[GitHub] spark pull request #17094: [SPARK-19762][ML][WIP] Hierarchy for consolidatin...

[sethah <gi...@git.apache.org>]

Github user sethah commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17094#discussion_r103370930
  
    --- Diff: mllib/src/test/scala/org/apache/spark/ml/optim/aggregator/DifferentiableLossAggregatorSuite.scala ---
    @@ -0,0 +1,147 @@
    +/*
    + * 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._
    +
    +class DifferentiableLossAggregatorSuite extends SparkFunSuite {
    +
    +  private val instances1 = 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))
    +  )
    +  private val instances2 = Seq(
    +    Instance(0.2, 0.4, Vectors.dense(0.8, 2.5)),
    +    Instance(0.8, 0.9, Vectors.dense(2.0, 1.3)),
    +    Instance(1.5, 0.2, Vectors.dense(3.0, 0.2))
    +  )
    +
    +  def assertEqual[T, Agg <: DifferentiableLossAggregator[T, Agg]](
    +      agg1: DifferentiableLossAggregator[T, Agg],
    +      agg2: DifferentiableLossAggregator[T, Agg]): Unit = {
    +    assert(agg1.weight === agg2.weight)
    +    assert(agg1.loss === agg2.loss)
    +    assert(agg1.gradient === agg2.gradient)
    +  }
    +
    +  test("empty aggregator") {
    +    val numFeatures = 5
    +    val coef = Vectors.dense(Array.fill(numFeatures)(1.0))
    +    val agg = new TestAggregator(numFeatures)(coef)
    +    withClue("cannot get loss for empty aggregator") {
    +      intercept[IllegalArgumentException] {
    +        agg.loss
    +      }
    +    }
    +    withClue("cannot get gradient for empty aggregator") {
    +      intercept[IllegalArgumentException] {
    +        agg.gradient
    +      }
    +    }
    +  }
    +
    +  test("aggregator initialization") {
    +    val numFeatures = 3
    +    val coef = Vectors.dense(Array.fill(numFeatures)(1.0))
    +    val agg = new TestAggregator(numFeatures)(coef)
    +    agg.add(Instance(1.0, 0.3, Vectors.dense(Array.fill(numFeatures)(1.0))))
    +    assert(agg.gradient.size === 3)
    +    assert(agg.weight === 0.3)
    +  }
    +
    +  test("merge aggregators") {
    +    val coefficients = Vectors.dense(0.5, -0.1)
    +    val agg1 = new TestAggregator(2)(coefficients)
    +    val agg2 = new TestAggregator(2)(coefficients)
    +    instances1.foreach(agg1.add)
    +
    +    // merge empty other
    +    val mergedEmptyOther = agg1.merge(agg2)
    +    assertEqual(mergedEmptyOther, agg1)
    +    assert(mergedEmptyOther === agg1)
    +
    +    // merge empty this
    +    val agg3 = new TestAggregator(2)(coefficients)
    +    val mergedEmptyThis = agg3.merge(agg1)
    +    assertEqual(mergedEmptyThis, agg1)
    +    assert(mergedEmptyThis !== agg1)
    +
    +    instances2.foreach(agg2.add)
    +    val (loss1, weight1, grad1) = (agg1.loss, agg1.weight, agg1.gradient)
    +    val (loss2, weight2, grad2) = (agg2.loss, agg2.weight, agg2.gradient)
    +    val merged = agg1.merge(agg2)
    +
    +    // check pointers are equal
    +    assert(merged === agg1)
    +
    +    // loss should be weighted average of the two individual losses
    +    assert(merged.loss === (loss1 * weight1 + loss2 * weight2) / (weight1 + weight2))
    +    assert(merged.weight === weight1 + weight2)
    +
    +    // gradient should be weighted average of individual gradients
    +    val addedGradients = Vectors.dense(grad1.toArray.clone())
    +    BLAS.scal(weight1, addedGradients)
    +    BLAS.axpy(weight2, grad2, addedGradients)
    +    BLAS.scal(1 / (weight1 + weight2), addedGradients)
    +    assert(merged.gradient === addedGradients)
    +  }
    +
    +  test("loss, gradient, weight") {
    +    val coefficients = Vectors.dense(0.5, -0.1)
    +    val agg = new TestAggregator(2)(coefficients)
    +    instances1.foreach(agg.add)
    +    val errors = instances1.map { case Instance(label, _, features) =>
    +      label - BLAS.dot(features, coefficients)
    +    }
    +    val expectedLoss = errors.zip(instances1).map { case (error: Double, instance: Instance) =>
    +      instance.weight * error * error / 2.0
    +    }
    +    val expectedGradient = Vectors.dense(0.0, 0.0)
    +    errors.zip(instances1).foreach { case (error, instance) =>
    +      BLAS.axpy(instance.weight * error, instance.features, expectedGradient)
    +    }
    +    BLAS.scal(1.0 / agg.weight, expectedGradient)
    +    val weightSum = instances1.map(_.weight).sum
    +
    +    assert(agg.weight ~== weightSum relTol 1e-5)
    +    assert(agg.loss ~== expectedLoss.sum / weightSum relTol 1e-5)
    +    assert(agg.gradient ~== expectedGradient relTol 1e-5)
    +  }
    +}
    +
    +/**
    + * Dummy aggregator that represents least squares cost with no intercept.
    + */
    +class TestAggregator(numFeatures: Int)(coefficients: Vector)
    --- End diff --
    
    move it into a companion object


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