diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
index 56b02b65d8724d34401f8dbac47750e13a0a591c..a6f78d2441db1eb95055480d22172ab38d5678ab 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
@@ -21,7 +21,7 @@ import org.apache.log4j.{Level, Logger}
import scopt.OptionParser
import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.mllib.classification.{LogisticRegressionWithSGD, SVMWithSGD}
+import org.apache.spark.mllib.classification.{LogisticRegressionWithLBFGS, SVMWithSGD}
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater}
@@ -66,7 +66,8 @@ object BinaryClassification {
.text("number of iterations")
.action((x, c) => c.copy(numIterations = x))
opt[Double]("stepSize")
- .text(s"initial step size, default: ${defaultParams.stepSize}")
+ .text("initial step size (ignored by logistic regression), " +
+ s"default: ${defaultParams.stepSize}")
.action((x, c) => c.copy(stepSize = x))
opt[String]("algorithm")
.text(s"algorithm (${Algorithm.values.mkString(",")}), " +
@@ -125,10 +126,9 @@ object BinaryClassification {
val model = params.algorithm match {
case LR =>
- val algorithm = new LogisticRegressionWithSGD()
+ val algorithm = new LogisticRegressionWithLBFGS()
algorithm.optimizer
.setNumIterations(params.numIterations)
- .setStepSize(params.stepSize)
.setUpdater(updater)
.setRegParam(params.regParam)
algorithm.run(training).clearThreshold()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 6790c86f651b4370eada5e53fb8c64a99104841e..486bdbfa9cb4793fce222984613ada340ee7fbb3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -73,6 +73,8 @@ class LogisticRegressionModel (
/**
* Train a classification model for Logistic Regression using Stochastic Gradient Descent.
* NOTE: Labels used in Logistic Regression should be {0, 1}
+ *
+ * Using [[LogisticRegressionWithLBFGS]] is recommended over this.
*/
class LogisticRegressionWithSGD private (
private var stepSize: Double,
@@ -191,51 +193,19 @@ object LogisticRegressionWithSGD {
/**
* Train a classification model for Logistic Regression using Limited-memory BFGS.
+ * Standard feature scaling and L2 regularization are used by default.
* 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)
+class LogisticRegressionWithLBFGS
extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable {
- /**
- * Construct a LogisticRegression object with default parameters
- */
- def this() = this(1E-4, 100, 0.0)
-
this.setFeatureScaling(true)
- private val gradient = new LogisticGradient()
- private val updater = new SimpleUpdater()
- // Have to return new LBFGS object every time since users can reset the parameters anytime.
- override def optimizer = new LBFGS(gradient, updater)
- .setNumCorrections(10)
- .setConvergenceTol(convergenceTol)
- .setMaxNumIterations(maxNumIterations)
- .setRegParam(regParam)
+ override val optimizer = new LBFGS(new LogisticGradient, new SquaredL2Updater)
override protected val validators = List(DataValidators.binaryLabelValidator)
- /**
- * 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(convergenceTol: Double): this.type = {
- this.convergenceTol = convergenceTol
- this
- }
-
- /**
- * Set the maximal number of iterations for L-BFGS. Default 100.
- */
- def setNumIterations(numIterations: Int): this.type = {
- this.maxNumIterations = numIterations
- this
- }
-
override protected def createModel(weights: Vector, intercept: Double) = {
new LogisticRegressionModel(weights, intercept)
}
-
}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
index 033fe44f34f3cb6c851eff30c224087be8fe345a..d16d0daf085650d4218825f37043555585b43c84 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
@@ -69,8 +69,17 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater)
/**
* Set the maximal number of iterations for L-BFGS. Default 100.
+ * @deprecated use [[LBFGS#setNumIterations]] instead
*/
+ @deprecated("use setNumIterations instead", "1.1.0")
def setMaxNumIterations(iters: Int): this.type = {
+ this.setNumIterations(iters)
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setNumIterations(iters: Int): this.type = {
this.maxNumIterations = iters
this
}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index bc05b2046878f6cbc93aaf89a653404962c5f673..862178694a50e7ee6f45b2194263f4b0aa717ba5 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -272,8 +272,9 @@ class LogisticRegressionClusterSuite extends FunSuite with LocalClusterSparkCont
}.cache()
// If we serialize data directly in the task closure, the size of the serialized task would be
// greater than 1MB and hence Spark would throw an error.
- val model =
- (new LogisticRegressionWithLBFGS().setIntercept(true).setNumIterations(2)).run(points)
+ val lr = new LogisticRegressionWithLBFGS().setIntercept(true)
+ lr.optimizer.setNumIterations(2)
+ val model = lr.run(points)
val predictions = model.predict(points.map(_.features))
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
index 5f4c24115ac80b98c8b9e1e5d5d1ada1cbe4a614..ccba004baa0077e2198b8e02e6575aae46f9e011 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
@@ -55,7 +55,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
val initialWeightsWithIntercept = Vectors.dense(1.0 +: initialWeights.toArray)
val convergenceTol = 1e-12
- val maxNumIterations = 10
+ val numIterations = 10
val (_, loss) = LBFGS.runLBFGS(
dataRDD,
@@ -63,7 +63,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
simpleUpdater,
numCorrections,
convergenceTol,
- maxNumIterations,
+ numIterations,
regParam,
initialWeightsWithIntercept)
@@ -99,7 +99,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
// Prepare another non-zero weights to compare the loss in the first iteration.
val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
val convergenceTol = 1e-12
- val maxNumIterations = 10
+ val numIterations = 10
val (weightLBFGS, lossLBFGS) = LBFGS.runLBFGS(
dataRDD,
@@ -107,7 +107,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
squaredL2Updater,
numCorrections,
convergenceTol,
- maxNumIterations,
+ numIterations,
regParam,
initialWeightsWithIntercept)
@@ -140,10 +140,10 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
/**
* For the first run, we set the convergenceTol to 0.0, so that the algorithm will
- * run up to the maxNumIterations which is 8 here.
+ * run up to the numIterations which is 8 here.
*/
val initialWeightsWithIntercept = Vectors.dense(0.0, 0.0)
- val maxNumIterations = 8
+ val numIterations = 8
var convergenceTol = 0.0
val (_, lossLBFGS1) = LBFGS.runLBFGS(
@@ -152,7 +152,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
squaredL2Updater,
numCorrections,
convergenceTol,
- maxNumIterations,
+ numIterations,
regParam,
initialWeightsWithIntercept)
@@ -167,7 +167,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
squaredL2Updater,
numCorrections,
convergenceTol,
- maxNumIterations,
+ numIterations,
regParam,
initialWeightsWithIntercept)
@@ -182,7 +182,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
squaredL2Updater,
numCorrections,
convergenceTol,
- maxNumIterations,
+ numIterations,
regParam,
initialWeightsWithIntercept)
@@ -200,12 +200,12 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
// Prepare another non-zero weights to compare the loss in the first iteration.
val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
val convergenceTol = 1e-12
- val maxNumIterations = 10
+ val numIterations = 10
val lbfgsOptimizer = new LBFGS(gradient, squaredL2Updater)
.setNumCorrections(numCorrections)
.setConvergenceTol(convergenceTol)
- .setMaxNumIterations(maxNumIterations)
+ .setNumIterations(numIterations)
.setRegParam(regParam)
val weightLBFGS = lbfgsOptimizer.optimize(dataRDD, initialWeightsWithIntercept)
@@ -241,7 +241,7 @@ class LBFGSClusterSuite extends FunSuite with LocalClusterSparkContext {
val lbfgs = new LBFGS(new LogisticGradient, new SquaredL2Updater)
.setNumCorrections(1)
.setConvergenceTol(1e-12)
- .setMaxNumIterations(1)
+ .setNumIterations(1)
.setRegParam(1.0)
val random = new Random(0)
// If we serialize data directly in the task closure, the size of the serialized task would be