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 test_merge(tmp_dir):
x_train = np.random.rand(100, 32)
y_train = np.random.rand(100)
input_node1 = ak.Input()
input_node2 = ak.Input()
output_node1 = ak.DenseBlock()(input_node1)
output_node2 = ak.DenseBlock()(input_node2)
output_node = ak.Merge()([output_node1, output_node2])
output_node = ak.RegressionHead()(output_node)
input_node1.shape = (32, )
input_node2.shape = (32, )
output_node[0].shape = (1, )
graph = ak.GraphAutoModel([input_node1, input_node2],
output_node,
directory=tmp_dir)
model = graph.build(kerastuner.HyperParameters())
model.fit([x_train, x_train],
y_train,
epochs=1,
batch_size=100,
verbose=False)
result = model.predict([x_train, x_train])
assert result.shape == (100, 1)
def test_preprocessing(tmp_dir):
x_train = np.random.rand(100, 32)
y_train = np.random.rand(100)
input_node1 = ak.Input()
temp_node1 = ak.Normalize()(input_node1)
output_node1 = ak.DenseBlock()(temp_node1)
output_node3 = ak.Normalize()(temp_node1)
output_node3 = ak.DenseBlock()(output_node3)
input_node2 = ak.Input()
output_node2 = ak.Normalize()(input_node2)
output_node2 = ak.DenseBlock()(output_node2)
output_node = ak.Merge()([output_node1, output_node2, output_node3])
output_node = ak.RegressionHead()(output_node)
graph = ak.GraphAutoModel([input_node1, input_node2],
output_node,
directory=tmp_dir,
max_trials=1)
graph.fit([x_train, x_train],
y_train,
epochs=1,
batch_size=100,
validation_data=([x_train, x_train], y_train),
validation_split=0.5,
verbose=False)
result = graph.predict([x_train, x_train])
assert result.shape == (100, 1)
def test_evaluate(tuner_fn, tmp_dir):
pg = mock.Mock()
pg.preprocess.return_value = (mock.Mock(), mock.Mock())
tuner_class = tuner_fn.return_value
tuner = tuner_class.return_value
tuner.get_best_model.return_value = (pg, mock.Mock())
x_train = np.random.rand(100, 32)
y_train = np.random.rand(100, 1)
input_node = ak.Input()
output_node = input_node
output_node = ak.DenseBlock()(output_node)
output_node = ak.RegressionHead()(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))
auto_model.evaluate(x_train, y_train)
assert tuner_fn.called
assert tuner_class.called
assert tuner.get_best_model.called
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_input_missing(tmp_dir):
input_node1 = ak.Input()
input_node2 = ak.Input()
output_node1 = ak.DenseBlock()(input_node1)
output_node2 = ak.DenseBlock()(input_node2)
output_node = ak.Merge()([output_node1, output_node2])
output_node = ak.RegressionHead()(output_node)
with pytest.raises(ValueError) as info:
ak.GraphAutoModel(input_node1, output_node, directory=tmp_dir)
assert str(info.value).startswith('A required input is missing for HyperModel')
def functional_api():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
input_node = ak.ImageInput()
output_node = input_node
output_node = ak.Normalization()(output_node)
output_node = ak.ConvBlock()(output_node)
output_node = ak.SpatialReduction()(output_node)
output_node = ak.DenseBlock()(output_node)
output_node = ak.ClassificationHead()(output_node)
clf = ak.GraphAutoModel(input_node, output_node, seed=5, max_trials=3)
clf.fit(x_train, y_train, validation_split=0.2)
return clf.evaluate(x_test, y_test)
def test_input_output_disconnect(tmp_dir):
input_node1 = ak.Input()
output_node = input_node1
_ = ak.DenseBlock()(output_node)
input_node = ak.Input()
output_node = input_node
output_node = ak.DenseBlock()(output_node)
output_node = ak.RegressionHead()(output_node)
with pytest.raises(ValueError) as info:
ak.GraphAutoModel(input_node1, output_node, directory=tmp_dir)
assert str(info.value) == 'Inputs and outputs not connected.'
def test_hyper_graph_cycle(tmp_dir):
input_node1 = ak.Input()
input_node2 = ak.Input()
output_node1 = ak.DenseBlock()(input_node1)
output_node2 = ak.DenseBlock()(input_node2)
output_node = ak.Merge()([output_node1, output_node2])
head = ak.RegressionHead()
output_node = head(output_node)
head.outputs = output_node1
with pytest.raises(ValueError) as info:
ak.GraphAutoModel([input_node1, input_node2],
output_node,
directory=tmp_dir)
assert str(info.value) == 'The network has a cycle.'
def test_auto_model_basic(tmp_dir):
x_train = np.random.rand(100, 32)
y_train = np.random.rand(100)
input_node = ak.Input()
output_node = input_node
output_node = ak.DenseBlock()(output_node)
output_node = ak.RegressionHead()(output_node)
auto_model = ak.GraphAutoModel(input_node, output_node, directory=tmp_dir)
ak.const.Constant.NUM_TRAILS = 2
auto_model.fit(x_train, y_train, epochs=2)
result = auto_model.predict(x_train)
assert result.shape == (100, 1)
def test_input_missing(tmp_dir):
input_node1 = ak.Input()
input_node2 = ak.Input()
output_node1 = ak.DenseBlock()(input_node1)
output_node2 = ak.DenseBlock()(input_node2)
output_node = ak.Merge()([output_node1, output_node2])
output_node = ak.RegressionHead()(output_node)
input_node1.shape = (32,)
input_node2.shape = (32,)
output_node[0].shape = (1,)
with pytest.raises(ValueError) as info:
graph = ak.GraphAutoModel(input_node1, output_node, directory=tmp_dir)
graph.build(kerastuner.HyperParameters())
assert str(info.value).startswith('A required input is missing for HyperModel')
def test_graph_auto_model_basic(tmp_dir):
x_train = np.random.rand(100, 32)
y_train = np.random.rand(100)
input_node = ak.Input()
output_node = input_node
output_node = ak.DenseBlock()(output_node)
output_node = ak.RegressionHead()(output_node)
graph = ak.GraphAutoModel(input_node,
output_node,
directory=tmp_dir,
max_trials=1)
graph.fit(x_train, y_train, epochs=1, validation_data=(x_train, y_train))
result = graph.predict(x_train)
assert result.shape == (100, 1)
def test_input_output_disconnect(tmp_dir):
input_node1 = ak.Input()
output_node = input_node1
_ = ak.DenseBlock()(output_node)
input_node = ak.Input()
output_node = input_node
output_node = ak.DenseBlock()(output_node)
output_node = ak.RegressionHead()(output_node)
input_node.shape = (32, )
output_node[0].shape = (1, )
with pytest.raises(ValueError) as info:
graph = ak.GraphAutoModel(input_node1, output_node, directory=tmp_dir)
graph.build(kerastuner.HyperParameters())
assert str(info.value) == 'Inputs and outputs not connected.'
def test_evaluate(tmp_dir):
x_train = np.random.rand(100, 32)
y_train = np.random.rand(100, 1)
input_node = ak.Input()
output_node = input_node
output_node = ak.DenseBlock()(output_node)
output_node = ak.RegressionHead()(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))
auto_model.evaluate(x_train, y_train)
def test_resnet_block(_, tmp_dir):
x_train = np.random.rand(100, 32, 32, 3)
y_train = np.random.randint(10, size=100)
y_train = tf.keras.utils.to_categorical(y_train)
input_node = ak.Input()
output_node = input_node
output_node = ak.ResNetBlock()(output_node)
output_node = ak.ClassificationHead()(output_node)
graph = ak.GraphAutoModel(input_node, output_node,
directory=tmp_dir,
max_trials=1)
graph.fit(x_train, y_train,
epochs=1,
batch_size=100,
verbose=False,
validation_split=0.2)
def test_hyper_graph_basic(tmp_dir):
x_train = np.random.rand(100, 32)
y_train = np.random.rand(100)
input_node = ak.Input()
output_node = input_node
output_node = ak.DenseBlock()(output_node)
output_node = ak.RegressionHead()(output_node)
input_node.shape = (32,)
output_node[0].shape = (1,)
graph = ak.GraphAutoModel(input_node, output_node, directory=tmp_dir)
model = graph.build(kerastuner.HyperParameters())
model.fit(x_train, y_train, epochs=1, batch_size=100, verbose=False)
result = model.predict(x_train)
assert result.shape == (100, 1)
def test_xception_block(tmp_dir):
x_train = np.random.rand(100, 32, 32, 3)
y_train = np.random.randint(10, size=100)
y_train = tf.keras.utils.to_categorical(y_train)
input_node = ak.Input()
output_node = input_node
output_node = ak.XceptionBlock()(output_node)
output_node = ak.ClassificationHead()(output_node)
input_node.shape = (32, 32, 3)
output_node[0].shape = (10, )
graph = ak.GraphAutoModel(input_node, output_node, directory=tmp_dir)
model = graph.build(kerastuner.HyperParameters())
model.fit(x_train, y_train, epochs=1, batch_size=100, verbose=False)
result = model.predict(x_train)
assert result.shape == (100, 10)
def test_merge(tmp_dir):
x_train = np.random.rand(100, 32)
y_train = np.random.rand(100)
input_node1 = ak.Input()
input_node2 = ak.Input()
output_node1 = ak.DenseBlock()(input_node1)
output_node2 = ak.DenseBlock()(input_node2)
output_node = ak.Merge()([output_node1, output_node2])
output_node = ak.RegressionHead()(output_node)
graph = ak.GraphAutoModel([input_node1, input_node2],
output_node,
directory=tmp_dir,
max_trials=1)
graph.fit([x_train, x_train], y_train, epochs=1, batch_size=100, verbose=False)
result = graph.predict([x_train, x_train])
assert result.shape == (100, 1)
def test_hyper_graph_cycle(tmp_dir):
input_node1 = ak.Input()
input_node2 = ak.Input()
output_node1 = ak.DenseBlock()(input_node1)
output_node2 = ak.DenseBlock()(input_node2)
output_node = ak.Merge()([output_node1, output_node2])
head = ak.RegressionHead()
output_node = head(output_node)
head.outputs = output_node1
input_node1.shape = (32,)
input_node2.shape = (32,)
output_node[0].shape = (1,)
with pytest.raises(ValueError) as info:
graph = ak.GraphAutoModel([input_node1, input_node2],
output_node,
directory=tmp_dir)
graph.build(kerastuner.HyperParameters())
assert str(info.value) == 'The network has a cycle.'
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)
def functional_api():
max_features = 20000
max_words = 400
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.imdb.load_data(
num_words=max_features, index_from=3)
x_train = tf.keras.preprocessing.sequence.pad_sequences(x_train,
maxlen=max_words)
x_test = tf.keras.preprocessing.sequence.pad_sequences(x_test,
maxlen=max_words)
print(x_train.dtype)
print(x_train[:10])
input_node = ak.Input()
output_node = input_node
output_node = ak.EmbeddingBlock(max_features=max_features)(output_node)
output_node = ak.ConvBlock()(output_node)
output_node = ak.SpatialReduction()(output_node)
output_node = ak.DenseBlock()(output_node)
output_node = ak.ClassificationHead()(output_node)
clf = ak.GraphAutoModel(input_node, output_node, seed=5, max_trials=3)
clf.fit(x_train, y_train, validation_split=0.2)
return clf.evaluate(x_test, y_test)
def test_evaluate(graph, tuner, tmp_dir):
mc = graph.return_value
mc.preprocess.return_value = (mock.Mock(), mock.Mock())
x_train = np.random.rand(100, 32)
y_train = np.random.rand(100, 1)
input_node = ak.Input()
output_node = input_node
output_node = ak.DenseBlock()(output_node)
output_node = ak.RegressionHead()(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))
auto_model.evaluate(x_train, y_train)
assert tuner.called
assert graph.called
def test_structured_data_input(tmp_dir):
num_data = 500
data = common.structured_data(num_data)
x_train = data
y = np.random.randint(0, 3, num_data)
y_train = y
input_node = ak.StructuredDataInput(
column_names=common.COLUMN_NAMES_FROM_NUMPY,
column_types=common.COLUMN_TYPES_FROM_NUMPY)
output_node = input_node
output_node = ak.StructuredDataBlock()(output_node)
output_node = ak.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))
auto_model.predict(x_train)
def test_conv_block(tmp_dir):
x_train = np.random.rand(100, 3, 3, 3)
y_train = np.random.randint(10, size=100)
y_train = tf.keras.utils.to_categorical(y_train)
input_node = ak.Input()
output_node = input_node
output_node = ak.ConvBlock()(output_node)
output_node = ak.ClassificationHead()(output_node)
input_node.shape = (3, 3, 3)
output_node[0].shape = (10,)
graph = ak.GraphAutoModel(input_node, output_node,
directory=tmp_dir,
max_trials=1)
graph.fit(x_train, y_train,
epochs=1,
batch_size=100,
verbose=False,
validation_split=0.2)
result = graph.predict(x_train)
assert result.shape == (100, 10)
y_test = y_test[:data_slice]
x_image = x_train.reshape(x_train.shape + (1,))
x_test = x_test.reshape(x_test.shape + (1,))
x_structured = np.random.rand(x_train.shape[0], 100)
y_regression = np.random.rand(x_train.shape[0], 1)
y_classification = y_classification.reshape(-1, 1)
# Build model and train.
inputs = ak.ImageInput(shape=(28, 28, 1))
outputs1 = ak.ResNetBlock(version='next')(inputs)
outputs2 = ak.XceptionBlock()(inputs)
image_outputs = ak.Merge()((outputs1, outputs2))
structured_inputs = ak.StructuredDataInput()
structured_outputs = ak.DenseBlock()(structured_inputs)
merged_outputs = ak.Merge()((structured_outputs, image_outputs))
classification_outputs = ak.ClassificationHead()(merged_outputs)
regression_outputs = ak.RegressionHead()(merged_outputs)
automodel = ak.GraphAutoModel(inputs=[inputs, structured_inputs],
outputs=[regression_outputs,
classification_outputs])
automodel.fit((x_image, x_structured),
(y_regression, y_classification),
# trials=100,
validation_split=0.2,
epochs=200,
callbacks=[tf.keras.callbacks.EarlyStopping()])
# Simple (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() auto_pipeline = ak.ImageClassifier(shape, num_classes) # Loss, optimizer are picked automatically auto_pipeline.fit(x_train, y_train) # The predict function should output the labels instead of numerical vectors. auto_pipeline.predict(x_test) # Intermediate inputs = ak.ImageInput(shape=...) x = ak.ImageBlock(inputs) head = ak.ClassificationHead(num_classes, metrics=['accuracy']) outputs = head(x) automodel = ak.GraphAutoModel(inputs=inputs, outputs=outputs) # Loss, optimizer are picked automatically automodel.fit(x_train, y_train) # Advanced inputs = ak.ImageInput(shape=...) outputs1 = ak.ResNetBlock()(inputs) outputs2 = ak.XceptionBlock()(inputs) outputs = ak.Merge()((outputs1, outputs2)) outputs = ak.ClassificationHead(num_classes)(outputs) automodel = ak.GraphAutoModel(inputs=inputs, outputs=outputs) learning_rate = 1.0 automodel.compile(optimizer=tf.keras.optimizers.Adam(learning_rate),
# Prepare the data.
(x_train, y_classification), (x_test, y_test) = mnist.load_data()
x_image = x_train.reshape(x_train.shape + (1,))
x_test = x_test.reshape(x_test.shape + (1,))
x_structured = np.random.rand(x_train.shape[0], 100)
y_regression = np.random.rand(x_train.shape[0], 1)
# Build model and train.
inputs = ak.ImageInput(shape=(28, 28, 1))
outputs1 = ak.ResNetBlock(version='next')(inputs)
outputs2 = ak.XceptionBlock()(inputs)
image_outputs = ak.Merge()((outputs1, outputs2))
structured_inputs = ak.StructuredInput()
structured_outputs = ak.DenseBlock()(structured_inputs)
merged_outputs = ak.Merge()((image_outputs, structured_outputs))
classification_outputs = ak.ClassificationHead()(merged_outputs)
regression_outputs = ak.RegressionHead()(merged_outputs)
automodel = ak.GraphAutoModel(inputs=inputs,
outputs=[regression_outputs,
classification_outputs])
automodel.fit((x_image, x_structured),
(y_regression, y_classification),
trials=100,
epochs=200,
callbacks=[tf.keras.callbacks.EarlyStopping(),
tf.keras.callbacks.LearningRateScheduler()])
