def test_io_api(tmp_dir):
(image_x, train_y), (test_x, test_y) = mnist.load_data()
(text_x, train_y), (test_x, test_y) = common.imdb_raw()
num_instances = 20
image_x = image_x[:num_instances]
text_x = text_x[:num_instances]
structured_data_x = common.generate_structured_data(
num_instances=num_instances)
classification_y = common.generate_one_hot_labels(
num_instances=num_instances, num_classes=3)
regression_y = common.generate_data(num_instances=num_instances,
shape=(1, ))
# Build model and train.
automodel = ak.AutoModel(
inputs=[ak.ImageInput(),
ak.TextInput(),
ak.StructuredDataInput()],
outputs=[
ak.RegressionHead(metrics=['mae']),
ak.ClassificationHead(loss='categorical_crossentropy',
metrics=['accuracy'])
],
directory=tmp_dir,
max_trials=2,
seed=common.SEED)
automodel.fit([image_x, text_x, structured_data_x],
[regression_y, classification_y],
epochs=2,
validation_split=0.2)
def test_image_classifier(tmp_dir):
train_x = common.generate_data(num_instances=100, shape=(32, 32, 3))
train_y = common.generate_one_hot_labels(num_instances=100, num_classes=10)
clf = ak.ImageClassifier(directory=tmp_dir, max_trials=2, seed=common.SEED)
clf.fit(train_x, train_y, epochs=1, validation_split=0.2)
keras_model = clf.export_model()
assert clf.predict(train_x).shape == (len(train_x), 10)
assert isinstance(keras_model, tf.keras.Model)
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_functional_api(tmp_dir):
# Prepare the data.
num_instances = 20
(image_x, train_y), (test_x, test_y) = mnist.load_data()
(text_x, train_y), (test_x, test_y) = common.imdb_raw()
(structured_data_x, train_y), (test_x, test_y) = common.dataframe_numpy()
image_x = image_x[:num_instances]
text_x = text_x[:num_instances]
structured_data_x = structured_data_x[:num_instances]
classification_y = common.generate_one_hot_labels(
num_instances=num_instances, num_classes=3)
regression_y = common.generate_data(num_instances=num_instances,
shape=(1, ))
# Build model and train.
image_input = ak.ImageInput()
output = ak.Normalization()(image_input)
output = ak.ImageAugmentation()(output)
outputs1 = ak.ResNetBlock(version='next')(image_input)
outputs2 = ak.XceptionBlock()(image_input)
image_output = ak.Merge()((outputs1, outputs2))
structured_data_input = ak.StructuredDataInput(
column_names=common.COLUMN_NAMES_FROM_CSV,
column_types=common.COLUMN_TYPES_FROM_CSV)
structured_data_output = ak.FeatureEngineering()(structured_data_input)
structured_data_output = ak.DenseBlock()(structured_data_output)
text_input = ak.TextInput()
outputs1 = ak.TextToIntSequence()(text_input)
outputs1 = ak.EmbeddingBlock()(outputs1)
outputs1 = ak.ConvBlock(separable=True)(outputs1)
outputs1 = ak.SpatialReduction()(outputs1)
outputs2 = ak.TextToNgramVector()(text_input)
outputs2 = ak.DenseBlock()(outputs2)
text_output = ak.Merge()((outputs1, outputs2))
merged_outputs = ak.Merge()(
(structured_data_output, image_output, text_output))
regression_outputs = ak.RegressionHead()(merged_outputs)
classification_outputs = ak.ClassificationHead()(merged_outputs)
automodel = ak.GraphAutoModel(
inputs=[image_input, text_input, structured_data_input],
directory=tmp_dir,
outputs=[regression_outputs, classification_outputs],
max_trials=2,
seed=common.SEED)
automodel.fit((image_x, text_x, structured_data_x),
(regression_y, classification_y),
validation_split=0.2,
epochs=2)
def test_structured_data_from_numpy_classifier(tmp_dir):
num_data = 500
num_train = 400
data = common.generate_structured_data(num_data)
x_train, x_test = data[:num_train], data[num_train:]
y = common.generate_one_hot_labels(num_instances=num_data, num_classes=3)
y_train, y_test = y[:num_train], y[num_train:]
clf = ak.StructuredDataClassifier(directory=tmp_dir,
max_trials=1,
seed=common.SEED)
clf.fit(x_train, y_train, epochs=2, validation_data=(x_train, y_train))
assert clf.predict(x_test).shape == (len(y_test), 3)
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_structured_classifier(init, fit):
num_data = 500
train_x = common.generate_structured_data(num_data)
train_y = common.generate_one_hot_labels(num_instances=num_data, num_classes=3)
clf = ak.StructuredDataClassifier(
column_names=common.COLUMN_NAMES_FROM_NUMPY,
directory=tmp_dir,
max_trials=1,
seed=common.SEED)
clf.fit(train_x, train_y, epochs=2, validation_data=(train_x, train_y))
assert init.called
assert fit.called
def test_add_early_stopping(_2, get_trials, _1, _, run_trial, tmp_dir):
trial = kerastuner.engine.trial.Trial()
trial.hyperparameters = kerastuner.HyperParameters()
get_trials.return_value = [trial]
input_shape = (32,)
num_instances = 100
num_classes = 10
x = common.generate_data(num_instances=num_instances,
shape=input_shape,
dtype='dataset')
y = common.generate_one_hot_labels(num_instances=num_instances,
num_classes=num_classes,
dtype='dataset')
input_node = ak.Input(shape=input_shape)
output_node = input_node
output_node = ak.DenseBlock()(output_node)
output_node = ak.ClassificationHead(num_classes=num_classes,
output_shape=(num_classes,))(output_node)
hypermodel = ak.hypermodel.graph.HyperBuiltGraphHyperModel(input_node,
output_node)
tuner = ak.tuner.RandomSearch(
hypermodel=hypermodel,
objective='val_loss',
max_trials=1,
directory=tmp_dir,
seed=common.SEED)
tuner.search(x=tf.data.Dataset.zip((x, y)),
validation_data=(x, y),
epochs=20,
callbacks=[])
_, kwargs = run_trial.call_args_list[0]
callbacks = kwargs['callbacks']
assert len(callbacks) == 1
assert isinstance(callbacks[0], tf.keras.callbacks.EarlyStopping)
_, kwargs = run_trial.call_args_list[1]
callbacks = kwargs['callbacks']
assert len(callbacks) == 0
