len(data.train_x), len(data.test_x), len(data.train_x[0]) ], columns=[ 'X_params', 'model_params', 'num_train', 'num_test', 'num_features' ]) hash_X = model_db.find_hash(fname_X) model_db.store_cur_data([hash_X], columns=['X_hash']) serial_num = model_db.find_serial_number() # Store the model with the appended serial_number fname_model = "pickled_files/models/mlp_regression_" + str( serial_num) + ".pkl" mlpr.dump(fname_model) hash_model = model_db.find_hash(fname_model) model_db.store_cur_data([hash_model], columns=['model_hash']) # Store the data that trained the model data.dump_X(serial_num=serial_num) data.dump_Y(serial_num=serial_num) # Store all the data in the data db and dump the db model_db.store_data(serial_num) model_db.dump() # Analyze the model mlpr_acc = mlpr.evaluate() print(mlpr_acc)