[Spark-24024][ML] Fix poisson deviance calculations in GLM to handle …

…y = 0

## What changes were proposed in this pull request?

It is reported by Spark users that the deviance calculation for poisson regression does not handle y = 0. Thus, the correct model summary cannot be obtained. The user has confirmed the the issue is in
```
override def deviance(y: Double, mu: Double, weight: Double): Double =
{ 2.0 * weight * (y * math.log(y / mu) - (y - mu)) }
when y = 0.
```

The user also mentioned there are many other places he believe we should check the same thing. However, no other changes are needed, including Gamma distribution.

## How was this patch tested?
Add a comparison with R deviance calculation to the existing unit test.

Author: Teng Peng <josephtengpeng@gmail.com>

Closes #21125 from tengpeng/Spark24024GLM.

mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala

@@ -471,6 +471,10 @@ object GeneralizedLinearRegression extends DefaultParamsReadable[GeneralizedLine
 
   private[regression] val epsilon: Double = 1E-16
 
+  private[regression] def ylogy(y: Double, mu: Double): Double = {
+    if (y == 0) 0.0 else y * math.log(y / mu)
+  }
+
   /**
    * Wrapper of family and link combination used in the model.
    */
@@ -725,10 +729,6 @@ object GeneralizedLinearRegression extends DefaultParamsReadable[GeneralizedLine
 
     override def variance(mu: Double): Double = mu * (1.0 - mu)
 
-    private def ylogy(y: Double, mu: Double): Double = {
-      if (y == 0) 0.0 else y * math.log(y / mu)
-    }
-
     override def deviance(y: Double, mu: Double, weight: Double): Double = {
       2.0 * weight * (ylogy(y, mu) + ylogy(1.0 - y, 1.0 - mu))
     }
@@ -783,7 +783,7 @@ object GeneralizedLinearRegression extends DefaultParamsReadable[GeneralizedLine
     override def variance(mu: Double): Double = mu
 
     override def deviance(y: Double, mu: Double, weight: Double): Double = {
-      2.0 * weight * (y * math.log(y / mu) - (y - mu))
+      2.0 * weight * (ylogy(y, mu) - (y - mu))
     }
 
     override def aic(

mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala

@@ -493,11 +493,20 @@ class GeneralizedLinearRegressionSuite extends MLTest with DefaultReadWriteTest
        }
        [1] -0.0457441 -0.6833928
        [1] 1.8121235  -0.1747493  -0.5815417
+
+       R code for deivance calculation:
+       data = cbind(y=c(0,1,0,0,0,1), x1=c(18, 12, 15, 13, 15, 16), x2=c(1,0,0,2,1,1))
+       summary(glm(y~x1+x2, family=poisson, data=data.frame(data)))$deviance
+       [1] 3.70055
+       summary(glm(y~x1+x2-1, family=poisson, data=data.frame(data)))$deviance
+       [1] 3.809296
      */
     val expected = Seq(
       Vectors.dense(0.0, -0.0457441, -0.6833928),
       Vectors.dense(1.8121235, -0.1747493, -0.5815417))
 
+    val residualDeviancesR = Array(3.809296, 3.70055)
+
     import GeneralizedLinearRegression._
 
     var idx = 0
@@ -510,6 +519,7 @@ class GeneralizedLinearRegressionSuite extends MLTest with DefaultReadWriteTest
       val actual = Vectors.dense(model.intercept, model.coefficients(0), model.coefficients(1))
       assert(actual ~= expected(idx) absTol 1e-4, "Model mismatch: GLM with poisson family, " +
         s"$link link and fitIntercept = $fitIntercept (with zero values).")
+      assert(model.summary.deviance ~== residualDeviancesR(idx) absTol 1E-3)
       idx += 1
     }
   }