100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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from config import names as gs
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from config import conf
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import sys
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import math
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import os
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def get_stats():
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annotation_times = np.genfromtxt(conf.annotation_path, delimiter=',', skip_header=1)[:, 1:]
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shop_duration = annotation_times[:, 1] - annotation_times[:, 0]
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print
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print 'Time spent in the shop:'
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print 'MEAN', np.mean(shop_duration/60.), 'min'
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print 'STD', np.std(shop_duration/60.), 'min'
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def get_feature_correlations():
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# find the window size that was most frequently chosen
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hist_sum = np.zeros((len(conf.all_window_sizes)), dtype=int)
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for trait in xrange(0, conf.n_traits):
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for si in xrange(0, 100):
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filename = conf.get_result_filename(conf.annotation_all, trait, False, si, add_suffix=True)
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if os.path.exists(filename):
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data = np.load(filename)
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chosen_window_indices = data['chosen_window_indices']
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hist, _ = np.histogram(chosen_window_indices, bins=np.arange(-0.5, len(conf.all_window_sizes), 1))
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hist_sum += hist
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else:
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print 'did not find', filename
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ws = conf.all_window_sizes[np.argmax(hist_sum)]
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# load features for the most frequently chosen time window
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x_file, y_file, id_file = conf.get_merged_feature_files(ws)
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x_ws = np.genfromtxt(x_file, delimiter=',', skip_header=1)
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ids_ws = np.genfromtxt(id_file, delimiter=',', skip_header=1).astype(int)[:,0]
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y = np.genfromtxt(conf.binned_personality_file, skip_header=1, usecols=xrange(1, conf.n_traits+1), delimiter=',')
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y_ws = np.genfromtxt(y_file, delimiter=',', skip_header=1).astype(int)
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# compute average feature per person
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avg_x_ws = np.zeros((conf.n_participants, conf.max_n_feat))
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for p in xrange(0,conf.n_participants):
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avg_x_ws[p,:] = np.mean(x_ws[ids_ws == p, :], axis=0)
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feature_correlations_avg = []
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for fi in xrange(0, conf.max_n_feat):
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C_avg = np.corrcoef(y.transpose(), avg_x_ws[:, fi])[-1][:-1]
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feature_correlations_avg.append(C_avg)
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feature_correlations_avg = np.array(feature_correlations_avg)
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# find the 5th to highest correlation for each trait and write them into a .tex table - see Table 4 in SI
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n = 15
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highest_correlated_features = []
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highest_correlated_features_lists = []
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highest_correlated_features_names = []
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for t in xrange(0, conf.n_traits):
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hcf = feature_correlations_avg[:,t].argsort()[-n:]
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locallist = []
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for f in hcf:
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if f not in highest_correlated_features:
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highest_correlated_features.append(f)
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highest_correlated_features_names.append(gs.full_long_label_list[f].lower())
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locallist.append(f)
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highest_correlated_features_lists.append(locallist)
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features = zip(highest_correlated_features_names, highest_correlated_features)
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highest_correlated_features = [y for (x,y) in sorted(features)]
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#highest_correlated_features.sort()
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filename = conf.figure_folder + '/table4.tex'
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print len(highest_correlated_features)
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with open(filename, 'w') as f:
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f.write('feature&Neur.&Extr.&Open.&Agree.&Consc.&PCS&CEI')
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f.write('\\\\\n\hline\n')
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for fi in highest_correlated_features:
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f.write(gs.full_long_label_list[fi])
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for t in xrange(0, conf.n_traits):
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fc = feature_correlations_avg[fi,t]
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if math.isnan(fc):
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f.write('&-')
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elif fi in highest_correlated_features_lists[t]:
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f.write('&\\textbf{'+'%.2f}'%fc)
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else:
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f.write('&'+'%.2f'%fc)
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f.write('\\\\\n')
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print
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print filename, 'written'
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if __name__ == "__main__":
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import os
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if not os.path.exists(conf.figure_folder):
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os.makedirs(conf.figure_folder)
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get_stats() # prints statistics on the time participants spent inside the shop
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get_feature_correlations() # Table 4
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