ai.h2o.sparkling.ml.params.HasBlendingDataFrame.scala Maven / Gradle / Ivy
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* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
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package ai.h2o.sparkling.ml.params
import ai.h2o.sparkling.H2OFrame
import org.apache.spark.expose.Logging
import org.apache.spark.sql.DataFrame
trait HasBlendingDataFrame extends H2OAlgoParamsBase with Logging with HasDataFrameSerializer {
val uid: String
private val blendingDataFrame = new NonSerializableNullableDataFrameParam(
this,
"blendingDataFrame",
"This parameter is used for computing the predictions that serve as the training frame for the meta-learner." +
" If provided, this triggers blending mode on the stacked ensemble training stage. Blending mode is faster" +
" than cross-validating the base learners (though these ensembles may not perform as well as the Super Learner" +
" ensemble).")
setDefault(blendingDataFrame -> null)
def getBlendingDataFrame(): DataFrame = $(blendingDataFrame)
def setBlendingDataFrame(value: DataFrame): this.type = set(blendingDataFrame, value)
private[sparkling] def getBlendingDataFrameParam(trainingFrame: H2OFrame): Map[String, Any] = {
Map("blending_frame" -> convertDataFrameToH2OFrameKey(getBlendingDataFrame()))
}
override private[sparkling] def getSWtoH2OParamNameMap(): Map[String, String] = {
super.getSWtoH2OParamNameMap() ++ Map("blendingDataFrame" -> "blending_frame")
}
}
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