[SPARK-4306] [MLlib] Python API for LogisticRegressionWithLBFGS

```
class LogisticRegressionWithLBFGS
 |  train(cls, data, iterations=100, initialWeights=None, corrections=10, tolerance=0.0001, regParam=0.01, intercept=False)
 |      Train a logistic regression model on the given data.
 |
 |      :param data:           The training data, an RDD of LabeledPoint.
 |      :param iterations:     The number of iterations (default: 100).
 |      :param initialWeights: The initial weights (default: None).
 |      :param regParam:       The regularizer parameter (default: 0.01).
 |      :param regType:        The type of regularizer used for training
 |                             our model.
 |                             :Allowed values:
 |                               - "l1" for using L1 regularization
 |                               - "l2" for using L2 regularization
 |                               - None for no regularization
 |                               (default: "l2")
 |      :param intercept:      Boolean parameter which indicates the use
 |                             or not of the augmented representation for
 |                             training data (i.e. whether bias features
 |                             are activated or not).
 |      :param corrections:    The number of corrections used in the LBFGS update (default: 10).
 |      :param tolerance:      The convergence tolerance of iterations for L-BFGS (default: 1e-4).
 |
 |      >>> data = [
 |      ...     LabeledPoint(0.0, [0.0, 1.0]),
 |      ...     LabeledPoint(1.0, [1.0, 0.0]),
 |      ... ]
 |      >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data))
 |      >>> lrm.predict([1.0, 0.0])
 |      1
 |      >>> lrm.predict([0.0, 1.0])
 |      0
 |      >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
 |      [1, 0]
```

Author: Davies Liu <davies@databricks.com>

Closes #3307 from davies/lbfgs and squashes the following commits:

34bd986 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into lbfgs
5a945a6 [Davies Liu] address comments
941061b [Davies Liu] Merge branch 'master' of github.com:apache/spark into lbfgs
03e5543 [Davies Liu] add it to docs
ed2f9a8 [Davies Liu] add regType
76cd1b6 [Davies Liu] reorder arguments
4429a74 [Davies Liu] Update classification.py
9252783 [Davies Liu] python api for LogisticRegressionWithLBFGS

1 files changed, 35 insertions, 0 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index c8476a5370..6f94b7f483 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -230,6 +230,41 @@ class PythonMLLibAPI extends Serializable {
   }
 
   /**
+   * Java stub for Python mllib LogisticRegressionWithLBFGS.train()
+   */
+  def trainLogisticRegressionModelWithLBFGS(
+      data: JavaRDD[LabeledPoint],
+      numIterations: Int,
+      initialWeights: Vector,
+      regParam: Double,
+      regType: String,
+      intercept: Boolean,
+      corrections: Int,
+      tolerance: Double): JList[Object] = {
+    val LogRegAlg = new LogisticRegressionWithLBFGS()
+    LogRegAlg.setIntercept(intercept)
+    LogRegAlg.optimizer
+      .setNumIterations(numIterations)
+      .setRegParam(regParam)
+      .setNumCorrections(corrections)
+      .setConvergenceTol(tolerance)
+    if (regType == "l2") {
+      LogRegAlg.optimizer.setUpdater(new SquaredL2Updater)
+    } else if (regType == "l1") {
+      LogRegAlg.optimizer.setUpdater(new L1Updater)
+    } else if (regType == null) {
+      LogRegAlg.optimizer.setUpdater(new SimpleUpdater)
+    } else {
+      throw new java.lang.IllegalArgumentException("Invalid value for 'regType' parameter."
+        + " Can only be initialized using the following string values: ['l1', 'l2', None].")
+    }
+    trainRegressionModel(
+      LogRegAlg,
+      data,
+      initialWeights)
+  }
+
+  /**
    * Java stub for NaiveBayes.train()
    */
   def trainNaiveBayes(