Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8884#discussion_r40518531
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
---
@@ -113,34 +115,37 @@ class LinearRegressionSuite extends SparkFunSuite
with MLlibTestSparkContext {
as.numeric.data.V2. 4.700706
as.numeric.data.V3. 7.199082
*/
- val interceptR = 6.298698
- val weightsR = Vectors.dense(4.700706, 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)
+ 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)
- }
+ 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 without regularization") {
- val trainer1 = (new LinearRegression).setFitIntercept(false)
- // Without regularization the results should be the same
- val trainer2 = (new
LinearRegression).setFitIntercept(false).setStandardization(false)
- val model1 = trainer1.fit(dataset)
- val modelWithoutIntercept1 = trainer1.fit(datasetWithoutIntercept)
- val model2 = trainer2.fit(dataset)
- val modelWithoutIntercept2 = trainer2.fit(datasetWithoutIntercept)
-
-
- /*
+ Seq("auto", "l-bfgs", "normal").foreach(solver => {
+ val trainer1 = (new
LinearRegression).setFitIntercept(false).setSolver(solver)
+ // Without regularization the results should be the same
+ val trainer2 = (new
LinearRegression).setFitIntercept(false).setStandardization(false)
+ .setSolver(solver)
+ val model1 = trainer1.fit(dataset)
+ val modelWithoutIntercept1 = trainer1.fit(datasetWithoutIntercept)
+ val model2 = trainer2.fit(dataset)
+ val modelWithoutIntercept2 = trainer2.fit(datasetWithoutIntercept)
+
+
+ /*
--- End diff --
ditto. indentation
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