def test_structured_data_input_less_col_name():
(x, _), _1 = common.dataframe_numpy()
with pytest.raises(ValueError) as info:
input_node = node.StructuredDataInput(
column_names=common.LESS_COLUMN_NAMES_FROM_CSV)
input_node.fit(x)
assert 'Expect column_names to have length' in str(info.value)
def test_partial_column_types():
input_node = node.StructuredDataInput(
column_names=common.COLUMN_NAMES_FROM_CSV,
column_types=common.PARTIAL_COLUMN_TYPES_FROM_CSV)
(x, y), (val_x, val_y) = common.dataframe_numpy()
dataset = x.values.astype(np.unicode)
input_node.transform(dataset)
assert input_node.column_types['fare'] == 'categorical'
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_partial_column_types():
input_node = node.StructuredDataInput(
column_names=common.COLUMN_NAMES_FROM_CSV,
column_types=common.PARTIAL_COLUMN_TYPES_FROM_CSV)
(x, y), (val_x, val_y) = common.dataframe_numpy()
dataset = tf.data.Dataset.zip(
((tf.data.Dataset.from_tensor_slices(x.values.astype(np.unicode)), ),
(tf.data.Dataset.from_tensor_slices(y), )))
hm = meta_model.assemble(input_node, ak.ClassificationHead(), dataset)
for block in hm._blocks:
if isinstance(block, ak.FeatureEngineering):
assert block.input_node.column_types['fare'] == 'categorical'
def test_structured_data_from_dataframe_numpy_classifier(tmp_dir):
clf = ak.StructuredDataClassifier(directory=tmp_dir, max_trials=1)
(x, y), (val_x, val_y) = common.dataframe_numpy()
clf.fit(x=x, y=y, epochs=2, validation_data=(val_x, val_y))
assert clf.predict(val_x).shape == (len(val_x), 1)