Fix edge case in AnchorTabular where no samples satisfying the anchor exist in the train data

Author: jklaise

alibi/explainers/anchors/anchor_tabular.py

@@ -290,6 +290,10 @@ def perturbation(self, anchor: tuple, num_samples: int) -> Tuple[np.ndarray, np.
             [allowed_rows[feat] for feat in uniq_feat_ids],
             np.intersect1d),
         )
+        if partial_anchor_rows == []:
+            # edge case - if there are no rows at all then `partial_anchor_rows` is the empty list, but it should
+            # be a list of an empty array to not cause an error in calculating coverage (which will be 0)
+            partial_anchor_rows = [np.array([], dtype=int)]
         nb_partial_anchors = np.array([len(n_records) for n_records in
                                        reversed(partial_anchor_rows)])  # reverse required for np.searchsorted later
         coverage = nb_partial_anchors[0] / self.n_records  # since we sorted, the correct coverage is first not last
@@ -383,7 +387,14 @@ def replace_features(self, samples: np.ndarray, allowed_rows: Dict[int, Any], un
         requested_samples = num_samples
         start, n_anchor_feats = 0, len(partial_anchor_rows)
         uniq_feat_ids = list(reversed(uniq_feat_ids))
-        start_idx = np.nonzero(nb_partial_anchors)[0][0]  # skip anchors with no samples in the database
+
+        try:
+            start_idx = np.nonzero(nb_partial_anchors)[0][0]  # skip anchors with no samples in the database
+        except IndexError:
+            # there are no samples in the database, need to break out of the function
+            # and go straight to treating unknown features
+            return
+
         end_idx = np.searchsorted(np.cumsum(nb_partial_anchors), num_samples)
 
         # replace partial anchors with partial anchors drawn from the training dataset