if __name__ == "__main__":
# parse the command line arguments
parser = argparse.ArgumentParser()
parser.set_defaults(epochs=200)
parser.add_argument('--data',
help='prepared data CSV file path',
type=validate_existing_filepath)
parser.add_argument('--model',
help='path to the trained model file',
type=validate_existing_filepath)
parser.add_argument(
'--print_stats',
action='store_true',
default=False,
help='print evaluation stats for the model predictions - if '
'your data has tagging')
parser.add_argument('--output',
help='path to location for inference output file',
type=validate_parent_exists)
args = parser.parse_args()
data_path = absolute_path(args.data)
model_path = absolute_path(args.model)
print_stats = args.print_stats
output_path = absolute_path(args.output)
data_set = NpSemanticSegData(data_path)
results = classify_collocation(data_set.test_set, model_path, args.epochs)
if print_stats and (data_set.is_y_labels is not None):
y_labels = data_set.test_set_y
print_evaluation(y_labels, results)
write_results(results, output_path)
writer.writerow([result])
print("Results of inference saved in {0}".format(output))
if __name__ == "__main__":
# parse the command line arguments
parser = NeonArgparser()
parser.set_defaults(epochs=200)
parser.add_argument('--data', help='prepared data CSV file path',
type=validate_existing_filepath)
parser.add_argument('--model', help='path to the trained model file',
type=validate_existing_filepath)
parser.add_argument('--print_stats', action='store_true', default=False,
help='print evaluation stats for the model predictions - if '
'your data has tagging')
parser.add_argument('--output', help='path to location for inference output file',
type=validate_parent_exists)
args = parser.parse_args()
data_path = absolute_path(args.data)
model_path = absolute_path(args.model)
print_stats = args.print_stats
output_path = absolute_path(args.output)
# generate backend
be = gen_backend(batch_size=10)
data_set = NpSemanticSegData(data_path, train_to_test_ratio=1)
results = classify_collocation(data_set, model_path, args.epochs, args.callback_args)
if print_stats and (data_set.is_y_labels is not None):
y_labels = extract_y_labels(data_path)
print_evaluation(y_labels, results.argmax(1))
write_results(results.argmax(1), output_path)
model.save(model_file_path)
# set evaluation error rates
error_rate, test_accuracy_rate, precision_recall_rate = model.eval(dataset.test_set)
neon_logger.display('Misclassification error = %.1f%%' %
(error_rate * 100))
neon_logger.display('Test accuracy rate = %.1f%%' %
(test_accuracy_rate * 100))
neon_logger.display('precision rate = %s!!' %
(str(precision_recall_rate[0])))
neon_logger.display('recall rate = %s!!' %
(str(precision_recall_rate[1])))
if __name__ == "__main__":
# parse the command line arguments
parser = NeonArgparser()
parser.set_defaults(epochs=200)
parser.add_argument('--data', type=validate_existing_filepath,
help='Path to the CSV file where the prepared dataset is saved')
parser.add_argument('--model_path', type=validate_parent_exists,
help='Path to save the model')
args = parser.parse_args()
data_path = absolute_path(args.data)
model_path = absolute_path(args.model_path)
# generate backend
be = gen_backend(batch_size=64)
# load data sets from file
data_set = NpSemanticSegData(data_path, train_to_test_ratio=0.8)
# train the mlp classifier
train_mlp_classifier(data_set, model_path, args.epochs, args.callback_args)
input_dim = dataset.train_set_x.shape[1]
model.build(input_dim)
# run fit
model.fit(dataset.train_set)
# save model params
model.save(model_file_path)
# set evaluation error rates
loss, binary_accuracy, precision, recall, f1 = model.eval(dataset.test_set)
print('loss = %.1f%%' % (loss))
print('Test binary_accuracy rate = %.1f%%' % (binary_accuracy * 100))
print('Test precision rate = %.1f%%' % (precision * 100))
print('Test recall rate = %.1f%%' % (recall * 100))
print('Test f1 rate = %.1f%%' % (f1 * 100))
if __name__ == "__main__":
# parse the command line arguments
parser = argparse.ArgumentParser()
parser.set_defaults(epochs=200)
parser.add_argument('--data', type=validate_existing_filepath,
help='Path to the CSV file where the prepared dataset is saved')
parser.add_argument('--model_path', type=validate_parent_exists,
help='Path to save the model')
args = parser.parse_args()
data_path = absolute_path(args.data)
model_path = absolute_path(args.model_path)
# load data sets from file
data_set = NpSemanticSegData(data_path, train_to_test_ratio=0.8)
# train the mlp classifier
train_mlp_classifier(data_set, model_path, args.epochs)
writer.writerow(result_row)
def preprocess_tratz_2011(folder_path):
"""
Pre-process tratz_2011 dataset
Args:
folder_path (str): path to the unzipped tratz_2011 dataset
"""
files = ['tratz2011_coarse_grained_random/train.tsv', 'tratz2011_coarse_grained_random/'
'val.tsv']
dicts = [tratz2011_train_labeled_dict, tratz2011_val_labeled_dict]
# 1. get abs path
if not os.path.isabs(folder_path):
# handle case using default value\relative paths
folder_path = os.path.join(os.path.dirname(__file__), folder_path)
# 2. add the location of the train file in the folder
for file, dic in zip(files, dicts):
file_full_path = os.path.join(folder_path, file)
read_from_tratz_2011(file_full_path, dic)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Pre-process Tratz 2011 data from tsv to csv')
parser.add_argument('--data', type=validate_existing_directory,
help='path the Tratz_2011_dataset folder local path')
args = parser.parse_args()
data_path = absolute_path(args.data)
preprocess_tratz_2011(data_path)