Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/8013#discussion_r37813105 --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala --- @@ -645,3 +666,179 @@ private class LeastSquaresCostFun( (leastSquaresAggregator.loss + regVal, new BDV(totalGradientArray)) } } + +/** + * HuberCostFun implements Breeze's DiffFunction[T] for Huber cost as used in Robust regression. + * The Huber M-estimator corresponds to a probability distribution for the errors which is normal + * in the centre but like a double exponential distribution in the tails (Hogg 1979: 109). + * L = 1/2 ||A weights-y||^2 if |A weights-y| <= k + * L = k |A weights-y| - 1/2 K^2 if |A weights-y| > k + * where k = 1.345 which produce 95% efficiency when the errors are normal and + * substantial resistance to outliers otherwise. + * See also the documentation for the precise formulation. + * It's used in Breeze's convex optimization routines. + */ + +private class HuberAggregator( + weights: Vector, + labelStd: Double, + labelMean: Double, + fitIntercept: Boolean, + featuresStd: Array[Double], + featuresMean: Array[Double], + robustEfficiency: Double) extends Serializable { + + private var totalCnt: Long = 0L + private var lossSum = 0.0 + + private val (effectiveWeightsArray: Array[Double], offset: Double, dim: Int) = { + val weightsArray = weights.toArray.clone() + var sum = 0.0 + var i = 0 + val len = weightsArray.length + while (i < len) { + if (featuresStd(i) != 0.0) { + weightsArray(i) /= featuresStd(i) + sum += weightsArray(i) * featuresMean(i) + } else { + weightsArray(i) = 0.0 + } + i += 1 + } + (weightsArray, if (fitIntercept) labelMean / labelStd - sum else 0.0, weightsArray.length) + } + + private val effectiveWeightsVector = Vectors.dense(effectiveWeightsArray) + + private val gradientSumArray = Array.ofDim[Double](dim) + + /** + * Add a new training data to this HuberAggregator, and update the loss and gradient + * of the objective function. + * + * @param label The label for this data point. + * @param data The features for one data point in dense/sparse vector format to be added + * into this aggregator. + * @return This HuberAggregator object. + */ + def add(label: Double, data: Vector): this.type = { + require(dim == data.size, s"Dimensions mismatch when adding new sample." + + s" Expecting $dim but got ${data.size}.") + + val diff = dot(data, effectiveWeightsVector) - label / labelStd + offset + val k = robustEfficiency + + if (diff < -k) { --- End diff -- This looks right now.
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