ENHANCEMENT: Handle mono classification with Shap

python/interpret-core/interpret/utils/_unify_predict.py

@@ -3,6 +3,7 @@
 
 import logging
 
+import numpy as np
 from sklearn.base import is_classifier, is_regressor
 
 from ._clean_simple import clean_dimensions
@@ -47,6 +48,30 @@ def determine_classes(model, data, n_samples):
             n_classes = 1
         else:
             n_classes = preds.shape[1]
+
+        if len(classes) == 1:
+            # for single-class problems, treat it as binary classification
+            # where the second class probability is always 0
+            n_classes = 2
+            orig_model = model
+            original_class = classes[0]
+
+            print(
+                f"Warning: Model was trained on single-class data. Model will always predict class {original_class}."
+            )
+
+            def mono_classification_model(data):
+                preds = orig_model(data)
+                if preds.ndim == 1:
+                    preds = preds.reshape(-1, 1)
+                # add zero probabilities for the synthetic class
+                return np.hstack([preds, np.zeros_like(preds)])
+
+            model = mono_classification_model
+            # keep original class and add any different value as synthetic class
+            synthetic_class = "other" if original_class != "other" else "synthetic"
+            classes = np.array([original_class, synthetic_class])
+
         if n_classes != len(classes):
             msg = "class number mismatch"
             _log.error(msg)