Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/11694#discussion_r56058139
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
---
@@ -466,6 +468,461 @@ class GeneralizedLinearRegressionSuite
}
}
+ test("glm summary: gaussian family with weight") {
+ /*
+ R code:
+
+ A <- matrix(c(0, 1, 2, 3, 5, 7, 11, 13), 4, 2)
+ b <- c(17, 19, 23, 29)
+ w <- c(1, 2, 3, 4)
+ df <- as.data.frame(cbind(A, b))
+ */
+ val datasetWithWeight = sqlContext.createDataFrame(sc.parallelize(Seq(
+ Instance(17.0, 1.0, Vectors.dense(0.0, 5.0).toSparse),
+ Instance(19.0, 2.0, Vectors.dense(1.0, 7.0)),
+ Instance(23.0, 3.0, Vectors.dense(2.0, 11.0)),
+ Instance(29.0, 4.0, Vectors.dense(3.0, 13.0))
+ ), 2))
+ /*
+ R code:
+
+ model <- glm(formula = "b ~ .", family="gaussian", data = df,
weights = w)
+ summary(model)
+
+ Deviance Residuals:
+ 1 2 3 4
+ 1.920 -1.358 -1.109 0.960
+
+ Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+ (Intercept) 18.080 9.608 1.882 0.311
+ V1 6.080 5.556 1.094 0.471
+ V2 -0.600 1.960 -0.306 0.811
+
+ (Dispersion parameter for gaussian family taken to be 7.68)
+
+ Null deviance: 202.00 on 3 degrees of freedom
+ Residual deviance: 7.68 on 1 degrees of freedom
+ AIC: 18.783
+
+ Number of Fisher Scoring iterations: 2
+
+ residuals(model, type="pearson")
+ 1 2 3 4
+ 1.920000 -1.357645 -1.108513 0.960000
+
+ residuals(model, type="working")
+ 1 2 3 4
+ 1.92 -0.96 -0.64 0.48
+
+ residuals(model, type="response")
+ 1 2 3 4
+ 1.92 -0.96 -0.64 0.48
+ */
+ val trainer = new GeneralizedLinearRegression()
+ .setWeightCol("weight")
+
+ val model = trainer.fit(datasetWithWeight)
+
+ val coefficientsR = Vectors.dense(Array(6.080, -0.600))
+ val interceptR = 18.080
+ val devianceResidualsR = Array(1.920, -1.358, -1.109, 0.960)
+ val pearsonResidualsR = Array(1.920000, -1.357645, -1.108513, 0.960000)
+ val workingResidualsR = Array(1.92, -0.96, -0.64, 0.48)
+ val responseResidualsR = Array(1.92, -0.96, -0.64, 0.48)
+ val seCoefR = Array(5.556, 1.960, 9.608)
+ val tValsR = Array(1.094, -0.306, 1.882)
+ val pValsR = Array(0.471, 0.811, 0.311)
+ val dispersionR = 7.68
+ val nullDevianceR = 202.00
+ val residualDevianceR = 7.68
+ val residualDegreeOfFreedomNullR = 3
+ val residualDegreeOfFreedomR = 1
+ val aicR = 18.783
+
+ val summary = model.summary
+
+ val devianceResiduals = summary.residuals()
+ .select(col("devianceResiduals"))
+ .collect()
+ .map(_.getAs[Double](0))
--- End diff --
`_.getDouble(0)`
---
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 infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
