docs: Modified docstring

- Fixed imports in class references
- Fixed examples outputs

src/imblearn_extra/clover/distribution/_density.py

@@ -118,10 +118,12 @@ class DensityDistributor(BaseDistributor):
             An array of unique cluster labels.
 
     Examples:
-        >>> from clover.distribution import DensityDistributor
+        >>> import numpy as np
+        >>> from imblearn_extra.clover.distribution import DensityDistributor
         >>> from sklearn.datasets import load_iris
         >>> from sklearn.cluster import KMeans
         >>> from imblearn.datasets import make_imbalance
+        >>> np.set_printoptions(legacy='1.25')
         >>> X, y = make_imbalance(
         ...     *load_iris(return_X_y=True),
         ...     sampling_strategy={0:50, 1:40, 2:30},
@@ -132,7 +134,7 @@ class DensityDistributor(BaseDistributor):
         >>> density_distributor.filtered_clusters_
         [(6, 1), (0, 1), (3, 1), (7, 1), (5, 2), (2, 2), (3, 2), (6, 2), (0, 2)]
         >>> density_distributor.intra_distribution_
-        {(6, 1): 0.50604609281056... (0, 1): 0.143311766542165...}
+        {(6, 1): 0.50604609281055... (0, 1): 0.143311766542168...}
         >>> density_distributor.inter_distribution_
         {}
     """

src/imblearn_extra/clover/over_sampling/_cluster.py

@@ -319,7 +319,7 @@ class ClusterOverSampler(BaseOverSampler):
             A fitted clone of the `clusterer` parameter or `None` when a
             clusterer is not given.
 
-        distributor_ (clover.distribution.base.BaseDistributor):
+        distributor_ (imblearn_extra.clover.distribution.base.BaseDistributor):
             A fitted clone of the `distributor` parameter or a fitted instance of
             the `DensityDistributor` when a distributor is not given.
 
@@ -338,20 +338,22 @@ class ClusterOverSampler(BaseOverSampler):
             Actual sampling strategy.
 
     Examples:
+        >>> import numpy as np
         >>> from collections import Counter
-        >>> from clover.over_sampling import ClusterOverSampler
+        >>> from imblearn_extra.clover.over_sampling import ClusterOverSampler
         >>> from sklearn.datasets import make_classification
         >>> from sklearn.cluster import KMeans
         >>> from imblearn.over_sampling import SMOTE
+        >>> np.set_printoptions(legacy='1.25')
         >>> X, y = make_classification(random_state=0, n_classes=2, weights=[0.9, 0.1])
         >>> print('Original dataset shape %s' % Counter(y))
-        Original dataset shape Counter({{0: 90, 1: 10}})
+        Original dataset shape Counter({0: 90, 1: 10})
         >>> cluster_oversampler = ClusterOverSampler(
         ... oversampler=SMOTE(random_state=5),
         ... clusterer=KMeans(random_state=10, n_init='auto'))
         >>> X_res, y_res = cluster_oversampler.fit_resample(X, y)
         >>> print('Resampled dataset shape %s' % Counter(y_res))
-        Resampled dataset shape Counter({{0: 90, 1: 90}})
+        Resampled dataset shape Counter({0: 90, 1: 90})
     """
 
     def __init__(

src/imblearn_extra/clover/over_sampling/_gsomo.py

@@ -86,9 +86,9 @@ class GeometricSOMO(ClusterOverSampler):
         som_estimator:
             Defines the SOM clusterer applied to the input space.
 
-            - If `None`, a `clover.clusterer.SOM` instance with default parameters is used.
+            - If `None`, a `imblearn_extra.clover.clusterer.SOM` instance with default parameters is used.
 
-            - If `clover.clusterer.SOM` object then is used with the given parameters.
+            - If `imblearn_extra.clover.clusterer.SOM` object then is used with the given parameters.
 
             - If `int`, the number of clusters to be used.
 
@@ -150,8 +150,8 @@ class GeometricSOMO(ClusterOverSampler):
         clusterer_ (SOM):
             A fitted `clovar.clusterer.SOM` instance.
 
-        distributor_ (clover.distribution.DensityDistributor):
-            A fitted `clover.distribution.DensityDistributor` instance.
+        distributor_ (imblearn_extra.clover.distribution.DensityDistributor):
+            A fitted `imblearn_extra.clover.distribution.DensityDistributor` instance.
 
         labels_ (Labels):
             Labels of each sample.
@@ -168,7 +168,7 @@ class GeometricSOMO(ClusterOverSampler):
 
     Examples:
         >>> import numpy as np
-        >>> from clover.over_sampling import GeometricSOMO # doctest: +SKIP
+        >>> from imblearn_extra.clover.over_sampling import GeometricSOMO # doctest: +SKIP
         >>> from sklearn.datasets import make_blobs
         >>> blobs = [100, 800, 100]
         >>> X, y  = make_blobs(blobs, centers=[(-10, 0), (0,0), (10, 0)])

src/imblearn_extra/clover/over_sampling/_kmeans_smote.py

@@ -132,8 +132,8 @@ class KMeansSMOTE(ClusterOverSampler):
         clusterer_ (sklearn.cluster.KMeans | sklearn.cluster.MiniBatchKMeans):
             A fitted `sklearn.cluster.KMeans` or `sklearn.cluster.MiniBatchKMeans` instance.
 
-        distributor_ (clover.distribution.DensityDistributor):
-            A fitted `clover.distribution.DensityDistributor` instance.
+        distributor_ (imblearn_extra.clover.distribution.DensityDistributor):
+            A fitted `imblearn_extra.clover.distribution.DensityDistributor` instance.
 
         labels_ (Labels):
             Cluster labels of each sample.
@@ -149,7 +149,7 @@ class KMeansSMOTE(ClusterOverSampler):
 
     Examples:
         >>> import numpy as np
-        >>> from clover.over_sampling import KMeansSMOTE
+        >>> from imblearn_extra.clover.over_sampling import KMeansSMOTE
         >>> from sklearn.datasets import make_blobs
         >>> blobs = [100, 800, 100]
         >>> X, y  = make_blobs(blobs, centers=[(-10, 0), (0,0), (10, 0)])

src/imblearn_extra/clover/over_sampling/_somo.py

@@ -78,7 +78,7 @@ class SOMO(ClusterOverSampler):
             - If `None`, `SOM` is used which
             tends to be better with large number of samples.
 
-            - If SOM object, then it is a `clover.clusterer.SOM` instance.
+            - If SOM object, then it is a `imblearn_extra.clover.clusterer.SOM` instance.
 
             - If `int`, the number of clusters to be used.
 
@@ -111,11 +111,11 @@ class SOMO(ClusterOverSampler):
         oversampler_ (imblearn.over_sampling.SMOTE):
             A fitted `imblearn.over_sampling.SMOTE` instance.
 
-        clusterer_ (clover.clusterer.SOM):
-            A fitted `clover.clusterer.SOM` instance.
+        clusterer_ (imblearn_extra.clover.clusterer.SOM):
+            A fitted `imblearn_extra.clover.clusterer.SOM` instance.
 
-        distributor_ (clover.distribution.DensityDistributor):
-            A fitted `clover.distribution.DensityDistributor` instance.
+        distributor_ (imblearn_extra.clover.distribution.DensityDistributor):
+            A fitted `imblearn_extra.clover.distribution.DensityDistributor` instance.
 
         labels_ (Labels):
             Cluster labels of each sample.
@@ -132,7 +132,7 @@ class SOMO(ClusterOverSampler):
 
     Examples:
         >>> import numpy as np
-        >>> from clover.over_sampling import SOMO # doctest: +SKIP
+        >>> from imblearn_extra.clover.over_sampling import SOMO # doctest: +SKIP
         >>> from sklearn.datasets import make_blobs
         >>> blobs = [100, 800, 100]
         >>> X, y  = make_blobs(blobs, centers=[(-10, 0), (0,0), (10, 0)])

src/imblearn_extra/gsmote/geometric_smote.py

@@ -200,9 +200,11 @@ class GeometricSMOTE(BaseOverSampler):
             Actual sampling strategy.
 
     Examples:
+        >>> import numpy as np
         >>> from collections import Counter
         >>> from sklearn.datasets import make_classification
-        >>> from gsmote import GeometricSMOTE # doctest: +NORMALIZE_WHITESPACE
+        >>> from imblearn_extra.gsmote import GeometricSMOTE # doctest: +NORMALIZE_WHITESPACE
+        >>> np.set_printoptions(legacy='1.25')
         >>> X, y = make_classification(n_classes=2, class_sep=2,
         ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
         ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)