com.tencent.angel.sona.ml.feature.Binarizer.scala Maven / Gradle / Ivy

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/*
 * 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 com.tencent.angel.sona.ml.feature

import org.apache.spark.linalg._
import com.tencent.angel.sona.ml.param.{DoubleParam, ParamMap}
import com.tencent.angel.sona.ml.Transformer
import com.tencent.angel.sona.ml.attribute.BinaryAttribute
import com.tencent.angel.sona.ml.param.shared.{HasInputCol, HasOutputCol}
import com.tencent.angel.sona.ml.util._
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import com.tencent.angel.sona.ml.util.DefaultParamsReadable

/**
  * Binarize a column of continuous features given a threshold.
  */
final class Binarizer(override val uid: String)
  extends Transformer with HasInputCol with HasOutputCol with DefaultParamsWritable {


  def this() = this(Identifiable.randomUID("binarizer"))

  /**
    * Param for threshold used to binarize continuous features.
    * The features greater than the threshold, will be binarized to 1.0.
    * The features equal to or less than the threshold, will be binarized to 0.0.
    * Default: 0.0
    *
    * @group param
    */

  val threshold: DoubleParam =
    new DoubleParam(this, "threshold", "threshold used to binarize continuous features")

  /** @group getParam */

  def getThreshold: Double = $(threshold)

  /** @group setParam */

  def setThreshold(value: Double): this.type = set(threshold, value)

  setDefault(threshold -> 0.0)

  /** @group setParam */

  def setInputCol(value: String): this.type = set(inputCol, value)

  /** @group setParam */

  def setOutputCol(value: String): this.type = set(outputCol, value)


  override def transform(dataset: Dataset[_]): DataFrame = {
    val outputSchema = transformSchema(dataset.schema, logging = true)
    val schema = dataset.schema
    val inputType = schema($(inputCol)).dataType
    val td = $(threshold)

    def trans(values: Array[Double]): (Array[Int], Array[Double]) = {
      values.zipWithIndex.collect {
        case (value, idx) if value > td => idx -> 1.0
      }.unzip
    }

    val binarizerDouble = udf { in: Double => if (in > td) 1.0 else 0.0 }
    val binarizerVector = udf { data: Vector =>
      data match {
        case DenseVector(values) =>
          val (newIndices, newValues) = trans(values)
          Vectors.sparse(data.size.toInt, newIndices, newValues).compressed
        case IntSparseVector(size, indices, values) =>
          val (newIndices, newValues) = trans(values)
          Vectors.sparse(size, newIndices.map(i => indices(i)), newValues).compressed
        case LongSparseVector(size, indices, values) =>
          val (newIndices, newValues) = trans(values)
          Vectors.sparse(size, newIndices.map(i => indices(i)), newValues).compressed
      }
    }

    val metadata = outputSchema($(outputCol)).metadata

    inputType match {
      case DoubleType =>
        dataset.select(col("*"), binarizerDouble(col($(inputCol))).as($(outputCol), metadata))
      case _: VectorUDT =>
        dataset.select(col("*"), binarizerVector(col($(inputCol))).as($(outputCol), metadata))
    }
  }


  override def transformSchema(schema: StructType): StructType = {
    val inputType = schema($(inputCol)).dataType
    val outputColName = $(outputCol)

    val outCol: StructField = inputType match {
      case DoubleType =>
        BinaryAttribute.defaultAttr.withName(outputColName).toStructField()
      case _: VectorUDT =>
        StructField(outputColName, new VectorUDT)
      case _ =>
        throw new IllegalArgumentException(s"Data type $inputType is not supported.")
    }

    if (schema.fieldNames.contains(outputColName)) {
      throw new IllegalArgumentException(s"Output column $outputColName already exists.")
    }
    StructType(schema.fields :+ outCol)
  }


  override def copy(extra: ParamMap): Binarizer = defaultCopy(extra)
}


object Binarizer extends DefaultParamsReadable[Binarizer] {


  override def load(path: String): Binarizer = super.load(path)
}