Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8884#discussion_r41810212
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
---
@@ -93,525 +94,559 @@ class LinearRegressionSuite extends SparkFunSuite
with MLlibTestSparkContext {
}
test("linear regression with intercept without regularization") {
- val trainer1 = new LinearRegression
- // The result should be the same regardless of standardization without
regularization
- val trainer2 = (new LinearRegression).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.
-
- library("glmnet")
- data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE)
- features <- as.matrix(data.frame(as.numeric(data$V2),
as.numeric(data$V3)))
- label <- as.numeric(data$V1)
- weights <- coef(glmnet(features, label, family="gaussian", alpha =
0, lambda = 0))
- > weights
- 3 x 1 sparse Matrix of class "dgCMatrix"
- s0
- (Intercept) 6.298698
- as.numeric.data.V2. 4.700706
- as.numeric.data.V3. 7.199082
- */
- val interceptR = 6.298698
- val weightsR = Vectors.dense(4.700706, 7.199082)
-
- assert(model1.intercept ~== interceptR relTol 1E-3)
- assert(model1.weights ~= weightsR relTol 1E-3)
- assert(model2.intercept ~== interceptR relTol 1E-3)
- assert(model2.weights ~= weightsR 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)
- }
+ Seq("auto", "l-bfgs", "normal").foreach { solver => {
--- End diff --
The `{` in ` solver => {` is not needed.
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