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