streaming.dsl.mmlib.algs.SQLLSVM.scala Maven / Gradle / Ivy
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* to you under the Apache License, Version 2.0 (the
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* 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 streaming.dsl.mmlib.algs
import org.apache.spark.ml.classification.{GBTClassificationModel, GBTClassifier, LinearSVC, LinearSVCModel}
import org.apache.spark.ml.linalg.SQLDataTypes._
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.sql.expressions.UserDefinedFunction
import org.apache.spark.sql.types.DoubleType
import streaming.dsl.mmlib.SQLAlg
import org.apache.spark.sql.{SparkSession, _}
/**
* Created by allwefantasy on 15/1/2018.
*/
class SQLLSVM extends SQLAlg with Functions {
override def train(df: DataFrame, path: String, params: Map[String, String]): DataFrame = {
val bayes = new LinearSVC()
configureModel(bayes, params)
val model = bayes.fit(df)
model.write.overwrite().save(path)
emptyDataFrame()(df)
}
override def load(sparkSession: SparkSession, path: String, params: Map[String, String]): Any = {
val model = LinearSVCModel.load(path)
model
}
override def predict(sparkSession: SparkSession, _model: Any, name: String, params: Map[String, String]): UserDefinedFunction = {
val model = sparkSession.sparkContext.broadcast(_model.asInstanceOf[LinearSVCModel])
val f = (vec: Vector) => {
val predictRaw = model.value.getClass.getMethod("predictRaw", classOf[Vector]).invoke(model.value, vec).asInstanceOf[Vector]
val raw2probability = model.value.getClass.getMethod("raw2prediction", classOf[Vector]).invoke(model.value, predictRaw).asInstanceOf[Double]
//model.getClass.getMethod("probability2prediction", classOf[Vector]).invoke(model, raw2probability).asInstanceOf[Vector]
raw2probability
}
UserDefinedFunction(f, DoubleType, Some(Seq(VectorType)))
}
}