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