from classifiers import encoder return encoder.Classifier_ENCODER(output_directory,input_shape, nb_classes, verbose) 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)
dataset_name) utils.create_directory(output_directory) 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':