def score_training_set():
# Generate a data frame that points to the challenge files
tr_files, te_files = phyc.get_files()
score = Challenge2018Score()
for i in range(0, np.size(tr_files, 0)):
gc.collect()
sys.stdout.write('Evaluating training subject: %d/%d' %
(i + 1, np.size(tr_files, 0)))
sys.stdout.flush()
record_name = tr_files.header.values[i][:-4]
predictions = R.classify_record(record_name)
arousals = phyc.import_arousals(tr_files.arousal.values[i])
arousals = np.ravel(arousals)
score.score_record(arousals, predictions, record_name)
auroc = score.record_auroc(record_name)
auprc = score.record_auprc(record_name)
print(' AUROC:%f AUPRC:%f' % (auroc, auprc))
print()
auroc_g = score.gross_auroc()
auprc_g = score.gross_auprc()
print('Training AUROC Performance (gross): %f' % auroc_g)
print('Training AUPRC Performance (gross): %f' % auprc_g)
print()
def evaluate_test_set():
# Generate a data frame that points to the challenge files
tr_files, te_files = phyc.get_files()
for i in range(0, np.size(te_files, 0)):
gc.collect()
print('Evaluating test subject: %d/%d' % (i + 1, np.size(te_files, 0)))
record_name = te_files.header.values[i][:-4]
output_file = os.path.basename(record_name) + '.vec'
predictions = R.classify_record(record_name)
np.savetxt(output_file, predictions, fmt='%.3f')
def score_training_set(model):
try:
os.mkdir('training_output')
except OSError:
pass
# Generate a data frame that points to the challenge files
tr_files, te_files = phyc.get_files()
j = 0
score = Challenge2018Score()
for i in range(0, np.size(tr_files, 0)):
gc.collect()
sys.stdout.write('\nEvaluating training subject: %d/%d' %
(i + 1, np.size(tr_files, 0)))
sys.stdout.flush()
record_name = tr_files.header.values[i][:-4]
predictions, pred_arousal_probabilities, model = T.classify_record(
record_name, model)
arousals = phyc.import_arousals(tr_files.arousal.values[i])
#appiattisce in un array 1D
arousals = np.ravel(arousals)
print("arousals.shape: " + str(arousals.shape))
print("predictions.shape: " + str(predictions.shape))
print("pred_arousal_probabilities.shape: " +
str(pred_arousal_probabilities.shape))
print_arousal_predictions(arousals, pred_arousal_probabilities)
score.score_record(arousals, pred_arousal_probabilities, record_name)
auroc = score.record_auroc(record_name)
auprc = score.record_auprc(record_name)
L.log_info(' AUROC:%f AUPRC:%f' % (auroc, auprc))
# save also training predictions to evaluate voting solutions between different models
output_file = "training_output/" + os.path.basename(
record_name) + '.vec'
L.log_info("Salvo i files esito del training in " + str(output_file))
np.savetxt(output_file, pred_arousal_probabilities, fmt='%.3f')
print()
auroc_g = score.gross_auroc()
auprc_g = score.gross_auprc()
L.log_info('Training AUROC Performance (gross): %f' % auroc_g)
L.log_info('Training AUPRC Performance (gross): %f' % auprc_g)
L.log_info("\n\r ")
return model
def train():
T.init()
# Generate a data frame that points to the challenge files
tr_files, te_files = phyc.get_files()
# For each subject in the training set...
for i in range(0, np.size(tr_files, 0)):
gc.collect()
print('Preprocessing training subject: %d/%d' %
(i + 1, np.size(tr_files, 0)))
record_name = tr_files.header.values[i][:-4]
T.preprocess_record(record_name)
T.finish()
def evaluate_test_set():
# Generate a data frame that points to the challenge files
tr_files, te_files = phyc.get_files()
try:
os.mkdir('test_output')
except OSError:
pass
for f in glob.glob('models/*.vec'):
os.remove(f)
for i in range(0, np.size(te_files, 0)):
gc.collect()
print('Evaluating test subject: %d/%d' % (i + 1, np.size(te_files, 0)))
record_name = te_files.header.values[i][:-4]
output_file = "test_output/" + os.path.basename(record_name) + '.vec'
print("Salvo i files esito del test in " + str(output_file))
predictions, pred_arousal_probabilities = T.classify_record(
record_name)
np.savetxt(output_file, pred_arousal_probabilities, fmt='%.3f')