Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/10702#discussion_r51354803 --- Diff: mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala --- @@ -558,6 +575,47 @@ class LinearRegressionSuite } } + test("linear regression model with constant label") { + /* + R code: + for (formula in c(b.const ~ . -1, b.const ~ .)) { + model <- lm(formula, data=df.const.label, weights=w) + print(as.vector(coef(model))) + } + [1] -9.221298 3.394343 + [1] 17 0 0 + */ + val expected = Seq( + Vectors.dense(0.0, -9.221298, 3.394343), + Vectors.dense(17.0, 0.0, 0.0)) + + Seq("auto", "l-bfgs", "normal").foreach { solver => + var idx = 0 + for (fitIntercept <- Seq(false, true)) { + val model = new LinearRegression() + .setFitIntercept(fitIntercept) + .setWeightCol("weight") + .setSolver(solver) + .fit(datasetWithWeightConstantLabel) + val actual = Vectors.dense(model.intercept, model.coefficients(0), model.coefficients(1)) + assert(actual ~== expected(idx) absTol 1e-4) + idx += 1 --- End diff -- When `fitInercept = true`, check the size of loss history is zero. (since the solution is returned without any optimization.) Will be nice to add one small test that `labelStd = 0` and `labelMean = 0` when `fitInercept = false`
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