streaming.dsl.mmlib.algs.SQLTfIdf.scala Maven / Gradle / Ivy
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* to you under the Apache License, Version 2.0 (the
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*
* http://www.apache.org/licenses/LICENSE-2.0
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package streaming.dsl.mmlib.algs
import org.apache.spark.ml.feature.{HashingTF, IDF, IDFModel, IntTF}
import org.apache.spark.ml.linalg.SQLDataTypes._
import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
import org.apache.spark.sql.expressions.UserDefinedFunction
import org.apache.spark.sql.types.{ArrayType, IntegerType, StringType}
import streaming.dsl.mmlib.SQLAlg
import org.apache.spark.sql.{SparkSession, _}
/**
* Created by allwefantasy on 17/1/2018.
*/
class SQLTfIdf extends SQLAlg with Functions {
override def train(df: DataFrame, path: String, params: Map[String, String]): DataFrame = {
val sparkSession = df.sparkSession
val rfc = new IntTF()
configureModel(rfc, params)
rfc.setOutputCol("__SQLTfIdf__")
val featurizedData = rfc.transform(df)
rfc.getBinary
val idf = new IDF()
configureModel(idf, params)
idf.setInputCol("__SQLTfIdf__")
val idfModel = idf.fit(featurizedData)
idfModel.write.overwrite().save(path)
emptyDataFrame()(df)
}
override def load(sparkSession: SparkSession, path: String, params: Map[String, String]): Any = {
val model = IDFModel.load(path)
model
}
override def predict(sparkSession: SparkSession, _model: Any, name: String, params: Map[String, String]): UserDefinedFunction = {
val res = internal_predict(sparkSession, _model, name)
UserDefinedFunction(res(name), VectorType, Some(Seq(ArrayType(IntegerType))))
}
def internal_predict(sparkSession: SparkSession, _model: Any, name: String) = {
val model = sparkSession.sparkContext.broadcast(_model.asInstanceOf[IDFModel])
val intTF = new org.apache.spark.mllib.feature.IntTF(model.value.idf.size).setBinary(false)
val idf = (words: Seq[Int]) => {
val idfModelField = model.value.getClass.getField("org$apache$spark$ml$feature$IDFModel$$idfModel")
idfModelField.setAccessible(true)
val idfModel = idfModelField.get(model.value).asInstanceOf[org.apache.spark.mllib.feature.IDFModel]
val vec = intTF.transform(words)
idfModel.transform(vec).asML
}
Map(name -> idf)
}
}