Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17715#discussion_r113071333
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
---
@@ -650,6 +711,34 @@ class LogisticRegressionSuite
assert(model2.coefficients ~= coefficientsR relTol 1E-2)
}
+ test("binary logistic regression without intercept without
regularization with bound") {
+ val upperBoundOfCoefficients = Matrices.dense(1, 4, Array(1.0, 0.0,
1.0, 0.0))
+
+ val trainer1 = new LogisticRegression()
+ .setUpperBoundOfCoefficients(upperBoundOfCoefficients)
+ .setFitIntercept(false)
+ .setStandardization(true)
+ .setWeightCol("weight")
+ val trainer2 = new LogisticRegression()
+ .setUpperBoundOfCoefficients(upperBoundOfCoefficients)
+ .setFitIntercept(false)
+ .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 coefficientsExpected = Vectors.dense(0.20847553, 0.0, -0.24240289,
-0.55568071)
+
+ assert(model1.intercept ~== 0.0 relTol 1E-3)
+ assert(model1.coefficients ~= coefficientsExpected relTol 1E-3)
+
+ // Without regularization, with or without standardization will
converge to the same solution.
+ assert(model2.intercept ~== 0.0 relTol 1E-3)
+ assert(model2.coefficients ~= coefficientsExpected relTol 1E-3)
+ }
+
test("binary logistic regression with intercept with L1 regularization")
{
--- End diff --
Can we add a test having lower bounds for non-negativity?
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