com.microsoft.ml.spark.vw.VowpalWabbitClassifier.scala Maven / Gradle / Ivy
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// Copyright (C) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License. See LICENSE in project root for information.
package com.microsoft.ml.spark.vw
import com.microsoft.ml.spark.core.serialize.ConstructorReadable
import org.apache.spark.ml.ComplexParamsReadable
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.ml.classification.{ProbabilisticClassificationModel, ProbabilisticClassifier}
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.sql._
import org.apache.spark.sql.functions.{col, udf}
import scala.math.exp
object VowpalWabbitClassifier extends DefaultParamsReadable[VowpalWabbitClassifier]
class VowpalWabbitClassifier(override val uid: String)
extends ProbabilisticClassifier[Row, VowpalWabbitClassifier, VowpalWabbitClassificationModel]
with VowpalWabbitBase
{
def this() = this(Identifiable.randomUID("VowpalWabbitClassifier"))
override protected def train(dataset: Dataset[_]): VowpalWabbitClassificationModel = {
val binaryModel = trainInternal(dataset)
new VowpalWabbitClassificationModel(uid)
.setModel(binaryModel)
.setFeaturesCol(getFeaturesCol)
.setAdditionalFeatures(getAdditionalFeatures)
.setPredictionCol(getPredictionCol)
.setProbabilityCol(getProbabilityCol)
.setRawPredictionCol(getRawPredictionCol)
}
override def copy(extra: ParamMap): VowpalWabbitClassifier = defaultCopy(extra)
}
// Preparation for multi-class learning, though it no fun as numClasses is spread around multiple reductions
class VowpalWabbitClassificationModel(override val uid: String)
extends ProbabilisticClassificationModel[Row, VowpalWabbitClassificationModel]
with VowpalWabbitBaseModel {
def numClasses: Int = 2
override def transform(dataset: Dataset[_]): DataFrame = {
val df = transformImplInternal(dataset)
// which mode one wants to use depends a bit on how this should be deployed
// 1. if you stay in spark w/o link=logistic is probably more convenient as it also returns the raw prediction
// 2. if you want to export the model *and* get probabilities at scoring term w/ link=logistic is preferable
// convert raw prediction to probability (if needed)
val probabilityUdf = if (vwArgs.getArgs.contains("--link logistic"))
udf { (pred: Double) => Vectors.dense(Array(1 - pred, pred)) }
else
udf { (pred: Double) => {
val prob = 1.0 / (1.0 + exp(-pred))
Vectors.dense(Array(1 - prob, prob))
} }
val df2 = df.withColumn($(probabilityCol), probabilityUdf(col($(rawPredictionCol))))
// convert probability to prediction
val probability2predictionUdf = udf(probability2prediction _)
df2.withColumn($(predictionCol), probability2predictionUdf(col($(probabilityCol))))
}
override def copy(extra: ParamMap): this.type = defaultCopy(extra)
protected override def predictRaw(features: Row): Vector = {
throw new NotImplementedError("Not implemented")
}
protected override def raw2probabilityInPlace(rawPrediction: Vector): Vector= {
throw new NotImplementedError("Not implemented")
}
}
object VowpalWabbitClassificationModel extends ComplexParamsReadable[VowpalWabbitClassificationModel]