dataset = datasets_dict[dataset_name]
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 = ''
if classifier_name=='cnn': # Time-CNN
from classifiers import cnn
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':
features=feature,
method=met,
iter=iter,
dense=dense)
print('\t\t\t\tDONE', flush=True)
# the creation of this directory means
create_directory(output_directory + '/DONE')
elif 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(
'', filename='results/inception_my_inception_compares.csv')
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.to_string())
# experiment with one data set to check the runtime
elif sys.argv[1] == 'Wafer':
archive_name = sys.argv[3]
datasets_dict = read_all_datasets(root_dir, archive_name)
classifier_name = sys.argv[4]
dataset_name = 'Wafer'
for size in range(100, 1001, 100):
print(f'size {size}')
tmp_output_directory = root_dir + 'results/' + classifier_name + '/' + archive_name + 'size' + str(
