edit python-package-conda.yml

.flake8

@@ -0,0 +1,2 @@
+[flake8]
+max-line-length = 127

.github/workflows/python-package-conda.yml

@@ -25,7 +25,7 @@ jobs:
       run: |
         conda install flake8
         # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        flake8 . --count --select=E9,F63,F7 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Black formatting

focus/core.py

@@ -24,7 +24,7 @@ class Focus:
     Parameters
     ----------
     distance_function: str, optional (default="euclidean")
-        Distance function - one of
+        Distance function - one of followings;
             - "euclidean"
             - "cosine"
             - "l1"
@@ -63,8 +63,7 @@ class Focus:
     ---------
     Lucic, A., Oosterhuis, H., Haned, H., & de Rijke, M. (2022, June).
     FOCUS: Flexible optimizable counterfactual explanations for tree ensembles.
-    In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36,
-    No. 5, pp. 5313-5322).
+    In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 5, pp. 5313-5322).
 
     Examples
     --------
@@ -125,11 +124,9 @@ def generate(self, model, X, x_train=None):
         This method generates counterfactual explanations for the
         predictions made by a tree-based model.
         It uses the gradient descent method to optimize the input features
-        based on a combination of hinge loss, approximate probability and
-        a distance term.
+        based on a combination of hinge loss, approximate probability and a distance term.
         The `model` should be an instance of a tree-based model,
-        such as DecisionTreeClassifier, RandomForestClassifier or
-        AdaBoostClassifier.
+        such as DecisionTreeClassifier, RandomForestClassifier or AdaBoostClassifier.
         The `X` parameter represents the input features for which
         counterfactual explanations are desired.
         The `x_train` parameter is an optional argument that