How to use the autokeras.StructuredDataClassifier function in autokeras

def test_structured_data_from_csv_partial_col_type_classifier(tmp_dir):
    clf = ak.StructuredDataClassifier(
        column_types=common.PARTIAL_COLUMN_TYPES_FROM_CSV,
        directory=tmp_dir,
        max_trials=1)
    clf.fit(x=common.TRAIN_FILE_PATH, y='survived', epochs=2,
            validation_data=common.TEST_FILE_PATH)

def test_structured_data_from_numpy_classifier(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
    clf = ak.StructuredDataClassifier(directory=tmp_dir, max_trials=1)
    clf.fit(x_train, y_train, epochs=2, validation_data=(x_train, y_train))

def test_structured_data_from_csv_less_col_name_classifier(tmp_dir):
    with pytest.raises(ValueError) as info:
        clf = ak.StructuredDataClassifier(
            column_names=common.LESS_COLUMN_NAMES_FROM_CSV,
            directory=tmp_dir,
            max_trials=1)
        clf.fit(x=common.TRAIN_FILE_PATH, y='survived', epochs=2,
                validation_data=common.TEST_FILE_PATH)
    assert 'Expect column_names to have length' in str(info.value)

def test_structured_data_from_numpy_col_name_classifier(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
    clf = ak.StructuredDataClassifier(
        column_names=common.COLUMN_NAMES_FROM_NUMPY,
        directory=tmp_dir,
        max_trials=1)
    clf.fit(x_train, y_train, epochs=2, validation_data=(x_train, y_train))

def test_structured_data_from_csv_false_col_type_classifier(tmp_dir):
    with pytest.raises(ValueError) as info:
        clf = ak.StructuredDataClassifier(
            column_types=common.FALSE_COLUMN_TYPES_FROM_CSV,
            directory=tmp_dir,
            max_trials=1)
        clf.fit(x=common.TRAIN_FILE_PATH, y='survived', epochs=2,
                validation_data=common.TEST_FILE_PATH)
    assert 'Column_types should be either "categorical"' in str(info.value)

def test_structured_data_from_numpy_col_type_classifier(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
    with pytest.raises(ValueError) as info:
        clf = ak.StructuredDataClassifier(
            column_types=common.COLUMN_TYPES_FROM_NUMPY,
            directory=tmp_dir,
            max_trials=1)
        clf.fit(x_train, y_train, epochs=2, validation_data=(x_train, y_train))
    assert str(info.value) == 'Column names must be specified.'

def test_structured_data_from_csv_col_name_classifier(tmp_dir):
    clf = ak.StructuredDataClassifier(
        column_names=common.COLUMN_NAMES_FROM_CSV,
        directory=tmp_dir,
        max_trials=1)
    clf.fit(x=common.TRAIN_FILE_PATH, y='survived', epochs=2,
            validation_data=common.TEST_FILE_PATH)

def test_structured_data_from_numpy_col_name_type_classifier(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
    clf = ak.StructuredDataClassifier(
        column_names=common.COLUMN_NAMES_FROM_NUMPY,
        column_types=common.COLUMN_TYPES_FROM_NUMPY,
        directory=tmp_dir,
        max_trials=1)
    clf.fit(x_train, y_train, epochs=2, validation_data=(x_train, y_train))

def test_structured_data_from_csv_classifier(tmp_dir):
    clf = ak.StructuredDataClassifier(directory=tmp_dir, max_trials=1)
    clf.fit(x=common.TRAIN_FILE_PATH, y='survived', epochs=2,
            validation_data=common.TEST_FILE_PATH)