style: Add a blank line after docstring

georgedouzas · Apr 13, 2023 · ce7524d · ce7524d
1 parent c5925db
commit ce7524d
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Showing 9 changed files with 2 additions and 26 deletions.
diff --git a/src/clover/distribution/_density.py b/src/clover/distribution/_density.py
@@ -200,7 +200,6 @@ def _check_parameters(
 
     def _identify_filtered_clusters(self: DensityDistributor, y: Targets) -> DensityDistributor:
         """Identify the filtered clusters."""
-
         # Generate multi-label
         multi_labels = list(zip(self.labels_, y, strict=True))
 
@@ -224,7 +223,6 @@ def _identify_filtered_clusters(self: DensityDistributor, y: Targets) -> Density
 
     def _calculate_clusters_density(self: DensityDistributor, X: InputData, y: Targets) -> DensityDistributor:
         """Calculate the density of the filtered clusters."""
-
         self.clusters_density_ = {}
 
         # Calculate density

diff --git a/src/clover/distribution/base.py b/src/clover/distribution/base.py
@@ -82,7 +82,7 @@ def _validate_fitting(self: BaseDistributor) -> BaseDistributor:
                 )
                 raise ValueError(msg)
             proportions[class_label1] += proportion
-        if not all([np.isclose(val, 0) or np.isclose(val, 1) for val in proportions.values()]):
+        if not all(np.isclose(val, 0) or np.isclose(val, 1) for val in proportions.values()):
             msg = (
                 'Intra-distribution and inter-distribution sum of proportions for each '
                 f'class label should be either equal to 0 or 1. Got {proportions} instead.'

diff --git a/src/clover/over_sampling/_cluster.py b/src/clover/over_sampling/_cluster.py
@@ -64,7 +64,6 @@ def clone_modify(oversampler: BaseOverSampler, class_label: int, y_in_cluster: T
     Returns:
         A cloned oversampler with modified number of nearest neighbors.
     """
-
     # Clone oversampler
     oversampler = clone(oversampler)
 

diff --git a/src/clover/over_sampling/_gsomo.py b/src/clover/over_sampling/_gsomo.py
@@ -221,7 +221,6 @@ def __init__(
 
     def _check_estimators(self: GeometricSOMO, X: InputData, y: Targets) -> GeometricSOMO:
         """Check various estimators."""
-
         # Check oversampler
         self.oversampler_ = GeometricSMOTE(
             sampling_strategy=self.sampling_strategy,

diff --git a/src/clover/over_sampling/_kmeans_smote.py b/src/clover/over_sampling/_kmeans_smote.py
@@ -183,7 +183,6 @@ def __init__(
 
     def _check_estimators(self: KMeansSMOTE, X: InputData, y: Targets) -> KMeansSMOTE:
         """Check various estimators."""
-
         # Check oversampler
         self.oversampler_ = SMOTE(
             sampling_strategy=self.sampling_strategy,
@@ -208,7 +207,7 @@ def _check_estimators(self: KMeansSMOTE, X: InputData, y: Targets) -> KMeansSMOT
             )
             n_clusters = round((X.shape[0] - 1) * self.kmeans_estimator + 1)
             self.clusterer_ = MiniBatchKMeans(n_clusters=n_clusters, random_state=self.random_state)
-        elif isinstance(self.kmeans_estimator, (KMeans, MiniBatchKMeans)):
+        elif isinstance(self.kmeans_estimator, KMeans | MiniBatchKMeans):
             self.clusterer_ = clone(self.kmeans_estimator)
         else:
             msg = (

diff --git a/src/clover/over_sampling/_somo.py b/src/clover/over_sampling/_somo.py
@@ -170,7 +170,6 @@ def __init__(
 
     def _check_estimators(self: SOMO, X: InputData, y: Targets) -> SOMO:
         """Check various estimators."""
-
         # Import SOM
         try:
             from somlearn import SOM

diff --git a/tests/over_sampling/test_gsomo.py b/tests/over_sampling/test_gsomo.py
@@ -35,7 +35,6 @@ def test_fit(k_neighbors, imbalance_ratio_threshold, distances_exponent):
 
     Multiple cases.
     """
-
     # Fit oversampler
     gsomo = clone(GSOMO).fit(X, y)
     y_count = Counter(y)
@@ -69,7 +68,6 @@ def test_fit_default():
 
     Default case.
     """
-
     # Fit oversampler
     gsomo = clone(GSOMO).fit(X, y)
 
@@ -88,7 +86,6 @@ def test_fit_number_of_clusters(n_clusters):
 
     Number of clusters case.
     """
-
     # Fit oversampler
     gsomo = clone(GSOMO).set_params(som_estimator=n_clusters).fit(X, y)
 
@@ -104,7 +101,6 @@ def test_fit_proportion_of_samples(proportion):
 
     Proportion of samples case.
     """
-
     # Fit oversampler
     gsomo = clone(GSOMO).set_params(som_estimator=proportion).fit(X, y)
 
@@ -119,7 +115,6 @@ def test_som_estimator():
 
     Clusterer case.
     """
-
     # Fit oversampler
     gsomo = clone(GSOMO).set_params(som_estimator=SOM()).fit(X, y)
 
@@ -167,7 +162,6 @@ def test_fit_resample():
 
     Default case.
     """
-
     # Fit oversampler
     gsomo = clone(GSOMO)
     _, y_res = gsomo.fit_resample(X, y)

diff --git a/tests/over_sampling/test_kmeans_smote.py b/tests/over_sampling/test_kmeans_smote.py
@@ -32,7 +32,6 @@ def test_fit(k_neighbors, imbalance_ratio_threshold, distances_exponent):
 
     Multiple cases.
     """
-
     # Fit oversampler
     kmeans_smote = clone(KMEANS_SMOTE).fit(X, y)
     y_count = Counter(y)
@@ -62,7 +61,6 @@ def test_fit_default():
 
     Default case.
     """
-
     # Fit oversampler
     kmeans_smote = clone(KMEANS_SMOTE).fit(X, y)
 
@@ -77,7 +75,6 @@ def test_fit_number_of_clusters(n_clusters):
 
     Number of clusters case.
     """
-
     # Fit oversampler
     kmeans_smote = clone(KMEANS_SMOTE).set_params(kmeans_estimator=n_clusters).fit(X, y)
 
@@ -92,7 +89,6 @@ def test_fit_proportion_of_samples(proportion):
 
     Proportion of samples case.
     """
-
     # Fit oversampler
     kmeans_smote = clone(KMEANS_SMOTE).set_params(kmeans_estimator=proportion).fit(X, y)
 
@@ -107,7 +103,6 @@ def test_fit_kmeans_estimator(kmeans_estimator):
 
     Clusterer case.
     """
-
     # Fit oversampler
     kmeans_smote = clone(KMEANS_SMOTE).set_params(kmeans_estimator=kmeans_estimator).fit(X, y)
 
@@ -151,7 +146,6 @@ def test_fit_resample():
 
     Default case.
     """
-
     # Fit oversampler
     kmeans_smote = clone(KMEANS_SMOTE)
     _, y_res = kmeans_smote.fit_resample(X, y)

diff --git a/tests/over_sampling/test_somo.py b/tests/over_sampling/test_somo.py
@@ -35,7 +35,6 @@ def test_fit(k_neighbors, imbalance_ratio_threshold, distances_exponent):
 
     Multiple cases.
     """
-
     # Fit oversampler
     somo = clone(SM).fit(X, y)
     y_count = Counter(y)
@@ -67,7 +66,6 @@ def test_fit_default():
 
     Default case.
     """
-
     # Fit oversampler
     somo = clone(SM).fit(X, y)
 
@@ -86,7 +84,6 @@ def test_fit_number_of_clusters(n_clusters):
 
     Number of clusters case.
     """
-
     # Fit oversampler
     somo = clone(SM).set_params(som_estimator=n_clusters).fit(X, y)
 
@@ -102,7 +99,6 @@ def test_fit_proportion_of_samples(proportion):
 
     Proportion of samples case.
     """
-
     # Fit oversampler
     somo = clone(SM).set_params(som_estimator=proportion).fit(X, y)
 
@@ -117,7 +113,6 @@ def test_fit_som_estimator():
 
     Clusterer case.
     """
-
     # Fit oversampler
     somo = clone(SM).set_params(som_estimator=SOM()).fit(X, y)
 
@@ -165,7 +160,6 @@ def test_fit_resample():
 
     Default case.
     """
-
     # Fit oversampler
     somo = clone(SM)
     _, y_res = somo.fit_resample(X, y)