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| author | Xiangrui Meng <meng@databricks.com> | 2016-06-17 21:22:29 -0700 |
|---|---|---|
| committer | Yanbo Liang <ybliang8@gmail.com> | 2016-06-17 21:22:29 -0700 |
| commit | edb23f9e47eecfe60992dde0e037ec1985c77e1d (patch) | |
| tree | 11158f998ed7ba18050f332835cbaaca65a286c7 /mllib | |
| parent | af2a4b0826b2358c0fe75c3e4d7fd8f7bccdd8e5 (diff) | |
| download | spark-edb23f9e47eecfe60992dde0e037ec1985c77e1d.tar.gz spark-edb23f9e47eecfe60992dde0e037ec1985c77e1d.tar.bz2 spark-edb23f9e47eecfe60992dde0e037ec1985c77e1d.zip | |
[SPARK-15946][MLLIB] Conversion between old/new vector columns in a DataFrame (Python)
## What changes were proposed in this pull request?
This PR implements python wrappers for #13662 to convert old/new vector columns in a DataFrame.
## How was this patch tested?
doctest in Python
cc: yanboliang
Author: Xiangrui Meng <meng@databricks.com>
Closes #13731 from mengxr/SPARK-15946.
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala | 14 |
1 files changed, 14 insertions, 0 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 7df61601fb..f2c70baf47 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -1201,6 +1201,20 @@ private[python] class PythonMLLibAPI extends Serializable { val spark = SparkSession.builder().sparkContext(sc).getOrCreate() spark.createDataFrame(blockMatrix.blocks) } + + /** + * Python-friendly version of [[MLUtils.convertVectorColumnsToML()]]. + */ + def convertVectorColumnsToML(dataset: DataFrame, cols: JArrayList[String]): DataFrame = { + MLUtils.convertVectorColumnsToML(dataset, cols.asScala: _*) + } + + /** + * Python-friendly version of [[MLUtils.convertVectorColumnsFromML()]] + */ + def convertVectorColumnsFromML(dataset: DataFrame, cols: JArrayList[String]): DataFrame = { + MLUtils.convertVectorColumnsFromML(dataset, cols.asScala: _*) + } } /** |