change black version

Pipfile

@@ -19,7 +19,7 @@ pytest-cov = "3.0.0"
 twine = "*"
 sphinx = "4.1.1"
 sphinx-rtd-theme = "0.5.2"
-black = "22.10.0"
+black = {version = "24.3.0", extras = ["colorama"]}
 pre-commit = "2.20.0"
 pyfakefs = "*"
 shap = "0.44.1"

hiclass/Pipeline.py

@@ -1,4 +1,5 @@
 """Custom Pipeline class that supports the `calibrate` method."""
+
 from sklearn.pipeline import Pipeline as skPipeline
 
 

hiclass/probability_combiner/ArithmeticMeanCombiner.py

@@ -1,4 +1,5 @@
 """Defines the ArithmeticMeanCombiner."""
+
 import numpy as np
 from hiclass.probability_combiner.ProbabilityCombiner import ProbabilityCombiner
 from typing import List

hiclass/probability_combiner/GeometricMeanCombiner.py

@@ -1,4 +1,5 @@
 """Defines the GeometricMeanCombiner."""
+
 import numpy as np
 from hiclass.probability_combiner.ProbabilityCombiner import ProbabilityCombiner
 from typing import List

hiclass/probability_combiner/MultiplyCombiner.py

@@ -1,4 +1,5 @@
 """Defines the MultiplyCombiner."""
+
 import numpy as np
 from hiclass.probability_combiner.ProbabilityCombiner import ProbabilityCombiner
 from typing import List

hiclass/probability_combiner/ProbabilityCombiner.py

@@ -1,4 +1,5 @@
 """Abstract class defining the structure of a probability combiner."""
+
 import abc
 import numpy as np
 from typing import List

tests/test_calibration.py

@@ -82,11 +82,12 @@ def binary_cal_X():
 @pytest.fixture
 def binary_mock_estimator(binary_calibration_data, binary_test_scores):
     # return calibration scores or test scores depending on input size
-    side_effect = (
-        lambda X: binary_calibration_data[0]
-        if len(X) == len(binary_calibration_data[0])
-        else binary_test_scores
-    )
+    def side_effect(X):
+        return (
+            binary_calibration_data[0]
+            if len(X) == len(binary_calibration_data[0])
+            else binary_test_scores
+        )
 
     lr = LogisticRegression()
     binary_estimator = Mock(spec=lr)
@@ -140,11 +141,12 @@ def multiclass_test_scores():
 @pytest.fixture
 def multiclass_mock_estimator(multiclass_calibration_data, multiclass_test_scores):
     # return calibration scores or test scores depending on input size
-    side_effect = (
-        lambda X: multiclass_calibration_data[0]
-        if len(X) == len(multiclass_calibration_data[0])
-        else multiclass_test_scores
-    )
+    def side_effect(X):
+        return (
+            multiclass_calibration_data[0]
+            if len(X) == len(multiclass_calibration_data[0])
+            else multiclass_test_scores
+        )
 
     multiclass_estimator = Mock(spec=LogisticRegression)
     multiclass_estimator.predict_proba.side_effect = side_effect