Liangcai Li created SPARK-29327:
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Summary: Support specifying features via multiple columns in
Predictor and PredictionModel
Key: SPARK-29327
URL: https://issues.apache.org/jira/browse/SPARK-29327
Project: Spark
Issue Type: Improvement
Components: ML, MLlib
Affects Versions: 3.0.0
Reporter: Liangcai Li
There are always more features than one in a classification/regression task,
however the current API to specify features columns in Predictor of Spark MLLib
only supports one single column, which requires users to assemble the multiple
features columns into a "org.apache.spark.ml.linalg.Vector" before fitting to
Spark ML pipeline.
This improvement is going to let users specify the features columns directly
without vectorization. To support this, we can introduce two new APIs in both
"Predictor" and "PredictionModel", and a new parameter named "featuresCols"
storing the features columns names as an Array. ( PR is ready here
[https://github.com/apache/spark/pull/25983])
*APIs:*
{{def setFeaturesCol(value: Array[String]): M = ...}}
{{protected def isSupportMultiColumnsForFeatures: Boolean = false}}
*Parameter:*
{{final val featuresCols: StringArrayParam = new StringArrayParam(this,
"featuresCols", ...)}}
Then ML implementations can get and use the features columns names from this
new parameter "featuresCols", along with the raw data of features in separate
columns directly in dataset.
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