notebooks/code005_principalComponentAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>Mapping the emotional face. How individual face parts contribute to successful emotion recognition.</h1></center>\n",
    "\n",
    "# 5. Principal Component Analysis (PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from myBasics import *\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.decomposition import PCA as sklearnPCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the big table, with values of each participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "participantDf = pd.read_csv('../outputs/weightStdDf.csv',index_col=[0,1,2])\n",
    "participantDf.index.names = ['p','ident','emo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <th>ident</th>\n",
       "      <th>emo</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",
       "      <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",
       "      <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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">p001</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">f</th>\n",
       "      <th>ang</th>\n",
       "      <td>-0.948401</td>\n",
       "      <td>-0.006541</td>\n",
       "      <td>1.667878</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>-0.948401</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-0.948401</td>\n",
       "      <td>1.039971</td>\n",
       "      <td>1.458576</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>0.935320</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-0.948401</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>1.039971</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-0.006541</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>-0.843750</td>\n",
       "      <td>2.400437</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>1.353925</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>1.458576</td>\n",
       "      <td>-0.006541</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>1.039971</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>1.353925</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>1.039971</td>\n",
       "      <td>1.353925</td>\n",
       "      <td>1.039971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.245679</td>\n",
       "      <td>1.036125</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>0.437950</td>\n",
       "      <td>1.548847</td>\n",
       "      <td>0.865218</td>\n",
       "      <td>-0.245679</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>1.121579</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>0.865218</td>\n",
       "      <td>0.779764</td>\n",
       "      <td>0.608857</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>0.608857</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>1.805208</td>\n",
       "      <td>1.890662</td>\n",
       "      <td>0.437950</td>\n",
       "      <td>-0.672947</td>\n",
       "      <td>0.352496</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>3.257920</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>0.523404</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>1.292486</td>\n",
       "      <td>0.352496</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>1.463394</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-0.416586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>0.582234</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>1.844366</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>1.844366</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>1.664062</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>1.123148</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>2.024671</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>1.844366</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>2.024671</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>0.221624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>0.352580</td>\n",
       "      <td>1.057741</td>\n",
       "      <td>-0.151106</td>\n",
       "      <td>-0.856267</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>1.460690</td>\n",
       "      <td>-0.453318</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>1.460690</td>\n",
       "      <td>0.755529</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>0.554055</td>\n",
       "      <td>-0.554055</td>\n",
       "      <td>-0.856267</td>\n",
       "      <td>-0.654792</td>\n",
       "      <td>-0.050369</td>\n",
       "      <td>0.151106</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>-0.957004</td>\n",
       "      <td>0.251843</td>\n",
       "      <td>1.057741</td>\n",
       "      <td>-0.957004</td>\n",
       "      <td>-0.957004</td>\n",
       "      <td>-0.453318</td>\n",
       "      <td>0.251843</td>\n",
       "      <td>-0.755529</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>0.050369</td>\n",
       "      <td>0.251843</td>\n",
       "      <td>-0.755529</td>\n",
       "      <td>-1.259216</td>\n",
       "      <td>1.158478</td>\n",
       "      <td>0.151106</td>\n",
       "      <td>0.554055</td>\n",
       "      <td>1.762902</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>1.762902</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>1.158478</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>2.065113</td>\n",
       "      <td>-0.554055</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>1.863639</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>0.251843</td>\n",
       "      <td>1.762902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-0.897600</td>\n",
       "      <td>-0.897600</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>0.733673</td>\n",
       "      <td>0.349844</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.801643</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.321856</td>\n",
       "      <td>1.213459</td>\n",
       "      <td>-0.321856</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>0.733673</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>-0.897600</td>\n",
       "      <td>-0.609728</td>\n",
       "      <td>0.733673</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.513771</td>\n",
       "      <td>0.061972</td>\n",
       "      <td>-0.321856</td>\n",
       "      <td>0.445801</td>\n",
       "      <td>-0.033985</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.705685</td>\n",
       "      <td>0.445801</td>\n",
       "      <td>0.349844</td>\n",
       "      <td>2.077074</td>\n",
       "      <td>0.541758</td>\n",
       "      <td>1.981117</td>\n",
       "      <td>0.829630</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.705685</td>\n",
       "      <td>-0.321856</td>\n",
       "      <td>0.829630</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>3.708346</td>\n",
       "      <td>0.061972</td>\n",
       "      <td>-0.417814</td>\n",
       "      <td>-0.705685</td>\n",
       "      <td>1.981117</td>\n",
       "      <td>0.253887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0         1         2         3         4         5  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -0.948401 -0.006541  1.667878 -1.262355 -1.262355 -0.320494   \n",
       "           dis -0.245679  1.036125 -0.074772 -1.100216 -1.100216  0.437950   \n",
       "           fea  0.582234 -1.040508 -1.040508 -1.040508  0.401929 -1.040508   \n",
       "           hap  0.352580  1.057741 -0.151106 -0.856267 -0.352580 -1.359953   \n",
       "           ntr -0.897600 -0.897600  0.157930  0.733673  0.349844 -1.089514   \n",
       "\n",
       "                       6         7         8         9        10        11  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -0.948401  0.412064 -1.262355 -0.948401  1.039971  1.458576   \n",
       "           dis  1.548847  0.865218 -0.245679 -1.100216  1.121579 -1.100216   \n",
       "           fea  0.401929  1.844366  0.401929  0.582234  1.844366  0.582234   \n",
       "           hap  1.460690 -0.453318 -1.359953  1.460690  0.755529 -0.352580   \n",
       "           ntr -1.089514 -0.801643 -1.089514 -0.321856  1.213459 -0.321856   \n",
       "\n",
       "                      12        13        14        15        16        17  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -1.262355  0.098110  0.935320 -1.262355 -0.948401  0.412064   \n",
       "           dis  0.865218  0.779764  0.608857 -1.100216  0.608857 -0.587494   \n",
       "           fea  0.221624  0.582234  1.664062  0.582234  0.582234 -1.040508   \n",
       "           hap  0.554055 -0.554055 -0.856267 -0.654792 -0.050369  0.151106   \n",
       "           ntr  0.157930  0.733673  0.157930  0.157930 -0.897600 -0.609728   \n",
       "\n",
       "                      18        19        20        21        22        23  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -1.262355  0.412064  1.039971 -1.262355 -0.006541 -0.320494   \n",
       "           dis -0.074772 -1.100216 -1.100216  1.805208  1.890662  0.437950   \n",
       "           fea  0.221624  0.582234 -1.040508  1.123148  0.221624 -1.040508   \n",
       "           hap -1.359953 -0.957004  0.251843  1.057741 -0.957004 -0.957004   \n",
       "           ntr  0.733673 -1.089514 -1.089514 -0.513771  0.061972 -0.321856   \n",
       "\n",
       "                      24        25        26        27        28        29  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -0.320494 -0.843750  2.400437  0.412064  1.353925  0.412064   \n",
       "           dis -0.672947  0.352496  0.096135 -1.100216 -0.074772 -1.100216   \n",
       "           fea -1.040508 -1.040508  0.221624  0.401929  0.221624  2.024671   \n",
       "           hap -0.453318  0.251843 -0.755529 -0.352580  0.050369  0.251843   \n",
       "           ntr  0.445801 -0.033985  0.157930 -1.089514 -0.705685  0.445801   \n",
       "\n",
       "                      30        31        32        33        34        35  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang  1.458576 -0.006541 -1.262355 -1.262355 -1.262355  0.412064   \n",
       "           dis  3.257920 -0.587494  0.523404 -0.074772  1.292486  0.352496   \n",
       "           fea -1.040508 -1.040508 -1.040508  0.401929  1.844366  0.582234   \n",
       "           hap -0.755529 -1.259216  1.158478  0.151106  0.554055  1.762902   \n",
       "           ntr  0.349844  2.077074  0.541758  1.981117  0.829630 -1.089514   \n",
       "\n",
       "                      36        37        38        39        40        41  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang  1.039971  0.098110 -0.320494 -0.320494  0.098110  0.098110   \n",
       "           dis -1.100216 -1.100216  1.463394 -0.074772 -1.100216 -0.587494   \n",
       "           fea -1.040508 -1.040508 -1.040508 -1.040508 -1.040508  2.024671   \n",
       "           hap -1.359953  1.762902 -0.352580  1.158478 -0.352580  2.065113   \n",
       "           ntr -0.705685 -0.321856  0.829630 -1.089514  0.157930 -1.089514   \n",
       "\n",
       "                      42        43        44        45        46        47  \n",
       "p    ident emo                                                              \n",
       "p001 f     ang  0.098110  1.353925 -1.262355  1.039971  1.353925  1.039971  \n",
       "           dis -0.074772 -0.074772 -0.587494 -0.587494 -1.100216 -0.416586  \n",
       "           fea -1.040508 -1.040508  0.221624 -1.040508  0.221624  0.221624  \n",
       "           hap -0.554055 -1.359953  1.863639 -1.359953  0.251843  1.762902  \n",
       "           ntr  3.708346  0.061972 -0.417814 -0.705685  1.981117  0.253887  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "participantDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the averaged data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weightDf = pd.read_csv('../outputs/weightAvgStdDf.csv',\n",
    "                      index_col=[0,1])\n",
    "weightDf.index.names = ['ident','emo']\n",
    "weightDf = weightDf.sortlevel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ident</th>\n",
       "      <th>emo</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",
       "      <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",
       "      <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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">f</th>\n",
       "      <th>ang</th>\n",
       "      <td>-0.107068</td>\n",
       "      <td>-0.090944</td>\n",
       "      <td>0.021346</td>\n",
       "      <td>-0.140837</td>\n",
       "      <td>-0.231097</td>\n",
       "      <td>0.120188</td>\n",
       "      <td>-0.101582</td>\n",
       "      <td>-0.155755</td>\n",
       "      <td>-0.212338</td>\n",
       "      <td>-0.097965</td>\n",
       "      <td>0.484374</td>\n",
       "      <td>0.035292</td>\n",
       "      <td>-0.089919</td>\n",
       "      <td>0.065455</td>\n",
       "      <td>-0.239384</td>\n",
       "      <td>-0.122578</td>\n",
       "      <td>-0.118693</td>\n",
       "      <td>0.146498</td>\n",
       "      <td>0.044670</td>\n",
       "      <td>0.344048</td>\n",
       "      <td>0.145883</td>\n",
       "      <td>0.255147</td>\n",
       "      <td>0.364067</td>\n",
       "      <td>-0.061439</td>\n",
       "      <td>-0.129267</td>\n",
       "      <td>-0.147019</td>\n",
       "      <td>0.547444</td>\n",
       "      <td>-0.020446</td>\n",
       "      <td>0.031519</td>\n",
       "      <td>0.969053</td>\n",
       "      <td>0.477879</td>\n",
       "      <td>-0.226840</td>\n",
       "      <td>-0.233784</td>\n",
       "      <td>-0.146768</td>\n",
       "      <td>0.651934</td>\n",
       "      <td>-0.001234</td>\n",
       "      <td>-0.250892</td>\n",
       "      <td>-0.034475</td>\n",
       "      <td>-0.133708</td>\n",
       "      <td>-0.271287</td>\n",
       "      <td>-0.215705</td>\n",
       "      <td>-0.245044</td>\n",
       "      <td>-0.127644</td>\n",
       "      <td>-0.094971</td>\n",
       "      <td>-0.183468</td>\n",
       "      <td>-0.170725</td>\n",
       "      <td>-0.187689</td>\n",
       "      <td>-0.114234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.197694</td>\n",
       "      <td>0.061481</td>\n",
       "      <td>-0.234455</td>\n",
       "      <td>-0.156955</td>\n",
       "      <td>-0.116681</td>\n",
       "      <td>-0.229027</td>\n",
       "      <td>-0.159949</td>\n",
       "      <td>0.004103</td>\n",
       "      <td>-0.161257</td>\n",
       "      <td>-0.209212</td>\n",
       "      <td>-0.077736</td>\n",
       "      <td>-0.004602</td>\n",
       "      <td>0.010296</td>\n",
       "      <td>0.081108</td>\n",
       "      <td>0.119742</td>\n",
       "      <td>-0.017898</td>\n",
       "      <td>-0.184315</td>\n",
       "      <td>-0.036822</td>\n",
       "      <td>-0.108139</td>\n",
       "      <td>-0.043717</td>\n",
       "      <td>0.015189</td>\n",
       "      <td>0.921797</td>\n",
       "      <td>1.060071</td>\n",
       "      <td>-0.102794</td>\n",
       "      <td>-0.215771</td>\n",
       "      <td>-0.190616</td>\n",
       "      <td>-0.372721</td>\n",
       "      <td>-0.040695</td>\n",
       "      <td>-0.193600</td>\n",
       "      <td>1.098819</td>\n",
       "      <td>1.436805</td>\n",
       "      <td>0.007758</td>\n",
       "      <td>-0.214569</td>\n",
       "      <td>-0.222747</td>\n",
       "      <td>-0.209609</td>\n",
       "      <td>-0.005113</td>\n",
       "      <td>-0.151673</td>\n",
       "      <td>0.039613</td>\n",
       "      <td>-0.103777</td>\n",
       "      <td>0.084613</td>\n",
       "      <td>0.003531</td>\n",
       "      <td>-0.158570</td>\n",
       "      <td>-0.139650</td>\n",
       "      <td>-0.259681</td>\n",
       "      <td>-0.120911</td>\n",
       "      <td>-0.063685</td>\n",
       "      <td>-0.158616</td>\n",
       "      <td>-0.081669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>0.077753</td>\n",
       "      <td>-0.066480</td>\n",
       "      <td>-0.141368</td>\n",
       "      <td>-0.121815</td>\n",
       "      <td>-0.012927</td>\n",
       "      <td>-0.287902</td>\n",
       "      <td>-0.147046</td>\n",
       "      <td>-0.017016</td>\n",
       "      <td>-0.061568</td>\n",
       "      <td>0.102944</td>\n",
       "      <td>0.736890</td>\n",
       "      <td>-0.114484</td>\n",
       "      <td>-0.240084</td>\n",
       "      <td>0.035541</td>\n",
       "      <td>-0.134807</td>\n",
       "      <td>-0.049666</td>\n",
       "      <td>-0.032427</td>\n",
       "      <td>-0.049715</td>\n",
       "      <td>-0.082939</td>\n",
       "      <td>-0.058181</td>\n",
       "      <td>-0.169369</td>\n",
       "      <td>0.198205</td>\n",
       "      <td>0.376390</td>\n",
       "      <td>-0.238898</td>\n",
       "      <td>-0.011844</td>\n",
       "      <td>0.122511</td>\n",
       "      <td>0.026201</td>\n",
       "      <td>-0.099165</td>\n",
       "      <td>-0.007230</td>\n",
       "      <td>0.956243</td>\n",
       "      <td>0.122989</td>\n",
       "      <td>-0.072396</td>\n",
       "      <td>-0.224578</td>\n",
       "      <td>0.057937</td>\n",
       "      <td>0.839029</td>\n",
       "      <td>0.044465</td>\n",
       "      <td>-0.080408</td>\n",
       "      <td>-0.194371</td>\n",
       "      <td>-0.158883</td>\n",
       "      <td>-0.196069</td>\n",
       "      <td>0.083162</td>\n",
       "      <td>-0.133403</td>\n",
       "      <td>-0.140680</td>\n",
       "      <td>-0.028346</td>\n",
       "      <td>-0.066560</td>\n",
       "      <td>-0.174366</td>\n",
       "      <td>0.049001</td>\n",
       "      <td>-0.214271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>-0.083151</td>\n",
       "      <td>-0.176273</td>\n",
       "      <td>-0.072354</td>\n",
       "      <td>-0.014752</td>\n",
       "      <td>-0.192790</td>\n",
       "      <td>-0.121763</td>\n",
       "      <td>-0.207403</td>\n",
       "      <td>-0.184612</td>\n",
       "      <td>-0.123583</td>\n",
       "      <td>-0.118115</td>\n",
       "      <td>0.042775</td>\n",
       "      <td>-0.155732</td>\n",
       "      <td>0.079402</td>\n",
       "      <td>0.812469</td>\n",
       "      <td>0.009954</td>\n",
       "      <td>-0.149744</td>\n",
       "      <td>-0.148304</td>\n",
       "      <td>-0.109140</td>\n",
       "      <td>-0.002345</td>\n",
       "      <td>-0.099668</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>0.759900</td>\n",
       "      <td>0.242644</td>\n",
       "      <td>-0.197336</td>\n",
       "      <td>-0.270702</td>\n",
       "      <td>-0.031605</td>\n",
       "      <td>-0.083610</td>\n",
       "      <td>-0.197457</td>\n",
       "      <td>-0.012785</td>\n",
       "      <td>0.837379</td>\n",
       "      <td>0.064816</td>\n",
       "      <td>-0.261093</td>\n",
       "      <td>0.001064</td>\n",
       "      <td>-0.101332</td>\n",
       "      <td>0.171326</td>\n",
       "      <td>0.087721</td>\n",
       "      <td>0.196049</td>\n",
       "      <td>0.715466</td>\n",
       "      <td>-0.149423</td>\n",
       "      <td>-0.221160</td>\n",
       "      <td>0.064848</td>\n",
       "      <td>0.033526</td>\n",
       "      <td>0.002885</td>\n",
       "      <td>-0.177391</td>\n",
       "      <td>-0.076576</td>\n",
       "      <td>0.016847</td>\n",
       "      <td>-0.201242</td>\n",
       "      <td>-0.197262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-0.123786</td>\n",
       "      <td>-0.018737</td>\n",
       "      <td>0.109495</td>\n",
       "      <td>-0.083957</td>\n",
       "      <td>-0.030939</td>\n",
       "      <td>-0.008957</td>\n",
       "      <td>-0.170157</td>\n",
       "      <td>-0.192194</td>\n",
       "      <td>-0.176686</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.661145</td>\n",
       "      <td>-0.199530</td>\n",
       "      <td>-0.004440</td>\n",
       "      <td>0.459792</td>\n",
       "      <td>-0.032845</td>\n",
       "      <td>-0.071983</td>\n",
       "      <td>-0.134917</td>\n",
       "      <td>0.030365</td>\n",
       "      <td>0.063706</td>\n",
       "      <td>-0.197195</td>\n",
       "      <td>-0.052643</td>\n",
       "      <td>0.393009</td>\n",
       "      <td>0.604447</td>\n",
       "      <td>-0.353003</td>\n",
       "      <td>-0.125066</td>\n",
       "      <td>-0.119348</td>\n",
       "      <td>-0.151487</td>\n",
       "      <td>0.079267</td>\n",
       "      <td>0.216065</td>\n",
       "      <td>0.114921</td>\n",
       "      <td>0.265603</td>\n",
       "      <td>-0.147549</td>\n",
       "      <td>-0.005426</td>\n",
       "      <td>-0.022756</td>\n",
       "      <td>0.952202</td>\n",
       "      <td>-0.034270</td>\n",
       "      <td>0.063469</td>\n",
       "      <td>-0.343780</td>\n",
       "      <td>-0.107934</td>\n",
       "      <td>-0.172750</td>\n",
       "      <td>-0.253165</td>\n",
       "      <td>0.041902</td>\n",
       "      <td>0.018439</td>\n",
       "      <td>-0.266517</td>\n",
       "      <td>-0.219938</td>\n",
       "      <td>-0.091623</td>\n",
       "      <td>-0.082322</td>\n",
       "      <td>-0.078240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1         2         3         4         5  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.107068 -0.090944  0.021346 -0.140837 -0.231097  0.120188   \n",
       "      dis -0.197694  0.061481 -0.234455 -0.156955 -0.116681 -0.229027   \n",
       "      fea  0.077753 -0.066480 -0.141368 -0.121815 -0.012927 -0.287902   \n",
       "      hap -0.083151 -0.176273 -0.072354 -0.014752 -0.192790 -0.121763   \n",
       "      ntr -0.123786 -0.018737  0.109495 -0.083957 -0.030939 -0.008957   \n",
       "\n",
       "                  6         7         8         9        10        11  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.101582 -0.155755 -0.212338 -0.097965  0.484374  0.035292   \n",
       "      dis -0.159949  0.004103 -0.161257 -0.209212 -0.077736 -0.004602   \n",
       "      fea -0.147046 -0.017016 -0.061568  0.102944  0.736890 -0.114484   \n",
       "      hap -0.207403 -0.184612 -0.123583 -0.118115  0.042775 -0.155732   \n",
       "      ntr -0.170157 -0.192194 -0.176686  0.000314  0.661145 -0.199530   \n",
       "\n",
       "                 12        13        14        15        16        17  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.089919  0.065455 -0.239384 -0.122578 -0.118693  0.146498   \n",
       "      dis  0.010296  0.081108  0.119742 -0.017898 -0.184315 -0.036822   \n",
       "      fea -0.240084  0.035541 -0.134807 -0.049666 -0.032427 -0.049715   \n",
       "      hap  0.079402  0.812469  0.009954 -0.149744 -0.148304 -0.109140   \n",
       "      ntr -0.004440  0.459792 -0.032845 -0.071983 -0.134917  0.030365   \n",
       "\n",
       "                 18        19        20        21        22        23  \\\n",
       "ident emo                                                               \n",
       "f     ang  0.044670  0.344048  0.145883  0.255147  0.364067 -0.061439   \n",
       "      dis -0.108139 -0.043717  0.015189  0.921797  1.060071 -0.102794   \n",
       "      fea -0.082939 -0.058181 -0.169369  0.198205  0.376390 -0.238898   \n",
       "      hap -0.002345 -0.099668 -0.000368  0.759900  0.242644 -0.197336   \n",
       "      ntr  0.063706 -0.197195 -0.052643  0.393009  0.604447 -0.353003   \n",
       "\n",
       "                 24        25        26        27        28        29  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.129267 -0.147019  0.547444 -0.020446  0.031519  0.969053   \n",
       "      dis -0.215771 -0.190616 -0.372721 -0.040695 -0.193600  1.098819   \n",
       "      fea -0.011844  0.122511  0.026201 -0.099165 -0.007230  0.956243   \n",
       "      hap -0.270702 -0.031605 -0.083610 -0.197457 -0.012785  0.837379   \n",
       "      ntr -0.125066 -0.119348 -0.151487  0.079267  0.216065  0.114921   \n",
       "\n",
       "                 30        31        32        33        34        35  \\\n",
       "ident emo                                                               \n",
       "f     ang  0.477879 -0.226840 -0.233784 -0.146768  0.651934 -0.001234   \n",
       "      dis  1.436805  0.007758 -0.214569 -0.222747 -0.209609 -0.005113   \n",
       "      fea  0.122989 -0.072396 -0.224578  0.057937  0.839029  0.044465   \n",
       "      hap  0.064816 -0.261093  0.001064 -0.101332  0.171326  0.087721   \n",
       "      ntr  0.265603 -0.147549 -0.005426 -0.022756  0.952202 -0.034270   \n",
       "\n",
       "                 36        37        38        39        40        41  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.250892 -0.034475 -0.133708 -0.271287 -0.215705 -0.245044   \n",
       "      dis -0.151673  0.039613 -0.103777  0.084613  0.003531 -0.158570   \n",
       "      fea -0.080408 -0.194371 -0.158883 -0.196069  0.083162 -0.133403   \n",
       "      hap  0.196049  0.715466 -0.149423 -0.221160  0.064848  0.033526   \n",
       "      ntr  0.063469 -0.343780 -0.107934 -0.172750 -0.253165  0.041902   \n",
       "\n",
       "                 42        43        44        45        46        47  \n",
       "ident emo                                                              \n",
       "f     ang -0.127644 -0.094971 -0.183468 -0.170725 -0.187689 -0.114234  \n",
       "      dis -0.139650 -0.259681 -0.120911 -0.063685 -0.158616 -0.081669  \n",
       "      fea -0.140680 -0.028346 -0.066560 -0.174366  0.049001 -0.214271  \n",
       "      hap  0.002885 -0.177391 -0.076576  0.016847 -0.201242 -0.197262  \n",
       "      ntr  0.018439 -0.266517 -0.219938 -0.091623 -0.082322 -0.078240  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weightDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform the PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sklearn_pca = sklearnPCA(n_components= 5 )\n",
    "sklearn_pca_avg = sklearnPCA(n_components= 5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_sklearn = sklearn_pca.fit_transform(participantDf)\n",
    "Y_sklearn_avg = sklearn_pca_avg.fit_transform(weightDf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eigenvectors  \n",
    "\n",
    "These are the n extracted principal components. Each PC is composed of as many values as there are dimensions/features. Each value of the PC tells us the weight of the original dimension/feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.04527311,  0.04126415,  0.03624892,  0.03417348,  0.03285534])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn_pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.37123251,  0.17716699,  0.11721804,  0.07849316,  0.05756008])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn_pca_avg.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YYVHic4H1wBnu/u9yRbibaefI5Bj60Uze+XJuzDaN6mfxwFUDaNFYZTMiIhUscTtHuvsn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\nAgcRERHJmgIHERERydr/A0HoOZCW0rN7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3937d8510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "expVar = sklearn_pca.explained_variance_ratio_\n",
    "\n",
    "plt.plot(expVar*100, '-o',linewidth=7,markersize=16)\n",
    "plt.xlim(-0.5,4.5)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xticks(np.arange(5),np.arange(1,5+1),fontsize=20)\n",
    "\n",
    "plt.xlabel('Principal Component #',fontsize=25)\n",
    "plt.ylabel('% Explained Variance',fontsize=25)\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "plt.savefig('../figures/pcaExpVar.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project the weigths of each PC onto the original feature space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plotPCWeights(Y_sklearn,components,explained_variance,figName='../figures/pcaCbar.png'):\n",
    "\n",
    "    # set basic figure properties\n",
    "    plt.figure(figsize=(16,6))\n",
    "\n",
    "    w,h = (6,8)\n",
    "    # loop through all eigenvectors\n",
    "    for index,thisPC in enumerate(components):\n",
    "\n",
    "        # make an empty w*h matrix with zeros\n",
    "        a = np.zeros(shape=(w,h))\n",
    "\n",
    "        # counter for the features\n",
    "        count = 0\n",
    "        \n",
    "        # we loop through the matrix in a structured way\n",
    "        for x in range(w):\n",
    "            for y in range(h):\n",
    "                a[x][y] = thisPC[count]\n",
    "                count+=1\n",
    "\n",
    "        # make a subplot\n",
    "        ax = plt.subplot(1,len(components),index+1)\n",
    "\n",
    "        # the heatmap: we have to transpose the matrix so it is oriented\n",
    "        # as the picture was; we write the weights into each tile and get\n",
    "        # rid of all other annotations\n",
    "        sns.heatmap(a.T,\n",
    "                    square=True,\n",
    "                    cbar=False,\n",
    "                    vmin=-0.4,vmax=0.4,\n",
    "                    xticklabels=False,\n",
    "                    yticklabels=False,\n",
    "                    cmap=None)\n",
    "\n",
    "        # the % explained variance is computed from the eigenvalue*100\n",
    "        # we round for plotting reasons\n",
    "        varExp = round(explained_variance[index]*100,1)\n",
    "        \n",
    "        # title with explained variance for each PC\n",
    "        plt.title('PC #'+str(index+1)+'\\nExplained. Var: '+str(varExp)+'%') \n",
    "        \n",
    "    # shows all subplots\n",
    "    plt.savefig(figName,dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plotPCWeights(Y_sklearn,components,explained_variance,ax,figName='../figures/pcaCbar.png'):\n",
    "\n",
    "    w,h = (6,8)\n",
    "    # loop through all eigenvectors\n",
    "    for index,thisPC in enumerate(components):\n",
    "\n",
    "        # make an empty w*h matrix with zeros\n",
    "        a = np.zeros(shape=(w,h))\n",
    "\n",
    "        # counter for the features\n",
    "        count = 0\n",
    "        \n",
    "        # we loop through the matrix in a structured way\n",
    "        for x in range(w):\n",
    "            for y in range(h):\n",
    "                a[x][y] = thisPC[count]\n",
    "                count+=1\n",
    "\n",
    "        # make a subplot\n",
    "        myAx = plt.subplot(1,len(components),index+1)\n",
    "\n",
    "        # the heatmap: we have to transpose the matrix so it is oriented\n",
    "        # as the picture was; we write the weights into each tile and get\n",
    "        # rid of all other annotations\n",
    "        ax = sns.heatmap(a.T,\n",
    "                    square=True,\n",
    "                    cbar=False,\n",
    "                    vmin=-0.4,vmax=0.4,\n",
    "                    xticklabels=False,\n",
    "                    yticklabels=False,\n",
    "                    cmap=None,\n",
    "                    ax=myAx)\n",
    "\n",
    "        # the % explained variance is computed from the eigenvalue*100\n",
    "        # we round for plotting reasons\n",
    "        varExp = round(explained_variance[index]*100,1)\n",
    "        \n",
    "        # title with explained variance for each PC\n",
    "        myAx.set_title('PC #'+str(index+1)+'\\nExplained. Var: '+str(varExp)+'%',fontsize=18) \n",
    "        \n",
    "    return ax;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With all data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NjEfdsnJ2I3UWzK5/fbam/m04QGuomfds9vwFfWXckUxJkiRJUjE2mZIkSZKk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\nkiSpGJtMSZIkSVIxNpmSJEmSpGL+D/75yg5I4QkTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe392def890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ( ax1 ) = plt.subplots( 1,2,figsize=(16,8) );\n",
    "\n",
    "plotPCWeights(Y_sklearn,\n",
    "              sklearn_pca.components_,\n",
    "              sklearn_pca.explained_variance_ratio_,\n",
    "              ax1);\n",
    "plt.savefig('../figures/pcaWeightsFaces.png',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With data averaged by condition (just out of curisity):  \n",
    "\n",
    "obviously, with only 14 data points, we can explain much more variance..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KxlOSJEmSVJWNpyRJkiSpKhtPSZIkSVJVNp6SJEmSpKpsPCVJkiRJVdl4SpIk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\nSZKqsvGUJEmSJFX1/wHCWK9c2MKELAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3c80cd7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ( ax1 ) = plt.subplots( 1,2,figsize=(16,8) );\n",
    "\n",
    "plotPCWeights(Y_sklearn,\n",
    "              sklearn_pca_avg.components_,\n",
    "              sklearn_pca_avg.explained_variance_ratio_,\n",
    "              ax1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Projecting the faces back into the PCA-space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
    "from matplotlib._png import read_png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform the values of each participant into the space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the data of each participant (48 tiles each) to the PCA space, where there are only 5 dimensions, each a combination of the 48 tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def makeParticipantPCA(participantDf,sklearn_pca=sklearn_pca):\n",
    "    pcaDf = pd.DataFrame()\n",
    "    for p in participantDf.index.levels[0]:\n",
    "        thisEntry = sklearn_pca.transform( participantDf.ix[p] )\n",
    "        thisDf = pd.DataFrame(thisEntry)\n",
    "        thisDf.index = participantDf.ix[p].index\n",
    "        # add participant name\n",
    "        thisDf['p'] = p\n",
    "        thisDf.set_index('p', append=True, inplace=True)\n",
    "        thisDf = thisDf.reorder_levels(['p', 'ident', 'emo'])\n",
    "        # add to group df\n",
    "        pcaDf = pd.concat([pcaDf,thisDf])\n",
    "    return pcaDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pcaDf = makeParticipantPCA(participantDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <th>ident</th>\n",
       "      <th>emo</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 rowspan=\"5\" valign=\"top\">p001</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">f</th>\n",
       "      <th>ang</th>\n",
       "      <td>1.037068</td>\n",
       "      <td>-0.412058</td>\n",
       "      <td>-2.594713</td>\n",
       "      <td>-0.848840</td>\n",
       "      <td>-0.719657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.657484</td>\n",
       "      <td>-2.868062</td>\n",
       "      <td>0.989515</td>\n",
       "      <td>1.414985</td>\n",
       "      <td>1.985290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>-1.093147</td>\n",
       "      <td>2.201858</td>\n",
       "      <td>0.527650</td>\n",
       "      <td>1.874243</td>\n",
       "      <td>-0.414441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>-0.403686</td>\n",
       "      <td>1.290129</td>\n",
       "      <td>0.843942</td>\n",
       "      <td>0.691269</td>\n",
       "      <td>-0.864649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-0.874690</td>\n",
       "      <td>0.397866</td>\n",
       "      <td>-0.769708</td>\n",
       "      <td>0.110667</td>\n",
       "      <td>-0.323192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0         1         2         3         4\n",
       "p    ident emo                                                  \n",
       "p001 f     ang  1.037068 -0.412058 -2.594713 -0.848840 -0.719657\n",
       "           dis -0.657484 -2.868062  0.989515  1.414985  1.985290\n",
       "           fea -1.093147  2.201858  0.527650  1.874243 -0.414441\n",
       "           hap -0.403686  1.290129  0.843942  0.691269 -0.864649\n",
       "           ntr -0.874690  0.397866 -0.769708  0.110667 -0.323192"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcaDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJMaO678AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe390288e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(stackColors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to plot the data as a scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeScatterPCA(pcaDf,ax,identDict=identDict,emoDict=emoDict):\n",
    "\n",
    "    # average over all participants\n",
    "    meanPca = pcaDf.groupby(level=[1,2]).mean()\n",
    "    n = len(pcaDf.index.levels[0])\n",
    "    stdPca = pcaDf.groupby(level=[1,2]).std()/np.sqrt(n)*1.96\n",
    "\n",
    "    # make the scatterplot\n",
    "    for ident,emo in meanPca.index:\n",
    "        \n",
    "        thisMean = meanPca.ix[ident].ix[emo]\n",
    "        thisStd = stdPca.ix[ident].ix[emo]\n",
    "\n",
    "        myAx = ax.errorbar(thisMean[0], thisMean[1],\n",
    "                           xerr = thisStd[0],yerr=thisStd[1],\n",
    "                           c=stackColors[emoReverse[emo]],\n",
    "                           ecolor='gray',\n",
    "                           marker='ov'[identReverse[ident]],\n",
    "                           markersize=15,\n",
    "                           markeredgecolor='gray',\n",
    "                           markeredgewidth=.5,\n",
    "                           label=ident+' '+emo\n",
    "                           );\n",
    "\n",
    "    sns.despine()\n",
    "    ax.set_xlim(-1.5,2);ax.set_ylim(-1,1)\n",
    "    ax.set_ylabel(\"PC 2\"); ax.set_xlabel(\"PC 1\")\n",
    "    #ax.legend(loc='upper right')\n",
    "\n",
    "    return myAx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to plot the pictures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def makePicturePCA(pcaDf,ax,identDict=identDict,emoDict=emoDict,myZoom=.15):\n",
    "    \n",
    "    # average over all participants\n",
    "    meanPca = pcaDf.groupby(level=[1,2]).mean()\n",
    "    # make the scatterplot\n",
    "    myAx = ax.scatter(meanPca[0], meanPca[1])\n",
    "\n",
    "    # annoate it with the index labels from the dataframe\n",
    "    for label, x, y in zip(meanPca.index, meanPca[0], meanPca[1]):\n",
    "\n",
    "        imagebox = OffsetImage(read_png(picList[identReverse[label[0]] ][emoReverse[label[1]] ]), zoom=myZoom)\n",
    "        ab = AnnotationBbox(imagebox, [x,y],pad=0)\n",
    "        ax.add_artist(ab)\n",
    "\n",
    "    sns.despine()        \n",
    "    ax.set_ylabel(\"PC 2\"); ax.set_xlabel(\"PC 1\")\n",
    "    ax.set_xlim(-1.5,2);ax.set_ylim(-1,1)\n",
    "    \n",
    "    return myAx;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe379da09d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ii5LtF88RtpeIkwmTvV2anTkc3yewDlkyIWh2KdKMtJxSOBanKAjjnLDeoBH6TPMxqU0Z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qG1voTRNBqNxs5Tjo+qasqsRErYvWuRXrtH4FqSZMZyv83hoMWR3XuIwwBHpFy/1Obm9/4YG088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yZTi6uEh0SjDe3GZhuc/pF86yef4CzXzO6SBiaXlE1usQhhFJEBCFAUkvo64aTFWzPZ5gmoad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\npB6w4JYkSZIkqQcsuCVJkiRJ6oH/D4yOzG1moQDlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3901cfc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1,ax2 ) = plt.subplots( 1,2,figsize=(16,8) );\n",
    "makeScatterPCA(pcaDf,ax1);\n",
    "makePicturePCA(pcaDf,ax2,myZoom=.15);\n",
    "#plt.savefig('../figures/pcaScatter.png',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With weights projected into face space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "participantList = participantDf.index.levels[0]\n",
    "for i in range(len(participantList)):\n",
    "    time.sleep(0.01)\n",
    "    thisDf = participantDf.ix[participantList[:i+1]]\n",
    "    this_pca = sklearnPCA(n_components= 5 )\n",
    "    this_Y = this_pca.fit_transform(thisDf)\n",
    "\n",
    "    f, ( ax1 ) = plt.subplots( 1,2,figsize=(16,8) );\n",
    "\n",
    "    plotPCWeights(this_Y,\n",
    "                  this_pca.components_,\n",
    "                  this_pca.explained_variance_ratio_,\n",
    "                  ax1);\n",
    "    f.suptitle(\"sample size: \"+str(i+1),fontsize=30)\n",
    "    f.savefig('../moviePics/pcaWeights'+('00'+str(i+1))[-3:]+'.png');\n",
    "    plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../moviePics/pcaWeights090.png',\n",
       " '../moviePics/pcaWeights091.png',\n",
       " '../moviePics/pcaWeights092.png',\n",
       " '../moviePics/pcaWeights093.png',\n",
       " '../moviePics/pcaWeights094.png']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcaPics = getFile('../moviePics/','pcaWeights*.png')\n",
    "pcaPics[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With scatterplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "participantList = participantDf.index.levels[0]\n",
    "for i in range(len(participantList)):\n",
    "    thisDf = participantDf.ix[participantList[:i+1]]\n",
    "    pcaDf = makeParticipantPCA(thisDf)\n",
    "    f, (ax1,ax2 ) = plt.subplots( 1,2,figsize=(16,8) );\n",
    "    makeScatterPCA(pcaDf,ax1);\n",
    "    makePicturePCA(pcaDf,ax2);\n",
    "    f.suptitle(\"sample size: \"+str(i+1),fontsize=30)\n",
    "    f.savefig('../moviePics/pcaHybrid'+('00'+str(i+1))[-3:]+'.png');\n",
    "    plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../moviePics/pcaHybrid090.png',\n",
       " '../moviePics/pcaHybrid091.png',\n",
       " '../moviePics/pcaHybrid092.png',\n",
       " '../moviePics/pcaHybrid093.png',\n",
       " '../moviePics/pcaHybrid094.png']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcaPics = getFile('../moviePics/','pcaHybrid*.png')\n",
    "pcaPics[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
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 },
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}