def test_io_api(tmp_path):
num_instances = 100
(image_x, train_y), (test_x, test_y) = mnist.load_data()
(text_x, train_y), (test_x,
test_y) = utils.imdb_raw(num_instances=num_instances)
image_x = image_x[:num_instances]
text_x = text_x[:num_instances]
structured_data_x = utils.generate_structured_data(
num_instances=num_instances)
classification_y = utils.generate_one_hot_labels(
num_instances=num_instances, num_classes=3)
regression_y = utils.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_path,
max_trials=2,
seed=utils.SEED)
automodel.fit([image_x, text_x, structured_data_x],
[regression_y, classification_y],
epochs=1,
validation_split=0.2)
def test_structured_reg_fit_call_auto_model_fit(fit, tmp_path):
auto_model = ak.StructuredDataRegressor(directory=tmp_path,
seed=utils.SEED)
auto_model.fit(x=utils.generate_structured_data(num_instances=100),
y=utils.generate_data(num_instances=100, shape=(1, )))
assert fit.is_called
def test_time_series_input_col_type_without_name():
num_data = 500
train_x = utils.generate_structured_data(num_data)
with pytest.raises(ValueError) as info:
adapter = input_adapter.TimeseriesInputAdapter(
lookback=2, column_types=utils.COLUMN_TYPES_FROM_NUMPY)
adapter.transform(train_x)
assert str(info.value) == 'Column names must be specified.'
def test_structured_data_input_col_type_without_name():
num_data = 500
train_x = utils.generate_structured_data(num_data)
with pytest.raises(ValueError) as info:
input_node = input_adapter.StructuredDataInputAdapter(
column_types=utils.COLUMN_TYPES_FROM_NUMPY)
input_node.transform(train_x)
assert str(info.value) == 'Column names must be specified.'
def test_structured_clf_fit_call_auto_model_fit(fit, tmp_path):
auto_model = ak.StructuredDataClassifier(directory=tmp_path, seed=utils.SEED)
auto_model.fit(
x=utils.generate_structured_data(num_instances=100),
y=utils.generate_one_hot_labels(num_instances=100, num_classes=3))
assert fit.is_called
def test_structured_data_from_numpy_classifier(tmp_path):
num_data = 500
num_train = 400
data = utils.generate_structured_data(num_data)
x_train, x_test = data[:num_train], data[num_train:]
y = utils.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_path,
max_trials=1,
seed=utils.SEED)
clf.fit(x_train, y_train, epochs=2, validation_data=(x_train, y_train))
clf.export_model()
assert clf.predict(x_test).shape == (len(y_test), 3)
def test_structured_data_from_numpy_regressor(tmp_path):
num_data = 500
num_train = 400
data = utils.generate_structured_data(num_data)
x_train, x_test = data[:num_train], data[num_train:]
y = utils.generate_data(num_instances=num_data, shape=(1, ))
y_train, y_test = y[:num_train], y[num_train:]
clf = ak.StructuredDataRegressor(directory=tmp_path,
max_trials=2,
seed=utils.SEED)
clf.fit(x_train, y_train, epochs=11, validation_data=(x_train, y_train))
clf.export_model()
assert clf.predict(x_test).shape == (len(y_test), 1)
def test_structured_regressor(init, fit, tmp_dir):
num_data = 500
train_x = utils.generate_structured_data(num_data)
train_y = utils.generate_data(num_instances=100, shape=(1,))
clf = structured_data.StructuredDataRegressor(
column_names=utils.COLUMN_NAMES_FROM_NUMPY,
directory=tmp_dir,
max_trials=1,
seed=utils.SEED)
clf.fit(train_x, train_y, epochs=2, validation_data=(train_x, train_y))
assert init.called
assert fit.called
def test_structured_classifier(init, fit, tmp_dir):
num_data = 500
train_x = utils.generate_structured_data(num_data)
train_y = utils.generate_one_hot_labels(num_instances=num_data, num_classes=3)
clf = structured_data.StructuredDataClassifier(
column_names=utils.COLUMN_NAMES_FROM_NUMPY,
directory=tmp_dir,
max_trials=1,
seed=utils.SEED)
clf.fit(train_x, train_y, epochs=2, validation_data=(train_x, train_y))
assert init.called
assert fit.called
