Github user ManchesterUnited16 commented on the issue:
https://github.com/apache/spark/pull/19687
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.recommendation.{ALS, ALSModel}
import org.apache.spark.ml.recommendation.ALS.Rating
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row}
// $example off$
import org.apache.spark.sql.SparkSession
object ALSExample {
// $example on$
case class Rating(userId: Int, movieId: Int, rating: Float, timestamp: Long)
def parseRating(str: String): Rating = {
val fields = str.split("::")
assert(fields.size == 4)
Rating(fields(0).toInt, fields(1).toInt, fields(2).toFloat,fields(3).toLong)
}
// $example off$
def main(args: Array[String]) {
val spark = SparkSession
.builder
.master("local")
.appName("ALSExample")
.getOrCreate()
import spark.implicits._
// $example on$
val ratings: DataFrame =
spark.read.textFile("D:\\xcar\\Spark_MLib\\ml_2.11.1\\src\\data\\mllib\\als\\sample_movielens_ratings.txt")
.map(parseRating)
.toDF()
val Array(training, test) = ratings.randomSplit(Array(0.8, 0.2))
// Build the recommendation model using ALS on the training data
val als = new ALS()
.setMaxIter(5)
.setRegParam(0.01)
.setUserCol("userId")
.setItemCol("movieId")
.setRatingCol("rating")
val model: ALSModel = als.fit(training)
// Evaluate the model by computing the RMSE on the test data
// Note we set cold start strategy to 'drop' to ensure we don't get
NaN evaluation metrics
model.setColdStartStrategy("drop")
val predictions: DataFrame = model.transform(test)
val evaluator: RegressionEvaluator = new RegressionEvaluator()
.setMetricName("rmse")
.setLabelCol("rating")
.setPredictionCol("prediction")
val rmse = evaluator.evaluate(predictions)
println(s"Root-mean-square error = $rmse")
// Generate top 10 movie recommendations for each user
val userRecs: DataFrame = model.recommendForAllUsers(10)
// Generate top 10 user recommendations for each movie
val movieRecs: DataFrame = model.recommendForAllItems(10)
// Generate top 10 movie recommendations for a specified set of users
// val users = ratings.select(als.getUserCol).distinct().limit(3)
// val userSubsetRecs = model.recommendForUserSubset(users, 10)
// // Generate top 10 user recommendations for a specified set of
movies
// val movies = ratings.select(als.getItemCol).distinct().limit(3)
// val movieSubSetRecs = model.recommendForItemSubset(movies, 10)
// $example off$
userRecs.show()
//movieRecs.show()
// userSubsetRecs.show()
// movieSubSetRecs.show()
spark.stop()
}
}
this is my code ,and when I run the line"userRecs.show()",there are some
error "java.io.NotSerializableException:
scala.reflect.api.TypeTags$PredefTypeCreator"
At 2017-11-09 05:29:55, "Shixiong Zhu" <notificati...@github.com> wrote:
@zsxwing commented on this pull request.
In
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoderSuite.scala:
> @@ -441,4 +443,28 @@ class ExpressionEncoderSuite extends PlanTest with
AnalysisTest {
}
}
}
+
+ /**
+ * Verify the size of scala.reflect.runtime.JavaUniverse.undoLog before
and after `func` to
+ * ensure we don't leak Scala reflection garbage.
+ *
+ * @see
org.apache.spark.sql.catalyst.ScalaReflection.cleanUpReflectionObjects
+ */
+ private def verifyNotLeakingReflectionObjects[T](func: => T): T = {
+ def undoLogSize: Int = {
+ import scala.reflect.runtime.{JavaUniverse, universe}
No special reason. I changed to use the fully qualified class name now.
â
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