Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8884#discussion_r40518653
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
---
@@ -206,30 +213,32 @@ class LinearRegressionSuite extends SparkFunSuite
with MLlibTestSparkContext {
as.numeric.data.V2. 3.893869
as.numeric.data.V3. 6.724286
*/
- val interceptR2 = 6.416948
- val weightsR2 = Vectors.dense(3.893869, 6.724286)
+ val interceptR2 = 6.416948
+ val weightsR2 = Vectors.dense(3.893869, 6.724286)
- assert(model2.intercept ~== interceptR2 relTol 1E-3)
- assert(model2.weights ~= weightsR2 relTol 1E-3)
+ assert(model2.intercept ~== interceptR2 relTol 1E-3)
+ assert(model2.weights ~= weightsR2 relTol 1E-3)
- model1.transform(dataset).select("features",
"prediction").collect().foreach {
- case Row(features: DenseVector, prediction1: Double) =>
- val prediction2 =
- features(0) * model1.weights(0) + features(1) *
model1.weights(1) + model1.intercept
- assert(prediction1 ~== prediction2 relTol 1E-5)
- }
+ model1.transform(dataset).select("features",
"prediction").collect().foreach {
+ case Row(features: DenseVector, prediction1: Double) =>
+ val prediction2 =
+ features(0) * model1.weights(0) + features(1) *
model1.weights(1) + model1.intercept
+ assert(prediction1 ~== prediction2 relTol 1E-5)
+ }
+ })
}
test("linear regression without intercept with L1 regularization") {
- val trainer1 = (new
LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
- .setFitIntercept(false)
- val trainer2 = (new
LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
- .setFitIntercept(false).setStandardization(false)
- val model1 = trainer1.fit(dataset)
- val model2 = trainer2.fit(dataset)
-
- /*
+ Seq("auto").foreach(solver => {
+ val trainer1 = (new
LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+ .setFitIntercept(false).setSolver(solver)
+ val trainer2 = (new
LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+ .setFitIntercept(false).setStandardization(false).setSolver(solver)
+ val model1 = trainer1.fit(dataset)
+ val model2 = trainer2.fit(dataset)
+
+ /*
weights <- coef(glmnet(features, label, family="gaussian", alpha =
1.0, lambda = 0.57,
--- End diff --
ditto
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