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From imatiach-msft <...@git.apache.org>
Subject [GitHub] spark pull request #16630: [SPARK-19270][ML] Add summary table to GLM summar...
Date Tue, 14 Feb 2017 00:12:48 GMT
Github user imatiach-msft commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16630#discussion_r100932182
  
    --- 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 --
    
    not related to this code review, but it's unfortunate that these aren't constants that
can be referenced from the model, it's messy to have to type strings like this everywhere
as opposed to referencing variables


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