{"action":"created","comment":{"url":"https://api.github.com/repos/pyro-ppl/pyro/pulls/comments/259260307","pull_request_review_id":206715619,"id":259260307,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDI1OTI2MDMwNw==","diff_hunk":"@@ -0,0 +1,12 @@\n+from .replay_messenger import ReplayMessenger\n+\n+\n+class SamplePosteriorMessenger(ReplayMessenger):","path":"pyro/poutine/posterior_messenger.py","position":4,"original_position":4,"commit_id":"0ab4f10cace19895cf8a56562017f0696ab38ba0","original_commit_id":"0ab4f10cace19895cf8a56562017f0696ab38ba0","user":{"login":"neerajprad","id":1762463,"node_id":"MDQ6VXNlcjE3NjI0NjM=","avatar_url":"https://avatars3.githubusercontent.com/u/1762463?v=4","gravatar_id":"","url":"https://api.github.com/users/neerajprad","html_url":"https://github.com/neerajprad","followers_url":"https://api.github.com/users/neerajprad/followers","following_url":"https://api.github.com/users/neerajprad/following{/other_user}","gists_url":"https://api.github.com/users/neerajprad/gists{/gist_id}","starred_url":"https://api.github.com/users/neerajprad/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/neerajprad/subscriptions","organizations_url":"https://api.github.com/users/neerajprad/orgs","repos_url":"https://api.github.com/users/neerajprad/repos","events_url":"https://api.github.com/users/neerajprad/events{/privacy}","received_events_url":"https://api.github.com/users/neerajprad/received_events","type":"User","site_admin":false},"body":"No worries! Also, there is no need to address everything in this PR. `AbstractInfer` is due a bigger refactor, so this will help clarify what should go into that refactor (in addition to #1725 for instance). I think that making those classes lazy might be the solution to this issue. ","created_at":"2019-02-22T09:06:04Z","updated_at":"2019-02-22T09:06:05Z","html_url":"https://github.com/pyro-ppl/pyro/pull/1770#discussion_r259260307","pull_request_url":"https://api.github.com/repos/pyro-ppl/pyro/pulls/1770","author_association":"COLLABORATOR","_links":{"self":{"href":"https://api.github.com/repos/pyro-ppl/pyro/pulls/comments/259260307"},"html":{"href":"https://github.com/pyro-ppl/pyro/pull/1770#discussion_r259260307"},"pull_request":{"href":"https://api.github.com/repos/pyro-ppl/pyro/pulls/1770"}},"in_reply_to_id":259206357},"pull_request":{"url":"https://api.github.com/repos/pyro-ppl/pyro/pulls/1770","id":254219882,"node_id":"MDExOlB1bGxSZXF1ZXN0MjU0MjE5ODgy","html_url":"https://github.com/pyro-ppl/pyro/pull/1770","diff_url":"https://github.com/pyro-ppl/pyro/pull/1770.diff","patch_url":"https://github.com/pyro-ppl/pyro/pull/1770.patch","issue_url":"https://api.github.com/repos/pyro-ppl/pyro/issues/1770","number":1770,"state":"open","locked":false,"title":"Change how `TracePredictive` works, so it resets plates on resampling","user":{"login":"ahmadsalim","id":1647846,"node_id":"MDQ6VXNlcjE2NDc4NDY=","avatar_url":"https://avatars0.githubusercontent.com/u/1647846?v=4","gravatar_id":"","url":"https://api.github.com/users/ahmadsalim","html_url":"https://github.com/ahmadsalim","followers_url":"https://api.github.com/users/ahmadsalim/followers","following_url":"https://api.github.com/users/ahmadsalim/following{/other_user}","gists_url":"https://api.github.com/users/ahmadsalim/gists{/gist_id}","starred_url":"https://api.github.com/users/ahmadsalim/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ahmadsalim/subscriptions","organizations_url":"https://api.github.com/users/ahmadsalim/orgs","repos_url":"https://api.github.com/users/ahmadsalim/repos","events_url":"https://api.github.com/users/ahmadsalim/events{/privacy}","received_events_url":"https://api.github.com/users/ahmadsalim/received_events","type":"User","site_admin":false},"body":"Hi Pyro Devs,\r\n\r\nWhile I was trying to use `TracePredictive` on test data of different size than training data, I noticed two issues:\r\n\r\n- The `marginal` uses the posterior one instead of the one that use the trace computed by the predictive posterior when calling `run`.\r\n-  The plates have fixed size from training data, and were not reset before. This meant that one could only run with test data of the same size as the training data. 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