Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8013#discussion_r36719832
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
---
@@ -505,4 +505,455 @@ class LinearRegressionSuite extends SparkFunSuite
with MLlibTestSparkContext {
.zip(testSummary.residuals.select("residuals").collect())
.forall { case (Row(r1: Double), Row(r2: Double)) => r1 ~== r2
relTol 1E-5 }
}
+
+ test("Robust params") {
+ ParamsSuite.checkParams((new LinearRegression).setRobust(true))
+ val model = new LinearRegressionModel("RobustReg", Vectors.dense(0.0),
0.0)
+ ParamsSuite.checkParams(model)
+ }
+
+ test("Robust regression: default params") {
+ val lir = (new LinearRegression).setRobust(true)
+ assert(lir.getLabelCol === "label")
+ assert(lir.getFeaturesCol === "features")
+ assert(lir.getPredictionCol === "prediction")
+ assert(lir.getRegParam === 0.0)
+ assert(lir.getElasticNetParam === 0.0)
+ assert(lir.getFitIntercept)
+ assert(lir.getStandardization)
+ val model = lir.fit(dataset)
+ model.transform(dataset)
+ .select("label", "prediction")
+ .collect()
+ assert(model.getFeaturesCol === "features")
+ assert(model.getPredictionCol === "prediction")
+ assert(model.intercept !== 0.0)
+ assert(model.hasParent)
+ }
+
+ test("Robust regression with intercept without regularization") {
+ val trainer1 = (new LinearRegression).setRobust(true)
+ // The result should be the same regardless of standardization without
regularization
+ val trainer2 = (new
LinearRegression).setRobust(true).setStandardization(false)
+ val model1 = trainer1.fit(dataset)
+ val model2 = trainer2.fit(dataset)
+
+ /*
+ Using the following R code to load the data and train the model
using glmnet package.
+
--- End diff --
Why do you test with `GLMNET`? Is it because all the data in this test
suite doesn't have outliner? Can you have a test with outliner proving this
algorithm works?
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