def test_generate_gated_recurrent_neural_network_model(self): _net_gen: object = NetworkGenerator( target=TARGET_TEXT, predictors=PREDICTORS_TEXT, output_layer_size=5, train_data_path=TRAIN_DATA_PATH_TEXT, test_data_path=TEST_DATA_PATH_TEXT, validation_data_path=VALIDATION_DATA_PATH_TEXT, models=['gru'], sep=',').generate_model() self.assertTrue(expr=isinstance(_net_gen.model, GRU))
def test_generate_multi_layer_perceptron_model(self): _net_gen: object = NetworkGenerator( target=TARGET_TEXT, predictors=PREDICTORS_TEXT, output_layer_size=5, train_data_path=TRAIN_DATA_PATH_TEXT, test_data_path=TEST_DATA_PATH_TEXT, validation_data_path=VALIDATION_DATA_PATH_TEXT, models=['mlp'], sep=',').generate_model() self.assertTrue(expr=isinstance(_net_gen.model, MLP))
def test_generate_long_short_term_memory_network_model(self): _net_gen: object = NetworkGenerator( target=TARGET_TEXT, predictors=PREDICTORS_TEXT, output_layer_size=5, train_data_path=TRAIN_DATA_PATH_TEXT, test_data_path=TEST_DATA_PATH_TEXT, validation_data_path=VALIDATION_DATA_PATH_TEXT, models=['lstm'], sep=',').generate_model() self.assertTrue(expr=isinstance(_net_gen.model, LSTM))
def test_forward(self): _network_generator: NetworkGenerator = NetworkGenerator(target='label', predictors=['text'], output_layer_size=2, train_data_path=DATA_FILE_PATH.get('train'), test_data_path=DATA_FILE_PATH.get('test'), validation_data_path=DATA_FILE_PATH.get('val'), models=['rcnn'] ) _network_generator.get_vanilla_model() _network_generator.train() self.assertTrue(expr=len(_network_generator.fitness.keys()) > 0)
def test_get_vanilla_transformer(self): _net_gen: object = NetworkGenerator( target=TARGET_TEXT, predictors=PREDICTORS_TEXT, output_layer_size=5, train_data_path=TRAIN_DATA_PATH_TEXT, test_data_path=TEST_DATA_PATH_TEXT, validation_data_path=VALIDATION_DATA_PATH_TEXT, models=['trans'], model_name='trans', sep=',') _model = _net_gen.get_vanilla_model()
def test_train(self): _net_gen: object = NetworkGenerator( target=TARGET_TEXT, predictors=PREDICTORS_TEXT, output_layer_size=5, train_data_path=TRAIN_DATA_PATH_TEXT, test_data_path=TEST_DATA_PATH_TEXT, validation_data_path=VALIDATION_DATA_PATH_TEXT, models=['trans'], #list(NETWORK_TYPE.keys()), sep=',').generate_model() _model = _net_gen.generate_model() _model.train() self.assertTrue(expr=_model.fitness.get('train') is not None)
def test_generate_params(self): _net_gen: object = NetworkGenerator( target=TARGET_TEXT, predictors=PREDICTORS_TEXT, output_layer_size=5, train_data_path=TRAIN_DATA_PATH_TEXT, test_data_path=TEST_DATA_PATH_TEXT, validation_data_path=VALIDATION_DATA_PATH_TEXT, models=list(NETWORK_TYPE.keys())).generate_model() _model = _net_gen.generate_model() _mutated_param: dict = copy.deepcopy(_model.model_param_mutated) _net_gen.generate_params(param_rate=0.1, force_param=None) self.assertTrue(expr=len(_mutated_param.keys()) < len( _net_gen.model_param_mutated.keys()))
def test_forward(self): _predictors: List[str] = ['x1', 'x2', 'x3', 'x4'] _network_generator: NetworkGenerator = NetworkGenerator(target='y', predictors=_predictors, output_layer_size=2, x_train=DATA_SET_MLP[_predictors].values, y_train=DATA_SET_MLP['y'].values, x_test=DATA_SET_MLP[_predictors].values, y_test=DATA_SET_MLP['y'].values, x_val=DATA_SET_MLP[_predictors].values, y_val=DATA_SET_MLP['y'].values, #models=['mlp'], model_name='mlp', sequential_type='numeric' ) _network_generator.generate_model() _network_generator.train() self.assertTrue(expr=len(_network_generator.fitness.keys()) > 0)