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d3a3b90 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 | '''Feature decoding evaluation.'''
import argparse
from itertools import product
import os
import re
from bdpy.dataform import Features, DecodedFeatures
from bdpy.evals.metrics import profile_correlation, pattern_correlation, pairwise_identification
import hdf5storage
import numpy as np
import pandas as pd
import yaml
# Main #######################################################################
def featdec_eval(
decoded_feature_dir,
true_feature_dir,
output_file='./accuracy.pkl.gz',
subjects=None,
rois=None,
features=None,
feature_index_file=None,
feature_decoder_dir=None,
single_trial=False
):
'''Evaluation of feature decoding.
Input:
- deocded_feature_dir
- true_feature_dir
Output:
- output_file
Parameters:
TBA
'''
# Display information
print('Subjects: {}'.format(subjects))
print('ROIs: {}'.format(rois))
print('')
print('Decoded features: {}'.format(decoded_feature_dir))
print('')
print('True features (Test): {}'.format(true_feature_dir))
print('')
print('Layers: {}'.format(features))
print('')
if feature_index_file is not None:
print('Feature index: {}'.format(feature_index_file))
print('')
# Loading data ###########################################################
# True features
if feature_index_file is not None:
features_test = Features(true_feature_dir, feature_index=feature_index_file)
else:
features_test = Features(true_feature_dir)
# Decoded features
decoded_features = DecodedFeatures(decoded_feature_dir)
# Evaluating decoding performances #######################################
if os.path.exists(output_file):
print('Loading {}'.format(output_file))
perf_df = pd.read_pickle(output_file)
else:
print('Creating an empty dataframe')
perf_df = pd.DataFrame(columns=[
'layer', 'subject', 'roi',
'profile correlation', 'pattern correlation', 'identification accuracy'
])
update_df = False
for layer in features:
print('Layer: {}'.format(layer))
for subject, roi in product(subjects, rois):
print('Subject: {} - ROI: {}'.format(subject, roi))
if len(perf_df.query(
'layer == "{}" and subject == "{}" and roi == "{}"'.format(
layer, subject, roi
)
)) > 0:
print('Already done. Skipped.')
continue
update_df = True
pred_y = decoded_features.get(layer=layer, subject=subject, roi=roi)
pred_labels = decoded_features.selected_label
if single_trial:
pred_labels = [re.match('sample\d*-(.*)', x).group(1) for x in pred_labels]
true_labels = pred_labels
true_y = features_test.get(layer=layer, label=true_labels)
if not np.array_equal(pred_labels, true_labels):
y_index = [np.where(np.array(true_labels) == x)[0][0] for x in pred_labels]
true_y_sorted = true_y[y_index]
else:
true_y_sorted = true_y
# Load Y mean and SD
# Proposed by Ken Shirakawa. See https://github.com/KamitaniLab/brain-decoding-cookbook/issues/13.
norm_param_dir = os.path.join(
feature_decoder_dir,
layer, subject, roi,
'model'
)
train_y_mean = hdf5storage.loadmat(os.path.join(norm_param_dir, 'y_mean.mat'))['y_mean']
train_y_std = hdf5storage.loadmat(os.path.join(norm_param_dir, 'y_norm.mat'))['y_norm']
r_prof = profile_correlation(pred_y, true_y_sorted)
r_patt = pattern_correlation(pred_y, true_y_sorted, mean=train_y_mean, std=train_y_std)
if single_trial:
ident = pairwise_identification(pred_y, true_y, single_trial=True, pred_labels=pred_labels, true_labels=true_labels)
else:
ident = pairwise_identification(pred_y, true_y_sorted)
print('Mean profile correlation: {}'.format(np.nanmean(r_prof)))
print('Mean pattern correlation: {}'.format(np.nanmean(r_patt)))
print('Mean identification accuracy: {}'.format(np.nanmean(ident)))
new_df = pd.DataFrame({
'layer': [layer],
'subject': [subject],
'roi': [roi],
'profile correlation': [r_prof.flatten()],
'pattern correlation': [r_patt.flatten()],
'identification accuracy': [ident.flatten()],
})
perf_df = pd.concat([perf_df, new_df], ignore_index=True)
print(perf_df)
# Save the results
if update_df:
perf_df.to_pickle(output_file, compression='gzip')
print('Saved {}'.format(output_file))
print('All done')
return output_file
# Entry point ################################################################
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'conf',
type=str,
help='analysis configuration file',
)
args = parser.parse_args()
conf_file = args.conf
with open(conf_file, 'r') as f:
conf = yaml.safe_load(f)
conf.update({
'__filename__': os.path.splitext(os.path.basename(conf_file))[0]
})
if 'analysis name' in conf:
analysis_name = conf['analysis name']
else:
analysis_name = ''
decoded_feature_dir = os.path.join(
conf['decoded feature dir'],
analysis_name,
'decoded_features',
conf['network']
)
if 'feature index file' in conf:
feature_index_file = os.path.join(conf['training feature dir'][0], conf['network'], conf['feature index file'])
else:
feature_index_file = None
if 'test single trial' in conf:
single_trial = conf['test single trial']
else:
single_trial = False
featdec_eval(
decoded_feature_dir,
os.path.join(conf['test feature dir'][0], conf['network']),
output_file=os.path.join(decoded_feature_dir, 'accuracy.pkl.gz'),
subjects=list(conf['test fmri'].keys()),
rois=list(conf['rois'].keys()),
features=conf['layers'],
feature_index_file=feature_index_file,
feature_decoder_dir=os.path.join(
conf['feature decoder dir'],
analysis_name,
conf['network']
),
single_trial=single_trial
)
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