Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8611#discussion_r39812017
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
---
@@ -0,0 +1,447 @@
+/*
+ * 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.{Since, 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.mllib.linalg.BLAS
+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 (AFT) regression.
+ */
+private[regression] trait AFTSurvivalRegressionParams extends Params
+ with HasFeaturesCol with HasLabelCol with HasPredictionCol with
HasMaxIter
+ with HasTol with HasFitIntercept {
+
+ /**
+ * Param for censored column name.
+ * The value of this column could be 0 or 1.
+ * If the value is 1, it means the event has occurred i.e. uncensored;
otherwise it censored.
+ * @group param
+ */
+ @Since("1.6.0")
+ final val censoredCol: Param[String] = new Param(this, "censoredCol",
"censored column name")
+
+ /** @group getParam */
+ @Since("1.6.0")
+ def getCensoredCol: String = $(censoredCol)
+
+ /**
+ * Param for quantile probabilities array.
+ * Values of the quantile probabilities array should be in the range [0,
1].
+ * @group param
+ */
+ @Since("1.6.0")
+ final val quantileProbabilities: DoubleArrayParam = new
DoubleArrayParam(this, "quantile",
+ "quantile probabilities array", (t: Array[Double]) =>
t.forall(ParamValidators.inRange(0, 1)))
+
+ /** @group getParam */
+ @Since("1.6.0")
+ def getQuantileProbabilities: Array[Double] = $(quantileProbabilities)
+
+ /** Checks whether the input has quantile probabilities array. */
+ protected[regression] def hasQuantileProbabilities: Boolean = {
+ isDefined(quantileProbabilities) && $(quantileProbabilities).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, $(censoredCol), DoubleType)
+ SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
+ }
+ SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType)
+ }
+}
+
+/**
+ * :: Experimental ::
+ * Fit a parametric survival regression model named accelerated failure
time (AFT) model
+ * ([[https://en.wikipedia.org/wiki/Accelerated_failure_time_model]])
+ * based on the Weibull distribution of the survival time.
+ */
+@Experimental
+@Since("1.6.0")
+class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val
uid: String)
+ extends Estimator[AFTSurvivalRegressionModel] with
AFTSurvivalRegressionParams with Logging {
+
+ @Since("1.6.0")
+ def this() = this(Identifiable.randomUID("aftSurvReg"))
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setLabelCol(value: String): this.type = set(labelCol, value)
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setCensoredCol(value: String): this.type = set(censoredCol, value)
+ setDefault(censoredCol -> "censored")
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /**
+ * Set if we should fit the intercept
+ * Default is true.
+ * @group setParam
+ */
+ @Since("1.6.0")
+ def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
+ setDefault(fitIntercept -> true)
+
+ /**
+ * Set the maximum number of iterations.
+ * Default is 100.
+ * @group setParam
+ */
+ @Since("1.6.0")
+ 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
+ */
+ @Since("1.6.0")
+ def setTol(value: Double): this.type = set(tol, value)
+ setDefault(tol -> 1E-6)
+
+ /**
+ * Extract [[featuresCol]], [[labelCol]] and [[censoredCol]] from input
dataset,
+ * and put it in an RDD with strong types.
+ */
+ protected[ml] def extractAFTPoints(
+ dataset: DataFrame): RDD[AFTPoint] = {
+ dataset.select($(featuresCol), $(labelCol), $(censoredCol)).map {
+ case Row(features: Vector, label: Double, censored: Double) =>
+ AFTPoint(features, label, censored)
+ }
+ }
+
+ @Since("1.6.0")
+ override def fit(dataset: DataFrame): AFTSurvivalRegressionModel = {
+ validateAndTransformSchema(dataset.schema, fitting = true)
+ val instances = extractAFTPoints(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, regression coefficients
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 coefficients = Vectors.dense(weights.slice(2, weights.length))
+ val intercept = weights(1)
+ val scale = math.exp(weights(0))
+ val model = new AFTSurvivalRegressionModel(uid, coefficients,
intercept, scale)
+ copyValues(model.setParent(this))
+ }
+
+ @Since("1.6.0")
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema, fitting = true)
+ }
+
+ @Since("1.6.0")
+ override def copy(extra: ParamMap): AFTSurvivalRegression =
defaultCopy(extra)
+}
+
+/**
+ * :: Experimental ::
+ * Model produced by [[AFTSurvivalRegression]].
+ */
+@Experimental
+@Since("1.6.0")
+class AFTSurvivalRegressionModel private[ml] (
+ @Since("1.6.0") override val uid: String,
+ @Since("1.6.0") val coefficients: Vector,
+ @Since("1.6.0") val intercept: Double,
+ @Since("1.6.0") val scale: Double)
+ extends Model[AFTSurvivalRegressionModel] with
AFTSurvivalRegressionParams {
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setQuantileProbabilities(value: Array[Double]): this.type =
set(quantileProbabilities, value)
+
+ @Since("1.6.0")
+ def predictQuantiles(features: Vector): Vector = {
+ require(hasQuantileProbabilities,
+ "AFTSurvivalRegressionModel predictQuantiles must set quantile
probabilities array")
+ // scale parameter for the Weibull distribution of lifetime
+ val lambda = math.exp(BLAS.dot(coefficients, features) + intercept)
+ // shape parameter for the Weibull distribution of lifetime
+ val k = 1 / scale
+ val quantiles = $(quantileProbabilities).map {
+ q => lambda * math.exp(math.log(-math.log(1-q)) / k)
+ }
+ Vectors.dense(quantiles)
+ }
+
+ @Since("1.6.0")
+ def predict(features: Vector): Double = {
+ math.exp(BLAS.dot(coefficients, features) + intercept)
+ }
+
+ @Since("1.6.0")
+ override def transform(dataset: DataFrame): DataFrame = {
+ transformSchema(dataset.schema)
+ val predictUDF = udf { features: Vector => predict(features) }
+ dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+ }
+
+ @Since("1.6.0")
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema, fitting = false)
+ }
+
+ @Since("1.6.0")
+ override def copy(extra: ParamMap): AFTSurvivalRegressionModel = {
+ copyValues(new AFTSurvivalRegressionModel(uid, coefficients,
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.
+ *
+ * Given the values of the covariates x^{'}, 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}}
+ * }}}
+ * Where \delta_{i} is the indicator of the event has occurred i.e.
uncensored or not.
+ * 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 of lifetime 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 log of scale parameter, the intercept and
+ * regression coefficients corresponding to the features.
+ * @param fitIntercept Whether to fit an intercept term.
+ */
+private class AFTAggregator(weights: BDV[Double], fitIntercept: Boolean)
+ extends Serializable {
+
+ // beta is the intercept and regression coefficients to the covariates
+ private val beta = weights.slice(1, weights.length)
+ // sigma is the scale parameter of the AFT model
+ 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)),
+ gradientBetaSum/totalCnt.toDouble)
+
+ /**
+ * Add a new training data to this AFTAggregator, and update the loss
and gradient
+ * of the objective function.
+ *
+ * @param data The AFTPoint representation for one data point to be
added into this aggregator.
+ * @return This AFTAggregator object.
+ */
+ def add(data: AFTPoint): this.type = {
+
+ val xi = if (fitIntercept) {
+ Vectors.dense(Array(1.0) ++ data.features.toArray).toBreeze
+ } else {
+ Vectors.dense(Array(0.0) ++ data.features.toArray).toBreeze
+ }
+ val ti = data.label
+ val delta = data.censored
+ val epsilon = (math.log(ti) - beta.dot(xi)) / sigma
+
+ lossSum += math.log(sigma) * delta
+ lossSum += (math.exp(epsilon) - delta * epsilon)
+
+ // Sanity check (should never occur):
+ assert(!lossSum.isInfinity,
+ s"AFTAggregator loss sum is infinity. Error for unknown reason.")
+
+ gradientBetaSum += xi * (delta - math.exp(epsilon)) / sigma
+ gradientLogSigmaSum += delta + (delta - math.exp(epsilon)) * epsilon
+
+ totalCnt += 1
+ this
+ }
+
+ /**
+ * Merge another AFTAggregator, and update the loss and gradient
+ * of the objective function.
+ * (Note that it's in place merging; as a result, `this` object will be
modified.)
+ *
+ * @param other The other AFTAggregator to be merged.
+ * @return This AFTAggregator object.
+ */
+ def merge(other: AFTAggregator): this.type = {
+ if (totalCnt != 0) {
+ totalCnt += other.totalCnt
+ lossSum += other.lossSum
+
+ gradientBetaSum += other.gradientBetaSum
+ gradientLogSigmaSum += other.gradientLogSigmaSum
+ }
+ this
+ }
+}
+
+/**
+ * AFTCostFun implements Breeze's DiffFunction[T] for AFT cost.
+ * It returns the loss and gradient at a particular point (coefficients).
+ * It's used in Breeze's convex optimization routines.
+ */
+private class AFTCostFun(data: RDD[AFTPoint], fitIntercept: Boolean)
+ extends DiffFunction[BDV[Double]] {
+
+ override def calculate(coefficients: BDV[Double]): (Double, BDV[Double])
= {
+
+ val aftAggregator = data.treeAggregate(new AFTAggregator(coefficients,
fitIntercept))(
+ seqOp = (c, v) => (c, v) match {
+ case (aggregator, instance) => aggregator.add(instance)
+ },
+ combOp = (c1, c2) => (c1, c2) match {
+ case (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
+ })
+
+ (aftAggregator.loss, aftAggregator.gradient)
+ }
+}
+
+/**
+ * Class that represents the (features, label, censored) of a data point.
+ *
+ * @param features List of features for this data point.
+ * @param label Label for this data point.
+ * @param censored Indicator of the event has occurred or not. If the
value is 1, it means
+ * the event has occurred i.e. uncensored; otherwise it
censored.
--- End diff --
remove `it`
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
