Github user yanboliang commented on the issue:
https://github.com/apache/spark/pull/15770
@thunterdb I have two concerns about making PIC as an Estimator:
* As your suggestion, model's transform will introduce extra data shuffle
compared with the original implementation.
* It can not fit into the pipeline well, since both the estimator ```fit```
and model ```transform``` method should work on the dataset with same schema,
like:
```
val model = new ALS().fit(dataset)
model.transform(dataset)
```
But in your suggestion, the input of PIC estimator is the edges or
RDD[(Long, Long, Double)], the input of PIC model is the vertices ID.
I totally agree making PIC as a Transformer is tricky, but I did not find a
better way. Thanks.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
