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| author | Yuhao Yang <hhbyyh@gmail.com> | 2016-06-23 11:00:00 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2016-06-23 11:00:00 -0700 |
| commit | 60398dabc50d402bbab4190fbe94ebed6d3a48dc (patch) | |
| tree | a189f8ab78eb58304a6151981d896dd563f4dca9 /mllib | |
| parent | d85bb10ce49926b8b661bd2cb97392205742fc14 (diff) | |
| download | spark-60398dabc50d402bbab4190fbe94ebed6d3a48dc.tar.gz spark-60398dabc50d402bbab4190fbe94ebed6d3a48dc.tar.bz2 spark-60398dabc50d402bbab4190fbe94ebed6d3a48dc.zip | |
[SPARK-16130][ML] model loading backward compatibility for ml.classfication.LogisticRegression
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-16130
model loading backward compatibility for ml.classfication.LogisticRegression
## How was this patch tested?
existing ut and manual test for loading old models.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #13841 from hhbyyh/lrcomp.
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index be69d46eeb..9c9f5ced4e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -674,12 +674,12 @@ object LogisticRegressionModel extends MLReadable[LogisticRegressionModel] { val dataPath = new Path(path, "data").toString val data = sparkSession.read.format("parquet").load(dataPath) - .select("numClasses", "numFeatures", "intercept", "coefficients").head() + // We will need numClasses, numFeatures in the future for multinomial logreg support. - // val numClasses = data.getInt(0) - // val numFeatures = data.getInt(1) - val intercept = data.getDouble(2) - val coefficients = data.getAs[Vector](3) + val Row(numClasses: Int, numFeatures: Int, intercept: Double, coefficients: Vector) = + MLUtils.convertVectorColumnsToML(data, "coefficients") + .select("numClasses", "numFeatures", "intercept", "coefficients") + .head() val model = new LogisticRegressionModel(metadata.uid, coefficients, intercept) DefaultParamsReader.getAndSetParams(model, metadata) |