Included warning TreeSHAP background dataset size. (#710)

* Included warning TreeSHAP background dataset size.

* Fixed background size when DenseData object returned by summarisation.

* Updated waring to emphasize sampling with replacement.

alibi/explainers/shap_wrappers.py

@@ -1000,6 +1000,7 @@ def reset_predictor(self, predictor: Callable) -> None:
 # TODO: Look into pyspark support requirements if requested
 # TODO: catboost.Pool not supported for fit stage (due to summarisation) but can do if there is a user need
 
+TREE_SHAP_BACKGROUND_SUPPORTED_SIZE = 100
 TREE_SHAP_BACKGROUND_WARNING_THRESHOLD = 1000
 TREE_SHAP_MODEL_OUTPUT = ['raw', 'probability', 'probability_doubled', 'log_loss']
 
@@ -1159,6 +1160,24 @@ def fit(self,  # type: ignore[override]
             else:
                 self._check_inputs(background_data)
 
+            # summarisation can return a DenseData object
+            n_samples = (background_data.data if isinstance(background_data, shap_utils.DenseData)
+                         else background_data).shape[0]
+
+            # Warns the user that TreeShap supports only up to TREE_SHAP_BACKGROUND_SIZE(100) samples in the
+            # background dataset. Note that there is a logic above related to the summarisation of the background
+            # dataset which uses TREE_SHAP_BACKGROUND_WARNING_THRESHOLD(1000) as (warning) threshold. Although the
+            # TREE_SHAP_BACKGROUND_WARNING_THRESHOLD > TREE_SHAP_BACKGROUND_SUPPORTED_SIZE which is contradictory, we
+            # leave the logic above untouched. This approach has at least two benefits:
+            #   i) minimal refactoring
+            #   ii) return the correct result if a newer version of shap which fixes the issue is used before we
+            #   update our dependencies in alibi (i.e. just ignore the warning)
+            if n_samples > TREE_SHAP_BACKGROUND_SUPPORTED_SIZE:
+                logger.warning(f'The upstream implementation of interventional TreeShap supports only up to '
+                               f'{TREE_SHAP_BACKGROUND_SUPPORTED_SIZE} samples in the background dataset. '
+                               f'A larger background dataset will be sampled with replacement to '
+                               f'{TREE_SHAP_BACKGROUND_SUPPORTED_SIZE} instances.')
+
         perturbation = 'interventional' if background_data is not None else 'tree_path_dependent'
         self.background_data = background_data
         self._explainer = shap.TreeExplainer(