Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/3833#discussion_r23823961
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
---
@@ -285,6 +377,97 @@ class LogisticRegressionSuite extends FunSuite with
MLlibTestSparkContext with M
assert(modelB1.weights(0) !~== modelB3.weights(0) * 1.0E6 absTol 0.1)
}
+ test("multinomial logistic regression with LBFGS") {
+ val nPoints = 10000
+
+ /**
+ * The following weights and xMean/xVariance are computed from iris
dataset with lambda = 0.2.
+ * As a result, we are actually drawing samples from probability
distribution of built model.
+ */
+ val weights = Array(
+ -0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
+ -0.16624, -0.84355, -0.048509, -0.301789, 4.170682)
+
+ val xMean = Array(5.843, 3.057, 3.758, 1.199)
+ val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
+
+ val testData =
LogisticRegressionSuite.generateMultinomialLogisticInput(
+ weights, xMean, xVariance, true, nPoints, 42)
+
+ val testRDD = sc.parallelize(testData, 2)
+ testRDD.cache()
+
+ val lr = new
LogisticRegressionWithLBFGS().setIntercept(true).setNumOfClasses(3)
+ lr.optimizer.setConvergenceTol(1E-15).setNumIterations(200)
+
+ val model = lr.run(testRDD)
+
+ /**
+ * The following is the instruction to reproduce the model using R's
glmnet package.
+ *
+ * First of all, using the following scala code to save the data into
`path`.
+ *
+ * testRDD.map(x => x.label+ ", " + x.features(0) + ", " +
x.features(1) + ", " +
+ * x.features(2) + ", " + x.features(3)).saveAsTextFile("path")
+ *
+ * Using the following R code to load the data and train the model
using glmnet package.
+ *
+ * library("glmnet")
+ * data <- read.csv("/Users/dbtsai/data.csv/a", header=FALSE)
--- End diff --
remove dbtsai
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