Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8013#discussion_r37813263
--- 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) {
+ lossSum += (-k * diff - 0.5 * k * k) / 2.0
+ } else if (diff > k) {
+ lossSum += (k * diff - 0.5 * k * k) / 2.0
+ } else if (diff != 0) {
+ val localGradientSumArray = gradientSumArray
+ data.foreachActive { (index, value) =>
+ if (featuresStd(index) != 0.0 && value != 0.0) {
+ localGradientSumArray(index) += diff * value / featuresStd(index)
+ }
+ }
+ lossSum += diff * diff / 4.0
+ }
+
+ totalCnt += 1
+ this
+ }
+
+ /**
+ * Merge another HuberAggregator, 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 HuberAggregator to be merged.
+ * @return This HuberAggregator object.
+ */
+ def merge(other: HuberAggregator): this.type = {
+ require(dim == other.dim, s"Dimensions mismatch when merging with
another " +
+ s"HuberAggregator. Expecting $dim but got ${other.dim}.")
+
+ if (other.totalCnt != 0) {
+ totalCnt += other.totalCnt
+ lossSum += other.lossSum
+
+ var i = 0
+ val localThisGradientSumArray = this.gradientSumArray
+ val localOtherGradientSumArray = other.gradientSumArray
+ while (i < dim) {
+ localThisGradientSumArray(i) += localOtherGradientSumArray(i)
+ i += 1
+ }
+ }
+ this
+ }
+
+ def count: Long = totalCnt
+
+ def loss: Double = lossSum / totalCnt
+
+ def gradient: Vector = {
+ val result = Vectors.dense(gradientSumArray.clone())
+ scal(1.0 / totalCnt, result)
+ result
+ }
+}
+
+private class HuberCostFun(
+ data: RDD[(Double, Vector)],
+ labelStd: Double,
+ labelMean: Double,
+ fitIntercept: Boolean,
+ standardization: Boolean,
+ featuresStd: Array[Double],
+ featuresMean: Array[Double],
+ effectiveL2regParam: Double,
+ robustEfficiency: Double) extends DiffFunction[BDV[Double]] {
+
--- End diff --
Still have lots of duplicated code. Do you think you can pass a parameter
to switch different loss?
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