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| author | Xiangrui Meng <meng@databricks.com> | 2014-08-18 18:20:54 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2014-08-18 18:21:04 -0700 |
| commit | 7d069bf0c57b75b53b449fcc51cf7fd616f8686d (patch) | |
| tree | 1c5bc64482486f82be5697543deff473b76fdfe3 /examples | |
| parent | e3f89e971b117e11d15e4b9b47e63da55f4e488b (diff) | |
| download | spark-7d069bf0c57b75b53b449fcc51cf7fd616f8686d.tar.gz spark-7d069bf0c57b75b53b449fcc51cf7fd616f8686d.tar.bz2 spark-7d069bf0c57b75b53b449fcc51cf7fd616f8686d.zip | |
[SPARK-3108][MLLIB] add predictOnValues to StreamingLR and fix predictOn
It is useful in streaming to allow users to carry extra data with the prediction, for monitoring the prediction error for example. freeman-lab
Author: Xiangrui Meng <meng@databricks.com>
Closes #2023 from mengxr/predict-on-values and squashes the following commits:
cac47b8 [Xiangrui Meng] add classtag
2821b3b [Xiangrui Meng] use mapValues
0925efa [Xiangrui Meng] add predictOnValues to StreamingLR and fix predictOn
(cherry picked from commit 217b5e915e2f21f047dfc4be680cd20d58baf9f8)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala index 0e992fa996..c5bd5b0b17 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala @@ -59,10 +59,10 @@ object StreamingLinearRegression { val testData = ssc.textFileStream(args(1)).map(LabeledPoint.parse) val model = new StreamingLinearRegressionWithSGD() - .setInitialWeights(Vectors.dense(Array.fill[Double](args(3).toInt)(0))) + .setInitialWeights(Vectors.zeros(args(3).toInt)) model.trainOn(trainingData) - model.predictOn(testData).print() + model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print() ssc.start() ssc.awaitTermination() |