f.write("Training time (s): \n\t{0}\n\n".format(train_time))
# Predictions
f.write("===================\n")
f.write("TEST SET\n")
f.write("===================\n")
print('Start predicting on test...')
start = time.time()
test_pred = model.predict(test_X, model_file)
end = time.time()
predict_test_time = end - start
f.write("Testing prediction time (s):\n\t{0}\n".format(predict_test_time))
print('Finished predicting on testing...')
# Calculate CE/class rate on testing set
test_ce, test_class_rate, test_precision, test_recall, test_f1_score = model.evaluate(test_y, test_pred, cross_entropy_flag = True)
f.write("testing CE:\n\t{0}\n".format(test_ce))
f.write("testing classification rate:\n\t{0}\n".format(test_class_rate))
f.write("testing precision:\n\t{0}\n".format(test_precision))
f.write("testing recall:\n\t{0}\n".format(test_recall))
f.write("testing f1 score:\n\t{0}\n".format(test_f1_score))
# Also write to the summary file
fieldnames = ['algorithm', 'configuration', 'model_file', 'data_set', 'train_time', 'predict_time', 'ce', 'classification_rate', 'precision', 'recall', 'f1']
if os.path.isfile(summary_file):
report = open(summary_file, 'a')
writer = csv.DictWriter(report, fieldnames=fieldnames)
else:
report = open(summary_file, 'w')
writer = csv.DictWriter(report, fieldnames=fieldnames)
f.write("Training time (s): \n\t{0}\n\n".format(train_time))
# Predictions
f.write("===================\n")
f.write("TRAINING\n")
f.write("===================\n")
print('Start predicting on training...')
start = time.time()
train_pred = model.predict(train_X, model_file)
end = time.time()
predict_train_time = end - start
f.write("Training prediction time (s):\n\t{0}\n".format(predict_train_time))
print('Finished predicting on training...')
# Calculate CE/class rate on training set
train_ce, train_class_rate, train_precision, train_recall, train_f1_score = model.evaluate(train_y, train_pred, cross_entropy_flag = True)
f.write("Training CE:\n\t{0}\n".format(train_ce))
f.write("Training classification rate:\n\t{0}\n".format(train_class_rate))
f.write("Training precision:\n\t{0}\n".format(train_precision))
f.write("Training recall:\n\t{0}\n".format(train_recall))
f.write("Training f1 score:\n\t{0}\n".format(train_f1_score))
# Evaluate on valid set
f.write("\n===================\n")
f.write("VALID\n")
f.write("===================\n")
print('Start predicting on validation...')
start = time.time()
valid_pred = model.predict(valid_X, model_file)
end = time.time()
predict_valid_time = end - start
