Python sklearn.impute.MissingIndicator() Examples

def test_missing_indicator_raise_on_sparse_with_missing_0(arr_type):
    # test for sparse input and missing_value == 0

    missing_values = 0
    X_fit = np.array([[missing_values, missing_values, 1],
                      [4, missing_values, 2]])
    X_trans = np.array([[missing_values, missing_values, 1],
                        [4, 12, 10]])

    # convert the input to the right array format
    X_fit_sparse = arr_type(X_fit)
    X_trans_sparse = arr_type(X_trans)

    indicator = MissingIndicator(missing_values=missing_values)

    with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
        indicator.fit_transform(X_fit_sparse)

    indicator.fit_transform(X_fit)
    with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
        indicator.transform(X_trans_sparse) 

def fit(self, X, y=None):
        """Fit the transformer on X.

        Parameters
        ----------
        X : {array-like}, shape (n_samples, n_features)
            Input data, where ``n_samples`` is the number of samples and
            ``n_features`` is the number of features.

        Returns
        -------
        self : RobustMissingIndicator
        """
        X = self._validate_input(X)

        self.vectorized_mask_function_ = self.mask_function or is_finite_numeric
        X = _apply_mask(X, _get_mask(X, self.vectorized_mask_function_))

        self.missing_indicator_ = MissingIndicator(features=self.features, error_on_new=self.error_on_new)
        self.missing_indicator_.fit(X)

        return self 

def test_missing_indicator_error(X_fit, X_trans, params, msg_err):
    indicator = MissingIndicator(missing_values=-1)
    indicator.set_params(**params)
    with pytest.raises(ValueError, match=msg_err):
        indicator.fit(X_fit).transform(X_trans) 

def test_missing_indicator_sparse_param(arr_type, missing_values,
                                        param_sparse):
    # check the format of the output with different sparse parameter
    X_fit = np.array([[missing_values, missing_values, 1],
                      [4, missing_values, 2]])
    X_trans = np.array([[missing_values, missing_values, 1],
                        [4, 12, 10]])
    X_fit = arr_type(X_fit).astype(np.float64)
    X_trans = arr_type(X_trans).astype(np.float64)

    indicator = MissingIndicator(missing_values=missing_values,
                                 sparse=param_sparse)
    X_fit_mask = indicator.fit_transform(X_fit)
    X_trans_mask = indicator.transform(X_trans)

    if param_sparse is True:
        assert X_fit_mask.format == 'csc'
        assert X_trans_mask.format == 'csc'
    elif param_sparse == 'auto' and missing_values == 0:
        assert isinstance(X_fit_mask, np.ndarray)
        assert isinstance(X_trans_mask, np.ndarray)
    elif param_sparse is False:
        assert isinstance(X_fit_mask, np.ndarray)
        assert isinstance(X_trans_mask, np.ndarray)
    else:
        if sparse.issparse(X_fit):
            assert X_fit_mask.format == 'csc'
            assert X_trans_mask.format == 'csc'
        else:
            assert isinstance(X_fit_mask, np.ndarray)
            assert isinstance(X_trans_mask, np.ndarray) 

def test_missing_indicator_string():
    X = np.array([['a', 'b', 'c'], ['b', 'c', 'a']], dtype=object)
    indicator = MissingIndicator(missing_values='a', features='all')
    X_trans = indicator.fit_transform(X)
    assert_array_equal(X_trans, np.array([[True, False, False],
                                          [False, False, True]])) 

def test_missing_indicator_no_missing():
    # check that all features are dropped if there are no missing values when
    # features='missing-only' (#13491)
    X = np.array([[1, 1],
                  [1, 1]])

    mi = MissingIndicator(features='missing-only', missing_values=-1)
    Xt = mi.fit_transform(X)

    assert Xt.shape[1] == 0 

def test_missing_indicator_sparse_no_explicit_zeros():
    # Check that non missing values don't become explicit zeros in the mask
    # generated by missing indicator when X is sparse. (#13491)
    X = sparse.csr_matrix([[0, 1, 2],
                           [1, 2, 0],
                           [2, 0, 1]])

    mi = MissingIndicator(features='all', missing_values=1)
    Xt = mi.fit_transform(X)

    assert Xt.getnnz() == Xt.sum() 

def fit(self, X, y=None):
        self._wrapped_model = SKLModel(**self._hyperparams)
        if (y is not None):
            self._wrapped_model.fit(X, y)
        else:
            self._wrapped_model.fit(X)
        return self 

def __init__(self, missing_values='nan', features='missing-only', sparse='auto', error_on_new=True):
        self._hyperparams = {
            'missing_values': missing_values,
            'features': features,
            'sparse': sparse,
            'error_on_new': error_on_new}
        self._wrapped_model = Op(**self._hyperparams) 

def transform(self, X):
        """Perform imputation using interpolation.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_timestamps)
            Data with missing values.

        Returns
        -------
        X_new : array-like, shape = (n_samples, n_timestamps)
            Data without missing values.

        """
        missing_values, force_all_finite = self._check_params()
        X = check_array(X, dtype='float64', force_all_finite=force_all_finite)
        n_samples, n_timestamps = X.shape

        indicator = MissingIndicator(
            missing_values=missing_values, features='all', sparse=False,
        )
        non_missing_idx = ~(indicator.fit_transform(X))
        x_new = np.arange(n_timestamps)
        X_imputed = np.asarray(
            [self._impute_one_sample(X[i], non_missing_idx[i], x_new)
             for i in range(n_samples)]
        )
        return X_imputed 

def test_missing_indicator_new(missing_values, arr_type, dtype, param_features,
                               n_features, features_indices):
    X_fit = np.array([[missing_values, missing_values, 1],
                      [4, 2, missing_values]])
    X_trans = np.array([[missing_values, missing_values, 1],
                        [4, 12, 10]])
    X_fit_expected = np.array([[1, 1, 0], [0, 0, 1]])
    X_trans_expected = np.array([[1, 1, 0], [0, 0, 0]])

    # convert the input to the right array format and right dtype
    X_fit = arr_type(X_fit).astype(dtype)
    X_trans = arr_type(X_trans).astype(dtype)
    X_fit_expected = X_fit_expected.astype(dtype)
    X_trans_expected = X_trans_expected.astype(dtype)

    indicator = MissingIndicator(missing_values=missing_values,
                                 features=param_features,
                                 sparse=False)
    X_fit_mask = indicator.fit_transform(X_fit)
    X_trans_mask = indicator.transform(X_trans)

    assert X_fit_mask.shape[1] == n_features
    assert X_trans_mask.shape[1] == n_features

    assert_array_equal(indicator.features_, features_indices)
    assert_allclose(X_fit_mask, X_fit_expected[:, features_indices])
    assert_allclose(X_trans_mask, X_trans_expected[:, features_indices])

    assert X_fit_mask.dtype == bool
    assert X_trans_mask.dtype == bool
    assert isinstance(X_fit_mask, np.ndarray)
    assert isinstance(X_trans_mask, np.ndarray)

    indicator.set_params(sparse=True)
    X_fit_mask_sparse = indicator.fit_transform(X_fit)
    X_trans_mask_sparse = indicator.transform(X_trans)

    assert X_fit_mask_sparse.dtype == bool
    assert X_trans_mask_sparse.dtype == bool
    assert X_fit_mask_sparse.format == 'csc'
    assert X_trans_mask_sparse.format == 'csc'
    assert_allclose(X_fit_mask_sparse.toarray(), X_fit_mask)
    assert_allclose(X_trans_mask_sparse.toarray(), X_trans_mask)