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https://issues.apache.org/jira/browse/SPARK-18715?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15722995#comment-15722995
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Apache Spark commented on SPARK-18715:
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User 'actuaryzhang' has created a pull request for this issue:
https://github.com/apache/spark/pull/16149
> Fix wrong AIC calculation in Binomial GLM
> -----------------------------------------
>
> Key: SPARK-18715
> URL: https://issues.apache.org/jira/browse/SPARK-18715
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.0.2
> Reporter: Wayne Zhang
> Priority: Critical
> Labels: patch
> Fix For: 2.2.0
>
> Original Estimate: 120h
> Remaining Estimate: 120h
>
> The AIC calculation in Binomial GLM seems to be wrong when there are weights.
> The result is different from that in R.
> The current implementation is:
> {code}
> -2.0 * predictions.map { case (y: Double, mu: Double, weight: Double) =>
> weight * dist.Binomial(1, mu).logProbabilityOf(math.round(y).toInt)
> }.sum()
> {code}
> Suggest changing this to
> {code}
> -2.0 * predictions.map { case (y: Double, mu: Double, weight: Double) =>
> val wt = math.round(weight).toInt
> if (wt == 0){
> 0.0
> } else {
> dist.Binomial(wt, mu).logProbabilityOf(math.round(y * weight).toInt)
> }
> }.sum()
> {code}
> ----
> ----
> The following is an example to illustrate the problem.
> {code}
> val dataset = Seq(
> LabeledPoint(0.0, Vectors.dense(18, 1.0)),
> LabeledPoint(0.5, Vectors.dense(12, 0.0)),
> LabeledPoint(1.0, Vectors.dense(15, 0.0)),
> LabeledPoint(0.0, Vectors.dense(13, 2.0)),
> LabeledPoint(0.0, Vectors.dense(15, 1.0)),
> LabeledPoint(0.5, Vectors.dense(16, 1.0))
> ).toDF().withColumn("weight", col("label") + 1.0)
> val glr = new GeneralizedLinearRegression()
> .setFamily("binomial")
> .setWeightCol("weight")
> .setRegParam(0)
> val model = glr.fit(dataset)
> model.summary.aic
> {code}
> This calculation shows the AIC is 14.189026847171382. To verify whether this
> is correct, I run the same analysis in R but got AIC = 11.66092, -2 * LogLik
> = 5.660918.
> {code}
> da <- scan(, what=list(y = 0, x1 = 0, x2 = 0, w = 0), sep = ",")
> 0,18,1,1
> 0.5,12,0,1.5
> 1,15,0,2
> 0,13,2,1
> 0,15,1,1
> 0.5,16,1,1.5
> da <- as.data.frame(da)
> f <- glm(y ~ x1 + x2 , data = da, family = binomial(), weight = w)
> AIC(f)
> -2 * logLik(f)
> {code}
> Now, I check whether the proposed change is correct. The following calculates
> -2 * LogLik manually and get 5.6609177228379055, the same as that in R.
> {code}
> val predictions = model.transform(dataset)
> -2.0 * predictions.select("label", "prediction", "weight").rdd.map {case
> Row(y: Double, mu: Double, weight: Double) =>
> val wt = math.round(weight).toInt
> if (wt == 0){
> 0.0
> } else {
> dist.Binomial(wt, mu).logProbabilityOf(math.round(y * weight).toInt)
> }
> }.sum()
> {code}
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