Github user actuaryzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/16630#discussion_r101159255
--- 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("V1", "V2"), Array("x1", "x2"))
+
+ var idx = 0
+ for (dataset <- Seq(dataset1, dataset2)) {
+ val model = new GeneralizedLinearRegression().fit(dataset)
+ model.summary.featureName.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("V1", "V2"),
+ Array("Intercept", "V1", "V2"))
+ val expectedEstimate = Seq(Vectors.dense(0.2884, 0.538),
+ Vectors.dense(0.7903, 0.2258, 0.4677))
+ val expectedStdError = Seq(Vectors.dense(1.724, 0.3787),
+ Vectors.dense(4.0129, 2.1153, 0.5815))
+ val expectedTValue = Seq(Vectors.dense(0.1673, 1.4205),
+ Vectors.dense(0.1969, 0.1067, 0.8043))
+ val expectedPValue = Seq(Vectors.dense(0.8778, 0.2506),
+ Vectors.dense(0.8621, 0.9247, 0.5056))
+
+ var idx = 0
+ for (fitIntercept <- Seq(false, true)) {
+ val trainer = new GeneralizedLinearRegression()
+ .setFamily("gaussian")
--- End diff --
Indeed, there is object `Gaussian` and one can use `Gaussian.name` for the
string name.
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