def __init__(self,
column_names=None,
column_types=None,
num_classes=None,
multi_label=False,
loss=None,
metrics=None,
name='structured_data_classifier',
max_trials=100,
directory=None,
objective='val_accuracy',
seed=None):
super().__init__(
outputs=head.ClassificationHead(num_classes=num_classes,
multi_label=multi_label,
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_classifier',
seed=seed)
def test_lgbm_classifier(tmp_dir):
x_train = np.random.rand(11, 32)
y_train = np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]])
input_node = ak.Input()
output_node = input_node
output_node = preprocessor.LightGBMBlock()(output_node)
output_node = head.ClassificationHead(loss='categorical_crossentropy',
metrics=['accuracy'])(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, 10)
def test_two_classes():
y = np.array(['a', 'a', 'a', 'b'])
head = head_module.ClassificationHead(name='a')
head.fit_transform(y)
head.output_shape = (1, )
head.build(kerastuner.HyperParameters(),
node_module.Input(shape=(32, )).build())
assert head.loss == 'binary_crossentropy'
def test_partial_column_types():
input_node = node.StructuredDataInput(
column_names=common.COLUMN_NAMES_FROM_CSV,
column_types=common.PARTIAL_COLUMN_TYPES_FROM_CSV)
(x, y), (val_x, val_y) = common.dataframe_numpy()
dataset = tf.data.Dataset.zip(
((tf.data.Dataset.from_tensor_slices(x.values.astype(np.unicode)), ),
(tf.data.Dataset.from_tensor_slices(y), )))
hm = meta_model.assemble(input_node, head.ClassificationHead(), dataset)
for block in hm._blocks:
if isinstance(block, preprocessor.FeatureEngineering):
assert block.input_node.column_types['fare'] == 'categorical'
def __init__(self,
num_classes=None,
multi_label=False,
loss=None,
metrics=None,
name='image_classifier',
max_trials=100,
directory=None,
seed=None):
super().__init__(outputs=head.ClassificationHead(
num_classes=num_classes,
multi_label=multi_label,
loss=loss,
metrics=metrics),
max_trials=max_trials,
directory=directory,
seed=seed)
def __init__(self,
num_classes=None,
multi_label=False,
loss=None,
metrics=None,
name='text_classifier',
max_trials=100,
directory=None,
objective='val_loss',
seed=None):
super().__init__(
outputs=head.ClassificationHead(num_classes=num_classes,
multi_label=multi_label,
loss=loss,
metrics=metrics),
max_trials=max_trials,
directory=directory,
name=name,
objective=objective,
tuner='text_classifier',
seed=seed)
def __init__(self,
num_classes: Optional[int] = None,
multi_label: bool = False,
loss: Union[str, Callable, None] = None,
metrics: Optional[List[Union[str, Callable]]] = None,
name: str = 'image_classifier',
max_trials: int = 100,
directory: Optional[str] = None,
objective: str = 'val_loss',
overwrite: bool = True,
seed: Optional[int] = None):
super().__init__(
outputs=head.ClassificationHead(num_classes=num_classes,
multi_label=multi_label,
loss=loss,
metrics=metrics),
max_trials=max_trials,
directory=directory,
name=name,
objective=objective,
tuner='image_classifier',
overwrite=overwrite,
seed=seed)
def test_y_is_pd_series():
(x, y), (val_x, val_y) = common.dataframe_series()
head = head_module.ClassificationHead()
head.fit(y)
assert isinstance(head.transform(y), tf.data.Dataset)
def test_three_classes():
y = np.array(['a', 'a', 'c', 'b'])
head = head_module.ClassificationHead(name='a')
head.fit_transform(y)
assert head.loss == 'categorical_crossentropy'
def test_one_class():
y = np.array(['a', 'a', 'a'])
head = head_module.ClassificationHead(name='a')
with pytest.raises(ValueError) as info:
head.fit_transform(y)
assert 'Expect the target data' in str(info.value)
def test_unsupported_types():
y = 1
head = head_module.ClassificationHead(name='a')
with pytest.raises(TypeError) as info:
head.fit_transform(y)
assert 'Expect the target data' in str(info.value)
