Add balanced models.

params.yaml

@@ -156,29 +156,36 @@ datasets:
 
 models:
   logistic_regression:
+    model: logistic_regression
     solver: liblinear
 
   logistic_regression_balanced:
+    model: logistic_regression
     solver: liblinear
     class_weight: balanced
 
   gradient_boosting_classifier:
+    model: gradient_boosting_classifier
     n_estimators: 40
     min_samples_split: 6
     max_depth: 2
 
   knn:
+    model: knn
     n_neighbors: 5
     weights: uniform
 
   svm:
+    model: svm
     kernel: rbf
 
   svm_balanced:
+    model: svm
     kernel: rbf
     class_weight: balanced
 
-  mlp: {}
+  mlp:
+    model: mlp
 
 valuation_methods:
   random:

scripts/evaluate_metrics.py

@@ -97,10 +97,8 @@ def _evaluate_metrics(
         / str(repetition_id)
         / valuation_method_name
     )
-    if (
-        False
-        and os.path.exists(output_dir / f"{metric_name}.csv")
-        and os.path.exists(output_dir / f"{metric_name}.curve.csv")
+    if os.path.exists(output_dir / f"{metric_name}.csv") and os.path.exists(
+        output_dir / f"{metric_name}.curve.csv"
     ):
         return logger.info(f"Metric data exists in '{output_dir}'. Skipping...")
 

src/re_classwise_shapley/model.py

@@ -33,26 +33,27 @@ def instantiate_model(
         The instantiated model.
     """
     random_state = np.random.RandomState(seed)
+    model = model_kwargs.pop("model")
 
-    if model_name == "gradient_boosting_classifier":
+    if model == "gradient_boosting_classifier":
         model = make_pipeline(
             GradientBoostingClassifier(**model_kwargs, random_state=random_state)
         )
-    elif model_name == "logistic_regression":
+    elif model == "logistic_regression":
         model = make_pipeline(
             StandardScaler(),
             LogisticRegression(**model_kwargs, random_state=random_state),
         )
-    elif model_name == "knn":
+    elif model == "knn":
         model = make_pipeline(
             StandardScaler(),
             KNeighborsClassifier(**model_kwargs),
         )
-    elif model_name == "svm":
+    elif model == "svm":
         model = make_pipeline(
             StandardScaler(), SVC(**model_kwargs, random_state=random_state)
         )
-    elif model_name == "mlp":
+    elif model == "mlp":
         model = make_pipeline(
             StandardScaler(), MLPClassifier(**model_kwargs, random_state=random_state)
         )