DOC add versionadded since 0.3

Author: glemaitre

doc/whats_new/v0.6.rst

@@ -11,16 +11,15 @@ Changelog
 
 - Allow column vectors to be passed as targets.
   :pr:`673` by :user:`Christos Aridas <chkoar>`.
-- Better input/output handling for pandas, numpy and plain lists. 
-  :pr:`681` by :user:`Christos Aridas <chkoar>`.
 
+- Better input/output handling for pandas, numpy and plain lists.
+  :pr:`681` by :user:`Christos Aridas <chkoar>`.
 
 .. _changes_0_6_1:
 
 Version 0.6.1
 ==============
 
-
 This is a bug-fix release to primarily resolve some packaging issues in version
 0.6.0. It also includes minor documentation improvements and some bug fixes.
 

imblearn/base.py

@@ -162,6 +162,8 @@ class FunctionSampler(BaseSampler):
         validation allows to use the ``FunctionSampler`` with any type of
         data.
 
+        .. versionadded:: 0.6
+
     See Also
     --------
 

imblearn/datasets/_zenodo.py

@@ -112,6 +112,8 @@ def fetch_datasets(
 ):
     """Load the benchmark datasets from Zenodo, downloading it if necessary.
 
+    .. versionadded:: 0.3
+
     Parameters
     ----------
     data_home : str, default=None

imblearn/ensemble/_easy_ensemble.py

@@ -37,6 +37,8 @@ class EasyEnsembleClassifier(BaggingClassifier):
 
     Read more in the :ref:`User Guide <boosting>`.
 
+    .. versionadded:: 0.4
+
     Parameters
     ----------
     n_estimators : int, default=10

imblearn/ensemble/_forest.py

@@ -86,6 +86,8 @@ class BalancedRandomForestClassifier(RandomForestClassifier):
 
     Read more in the :ref:`User Guide <forest>`.
 
+    .. versionadded:: 0.4
+
     Parameters
     ----------
     n_estimators : int, default=100

imblearn/ensemble/_weight_boosting.py

@@ -27,6 +27,8 @@ class RUSBoostClassifier(AdaBoostClassifier):
 
     Read more in the :ref:`User Guide <boosting>`.
 
+    .. versionadded:: 0.4
+
     Parameters
     ----------
     base_estimator : estimator object, default=None
@@ -176,7 +178,8 @@ def _validate_estimator(self):
         super()._validate_estimator()
 
         self.base_sampler_ = RandomUnderSampler(
-            sampling_strategy=self.sampling_strategy, replacement=self.replacement,
+            sampling_strategy=self.sampling_strategy,
+            replacement=self.replacement,
         )
 
     def _make_sampler_estimator(self, append=True, random_state=None):

imblearn/keras/_generator.py

@@ -62,6 +62,8 @@ class BalancedBatchGenerator(*ParentClass):
     creating the batch. The sampler should have an attribute
     ``sample_indices_``.
 
+    .. versionadded:: 0.4
+
     Parameters
     ----------
     X : ndarray of shape (n_samples, n_features)
@@ -163,9 +165,7 @@ def _sample(self):
             self.sampler_ = clone(self.sampler)
         self.sampler_.fit_resample(self.X, self.y)
         if not hasattr(self.sampler_, "sample_indices_"):
-            raise ValueError(
-                "'sampler' needs to have an attribute 'sample_indices_'."
-            )
+            raise ValueError("'sampler' needs to have an attribute 'sample_indices_'.")
         self.indices_ = self.sampler_.sample_indices_
         # shuffle the indices since the sampler are packing them by class
         random_state.shuffle(self.indices_)

imblearn/metrics/_classification.py

@@ -831,13 +831,13 @@ def classification_report_imbalanced(
     output_dict : bool, default=False
         If True, return output as dict.
 
-        .. versionadded:: 0.7
+        .. versionadded:: 0.8
 
     zero_division : "warn" or {0, 1}, default="warn"
         Sets the value to return when there is a zero division. If set to
         "warn", this acts as 0, but warnings are also raised.
 
-        .. versionadded:: 0.7
+        .. versionadded:: 0.8
 
     Returns
     -------
@@ -999,6 +999,8 @@ def macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None):
 
     Read more in the :ref:`User Guide <macro_averaged_mean_absolute_error>`.
 
+    .. versionadded:: 0.8
+
     Parameters
     ----------
     y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)

imblearn/metrics/pairwise.py

@@ -40,6 +40,8 @@ class ValueDifferenceMetric(BaseEstimator):
 
     Read more in the :ref:`User Guide <vdm>`.
 
+    .. versionadded:: 0.8
+
     Parameters
     ----------
     n_categories : "auto" or array-like of shape (n_features,), default="auto"

imblearn/over_sampling/_random_over_sampler.py

@@ -53,7 +53,7 @@ class RandomOverSampler(BaseOverSampler):
         The value needs of the shrinkage parameter needs to be higher or equal
         to 0.
 
-        .. versionadded:: 0.7
+        .. versionadded:: 0.8
 
     Attributes
     ----------
@@ -66,7 +66,7 @@ class RandomOverSampler(BaseOverSampler):
         The per-class shrinkage factor used to generate the smoothed bootstrap
         sample. When `shrinkage=None` a normal bootstrap will be generated.
 
-        .. versionadded:: 0.7
+        .. versionadded:: 0.8
 
     See Also
     --------