "      <td>0.581546</td>\n",
       "      <td>0.473821</td>\n",
       "      <td>0.611202</td>\n",
       "      <td>0.351684</td>\n",
       "      <td>0.507992</td>\n",
       "      <td>0.356798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          TimbreAvg1         ...          TimbreCovariance78\n",
       "count  463715.000000         ...               463715.000000\n",
       "mean        0.691393         ...                    0.347715\n",
       "std         0.100947         ...                    0.023989\n",
       "min         0.000000         ...                    0.000000\n",
       "25%         0.634471         ...                    0.336685\n",
       "50%         0.705958         ...                    0.346333\n",
       "75%         0.765257         ...                    0.356798\n",
       "max         1.000000         ...                    1.000000\n",
       "\n",
       "[8 rows x 90 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90 features is lot of features and so we attempt dimensionality reduction by implementing PCA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we normalise our data using scikits StandardScalar.\n",
    "This is a necessary step for pca:\n",
    "> Principle Component Analysis (PCA) as being a prime example of when normalization is important. In PCA we are interested in the components that maximize the variance. If one component (e.g. human height) varies less than another (e.g. weight) because of their respective scales (meters vs. kilos), PCA might determine that the direction of maximal variance more closely corresponds with the ‘weight’ axis, if those features are not scaled. As a change in height of one meter can be considered much more important than the change in weight of one kilogram, this is clearly incorrect.\n",
    "(https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html#sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "# Fit on training set only.\n",
    "scaler.fit(X_train)\n",
    "# Apply transform to both the train set and the test set.\n",
    "X_train_std = scaler.transform(X_train)\n",
    "X_test_std = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimbreAvg1</th>\n",
       "      <th>TimbreAvg2</th>\n",
       "      <th>TimbreAvg3</th>\n",
       "      <th>TimbreAvg4</th>\n",
       "      <th>TimbreAvg5</th>\n",
       "      <th>TimbreAvg6</th>\n",
       "      <th>TimbreAvg7</th>\n",
       "      <th>TimbreAvg8</th>\n",
       "      <th>TimbreAvg9</th>\n",
       "      <th>TimbreAvg10</th>\n",
       "      <th>TimbreAvg11</th>\n",
       "      <th>TimbreAvg12</th>\n",
       "      <th>TimbreCovariance1</th>\n",
       "      <th>TimbreCovariance2</th>\n",
       "      <th>TimbreCovariance3</th>\n",
       "      <th>TimbreCovariance4</th>\n",
       "      <th>TimbreCovariance5</th>\n",
       "      <th>TimbreCovariance6</th>\n",
       "      <th>TimbreCovariance7</th>\n",
       "      <th>TimbreCovariance8</th>\n",
       "      <th>TimbreCovariance9</th>\n",
       "      <th>TimbreCovariance10</th>\n",
       "      <th>TimbreCovariance11</th>\n",
       "      <th>TimbreCovariance12</th>\n",
       "      <th>TimbreCovariance13</th>\n",
       "      <th>TimbreCovariance14</th>\n",
       "      <th>TimbreCovariance15</th>\n",
       "      <th>TimbreCovariance16</th>\n",
       "      <th>TimbreCovariance17</th>\n",
       "      <th>TimbreCovariance18</th>\n",
       "      <th>TimbreCovariance19</th>\n",
       "      <th>TimbreCovariance20</th>\n",
       "      <th>TimbreCovariance21</th>\n",
       "      <th>TimbreCovariance22</th>\n",
       "      <th>TimbreCovariance23</th>\n",
       "      <th>TimbreCovariance24</th>\n",
       "      <th>TimbreCovariance25</th>\n",
       "      <th>TimbreCovariance26</th>\n",
       "      <th>TimbreCovariance27</th>\n",
       "      <th>TimbreCovariance28</th>\n",
       "      <th>...</th>\n",
       "      <th>TimbreCovariance39</th>\n",
       "      <th>TimbreCovariance40</th>\n",
       "      <th>TimbreCovariance41</th>\n",
       "      <th>TimbreCovariance42</th>\n",
       "      <th>TimbreCovariance43</th>\n",
       "      <th>TimbreCovariance44</th>\n",
       "      <th>TimbreCovariance45</th>\n",
       "      <th>TimbreCovariance46</th>\n",
       "      <th>TimbreCovariance47</th>\n",
       "      <th>TimbreCovariance48</th>\n",
       "      <th>TimbreCovariance49</th>\n",
       "      <th>TimbreCovariance50</th>\n",
       "      <th>TimbreCovariance51</th>\n",
       "      <th>TimbreCovariance52</th>\n",
       "      <th>TimbreCovariance53</th>\n",
       "      <th>TimbreCovariance54</th>\n",
       "      <th>TimbreCovariance55</th>\n",
       "      <th>TimbreCovariance56</th>\n",
       "      <th>TimbreCovariance57</th>\n",
       "      <th>TimbreCovariance58</th>\n",
       "      <th>TimbreCovariance59</th>\n",
       "      <th>TimbreCovariance60</th>\n",
       "      <th>TimbreCovariance61</th>\n",
       "      <th>TimbreCovariance62</th>\n",
       "      <th>TimbreCovariance63</th>\n",
       "      <th>TimbreCovariance64</th>\n",
       "      <th>TimbreCovariance65</th>\n",
       "      <th>TimbreCovariance66</th>\n",
       "      <th>TimbreCovariance67</th>\n",
       "      <th>TimbreCovariance68</th>\n",
       "      <th>TimbreCovariance69</th>\n",
       "      <th>TimbreCovariance70</th>\n",
       "      <th>TimbreCovariance71</th>\n",
       "      <th>TimbreCovariance72</th>\n",
       "      <th>TimbreCovariance73</th>\n",
       "      <th>TimbreCovariance74</th>\n",
       "      <th>TimbreCovariance75</th>\n",
       "      <th>TimbreCovariance76</th>\n",
       "      <th>TimbreCovariance77</th>\n",
       "      <th>TimbreCovariance78</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "      <td>4.637150e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.860182e-16</td>\n",
       "      <td>-2.091763e-15</td>\n",
       "      <td>1.355798e-16</td>\n",
       "      <td>-1.357427e-15</td>\n",
       "      <td>-8.698030e-16</td>\n",
       "      <td>1.346587e-15</td>\n",
       "      <td>-5.625596e-16</td>\n",
       "      <td>4.602650e-16</td>\n",
       "      <td>-3.256487e-16</td>\n",
       "      <td>1.911129e-15</td>\n",
       "      <td>4.018197e-15</td>\n",
       "      <td>-2.502608e-15</td>\n",
       "      <td>-6.190568e-17</td>\n",
       "      <td>-1.069821e-17</td>\n",
       "      <td>-3.529729e-16</td>\n",
       "      <td>-9.763661e-17</td>\n",
       "      <td>-2.724692e-16</td>\n",
       "      <td>2.504086e-16</td>\n",
       "      <td>8.627746e-16</td>\n",
       "      <td>4.121119e-16</td>\n",
       "      <td>-6.631095e-16</td>\n",
       "      <td>3.394529e-16</td>\n",
       "      <td>-7.586569e-16</td>\n",
       "      <td>-8.156104e-17</td>\n",
       "      <td>-8.968334e-15</td>\n",
       "      <td>-2.391046e-15</td>\n",
       "      <td>6.454967e-15</td>\n",
       "      <td>-2.494976e-15</td>\n",
       "      <td>4.325894e-15</td>\n",
       "      <td>2.140151e-17</td>\n",
       "      <td>5.876518e-15</td>\n",
       "      <td>-1.396214e-15</td>\n",
       "      <td>3.546603e-15</td>\n",
       "      <td>1.237439e-14</td>\n",
       "      <td>1.326574e-14</td>\n",
       "      <td>-1.499921e-15</td>\n",
       "      <td>-2.790937e-15</td>\n",
       "      <td>-4.764826e-16</td>\n",
       "      <td>8.122309e-15</td>\n",
       "      <td>5.200963e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.736401e-16</td>\n",
       "      <td>8.136067e-15</td>\n",
       "      <td>2.407077e-14</td>\n",
       "      <td>5.731586e-15</td>\n",
       "      <td>1.305549e-15</td>\n",
       "      <td>5.385272e-16</td>\n",
       "      <td>1.614794e-15</td>\n",
       "      <td>-3.452024e-15</td>\n",
       "      <td>-1.443823e-15</td>\n",
       "      <td>1.322732e-17</td>\n",
       "      <td>2.803837e-15</td>\n",
       "      <td>8.785695e-15</td>\n",
       "      <td>-9.397954e-16</td>\n",
       "      <td>-6.418378e-15</td>\n",
       "      <td>2.609099e-15</td>\n",
       "      <td>-8.714935e-16</td>\n",
       "      <td>2.895461e-15</td>\n",
       "      <td>-1.027189e-14</td>\n",
       "      <td>3.537959e-16</td>\n",
       "      <td>3.607981e-15</td>\n",
       "      <td>-5.811512e-15</td>\n",
       "      <td>3.722570e-15</td>\n",
       "      <td>-4.277726e-15</td>\n",
       "      <td>5.148444e-15</td>\n",
       "      <td>7.730112e-16</td>\n",
       "      <td>-2.349867e-15</td>\n",
       "      <td>-7.733918e-16</td>\n",
       "      <td>2.570563e-15</td>\n",
       "      <td>7.668392e-15</td>\n",
       "      <td>1.063528e-14</td>\n",
       "      <td>1.832125e-16</td>\n",
       "      <td>8.840391e-16</td>\n",
       "      <td>-4.720778e-15</td>\n",
       "      <td>-1.128972e-15</td>\n",
       "      <td>-4.590983e-15</td>\n",
       "      <td>-8.157310e-15</td>\n",
       "      <td>-2.556776e-15</td>\n",
       "      <td>-1.843250e-15</td>\n",
       "      <td>-7.529481e-15</td>\n",
       "      <td>4.832588e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-6.849083e+00</td>\n",
       "      <td>-6.555536e+00</td>\n",
       "      <td>-8.780881e+00</td>\n",
       "      <td>-9.508272e+00</td>\n",
       "      <td>-7.675981e+00</td>\n",
       "      <td>-5.626720e+00</td>\n",
       "      <td>-1.274530e+01</td>\n",
       "      <td>-8.881098e+00</td>\n",
       "      <td>-1.230653e+01</td>\n",
       "      <td>-6.669490e+00</td>\n",
       "      <td>-1.591731e+01</td>\n",
       "      <td>-1.160387e+01</td>\n",
       "      <td>-1.508788e+00</td>\n",
       "      <td>-1.388673e+00</td>\n",
       "      <td>-1.540662e+00</td>\n",
       "      <td>-1.367069e+00</td>\n",
       "      <td>-1.888816e+00</td>\n",
       "      <td>-1.512189e+00</td>\n",
       "      <td>-1.841663e+00</td>\n",
       "      <td>-1.652227e+00</td>\n",
       "      <td>-1.810713e+00</td>\n",
       "      <td>-1.875352e+00</td>\n",
       "      <td>-1.515879e+00</td>\n",
       "      <td>-1.866748e+00</td>\n",
       "      <td>-2.355239e+01</td>\n",
       "      <td>-1.875925e+01</td>\n",
       "      <td>-2.196547e+01</td>\n",
       "      <td>-1.969503e+01</td>\n",
       "      <td>-2.065124e+01</td>\n",
       "      <td>-2.343220e+01</td>\n",
       "      <td>-3.843378e+01</td>\n",
       "      <td>-2.147554e+01</td>\n",
       "      <td>-2.268682e+01</td>\n",
       "      <td>-2.967671e+01</td>\n",
       "      <td>-2.335012e+01</td>\n",
       "      <td>-1.652946e+01</td>\n",
       "      <td>-2.020461e+01</td>\n",
       "      <td>-2.255056e+01</td>\n",
       "      <td>-3.686078e+01</td>\n",
       "      <td>-3.847736e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.433150e+01</td>\n",
       "      <td>-2.362536e+01</td>\n",
       "      <td>-4.573577e+01</td>\n",
       "      <td>-2.637486e+01</td>\n",
       "      <td>-2.917145e+01</td>\n",
       "      <td>-1.682569e+01</td>\n",
       "      <td>-1.682179e+01</td>\n",
       "      <td>-1.766429e+01</td>\n",
       "      <td>-1.810442e+01</td>\n",
       "      <td>-1.715994e+01</td>\n",
       "      <td>-3.210613e+01</td>\n",
       "      <td>-2.300836e+01</td>\n",
       "      <td>-1.548023e+01</td>\n",
       "      <td>-3.319885e+01</td>\n",
       "      <td>-3.319514e+01</td>\n",
       "      <td>-3.038489e+01</td>\n",
       "      <td>-1.204649e+01</td>\n",
       "      <td>-2.226450e+01</td>\n",
       "      <td>-1.829168e+01</td>\n",
       "      <td>-2.529849e+01</td>\n",
       "      <td>-2.803944e+01</td>\n",
       "      <td>-3.034925e+01</td>\n",
       "      <td>-5.106798e+01</td>\n",
       "      <td>-2.849609e+01</td>\n",
       "      <td>-2.111252e+01</td>\n",
       "      <td>-2.706919e+01</td>\n",
       "      <td>-3.659346e+01</td>\n",
       "      <td>-1.422095e+01</td>\n",
       "      <td>-4.128419e+01</td>\n",
       "      <td>-3.856717e+01</td>\n",
       "      <td>-1.413267e+01</td>\n",
       "      <td>-2.468577e+01</td>\n",
       "      <td>-1.520687e+01</td>\n",
       "      <td>-3.305562e+01</td>\n",
       "      <td>-2.117983e+01</td>\n",
       "      <td>-2.786972e+01</td>\n",
       "      <td>-2.464943e+01</td>\n",
       "      <td>-1.805304e+01</td>\n",
       "      <td>-4.045652e+01</td>\n",
       "      <td>-1.449495e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-5.638876e-01</td>\n",
       "      <td>-5.311584e-01</td>\n",
       "      <td>-5.697513e-01</td>\n",
       "      <td>-5.905186e-01</td>\n",
       "      <td>-6.179717e-01</td>\n",
       "      <td>-6.935698e-01</td>\n",
       "      <td>-5.755554e-01</td>\n",
       "      <td>-5.862542e-01</td>\n",
       "      <td>-5.688679e-01</td>\n",
       "      <td>-6.624309e-01</td>\n",
       "      <td>-5.732855e-01</td>\n",
       "      <td>-6.131185e-01</td>\n",
       "      <td>-6.951746e-01</td>\n",
       "      <td>-6.419074e-01</td>\n",
       "      <td>-6.759057e-01</td>\n",
       "      <td>-6.390212e-01</td>\n",
       "      <td>-6.390820e-01</td>\n",
       "      <td>-6.777498e-01</td>\n",
       "      <td>-6.664736e-01</td>\n",
       "      <td>-6.420709e-01</td>\n",
       "      <td>-6.365427e-01</td>\n",
       "      <td>-6.744530e-01</td>\n",
       "      <td>-6.491041e-01</td>\n",
       "      <td>-6.149120e-01</td>\n",
       "      <td>-4.493756e-01</td>\n",
       "      <td>-4.635728e-01</td>\n",
       "      <td>-3.744862e-01</td>\n",
       "      <td>-4.156852e-01</td>\n",
       "      <td>-4.593146e-01</td>\n",
       "      <td>-5.116203e-01</td>\n",
       "      <td>-4.403281e-01</td>\n",
       "      <td>-4.248182e-01</td>\n",
       "      <td>-4.611867e-01</td>\n",
       "      <td>-4.578923e-01</td>\n",
       "      <td>-4.124200e-01</td>\n",
       "      <td>-5.390717e-01</td>\n",
       "      <td>-4.136821e-01</td>\n",
       "      <td>-4.785796e-01</td>\n",
       "      <td>-3.822540e-01</td>\n",
       "      <td>-4.504104e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.819230e-01</td>\n",
       "      <td>-4.976248e-01</td>\n",
       "      <td>-4.397595e-01</td>\n",
       "      <td>-4.269083e-01</td>\n",
       "      <td>-3.237309e-01</td>\n",
       "      <td>-4.841597e-01</td>\n",
       "      <td>-4.615884e-01</td>\n",
       "      <td>-4.371545e-01</td>\n",
       "      <td>-4.929491e-01</td>\n",
       "      <td>-4.600017e-01</td>\n",
       "      <td>-4.795339e-01</td>\n",
       "      <td>-4.462117e-01</td>\n",
       "      <td>-4.839344e-01</td>\n",
       "      <td>-4.044806e-01</td>\n",
       "      <td>-3.880277e-01</td>\n",
       "      <td>-4.075018e-01</td>\n",
       "      <td>-4.957911e-01</td>\n",
       "      <td>-4.042848e-01</td>\n",
       "      <td>-4.855927e-01</td>\n",
       "      <td>-4.151934e-01</td>\n",
       "      <td>-3.462071e-01</td>\n",
       "      <td>-4.163205e-01</td>\n",
       "      <td>-3.595521e-01</td>\n",
       "      <td>-4.896345e-01</td>\n",
       "      <td>-4.511315e-01</td>\n",
       "      <td>-3.588562e-01</td>\n",
       "      <td>-4.708746e-01</td>\n",
       "      <td>-4.344471e-01</td>\n",
       "      <td>-2.916292e-01</td>\n",
       "      <td>-4.425110e-01</td>\n",
       "      <td>-5.466954e-01</td>\n",
       "      <td>-3.763002e-01</td>\n",
       "      <td>-5.118673e-01</td>\n",
       "      <td>-4.478800e-01</td>\n",
       "      <td>-4.397373e-01</td>\n",
       "      <td>-4.299846e-01</td>\n",
       "      <td>-4.307504e-01</td>\n",
       "      <td>-5.268242e-01</td>\n",
       "      <td>-4.292140e-01</td>\n",
       "      <td>-4.598088e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.442775e-01</td>\n",
       "      <td>1.377638e-01</td>\n",
       "      <td>5.161888e-02</td>\n",
       "      <td>-1.115648e-01</td>\n",
       "      <td>2.275066e-02</td>\n",
       "      <td>-1.280171e-01</td>\n",
       "      <td>2.309700e-02</td>\n",
       "      <td>7.320083e-03</td>\n",
       "      <td>9.665689e-03</td>\n",
       "      <td>-1.489907e-02</td>\n",
       "      <td>1.064278e-02</td>\n",
       "      <td>-2.832901e-02</td>\n",
       "      <td>-2.070092e-01</td>\n",
       "      <td>-2.455417e-01</td>\n",
       "      <td>-2.220075e-01</td>\n",
       "      <td>-2.499821e-01</td>\n",
       "      <td>-1.967996e-01</td>\n",
       "      <td>-2.416020e-01</td>\n",
       "      <td>-1.959821e-01</td>\n",
       "      <td>-2.336870e-01</td>\n",
       "      <td>-2.061242e-01</td>\n",
       "      <td>-1.984461e-01</td>\n",
       "      <td>-2.502268e-01</td>\n",
       "      <td>-1.817949e-01</td>\n",
       "      <td>-1.266212e-01</td>\n",
       "      <td>-1.206790e-01</td>\n",
       "      <td>1.372887e-02</td>\n",
       "      <td>1.965026e-02</td>\n",
       "      <td>-4.398765e-02</td>\n",
       "      <td>-1.080744e-01</td>\n",
       "      <td>-1.039974e-02</td>\n",
       "      <td>-1.267623e-02</td>\n",
       "      <td>-2.402420e-02</td>\n",
       "      <td>-1.039665e-01</td>\n",
       "      <td>6.226814e-02</td>\n",
       "      <td>-1.525126e-01</td>\n",
       "      <td>-5.164891e-02</td>\n",
       "      <td>-8.316110e-02</td>\n",
       "      <td>1.128178e-01</td>\n",
       "      <td>-9.200374e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.347085e-02</td>\n",
       "      <td>-3.418526e-02</td>\n",
       "      <td>-5.398361e-02</td>\n",
       "      <td>-1.315678e-02</td>\n",
       "      <td>1.424748e-01</td>\n",
       "      <td>-1.248806e-01</td>\n",
       "      <td>-6.082867e-02</td>\n",
       "      <td>-6.022220e-02</td>\n",
       "      <td>-6.531348e-02</td>\n",
       "      <td>-2.276454e-02</td>\n",
       "      <td>-5.982990e-02</td>\n",
       "      <td>2.751061e-02</td>\n",
       "      <td>-7.488806e-02</td>\n",
       "      <td>7.243625e-02</td>\n",
       "      <td>4.084039e-02</td>\n",
       "      <td>3.523497e-02</td>\n",
       "      <td>-8.535268e-03</td>\n",
       "      <td>-8.404490e-02</td>\n",
       "      <td>-9.020841e-02</td>\n",
       "      <td>1.888310e-03</td>\n",
       "      <td>9.409576e-02</td>\n",
       "      <td>3.146517e-02</td>\n",
       "      <td>4.577475e-02</td>\n",
       "      <td>-7.307116e-03</td>\n",
       "      <td>7.474668e-02</td>\n",
       "      <td>1.185304e-01</td>\n",
       "      <td>-1.230538e-01</td>\n",
       "      <td>5.269330e-02</td>\n",
       "      <td>1.667031e-01</td>\n",
       "      <td>-1.049565e-03</td>\n",
       "      <td>-2.051342e-01</td>\n",
       "      <td>1.151222e-01</td>\n",
       "      <td>-1.043898e-01</td>\n",
       "      <td>-4.517888e-02</td>\n",
       "      <td>2.990574e-02</td>\n",
       "      <td>-1.788398e-02</td>\n",
       "      <td>3.008970e-02</td>\n",
       "      <td>-1.001326e-01</td>\n",
       "      <td>-6.574410e-02</td>\n",
       "      <td>-5.763047e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.317100e-01</td>\n",
       "      <td>6.758454e-01</td>\n",
       "      <td>5.980758e-01</td>\n",
       "      <td>4.668744e-01</td>\n",
       "      <td>6.240130e-01</td>\n",
       "      <td>5.564446e-01</td>\n",
       "      <td>6.104375e-01</td>\n",
       "      <td>5.901695e-01</td>\n",
       "      <td>5.894924e-01</td>\n",
       "      <td>6.525343e-01</td>\n",
       "      <td>5.907976e-01</td>\n",
       "      <td>5.793166e-01</td>\n",
       "      <td>4.358331e-01</td>\n",
       "      <td>3.439870e-01</td>\n",
       "      <td>4.123615e-01</td>\n",
       "      <td>3.435088e-01</td>\n",
       "      <td>3.989686e-01</td>\n",
       "      <td>4.066661e-01</td>\n",
       "      <td>4.317881e-01</td>\n",
       "      <td>3.536420e-01</td>\n",
       "      <td>3.836953e-01</td>\n",
       "      <td>4.342104e-01</td>\n",
       "      <td>3.615573e-01</td>\n",
       "      <td>3.741295e-01</td>\n",
       "      <td>3.758779e-01</td>\n",
       "      <td>3.356775e-01</td>\n",
       "      <td>3.924052e-01</td>\n",
       "      <td>4.288088e-01</td>\n",
       "      <td>4.257723e-01</td>\n",
       "      <td>3.909540e-01</td>\n",
       "      <td>4.383029e-01</td>\n",
       "      <td>4.399820e-01</td>\n",
       "      <td>4.221423e-01</td>\n",
       "      <td>3.074795e-01</td>\n",
       "      <td>4.768112e-01</td>\n",
       "      <td>4.088835e-01</td>\n",
       "      <td>3.289584e-01</td>\n",
       "      <td>4.179632e-01</td>\n",
       "      <td>4.965377e-01</td>\n",
       "      <td>4.477446e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.722113e-01</td>\n",
       "      <td>4.537573e-01</td>\n",
       "      <td>3.802789e-01</td>\n",
       "      <td>4.260445e-01</td>\n",
       "      <td>4.979216e-01</td>\n",
       "      <td>3.629212e-01</td>\n",
       "      <td>3.882181e-01</td>\n",
       "      <td>3.226990e-01</td>\n",
       "      <td>4.285659e-01</td>\n",
       "      <td>4.066440e-01</td>\n",
       "      <td>4.031240e-01</td>\n",
       "      <td>4.755073e-01</td>\n",
       "      <td>4.060183e-01</td>\n",
       "      <td>4.530141e-01</td>\n",
       "      <td>4.780044e-01</td>\n",
       "      <td>4.192291e-01</td>\n",
       "      <td>4.857568e-01</td>\n",
       "      <td>2.854014e-01</td>\n",
       "      <td>3.849043e-01</td>\n",
       "      <td>4.246213e-01</td>\n",
       "      <td>4.712277e-01</td>\n",
       "      <td>4.293974e-01</td>\n",
       "      <td>4.251828e-01</td>\n",
       "      <td>4.622619e-01</td>\n",
       "      <td>5.105105e-01</td>\n",
       "      <td>4.982904e-01</td>\n",
       "      <td>3.183511e-01</td>\n",
       "      <td>5.150252e-01</td>\n",
       "      <td>4.859609e-01</td>\n",
       "      <td>4.736714e-01</td>\n",
       "      <td>3.273959e-01</td>\n",
       "      <td>4.942984e-01</td>\n",
       "      <td>3.940738e-01</td>\n",
       "      <td>4.204911e-01</td>\n",
       "      <td>5.045541e-01</td>\n",
       "      <td>4.385694e-01</td>\n",
       "      <td>4.516087e-01</td>\n",
       "      <td>4.131805e-01</td>\n",
       "      <td>3.595684e-01</td>\n",
       "      <td>3.786469e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.057122e+00</td>\n",
       "      <td>7.416766e+00</td>\n",
       "      <td>8.909792e+00</td>\n",
       "      <td>1.765551e+01</td>\n",
       "      <td>1.175114e+01</td>\n",
       "      <td>1.007897e+01</td>\n",
       "      <td>1.198797e+01</td>\n",
       "      <td>1.343956e+01</td>\n",
       "      <td>1.347767e+01</td>\n",
       "      <td>8.964296e+00</td>\n",
       "      <td>7.090614e+00</td>\n",
       "      <td>1.025687e+01</td>\n",
       "      <td>2.321918e+01</td>\n",
       "      <td>3.621242e+01</td>\n",
       "      <td>2.762232e+01</td>\n",
       "      <td>2.775638e+01</td>\n",
       "      <td>3.985768e+01</td>\n",
       "      <td>2.766484e+01</td>\n",
       "      <td>3.177211e+01</td>\n",
       "      <td>2.933540e+01</td>\n",
       "      <td>4.308266e+01</td>\n",
       "      <td>2.056735e+01</td>\n",
       "      <td>3.465084e+01</td>\n",
       "      <td>6.193636e+01</td>\n",
       "      <td>1.649785e+01</td>\n",
       "      <td>3.416372e+01</td>\n",
       "      <td>2.670132e+01</td>\n",
       "      <td>1.576088e+01</td>\n",
       "      <td>1.998443e+01</td>\n",
       "      <td>2.601625e+01</td>\n",
       "      <td>1.838174e+01</td>\n",
       "      <td>2.691332e+01</td>\n",
       "      <td>3.865847e+01</td>\n",
       "      <td>4.366883e+01</td>\n",
       "      <td>4.273974e+01</td>\n",
       "      <td>2.245118e+01</td>\n",
       "      <td>3.417137e+01</td>\n",
       "      <td>1.753563e+01</td>\n",
       "      <td>2.398130e+01</td>\n",
       "      <td>4.057395e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.517978e+01</td>\n",
       "      <td>3.237494e+01</td>\n",
       "      <td>3.403535e+01</td>\n",
       "      <td>3.472804e+01</td>\n",
       "      <td>1.003386e+01</td>\n",
       "      <td>2.520328e+01</td>\n",
       "      <td>2.833136e+01</td>\n",
       "      <td>4.855057e+01</td>\n",
       "      <td>3.186487e+01</td>\n",
       "      <td>3.969876e+01</td>\n",
       "      <td>3.049951e+01</td>\n",
       "      <td>1.980749e+01</td>\n",
       "      <td>2.159619e+01</td>\n",
       "      <td>3.638141e+01</td>\n",
       "      <td>1.295372e+01</td>\n",
       "      <td>2.687900e+01</td>\n",
       "      <td>2.056303e+01</td>\n",
       "      <td>4.080572e+01</td>\n",
       "      <td>4.962670e+01</td>\n",
       "      <td>2.031260e+01</td>\n",
       "      <td>1.577953e+01</td>\n",
       "      <td>2.065479e+01</td>\n",
       "      <td>1.312081e+01</td>\n",
       "      <td>2.613535e+01</td>\n",
       "      <td>2.009278e+01</td>\n",
       "      <td>2.834790e+01</td>\n",
       "      <td>3.747219e+01</td>\n",
       "      <td>2.352090e+01</td>\n",
       "      <td>2.405977e+01</td>\n",
       "      <td>2.990595e+01</td>\n",
       "      <td>2.571906e+01</td>\n",
       "      <td>2.590152e+01</td>\n",
       "      <td>2.602152e+01</td>\n",
       "      <td>1.723191e+01</td>\n",
       "      <td>1.610766e+01</td>\n",
       "      <td>3.187494e+01</td>\n",
       "      <td>1.641892e+01</td>\n",
       "      <td>3.445501e+01</td>\n",
       "      <td>3.989141e+01</td>\n",
       "      <td>2.719132e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         TimbreAvg1         ...          TimbreCovariance78\n",
       "count  4.637150e+05         ...                4.637150e+05\n",
       "mean   7.860182e-16         ...                4.832588e-15\n",
       "std    1.000001e+00         ...                1.000001e+00\n",
       "min   -6.849083e+00         ...               -1.449495e+01\n",
       "25%   -5.638876e-01         ...               -4.598088e-01\n",
       "50%    1.442775e-01         ...               -5.763047e-02\n",
       "75%    7.317100e-01         ...                3.786469e-01\n",
       "max    3.057122e+00         ...                2.719132e+01\n",
       "\n",
       "[8 rows x 90 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_std = pd.DataFrame(X_train_std,columns=X_train.columns)\n",
    "X_train_std.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our values are normalised and so we now actually apply PCA\n",
    "(https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# Make an instance of the Model\n",
    "pca = PCA(.90)\n",
    "\n",
    "# We fit to only our training set\n",
    "pca.fit(X_train_std)\n",
    "# Print number of components generated\n",
    "pca.n_components_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA(.90) means that scikit-learn choose the minimum number of principal components such that 90% of the variance is retained.\n",
    "\n",
    "In this case, 90% of the variance amounts to 55 principal components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_proc = pca.transform(X_train_std)\n",
    "X_test_proc = pca.transform(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_proc = y_train - min(y_train)\n",
    "y_test_proc = y_test - min(y_test)\n",
    "# y_train_proc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# logisticRegr = LogisticRegression(verbose=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# logisticRegr.fit(X_train_proc, y_train_proc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictions = logisticRegr.predict(X_test_proc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# score = logisticRegr.score(X_test_proc, y_test_proc)\n",
    "# score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [],
   "source": [
    "# predictions = logisticRegr.predict(X_test_proc)\n",
    "# np.mean(np.absolute((predictions-np.array(y_test_proc))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linearRegr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegr.fit(X_train_proc, y_train_proc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.07339863984608797"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_linearReg = linearRegr.score(X_test_proc, y_test_proc)\n",
    "score_linearReg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.629861386606082"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_linearRegr = linearRegr.predict(X_test_proc)\n",
    "np.mean(np.absolute((predictions_linearRegr-np.array(y_test_proc))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import mean_squared_error, r2_score\n",
    "# mean_squared_error(predictions_linearRegr, np.array(y_test_proc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import accuracy_score\n",
    "\n",
    "# print(accuracy_score(predictions, y_test_proc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f530723abe0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = pd.DataFrame([np.array(predictions_linearRegr).T, np.array(y_test_proc).T])\n",
    "df_plot = df_plot.transpose()\n",
    "df_plot = df_plot.sort_values([1]).reset_index(drop=True)\n",
    "plt.scatter(df_plot[1], df_plot[0], s=1)\n",
    "# plt.line()\n",
    "# plt.plot(df_plot)google"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(np.round_(np.array(predictions_linearRegr), decimals=-1), np.round_(np.array(y_test_proc), decimals=-1))\n",
    "# np.round_(np.array(predictions_linearRegr), decimals=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1920</th>\n",
       "      <th>1930</th>\n",
       "      <th>1940</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2020</th>\n",
       "      <th>2030</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>83</td>\n",
       "      <td>182</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>57</td>\n",
       "      <td>253</td>\n",
       "      <td>1288</td>\n",
       "      <td>1461</td>\n",
       "      <td>3278</td>\n",
       "      <td>4646</td>\n",
       "      <td>5510</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>469</td>\n",
       "      <td>907</td>\n",
       "      <td>2615</td>\n",
       "      <td>9162</td>\n",
       "      <td>20271</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>215</td>\n",
       "      <td>838</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1920  1930  1940  1950  1960  ...   1990   2000  2010  2020  2030\n",
       "1920     0     0     0     0     0  ...      0      0     0     0     0\n",
       "1930     0     0     0     0     0  ...      0      0     0     0     0\n",
       "1940     0     0     0     0     0  ...      0      0     0     0     0\n",
       "1950     0     0     0     0     0  ...      0      0     0     0     0\n",
       "1960     0     0     0     0     0  ...      0      0     0     0     0\n",
       "1970     0     0     0     4     0  ...      1      1     0     0     0\n",
       "1980     5    24    11     8    33  ...    182    171     0     0     0\n",
       "1990    21    22    57   253  1288  ...   4646   5510     0     0     0\n",
       "2000     0     0     3    37   469  ...   9162  20271     0     0     0\n",
       "2010     0     0     0     0     0  ...    215    838     0     0     0\n",
       "2020     0     0     0     0     0  ...      6     16     0     0     0\n",
       "2030     0     0     0     0     0  ...      0      5     0     0     0\n",
       "\n",
       "[12 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "ind = list(range(1920,2040,10))\n",
    "df_heat = pd.DataFrame(cm, index=ind, columns=ind)\n",
    "len(ind)\n",
    "# lab = pd.unique(df_heat[0])\n",
    "sns.heatmap(df_heat)\n",
    "df_heat\n",
    "# df_plot.transpose().corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}