def test_structured_data_unknown_str_in_col_type(tmp_dir):
with pytest.raises(ValueError) as info:
structured_data.StructuredDataClassifier(
column_types=utils.FALSE_COLUMN_TYPES_FROM_CSV,
directory=tmp_dir,
max_trials=1,
seed=utils.SEED)
assert 'Column_types should be either "categorical"' in str(info.value)
def test_structured_data_col_name_type_mismatch(tmp_dir):
with pytest.raises(ValueError) as info:
structured_data.StructuredDataClassifier(
column_names=utils.COLUMN_NAMES_FROM_NUMPY,
column_types=utils.COLUMN_TYPES_FROM_CSV,
directory=tmp_dir,
max_trials=1,
seed=utils.SEED)
assert 'Column_names and column_types are mismatched.' in str(info.value)
def test_structured_data_classifier_from_csv(init, fit, tmp_dir):
clf = structured_data.StructuredDataClassifier(
directory=tmp_dir,
max_trials=1,
seed=utils.SEED)
clf.fit(x=utils.TRAIN_FILE_PATH, y='survived', epochs=2,
validation_data=(utils.TEST_FILE_PATH, 'survived'))
assert init.called
_, kwargs = fit.call_args_list[0]
assert isinstance(kwargs['x'], pandas.DataFrame)
assert isinstance(kwargs['y'], np.ndarray)
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