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[SPARK-18608][ML] Fix double-caching in ML algorithms #17014

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[SPARK-18608][ML] Fix double-caching in ML algorithms #17014

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45adf7e
recreate pr
zhengruifeng Mar 31, 2017
208e100
del unused equation
zhengruifeng Jul 6, 2017
936d466
revert Predictor
zhengruifeng Aug 29, 2017
df4d263
create param HasHandlePersistence
zhengruifeng Sep 4, 2017
971e52c
fix
zhengruifeng Sep 4, 2017
f8fa957
fix mima
zhengruifeng Sep 4, 2017
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fix
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@zhengruifeng
zhengruifeng committed Sep 4, 2017
commit 971e52c4a18b4261d82ac14fefa6bb849367562c
6 changes: 3 additions & 3 deletions mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
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Expand Up @@ -444,13 +444,13 @@ class LogisticRegressionWithLBFGS
lr.setFitIntercept(addIntercept)
lr.setMaxIter(optimizer.getNumIterations())
lr.setTol(optimizer.getConvergenceTol())
// Determine if we should cache the DF
lr.setHandlePersistence(input.getStorageLevel == StorageLevel.NONE)
// Convert our input into a DataFrame
val spark = SparkSession.builder().sparkContext(input.context).getOrCreate()
val df = spark.createDataFrame(input.map(_.asML))
// Determine if we should cache the DF
val handlePersistence = input.getStorageLevel == StorageLevel.NONE
// Train our model
val mlLogisticRegressionModel = lr.train(df, handlePersistence)
val mlLogisticRegressionModel = lr.train(df)
// convert the model
val weights = Vectors.dense(mlLogisticRegressionModel.coefficients.toArray)
createModel(weights, mlLogisticRegressionModel.intercept)
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