{"action":"created","comment":{"url":"https://api.github.com/repos/databricks/spark-sql-perf/pulls/comments/67933347","id":67933347,"diff_hunk":"@@ -0,0 +1,60 @@\n+package com.databricks.spark.sql.perf.mllib.clustering\n+\n+import com.databricks.spark.sql.perf.mllib.{ClassificationContext, TestFromTraining, BenchmarkAlgorithm}\n+import com.databricks.spark.sql.perf.mllib.OptionImplicits._\n+import org.apache.commons.math3.random.Well19937c\n+import org.apache.spark.ml.Transformer\n+import org.apache.spark.ml.clustering.{LDA => ML_LDA}","path":"src/main/scala/com/databricks/spark/sql/perf/mllib/clustering/LDA.scala","position":7,"original_position":7,"commit_id":"5ce08a6f220bd478f8c946a9b1b680affe495389","original_commit_id":"5ce08a6f220bd478f8c946a9b1b680affe495389","user":{"login":"jkbradley","id":5084283,"avatar_url":"https://avatars.githubusercontent.com/u/5084283?v=3","gravatar_id":"","url":"https://api.github.com/users/jkbradley","html_url":"https://github.com/jkbradley","followers_url":"https://api.github.com/users/jkbradley/followers","following_url":"https://api.github.com/users/jkbradley/following{/other_user}","gists_url":"https://api.github.com/users/jkbradley/gists{/gist_id}","starred_url":"https://api.github.com/users/jkbradley/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jkbradley/subscriptions","organizations_url":"https://api.github.com/users/jkbradley/orgs","repos_url":"https://api.github.com/users/jkbradley/repos","events_url":"https://api.github.com/users/jkbradley/events{/privacy}","received_events_url":"https://api.github.com/users/jkbradley/received_events","type":"User","site_admin":false},"body":"Why rename?","created_at":"2016-06-21T19:13:58Z","updated_at":"2016-06-21T19:13:58Z","html_url":"https://github.com/databricks/spark-sql-perf/pull/69#discussion_r67933347","pull_request_url":"https://api.github.com/repos/databricks/spark-sql-perf/pulls/69","_links":{"self":{"href":"https://api.github.com/repos/databricks/spark-sql-perf/pulls/comments/67933347"},"html":{"href":"https://github.com/databricks/spark-sql-perf/pull/69#discussion_r67933347"},"pull_request":{"href":"https://api.github.com/repos/databricks/spark-sql-perf/pulls/69"}}},"pull_request":{"url":"https://api.github.com/repos/databricks/spark-sql-perf/pulls/69","id":74531183,"html_url":"https://github.com/databricks/spark-sql-perf/pull/69","diff_url":"https://github.com/databricks/spark-sql-perf/pull/69.diff","patch_url":"https://github.com/databricks/spark-sql-perf/pull/69.patch","issue_url":"https://api.github.com/repos/databricks/spark-sql-perf/issues/69","number":69,"state":"open","locked":false,"title":"Initial commit for adding MLlib reporting in spark-sql-perf","user":{"login":"thunterdb","id":7594753,"avatar_url":"https://avatars.githubusercontent.com/u/7594753?v=3","gravatar_id":"","url":"https://api.github.com/users/thunterdb","html_url":"https://github.com/thunterdb","followers_url":"https://api.github.com/users/thunterdb/followers","following_url":"https://api.github.com/users/thunterdb/following{/other_user}","gists_url":"https://api.github.com/users/thunterdb/gists{/gist_id}","starred_url":"https://api.github.com/users/thunterdb/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/thunterdb/subscriptions","organizations_url":"https://api.github.com/users/thunterdb/orgs","repos_url":"https://api.github.com/users/thunterdb/repos","events_url":"https://api.github.com/users/thunterdb/events{/privacy}","received_events_url":"https://api.github.com/users/thunterdb/received_events","type":"User","site_admin":false},"body":"This PR adds basic MLlib infrastructure to run some benchmarks against ML pipelines.\r\n\r\nThere are 2 ways to describe and run ML pipelines:\r\n - programatically, in scala (see MLBenchmarks.scala)\r\n - using a simple YAML file (see mllib-small.yaml for an example)\r\nThe YAML approach is preferred because it generates programmatically the cartesian product of all the experiments to run and validates the types of the objects in the yaml file.\r\n\r\nIn both cases, all the ML experiments are standard benchmarks.\r\n\r\nThis PR also moves some code in `Benchmark.scala` : the current code generates path-dependent structural signatures and confuses intellij.\r\n\r\nIt does not include tests, but some small benchmarks can be run locally against a spark 2 installation:\r\n\r\n```\r\n$SPARK_HOME/bin/spark-shell --jars $PWD/target/scala-2.10/spark-sql-perf-assembly-0.4.9-SNAPSHOT.jar\r\n```\r\nand 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