Vyacheslav Baranov created SPARK-10182:
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Summary: GeneralizedLinearModel doesn't unpersist cached data
Key: SPARK-10182
URL: https://issues.apache.org/jira/browse/SPARK-10182
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.4.1
Reporter: Vyacheslav Baranov
The problem might be reproduced in spark-shell with following code snippet:
{code}
import org.apache.spark.SparkContext
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
val samples = Seq[LabeledPoint](
LabeledPoint(1.0, Vectors.dense(1.0, 0.0)),
LabeledPoint(1.0, Vectors.dense(0.0, 1.0)),
LabeledPoint(0.0, Vectors.dense(1.0, 1.0)),
LabeledPoint(0.0, Vectors.dense(0.0, 0.0))
)
val rdd = sc.parallelize(samples)
for (i <- 0 until 10) {
val model = {
new LogisticRegressionWithLBFGS()
.setNumClasses(2)
.run(rdd)
.clearThreshold()
}
}
{code}
After code execution there are 10 {{MapPartitionsRDD}} objects.
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