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VectorSHAP.scala
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VectorSHAP.scala
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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.azure.synapse.ml.explainers
import breeze.stats.distributions.RandBasis
import com.microsoft.azure.synapse.ml.core.schema.DatasetExtensions
import com.microsoft.azure.synapse.ml.logging.FeatureNames
import org.apache.spark.injections.UDFUtils
import org.apache.spark.ml.ComplexParamsReadable
import org.apache.spark.ml.linalg.SQLDataTypes.VectorType
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param.shared.HasInputCol
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions.{col, explode}
import org.apache.spark.sql.types._
class VectorSHAP(override val uid: String)
extends KernelSHAPBase(uid)
with HasInputCol
with HasBackgroundData {
logClass(FeatureNames.Explainers)
def this() = {
this(Identifiable.randomUID("VectorSHAP"))
}
def setInputCol(value: String): this.type = this.set(inputCol, value)
override protected def createSamples(df: DataFrame,
idCol: String,
coalitionCol: String,
weightCol: String,
targetClassesCol: String): DataFrame = {
val instanceCol = DatasetExtensions.findUnusedColumnName("instance", df)
val backgroundCol = DatasetExtensions.findUnusedColumnName("background", df)
val instances = df.select(col(idCol), col(targetClassesCol), col(getInputCol).alias(instanceCol))
val background = this.getBackgroundData
.select(col(getInputCol).alias(backgroundCol))
val numSampleOpt = this.getNumSamplesOpt
val infWeightVal = this.getInfWeight
val samplesUdf = UDFUtils.oldUdf(
{
(instance: Vector, background: Vector) =>
val effectiveNumSamples = KernelSHAPBase.getEffectiveNumSamples(numSampleOpt, instance.size)
val sampler = new KernelSHAPVectorSampler(instance, background, effectiveNumSamples, infWeightVal)
(1 to effectiveNumSamples) map {
_ =>
implicit val randBasis: RandBasis = RandBasis.mt0
sampler.sample
}
},
getSampleSchema(VectorType)
)
val samplesCol = DatasetExtensions.findUnusedColumnName("samples", df)
instances.crossJoin(background)
.withColumn(samplesCol, explode(samplesUdf(col(instanceCol), col(backgroundCol))))
.select(
col(idCol),
col(samplesCol).getField(sampleField).alias(getInputCol),
col(samplesCol).getField(coalitionField).alias(coalitionCol),
col(samplesCol).getField(weightField).alias(weightCol),
col(targetClassesCol)
)
}
protected override def validateSchema(schema: StructType): Unit = {
super.validateSchema(schema)
require(
schema(getInputCol).dataType == VectorType,
s"Field $getInputCol from input must be Vector type, but got ${schema(getInputCol).dataType} instead."
)
if (this.get(backgroundData).isDefined) {
val dataType = getBackgroundData.schema(getInputCol).dataType
require(
dataType == VectorType,
s"Field $getInputCol from background dataset must be Vector type, but got $dataType instead."
)
}
}
}
object VectorSHAP extends ComplexParamsReadable[VectorSHAP]