def main(args): logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(message)s') args = parse_arguments(args) input_file_path_list = args.files output_file_path = args.output_file chromosome_set = set(args.chromosome_list) if args.chromosome_list else None logging.info("Starting JointSV") jointsv(input_file_path_list, output_file_path, chromosome_set) logging.info("JointSV finished successfully")
# f.write("\n") correct_labels.extend( np.argmax(test_batch_data["batch_labels_one_hot"], axis=1)) predictions.extend(np.argmax(scores[0], axis=1)) print(correct_labels) print(predictions) target_names = [str(i) for i in range(1, 11)] precision = float( precision_score(correct_labels, predictions, average="micro")) recall = float( recall_score(correct_labels, predictions, average="micro")) f1 = float(f1_score(correct_labels, predictions, average="micro")) print(classification_report(correct_labels, predictions)) print("Finished producing test results for model : ", str(test_opt.model_path)) print('Precision:', recall) print('Recall:', recall) print('F1:', f1) # print(confusion_matrix(correct_labels, predictions)) if __name__ == "__main__": test_opt = argument_parser.parse_arguments() os.environ['CUDA_VISIBLE_DEVICES'] = test_opt.cuda main(test_opt)
np.argmax(test_batch_data["batch_labels_one_hot"], axis=1)) predictions.extend(np.argmax(scores[0], axis=1)) print(correct_labels) print(predictions) f1 = float( f1_score(correct_labels, predictions, average="micro")) print(classification_report(correct_labels, predictions)) print('F1:', f1) print('Best F1:', best_f1) # print(confusion_matrix(correct_labels, predictions)) if f1 > best_f1: best_f1 = f1 saver.save(sess, checkfile) print('Checkpoint saved, epoch:' + str(epoch) + ', step: ' + str(train_step) + ', loss: ' + str(err) + '.') if __name__ == "__main__": train_opt = argument_parser.parse_arguments() test_opt = copy.deepcopy(train_opt) # test_opt.data_path = "OJ_rs/OJ_rs-buckets-test.pkl" os.environ['CUDA_VISIBLE_DEVICES'] = train_opt.cuda main(train_opt, test_opt)
baselines_output_folder = None embedding_stats = test_embeddings.run_test( model, train_dataloader.dataset, val_dataloader.dataset, test_dataloader.dataset, epochs=100, batch_size=16, lr=1e-3, embedding_dim=args.embedding_dim, es_tmpdir=args.es_tmpdir, hidden_dim=args.embedding_dim, early_stopping=True, output_folder=baselines_output_folder, device=args.device) cv_baselines_test_stats.update(embedding_stats) print("\n\n############## Baseline Multitask GCN ##############") ut.print_cv_stats(cv_test_stats) if args.test_emb: cv_baselines_test_stats.print_stats() return cv_test_stats, model if __name__ == "__main__": args = parse_arguments("ConcurrentMultiTaskGCN") ut.set_seeds() ut.print_arguments(args) cv_stats, model = run(args)
meta_val_dataloader.dataset, meta_test_dataloader.dataset, epochs=100, batch_size=8, lr=1e-3, embedding_dim=args.embedding_dim, hidden_dim=args.embedding_dim, early_stopping=True, es_tmpdir=args.es_tmpdir, output_folder=baselines_output_folder, device=args.device) cv_baselines_test_stats.update(embedding_stats) print("\n\n############## Meta-Learned Multitask GCN ##############") print("Best Val Acc") ut.print_cv_stats(cv_test_stats) if args.early_stopping: print("\nBest Val Loss") ut.print_cv_stats(cv_test_stats_best_loss) if args.test_emb: cv_baselines_test_stats.print_stats() return cv_test_stats, model if __name__ == "__main__": args = parse_arguments("MultitaskGCN") ut.set_seeds() ut.print_arguments(args) cv_stats, model = run(args)
training_data=training_data_minus_fold_for_test, test_data=holdout_data, s=s, p=p, K=K) # Attach values for precision and recall, training outcomes and probabilities for each tree in the random forest. metrics_dict_test = dict_list_appender(metrics_dict_test, run_metrics_test[:-1]) for key in metrics_dict_tree_master_test.keys(): metrics_dict_tree_master_test[key] = metrics_dict_tree_master_test[ key] + run_metrics_test[-2][key] # Extract values for false positive rate (fpr), true positive rate(tpr), precision, and area under curves for the # decision trees generated from the test data and plot the associated prc and roc curves. testing_data_curves = curve_generator(*prc_roc_curve( np.array(metrics_dict_tree_master_test['training_outcomes']), np.array(metrics_dict_tree_master_test['probabilities']))) testing_data_curves.gen_roc(title='Testing Data') testing_data_curves.gen_prc(title='Testing Data') # Print out relevant run metrics for testing data. print(f"Testing Data AUROC = {testing_data_curves.get_auc_roc()}") print(f"Testing Data AUPRC = {testing_data_curves.get_auc_prc()}") print(f"Testing data metrics (AVG) = {metrics_dict_test}") if __name__ == "__main__": arguments = argument_parser.parse_arguments() main(*arguments)
if len(data) >= MAX_LOGIN_ATTEMPTS: if i_email: global_logger.log('Sending email to', i_email) send_mail(i_email, 'Intrusion Attempt', time.time(), '/var/log/auth.log') subprocess.call('sudo shutdown -h now', shell=True) ''' Main entry point ''' if __name__ == "__main__": log_filename = global_logger.create_name_from_filename(__file__) global_logger.register_global_file(log_filename) global_logger.log('----- Starting -----') # Parse command line arguments cmdargs = parse_arguments() try: attempts = get_login_attempts() handle_login_attempts(attempts, cmdargs.email) except: global_logger.log('Exception: ', sys.exc_info(), traceback.format_exc()) if cmdargs.email: send_report(log_filename, cmdargs.email)
return payload def create_header(): """ Constructs the header for the REST request """ header = {} header['Content-type'] = 'application/json' return header """ Main function starts here. """ (result, v_operation, v_argument) = argument_parser.parse_arguments(sys.argv) if result == 0: sys.exit() """ Load the config file """ try: config = fb_config.fb_config("config.real") except Exception as excep: print(excep.args[0]) sys.exit() """ Prepare the REST url """ if v_operation == 'interrogate': v_url = create_interrogate_url(config) else: sys.exit() """ Create a JSON payload """ v_payload = create_payload(config)
baselines_output_folder = None embedding_stats = test_embeddings.run_test( model, train_dataloader.dataset, val_dataloader.dataset, test_dataloader.dataset, epochs=100, batch_size=16, lr=1e-3, embedding_dim=args.embedding_dim, es_tmpdir=args.es_tmpdir, hidden_dim=args.embedding_dim, early_stopping=True, output_folder=baselines_output_folder, device=args.device) cv_baselines_test_stats.update(embedding_stats) print("\n\n############## Single Task GCN ##############") ut.print_cv_stats(cv_test_stats) if args.test_emb: cv_baselines_test_stats.print_stats() return cv_test_stats, model if __name__ == "__main__": args = parse_arguments("SingleTaskGCN") ut.set_seeds() ut.print_arguments(args) cv_stats, model = run(args)
def main(): args = parse_arguments() api = get_twitter_api() usernames = load_file(args.filename) users_chunks = chunks(usernames, args.max_query_size) fetch_data(users_chunks, args.minutes_to_sleep, api, args.statuses)