Skip to content

[SPARK-16558][Examples][MLlib] examples/mllib/LDAExample should use MLVector instead of MLlib Vector #14212

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
yinxusen wants to merge 2 commits into from
Closed

[SPARK-16558][Examples][MLlib] examples/mllib/LDAExample should use MLVector instead of MLlib Vector #14212

Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
596aba6
fix vector
yinxusen Jul 14, 2016
de5eeab
use API instead of implicits
yinxusen Jul 21, 2016
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
use API instead of implicits
  • Loading branch information
@yinxusen
yinxusen committed Jul 21, 2016
commit de5eeabfb10a684b2c8f938d88ebc3dec88c387c
5 changes: 2 additions & 3 deletions examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,8 +26,7 @@ import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.{CountVectorizer, CountVectorizerModel, RegexTokenizer, StopWordsRemover}
import org.apache.spark.ml.linalg.{Vector => MLVector}
import org.apache.spark.mllib.clustering.{DistributedLDAModel, EMLDAOptimizer, LDA, OnlineLDAOptimizer}
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.linalg.VectorImplicits
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{Row, SparkSession}

Expand Down Expand Up @@ -227,7 +226,7 @@ object LDAExample {
val documents = model.transform(df)
.select("features")
.rdd
.map { case Row(features: MLVector) => VectorImplicits.mlVectorToMLlibVector(features) }
.map { case Row(features: MLVector) => Vectors.fromML(features) }
.zipWithIndex()
.map(_.swap)

Expand Down