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author | Yanbo Liang <ybliang8@gmail.com> | 2015-07-30 23:03:48 -0700 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-07-30 23:03:48 -0700 |
commit | 69b62f76fced18efa35a107c9be4bc22eba72878 (patch) | |
tree | 9cef7ff52d64a096694765badf01e4ea7352d881 /mllib/src/test/java | |
parent | 4e5919bfb47a58bcbda90ae01c1bed2128ded983 (diff) | |
download | spark-69b62f76fced18efa35a107c9be4bc22eba72878.tar.gz spark-69b62f76fced18efa35a107c9be4bc22eba72878.tar.bz2 spark-69b62f76fced18efa35a107c9be4bc22eba72878.zip |
[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
Diffstat (limited to 'mllib/src/test/java')
-rw-r--r-- | mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java | 4 |
1 files changed, 2 insertions, 2 deletions
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"); |