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)
