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