Github user actuaryzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/16630#discussion_r101822942
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
---
@@ -1104,6 +1103,83 @@ class GeneralizedLinearRegressionSuite
.fit(datasetGaussianIdentity.as[LabeledPoint])
}
+
+ test("glm summary: feature name") {
+ // dataset1 with no attribute
+ val dataset1 = Seq(
+ Instance(2.0, 1.0, Vectors.dense(0.0, 5.0)),
+ Instance(8.0, 2.0, Vectors.dense(1.0, 7.0)),
+ Instance(3.0, 3.0, Vectors.dense(2.0, 11.0)),
+ Instance(9.0, 4.0, Vectors.dense(3.0, 13.0)),
+ Instance(2.0, 5.0, Vectors.dense(2.0, 3.0))
+ ).toDF()
+
+ // dataset2 with attribute
+ val datasetTmp = Seq(
+ (2.0, 1.0, 0.0, 5.0),
+ (8.0, 2.0, 1.0, 7.0),
+ (3.0, 3.0, 2.0, 11.0),
+ (9.0, 4.0, 3.0, 13.0),
+ (2.0, 5.0, 2.0, 3.0)
+ ).toDF("y", "w", "x1", "x2")
+ val formula = new RFormula().setFormula("y ~ x1 + x2")
+ val dataset2 = formula.fit(datasetTmp).transform(datasetTmp)
+
+ val expectedFeature = Seq(Array("features_0", "features_1"),
Array("x1", "x2"))
+
+ var idx = 0
+ for (dataset <- Seq(dataset1, dataset2)) {
+ val model = new GeneralizedLinearRegression().fit(dataset)
+ model.summary.featureNames.zip(expectedFeature(idx))
+ .foreach{ x => assert(x._1 === x._2) }
+ idx += 1
+ }
+ }
+
+ test("glm summary: summaryTable") {
+ val dataset = Seq(
+ Instance(2.0, 1.0, Vectors.dense(0.0, 5.0)),
+ Instance(8.0, 2.0, Vectors.dense(1.0, 7.0)),
+ Instance(3.0, 3.0, Vectors.dense(2.0, 11.0)),
+ Instance(9.0, 4.0, Vectors.dense(3.0, 13.0)),
+ Instance(2.0, 5.0, Vectors.dense(2.0, 3.0))
+ ).toDF()
+
+ val expectedFeature = Seq(Array("features_0", "features_1"),
+ Array("(Intercept)", "features_0", "features_1"))
+ val expectedEstimate = Seq(Vectors.dense(0.2884, 0.538),
--- End diff --
Thanks. Added in R code.
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