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_lgbm_regressor():
dataset = common.generate_data(11, (32, ), dtype='dataset')
y = common.generate_data(11, (1, ), dtype='dataset')
instance = preprocessor_module.LightGBMBlock(seed=common.SEED)
instance.lightgbm_block = preprocessor_module.LightGBMRegressor(
seed=common.SEED)
new_dataset = run_preprocessor(instance, dataset, y, tf.float32)
assert isinstance(new_dataset, tf.data.Dataset)
def test_lgbm_classifier():
dataset = common.generate_data(11, (32,), dtype='dataset')
y = common.generate_one_hot_labels(11, dtype='dataset')
instance = preprocessor.LightGBMBlock(seed=common.SEED)
instance.lightgbm_block = preprocessor.LightGBMClassifier(seed=common.SEED)
new_dataset = run_preprocessor(instance,
dataset,
y,
tf.float32)
assert isinstance(new_dataset, tf.data.Dataset)
def test_lgbm_classifier():
dataset = common.generate_data(100, (32, ), dtype='dataset')
y = common.generate_one_hot_labels(100, num_classes=3, dtype='dataset')
instance = preprocessor_module.LightGBMBlock(seed=common.SEED)
instance.lightgbm_block = preprocessor_module.LightGBMClassifier(
seed=common.SEED)
instance.lightgbm_block.num_classes = 3
new_dataset = run_preprocessor(instance, dataset, y, tf.float32)
for (x, ) in new_dataset:
assert x.shape == (3, )
break
assert isinstance(new_dataset, tf.data.Dataset)
def test_lgbm_classifier_two_classes():
dataset = common.generate_data(11, (32, ), dtype='dataset')
y = tf.data.Dataset.from_tensor_slices(
np.random.randint(0, 2, 11).reshape(-1, 1))
instance = preprocessor_module.LightGBMBlock(seed=common.SEED)
instance.lightgbm_block = preprocessor_module.LightGBMClassifier(
seed=common.SEED)
instance.lightgbm_block.num_classes = 11
new_dataset = run_preprocessor(instance, dataset, y, tf.float32)
for (x, ) in new_dataset:
assert x.shape == (1, )
break
assert isinstance(new_dataset, tf.data.Dataset)
def build_body(self, hp, input_node):
if len(self.heads) > 1:
module_type_choices = ['dense']
else:
module_type_choices = ['lightgbm', 'dense']
module_type = self.module_type or hp.Choice(
'module_type', module_type_choices, default=module_type_choices[0])
if module_type == 'dense':
output_node = block.DenseBlock()(input_node)
elif module_type == 'lightgbm':
output_node = preprocessor.LightGBMBlock(
seed=self.seed)(input_node)
else:
raise ValueError(
'Unsupported module'
'type: {module_type}'.format(module_type=module_type))
nest.flatten(output_node)[0].shape = self.output_shape
return output_node
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)
