[SPARK-2934][MLlib] Adding LogisticRegressionWithLBFGS Interface

for training with LBFGS Optimizer which will converge faster than SGD.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #1862 from dbtsai/dbtsai-lbfgs-lor and squashes the following commits:

aa84b81 [DB Tsai] small change
f852bcd [DB Tsai] Remove duplicate method
f119fdc [DB Tsai] Formatting
97776aa [DB Tsai] address more feedback
85b4a91 [DB Tsai] address feedback
3cf50c2 [DB Tsai] LogisticRegressionWithLBFGS interface

1 files changed, 50 insertions, 1 deletions

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 2242329b79..31d474a20f 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
@@ -101,7 +101,7 @@ class LogisticRegressionWithSGD private (
 }
 
 /**
- * Top-level methods for calling Logistic Regression.
+ * Top-level methods for calling Logistic Regression using Stochastic Gradient Descent.
  * NOTE: Labels used in Logistic Regression should be {0, 1}
  */
 object LogisticRegressionWithSGD {
@@ -188,3 +188,52 @@ 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 {
+
+  /**
+   * Construct a LogisticRegression object with default parameters
+   */
+  def this() = this(1E-4, 100, 0.0)
+
+  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 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)
+  }
+
+}