Fix overflow warning in nearest neighbors code (#755)

During the unittest we were seeing a warning saying

```
tests/knn_test.py::BM25Test::test_rank_items_batch
tests/knn_test.py::BM25Test::test_similar_items_filter
tests/knn_test.py::TFIDFTest::test_rank_items_batch
tests/knn_test.py::TFIDFTest::test_similar_items_filter
tests/knn_test.py::CosineTest::test_rank_items_batch
tests/knn_test.py::CosineTest::test_similar_items_filter
  /home/ben/code/implicit/implicit/utils.py:134: RuntimeWarning: overflow encountered in cast
    output_scores[i] = batch_scores[:N]
```

This is because the `_batch_call` was generating output in float32, but the
KNN models were returning float64 results. Fix.

Author: benfred

implicit/nearest_neighbours.py

@@ -62,6 +62,7 @@ def recommend(
                 userid,
                 user_items=user_items,
                 N=N,
+                score_dtype=np.float64,
                 filter_already_liked_items=filter_already_liked_items,
                 filter_items=filter_items,
                 recalculate_user=recalculate_user,
@@ -115,7 +116,12 @@ def similar_items(
 
         if not np.isscalar(itemid):
             return _batch_call(
-                self.similar_items, itemid, N=N, filter_items=filter_items, items=items
+                self.similar_items,
+                itemid,
+                N=N,
+                score_dtype=np.float64,
+                filter_items=filter_items,
+                items=items,
             )
 
         if filter_items is not None and items is not None:

implicit/utils.py

@@ -103,11 +103,11 @@ def augment_inner_product_matrix(factors):
     return max_norm, np.append(factors, extra_dimension.reshape(norms.shape[0], 1), axis=1)
 
 
-def _batch_call(func, ids, *args, N=10, **kwargs):
+def _batch_call(func, ids, *args, N=10, id_dtype=np.int32, score_dtype=np.float32, **kwargs):
     # we're running in batch mode, just loop over each item and call the scalar version of the
     # function
-    output_ids = np.zeros((len(ids), N), dtype=np.int32)
-    output_scores = np.zeros((len(ids), N), dtype=np.float32)
+    output_ids = np.zeros((len(ids), N), dtype=id_dtype)
+    output_scores = np.zeros((len(ids), N), dtype=score_dtype)
 
     user_items = kwargs.pop("user_items") if "user_items" in kwargs else None
     item_users = kwargs.pop("item_users") if "item_users" in kwargs else None