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.LightGBMClassifier()(output_node)
output_node = block.IdentityBlock()(output_node)
output_node = head.EmptyHead(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)
auto_model.tuner.get_best_models()[0].summary()
assert result.shape == (11, 10)
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
output_node = preprocessor.LightGBMClassifier()(output_node)
output_node = block.IdentityBlock()(output_node)
output_node = head.EmptyHead(loss=self.loss,
metrics=[self.metrics])(output_node)
return output_node
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 lightgbm_head(lightgbm_block):
lightgbm_block.heads = fetch_heads(lightgbm_block)
if len(lightgbm_block.heads) > 1:
raise ValueError('LightGBMBlock can only be connected to one head.')
head = lightgbm_block.heads[0]
if isinstance(head, head_module.ClassificationHead):
classifier = preprocessor_module.LightGBMClassifier(seed=lightgbm_block.seed)
classifier.num_classes = head.num_classes
lightgbm_block.lightgbm_block = classifier
if isinstance(head, head_module.RegressionHead):
lightgbm_block.lightgbm_block = preprocessor_module.LightGBMRegressor(
seed=lightgbm_block.seed)
in_block = head
# Check if the head has no other input but only LightGBMBlock.
while in_block is not lightgbm_block:
# The head has other inputs.
if len(in_block.inputs) > 1:
return
in_block = in_block.inputs[0].in_blocks[0]
head.identity = True