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 build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
output_node = preprocessor.LightGBMRegressor()(output_node)
output_node = block.IdentityBlock()(output_node)
output_node = head.EmptyHead(loss='mean_squared_error',
metrics=[self.metrics])(output_node)
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])
input_node = ak.Input()
output_node = input_node
output_node = preprocessor.LightGBMRegressor()(output_node)
output_node = block.IdentityBlock()(output_node)
output_node = head.EmptyHead(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)
auto_model.tuner.get_best_models()[0].summary()
assert result.shape == (11, 1)
