if __name__ == "__main__": args = parser.parse_args() configs = generate_all_model_configs( activations=args.activation, init_methods=args.init, max_ic50_values=args.max_ic50, dropout_values=args.dropout, minibatch_sizes=args.minibatch_size, embedding_sizes=args.embedding_size, n_pretrain_epochs_values=args.pretrain_epochs, n_training_epochs_values=args.training_epochs, hidden_layer_sizes=args.hidden_layer_size, learning_rates=args.learning_rate, optimizers=args.optimizer) print("Total # configurations = %d" % len(configs)) training_datasets, _ = load_data( args.binding_data_csv_path, max_ic50=args.max_ic50, peptide_length=9, binary_encoding=False) combined_df = evaluate_model_configs( configs=configs, results_filename=args.output, train_fn=lambda config: evaluate_model_config_by_cross_validation( config, training_datasets, min_samples_per_allele=args.min_samples_per_allele, cv_folds=args.cv_folds)) hyperparameter_performance(combined_df)
init=args.init, n_pretrain_epochs=args.pretrain_epochs, n_epochs=args.training_epochs, dropout_probability=args.dropout, max_ic50=args.max_ic50, minibatch_size=args.minibatch_size, learning_rate=args.learning_rate, optimizer=args.optimizer) models = [make_model(config) for _ in range(args.ensemble_size)] binary_encoding = (args.embedding_size == 0) training_datasets, _ = load_data( filename=args.training_csv, peptide_length=9, max_ic50=args.max_ic50, binary_encoding=binary_encoding) X_all = np.vstack([dataset.X for dataset in training_datasets.values()]) Y_all = np.concatenate([ dataset.Y for dataset in training_datasets.values() ]) for model in models: model.fit( X_all, Y_all, nb_epoch=args.pretrain_epochs, batch_size=args.minibatch_size,