Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15394#discussion_r82980460
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
---
@@ -132,24 +197,234 @@ class WeightedLeastSquaresSuite extends
SparkFunSuite with MLlibTestSparkContext
var idx = 0
for (fitIntercept <- Seq(false, true)) {
for (standardization <- Seq(false, true)) {
- val wls = new WeightedLeastSquares(
- fitIntercept, regParam = 0.0, standardizeFeatures =
standardization,
- standardizeLabel = standardization).fit(instancesConstLabel)
- val actual = Vectors.dense(wls.intercept, wls.coefficients(0),
wls.coefficients(1))
- assert(actual ~== expected(idx) absTol 1e-4)
+ for (solver <- WeightedLeastSquares.supportedSolvers) {
+ val wls = new WeightedLeastSquares(fitIntercept, regParam = 0.0,
elasticNetParam = 0.0,
+ standardizeFeatures = standardization,
+ standardizeLabel = standardization, solverType =
solver).fit(instancesConstLabel)
+ val actual = Vectors.dense(wls.intercept, wls.coefficients(0),
wls.coefficients(1))
+ assert(actual ~== expected(idx) absTol 1e-4)
+ }
}
idx += 1
}
+
+ // when label is constant zero, and fitIntercept is false, we should
not train and get all zeros
+ val instancesConstZeroLabel = instancesConstLabel.map { case
Instance(l, w, f) =>
+ Instance(0.0, w, f)
+ }
+ for (solver <- WeightedLeastSquares.supportedSolvers) {
+ val wls = new WeightedLeastSquares(false, 0.0, 0.0, true, true,
solverType = solver)
+ .fit(instancesConstZeroLabel)
+ val actual = Vectors.dense(wls.intercept, wls.coefficients(0),
wls.coefficients(1))
+ assert(actual === Vectors.dense(0.0, 0.0, 0.0))
+ assert(wls.objectiveHistory === Array(0.0))
+ }
}
test("WLS with regularization when label is constant") {
// if regParam is non-zero and standardization is true, the problem is
ill-defined and
// an exception is thrown.
- val wls = new WeightedLeastSquares(
- fitIntercept = false, regParam = 0.1, standardizeFeatures = true,
- standardizeLabel = true)
- intercept[IllegalArgumentException]{
- wls.fit(instancesConstLabel)
+ for (solver <- WeightedLeastSquares.supportedSolvers) {
+ val wls = new WeightedLeastSquares(
+ fitIntercept = false, regParam = 0.1, elasticNetParam = 0.0,
standardizeFeatures = true,
+ standardizeLabel = true, solverType = solver)
+ intercept[IllegalArgumentException]{
+ wls.fit(instancesConstLabel)
+ }
+ }
+ }
+
+ test("WLS against glmnet with constant features") {
+ /*
+ R code:
+
+ A <- matrix(c(1, 1, 1, 1, 5, 7, 11, 13), 4, 2)
+ b <- c(17, 19, 23, 29)
+ w <- c(1, 2, 3, 4)
+ */
+ val constantFeatures = sc.parallelize(Seq(
--- End diff --
It's better to move the dataset as a member variable of the class like
```instancesConstLabel```, then other test cases can leverage it later.
---
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