Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/4259#discussion_r29218110
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -97,3 +190,146 @@ class LinearRegressionModel private[ml] (
m
}
}
+
+private class LeastSquaresAggregator(
+ weights: Vector,
+ labelStd: Double,
+ labelMean: Double,
+ featuresStd: Array[Double],
+ featuresMean: Array[Double]) extends Serializable {
+
+ private var totalCnt: Long = 0
+ private var lossSum = 0.0
+ private var diffSum = 0.0
+
+ private val (effectiveWeightsArray: Array[Double], offset: Double, dim:
Int) = {
+ val weightsArray = weights.toArray.clone()
+ var sum = 0.0
+ var i = 0
+ while (i < weightsArray.length) {
+ if (featuresStd(i) != 0.0) {
+ weightsArray(i) /= featuresStd(i)
+ sum += weightsArray(i) * featuresMean(i)
+ } else {
+ weightsArray(i) = 0.0
+ }
+ i += 1
+ }
+ (weightsArray, -sum + labelMean / labelStd, weightsArray.length)
+ }
+ private val effectiveWeightsVector = Vectors.dense(effectiveWeightsArray)
+
+ private val gradientSumArray: Array[Double] = Array.ofDim[Double](dim)
+
+ /**
+ * Add a new training data to this LeastSquaresAggregator, 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 LeastSquaresAggregator 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
+
+ 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 / 2.0
+ diffSum += diff
+ }
+
+ totalCnt += 1
+ this
+ }
+
+ /**
+ * Merge another LeastSquaresAggregator, 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 LeastSquaresAggregator to be merged.
+ * @return This LeastSquaresAggregator object.
+ */
+ def merge(other: LeastSquaresAggregator): this.type = {
+ require(dim == other.dim, s"Dimensions mismatch when merging with
another " +
+ s"LeastSquaresAggregator. Expecting $dim but got ${other.dim}.")
+
+ if (other.totalCnt != 0) {
+ totalCnt += other.totalCnt
+ lossSum += other.lossSum
+ diffSum += other.diffSum
+
+ 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())
+
+ val correction = {
+ val temp = effectiveWeightsArray.clone()
+ var i = 0
+ while (i < temp.length) {
+ temp(i) *= featuresMean(i)
+ i += 1
+ }
+ Vectors.dense(temp)
+ }
+
+ axpy(-diffSum, correction, result)
+ scal(1.0 / totalCnt, result)
--- End diff --
Okay, I finally found why `correction` effect is zero. It's because
`diffSum` is zero in our test dataset. `diffSum` is sum of `diff`, and for a
synthetic dataset generated from linear equation with noise, the average of
`diff` will be zero. As a result, for a real non-linear dataset, `diffSum` will
not be zero, so we need some non-linear dataset for testing correctness. I'll
add famous prostate cancer dataset used in the linear regression lasso paper
into the unit-test.
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