🖍️ improving formatting

Author: jonasvdd

powershap/shap_wrappers/shap_explainer.py

@@ -1,5 +1,6 @@
 __author__ = "Jarne Verhaeghe, Jeroen Van Der Donckt"
 
+import gc
 import warnings
 from abc import ABC
 from copy import copy
@@ -12,8 +13,6 @@
 from sklearn.model_selection import train_test_split
 from tqdm.auto import tqdm
 
-import gc
-
 
 class ShapExplainer(ABC):
     """Interface class for a (POWERshap explainer class."""
@@ -185,7 +184,6 @@ def explain(
             # on every iteration.
             gc.collect()
 
-
         shaps = np.array(shaps)
 
         return pd.DataFrame(data=shaps, columns=X.columns.values)

pyproject.toml

@@ -53,5 +53,5 @@ multi_line_output = 3
 include_trailing_comma = true
 force_grid_wrap = 0
 combine_as_imports = true
-color_output = true
+color_output = false
 skip = "tests/toml_test.py"

tests/test_powershap.py

@@ -1,5 +1,7 @@
 __author__ = "Jeroen Van Der Donckt"
 
+import hashlib
+
 import numpy as np
 import pandas as pd
 from catboost import CatBoostClassifier, CatBoostRegressor
@@ -8,8 +10,6 @@
 
 from .conftest import dummy_classification, dummy_regression
 
-import hashlib
-
 ### DEFAULT MODEL & AUTOMATIC MODE
 
 
@@ -251,8 +251,7 @@ def test_powershap_cv_groupshufflesplit(dummy_classification):
 
 
 def test_no_mutate_df(dummy_classification):
-    """Ensure that powershap fit doesn't mutate an input pandas dataframe.
-    """
+    """Ensure that powershap fit doesn't mutate an input pandas dataframe."""
     X, y = dummy_classification
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
@@ -277,8 +276,7 @@ def test_no_mutate_df(dummy_classification):
 
 
 def test_no_mutate_numpy(dummy_classification):
-    """Ensure that powershap fit doesn't mutate an input numpy array.
-    """
+    """Ensure that powershap fit doesn't mutate an input numpy array."""
     X, y = dummy_classification
 
     X = X.to_numpy()