Update diagnosis tests

validmind/tests/model_validation/sklearn/OverfitDiagnosis.py

@@ -220,6 +220,16 @@ def OverfitDiagnosis(
     - May not capture more subtle forms of overfitting that do not exceed the threshold.
     - Assumes that the binning of features adequately represents the data segments.
     """
+
+    numeric_and_categorical_feature_columns = (
+        datasets[0].feature_columns_numeric + datasets[0].feature_columns_categorical
+    )
+
+    if not numeric_and_categorical_feature_columns:
+        raise ValueError(
+            "No valid numeric or categorical columns found in features_columns"
+        )
+
     is_classification = bool(datasets[0].probability_column(model))
 
     if not metric:
@@ -246,7 +256,7 @@ def OverfitDiagnosis(
     figures = []
     results_headers = ["slice", "shape", "feature", metric]
 
-    for feature_column in datasets[0].feature_columns:
+    for feature_column in numeric_and_categorical_feature_columns:
         bins = 10
         if feature_column in datasets[0].feature_columns_categorical:
             bins = len(train_df[feature_column].unique())

validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.py

@@ -211,6 +211,19 @@ def WeakspotsDiagnosis(
     improvement.
     """
     feature_columns = features_columns or datasets[0].feature_columns
+    numeric_and_categorical_columns = (
+        datasets[0].feature_columns_numeric + datasets[0].feature_columns_categorical
+    )
+
+    feature_columns = [
+        col for col in feature_columns if col in numeric_and_categorical_columns
+    ]
+
+    if not feature_columns:
+        raise ValueError(
+            "No valid numeric or categorical columns found in features_columns"
+        )
+
     if not all(col in datasets[0].feature_columns for col in feature_columns):
         raise ValueError(
             "Column(s) provided in features_columns do not exist in the dataset"