Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8884#discussion_r40518703
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
---
@@ -350,28 +364,31 @@ class LinearRegressionSuite extends SparkFunSuite
with MLlibTestSparkContext {
as.numeric.data.V2. 5.263704
as.numeric.data.V3. 4.187419
*/
- val interceptR2 = 0.0
- val weightsR2 = Vectors.dense(5.263704, 4.187419)
-
- assert(model2.intercept ~== interceptR2 absTol 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)
- }
+ val interceptR2 = 0.0
+ val weightsR2 = Vectors.dense(5.263704, 4.187419)
+
+ assert(model2.intercept ~== interceptR2 absTol 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)
+ }
+ })
}
test("linear regression with intercept with ElasticNet regularization") {
- val trainer1 = (new
LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
- val trainer2 = (new
LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
- .setStandardization(false)
- val model1 = trainer1.fit(dataset)
- val model2 = trainer2.fit(dataset)
-
- /*
+ Seq("auto").foreach(solver => {
+ val trainer1 = (new
LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+ .setSolver(solver)
+ val trainer2 = (new
LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+ .setStandardization(false).setSolver(solver)
+ val model1 = trainer1.fit(dataset)
+ val model2 = trainer2.fit(dataset)
+
+ /*
weights <- coef(glmnet(features, label, family="gaussian", alpha =
0.3, lambda = 1.6))
--- End diff --
ditto. indentation
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