Python utils.deprecated方法代碼示例

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def fit_predict(self, X, y=None):
        """Fit detector first and then predict whether a particular sample
        is an outlier or not. y is ignored in unsupervised models.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The input samples.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        outlier_labels : numpy array of shape (n_samples,)
            For each observation, tells whether or not
            it should be considered as an outlier according to the
            fitted model. 0 stands for inliers and 1 for outliers.

        .. deprecated:: 0.6.9
          `fit_predict` will be removed in pyod 0.8.0.; it will be
          replaced by calling `fit` function first and then accessing
          `labels_` attribute for consistency.
        """

        self.fit(X, y)
        return self.labels_ 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def get_params(self, deep=True):
        """Get parameters for this estimator.

        See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
        and sklearn/base.py for more information.

        Parameters
        ----------
        deep : bool, optional (default=True)
            If True, will return the parameters for this estimator and
            contained subobjects that are estimators.

        Returns
        -------
        params : mapping of string to any
            Parameter names mapped to their values.
        """

        out = dict()
        for key in self._get_param_names():
            # We need deprecation warnings to always be on in order to
            # catch deprecated param values.
            # This is set in utils/__init__.py but it gets overwritten
            # when running under python3 somehow.
            warnings.simplefilter("always", DeprecationWarning)
            try:
                with warnings.catch_warnings(record=True) as w:
                    value = getattr(self, key, None)
                if len(w) and w[0].category == DeprecationWarning:
                    # if the parameter is deprecated, don't show it
                    continue
            finally:
                warnings.filters.pop(0)

            # XXX: should we rather test if instance of estimator?
            if deep and hasattr(value, 'get_params'):
                deep_items = value.get_params().items()
                out.update((key + '__' + k, val) for k, val in deep_items)
            out[key] = value
        return out 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def fake_mldata(columns_dict, dataname, matfile, ordering=None):
    """Create a fake mldata data set.

    .. deprecated:: 0.20
        Will be removed in version 0.22

    Parameters
    ----------
    columns_dict : dict, keys=str, values=ndarray
        Contains data as columns_dict[column_name] = array of data.

    dataname : string
        Name of data set.

    matfile : string or file object
        The file name string or the file-like object of the output file.

    ordering : list, default None
        List of column_names, determines the ordering in the data set.

    Notes
    -----
    This function transposes all arrays, while fetch_mldata only transposes
    'data', keep that into account in the tests.
    """
    datasets = dict(columns_dict)

    # transpose all variables
    for name in datasets:
        datasets[name] = datasets[name].T

    if ordering is None:
        ordering = sorted(list(datasets.keys()))
    # NOTE: setting up this array is tricky, because of the way Matlab
    # re-packages 1D arrays
    datasets['mldata_descr_ordering'] = sp.empty((1, len(ordering)),
                                                 dtype='object')
    for i, name in enumerate(ordering):
        datasets['mldata_descr_ordering'][0, i] = name

    scipy.io.savemat(matfile, datasets, oned_as='column') 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def test_check_fit_score_takes_y_works_on_deprecated_fit():
    # Tests that check_fit_score_takes_y works on a class with
    # a deprecated fit method

    class TestEstimatorWithDeprecatedFitMethod(BaseEstimator):
        @deprecated("Deprecated for the purpose of testing "
                    "check_fit_score_takes_y")
        def fit(self, X, y):
            return self

    check_fit_score_takes_y("test", TestEstimatorWithDeprecatedFitMethod()) 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def test_check_array_warn_on_dtype_deprecation():
    X = np.asarray([[0.0], [1.0]])
    Y = np.asarray([[2.0], [3.0]])
    with pytest.warns(DeprecationWarning,
                      match="'warn_on_dtype' is deprecated"):
        check_array(X, warn_on_dtype=True)
    with pytest.warns(DeprecationWarning,
                      match="'warn_on_dtype' is deprecated"):
        check_X_y(X, Y, warn_on_dtype=True) 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def test_has_fit_parameter():
    assert not has_fit_parameter(KNeighborsClassifier, "sample_weight")
    assert has_fit_parameter(RandomForestRegressor, "sample_weight")
    assert has_fit_parameter(SVR, "sample_weight")
    assert has_fit_parameter(SVR(), "sample_weight")

    class TestClassWithDeprecatedFitMethod:
        @deprecated("Deprecated for the purpose of testing has_fit_parameter")
        def fit(self, X, y, sample_weight=None):
            pass

    assert has_fit_parameter(TestClassWithDeprecatedFitMethod,
                             "sample_weight"), \
        "has_fit_parameter fails for class with deprecated fit method." 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def test_deprecated():
    # Test whether the deprecated decorator issues appropriate warnings
    # Copied almost verbatim from https://docs.python.org/library/warnings.html

    # First a function...
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")

        @deprecated()
        def ham():
            return "spam"

        spam = ham()

        assert_equal(spam, "spam")     # function must remain usable

        assert_equal(len(w), 1)
        assert issubclass(w[0].category, DeprecationWarning)
        assert "deprecated" in str(w[0].message).lower()

    # ... then a class.
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")

        @deprecated("don't use this")
        class Ham:
            SPAM = 1

        ham = Ham()

        assert hasattr(ham, "SPAM")

        assert_equal(len(w), 1)
        assert issubclass(w[0].category, DeprecationWarning)
        assert "deprecated" in str(w[0].message).lower() 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def support_vectors_time_series_(self, X=None):
        warnings.warn('The use of '
                      '`support_vectors_time_series_` is deprecated in '
                      'tslearn v0.4 and will be removed in v0.6. Use '
                      '`support_vectors_` property instead.')
        check_is_fitted(self, '_X_fit')
        return self._X_fit[self.svm_estimator_.support_] 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def test_deprecated():
    # Test whether the deprecated decorator issues appropriate warnings
    # Copied almost verbatim from http://docs.python.org/library/warnings.html

    # First a function...
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")

        @deprecated()
        def ham():
            return "spam"

        spam = ham()

        assert_equal(spam, "spam")     # function must remain usable

        assert_equal(len(w), 1)
        assert_true(issubclass(w[0].category, DeprecationWarning))
        assert_true("deprecated" in str(w[0].message).lower())

    # ... then a class.
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")

        @deprecated("don't use this")
        class Ham(object):
            SPAM = 1

        ham = Ham()

        assert_true(hasattr(ham, "SPAM"))

        assert_equal(len(w), 1)
        assert_true(issubclass(w[0].category, DeprecationWarning))
        assert_true("deprecated" in str(w[0].message).lower()) 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def __init__(self, a=None, b=None):
        self.a = a
        if b is not None:
            DeprecationWarning("b is deprecated and renamed 'a'")
            self.a = b 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def test_clone_copy_init_params():
    # test for deprecation warning when copying or casting an init parameter
    est = ModifyInitParams()
    message = ("Estimator ModifyInitParams modifies parameters in __init__. "
               "This behavior is deprecated as of 0.18 and support "
               "for this behavior will be removed in 0.20.")

    assert_warns_message(DeprecationWarning, message, clone, est) 

# 需要導入模塊: from sklearn import utils [as 別名]
# 或者: from sklearn.utils import deprecated [as 別名]
def test_get_params_deprecated():
    # deprecated attribute should not show up as params
    est = DeprecatedAttributeEstimator(a=1)

    assert_true('a' in est.get_params())
    assert_true('a' in est.get_params(deep=True))
    assert_true('a' in est.get_params(deep=False))

    assert_true('b' not in est.get_params())
    assert_true('b' not in est.get_params(deep=True))
    assert_true('b' not in est.get_params(deep=False))