Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/19020#discussion_r136071530
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/optim/aggregator/HuberAggregatorSuite.scala
---
@@ -0,0 +1,170 @@
+/*
+ * 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.optim.aggregator
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.linalg.{BLAS, Vector, Vectors}
+import org.apache.spark.ml.util.TestingUtils._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class HuberAggregatorSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ import DifferentiableLossAggregatorSuite.getRegressionSummarizers
+
+ @transient var instances: Array[Instance] = _
+ @transient var instancesConstantFeature: Array[Instance] = _
+ @transient var instancesConstantFeatureFiltered: Array[Instance] = _
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ instances = Array(
+ Instance(0.0, 0.1, Vectors.dense(1.0, 2.0)),
+ Instance(1.0, 0.5, Vectors.dense(1.5, 1.0)),
+ Instance(2.0, 0.3, Vectors.dense(4.0, 0.5))
+ )
+ instancesConstantFeature = Array(
+ Instance(0.0, 0.1, Vectors.dense(1.0, 2.0)),
+ Instance(1.0, 0.5, Vectors.dense(1.0, 1.0)),
+ Instance(2.0, 0.3, Vectors.dense(1.0, 0.5))
+ )
+ instancesConstantFeatureFiltered = Array(
+ Instance(0.0, 0.1, Vectors.dense(2.0)),
+ Instance(1.0, 0.5, Vectors.dense(1.0)),
+ Instance(2.0, 0.3, Vectors.dense(0.5))
+ )
+ }
+
+ /** Get summary statistics for some data and create a new
HuberAggregator. */
+ private def getNewAggregator(
+ instances: Array[Instance],
+ parameters: Vector,
+ fitIntercept: Boolean,
+ m: Double): HuberAggregator = {
+ val (featuresSummarizer, _) = getRegressionSummarizers(instances)
+ val featuresStd = featuresSummarizer.variance.toArray.map(math.sqrt)
+ val bcFeaturesStd = spark.sparkContext.broadcast(featuresStd)
+ val bcParameters = spark.sparkContext.broadcast(parameters)
+ new HuberAggregator(fitIntercept, m, bcFeaturesStd)(bcParameters)
+ }
+
+ test("aggregator add method should check input size") {
+ val parameters = Vectors.dense(1.0, 2.0, 3.0, 4.0)
+ val agg = getNewAggregator(instances, parameters, fitIntercept = true,
m = 1.35)
+ withClue("HuberAggregator features dimension must match parameters
dimension") {
+ intercept[IllegalArgumentException] {
+ agg.add(Instance(1.0, 1.0, Vectors.dense(2.0)))
+ }
+ }
+ }
+
+ test("negative weight") {
+ val parameters = Vectors.dense(1.0, 2.0, 3.0, 4.0)
+ val agg = getNewAggregator(instances, parameters, fitIntercept = true,
m = 1.35)
+ withClue("HuberAggregator does not support negative instance
weights.") {
+ intercept[IllegalArgumentException] {
+ agg.add(Instance(1.0, -1.0, Vectors.dense(2.0, 1.0)))
+ }
+ }
+ }
+
+ test("check sizes") {
+ val paramWithIntercept = Vectors.dense(1.0, 2.0, 3.0, 4.0)
+ val paramWithoutIntercept = Vectors.dense(1.0, 2.0, 4.0)
+ val aggIntercept = getNewAggregator(instances, paramWithIntercept,
+ fitIntercept = true, m = 1.35)
+ val aggNoIntercept = getNewAggregator(instances, paramWithoutIntercept,
+ fitIntercept = false, m = 1.35)
+ instances.foreach(aggIntercept.add)
+ instances.foreach(aggNoIntercept.add)
+
+ assert(aggIntercept.gradient.size === 4)
+ assert(aggNoIntercept.gradient.size === 3)
+ }
+
+ test("check correctness") {
+ val parameters = Vectors.dense(1.0, 2.0, 3.0, 4.0)
+ val numFeatures = 2
+ val (featuresSummarizer, _) = getRegressionSummarizers(instances)
+ val featuresStd = featuresSummarizer.variance.toArray.map(math.sqrt)
+ val m = 1.35
+ val weightSum = instances.map(_.weight).sum
+
+ val agg = getNewAggregator(instances, parameters, fitIntercept = true,
m)
+ instances.foreach(agg.add)
+
+ // compute expected loss sum
+ val coefficients = parameters.toArray.slice(0, 2)
+ val intercept = parameters(2)
+ val sigma = parameters(3)
+ val stdCoef = coefficients.indices.map(i => coefficients(i) /
featuresStd(i)).toArray
+ val lossSum = instances.map { case Instance(label, weight, features) =>
+ val margin = BLAS.dot(Vectors.dense(stdCoef), features) + intercept
+ val linearLoss = label - margin
+ if (math.abs(linearLoss) <= sigma * m) {
+ 0.5 * weight * (sigma + math.pow(linearLoss, 2.0) / sigma)
+ } else {
+ 0.5 * weight * (sigma + 2.0 * m * math.abs(linearLoss) - sigma * m
* m)
+ }
+ }.sum
+ val loss = lossSum / weightSum
+
+ // compute expected gradients
+ val gradientCoef = new Array[Double](numFeatures + 2)
+ instances.foreach { case Instance(label, weight, features) =>
+ val margin = BLAS.dot(Vectors.dense(stdCoef), features) + intercept
+ val linearLoss = label - margin
+ if (math.abs(linearLoss) <= sigma * m) {
+ features.toArray.indices.foreach { i =>
+ gradientCoef(i) +=
+ -1.0 * weight * linearLoss / sigma * (features(i) /
featuresStd(i))
+ }
+ gradientCoef(2) += -1.0 * weight * linearLoss / sigma
+ gradientCoef(3) += 0.5 * weight * (1.0 - math.pow(linearLoss /
sigma, 2.0))
+ } else {
+ val sign = if (linearLoss >= 0) -1.0 else 1.0
+ features.toArray.indices.foreach { i =>
+ gradientCoef(i) += weight * sign * m * (features(i) /
featuresStd(i))
+ }
+ gradientCoef(2) += weight * sign * m
+ gradientCoef(3) += 0.5 * weight * (1.0 - m * m)
+ }
+ }
+ val gradient = Vectors.dense(gradientCoef.map(_ / weightSum))
+
+ assert(loss ~== agg.loss relTol 0.01)
+ assert(gradient ~== agg.gradient relTol 0.01)
+ }
+
+ test("check with zero standard deviation") {
+ val parameters = Vectors.dense(1.0, 2.0, 3.0, 4.0)
+ val parametersFiltered = Vectors.dense(2.0, 3.0, 4.0)
+ val aggConstantFeature = getNewAggregator(instancesConstantFeature,
parameters,
+ fitIntercept = true, m = 1.35)
+ val aggConstantFeatureFiltered =
getNewAggregator(instancesConstantFeatureFiltered,
+ parametersFiltered, fitIntercept = true, m = 1.35)
+ instances.foreach(aggConstantFeature.add)
+
instancesConstantFeatureFiltered.foreach(aggConstantFeatureFiltered.add)
+ // constant features should not affect gradient
+ def validateGradient(grad: Vector, gradFiltered: Vector): Unit = {
+ assert(grad(0) === 0.0)
+ assert(grad(1) === gradFiltered(0))
--- End diff --
Use `~== relTol` instead of `===`
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