[SPARK-9214] [ML] [PySpark] support ml.NaiveBayes for Python

support ml.NaiveBayes for Python

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7568 from yanboliang/spark-9214 and squashes the following commits:

5ee3fd6 [Yanbo Liang] fix typos
3ecd046 [Yanbo Liang] fix typos
f9c94d1 [Yanbo Liang] change lambda_ to smoothing and fix other issues
180452a [Yanbo Liang] fix typos
7dda1f4 [Yanbo Liang] support ml.NaiveBayes for Python

3 files changed, 10 insertions, 10 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 1f547e4a98..5be35fe209 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -38,11 +38,11 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
    * (default = 1.0).
    * @group param
    */
-  final val lambda: DoubleParam = new DoubleParam(this, "lambda", "The smoothing parameter.",
+  final val smoothing: DoubleParam = new DoubleParam(this, "smoothing", "The smoothing parameter.",
     ParamValidators.gtEq(0))
 
   /** @group getParam */
-  final def getLambda: Double = $(lambda)
+  final def getSmoothing: Double = $(smoothing)
 
   /**
    * The model type which is a string (case-sensitive).
@@ -79,8 +79,8 @@ class NaiveBayes(override val uid: String)
    * Default is 1.0.
    * @group setParam
    */
-  def setLambda(value: Double): this.type = set(lambda, value)
-  setDefault(lambda -> 1.0)
+  def setSmoothing(value: Double): this.type = set(smoothing, value)
+  setDefault(smoothing -> 1.0)
 
   /**
    * Set the model type using a string (case-sensitive).
@@ -92,7 +92,7 @@ class NaiveBayes(override val uid: String)
 
   override protected def train(dataset: DataFrame): NaiveBayesModel = {
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
-    val oldModel = OldNaiveBayes.train(oldDataset, $(lambda), $(modelType))
+    val oldModel = OldNaiveBayes.train(oldDataset, $(smoothing), $(modelType))
     NaiveBayesModel.fromOld(oldModel, this)
   }
 
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
index 09a9fba0c1..a700c9cddb 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
@@ -68,7 +68,7 @@ public class JavaNaiveBayesSuite implements Serializable {
     assert(nb.getLabelCol() == "label");
     assert(nb.getFeaturesCol() == "features");
     assert(nb.getPredictionCol() == "prediction");
-    assert(nb.getLambda() == 1.0);
+    assert(nb.getSmoothing() == 1.0);
     assert(nb.getModelType() == "multinomial");
   }
 
@@ -89,7 +89,7 @@ public class JavaNaiveBayesSuite implements Serializable {
     });
 
     DataFrame dataset = jsql.createDataFrame(jrdd, schema);
-    NaiveBayes nb = new NaiveBayes().setLambda(0.5).setModelType("multinomial");
+    NaiveBayes nb = new NaiveBayes().setSmoothing(0.5).setModelType("multinomial");
     NaiveBayesModel model = nb.fit(dataset);
 
     DataFrame predictionAndLabels = model.transform(dataset).select("prediction", "label");
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
index 76381a2741..264bde3703 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
@@ -58,7 +58,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(nb.getLabelCol === "label")
     assert(nb.getFeaturesCol === "features")
     assert(nb.getPredictionCol === "prediction")
-    assert(nb.getLambda === 1.0)
+    assert(nb.getSmoothing === 1.0)
     assert(nb.getModelType === "multinomial")
   }
 
@@ -75,7 +75,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     val testDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
       piArray, thetaArray, nPoints, 42, "multinomial"))
-    val nb = new NaiveBayes().setLambda(1.0).setModelType("multinomial")
+    val nb = new NaiveBayes().setSmoothing(1.0).setModelType("multinomial")
     val model = nb.fit(testDataset)
 
     validateModelFit(pi, theta, model)
@@ -101,7 +101,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     val testDataset = sqlContext.createDataFrame(generateNaiveBayesInput(
       piArray, thetaArray, nPoints, 45, "bernoulli"))
-    val nb = new NaiveBayes().setLambda(1.0).setModelType("bernoulli")
+    val nb = new NaiveBayes().setSmoothing(1.0).setModelType("bernoulli")
     val model = nb.fit(testDataset)
 
     validateModelFit(pi, theta, model)