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evaluation code

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Sabrina Hoppe 2018-05-05 22:22:21 +02:00
parent fc7973a49b
commit 3d3cebb956
6 changed files with 660 additions and 13 deletions
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234
05_plot_weights.py Normal file
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import numpy as np
import matplotlib.pyplot as plt
from config import conf
import os, sys
import pandas as pns
from config import names as gs
import getopt
import matplotlib.gridspec as gridspec
from sklearn.metrics import f1_score
import seaborn as sns
sns.set(style='whitegrid', color_codes=True)
sns.set_context('poster')
dark_color = sns.xkcd_rgb['charcoal grey']
light_color = sns.xkcd_rgb['cloudy blue']
def plot_weights():
# for each personality trait, compute the list of median feature importances across all cross validation folds and iterations
medianlist = []
for t in xrange(0, conf.n_traits):
medianlist.append(
list(imp_df.loc[imp_df['T'] == t].groupby(by='feat_num')['feature importance'].median()))
# find the 5th to highest feature importance for each trait and write their importances into a .tex table - see Table 2, SI
n = 15
most_important_features = []
most_important_features_lists = []
for ml in medianlist:
locallist = []
for i in xrange(1,(n+1)):
fn = gs.full_long_label_list[int(np.argsort(np.array(ml))[-i])]
locallist.append(fn)
if fn not in most_important_features:
most_important_features.append(fn)
most_important_features_lists.append(locallist)
most_important_features.sort()
# write the full list of feature importances into a .tex table - shown in Table 2, SI
filename = conf.figure_folder + '/table2.tex'
with open(filename, 'w') as f:
f.write('feature&Neur.&Extr.&Open.&Agree.&Consc.&PCS&CEI')
f.write('\\\\\n\hline\n')
for fi in xrange(0, len(most_important_features)):
f.write(most_important_features[fi])
for t in xrange(0, conf.n_traits):
m = imp_df[(imp_df['T'] == t)&(imp_df.feature == most_important_features[fi])]['feature importance'].median()
if most_important_features[fi] in most_important_features_lists[t]:
f.write('& \\textbf{' + '%.3f}' % m)
else:
f.write('&' + '%.3f' % m)
f.write('\\\\\n')
print filename, 'written.'
# create Figure 2
# first collect the set of individual top TOP_N features per trait:
TOP_N = 10
featlabels = []
for trait in xrange(0, conf.n_traits):
basedata = imp_df.loc[imp_df['T'] == trait]
gp = basedata.groupby(by='feature')['feature importance'].median()
order = gp.sort_values(ascending=False)
featlabels.extend(order[:TOP_N].keys())
super_feats = np.unique(np.array(featlabels))
# collect the sum of feature importances for these labels, to sort the features by their median
super_feats_importance_sum = np.zeros((len(super_feats)))
for i in xrange(0, len(super_feats)):
super_feats_importance_sum[i] = imp_df[imp_df.feature==super_feats[i]].groupby(by=['T'])['feature importance'].median().sum()
super_feats_sort_indices = np.argsort(super_feats_importance_sum)[::-1]
# add some interesting features from related work to the list of features whose importance will be shown
must_have_feats = [
'inter quartile range x', 'range x', 'maximum x', 'std x', '1st quartile x', 'range pupil diameter', 'median y',
'mean difference of subsequent x', 'mean fixation duration', '3rd quartile y',
'fixation rate', 'mean saccade amplitude', 'dwelling time'
]
# but only add them if they are not in the list yet
additional_feats = np.array([a for a in must_have_feats if a not in super_feats], dtype=object)
# collect the sum of feature importances for these labels as well, so they can be sorted by their median importance in the plot
additional_feats_importance_sum = np.zeros((len(additional_feats)))
for trait in xrange(0, conf.n_traits):
basedata = imp_df.loc[imp_df['T'] == trait]
for i in xrange(0, len(additional_feats)):
logi = basedata.feature == additional_feats[i]
additional_feats_importance_sum[i] += float(basedata[logi]['feature importance'].median())
additional_feats_sort_indices = np.argsort(additional_feats_importance_sum)[::-1]
# create the figure
plt.figure(figsize=(20, 12))
grs = gridspec.GridSpec(len(super_feats) + len(additional_feats) + 1, conf.n_traits)
for trait in xrange(0, conf.n_traits):
# upper part of the figure, i.e. important features
ax = plt.subplot(grs[:len(super_feats),trait])
basedata = imp_df.loc[imp_df['T'] == trait]
feat_importances = []
for i in xrange(0, len(super_feats)):
logi = basedata.feature == super_feats[super_feats_sort_indices][i]
feat_importances.append(list(basedata[logi]['feature importance']))
bp = plt.boxplot(x=feat_importances, #notch=True, labels=super_feats[super_feats_sort_indices],
patch_artist=True, sym='', vert=False, whis='range', positions=np.arange(0,len(feat_importances)))
# asthetics
for i in xrange(0, len(super_feats)):
bp['boxes'][i].set(color=dark_color)
bp['boxes'][i].set(facecolor=light_color)
bp['whiskers'][2 * i].set(color=dark_color, linestyle='-')
bp['whiskers'][2 * i + 1].set(color=dark_color, linestyle='-')
bp['caps'][2 * i].set(color=dark_color)
bp['caps'][2 * i + 1].set(color=dark_color)
bp['medians'][i].set(color=dark_color)
if not trait == 0:
plt.ylabel('')
plt.setp(ax.get_yticklabels(), visible=False)
else:
ax.set_yticklabels(super_feats[super_feats_sort_indices])
xlimmax = 0.47
xticks = [0.15, 0.35]
plt.xlim((0, xlimmax))
plt.xticks(xticks)
plt.setp(ax.get_xticklabels(), visible=False)
# lower part of the figure, i.e. features from related work
ax = plt.subplot(grs[(-len(additional_feats)):, trait])
basedata = imp_df.loc[imp_df['T'] == trait]
feat_importances = []
for i in xrange(0, len(additional_feats)):
logi = basedata.feature == additional_feats[additional_feats_sort_indices][i]
feat_importances.append(basedata[logi]['feature importance'])
bp = plt.boxplot(x=feat_importances, patch_artist=True, sym='', vert=False, whis='range',
positions=np.arange(0,len(feat_importances)))
# asthetics
for i in xrange(0, len(additional_feats)):
bp['boxes'][i].set(color=dark_color)
bp['boxes'][i].set(facecolor=light_color) #, alpha=0.5)
bp['whiskers'][2 * i].set(color=dark_color, linestyle='-')
bp['whiskers'][2 * i + 1].set(color=dark_color, linestyle='-')
bp['caps'][2 * i].set(color=dark_color)
bp['caps'][2 * i + 1].set(color=dark_color)
bp['medians'][i].set(color=dark_color) #, linewidth=.1)
if not trait == 0:
plt.ylabel('')
plt.setp(ax.get_yticklabels(), visible=False)
else:
ax.set_yticklabels(additional_feats[additional_feats_sort_indices])
plt.xlim((0, xlimmax))
plt.xticks(xticks)
if trait == 3:
plt.xlabel(conf.medium_traitlabels[trait] + '\n\nFeature Importance')
else:
plt.xlabel(conf.medium_traitlabels[trait])
filename = conf.figure_folder + '/figure2.pdf'
plt.savefig(filename, bbox_inches='tight')
print filename.split('/')[-1], 'written.'
plt.close()
if __name__ == "__main__":
# target file names - save table of F1 scores, feature importances and majority predictions there
datapathI = conf.get_result_folder(conf.annotation_all) + '/f1s.csv' # F1 scores from each iteration
datapathII = conf.get_result_folder(conf.annotation_all) + '/feature_importance.csv' # Feature importance from each iteration
datapathIII = conf.get_result_folder(conf.annotation_all) + '/majority_predictions.csv' # Majority voting result for each participant over all iterations
if not os.path.exists(conf.figure_folder):
os.mkdir(conf.figure_folder)
# if target files do not exist yet, create them
if (not os.path.exists(datapathI)) or (not os.path.exists(datapathII)) or (not os.path.exists(datapathIII)):
f1s = []
feature_importances = []
majority_predictions = []
for trait in xrange(0, conf.n_traits):
predictions = np.zeros((conf.n_participants, conf.max_n_iter),dtype=int)-1
ground_truth = np.loadtxt(conf.binned_personality_file, delimiter=',', skiprows=1, usecols=(trait+1,))
for si in xrange(0, conf.max_n_iter):
filename = conf.get_result_filename(conf.annotation_all, trait, False, si, add_suffix=True)
if os.path.exists(filename):
data = np.load(filename)
if (data['predictions'] > 0).all():
assert data['f1'] == f1_score(ground_truth, data['predictions'], average='macro')
f1s.append([data['f1'], conf.medium_traitlabels[trait]])
else:
# if there was no time window for a condition, like if shopping data only is evaluated,
# the F1 score for each person without a single time window will be set to -1
# but should not be used as such to compute the mean F1 score.
# Thus, here the F1 score is re-computed on the relevant participants only.
pr = data['predictions']
pr = pr[pr > 0]
dt = ground_truth[pr > 0]
f1s.append([f1_score(dt, pr, average='macro'), conf.medium_traitlabels[trait]])
for outer_cv_i in xrange(0, 5): # number outer CV, not person anymore
for fi in xrange(0, conf.max_n_feat):
feature_importances.append([data['feature_importances'][outer_cv_i, fi], trait, gs.full_long_label_list[fi], fi])
predictions[:,si] = data['predictions']
else:
print 'did not find', filename
# compute majority voting for each participant over all iterations
for p in xrange(0, conf.n_participants):
(values, counts) = np.unique(predictions[p, predictions[p,:]>0], return_counts=True)
ind = np.argmax(counts)
majority_predictions.append([values[ind], p, conf.medium_traitlabels[trait]])
f1s_df = pns.DataFrame(data=f1s, columns=['F1', 'trait'])
f1s_df.to_csv(datapathI)
imp_df = pns.DataFrame(data=feature_importances, columns=['feature importance', 'T', 'feature', 'feat_num'])
imp_df.to_csv(datapathII)
majority_predictions_df = pns.DataFrame(data=majority_predictions, columns=['prediction','participant','trait'])
majority_predictions_df.to_csv(datapathIII)
else:
print 'No new results are collected as previous results were available. If you want to overwrite them, please delete the following files:'
print datapathI
print datapathII
print datapathIII
f1s_df = pns.read_csv(datapathI)
imp_df = pns.read_csv(datapathII)
majority_predictions_df = pns.read_csv(datapathIII)
plot_weights() # Figure 2

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import numpy as np
import matplotlib.pyplot as plt
from config import conf
import os, sys
import pandas as pns
from config import names as gs
import getopt
import matplotlib.gridspec as gridspec
from sklearn.metrics import f1_score, accuracy_score
import seaborn as sns
sns.set(style='whitegrid', color_codes=True)
sns.set_context('poster')
dark_color = sns.xkcd_rgb['charcoal grey']
light_color = sns.xkcd_rgb['cloudy blue']
max_n_feat = conf.max_n_feat
m_iter = conf.max_n_iter
featurelabels = gs.full_long_label_list
participant_ids = np.arange(0, conf.n_participants)
def plot_overview():
all_baselines.groupby(by=['trait', 'clf_name'])['F1'].mean().to_csv(conf.figure_folder +
'/figure1.csv')
print 'Figure1.csv written'
sns.set(font_scale=2.1)
plt.figure(figsize=(20, 10))
ax = plt.subplot(1,1,1)
sns.barplot(x='trait', y='F1', hue='clf_name', data=all_baselines, capsize=.05, errwidth=3,
linewidth=3, estimator=np.mean, edgecolor=dark_color,
palette={'our classifier': sns.xkcd_rgb['windows blue'],
'most frequent class': sns.xkcd_rgb['faded green'],
'random guess':sns.xkcd_rgb['greyish brown'],
'label permutation':sns.xkcd_rgb['dusky pink']
}
)
plt.plot([-0.5,6.5], [0.33, 0.33], c=dark_color, linestyle='--', linewidth=3, label='theoretical chance level')
handles, labels = ax.get_legend_handles_labels()
ax.legend([handles[1], handles[2], handles[3], handles[4], handles[0]], [labels[1], labels[2], labels[3], labels[4], labels[0]], fontsize=20)
plt.xlabel('')
plt.ylabel('F1 score', fontsize=20)
plt.ylim((0, 0.55))
filename = conf.figure_folder + '/figure1.pdf'
plt.savefig(filename, bbox_inches='tight')
plt.close()
print 'wrote', filename.split('/')[-1]
if __name__ == "__main__":
# collect F1 scores for classifiers on all data from a file that was written by evaluation_single_context.py
datapath = conf.get_result_folder(conf.annotation_all) + '/f1s.csv'
if not os.path.exists(datapath):
print 'could not find', datapath
print 'consider (re-)running evaluation_single_context.py'
sys.exit(1)
our_classifier = pns.read_csv(datapath)
our_classifier['clf_name'] = 'our classifier'
# baseline 1: guess the most frequent class from each training set that was written by train_baseline.py
datapath = conf.result_folder + '/most_frequ_class_baseline.csv'
if not os.path.exists(datapath):
print 'could not find', datapath
print 'consider (re-)running train_baseline.py'
sys.exit(1)
most_frequent_class_df = pns.read_csv(datapath)
most_frequent_class_df['clf_name'] = 'most frequent class'
# compute all other baselines ad hoc
collection = []
for trait in xrange(0, conf.n_traits):
# baseline 2: random guess
truth = np.genfromtxt(conf.binned_personality_file, skip_header=1, usecols=(trait+1,), delimiter=',')
for i in xrange(0, 100):
rand_guess = np.random.randint(1, 4, conf.n_participants)
f1 = f1_score(truth, rand_guess, average='macro')
collection.append([f1, conf.medium_traitlabels[trait], i, 'random guess'])
# baseline 3: label permutation test
# was computed using label_permutation_test.sh and written into results. ie. is just loaded here
for si in xrange(0, m_iter):
filename_rand = conf.get_result_filename(conf.annotation_all, trait, True, si, add_suffix=True)
if os.path.exists(filename_rand):
data = np.load(filename_rand)
pr = data['predictions']
dt = truth[pr > 0]
pr = pr[pr > 0]
f1 = f1_score(dt, pr, average='macro')
collection.append([f1, conf.medium_traitlabels[trait], si, 'label permutation'])
else:
print 'did not find', filename_rand
print 'consider (re-)running label_permutation_test.sh'
sys.exit(1)
collectiondf = pns.DataFrame(data=collection,columns=['F1','trait','iteration','clf_name'])
all_baselines = pns.concat([our_classifier, most_frequent_class_df, collectiondf])
plot_overview() # Figure 1

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import numpy as np
from config import conf
import os, sys
from config import names as gs
import pandas as pd
truth = np.genfromtxt(conf.binned_personality_file, skip_header=1, usecols=xrange(1, conf.n_traits+1), delimiter=',')
# all comparisons to perform. Each has
# a name,
# two annotation values that determine if classifiers trained on all data or on specific subsets only will be examined;
# names for both tasks to compare
comparisons = dict({'split halves': [conf.annotation_all, conf.annotation_all, 'first half', 'second half'],
'two ways': [conf.annotation_ways, conf.annotation_ways, 'way there', 'way back'],
'way vs shop in general classifier': [conf.annotation_all, conf.annotation_all, 'both ways' ,'shop'],
'way vs shop in specialised classifier': [conf.annotation_ways, conf.annotation_shop, 'both ways', 'shop'],
'way in specialised classifier vs way in general classifier': [conf.annotation_ways, conf.annotation_all, 'both ways', 'both ways'],
'shop in specialised classifier vs shop in general classifier': [conf.annotation_shop, conf.annotation_all, 'shop', 'shop']
})
def get_majority_vote(predictions):
if len(predictions) == 0:
return -1
(values, counts) = np.unique(predictions, return_counts=True)
ind = np.argmax(counts)
return values[ind]
def get_average_correlation(predA, predB, m_iter):
"""
:param predA: predictions for task A, n_participants x m_iter
:param predB: predictions for task B, n_participants x m_iter
:return:
"""
correlations = []
for si in xrange(0, m_iter):
if predB.ndim == 1:
if np.sum(predA[:,si]) > 0:
A = predA[:,si]
B = predB
consider = (A>0)
A = A[consider]
B = B[consider]
else:
continue
else:
if np.sum(predA[:,si]) > 0 and (np.sum(predB[:,si]) > 0):
A = predA[:,si]
B = predB[:,si]
consider = (A>0) & (B>0)
A = A[consider]
B = B[consider]
else:
continue
correlation = np.corrcoef(np.array([A, B]))[0][1]
correlations.append(correlation)
avg = np.tanh(np.mean(np.arctanh(np.array(correlations))))
return avg
if __name__ == "__main__":
# check if the output target folder already exists and create if not
if not os.path.exists(conf.figure_folder):
os.mkdir(conf.figure_folder)
# collect masks for each participant, annotation (all data, shop, way), window size and subset in question (e.g. first half, or way to the shop)
# each mask is True for samples of a particular participant and subset; False for all others
window_masks = []
for wsi in xrange(0, len(conf.all_window_sizes)):
x_file, y_file, id_file = conf.get_merged_feature_files(conf.all_window_sizes[wsi])
for annotation_value in conf.annotation_values:
ids_ws = np.genfromtxt(id_file, delimiter=',', skip_header=1).astype(int)
if annotation_value == conf.annotation_shop:
ids_ws = ids_ws[ids_ws[:, 1] == conf.time_window_annotation_shop, :]
elif annotation_value == conf.annotation_ways:
ids_ws = ids_ws[(ids_ws[:, 1] == conf.time_window_annotation_wayI) | (ids_ws[:, 1] == conf.time_window_annotation_wayII), :]
for p in xrange(0, conf.n_participants):
ids_ws_p = ids_ws[(ids_ws[:, 0] == p), :]
window_masks.append([annotation_value, p, wsi, 'first half', ids_ws_p[:, 2] == conf.time_window_annotation_halfI])
window_masks.append([annotation_value, p, wsi, 'second half', ids_ws_p[:, 2] == conf.time_window_annotation_halfII])
window_masks.append([annotation_value, p, wsi, 'way there', ids_ws_p[:, 1] == conf.time_window_annotation_wayI])
window_masks.append([annotation_value, p, wsi, 'way back', ids_ws_p[:, 1] == conf.time_window_annotation_wayII])
window_masks.append([annotation_value, p, wsi, 'shop', ids_ws_p[:, 1] == conf.time_window_annotation_shop])
window_masks.append([annotation_value, p, wsi, 'both ways', np.logical_or(ids_ws_p[:, 1] == conf.time_window_annotation_wayI,ids_ws_p[:, 1] == conf.time_window_annotation_wayII)])
window_masks_df = pd.DataFrame(window_masks, columns=['annotation', 'participant', 'window size index', 'subtask', 'mask'])
# collect predictions for each participant and each setting that is interesting for one of the comparisons
# Results are directly written into figures/table1-5.csv
with open(conf.figure_folder + '/table1-5.csv', 'w') as f:
f.write('comparison')
for trait in xrange(0, conf.n_traits):
f.write(',' + conf.medium_traitlabels[trait])
f.write('\n')
for comp_title, (annotation_value_I, annotation_value_II, subtaskI, subtaskII) in comparisons.items():
f.write(comp_title)
result_filename = conf.result_folder + '/predictions_' + comp_title.replace(' ','_') + '.npz'
if not os.path.exists(result_filename):
print 'computing data for', comp_title
print 'Note taht this might take a while - if the script is run again, intermediate results will be available and speed up all computations.'
predictions_I = np.zeros((conf.n_participants, conf.n_traits, conf.max_n_iter), dtype=int)
predictions_II = np.zeros((conf.n_participants, conf.n_traits, conf.max_n_iter), dtype=int)
for trait in xrange(0, conf.n_traits):
for si in xrange(0, conf.max_n_iter):
filenameI = conf.get_result_filename(annotation_value_I, trait, False, si, add_suffix=True)
filenameII = conf.get_result_filename(annotation_value_II, trait, False, si, add_suffix=True)
if os.path.exists(filenameI) and os.path.exists(filenameII):
dataI = np.load(filenameI)
detailed_predictions_I = dataI['detailed_predictions']
chosen_window_indices_I = dataI['chosen_window_indices']
dataII = np.load(filenameII)
detailed_predictions_II = dataII['detailed_predictions']
chosen_window_indices_II = dataII['chosen_window_indices']
for p, window_index_I, window_index_II, local_detailed_preds_I, local_detailed_preds_II in zip(xrange(0, conf.n_participants), chosen_window_indices_I, chosen_window_indices_II, detailed_predictions_I, detailed_predictions_II):
maskI = window_masks_df[(window_masks_df.annotation == annotation_value_I) &
(window_masks_df.participant == p) &
(window_masks_df['window size index'] == window_index_I) &
(window_masks_df.subtask == subtaskI)
].as_matrix(columns=['mask'])[0][0]
maskII = window_masks_df[(window_masks_df.annotation == annotation_value_II) &
(window_masks_df.participant == p) &
(window_masks_df['window size index'] == window_index_II) &
(window_masks_df.subtask == subtaskII)
].as_matrix(columns=['mask'])[0][0]
predictions_I[p, trait, si] = get_majority_vote(np.array(local_detailed_preds_I)[maskI])
predictions_II[p, trait, si] = get_majority_vote(np.array(local_detailed_preds_II)[maskII])
else:
print 'did not find', filenameI, 'or', filenameII
sys.exit(1)
np.savez(result_filename, predictions_I=predictions_I, predictions_II=predictions_II)
else:
data = np.load(result_filename)
predictions_I = data['predictions_I']
predictions_II = data['predictions_II']
# predictions_I are predictions from one context, predictions_II is the other context
# compute their average correlation and write it to file
for t in xrange(0, conf.n_traits):
corrI = get_average_correlation(predictions_I[:, t, :], predictions_II[:, t, :], 100)
f.write(','+'%.2f'%corrI)
f.write('\n')

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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

37
09_plot_ws_hist.py Normal file
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@ -0,0 +1,37 @@
import numpy as np
import matplotlib.pyplot as plt
import seaborn
from config import conf
import os
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
hist_sum_sum = np.sum(hist_sum)
plt.figure()
ax = plt.subplot(111)
bars = ax.bar(conf.all_window_sizes, hist_sum/float(hist_sum_sum)*100, width=8, tick_label=[str(x) for x in conf.all_window_sizes])
for rect in bars:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width()/2., 1.01*height,
'%d' % (height/100.*hist_sum_sum),
ha='center', va='bottom')
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.xlabel('window size in s')
plt.ylabel('percentage')
plt.savefig('figures/ws_hist.pdf')
plt.close()

47
README.md
View file

@ -25,27 +25,48 @@ reproducing the paper results step by step:
1. __Extract features from raw gaze data__:
`python 00_compute_features.py` to compute gaze features for all participants
Once extracted, the features are stored in `features/ParticipantXX/window_features_YY.npy` where XX is the participant number and YY the length of the sliding window in seconds.
2. __Train random forest classifiers__
`./01 train_classifiers.sh` to reproduce the evaluation setting described in the paper in which each classifier was trained 100 times.
`./02_train_specialized_classifiers.sh` to train specialized classifiers on parts of the data (specifically on data from inside the shop or on the way).
`./01 train_classifiers.sh` to reproduce the evaluation setting described in the paper in which each classifier was trained 100 times.
`./02_train_specialized_classifiers.sh` to train specialized classifiers on parts of the data (specifically on data from inside the shop or on the way).
If the scripts cannot be executed, you might not have the right access permissions to do so. On Linux, you can try `chmod +x 01_train_classifiers.sh`,`chmod +x 02_train_specialized_classifiers.sh` and `chmod +x 03_label_permutation_test.sh` (see below for when/how to use the last script).
If the scripts cannot be executed, you might not have the right access permissions to do so. On Linux, you can try `chmod +x 01_train_classifiers.sh`,`chmod +x 02_train_specialized_classifiers.sh` and `chmod +x 03_label_permutation_test.sh` (see below for when/how to use the last script).
In case you want to call the script differently, e.g. to speed-up the computation or try with different parameters, you can pass the following arguments to `classifiers.train_classifier`:
`-t` trait index between 0 and 6
`-l` lowest number of repetitions, e.g. 0
`-m` max number of repetitions, e.g. 100
`-a` using partial data only: 0 (all data), 1 (way data), 2(shop data)
In case you want to call the script differently, e.g. to speed-up the computation or try with different parameters, you can pass the following arguments to `classifiers.train_classifier`:
`-t` trait index between 0 and 6
`-l` lowest number of repetitions, e.g. 0
`-m` max number of repetitions, e.g. 100
`-a` using partial data only: 0 (all data), 1 (way data), 2(shop data)
In case of performance issues, it might be useful to check `_conf.py` and change `max_n_jobs` to restrict the number of jobs (i.e. threads) running in parallel.
In case of performance issues, it might be useful to check `_conf.py` and change `max_n_jobs` to restrict the number of jobs (i.e. threads) running in parallel.
The results will be saved in `results/A0` for all data, `results/A1` for way data only and `results/A2` for data inside a shop. Each file is named `TTT_XXX.npz`, where TTT is the abbreviation of the personality trait (`O`,`C`,`E`,`A`,`N` for the Big Five and `CEI` or `PCS` for the two curiosity measures). XXX enumerates the classifiers (remember that we always train 100 classifiers for evaluation because there is some randomness involved in the training process).
The results will be saved in `results/A0` for all data, `results/A1` for way data only and `results/A2` for data inside a shop. Each file is named `TTT_XXX.npz`, where TTT is the abbreviation of the personality trait (`O`,`C`,`E`,`A`,`N` for the Big Five and `CEI` or `PCS` for the two curiosity measures). XXX enumerates the classifiers (remember that we always train 100 classifiers for evaluation because there is some randomness involved in the training process).
3. __Evaluate Baselines__
* To train a classifier that always predicts the most frequent personality score range from its current training set, please execute `python 03_train_baseline.py`
* To train classifiers on permuted labels, i.e. perform the so-called label permutation test, please execute `./04_label_permutation_test.sh`
3. __Train baselines__
* To train a classifier that always predicts the most frequent personality score range from its current training set, please execute `python 03_train_baseline.py`
* To train classifiers on permuted labels, i.e. perform the so-called label permutation test, please execute `./04_label_permutation_test.sh`
4. __Performance analysis__
* Run `python 05_plot_weights.py` to extract feature importance scores. These scores will be visualized in `figures/figure2.pdf` which corresponds to Figure 2 in the paper and `figures/table2.tex` which is shown in Table 2 in the supplementary information.
(additionally this step computes F1 scores which are required for the next step, so do not skip it)
* The results obtained from both baselines will be written to disk and read once you execute `python 06_baselines.py`.
A figure illustrating the actual classifiers' performance along with the random results will be written to `figures/figure1.pdf` as well as `figures/figure1.csv` and correspond to Figure 1 in the paper.
5. __Context comparison__
`python 07_evaluation_across_contexts.py` to compute the average correlation coefficients between predictions based on data from different contexts. The table with all coefficients will be written to `figures/table1-5.csv` which can be found in Table 1 and Table 5 in supplementary information.
If (some) files in the results folder are missing, try re-running all one of the bash (\*.sh) scripts again.
6. __Descriptive analysis__
`python 08_descriptive.py` to compute the correlation between each participant's average feature for the most frequently chosen time window and their personality score range. Results are written to four files `figures/table4-1.tex`,`figures/table4-2.tex`,`figures/table4-3.tex`,`figures/table4-4.tex` and are shown together in Table 4 in the supplementary information.
7. __Window Size Histogram__
`python 09_plot_ws_hist.py` to plot a histogram of window sizes chosen during the nested cross validation routine to `figures/ws_hist.pdf`.
All these scripts write intermediate results to disk, i.e. if you start a script a second time, it will be much faster - but the first run can take some time, e.g. up to 8 hours to train classifiers for one context on a 16 core machine; 1 hour to compute correlations between contexts.
## Citation
If you want to cite this project, please use the following Bibtex format: