{"action":"created","issue":{"url":"https://api.github.com/repos/apache/spark/issues/11601","repository_url":"https://api.github.com/repos/apache/spark","labels_url":"https://api.github.com/repos/apache/spark/issues/11601/labels{/name}","comments_url":"https://api.github.com/repos/apache/spark/issues/11601/comments","events_url":"https://api.github.com/repos/apache/spark/issues/11601/events","html_url":"https://github.com/apache/spark/pull/11601","id":139504374,"number":11601,"title":"[SPARK-13568] [ML] Create feature transformer to impute missing values","user":{"login":"hhbyyh","id":7981698,"avatar_url":"https://avatars.githubusercontent.com/u/7981698?v=3","gravatar_id":"","url":"https://api.github.com/users/hhbyyh","html_url":"https://github.com/hhbyyh","followers_url":"https://api.github.com/users/hhbyyh/followers","following_url":"https://api.github.com/users/hhbyyh/following{/other_user}","gists_url":"https://api.github.com/users/hhbyyh/gists{/gist_id}","starred_url":"https://api.github.com/users/hhbyyh/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hhbyyh/subscriptions","organizations_url":"https://api.github.com/users/hhbyyh/orgs","repos_url":"https://api.github.com/users/hhbyyh/repos","events_url":"https://api.github.com/users/hhbyyh/events{/privacy}","received_events_url":"https://api.github.com/users/hhbyyh/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":8,"created_at":"2016-03-09T08:01:04Z","updated_at":"2016-03-23T09:58:46Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/apache/spark/pulls/11601","html_url":"https://github.com/apache/spark/pull/11601","diff_url":"https://github.com/apache/spark/pull/11601.diff","patch_url":"https://github.com/apache/spark/pull/11601.patch"},"body":"## What changes were proposed in this pull request?\r\n\r\nIt is quite common to encounter missing values in data sets. It would be useful to implement a Transformer that can impute missing data points, similar to e.g. Imputer in scikit-learn.\r\nInitially, options for imputation could include mean, median and most frequent, but we could add various other approaches. Where possible existing DataFrame code can be used (e.g. for approximate quantiles etc).\r\n\r\nCurrently this PR supports imputation for Double and Vector (null and NaN in Vector).\r\n\r\n\r\n## How was this patch tested?\r\n\r\nnew unit tests and manual test\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/apache/spark/issues/comments/200275398","html_url":"https://github.com/apache/spark/pull/11601#issuecomment-200275398","issue_url":"https://api.github.com/repos/apache/spark/issues/11601","id":200275398,"user":{"login":"SparkQA","id":8108735,"avatar_url":"https://avatars.githubusercontent.com/u/8108735?v=3","gravatar_id":"","url":"https://api.github.com/users/SparkQA","html_url":"https://github.com/SparkQA","followers_url":"https://api.github.com/users/SparkQA/followers","following_url":"https://api.github.com/users/SparkQA/following{/other_user}","gists_url":"https://api.github.com/users/SparkQA/gists{/gist_id}","starred_url":"https://api.github.com/users/SparkQA/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/SparkQA/subscriptions","organizations_url":"https://api.github.com/users/SparkQA/orgs","repos_url":"https://api.github.com/users/SparkQA/repos","events_url":"https://api.github.com/users/SparkQA/events{/privacy}","received_events_url":"https://api.github.com/users/SparkQA/received_events","type":"User","site_admin":false},"created_at":"2016-03-23T09:58:45Z","updated_at":"2016-03-23T09:58:45Z","body":"**[Test build #53923 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/53923/consoleFull)** for PR 11601 at commit [`1b36deb`](https://github.com/apache/spark/commit/1b36deb3eb0391ec7080bafebd2dfb662d09a6e4).\n * This patch **fails Scala style tests**.\n * This patch merges cleanly.\n * This patch adds no public classes."}}