Function _batch_call

implicit/utils.py:106–136

(func, ids, *args, N=10, id_dtype=np.int32, score_dtype=np.float32, **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=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

    # pylint: disable=unsubscriptable-object
    for i, idx in enumerate(ids):
        current_kwargs = kwargs
        if user_items is not None:
            current_kwargs = {"user_items": user_items[i], **kwargs}
        elif item_users is not None:
            current_kwargs = {"item_users": item_users[i], **kwargs}

        batch_ids, batch_scores = func(idx, *args, N=N, **current_kwargs)

        # pad out to N items if we're returned fewer
        missing_items = N - len(batch_ids)
        if missing_items > 0:
            batch_ids = np.append(batch_ids, np.full(missing_items, -1))
            batch_scores = np.append(
                batch_scores, np.full(missing_items, -np.finfo(np.float32).max)
            )

        output_ids[i] = batch_ids[:N]
        output_scores[i] = batch_scores[:N]

    return output_ids, output_scores


def _filter_items_from_results(queryid, ids, scores, filter_items, N):