Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1862#discussion_r16023020
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
---
@@ -188,3 +188,54 @@ object LogisticRegressionWithSGD {
train(input, numIterations, 1.0, 1.0)
}
}
+
+/**
+ * Train a classification model for Logistic Regression using
Limited-memory BFGS.
+ * NOTE: Labels used in Logistic Regression should be {0, 1}
+ */
+class LogisticRegressionWithLBFGS private (
+ private var convergenceTol: Double,
+ private var maxNumIterations: Int,
+ private var regParam: Double)
+ extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with
Serializable {
+
+ private val gradient = new LogisticGradient()
+ private val updater = new SimpleUpdater()
+ // Have to be lazy since users can change the parameters after the class
is created.
+ // PS, after the first train, the optimizer variable will be computed,
so the parameters
+ // can not be changed anymore.
+ override lazy val optimizer = new LBFGS(gradient, updater)
+ .setNumCorrections(10)
+ .setConvergenceTol(convergenceTol)
+ .setMaxNumIterations(maxNumIterations)
+ .setRegParam(regParam)
+
+ override protected val validators =
List(DataValidators.binaryLabelValidator)
+
+ /**
+ * Construct a LogisticRegression object with default parameters
+ */
+ def this() = this(1E-4, 100, 0.0)
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvergenceTol(tolerance: Double): this.type = {
--- End diff --
`tolerance` -> `convergenceTol`?
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