correction of the shap explainer and refactoring

imputegap/explainer/explainer.py

@@ -114,8 +114,8 @@ def print(shap_values, shap_details=None):
                 print(f"\tRMSE SERIES {i:<5} : {output:<15}")
 
         print("\n\nSHAP Results details : ")
-        for (x, algo, rate, description, feature, categorie, mean_features) in shap_values:
-            print(f"\tFeature : {x:<5} {algo:<10} with a score of {rate:<10} {categorie:<18} {description:<75} {feature}\n")
+        for (x, algo, rate, description, feature, category, mean_features) in shap_values:
+            print(f"\tFeature : {x:<5} {algo:<10} with a score of {rate:<10} {category:<18} {description:<75} {feature}\n")
 
     def convert_results(tmp, file, algo, descriptions, features, categories, mean_features, to_save):
         """
@@ -145,9 +145,9 @@ def convert_results(tmp, file, algo, descriptions, features, categories, mean_fe
             print(tup[2], end=",")
 
         with open(to_save + "_results.txt", 'w') as file_output:
-            for (x, algo, rate, description, feature, categorie, mean_features) in result_display:
-                file_output.write(f"Feature : {x:<5} {algo:<10} with a score of {rate:<10} {categorie:<18} {description:<65} {feature}\n")
-                result_shap.append([file, algo, rate, description, feature, categorie, mean_features])
+            for (x, algo, rate, description, feature, category, mean_features) in result_display:
+                file_output.write(f"Feature : {x:<5} {algo:<10} with a score of {rate:<10} {category:<18} {description:<65} {feature}\n")
+                result_shap.append([file, algo, rate, description, feature, category, mean_features])
 
         return result_shap
 
@@ -426,7 +426,7 @@ def shap_explainer(ground_truth, algorithm="cdrec", params=None, contamination="
         categories, features = Explainer.load_configuration()
 
         for current_series in range(0, limitation):
-            print("Generation ", current_series, "____________________________________________________________________")
+            print("Generation ", current_series, "___________________________________________________________________")
             print("\tContamination ", current_series, "...")
 
             if contamination == "mcar":

tests/test_explainer.py

@@ -22,6 +22,7 @@ def resolve_path(local_path, github_actions_path):
     else:
         raise FileNotFoundError("File not found in both: ", local_path, " and ", github_actions_path)
 
+
 def get_file_path(set_name="test"):
     """
     Find the accurate path for loading files of tests
@@ -46,7 +47,6 @@ def test_explainer_shap(self):
 
         expected_categories, expected_features = Explainer.load_configuration()
 
-
         gap = TimeSeries(data=get_file_path(filename))
 
         shap_values, shap_details = Explainer.shap_explainer(ground_truth=gap.ts, file_name=filename, use_seed=True,
@@ -58,33 +58,30 @@ def test_explainer_shap(self):
         for i, (_, output) in enumerate(shap_details):
             assert np.isclose(RMSE[i], output, atol=0.01)
 
-
-
-        for i, (x, algo, rate, description, feature, categorie, mean_features) in enumerate(shap_values):
+        for i, (x, algo, rate, description, feature, category, mean_features) in enumerate(shap_values):
             assert np.isclose(SHAP_VAL[i], rate, atol=0.01)
 
             self.assertTrue(x is not None and not (isinstance(x, (int, float)) and np.isnan(x)))
             self.assertTrue(algo is not None)
             self.assertTrue(rate is not None and not (isinstance(rate, (int, float)) and np.isnan(rate)))
             self.assertTrue(description is not None)
             self.assertTrue(feature is not None)
-            self.assertTrue(categorie is not None)
-            self.assertTrue(mean_features is not None and not (isinstance(mean_features, (int, float)) and np.isnan(mean_features)))
+            self.assertTrue(category is not None)
+            self.assertTrue(
+                mean_features is not None and not (isinstance(mean_features, (int, float)) and np.isnan(mean_features)))
 
             # Check relation feature/category
             feature_found_in_category = False
-            for category, features in expected_categories.items():
-                if feature in features:
-                    assert categorie == category, f"Feature '{feature}' should be in category '{category}', but is in '{categorie}'"
+            for exp_category, exp_features in expected_categories.items():
+                if feature in exp_features:
+                    assert category == exp_category, f"Feature '{feature}' must in '{exp_category}', but is in '{category}'"
                     feature_found_in_category = True
                     break
             assert feature_found_in_category, f"Feature '{feature}' not found in any category"
 
             # Check relation description/feature
             if feature in expected_features:
                 expected_description = expected_features[feature]
-                assert description == expected_description, f"Feature '{feature}' has wrong description. Expected '{expected_description}', got '{description}'"
+                assert description == expected_description, f"Feature '{feature}' has wrong description. Expected '{expected_description}', got '{description}' "
             else:
                 assert False, f"Feature '{feature}' not found in the FEATURES dictionary"
-
-