def predict_all(): # add threads to a list, and wait for all of them in the end threads = [] for trait in trait_list: for si in xrange(low_repetitions, num_repetitions): fname = conf.get_result_filename(annotation_value, trait, shuffle_labels, si, add_suffix=True) if not os.path.exists(fname): thread = threading.Thread(target=save_predictions, args=(trait, conf.get_result_filename(annotation_value, trait, shuffle_labels, si), si)) sys.stdout.flush() thread.start() threads.append(thread) else: print "existing solution:", fname for thread in threads: thread.join() print 'waiting to join'
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,
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 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[
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)