How to use the tpot.builtins.one_hot_encoder.OneHotEncoder function in TPOT

def fit_then_transform_dense(expected, input,
                             categorical_features='all',
                             minimum_fraction=None):
    ohe = OneHotEncoder(categorical_features=categorical_features,
                        sparse=False, minimum_fraction=minimum_fraction)
    transformation = ohe.fit_transform(input.copy())
    assert_array_almost_equal(expected, transformation)

    ohe2 = OneHotEncoder(categorical_features=categorical_features,
                         sparse=False, minimum_fraction=minimum_fraction)
    ohe2.fit(input.copy())
    transformation = ohe2.transform(input.copy())
    assert_array_almost_equal(expected, transformation)

def fit_then_transform(expected, input, categorical_features='all',
                       minimum_fraction=None):
    # Test fit_transform
    ohe = OneHotEncoder(categorical_features=categorical_features,
                        minimum_fraction=minimum_fraction)
    transformation = ohe.fit_transform(input.copy())
    assert_array_almost_equal(expected.astype(float),
                              transformation.todense())

    # Test fit, and afterwards transform
    ohe2 = OneHotEncoder(categorical_features=categorical_features,
                         minimum_fraction=minimum_fraction)
    ohe2.fit(input.copy())
    transformation = ohe2.transform(input.copy())
    assert_array_almost_equal(expected, transformation.todense())

def fit_then_transform(expected, input, categorical_features='all',
                       minimum_fraction=None):
    # Test fit_transform
    ohe = OneHotEncoder(categorical_features=categorical_features,
                        minimum_fraction=minimum_fraction)
    transformation = ohe.fit_transform(input.copy())
    assert_array_almost_equal(expected.astype(float),
                              transformation.todense())

    # Test fit, and afterwards transform
    ohe2 = OneHotEncoder(categorical_features=categorical_features,
                         minimum_fraction=minimum_fraction)
    ohe2.fit(input.copy())
    transformation = ohe2.transform(input.copy())
    assert_array_almost_equal(expected, transformation.todense())

def fit_then_transform_dense(expected, input,
                             categorical_features='all',
                             minimum_fraction=None):
    ohe = OneHotEncoder(categorical_features=categorical_features,
                        sparse=False, minimum_fraction=minimum_fraction)
    transformation = ohe.fit_transform(input.copy())
    assert_array_almost_equal(expected, transformation)

    ohe2 = OneHotEncoder(categorical_features=categorical_features,
                         sparse=False, minimum_fraction=minimum_fraction)
    ohe2.fit(input.copy())
    transformation = ohe2.transform(input.copy())
    assert_array_almost_equal(expected, transformation)

def test_transform():
    """Test OneHotEncoder with both dense and sparse matrixes."""
    input = np.array(((0, 1, 2, 3, 4, 5), (0, 1, 2, 3, 4, 5))).transpose()
    ohe = OneHotEncoder()
    ohe.fit(input)
    test_data = np.array(((0, 1, 2, 6), (0, 1, 6, 7))).transpose()
    output = ohe.transform(test_data).todense()
    assert np.sum(output) == 5

    input = np.array(((0, 1, 2, 3, 4, 5), (0, 1, 2, 3, 4, 5))).transpose()
    ips = scipy.sparse.csr_matrix(input)
    ohe = OneHotEncoder()
    ohe.fit(ips)
    test_data = np.array(((0, 1, 2, 6), (0, 1, 6, 7))).transpose()
    tds = scipy.sparse.csr_matrix(test_data)
    output = ohe.transform(tds).todense()
    assert np.sum(output) == 3

def test_k_fold_cv():
    """Test OneHotEncoder with categorical_features='auto'."""
    boston = load_boston()

    clf = make_pipeline(
        OneHotEncoder(
            categorical_features='auto',
            sparse=False,
            minimum_fraction=0.05
        ),
        LinearRegression()
    )

    cross_val_score(clf, boston.data, boston.target, cv=KFold(n_splits=10, shuffle=True))

----------
        X: numpy ndarray, {n_samples, n_components}
            New data, where n_samples is the number of samples and n_components is the number of components.

        Returns
        -------
        array-like, {n_samples, n_components}
        """
        selected = auto_select_categorical_features(X, threshold=self.threshold)
        X_sel, _, n_selected, _ = _X_selected(X, selected)

        if n_selected == 0:
            # No features selected.
            raise ValueError('No categorical feature was found!')
        else:
            ohe = OneHotEncoder(categorical_features='all', sparse=False, minimum_fraction=self.minimum_fraction)
            return ohe.fit_transform(X_sel)