org.apache.spark.ml.algs.LinearRegressionEstimator.scala Maven / Gradle / Ivy
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* 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 org.apache.spark.ml.algs
import org.apache.spark.ml.evaluation.{Evaluator, RegressionEvaluator}
import org.apache.spark.ml.regression.{LinearRegressionModel, LinearRegression}
import org.apache.spark.ml.tuning.TrainValidationSplit
import org.apache.spark.ml.{BaseAlgorithmEstimator, Estimator, Model}
import org.apache.spark.sql.DataFrame
/**
* 7/27/16 WilliamZhu([email protected])
*/
class LinearRegressionEstimator(training: DataFrame, params: Array[Map[String, Any]]) extends BaseAlgorithmEstimator {
val lr = new LinearRegression()
override def name: String = "lr"
override def fit: Model[_] = {
val paramGrid = mlParams(params.tail)
val vectorSize = if (params.head.contains("dicTable")) {
training.sqlContext.table(params.head.getOrElse("dicTable", "").toString).count()
} else 0l
if (params.length <= 1) {
lr.fit(source(training,vectorSize.toInt), paramGrid(0))
} else {
val trainValidationSplit = new TrainValidationSplit()
.setEstimator(lr)
.setEvaluator(evaluator)
.setEstimatorParamMaps(paramGrid)
.setTrainRatio(0.8)
trainValidationSplit.fit(source(training,vectorSize.toInt))
}
}
override def algorithm: Estimator[_] = lr
override def evaluator: Evaluator = new RegressionEvaluator()
}