Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15394#discussion_r84838671
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
---
@@ -132,28 +232,256 @@ 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
+ for (solver <- WeightedLeastSquares.supportedSolvers) {
+ val wls = new WeightedLeastSquares(fitIntercept = false, regParam =
0.0,
+ elasticNetParam = 0.0, standardizeFeatures = true,
standardizeLabel = 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") {
+ test("WLS against glmnet with constant features") {
+ // Cholesky solver does not handle singular input with no
regularization
+ for (fitIntercept <- Seq(false, true);
+ standardization <- Seq(false, true)) {
+ val wls = new WeightedLeastSquares(fitIntercept, regParam = 0.0,
elasticNetParam = 0.0,
+ standardizeFeatures = standardization, standardizeLabel =
standardization,
+ solverType = WeightedLeastSquares.Cholesky)
+ intercept[SingularMatrixException] {
+ wls.fit(constantFeaturesInstances)
+ }
+ }
+
+ // Cholesky also fails when regularization is added but we don't wish
to standardize
+ val wls = new WeightedLeastSquares(true, regParam = 0.5,
elasticNetParam = 0.0,
+ standardizeFeatures = false, standardizeLabel = false,
+ solverType = WeightedLeastSquares.Cholesky)
+ intercept[SingularMatrixException] {
+ wls.fit(constantFeaturesInstances)
+ }
+
+ /*
+ for (intercept in c(FALSE, TRUE)) {
+ model <- glmnet(A, b, weights=w, intercept=intercept, lambda=0.5,
+ standardize=T, alpha=0.0, thresh=1E-14)
+ print(as.vector(coef(model)))
+ }
+ [1] 0.000000 0.000000 2.235802
+ [1] 9.798771 0.000000 1.365503
+ */
+ // should not fail when regularization and standardization are added
+ val expectedCholesky = Seq(
+ Vectors.dense(0.0, 0.0, 2.235802),
+ Vectors.dense(9.798771, 0.0, 1.365503)
+ )
+ var idx = 0
+ for (fitIntercept <- Seq(false, true)) {
+ val wls = new WeightedLeastSquares(fitIntercept = fitIntercept,
regParam = 0.5,
+ elasticNetParam = 0.0, standardizeFeatures = true,
+ standardizeLabel = true, solverType =
WeightedLeastSquares.Cholesky)
+ .fit(constantFeaturesInstances)
+ val actual = Vectors.dense(wls.intercept, wls.coefficients(0),
wls.coefficients(1))
+ assert(actual ~== expectedCholesky(idx) absTol 1e-6)
+ idx += 1
+ }
+
+ /*
+ for (intercept in c(FALSE, TRUE)) {
+ for (standardize in c(FALSE, TRUE)) {
+ for (regParams in list(c(0.0, 0.0), c(0.5, 0.0), c(0.5, 0.5),
c(0.5, 1.0))) {
+ model <- glmnet(A, b, weights=w, intercept=intercept,
lambda=regParams[1],
+ standardize=standardize, alpha=regParams[2],
thresh=1E-14)
+ print(as.vector(coef(model)))
+ }
+ }
+ }
+ [1] 0.000000 0.000000 2.253012
+ [1] 0.000000 0.000000 2.250857
+ [1] 0.000000 0.000000 2.249784
+ [1] 0.000000 0.000000 2.248709
+ [1] 0.000000 0.000000 2.253012
+ [1] 0.000000 0.000000 2.235802
+ [1] 0.000000 0.000000 2.238297
+ [1] 0.000000 0.000000 2.240811
+ [1] 8.218905 0.000000 1.517413
+ [1] 8.434286 0.000000 1.496703
+ [1] 8.648497 0.000000 1.476106
+ [1] 8.865672 0.000000 1.455224
+ [1] 8.218905 0.000000 1.517413
+ [1] 9.798771 0.000000 1.365503
+ [1] 9.919095 0.000000 1.353933
+ [1] 10.052804 0.000000 1.341077
+ */
+ val expectedQuasiNewton = Seq(
+ Vectors.dense(0.000000, 0.000000, 2.253012),
+ Vectors.dense(0.000000, 0.000000, 2.250857),
+ Vectors.dense(0.000000, 0.000000, 2.249784),
+ Vectors.dense(0.000000, 0.000000, 2.248709),
+ Vectors.dense(0.000000, 0.000000, 2.253012),
+ Vectors.dense(0.000000, 0.000000, 2.235802),
+ Vectors.dense(0.000000, 0.000000, 2.238297),
+ Vectors.dense(0.000000, 0.000000, 2.240811),
+ Vectors.dense(8.218905, 0.000000, 1.517413),
+ Vectors.dense(8.434286, 0.000000, 1.496703),
+ Vectors.dense(8.648497, 0.000000, 1.476106),
+ Vectors.dense(8.865672, 0.000000, 1.455224),
+ Vectors.dense(8.218905, 0.000000, 1.517413),
+ Vectors.dense(9.798771, 0.000000, 1.365503),
+ Vectors.dense(9.919095, 0.000000, 1.353933),
+ Vectors.dense(10.052804, 0.000000, 1.341077))
+
+ idx = 0
+ for (fitIntercept <- Seq(false, true);
+ standardization <- Seq(false, true);
+ (lambda, alpha) <- Seq((0.0, 0.0), (0.5, 0.0), (0.5, 0.5), (0.5,
1.0))) {
+ for (solver <- Seq(WeightedLeastSquares.Auto,
WeightedLeastSquares.Cholesky)) {
--- End diff --
Should the enumeration be removed? Since it's not used. This is a very
minor issue, we can address it in follow up work.
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