type="test", fold=str(i), verbose=False)[1].values()) manager = ModelManager() manager.assign_sets(train=train) tup = manager.create_mask( train.iloc[:, :-1], global_dirs.variable_selection[0], select=global_dirs.variable_selection[1] ) # This tuple shouldn't take care about y_column index scalers = manager.preprocess_train(tup, scale_Y=False) dnn_model = manager.fit_dnn_regression([(9, 'relu'), (18, 'relu'), (56, 'relu'), (11, 'relu'), (10, 'relu')], epochs=300, batch_size=30, use_dropout=False) results = manager.predict_dnn_regression(test, tup) #X_train = train.loc[:, train.columns != train.columns[-1]] #X_test = test.loc[:, test.columns != test.columns[-1]] #var_selection = reader.create_mask(X_train, global_dirs.variable_selection[0], select=global_dirs.variable_selection[1]) #X_train = X_train.loc[:, var_selection] #X_test = X_test.loc[:, var_selection] #y_train = train.loc[:, train.columns[-1]] #y_test = test.loc[:, test.columns[-1]]
if not os.path.isdir(global_dirs.results_path): os.mkdir(global_dirs.results_path) if not os.path.isdir(global_dirs.dnn_path): os.mkdir(global_dirs.dnn_path) if not os.path.isdir(global_dirs.dnn_path + "scalers/"): os.mkdir(global_dirs.dnn_path + "scalers/") if not os.path.isdir(global_dirs.dnn_path + "model/"): os.mkdir(global_dirs.dnn_path + "model/") if not os.path.isdir(global_dirs.dnn_path + "results/"): os.mkdir(global_dirs.dnn_path + "results/") dnn_model = manager.fit_dnn_regression( [(9, 'relu'), (18, 'relu'), (56, 'relu'), (11, 'relu'), (10, 'relu')], epochs=500, batch_size=30, save_dir=global_dirs.dnn_path + "model/", save=True, use_dropout=False) if (isinstance(scalers, tuple)): joblib.dump(scalers[0], global_dirs.dnn_path + "scalers/scaler_X.h5") joblib.dump(scalers[1], global_dirs.dnn_path + "scalers/scaler_y.h5") else: joblib.dump(scalers, global_dirs.dnn_path + "scalers/scaler_X.h5") test = { name.split("-")[1]: data for name, data in manager.read_data(global_dirs.splitted_data_path, formats=["hdf"], sim="qgsjet",