"""
Test the pipeline module.
"""
import numpy as np
from scipy import sparse

from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regex
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import assert_dict_equal

from sklearn.base import clone, BaseEstimator
from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline, make_union
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
from sklearn.cluster import KMeans
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.feature_extraction.text import CountVectorizer


JUNK_FOOD_DOCS = (
    "the pizza pizza beer copyright",
    "the pizza burger beer copyright",
    "the the pizza beer beer copyright",
    "the burger beer beer copyright",
    "the coke burger coke copyright",
    "the coke burger burger",
)


class NoFit(object):
    """Small class to test parameter dispatching.
    """

    def __init__(self, a=None, b=None):
        self.a = a
        self.b = b


class NoTrans(NoFit):

    def fit(self, X, y):
        return self

    def get_params(self, deep=False):
        return {'a': self.a, 'b': self.b}

    def set_params(self, **params):
        self.a = params['a']
        return self


class NoInvTransf(NoTrans):
    def transform(self, X, y=None):
        return X


class Transf(NoInvTransf):
    def transform(self, X, y=None):
        return X

    def inverse_transform(self, X):
        return X


class TransfFitParams(Transf):

    def fit(self, X, y, **fit_params):
        self.fit_params = fit_params
        return self


class Mult(BaseEstimator):
    def __init__(self, mult=1):
        self.mult = mult

    def fit(self, X, y):
        return self

    def transform(self, X):
        return np.asarray(X) * self.mult

    def inverse_transform(self, X):
        return np.asarray(X) / self.mult

    def predict(self, X):
        return (np.asarray(X) * self.mult).sum(axis=1)

    predict_proba = predict_log_proba = decision_function = predict

    def score(self, X, y=None):
        return np.sum(X)


class FitParamT(BaseEstimator):
    """Mock classifier
    """

    def __init__(self):
        self.successful = False

    def fit(self, X, y, should_succeed=False):
        self.successful = should_succeed

    def predict(self, X):
        return self.successful

    def fit_predict(self, X, y, should_succeed=False):
        self.fit(X, y, should_succeed=should_succeed)
        return self.predict(X)

    def score(self, X, y=None, sample_weight=None):
        if sample_weight is not None:
            X = X * sample_weight
        return np.sum(X)


def test_pipeline_init():
    # Test the various init parameters of the pipeline.
    assert_raises(TypeError, Pipeline)
    # Check that we can't instantiate pipelines with objects without fit
    # method
    assert_raises_regex(TypeError,
                        'Last step of Pipeline should implement fit. '
                        '.*NoFit.*',
                        Pipeline, [('clf', NoFit())])
    # Smoke test with only an estimator
    clf = NoTrans()
    pipe = Pipeline([('svc', clf)])
    assert_equal(pipe.get_params(deep=True),
                 dict(svc__a=None, svc__b=None, svc=clf,
                      **pipe.get_params(deep=False)))

    # Check that params are set
    pipe.set_params(svc__a=0.1)
    assert_equal(clf.a, 0.1)
    assert_equal(clf.b, None)
    # Smoke test the repr:
    repr(pipe)

    # Test with two objects
    clf = SVC()
    filter1 = SelectKBest(f_classif)
    pipe = Pipeline([('anova', filter1), ('svc', clf)])

    # Check that we can't instantiate with non-transformers on the way
    # Note that NoTrans implements fit, but not transform
    assert_raises_regex(TypeError,
                        'All intermediate steps should be transformers'
                        '.*\\bNoTrans\\b.*',
                        Pipeline, [('t', NoTrans()), ('svc', clf)])

    # Check that params are set
    pipe.set_params(svc__C=0.1)
    assert_equal(clf.C, 0.1)
    # Smoke test the repr:
    repr(pipe)

    # Check that params are not set when naming them wrong
    assert_raises(ValueError, pipe.set_params, anova__C=0.1)

    # Test clone
    pipe2 = clone(pipe)
    assert_false(pipe.named_steps['svc'] is pipe2.named_steps['svc'])

    # Check that apart from estimators, the parameters are the same
    params = pipe.get_params(deep=True)
    params2 = pipe2.get_params(deep=True)

    for x in pipe.get_params(deep=False):
        params.pop(x)

    for x in pipe2.get_params(deep=False):
        params2.pop(x)

    # Remove estimators that where copied
    params.pop('svc')
    params.pop('anova')
    params2.pop('svc')
    params2.pop('anova')
    assert_equal(params, params2)


def test_pipeline_methods_anova():
    # Test the various methods of the pipeline (anova).
    iris = load_iris()
    X = iris.data
    y = iris.target
    # Test with Anova + LogisticRegression
    clf = LogisticRegression()
    filter1 = SelectKBest(f_classif, k=2)
    pipe = Pipeline([('anova', filter1), ('logistic', clf)])
    pipe.fit(X, y)
    pipe.predict(X)
    pipe.predict_proba(X)
    pipe.predict_log_proba(X)
    pipe.score(X, y)


def test_pipeline_fit_params():
    # Test that the pipeline can take fit parameters
    pipe = Pipeline([('transf', Transf()), ('clf', FitParamT())])
    pipe.fit(X=None, y=None, clf__should_succeed=True)
    # classifier should return True
    assert_true(pipe.predict(None))
    # and transformer params should not be changed
    assert_true(pipe.named_steps['transf'].a is None)
    assert_true(pipe.named_steps['transf'].b is None)
    # invalid parameters should raise an error message
    assert_raise_message(
        TypeError,
        "fit() got an unexpected keyword argument 'bad'",
        pipe.fit, None, None, clf__bad=True
    )


def test_pipeline_sample_weight_supported():
    # Pipeline should pass sample_weight
    X = np.array([[1, 2]])
    pipe = Pipeline([('transf', Transf()), ('clf', FitParamT())])
    pipe.fit(X, y=None)
    assert_equal(pipe.score(X), 3)
    assert_equal(pipe.score(X, y=None), 3)
    assert_equal(pipe.score(X, y=None, sample_weight=None), 3)
    assert_equal(pipe.score(X, sample_weight=np.array([2, 3])), 8)


def test_pipeline_sample_weight_unsupported():
    # When sample_weight is None it shouldn't be passed
    X = np.array([[1, 2]])
    pipe = Pipeline([('transf', Transf()), ('clf', Mult())])
    pipe.fit(X, y=None)
    assert_equal(pipe.score(X), 3)
    assert_equal(pipe.score(X, sample_weight=None), 3)
    assert_raise_message(
        TypeError,
        "score() got an unexpected keyword argument 'sample_weight'",
        pipe.score, X, sample_weight=np.array([2, 3])
    )


def test_pipeline_raise_set_params_error():
    # Test pipeline raises set params error message for nested models.
    pipe = Pipeline([('cls', LinearRegression())])

    # expected error message
    error_msg = ('Invalid parameter %s for estimator %s. '
                 'Check the list of available parameters '
                 'with `estimator.get_params().keys()`.')

    assert_raise_message(ValueError,
                         error_msg % ('fake', 'Pipeline'),
                         pipe.set_params,
                         fake='nope')

    # nested model check
    assert_raise_message(ValueError,
                         error_msg % ("fake", pipe),
                         pipe.set_params,
                         fake__estimator='nope')


def test_pipeline_methods_pca_svm():
    # Test the various methods of the pipeline (pca + svm).
    iris = load_iris()
    X = iris.data
    y = iris.target
    # Test with PCA + SVC
    clf = SVC(probability=True, random_state=0)
    pca = PCA(svd_solver='full', n_components='mle', whiten=True)
    pipe = Pipeline([('pca', pca), ('svc', clf)])
    pipe.fit(X, y)
    pipe.predict(X)
    pipe.predict_proba(X)
    pipe.predict_log_proba(X)
    pipe.score(X, y)


def test_pipeline_methods_preprocessing_svm():
    # Test the various methods of the pipeline (preprocessing + svm).
    iris = load_iris()
    X = iris.data
    y = iris.target
    n_samples = X.shape[0]
    n_classes = len(np.unique(y))
    scaler = StandardScaler()
    pca = PCA(n_components=2, svd_solver='randomized', whiten=True)
    clf = SVC(probability=True, random_state=0, decision_function_shape='ovr')

    for preprocessing in [scaler, pca]:
        pipe = Pipeline([('preprocess', preprocessing), ('svc', clf)])
        pipe.fit(X, y)

        # check shapes of various prediction functions
        predict = pipe.predict(X)
        assert_equal(predict.shape, (n_samples,))

        proba = pipe.predict_proba(X)
        assert_equal(proba.shape, (n_samples, n_classes))

        log_proba = pipe.predict_log_proba(X)
        assert_equal(log_proba.shape, (n_samples, n_classes))

        decision_function = pipe.decision_function(X)
        assert_equal(decision_function.shape, (n_samples, n_classes))

        pipe.score(X, y)


def test_fit_predict_on_pipeline():
    # test that the fit_predict method is implemented on a pipeline
    # test that the fit_predict on pipeline yields same results as applying
    # transform and clustering steps separately
    iris = load_iris()
    scaler = StandardScaler()
    km = KMeans(random_state=0)
    # As pipeline doesn't clone estimators on construction,
    # it must have its own estimators
    scaler_for_pipeline = StandardScaler()
    km_for_pipeline = KMeans(random_state=0)

    # first compute the transform and clustering step separately
    scaled = scaler.fit_transform(iris.data)
    separate_pred = km.fit_predict(scaled)

    # use a pipeline to do the transform and clustering in one step
    pipe = Pipeline([
        ('scaler', scaler_for_pipeline),
        ('Kmeans', km_for_pipeline)
    ])
    pipeline_pred = pipe.fit_predict(iris.data)

    assert_array_almost_equal(pipeline_pred, separate_pred)


def test_fit_predict_on_pipeline_without_fit_predict():
    # tests that a pipeline does not have fit_predict method when final
    # step of pipeline does not have fit_predict defined
    scaler = StandardScaler()
    pca = PCA(svd_solver='full')
    pipe = Pipeline([('scaler', scaler), ('pca', pca)])
    assert_raises_regex(AttributeError,
                        "'PCA' object has no attribute 'fit_predict'",
                        getattr, pipe, 'fit_predict')


def test_fit_predict_with_intermediate_fit_params():
    # tests that Pipeline passes fit_params to intermediate steps
    # when fit_predict is invoked
    pipe = Pipeline([('transf', TransfFitParams()), ('clf', FitParamT())])
    pipe.fit_predict(X=None,
                     y=None,
                     transf__should_get_this=True,
                     clf__should_succeed=True)
    assert_true(pipe.named_steps['transf'].fit_params['should_get_this'])
    assert_true(pipe.named_steps['clf'].successful)
    assert_false('should_succeed' in pipe.named_steps['transf'].fit_params)


def test_feature_union():
    # basic sanity check for feature union
    iris = load_iris()
    X = iris.data
    X -= X.mean(axis=0)
    y = iris.target
    svd = TruncatedSVD(n_components=2, random_state=0)
    select = SelectKBest(k=1)
    fs = FeatureUnion([("svd", svd), ("select", select)])
    fs.fit(X, y)
    X_transformed = fs.transform(X)
    assert_equal(X_transformed.shape, (X.shape[0], 3))

    # check if it does the expected thing
    assert_array_almost_equal(X_transformed[:, :-1], svd.fit_transform(X))
    assert_array_equal(X_transformed[:, -1],
                       select.fit_transform(X, y).ravel())

    # test if it also works for sparse input
    # We use a different svd object to control the random_state stream
    fs = FeatureUnion([("svd", svd), ("select", select)])
    X_sp = sparse.csr_matrix(X)
    X_sp_transformed = fs.fit_transform(X_sp, y)
    assert_array_almost_equal(X_transformed, X_sp_transformed.toarray())

    # test setting parameters
    fs.set_params(select__k=2)
    assert_equal(fs.fit_transform(X, y).shape, (X.shape[0], 4))

    # test it works with transformers missing fit_transform
    fs = FeatureUnion([("mock", Transf()), ("svd", svd), ("select", select)])
    X_transformed = fs.fit_transform(X, y)
    assert_equal(X_transformed.shape, (X.shape[0], 8))

    # test error if some elements do not support transform
    assert_raises_regex(TypeError,
                        'All estimators should implement fit and '
                        'transform.*\\bNoTrans\\b',
                        FeatureUnion,
                        [("transform", Transf()), ("no_transform", NoTrans())])


def test_make_union():
    pca = PCA(svd_solver='full')
    mock = Transf()
    fu = make_union(pca, mock)
    names, transformers = zip(*fu.transformer_list)
    assert_equal(names, ("pca", "transf"))
    assert_equal(transformers, (pca, mock))


def test_pipeline_transform():
    # Test whether pipeline works with a transformer at the end.
    # Also test pipeline.transform and pipeline.inverse_transform
    iris = load_iris()
    X = iris.data
    pca = PCA(n_components=2, svd_solver='full')
    pipeline = Pipeline([('pca', pca)])

    # test transform and fit_transform:
    X_trans = pipeline.fit(X).transform(X)
    X_trans2 = pipeline.fit_transform(X)
    X_trans3 = pca.fit_transform(X)
    assert_array_almost_equal(X_trans, X_trans2)
    assert_array_almost_equal(X_trans, X_trans3)

    X_back = pipeline.inverse_transform(X_trans)
    X_back2 = pca.inverse_transform(X_trans)
    assert_array_almost_equal(X_back, X_back2)


def test_pipeline_fit_transform():
    # Test whether pipeline works with a transformer missing fit_transform
    iris = load_iris()
    X = iris.data
    y = iris.target
    transf = Transf()
    pipeline = Pipeline([('mock', transf)])

    # test fit_transform:
    X_trans = pipeline.fit_transform(X, y)
    X_trans2 = transf.fit(X, y).transform(X)
    assert_array_almost_equal(X_trans, X_trans2)


def test_set_pipeline_steps():
    transf1 = Transf()
    transf2 = Transf()
    pipeline = Pipeline([('mock', transf1)])
    assert_true(pipeline.named_steps['mock'] is transf1)

    # Directly setting attr
    pipeline.steps = [('mock2', transf2)]
    assert_true('mock' not in pipeline.named_steps)
    assert_true(pipeline.named_steps['mock2'] is transf2)
    assert_equal([('mock2', transf2)], pipeline.steps)

    # Using set_params
    pipeline.set_params(steps=[('mock', transf1)])
    assert_equal([('mock', transf1)], pipeline.steps)

    # Using set_params to replace single step
    pipeline.set_params(mock=transf2)
    assert_equal([('mock', transf2)], pipeline.steps)

    # With invalid data
    pipeline.set_params(steps=[('junk', ())])
    assert_raises(TypeError, pipeline.fit, [[1]], [1])
    assert_raises(TypeError, pipeline.fit_transform, [[1]], [1])


def test_set_pipeline_step_none():
    # Test setting Pipeline steps to None
    X = np.array([[1]])
    y = np.array([1])
    mult2 = Mult(mult=2)
    mult3 = Mult(mult=3)
    mult5 = Mult(mult=5)

    def make():
        return Pipeline([('m2', mult2), ('m3', mult3), ('last', mult5)])

    pipeline = make()

    exp = 2 * 3 * 5
    assert_array_equal([[exp]], pipeline.fit_transform(X, y))
    assert_array_equal([exp], pipeline.fit(X).predict(X))
    assert_array_equal(X, pipeline.inverse_transform([[exp]]))

    pipeline.set_params(m3=None)
    exp = 2 * 5
    assert_array_equal([[exp]], pipeline.fit_transform(X, y))
    assert_array_equal([exp], pipeline.fit(X).predict(X))
    assert_array_equal(X, pipeline.inverse_transform([[exp]]))
    assert_dict_equal(pipeline.get_params(deep=True),
                      {'steps': pipeline.steps,
                       'm2': mult2,
                       'm3': None,
                       'last': mult5,
                       'm2__mult': 2,
                       'last__mult': 5,
                       })

    pipeline.set_params(m2=None)
    exp = 5
    assert_array_equal([[exp]], pipeline.fit_transform(X, y))
    assert_array_equal([exp], pipeline.fit(X).predict(X))
    assert_array_equal(X, pipeline.inverse_transform([[exp]]))

    # for other methods, ensure no AttributeErrors on None:
    other_methods = ['predict_proba', 'predict_log_proba',
                     'decision_function', 'transform', 'score']
    for method in other_methods:
        getattr(pipeline, method)(X)

    pipeline.set_params(m2=mult2)
    exp = 2 * 5
    assert_array_equal([[exp]], pipeline.fit_transform(X, y))
    assert_array_equal([exp], pipeline.fit(X).predict(X))
    assert_array_equal(X, pipeline.inverse_transform([[exp]]))

    pipeline = make()
    pipeline.set_params(last=None)
    # mult2 and mult3 are active
    exp = 6
    assert_array_equal([[exp]], pipeline.fit(X, y).transform(X))
    assert_array_equal([[exp]], pipeline.fit_transform(X, y))
    assert_array_equal(X, pipeline.inverse_transform([[exp]]))
    assert_raise_message(AttributeError,
                         "'NoneType' object has no attribute 'predict'",
                         getattr, pipeline, 'predict')

    # Check None step at construction time
    exp = 2 * 5
    pipeline = Pipeline([('m2', mult2), ('m3', None), ('last', mult5)])
    assert_array_equal([[exp]], pipeline.fit_transform(X, y))
    assert_array_equal([exp], pipeline.fit(X).predict(X))
    assert_array_equal(X, pipeline.inverse_transform([[exp]]))


def test_pipeline_ducktyping():
    pipeline = make_pipeline(Mult(5))
    pipeline.predict
    pipeline.transform
    pipeline.inverse_transform

    pipeline = make_pipeline(Transf())
    assert_false(hasattr(pipeline, 'predict'))
    pipeline.transform
    pipeline.inverse_transform

    pipeline = make_pipeline(None)
    assert_false(hasattr(pipeline, 'predict'))
    pipeline.transform
    pipeline.inverse_transform

    pipeline = make_pipeline(Transf(), NoInvTransf())
    assert_false(hasattr(pipeline, 'predict'))
    pipeline.transform
    assert_false(hasattr(pipeline, 'inverse_transform'))

    pipeline = make_pipeline(NoInvTransf(), Transf())
    assert_false(hasattr(pipeline, 'predict'))
    pipeline.transform
    assert_false(hasattr(pipeline, 'inverse_transform'))


def test_make_pipeline():
    t1 = Transf()
    t2 = Transf()
    pipe = make_pipeline(t1, t2)
    assert_true(isinstance(pipe, Pipeline))
    assert_equal(pipe.steps[0][0], "transf-1")
    assert_equal(pipe.steps[1][0], "transf-2")

    pipe = make_pipeline(t1, t2, FitParamT())
    assert_true(isinstance(pipe, Pipeline))
    assert_equal(pipe.steps[0][0], "transf-1")
    assert_equal(pipe.steps[1][0], "transf-2")
    assert_equal(pipe.steps[2][0], "fitparamt")


def test_feature_union_weights():
    # test feature union with transformer weights
    iris = load_iris()
    X = iris.data
    y = iris.target
    pca = PCA(n_components=2, svd_solver='randomized', random_state=0)
    select = SelectKBest(k=1)
    # test using fit followed by transform
    fs = FeatureUnion([("pca", pca), ("select", select)],
                      transformer_weights={"pca": 10})
    fs.fit(X, y)
    X_transformed = fs.transform(X)
    # test using fit_transform
    fs = FeatureUnion([("pca", pca), ("select", select)],
                      transformer_weights={"pca": 10})
    X_fit_transformed = fs.fit_transform(X, y)
    # test it works with transformers missing fit_transform
    fs = FeatureUnion([("mock", Transf()), ("pca", pca), ("select", select)],
                      transformer_weights={"mock": 10})
    X_fit_transformed_wo_method = fs.fit_transform(X, y)
    # check against expected result

    # We use a different pca object to control the random_state stream
    assert_array_almost_equal(X_transformed[:, :-1], 10 * pca.fit_transform(X))
    assert_array_equal(X_transformed[:, -1],
                       select.fit_transform(X, y).ravel())
    assert_array_almost_equal(X_fit_transformed[:, :-1],
                              10 * pca.fit_transform(X))
    assert_array_equal(X_fit_transformed[:, -1],
                       select.fit_transform(X, y).ravel())
    assert_equal(X_fit_transformed_wo_method.shape, (X.shape[0], 7))


def test_feature_union_parallel():
    # test that n_jobs work for FeatureUnion
    X = JUNK_FOOD_DOCS

    fs = FeatureUnion([
        ("words", CountVectorizer(analyzer='word')),
        ("chars", CountVectorizer(analyzer='char')),
    ])

    fs_parallel = FeatureUnion([
        ("words", CountVectorizer(analyzer='word')),
        ("chars", CountVectorizer(analyzer='char')),
    ], n_jobs=2)

    fs_parallel2 = FeatureUnion([
        ("words", CountVectorizer(analyzer='word')),
        ("chars", CountVectorizer(analyzer='char')),
    ], n_jobs=2)

    fs.fit(X)
    X_transformed = fs.transform(X)
    assert_equal(X_transformed.shape[0], len(X))

    fs_parallel.fit(X)
    X_transformed_parallel = fs_parallel.transform(X)
    assert_equal(X_transformed.shape, X_transformed_parallel.shape)
    assert_array_equal(
        X_transformed.toarray(),
        X_transformed_parallel.toarray()
    )

    # fit_transform should behave the same
    X_transformed_parallel2 = fs_parallel2.fit_transform(X)
    assert_array_equal(
        X_transformed.toarray(),
        X_transformed_parallel2.toarray()
    )

    # transformers should stay fit after fit_transform
    X_transformed_parallel2 = fs_parallel2.transform(X)
    assert_array_equal(
        X_transformed.toarray(),
        X_transformed_parallel2.toarray()
    )


def test_feature_union_feature_names():
    word_vect = CountVectorizer(analyzer="word")
    char_vect = CountVectorizer(analyzer="char_wb", ngram_range=(3, 3))
    ft = FeatureUnion([("chars", char_vect), ("words", word_vect)])
    ft.fit(JUNK_FOOD_DOCS)
    feature_names = ft.get_feature_names()
    for feat in feature_names:
        assert_true("chars__" in feat or "words__" in feat)
    assert_equal(len(feature_names), 35)

    ft = FeatureUnion([("tr1", Transf())]).fit([[1]])
    assert_raise_message(AttributeError,
                         'Transformer tr1 (type Transf) does not provide '
                         'get_feature_names', ft.get_feature_names)


def test_classes_property():
    iris = load_iris()
    X = iris.data
    y = iris.target

    reg = make_pipeline(SelectKBest(k=1), LinearRegression())
    reg.fit(X, y)
    assert_raises(AttributeError, getattr, reg, "classes_")

    clf = make_pipeline(SelectKBest(k=1), LogisticRegression(random_state=0))
    assert_raises(AttributeError, getattr, clf, "classes_")
    clf.fit(X, y)
    assert_array_equal(clf.classes_, np.unique(y))


def test_X1d_inverse_transform():
    transformer = Transf()
    pipeline = make_pipeline(transformer)
    X = np.ones(10)
    msg = "1d X will not be reshaped in pipeline.inverse_transform"
    assert_warns_message(FutureWarning, msg, pipeline.inverse_transform, X)


def test_set_feature_union_steps():
    mult2 = Mult(2)
    mult2.get_feature_names = lambda: ['x2']
    mult3 = Mult(3)
    mult3.get_feature_names = lambda: ['x3']
    mult5 = Mult(5)
    mult5.get_feature_names = lambda: ['x5']

    ft = FeatureUnion([('m2', mult2), ('m3', mult3)])
    assert_array_equal([[2, 3]], ft.transform(np.asarray([[1]])))
    assert_equal(['m2__x2', 'm3__x3'], ft.get_feature_names())

    # Directly setting attr
    ft.transformer_list = [('m5', mult5)]
    assert_array_equal([[5]], ft.transform(np.asarray([[1]])))
    assert_equal(['m5__x5'], ft.get_feature_names())

    # Using set_params
    ft.set_params(transformer_list=[('mock', mult3)])
    assert_array_equal([[3]], ft.transform(np.asarray([[1]])))
    assert_equal(['mock__x3'], ft.get_feature_names())

    # Using set_params to replace single step
    ft.set_params(mock=mult5)
    assert_array_equal([[5]], ft.transform(np.asarray([[1]])))
    assert_equal(['mock__x5'], ft.get_feature_names())


def test_set_feature_union_step_none():
    mult2 = Mult(2)
    mult2.get_feature_names = lambda: ['x2']
    mult3 = Mult(3)
    mult3.get_feature_names = lambda: ['x3']
    X = np.asarray([[1]])

    ft = FeatureUnion([('m2', mult2), ('m3', mult3)])
    assert_array_equal([[2, 3]], ft.fit(X).transform(X))
    assert_array_equal([[2, 3]], ft.fit_transform(X))
    assert_equal(['m2__x2', 'm3__x3'], ft.get_feature_names())

    ft.set_params(m2=None)
    assert_array_equal([[3]], ft.fit(X).transform(X))
    assert_array_equal([[3]], ft.fit_transform(X))
    assert_equal(['m3__x3'], ft.get_feature_names())

    ft.set_params(m3=None)
    assert_array_equal([[]], ft.fit(X).transform(X))
    assert_array_equal([[]], ft.fit_transform(X))
    assert_equal([], ft.get_feature_names())

    # check we can change back
    ft.set_params(m3=mult3)
    assert_array_equal([[3]], ft.fit(X).transform(X))


def test_step_name_validation():
    bad_steps1 = [('a__q', Mult(2)), ('b', Mult(3))]
    bad_steps2 = [('a', Mult(2)), ('a', Mult(3))]
    for cls, param in [(Pipeline, 'steps'),
                       (FeatureUnion, 'transformer_list')]:
        # we validate in construction (despite scikit-learn convention)
        bad_steps3 = [('a', Mult(2)), (param, Mult(3))]
        for bad_steps, message in [
            (bad_steps1, "Step names must not contain __: got ['a__q']"),
            (bad_steps2, "Names provided are not unique: ['a', 'a']"),
            (bad_steps3, "Step names conflict with constructor "
                         "arguments: ['%s']" % param),
        ]:
            # three ways to make invalid:
            # - construction
            assert_raise_message(ValueError, message, cls,
                                 **{param: bad_steps})

            # - setattr
            est = cls(**{param: [('a', Mult(1))]})
            setattr(est, param, bad_steps)
            assert_raise_message(ValueError, message, est.fit, [[1]], [1])
            assert_raise_message(ValueError, message, est.fit_transform,
                                 [[1]], [1])

            # - set_params
            est = cls(**{param: [('a', Mult(1))]})
            est.set_params(**{param: bad_steps})
            assert_raise_message(ValueError, message, est.fit, [[1]], [1])
            assert_raise_message(ValueError, message, est.fit_transform,
                                 [[1]], [1])