Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17715#discussion_r113073040
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
---
@@ -815,6 +904,40 @@ class LogisticRegressionSuite
assert(model2.coefficients ~= coefficientsR relTol 1E-3)
}
+ test("binary logistic regression with intercept with L2 regularization
with bound") {
+ val upperBoundOfCoefficients = Matrices.dense(1, 4, Array(1.0, 0.0,
1.0, 0.0))
+ val upperBoundOfIntercept = Vectors.dense(1.0)
+
+ val trainer1 = new LogisticRegression()
+ .setUpperBoundOfCoefficients(upperBoundOfCoefficients)
+ .setUpperBoundOfIntercept(upperBoundOfIntercept)
+ .setRegParam(1.37)
+ .setFitIntercept(true)
+ .setStandardization(true)
+ .setWeightCol("weight")
+ val trainer2 = new LogisticRegression()
+ .setUpperBoundOfCoefficients(upperBoundOfCoefficients)
+ .setUpperBoundOfIntercept(upperBoundOfIntercept)
+ .setRegParam(1.37)
+ .setFitIntercept(true)
+ .setStandardization(false)
+ .setWeightCol("weight")
+
+ val model1 = trainer1.fit(binaryDataset)
+ val model2 = trainer2.fit(binaryDataset)
+
+ // The solution is generated by
https://github.com/yanboliang/bound-optimization.
+ val coefficientsExpectedStd = Vectors.dense(-0.06985003, 0.0,
-0.04794278, -0.10168595)
--- End diff --
`coefficientsExpectedWithStd` is easier to read :)
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