streaming.dsl.mmlib.algs.SQLStringIndex.scala Maven / Gradle / Ivy

Show more of this group Show more artifacts with this name
Show all versions of streamingpro-mlsql-spark_2.4 Show documentation
The newest version!
/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more 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
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package streaming.dsl.mmlib.algs

import org.apache.spark.ml.feature.{StringIndexer, StringIndexerModel}
import org.apache.spark.ml.help.HSQLStringIndex
import org.apache.spark.ml.param.Param
import org.apache.spark.sql.expressions.UserDefinedFunction
import org.apache.spark.sql.types.{ArrayType, StringType}
import org.apache.spark.sql.{DataFrame, SparkSession, functions => F}
import streaming.dsl.mmlib.SQLAlg
import streaming.dsl.mmlib.algs.bigdl.BigDLFunctions
import streaming.dsl.mmlib.algs.classfication.BaseClassification
import streaming.dsl.mmlib.algs.param.BaseParams

/**
  * Created by allwefantasy on 15/1/2018.
  */
class SQLStringIndex(override val uid: String) extends SQLAlg with MllibFunctions with BigDLFunctions with BaseClassification {

  def this() = this(BaseParams.randomUID())

  override def train(df: DataFrame, path: String, params: Map[String, String]): DataFrame = {

    params.get(inputCol.name).
      map(m => set(inputCol, m)).
      getOrElse {
        require(params.contains("inputCol"), "inputCol is required")
      }

    params.get(outputCol.name).
      map(m => set(outputCol, m)).
      getOrElse {
        set(outputCol, $(inputCol))
      }


    var newDf = df
    df.schema.filter(f => f.name == $(inputCol)).head.dataType match {
      case ArrayType(StringType, _) =>
        newDf = df.select(F.explode(F.col($(inputCol))).as($(inputCol)))
      case StringType => // do nothing
      case _ => throw new IllegalArgumentException(s"${$(inputCol)} should be arraytype or stringtype")
    }
    val rfc = new StringIndexer()
    configureModel(rfc, params)
    val model = rfc.fit(newDf)
    model.write.overwrite().save(path)
    emptyDataFrame()(df)
  }

  override def load(sparkSession: SparkSession, path: String, params: Map[String, String]): Any = {
    val model = StringIndexerModel.load(path)
    model
  }


  override def batchPredict(df: DataFrame, path: String, params: Map[String, String]): DataFrame = {
    val model = load(df.sparkSession, path, params).asInstanceOf[StringIndexerModel]
    model.transform(df)
  }

  override def predict(sparkSession: SparkSession, _model: Any, name: String, params: Map[String, String]): UserDefinedFunction = {
    HSQLStringIndex.predict(sparkSession, _model, name)
  }

  def internal_predict(sparkSession: SparkSession, _model: Any, name: String) = {
    HSQLStringIndex.internal_predict(sparkSession, _model, name)
  }


  override def explainParams(sparkSession: SparkSession): DataFrame = {
    _explainParams(sparkSession)
  }

  final val inputCol: Param[String] = new Param[String](this, "inputCol",
    s"""inputCol""")

  final val outputCol: Param[String] = new Param[String](this, "outputCol",
    s"""outputCol""")


}