Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8631#discussion_r39937401
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
---
@@ -510,4 +513,90 @@ 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("linear regression with weighted samples"){
+ val (data, weightedData) = {
+ val activeData = LinearDataGenerator.generateLinearInput(
+ 6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 500, 1,
0.1)
+
+ val rnd = new Random(8392)
+ val signedData = activeData map { case p: LabeledPoint =>
+ (rnd.nextGaussian() > 0.0, p)
+ }
+
+ val data1 = signedData flatMap {
+ case (true, p) => Iterator(p, p)
+ case (false, p) => Iterator(p)
+ }
+
+ val weightedSignedData = signedData flatMap {
+ case (true, LabeledPoint(label, features)) =>
+ Iterator(
+ Instance(label, 1.2, features),
+ Instance(label, 0.8, features)
+ )
+ case (false, LabeledPoint(label, features)) =>
+ Iterator(
+ Instance(label, 0.3, features),
+ Instance(label, 0.1, features),
+ Instance(label, 0.6, features)
+ )
+ }
+
+ val noiseData = LinearDataGenerator.generateLinearInput(
+ 2, Array(1, 3), Array(0.9, -1.3), Array(0.7, 1.2), 500, 1, 0.1)
+ val weightedNoiseData = noiseData map {
+ case LabeledPoint(label, features) => Instance(label, 0, features)
+ }
+ val data2 = weightedSignedData ++ weightedNoiseData
+
+ (sqlContext.createDataFrame(sc.parallelize(data1, 4)),
+ sqlContext.createDataFrame(sc.parallelize(data2, 4)))
+ }
+
+ val trainer1a = (new LinearRegression).setFitIntercept(true)
+ .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true)
+ val trainer1b = (new
LinearRegression).setFitIntercept(true).setWeightCol("weight")
+ .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true)
+ val model1a0 = trainer1a.fit(data)
+ val model1a1 = trainer1a.fit(weightedData)
+ val model1b = trainer1b.fit(weightedData)
+ assert(model1a0.weights !~= model1a1.weights absTol 1E-3)
+ assert(model1a0.intercept !~= model1a1.intercept absTol 1E-3)
+ assert(model1a0.weights ~== model1b.weights absTol 1E-3)
+ assert(model1a0.intercept ~== model1b.intercept absTol 1E-3)
+
+ val trainer2a = (new LinearRegression).setFitIntercept(true)
+ .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(false)
+ val trainer2b = (new
LinearRegression).setFitIntercept(true).setWeightCol("weight")
+ .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(false)
+ val model2a0 = trainer2a.fit(data)
+ val model2a1 = trainer2a.fit(weightedData)
+ val model2b = trainer2b.fit(weightedData)
+ assert(model2a0.weights !~= model2a1.weights absTol 1E-3)
+ assert(model2a0.intercept !~= model2a1.intercept absTol 1E-3)
+ assert(model2a0.weights ~== model2b.weights absTol 1E-3)
+ assert(model2a0.intercept ~== model2b.intercept absTol 1E-3)
+
+ val trainer3a = (new LinearRegression).setFitIntercept(false)
+ .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true)
+ val trainer3b = (new
LinearRegression).setFitIntercept(false).setWeightCol("weight")
+ .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true)
+ val model3a0 = trainer3a.fit(data)
+ val model3a1 = trainer3a.fit(weightedData)
+ val model3b = trainer3b.fit(weightedData)
+ assert(model3a0.weights !~= model3a1.weights absTol 1E-3)
+ assert(model3a0.weights ~== model3b.weights absTol 1E-3)
+
+ val trainer4a = (new LinearRegression).setFitIntercept(false)
+ .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(false)
+ val trainer4b = (new
LinearRegression).setFitIntercept(false).setWeightCol("weight")
+ .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(false)
+ val model4a0 = trainer4a.fit(data)
+ val model4a1 = trainer4a.fit(weightedData)
+ val model4b = trainer4b.fit(weightedData)
+ assert(model4a0.weights !~= model4a1.weights absTol 1E-3)
+ assert(model4a0.weights ~== model4b.weights absTol 1E-3)
+
--- End diff --
remove this extra line.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
