feature extraction code

config/conf.py

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+import numpy as np
+
+# global parameters
+n_participants = 42
+n_traits = 7
+max_n_feat = 207
+max_n_iter = 100
+all_window_sizes = [5, 15, 30, 45, 60, 75, 90, 105, 120, 135]
+all_shop_window_sizes = [5, 15]  # at least 3/4 of the people have a time window in these times
+
+# cross validation paramters
+n_inner_folds = 3
+n_outer_folds = 5
+
+# Random Forest Parameters
+tree_max_features = 15
+tree_max_depth = 5
+n_estimators = 100
+max_n_jobs = 5
+
+# given a window size, determine step size correctly for even and odd numbers
+def get_step_size(window_size):
+	step_size = window_size / 2.0
+	if step_size * 10 % 2 == 0:
+		step_size = int(step_size)
+	return step_size
+
+# relative paths
+data_folder = 'data'
+info_folder = 'info'
+feature_folder = 'features'
+result_folder = 'results'
+figure_folder = 'figures'
+annotation_path = info_folder + '/annotation.csv'
+binned_personality_file = info_folder + '/binned_personality.csv'
+personality_sex_age_file = info_folder + '/personality_sex_age.csv'
+
+# load the personality trait names from file and map them to abbreviations
+traitlabels = np.loadtxt(binned_personality_file, delimiter=',', dtype=str)[0, 1:]
+def get_abbr(s):
+	return ''.join(item[0] for item in s.split() if item[0].isupper())
+medium_traitlabels = [get_abbr(s) if (" " in s) else s for s in traitlabels]
+short_traitlabels = [''.join(item[0] for item in tl.split() if item[0].isupper()) for tl in traitlabels]
+
+
+# dynamically create relative paths for result files to create
+def get_result_folder(annotation_val):
+	return result_folder + '/A' + str(annotation_val)
+
+def get_result_filename(annotation_val, trait, shuffle_labels, i, add_suffix=False):
+	filename = get_result_folder(annotation_val) + '/' + short_traitlabels[trait]
+	if shuffle_labels:
+		filename += '_rnd'
+	filename += '_' + str(i).zfill(3)
+	if add_suffix:
+		filename += '.npz'
+	return filename
+
+def get_feature_folder(participant):
+	return feature_folder + '/Participant' + str(participant).zfill(2)
+
+def get_merged_feature_files(window_size):
+	return feature_folder + '/merged_features_' + str(window_size) + '.csv', feature_folder + '/merged_traits_' + str(window_size) + '.csv', feature_folder + '/merged_ids_' + str(window_size) + '.csv'
+
+def get_data_folder(participant):
+	return data_folder + '/Participant' + str(participant).zfill(2)
+
+def get_window_times_file(participant, window_size):
+	return get_feature_folder(participant) + "/window_times_" + str(window_size) + '.npy'
+
+def get_window_features_file(participant, window_size):
+	return get_feature_folder(participant) + "/window_features_" + str(window_size) + '.npy'
+
+def get_overall_features_file(participant):
+	return get_feature_folder(participant) + "/overall_features.npy"
+
+
+# parameters for fixation/saccade detection
+fixation_radius_threshold = 0.025
+fixation_duration_threshold = 0.1
+saccade_min_velocity = 2
+max_saccade_duration = 0.5
+
+# annotation constants (as given as arguments to train_classifier, and as used for file names in result_folder)
+annotation_all = 0
+annotation_ways = 1
+annotation_shop = 2
+annotation_values = [annotation_all, annotation_ways, annotation_shop]
+
+# annotations used in merged_ids_* files in the feature_folder
+# column 1
+time_window_annotation_wayI = 1
+time_window_annotation_shop = 2
+time_window_annotation_wayII = 3
+# column 2
+time_window_annotation_halfI = 1
+time_window_annotation_halfII = 2