Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8611#discussion_r39750108
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
---
@@ -0,0 +1,400 @@
+/*
+ * 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 org.apache.spark.ml.regression
+
+import scala.collection.mutable
+
+import breeze.linalg.{DenseVector => BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS =>
BreezeLBFGS}
+
+import org.apache.spark.{SparkException, Logging}
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.{Model, Estimator}
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.util.{SchemaUtils, Identifiable}
+import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{Row, DataFrame}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructType}
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * Params for Accelerated Failure Time regression.
+ */
+private[regression] trait AFTSurvivalRegressionParams extends Params
+ with HasFeaturesCol with HasLabelCol with HasPredictionCol with
HasMaxIter
+ with HasTol with HasFitIntercept {
+
+ /**
+ * Param for censored column name.
+ * @group param
+ */
+ final val censorCol: Param[String] = new Param[String](this,
"censorCol", "censored column name")
+
+ /** @group getParam */
+ final def getCensorCol: String = $(censorCol)
+
+ /**
+ * Param for quantile vector.
+ * @group param
+ */
+ final val quantile: Param[Vector] = new Param[Vector](this,
+ "quantileCol", "quantile column name")
+
+ /** @group getParam */
+ final def getQuantile: Vector = $(quantile)
+
+ /** Checks whether the input has quantile vector. */
+ protected[ml] def hasQuantile: Boolean = {
+ isDefined(quantile) && $(quantile).size != 0
+ }
+
+ /**
+ * Validates and transforms the input schema with the provided param map.
+ * @param schema input schema
+ * @param fitting whether this is in fitting or prediction
+ * @return output schema
+ */
+ protected def validateAndTransformSchema(
+ schema: StructType,
+ fitting: Boolean): StructType = {
+ SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
+ if (fitting) {
+ SchemaUtils.checkColumnType(schema, $(censorCol), DoubleType)
+ SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
+ }
+ SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType)
+ }
+}
+
+/**
+ * :: Experimental ::
+ * Fit a parametric survival regression model named Accelerated failure
time model
+ * ([[https://en.wikipedia.org/wiki/Accelerated_failure_time_model]])
+ * based on the Weibull distribution of the survival time.
+ */
+@Experimental
+class AFTSurvivalRegression(override val uid: String)
+ extends Estimator[AFTSurvivalRegressionModel] with
AFTSurvivalRegressionParams with Logging {
+
+ def this() = this(Identifiable.randomUID("aftReg"))
+
+ /** @group setParam */
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ def setLabelCol(value: String): this.type = set(labelCol, value)
+
+ /** @group setParam */
+ def setCensorCol(value: String): this.type = set(censorCol, value)
+ setDefault(censorCol -> "censored")
+
+ /** @group setParam */
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /**
+ * Set if we should fit the intercept
+ * Default is true.
+ * @group setParam
+ */
+ def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
+ setDefault(fitIntercept -> true)
+
+ /**
+ * Set the maximum number of iterations.
+ * Default is 100.
+ * @group setParam
+ */
+ def setMaxIter(value: Int): this.type = set(maxIter, value)
+ setDefault(maxIter -> 100)
+
+ /**
+ * Set the convergence tolerance of iterations.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ * Default is 1E-6.
+ * @group setParam
+ */
+ def setTol(value: Double): this.type = set(tol, value)
+ setDefault(tol -> 1E-6)
+
+ /**
+ * Extracts (features, label, censored) from input dataset.
+ */
+ protected[ml] def extractCensoredLabeledPoints(
+ dataset: DataFrame): RDD[(Vector, Double, Double)] = {
+ dataset.select($(featuresCol), $(labelCol), $(censorCol))
+ .map { case Row(features: Vector, label: Double, censored: Double) =>
+ (features, label, censored)
+ }
+ }
+
+ override def fit(dataset: DataFrame): AFTSurvivalRegressionModel = {
+ validateAndTransformSchema(dataset.schema, fitting = true)
+ val instances = extractCensoredLabeledPoints(dataset)
+ val handlePersistence = dataset.rdd.getStorageLevel ==
StorageLevel.NONE
+ if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
+
+ val costFun = new AFTCostFun(instances, $(fitIntercept))
+ val optimizer = new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
+
+ val numFeatures =
dataset.select($(featuresCol)).take(1)(0).getAs[Vector](0).size
+ /*
+ The weights vector has three parts:
+ the first element: Double, log(sigma), the log of scale parameter
+ the second element: Double, intercept of the beta parameter
+ the third to the end elements: Doubles, weights vector of the beta
parameter
+ */
+ val initialWeights = Vectors.zeros(numFeatures + 2)
+
+ val states = optimizer.iterations(new CachedDiffFunction(costFun),
+ initialWeights.toBreeze.toDenseVector)
+
+ val weights = {
+ val arrayBuilder = mutable.ArrayBuilder.make[Double]
+ var state: optimizer.State = null
+ while (states.hasNext) {
+ state = states.next()
+ arrayBuilder += state.adjustedValue
+ }
+ if (state == null) {
+ val msg = s"${optimizer.getClass.getName} failed."
+ throw new SparkException(msg)
+ }
+
+ val rawWeights = state.x.toArray.clone()
+ rawWeights
+ }
+
+ if (handlePersistence) instances.unpersist()
+
+ val realWeights = Vectors.dense(weights.slice(2, weights.length))
+ val intercept = weights(1)
+ val scale = math.exp(weights(0))
+ val model = new AFTSurvivalRegressionModel(uid, realWeights,
intercept, scale)
+ copyValues(model.setParent(this))
+ }
+
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema, fitting = true)
+ }
+
+ override def copy(extra: ParamMap): AFTSurvivalRegression =
defaultCopy(extra)
+}
+
+/**
+ * :: Experimental ::
+ * Model produced by [[AFTSurvivalRegression]].
+ */
+@Experimental
+class AFTSurvivalRegressionModel private[ml] (
+ override val uid: String,
+ val weights: Vector,
+ val intercept: Double,
+ val scale: Double)
+ extends Model[AFTSurvivalRegressionModel] with
AFTSurvivalRegressionParams {
+
+ /** @group setParam */
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /** @group setParam */
+ def setQuantile(value: Vector): this.type = set(quantile, value)
+
+ def quantilePredict(features: Vector): Vector = {
+ require(hasQuantile, "AFTSurvivalRegressionModel quantilePredict must
set quantile vector")
+ // scale parameter of the Weibull distribution
+ val lambda = math.exp(weights.toBreeze.dot(features.toBreeze) +
intercept)
+ // shape parameter of the Weibull distribution
+ val k = 1 / scale
+ val array = $(quantile).toArray.map { q => lambda *
math.exp(math.log(-math.log(1-q)) / k) }
+ Vectors.dense(array)
+ }
+
+ def predict(features: Vector): Double = {
+ val lambda = math.exp(weights.toBreeze.dot(features.toBreeze) +
intercept)
+ lambda
+ }
+
+ override def transform(dataset: DataFrame): DataFrame = {
+ transformSchema(dataset.schema)
+ val predictUDF = udf { features: Vector => predict(features) }
+ dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+ }
+
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema, fitting = false)
+ }
+
+ override def copy(extra: ParamMap): AFTSurvivalRegressionModel = {
+ copyValues(new AFTSurvivalRegressionModel(uid, weights, intercept,
scale), extra)
+ .setParent(parent)
+ }
+}
+
+/**
+ * AFTAggregator computes the gradient and loss for a AFT loss function,
+ * as used in AFT survival regression for samples in sparse or dense
vector in a online fashion.
+ *
+ * The loss function and likelihood function under the AFT model based on:
+ * Lawless, J. F., Statistical Models and Methods for Lifetime Data,
+ * New York: John Wiley & Sons, Inc. 2003.
+ *
+ * Two AFTAggregator can be merged together to have a summary of loss and
gradient of
+ * the corresponding joint dataset.
+ *
+ * For random lifetime T_{i} of subjects i = 1, ..., n, with possible
right-censoring,
+ * the likelihood function under the AFT model is given as
+ * {{{
+ * L(\beta,\sigma)=\prod_{i=1}^n[\frac{1}{\sigma}f_{0}
+ * (\frac{\log{t_{i}}-x^{'}\beta}{\sigma})]^{\delta_{i}}S_{0}
+ * (\frac{\log{t_{i}}-x^{'}\beta}{\sigma})^{1-\delta_{i}}
+ * }}}
+ * Using \epsilon_{i}=\frac{\log{t_{i}}-x^{'}\beta}{\sigma}, the
log-likelihood function
+ * assumes the form
+ * {{{
+ * \iota(\beta,\sigma)=\sum_{i=1}^{n}[-\delta_{i}\log\sigma+
+ *
\delta_{i}\log{f_{0}}(\epsilon_{i})+(1-\delta_{i})\log{S_{0}(\epsilon_{i})}]
+ * }}}
+ * Where S_{0}(\epsilon_{i}) is the baseline survivor function
+ * and f_{0}(\epsilon_{i}) is corresponding density function.
+ * The most commonly used log-linear survival regression method is based
on the Weibull
+ * distribution of the survival time. The Weibull distribution for
lifetime corresponding
+ * to extreme value distribution for log of the lifetime,
+ * and the S_{0}(\epsilon) function is
+ * {{{
+ * S_{0}(\epsilon_{i})=\exp(-e^{\epsilon_{i}})
+ * }}}
+ * the f_{0}(\epsilon_{i}) function is
+ * {{{
+ * f_{0}(\epsilon_{i})=e^{\epsilon_{i}}\exp(-e^{\epsilon_{i}})
+ * }}}
+ * The log-likelihood function for Weibull distribution is
+ * {{{
+ * \iota(\beta,\sigma)=
+ *
-\sum_{i=1}^n[\delta_{i}\log\sigma-\delta_{i}\epsilon_{i}+e^{\epsilon_{i}}]
+ * }}}
+ * Due to minimizing the negative log-likelihood equivalent to maximum a
posteriori probability,
+ * the loss function we use to optimize is -\iota(\beta,\sigma).
+ * The gradient functions for \beta and \log\sigma respectively are
+ * {{{
+ * \frac{\partial (-\iota)}{\partial \beta}=
+ * \sum_{1=1}^{n}[\delta_{i}-e^{\epsilon_{i}}]\frac{x_{i}}{\sigma}
+ * }}}
+ * {{{
+ * \frac{\partial (-\iota)}{\partial (\log\sigma)}=
+ * \sum_{i=1}^{n}[\delta_{i}+(\delta_{i}-e^{\epsilon_{i}})\epsilon_{i}]
+ * }}}
+ * @param weights The the log of scale parameter,
+ * the intercept and weights/coefficients corresponding to
the features.
+ * @param fitIntercept Whether to fit an intercept term.
+ */
+private class AFTAggregator(weights: BDV[Double], fitIntercept: Boolean)
+ extends Serializable {
+
+ private val beta = weights.slice(1, weights.length)
+ private val sigma = math.exp(weights(0))
+
+ private var totalCnt: Long = 0L
+ private var lossSum = 0.0
+ private var gradientBetaSum = BDV.zeros[Double](beta.length)
+ private var gradientLogSigmaSum = 0.0
+
+ def count: Long = totalCnt
+ def loss: Double = if (totalCnt == 0) 1.0 else lossSum / totalCnt
+ // Here we optimize loss function over beta and log(sigma)
+ def gradient: BDV[Double] = BDV.vertcat(BDV(Array(gradientLogSigmaSum /
totalCnt.toDouble)),
--- End diff --
It has been checked
[here](https://github.com/apache/spark/pull/8611/files#diff-e277fd0bc21f825d3196b4551c01fe5fR359).
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