return cnn.Classifier_CNN(output_directory,input_shape, nb_classes, verbose)
############################################### main
# change this directory for your machine
# it should contain the archive folder containing both univariate and multivariate archives
root_dir ='/scratch/Project-CTI/data/SynCAN/classification_SOA'
if sys.argv[1]=='transform_mts_to_ucr_format':
transform_mts_to_ucr_format()
elif sys.argv[1]=='visualize_filter':
visualize_filter(root_dir)
elif sys.argv[1]=='viz_for_survey_paper':
viz_for_survey_paper(root_dir)
elif sys.argv[1]=='viz_cam':
viz_cam(root_dir)
elif sys.argv[1]=='generate_results_csv':
res = generate_results_csv('results.csv',root_dir)
print(res)
elif sys.argv[1]=='mts_benchmark':
info_dict = get_info_run()
archive_names = info_dict['archive_names']
mts_data_names = info_dict['mts_dataset_names']
classifier_names = info_dict['classifiers_names']
print(archive_names, mts_data_names, classifier_names)
itr = sys.argv[2]
if itr == '_itr_0':
itr = ''
fit_classifier(classifier_name, dataset, output_directory)
print('\t\t\t\tDONE')
# the creation of this directory means
utils.create_directory(
os.path.join(output_directory, 'DONE'))
elif sys.argv[1] == 'transform_mts_to_ucr_format':
utils.transform_mts_to_ucr_format()
elif sys.argv[1] == 'visualize_filter':
utils.visualize_filter(ROOT_DIR)
elif sys.argv[1] == 'viz_for_survey_paper':
utils.viz_for_survey_paper(ROOT_DIR)
elif sys.argv[1] == 'viz_cam':
utils.viz_cam(ROOT_DIR)
elif sys.argv[1] == 'generate_results_csv':
res = utils.generate_results_csv('results.csv', ROOT_DIR)
print(res.to_string())
else:
# this is the code used to launch an experiment on a dataset
archive_name = sys.argv[1]
dataset_name = sys.argv[2]
classifier_name = sys.argv[3]
itr = sys.argv[4]
if itr == '_itr_0':
itr = ''
output_directory = os.path.join(ROOT_DIR, 'results', classifier_name,
archive_name + itr, dataset_name)
fit_classifier()
print('\t\t\t\tDONE')
# the creation of this directory means
create_directory(output_directory + '/DONE')
elif args.action == 'transform_mts_to_ucr_format':
transform_mts_to_ucr_format()
elif args.action == 'visualize_filter':
visualize_filter(root_dir)
elif args.action == 'viz_for_survey_paper':
viz_for_survey_paper(root_dir)
elif args.action == 'viz_cam':
viz_cam(root_dir, classifier_name, archive_name,
dataset_name, itr, args.file_ext, args.remove_docstr)
elif args.action == 'generate_results_csv':
res = generate_results_csv('results.csv', root_dir)
print(res.to_string())
else:
# this is the code used to launch an experiment on a dataset
# archive_name = sys.argv[1]
# dataset_name = sys.argv[2]
# classifier_name = sys.argv[3]
# itr = sys.argv[4]
output_directory = root_dir + '/results/' + classifier_name + '/' + archive_name + itr + '/' + \
dataset_name + '/'
test_dir_df_metrics = output_directory + 'df_metrics.csv'
fit_classifier()
print('\t\t\t\tDONE')
# the creation of this directory means
create_directory(output_directory + '/DONE')
elif args.action == 'transform_mts_to_ucr_format':
transform_mts_to_ucr_format()
elif args.action == 'visualize_filter':
visualize_filter(root_dir)
elif args.action == 'viz_for_survey_paper':
viz_for_survey_paper(root_dir)
elif args.action == 'viz_cam':
viz_cam(root_dir, classifier_name, archive_name, dataset_name, itr,
args.file_ext, args.remove_docstr, args.swap_repr,
args.viz_frame, args.out_viz_file, args.viz_model,
args.viz_class, args.viz_example)
elif args.action == 'generate_results_csv':
res = generate_results_csv('results.csv', root_dir)
print(res.to_string())
else:
# this is the code used to launch an experiment on a dataset
# archive_name = sys.argv[1]
# dataset_name = sys.argv[2]
# classifier_name = sys.argv[3]
# itr = sys.argv[4]
output_directory = root_dir + '/results/' + classifier_name + '/' + archive_name + itr + '/' + \
dataset_name + '/'
test_dir_df_metrics = output_directory + 'df_metrics.csv'