def __init__(self, column_names=None, column_types=None, output_dim=None, loss='mean_squared_error', metrics=None, name='structured_data_regressor', max_trials=100, directory=None, objective='val_loss', overwrite=True, seed=None): super().__init__( outputs=head.RegressionHead(output_dim=output_dim, loss=loss, metrics=metrics), column_names=column_names, column_types=column_types, max_trials=max_trials, directory=directory, name=name, objective=objective, tuner='structured_data_regressor', overwrite=overwrite, seed=seed)
def __init__(self, output_dim=None, loss=None, metrics=None, name='image_regressor', max_trials=100, directory=None, seed=None): super().__init__(outputs=head.RegressionHead(output_dim=output_dim, loss=loss, metrics=metrics), max_trials=max_trials, directory=directory, seed=seed)
def __init__(self, output_dim=None, loss=None, metrics=None, name='text_regressor', max_trials=100, directory=None, objective='val_loss', seed=None): super().__init__(outputs=head.RegressionHead(output_dim=output_dim, loss=loss, metrics=metrics), max_trials=max_trials, directory=directory, name=name, objective=objective, seed=seed)
def __init__(self, column_names=None, column_types=None, output_dim=None, loss=None, metrics=None, name='structured_data_regressor', max_trials=100, directory=None, seed=None): super().__init__(outputs=head.RegressionHead(output_dim=output_dim, loss=loss, metrics=metrics), column_names=column_names, column_types=column_types, max_trials=max_trials, directory=directory, seed=seed)
def test_lgbm_regressor(tmp_dir): x_train = np.random.rand(11, 32) y_train = np.array([1.1, 2.1, 4.2, 0.3, 2.4, 8.5, 7.3, 8.4, 9.4, 4.3]) y_train = y_train.reshape(-1, 1) input_node = ak.Input() output_node = input_node output_node = preprocessor.LightGBMBlock()(output_node) output_node = head.RegressionHead(loss='mean_squared_error', metrics=['mean_squared_error' ])(output_node) auto_model = ak.GraphAutoModel(input_node, output_node, directory=tmp_dir, max_trials=1) auto_model.fit(x_train, y_train, epochs=1, validation_data=(x_train, y_train)) result = auto_model.predict(x_train) assert result.shape == (11, 1)