[ASK] Is binary relevance the only option in RankingMetric class for pyspark evaluation?

[lgabs]

While testing metrics for pyspark evaluation, I've noticed that the ranking metrics like NDCG seems to be using binary relevances only, while python evaluation has a parameter to chose between binary, exponential or raw relevances. The snippet below shows that behavior (it will only consider which items are relevant, but not accessing their relevances):

recommenders/recommenders/evaluation/spark_evaluation.py

Lines 292 to 295 in c2ea583

	self._items_for_user_true = (
	self.rating_true.groupBy(self.col_user)
	.agg(expr("collect_list(" + self.col_item + ") as ground_truth"))
	.select(self.col_user, "ground_truth")

Is it possible to use exponencial or raw relevances in spark evaluation currently or am I wrong in this analysis?