[MRG] More deprecation for 0.24 (scikit-learn#17142)

* normalize_components in sparsePCA

* changed default strategy of Dummy to prior and removed outputs_2d ttribute

* removed usage of None to drop estimator in ensemble and behaviour param of IsolationForest

* remove support for drop=None in Voting

* removed some warning decorators

* remove feature_extraction.extract_patches

* removed VectorizerMixin and copy parameter from TFIDFVectorizer

* kernel.set_params now raises attributeerror

* removed fig from plot_partial_dependence

* removed iid parameter of search estimators

* removed brier_scorer

* raise error in split when shuffle is False and random_state is not None

* removed MultiOutputEstimator

* removed base classes of NaiveBayes

* removed drop from pipeline

* removed utils in random_projection

* removed presort and classes_ in trees

* flake8

* fixed some tests

* flake

* fixed docstring

* fixed other one

* some left

* mmmm

sklearn/decomposition/_sparse_pca.py

@@ -2,8 +2,6 @@
 # Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
 # License: BSD 3 clause
 
-import warnings
-
 import numpy as np
 
 from ..utils import check_random_state, check_array
@@ -14,22 +12,6 @@
 from ._dict_learning import dict_learning, dict_learning_online
 
 
-# FIXME: remove in 0.24
-def _check_normalize_components(normalize_components, estimator_name):
-    if normalize_components != 'deprecated':
-        if normalize_components:
-            warnings.warn(
-                "'normalize_components' has been deprecated in 0.22 and "
-                "will be removed in 0.24. Remove the parameter from the "
-                " constructor.", FutureWarning
-            )
-        else:
-            raise NotImplementedError(
-                "normalize_components=False is not supported starting from "
-                "0.22. Remove this parameter from the constructor."
-            )
-
-
 class SparsePCA(TransformerMixin, BaseEstimator):
     """Sparse Principal Components Analysis (SparsePCA)
 
@@ -85,16 +67,6 @@ class SparsePCA(TransformerMixin, BaseEstimator):
         across multiple function calls.
         See :term:`Glossary <random_state>`.
 
-    normalize_components : 'deprecated'
-        This parameter does not have any effect. The components are always
-        normalized.
-
-        .. versionadded:: 0.20
-
-        .. deprecated:: 0.22
-           ``normalize_components`` is deprecated in 0.22 and will be removed
-           in 0.24.
-
     Attributes
     ----------
     components_ : array, [n_components, n_features]
@@ -140,8 +112,7 @@ class SparsePCA(TransformerMixin, BaseEstimator):
     @_deprecate_positional_args
     def __init__(self, n_components=None, *, alpha=1, ridge_alpha=0.01,
                  max_iter=1000, tol=1e-8, method='lars', n_jobs=None,
-                 U_init=None, V_init=None, verbose=False, random_state=None,
-                 normalize_components='deprecated'):
+                 U_init=None, V_init=None, verbose=False, random_state=None):
         self.n_components = n_components
         self.alpha = alpha
         self.ridge_alpha = ridge_alpha
@@ -153,7 +124,6 @@ def __init__(self, n_components=None, *, alpha=1, ridge_alpha=0.01,
         self.V_init = V_init
         self.verbose = verbose
         self.random_state = random_state
-        self.normalize_components = normalize_components
 
     def fit(self, X, y=None):
         """Fit the model from data in X.
@@ -174,10 +144,6 @@ def fit(self, X, y=None):
         random_state = check_random_state(self.random_state)
         X = self._validate_data(X)
 
-        _check_normalize_components(
-            self.normalize_components, self.__class__.__name__
-        )
-
         self.mean_ = X.mean(axis=0)
         X = X - self.mean_
 
@@ -304,16 +270,6 @@ class MiniBatchSparsePCA(SparsePCA):
         across multiple function calls.
         See :term:`Glossary <random_state>`.
 
-    normalize_components : 'deprecated'
-        This parameter does not have any effect. The components are always
-        normalized.
-
-        .. versionadded:: 0.20
-
-        .. deprecated:: 0.22
-           ``normalize_components`` is deprecated in 0.22 and will be removed
-           in 0.24.
-
     Attributes
     ----------
     components_ : array, [n_components, n_features]
@@ -357,13 +313,11 @@ class MiniBatchSparsePCA(SparsePCA):
     @_deprecate_positional_args
     def __init__(self, n_components=None, *, alpha=1, ridge_alpha=0.01,
                  n_iter=100, callback=None, batch_size=3, verbose=False,
-                 shuffle=True, n_jobs=None, method='lars', random_state=None,
-                 normalize_components='deprecated'):
+                 shuffle=True, n_jobs=None, method='lars', random_state=None):
         super().__init__(
             n_components=n_components, alpha=alpha, verbose=verbose,
             ridge_alpha=ridge_alpha, n_jobs=n_jobs, method=method,
-            random_state=random_state,
-            normalize_components=normalize_components)
+            random_state=random_state)
         self.n_iter = n_iter
         self.callback = callback
         self.batch_size = batch_size
@@ -388,10 +342,6 @@ def fit(self, X, y=None):
         random_state = check_random_state(self.random_state)
         X = self._validate_data(X)
 
-        _check_normalize_components(
-            self.normalize_components, self.__class__.__name__
-        )
-
         self.mean_ = X.mean(axis=0)
         X = X - self.mean_
 

sklearn/decomposition/_truncated_svd.py

@@ -88,7 +88,6 @@ class TruncatedSVD(TransformerMixin, BaseEstimator):
     --------
     >>> from sklearn.decomposition import TruncatedSVD
     >>> from scipy.sparse import random as sparse_random
-    >>> from sklearn.random_projection import sparse_random_matrix
     >>> X = sparse_random(100, 100, density=0.01, format='csr',
     ...                   random_state=42)
     >>> svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)

sklearn/decomposition/tests/test_sparse_pca.py

@@ -189,26 +189,6 @@ def test_pca_vs_spca():
     assert_allclose(results_test_pca, results_test_spca)
 
 
-@pytest.mark.parametrize("spca", [SparsePCA, MiniBatchSparsePCA])
-def test_spca_deprecation_warning(spca):
-    rng = np.random.RandomState(0)
-    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)
-
-    warn_msg = "'normalize_components' has been deprecated in 0.22"
-    with pytest.warns(FutureWarning, match=warn_msg):
-        spca(normalize_components=True).fit(Y)
-
-
-@pytest.mark.parametrize("spca", [SparsePCA, MiniBatchSparsePCA])
-def test_spca_error_unormalized_components(spca):
-    rng = np.random.RandomState(0)
-    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)
-
-    err_msg = "normalize_components=False is not supported starting "
-    with pytest.raises(NotImplementedError, match=err_msg):
-        spca(normalize_components=False).fit(Y)
-
-
 @pytest.mark.parametrize("SPCA", [SparsePCA, MiniBatchSparsePCA])
 @pytest.mark.parametrize("n_components", [None, 3])
 def test_spca_n_components_(SPCA, n_components):

sklearn/dummy.py

@@ -17,9 +17,9 @@
 from .utils.random import _random_choice_csc
 from .utils.stats import _weighted_percentile
 from .utils.multiclass import class_distribution
-from .utils import deprecated
 from .utils.validation import _deprecate_positional_args
 
+
 class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
     """
     DummyClassifier is a classifier that makes predictions using simple rules.
@@ -33,7 +33,7 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
 
     Parameters
     ----------
-    strategy : str, default="stratified"
+    strategy : str, default="prior"
         Strategy to use to generate predictions.
 
         * "stratified": generates predictions by respecting the training
@@ -47,14 +47,9 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
           the user. This is useful for metrics that evaluate a non-majority
           class
 
-          .. versionchanged:: 0.22
-             The default value of `strategy` will change to "prior" in version
-             0.24. Starting from version 0.22, a warning will be raised if
-             `strategy` is not explicitly set.
-
-          .. versionadded:: 0.17
-             Dummy Classifier now supports prior fitting strategy using
-             parameter *prior*.
+          .. versionchanged:: 0.24
+             The default value of `strategy` has changed to "prior" in version
+             0.24.
 
     random_state : int, RandomState instance or None, optional, default=None
         Controls the randomness to generate the predictions when
@@ -99,7 +94,7 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
     0.75
     """
     @_deprecate_positional_args
-    def __init__(self, *, strategy="warn", random_state=None,
+    def __init__(self, *, strategy="prior", random_state=None,
                  constant=None):
         self.strategy = strategy
         self.random_state = random_state
@@ -126,16 +121,11 @@ def fit(self, X, y, sample_weight=None):
         allowed_strategies = ("most_frequent", "stratified", "uniform",
                               "constant", "prior")
 
-        # TODO: Remove in 0.24
-        if self.strategy == "warn":
-            warnings.warn("The default value of strategy will change from "
-                          "stratified to prior in 0.24.", FutureWarning)
-            self._strategy = "stratified"
-        elif self.strategy not in allowed_strategies:
+        if self.strategy not in allowed_strategies:
             raise ValueError("Unknown strategy type: %s, expected one of %s."
                              % (self.strategy, allowed_strategies))
-        else:
-            self._strategy = self.strategy
+
+        self._strategy = self.strategy
 
         if self._strategy == "uniform" and sp.issparse(y):
             y = y.toarray()
@@ -395,16 +385,6 @@ def score(self, X, y, sample_weight=None):
             X = np.zeros(shape=(len(y), 1))
         return super().score(X, y, sample_weight)
 
-    # mypy error: Decorated property not supported
-    @deprecated(  # type: ignore
-        "The outputs_2d_ attribute is deprecated in version 0.22 "
-        "and will be removed in version 0.24. It is equivalent to "
-        "n_outputs_ > 1."
-    )
-    @property
-    def outputs_2d_(self):
-        return self.n_outputs_ != 1
-
 
 class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
     """
@@ -624,13 +604,3 @@ def score(self, X, y, sample_weight=None):
         if X is None:
             X = np.zeros(shape=(len(y), 1))
         return super().score(X, y, sample_weight)
-
-    # mypy error: Decorated property not supported
-    @deprecated(  # type: ignore
-        "The outputs_2d_ attribute is deprecated in version 0.22 "
-        "and will be removed in version 0.24. It is equivalent to "
-        "n_outputs_ > 1."
-    )
-    @property
-    def outputs_2d_(self):
-        return self.n_outputs_ != 1

sklearn/ensemble/_base.py

@@ -5,7 +5,6 @@
 
 from abc import ABCMeta, abstractmethod
 import numbers
-import warnings
 from typing import List
 
 import numpy as np
@@ -227,16 +226,7 @@ def _validate_estimators(self):
         # defined by MetaEstimatorMixin
         self._validate_names(names)
 
-        # FIXME: deprecate the usage of None to drop an estimator from the
-        # ensemble. Remove in 0.24
-        if any(est is None for est in estimators):
-            warnings.warn(
-                "Using 'None' to drop an estimator from the ensemble is "
-                "deprecated in 0.22 and support will be dropped in 0.24. "
-                "Use the string 'drop' instead.", FutureWarning
-            )
-
-        has_estimator = any(est not in (None, 'drop') for est in estimators)
+        has_estimator = any(est != 'drop' for est in estimators)
         if not has_estimator:
             raise ValueError(
                 "All estimators are dropped. At least one is required "
@@ -247,7 +237,7 @@ def _validate_estimators(self):
                              else is_regressor)
 
         for est in estimators:
-            if est not in (None, 'drop') and not is_estimator_type(est):
+            if est != 'drop' and not is_estimator_type(est):
                 raise ValueError(
                     "The estimator {} should be a {}.".format(
                         est.__class__.__name__, is_estimator_type.__name__[3:]

sklearn/ensemble/_iforest.py

@@ -93,20 +93,6 @@ class IsolationForest(OutlierMixin, BaseBagging):
         :obj:`joblib.parallel_backend` context. ``-1`` means using all
         processors. See :term:`Glossary <n_jobs>` for more details.
 
-    behaviour : str, default='deprecated'
-        This parameter has no effect, is deprecated, and will be removed.
-
-        .. versionadded:: 0.20
-           ``behaviour`` is added in 0.20 for back-compatibility purpose.
-
-        .. deprecated:: 0.20
-           ``behaviour='old'`` is deprecated in 0.20 and will not be possible
-           in 0.22.
-
-        .. deprecated:: 0.22
-           ``behaviour`` parameter is deprecated in 0.22 and removed in
-           0.24.
-
     random_state : int or RandomState, default=None
         Controls the pseudo-randomness of the selection of the feature
         and split values for each branching step and each tree in the forest.
@@ -192,7 +178,6 @@ def __init__(self, *,
                  max_features=1.,
                  bootstrap=False,
                  n_jobs=None,
-                 behaviour='deprecated',
                  random_state=None,
                  verbose=0,
                  warm_start=False):
@@ -212,7 +197,6 @@ def __init__(self, *,
             random_state=random_state,
             verbose=verbose)
 
-        self.behaviour = behaviour
         self.contamination = contamination
 
     def _set_oob_score(self, X, y):
@@ -247,19 +231,6 @@ def fit(self, X, y=None, sample_weight=None):
         self : object
             Fitted estimator.
         """
-        if self.behaviour != 'deprecated':
-            if self.behaviour == 'new':
-                warn(
-                    "'behaviour' is deprecated in 0.22 and will be removed "
-                    "in 0.24. You should not pass or set this parameter.",
-                    FutureWarning
-                )
-            else:
-                raise NotImplementedError(
-                    "The old behaviour of IsolationForest is not implemented "
-                    "anymore. Remove the 'behaviour' parameter."
-                )
-
         X = check_array(X, accept_sparse=['csc'])
         if issparse(X):
             # Pre-sort indices to avoid that each individual tree of the