MAINT remove deprecated params for 0.13 (#1112)

Author: glemaitre

imblearn/combine/_smote_enn.py

@@ -141,7 +141,6 @@ def _validate_estimator(self):
             self.smote_ = SMOTE(
                 sampling_strategy=self.sampling_strategy,
                 random_state=self.random_state,
-                n_jobs=self.n_jobs,
             )
 
         if self.enn is not None:

imblearn/combine/_smote_tomek.py

@@ -140,7 +140,6 @@ def _validate_estimator(self):
             self.smote_ = SMOTE(
                 sampling_strategy=self.sampling_strategy,
                 random_state=self.random_state,
-                n_jobs=self.n_jobs,
             )
 
         if self.tomek is not None:

imblearn/combine/tests/test_smote_enn.py

@@ -146,12 +146,10 @@ def test_parallelisation():
     smt = SMOTEENN(random_state=RND_SEED)
     smt._validate_estimator()
     assert smt.n_jobs is None
-    assert smt.smote_.n_jobs is None
     assert smt.enn_.n_jobs is None
 
     # Check if job count is set
     smt = SMOTEENN(random_state=RND_SEED, n_jobs=8)
     smt._validate_estimator()
     assert smt.n_jobs == 8
-    assert smt.smote_.n_jobs == 8
     assert smt.enn_.n_jobs == 8

imblearn/combine/tests/test_smote_tomek.py

@@ -156,12 +156,10 @@ def test_parallelisation():
     smt = SMOTETomek(random_state=RND_SEED)
     smt._validate_estimator()
     assert smt.n_jobs is None
-    assert smt.smote_.n_jobs is None
     assert smt.tomek_.n_jobs is None
 
     # Check if job count is set
     smt = SMOTETomek(random_state=RND_SEED, n_jobs=8)
     smt._validate_estimator()
     assert smt.n_jobs == 8
-    assert smt.smote_.n_jobs == 8
     assert smt.tomek_.n_jobs == 8

imblearn/ensemble/_bagging.py

@@ -10,22 +10,17 @@
 import numpy as np
 from sklearn.base import clone
 from sklearn.ensemble import BaggingClassifier
-from sklearn.ensemble._bagging import _parallel_decision_function
-from sklearn.ensemble._base import _partition_estimators
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils._param_validation import HasMethods, Interval, StrOptions
 from sklearn.utils.fixes import parse_version
-from sklearn.utils.metaestimators import available_if
-from sklearn.utils.parallel import Parallel, delayed
-from sklearn.utils.validation import check_is_fitted
 
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
 from ..under_sampling.base import BaseUnderSampler
 from ..utils import Substitution, check_sampling_strategy, check_target_type
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
-from ..utils._sklearn_compat import _fit_context, sklearn_version, validate_data
-from ._common import _bagging_parameter_constraints, _estimator_has
+from ..utils._sklearn_compat import _fit_context, sklearn_version
+from ._common import _bagging_parameter_constraints
 
 
 @Substitution(
@@ -356,54 +351,6 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
         # None.
         return super()._fit(X, y, self.max_samples)
 
-    # TODO: remove when minimum supported version of scikit-learn is 1.1
-    @available_if(_estimator_has("decision_function"))
-    def decision_function(self, X):
-        """Average of the decision functions of the base classifiers.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            The training input samples. Sparse matrices are accepted only if
-            they are supported by the base estimator.
-
-        Returns
-        -------
-        score : ndarray of shape (n_samples, k)
-            The decision function of the input samples. The columns correspond
-            to the classes in sorted order, as they appear in the attribute
-            ``classes_``. Regression and binary classification are special
-            cases with ``k == 1``, otherwise ``k==n_classes``.
-        """
-        check_is_fitted(self)
-
-        # Check data
-        X = validate_data(
-            self,
-            X=X,
-            accept_sparse=["csr", "csc"],
-            dtype=None,
-            ensure_all_finite=False,
-            reset=False,
-        )
-
-        # Parallel loop
-        n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs)
-
-        all_decisions = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
-            delayed(_parallel_decision_function)(
-                self.estimators_[starts[i] : starts[i + 1]],
-                self.estimators_features_[starts[i] : starts[i + 1]],
-                X,
-            )
-            for i in range(n_jobs)
-        )
-
-        # Reduce
-        decisions = sum(all_decisions) / self.n_estimators
-
-        return decisions
-
     @property
     def base_estimator_(self):
         """Attribute for older sklearn version compatibility."""

imblearn/ensemble/_easy_ensemble.py

@@ -6,7 +6,6 @@
 
 import copy
 import numbers
-import warnings
 
 import numpy as np
 from sklearn.base import clone
@@ -248,20 +247,6 @@ def _validate_estimator(self, default=AdaBoostClassifier(algorithm="SAMME")):
         )
         self.estimator_ = Pipeline([("sampler", sampler), ("classifier", estimator)])
 
-    # TODO: remove when supporting scikit-learn>=1.2
-    @property
-    def n_features_(self):
-        """Number of features when ``fit`` is performed."""
-        warnings.warn(
-            (
-                "`n_features_` was deprecated in scikit-learn 1.0. This attribute will "
-                "not be accessible when the minimum supported version of scikit-learn "
-                "is 1.2."
-            ),
-            FutureWarning,
-        )
-        return self.n_features_in_
-
     @_fit_context(prefer_skip_nested_validation=False)
     def fit(self, X, y):
         """Build a Bagging ensemble of estimators from the training set (X, y).

imblearn/ensemble/_forest.py

@@ -437,10 +437,10 @@ def __init__(
         max_features="sqrt",
         max_leaf_nodes=None,
         min_impurity_decrease=0.0,
-        bootstrap="warn",
+        bootstrap=False,
         oob_score=False,
-        sampling_strategy="warn",
-        replacement="warn",
+        sampling_strategy="all",
+        replacement=True,
         n_jobs=None,
         random_state=None,
         verbose=0,
@@ -498,7 +498,7 @@ def _validate_estimator(self, default=DecisionTreeClassifier()):
 
         self.base_sampler_ = RandomUnderSampler(
             sampling_strategy=self._sampling_strategy,
-            replacement=self._replacement,
+            replacement=self.replacement,
         )
 
     def _make_sampler_estimator(self, random_state=None):
@@ -544,49 +544,6 @@ def fit(self, X, y, sample_weight=None):
             The fitted instance.
         """
         self._validate_params()
-        # TODO: remove in 0.13
-        if self.sampling_strategy == "warn":
-            warn(
-                (
-                    "The default of `sampling_strategy` will change from `'auto'` to"
-                    " `'all'` in version 0.13. This change will follow the"
-                    " implementation proposed in the original paper. Set to `'all'` to"
-                    " silence this warning and adopt the future behaviour."
-                ),
-                FutureWarning,
-            )
-            self._sampling_strategy = "auto"
-        else:
-            self._sampling_strategy = self.sampling_strategy
-
-        if self.replacement == "warn":
-            warn(
-                (
-                    "The default of `replacement` will change from `False` to `True` in"
-                    " version 0.13. This change will follow the implementation proposed"
-                    " in the original paper. Set to `True` to silence this warning and"
-                    " adopt the future behaviour."
-                ),
-                FutureWarning,
-            )
-            self._replacement = False
-        else:
-            self._replacement = self.replacement
-
-        if self.bootstrap == "warn":
-            warn(
-                (
-                    "The default of `bootstrap` will change from `True` to `False` in"
-                    " version 0.13. This change will follow the implementation proposed"
-                    " in the original paper. Set to `False` to silence this warning and"
-                    " adopt the future behaviour."
-                ),
-                FutureWarning,
-            )
-            self._bootstrap = True
-        else:
-            self._bootstrap = self.bootstrap
-
         # Validate or convert input data
         if issparse(y):
             raise ValueError("sparse multilabel-indicator for y is not supported.")
@@ -657,7 +614,7 @@ def fit(self, X, y, sample_weight=None):
         if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
             y_encoded = np.ascontiguousarray(y_encoded, dtype=DOUBLE)
 
-        if isinstance(self._sampling_strategy, dict):
+        if isinstance(self.sampling_strategy, dict):
             self._sampling_strategy = {
                 np.where(self.classes_[0] == key)[0][0]: value
                 for key, value in check_sampling_strategy(
@@ -667,7 +624,7 @@ def fit(self, X, y, sample_weight=None):
                 ).items()
             }
         else:
-            self._sampling_strategy = self._sampling_strategy
+            self._sampling_strategy = self.sampling_strategy
 
         if expanded_class_weight is not None:
             if sample_weight is not None:
@@ -683,7 +640,7 @@ def fit(self, X, y, sample_weight=None):
         # Check parameters
         self._validate_estimator()
 
-        if not self._bootstrap and self.oob_score:
+        if not self.bootstrap and self.oob_score:
             raise ValueError("Out of bag estimation only available if bootstrap=True")
 
         random_state = check_random_state(self.random_state)
@@ -735,7 +692,7 @@ def fit(self, X, y, sample_weight=None):
                 delayed(_local_parallel_build_trees)(
                     s,
                     t,
-                    self._bootstrap,
+                    self.bootstrap,
                     X,
                     y_encoded,
                     sample_weight,

imblearn/ensemble/tests/test_forest.py

@@ -217,24 +217,6 @@ def test_balanced_random_forest_oob_binomial(ratio):
     assert np.abs(erf.oob_score_ - 0.5) < 0.1
 
 
-# TODO: remove in 0.13
-def test_balanced_random_forest_change_behaviour(imbalanced_dataset):
-    """Check that we raise a change of behaviour for the parameters `sampling_strategy`
-    and `replacement`.
-    """
-    estimator = BalancedRandomForestClassifier(sampling_strategy="all", bootstrap=False)
-    with pytest.warns(FutureWarning, match="The default of `replacement`"):
-        estimator.fit(*imbalanced_dataset)
-    estimator = BalancedRandomForestClassifier(replacement=True, bootstrap=False)
-    with pytest.warns(FutureWarning, match="The default of `sampling_strategy`"):
-        estimator.fit(*imbalanced_dataset)
-    estimator = BalancedRandomForestClassifier(
-        sampling_strategy="all", replacement=True
-    )
-    with pytest.warns(FutureWarning, match="The default of `bootstrap`"):
-        estimator.fit(*imbalanced_dataset)
-
-
 @pytest.mark.skipif(
     parse_version(sklearn_version.base_version) < parse_version("1.4"),
     reason="scikit-learn should be >= 1.4",

imblearn/over_sampling/_adasyn.py

@@ -5,21 +5,19 @@
 # License: MIT
 
 import numbers
-import warnings
 
 import numpy as np
 from scipy import sparse
 from sklearn.utils import _safe_indexing, check_random_state
 from sklearn.utils._param_validation import HasMethods, Interval
 
 from ..utils import Substitution, check_neighbors_object
-from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
+from ..utils._docstring import _random_state_docstring
 from .base import BaseOverSampler
 
 
 @Substitution(
     sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
-    n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
 class ADASYN(BaseOverSampler):
@@ -50,14 +48,6 @@ class ADASYN(BaseOverSampler):
           :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to
           any compatible class.
 
-    {n_jobs}
-
-        .. deprecated:: 0.10
-           `n_jobs` has been deprecated in 0.10 and will be removed in 0.12.
-           It was previously used to set `n_jobs` of nearest neighbors
-           algorithm. From now on, you can pass an estimator where `n_jobs` is
-           already set instead.
-
     Attributes
     ----------
     sampling_strategy_ : dict
@@ -128,7 +118,6 @@ class ADASYN(BaseOverSampler):
             Interval(numbers.Integral, 1, None, closed="left"),
             HasMethods(["kneighbors", "kneighbors_graph"]),
         ],
-        "n_jobs": [numbers.Integral, None],
     }
 
     def __init__(
@@ -137,12 +126,10 @@ def __init__(
         sampling_strategy="auto",
         random_state=None,
         n_neighbors=5,
-        n_jobs=None,
     ):
         super().__init__(sampling_strategy=sampling_strategy)
         self.random_state = random_state
         self.n_neighbors = n_neighbors
-        self.n_jobs = n_jobs
 
     def _validate_estimator(self):
         """Create the necessary objects for ADASYN"""
@@ -151,17 +138,6 @@ def _validate_estimator(self):
         )
 
     def _fit_resample(self, X, y):
-        # FIXME: to be removed in 0.12
-        if self.n_jobs is not None:
-            warnings.warn(
-                (
-                    "The parameter `n_jobs` has been deprecated in 0.10 and will be"
-                    " removed in 0.12. You can pass an nearest neighbors estimator"
-                    " where `n_jobs` is already set instead."
-                ),
-                FutureWarning,
-            )
-
         self._validate_estimator()
         random_state = check_random_state(self.random_state)