Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/17715#discussion_r112736608
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
---
@@ -1149,6 +1300,49 @@ class LogisticRegressionSuite
assert(model2.interceptVector.toArray.sum ~== 0.0 absTol eps)
}
+ test("multinomial logistic regression with intercept without
regularization with bound") {
+ val lowerBoundOfCoefficients = Matrices.dense(3, 4,
Array.fill(12)(1.0))
+ val lowerBoundOfIntercept = Vectors.dense(Array.fill(3)(1.0))
+
+ val trainer1 = new LogisticRegression()
+ .setLowerBoundOfCoefficients(lowerBoundOfCoefficients)
+ .setLowerBoundOfIntercept(lowerBoundOfIntercept)
+ .setFitIntercept(true)
+ .setStandardization(true)
+ .setWeightCol("weight")
+ val trainer2 = new LogisticRegression()
+ .setLowerBoundOfCoefficients(lowerBoundOfCoefficients)
+ .setLowerBoundOfIntercept(lowerBoundOfIntercept)
+ .setFitIntercept(true)
+ .setStandardization(false)
+ .setWeightCol("weight")
+
+ val model1 = trainer1.fit(multinomialDataset)
+ val model2 = trainer2.fit(multinomialDataset)
+
+ // The solution is generated by
https://github.com/yanboliang/bound-optimization.
+ val coefficientsExpected = new DenseMatrix(3, 4, Array(
+ 2.52076464, 2.73596057, 1.87984904, 2.73264492,
+ 1.93302281, 3.71363303, 1.50681746, 1.93398782,
+ 2.37839917, 1.93601818, 1.81924758, 2.45191255), isTransposed = true)
+ val interceptsExpected = Vectors.dense(1.00010477, 3.44237083,
4.86740286)
+
+ model1.coefficientMatrix.colIter.zip(coefficientsExpected.colIter)
+ .foreach { case (col1: Vector, col2: Vector) =>
+ (col1.asBreeze - col2.asBreeze).toArray.toSeq.sliding(2).foreach {
+ case Seq(v1, v2) => assert(v1 ~== v2 absTol 1E-3)
+ }
+ }
--- End diff --
When reg == 0, multinomial logistic regression has multiple solutions and
we centralize the coefficients to get identical result for non-bound
regression, but we didn't do this for bound constrained regression, since it
may cross the bound if we centralize them. So here we check whether
```coefficients1``` equals to ```coefficientsExpected + constant value``` for
each column.
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