def test_imputation_pipeline_grid_search():
    # Test imputation within a pipeline + gridsearch.
    pipeline = Pipeline([("imputer", Imputer(missing_values=0)), ("tree", tree.DecisionTreeRegressor(random_state=0))])

    parameters = {"imputer__strategy": ["mean", "median", "most_frequent"], "imputer__axis": [0, 1]}

    l = 100
    X = sparse_random_matrix(l, l, density=0.10)
    Y = sparse_random_matrix(l, 1, density=0.10).toarray()
    gs = grid_search.GridSearchCV(pipeline, parameters)
    gs.fit(X, Y)

def test_imputation_pipeline_grid_search():
    # Test imputation within a pipeline + gridsearch.
    pipeline = Pipeline([('imputer', SimpleImputer(missing_values=0)),
                         ('tree', tree.DecisionTreeRegressor(random_state=0))])

    parameters = {
        'imputer__strategy': ["mean", "median", "most_frequent"]
    }

    X = sparse_random_matrix(100, 100, density=0.10)
    Y = sparse_random_matrix(100, 1, density=0.10).toarray()
    gs = GridSearchCV(pipeline, parameters)
    gs.fit(X, Y)

def test_imputation_pipeline_grid_search():
    """Test imputation within a pipeline + gridsearch."""
    pipeline = Pipeline([('imputer', Imputer(missing_values=0)),
                         ('tree', tree.DecisionTreeRegressor(random_state=0))])

    parameters = {
        'imputer__strategy': ["mean", "median", "most_frequent"],
        'imputer__axis': [0, 1]
    }

    l = 100
    X = sparse_random_matrix(l, l, density=0.10)
    Y = sparse_random_matrix(l, 1, density=0.10).todense()
    gs = grid_search.GridSearchCV(pipeline, parameters)
    gs.fit(X, Y)

def test_mice_imputation_order(imputation_order):
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
    X[:, 0] = 1  # this column should not be discarded by MICEImputer

    imputer = MICEImputer(missing_values=0,
                          n_imputations=1,
                          n_burn_in=1,
                          n_nearest_features=5,
                          min_value=0,
                          max_value=1,
                          verbose=False,
                          imputation_order=imputation_order,
                          random_state=rng)
    imputer.fit_transform(X)
    ordered_idx = [i.feat_idx for i in imputer.imputation_sequence_]
    if imputation_order == 'roman':
        assert np.all(ordered_idx[:d-1] == np.arange(1, d))
    elif imputation_order == 'arabic':
        assert np.all(ordered_idx[:d-1] == np.arange(d-1, 0, -1))
    elif imputation_order == 'random':
        ordered_idx_round_1 = ordered_idx[:d-1]
        ordered_idx_round_2 = ordered_idx[d-1:]
        assert ordered_idx_round_1 != ordered_idx_round_2
    elif 'ending' in imputation_order:
        assert len(ordered_idx) == 2 * (d - 1)

def test_iterative_imputer_imputation_order(imputation_order):
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    max_iter = 2
    X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
    X[:, 0] = 1  # this column should not be discarded by IterativeImputer

    imputer = IterativeImputer(missing_values=0,
                               max_iter=max_iter,
                               n_nearest_features=5,
                               sample_posterior=False,
                               min_value=0,
                               max_value=1,
                               verbose=1,
                               imputation_order=imputation_order,
                               random_state=rng)
    imputer.fit_transform(X)
    ordered_idx = [i.feat_idx for i in imputer.imputation_sequence_]

    assert (len(ordered_idx) // imputer.n_iter_ ==
            imputer.n_features_with_missing_)

    if imputation_order == 'roman':
        assert np.all(ordered_idx[:d-1] == np.arange(1, d))
    elif imputation_order == 'arabic':
        assert np.all(ordered_idx[:d-1] == np.arange(d-1, 0, -1))
    elif imputation_order == 'random':
        ordered_idx_round_1 = ordered_idx[:d-1]
        ordered_idx_round_2 = ordered_idx[d-1:]
        assert ordered_idx_round_1 != ordered_idx_round_2
    elif 'ending' in imputation_order:
        assert len(ordered_idx) == max_iter * (d - 1)

def check_alternative_lrap_implementation(lrap_score, n_classes=5,
                                          n_samples=20, random_state=0):
    _, y_true = make_multilabel_classification(n_features=1,
                                               allow_unlabeled=False,
                                               random_state=random_state,
                                               n_classes=n_classes,
                                               n_samples=n_samples)

    # Score with ties
    y_score = sparse_random_matrix(n_components=y_true.shape[0],
                                   n_features=y_true.shape[1],
                                   random_state=random_state)

    if hasattr(y_score, "toarray"):
        y_score = y_score.toarray()
    score_lrap = label_ranking_average_precision_score(y_true, y_score)
    score_my_lrap = _my_lrap(y_true, y_score)
    assert_almost_equal(score_lrap, score_my_lrap)

    # Uniform score
    random_state = check_random_state(random_state)
    y_score = random_state.uniform(size=(n_samples, n_classes))
    score_lrap = label_ranking_average_precision_score(y_true, y_score)
    score_my_lrap = _my_lrap(y_true, y_score)
    assert_almost_equal(score_lrap, score_my_lrap)

def test_as_float_array():
    # Test function for as_float_array
    X = np.ones((3, 10), dtype=np.int32)
    X = X + np.arange(10, dtype=np.int32)
    # Checks that the return type is ok
    X2 = as_float_array(X, copy=False)
    np.testing.assert_equal(X2.dtype, np.float32)
    # Another test
    X = X.astype(np.int64)
    X2 = as_float_array(X, copy=True)
    # Checking that the array wasn't overwritten
    assert_true(as_float_array(X, False) is not X)
    # Checking that the new type is ok
    np.testing.assert_equal(X2.dtype, np.float64)
    # Here, X is of the right type, it shouldn't be modified
    X = np.ones((3, 2), dtype=np.float32)
    assert_true(as_float_array(X, copy=False) is X)
    # Test that if X is fortran ordered it stays
    X = np.asfortranarray(X)
    assert_true(np.isfortran(as_float_array(X, copy=True)))

    # Test the copy parameter with some matrices
    matrices = [
        np.matrix(np.arange(5)),
        sp.csc_matrix(np.arange(5)).toarray(),
        sparse_random_matrix(10, 10, density=0.10).toarray()
    ]
    for M in matrices:
        N = as_float_array(M, copy=True)
        N[0, 0] = np.nan
        assert_false(np.isnan(M).any())

    def fit(self, X, y):
        if self.activation is None:
            # Useful to quantify the impact of the non-linearity
            self._activate = lambda x: x
        else:
            self._activate = self.activations[self.activation]
        rng = check_random_state(self.random_state)

        # one-of-K coding for output values
        self.classes_ = unique_labels(y)
        Y = label_binarize(y, self.classes_)

        # set hidden layer parameters randomly
        n_features = X.shape[1]
        if self.rank is None:
            if self.density == 1:
                self.weights_ = rng.randn(n_features, self.n_hidden)
            else:
                self.weights_ = sparse_random_matrix(
                    self.n_hidden, n_features, density=self.density,
                    random_state=rng).T
        else:
            # Low rank weight matrix
            self.weights_u_ = rng.randn(n_features, self.rank)
            self.weights_v_ = rng.randn(self.rank, self.n_hidden)
        self.biases_ = rng.randn(self.n_hidden)

        # map the input data through the hidden layer
        H = self.transform(X)

        # fit the linear model on the hidden layer activation
        self.beta_ = np.dot(pinv2(H), Y)
        return self

def test_imputation_copy():
    # Test imputation with copy
    X_orig = sparse_random_matrix(5, 5, density=0.75, random_state=0)

    # copy=True, dense => copy
    X = X_orig.copy().toarray()
    imputer = Imputer(missing_values=0, strategy="mean", copy=True)
    Xt = imputer.fit(X).transform(X)
    Xt[0, 0] = -1
    assert_false(np.all(X == Xt))

    # copy=True, sparse csr => copy
    X = X_orig.copy()
    imputer = Imputer(missing_values=X.data[0], strategy="mean", copy=True)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert_false(np.all(X.data == Xt.data))

    # copy=False, dense => no copy
    X = X_orig.copy().toarray()
    imputer = Imputer(missing_values=0, strategy="mean", copy=False)
    Xt = imputer.fit(X).transform(X)
    Xt[0, 0] = -1
    assert_true(np.all(X == Xt))

    # copy=False, sparse csr, axis=1 => no copy
    X = X_orig.copy()
    imputer = Imputer(missing_values=X.data[0], strategy="mean", copy=False, axis=1)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert_true(np.all(X.data == Xt.data))

    # copy=False, sparse csc, axis=0 => no copy
    X = X_orig.copy().tocsc()
    imputer = Imputer(missing_values=X.data[0], strategy="mean", copy=False, axis=0)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert_true(np.all(X.data == Xt.data))

    # copy=False, sparse csr, axis=0 => copy
    X = X_orig.copy()
    imputer = Imputer(missing_values=X.data[0], strategy="mean", copy=False, axis=0)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert_false(np.all(X.data == Xt.data))

    # copy=False, sparse csc, axis=1 => copy
    X = X_orig.copy().tocsc()
    imputer = Imputer(missing_values=X.data[0], strategy="mean", copy=False, axis=1)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert_false(np.all(X.data == Xt.data))

    # copy=False, sparse csr, axis=1, missing_values=0 => copy
    X = X_orig.copy()
    imputer = Imputer(missing_values=0, strategy="mean", copy=False, axis=1)
    Xt = imputer.fit(X).transform(X)
    assert_false(sparse.issparse(Xt))

def test_deprecated_imputer_axis():
    depr_message = ("Parameter 'axis' has been deprecated in 0.20 and will "
                    "be removed in 0.22. Future (and default) behavior is "
                    "equivalent to 'axis=0' (impute along columns). Row-wise "
                    "imputation can be performed with FunctionTransformer.")
    X = sparse_random_matrix(5, 5, density=0.75, random_state=0)
    imputer = Imputer(missing_values=0, axis=0)
    assert_warns_message(DeprecationWarning, depr_message, imputer.fit, X)
    imputer = Imputer(missing_values=0, axis=1)
    assert_warns_message(DeprecationWarning, depr_message, imputer.fit, X)

def test_iterative_imputer_verbose():
    rng = np.random.RandomState(0)

    n = 100
    d = 3
    X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
    imputer = IterativeImputer(missing_values=0, max_iter=1, verbose=1)
    imputer.fit(X)
    imputer.transform(X)
    imputer = IterativeImputer(missing_values=0, max_iter=1, verbose=2)
    imputer.fit(X)
    imputer.transform(X)

def test_iterative_imputer_transform_stochasticity():
    pytest.importorskip("scipy", minversion="0.17.0")
    rng1 = np.random.RandomState(0)
    rng2 = np.random.RandomState(1)
    n = 100
    d = 10
    X = sparse_random_matrix(n, d, density=0.10,
                             random_state=rng1).toarray()

    # when sample_posterior=True, two transforms shouldn't be equal
    imputer = IterativeImputer(missing_values=0,
                               max_iter=1,
                               sample_posterior=True,
                               random_state=rng1)
    imputer.fit(X)

    X_fitted_1 = imputer.transform(X)
    X_fitted_2 = imputer.transform(X)

    # sufficient to assert that the means are not the same
    assert np.mean(X_fitted_1) != pytest.approx(np.mean(X_fitted_2))

    # when sample_posterior=False, and n_nearest_features=None
    # and imputation_order is not random
    # the two transforms should be identical even if rng are different
    imputer1 = IterativeImputer(missing_values=0,
                                max_iter=1,
                                sample_posterior=False,
                                n_nearest_features=None,
                                imputation_order='ascending',
                                random_state=rng1)

    imputer2 = IterativeImputer(missing_values=0,
                                max_iter=1,
                                sample_posterior=False,
                                n_nearest_features=None,
                                imputation_order='ascending',
                                random_state=rng2)
    imputer1.fit(X)
    imputer2.fit(X)

    X_fitted_1a = imputer1.transform(X)
    X_fitted_1b = imputer1.transform(X)
    X_fitted_2 = imputer2.transform(X)

    assert_allclose(X_fitted_1a, X_fitted_1b)
    assert_allclose(X_fitted_1a, X_fitted_2)

def test_sparse_random_matrix():
    # Check some statical properties of sparse random matrix
    n_components = 100
    n_features = 500

    for density in [0.3, 1.]:
        s = 1 / density

        A = sparse_random_matrix(n_components,
                                 n_features,
                                 density=density,
                                 random_state=0)
        A = densify(A)

        # Check possible values
        values = np.unique(A)
        assert_in(np.sqrt(s) / np.sqrt(n_components), values)
        assert_in(- np.sqrt(s) / np.sqrt(n_components), values)

        if density == 1.0:
            assert_equal(np.size(values), 2)
        else:
            assert_in(0., values)
            assert_equal(np.size(values), 3)

        # Check that the random matrix follow the proper distribution.
        # Let's say that each element of a_{ij} of A is taken from
        #
        # - -sqrt(s) / sqrt(n_components)   with probability 1 / 2s
        # -  0                              with probability 1 - 1 / s
        # - +sqrt(s) / sqrt(n_components)   with probability 1 / 2s
        #
        assert_almost_equal(np.mean(A == 0.0),
                            1 - 1 / s, decimal=2)
        assert_almost_equal(np.mean(A == np.sqrt(s) / np.sqrt(n_components)),
                            1 / (2 * s), decimal=2)
        assert_almost_equal(np.mean(A == - np.sqrt(s) / np.sqrt(n_components)),
                            1 / (2 * s), decimal=2)

        assert_almost_equal(np.var(A == 0.0, ddof=1),
                            (1 - 1 / s) * 1 / s, decimal=2)
        assert_almost_equal(np.var(A == np.sqrt(s) / np.sqrt(n_components),
                                   ddof=1),
                            (1 - 1 / (2 * s)) * 1 / (2 * s), decimal=2)
        assert_almost_equal(np.var(A == - np.sqrt(s) / np.sqrt(n_components),
                                   ddof=1),
                            (1 - 1 / (2 * s)) * 1 / (2 * s), decimal=2)

def test_iterative_imputer_clip():
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    X = sparse_random_matrix(n, d, density=0.10,
                             random_state=rng).toarray()

    imputer = IterativeImputer(missing_values=0,
                               max_iter=1,
                               min_value=0.1,
                               max_value=0.2,
                               random_state=rng)

    Xt = imputer.fit_transform(X)
    assert_allclose(np.min(Xt[X == 0]), 0.1)
    assert_allclose(np.max(Xt[X == 0]), 0.2)
    assert_allclose(Xt[X != 0], X[X != 0])

def test_imputation_pickle():
    """Test for pickling imputers."""
    import pickle

    l = 100
    X = sparse_random_matrix(l, l, density=0.10)

    for strategy in ["mean", "median", "most_frequent"]:
        imputer = Imputer(missing_values=0, strategy=strategy)
        imputer.fit(X)

        imputer_pickled = pickle.loads(pickle.dumps(imputer))

        assert_array_equal(imputer.transform(X.copy()),
                           imputer_pickled.transform(X.copy()),
                           "Fail to transform the data after pickling "
                           "(strategy = %s)" % (strategy))

def test_mice_transform_stochasticity():
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    X = sparse_random_matrix(n, d, density=0.10,
                             random_state=rng).toarray()

    imputer = MICEImputer(missing_values=0,
                          n_imputations=1,
                          n_burn_in=1,
                          random_state=rng)
    imputer.fit(X)

    X_fitted_1 = imputer.transform(X)
    X_fitted_2 = imputer.transform(X)

    # sufficient to assert that the means are not the same
    assert np.mean(X_fitted_1) != pytest.approx(np.mean(X_fitted_2))

def test_as_float_array():
    # Test function for as_float_array
    X = np.ones((3, 10), dtype=np.int32)
    X = X + np.arange(10, dtype=np.int32)
    X2 = as_float_array(X, copy=False)
    assert_equal(X2.dtype, np.float32)
    # Another test
    X = X.astype(np.int64)
    X2 = as_float_array(X, copy=True)
    # Checking that the array wasn't overwritten
    assert as_float_array(X, False) is not X
    assert_equal(X2.dtype, np.float64)
    # Test int dtypes <= 32bit
    tested_dtypes = [np.bool,
                     np.int8, np.int16, np.int32,
                     np.uint8, np.uint16, np.uint32]
    for dtype in tested_dtypes:
        X = X.astype(dtype)
        X2 = as_float_array(X)
        assert_equal(X2.dtype, np.float32)

    # Test object dtype
    X = X.astype(object)
    X2 = as_float_array(X, copy=True)
    assert_equal(X2.dtype, np.float64)

    # Here, X is of the right type, it shouldn't be modified
    X = np.ones((3, 2), dtype=np.float32)
    assert as_float_array(X, copy=False) is X
    # Test that if X is fortran ordered it stays
    X = np.asfortranarray(X)
    assert np.isfortran(as_float_array(X, copy=True))

    # Test the copy parameter with some matrices
    matrices = [
        np.matrix(np.arange(5)),
        sp.csc_matrix(np.arange(5)).toarray(),
        sparse_random_matrix(10, 10, density=0.10).toarray()
    ]
    for M in matrices:
        N = as_float_array(M, copy=True)
        N[0, 0] = np.nan
        assert not np.isnan(M).any()

def test_iterative_imputer_clip_truncnorm():
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
    X[:, 0] = 1

    imputer = IterativeImputer(missing_values=0,
                               max_iter=2,
                               n_nearest_features=5,
                               sample_posterior=True,
                               min_value=0.1,
                               max_value=0.2,
                               verbose=1,
                               imputation_order='random',
                               random_state=rng)
    Xt = imputer.fit_transform(X)
    assert_allclose(np.min(Xt[X == 0]), 0.1)
    assert_allclose(np.max(Xt[X == 0]), 0.2)
    assert_allclose(Xt[X != 0], X[X != 0])

def test_imputation_copy():
    # Test imputation with copy
    X_orig = sparse_random_matrix(5, 5, density=0.75, random_state=0)

    # copy=True, dense => copy
    X = X_orig.copy().toarray()
    imputer = SimpleImputer(missing_values=0, strategy="mean", copy=True)
    Xt = imputer.fit(X).transform(X)
    Xt[0, 0] = -1
    assert not np.all(X == Xt)

    # copy=True, sparse csr => copy
    X = X_orig.copy()
    imputer = SimpleImputer(missing_values=X.data[0], strategy="mean",
                            copy=True)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert not np.all(X.data == Xt.data)

    # copy=False, dense => no copy
    X = X_orig.copy().toarray()
    imputer = SimpleImputer(missing_values=0, strategy="mean", copy=False)
    Xt = imputer.fit(X).transform(X)
    Xt[0, 0] = -1
    assert_array_almost_equal(X, Xt)

    # copy=False, sparse csc => no copy
    X = X_orig.copy().tocsc()
    imputer = SimpleImputer(missing_values=X.data[0], strategy="mean",
                            copy=False)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert_array_almost_equal(X.data, Xt.data)

    # copy=False, sparse csr => copy
    X = X_orig.copy()
    imputer = SimpleImputer(missing_values=X.data[0], strategy="mean",
                            copy=False)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert not np.all(X.data == Xt.data)

def test_mice_predictors(predictor):
    rng = np.random.RandomState(0)

    n = 100
    d = 10
    X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()

    imputer = MICEImputer(missing_values=0,
                          n_imputations=1,
                          n_burn_in=1,
                          predictor=predictor,
                          random_state=rng)
    imputer.fit_transform(X)

    # check that types are correct for predictors
    hashes = []
    for triplet in imputer.imputation_sequence_:
        assert triplet.predictor
        hashes.append(id(triplet.predictor))

    # check that each predictor is unique
    assert len(set(hashes)) == len(hashes)