[SPARK-9016] [ML] make random forest classifiers implement classification trait

Implement the classification trait for RandomForestClassifiers. The plan is to use this in the future to providing thresholding for RandomForestClassifiers (as well as other classifiers that implement that trait).

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7432 from holdenk/SPARK-9016-make-random-forest-classifiers-implement-classification-trait and squashes the following commits:

bf22fa6 [Holden Karau] Add missing imports for testing suite
e948f0d [Holden Karau] Check the prediction generation from rawprediciton
25320c3 [Holden Karau] Don't supply numClasses when not needed, assert model classes are as expected
1a67e04 [Holden Karau] Use old decission tree stuff instead
673e0c3 [Holden Karau] Merge branch 'master' into SPARK-9016-make-random-forest-classifiers-implement-classification-trait
0d15b96 [Holden Karau] FIx typo
5eafad4 [Holden Karau] add a constructor for rootnode + num classes
fc6156f [Holden Karau] scala style fix
2597915 [Holden Karau] take num classes in constructor
3ccfe4a [Holden Karau] Merge in master, make pass numClasses through randomforest for training
222a10b [Holden Karau] Increase numtrees to 3 in the python test since before the two were equal and the argmax was selecting the last one
16aea1c [Holden Karau] Make tests match the new models
b454a02 [Holden Karau] Make the Tree classifiers extends the Classifier base class
77b4114 [Holden Karau] Import vectors lib

1 files changed, 2 insertions, 2 deletions

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 89117e4928..5a82bc286d 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -299,9 +299,9 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
     >>> si_model = stringIndexer.fit(df)
     >>> td = si_model.transform(df)
-    >>> rf = RandomForestClassifier(numTrees=2, maxDepth=2, labelCol="indexed", seed=42)
+    >>> rf = RandomForestClassifier(numTrees=3, maxDepth=2, labelCol="indexed", seed=42)
     >>> model = rf.fit(td)
-    >>> allclose(model.treeWeights, [1.0, 1.0])
+    >>> allclose(model.treeWeights, [1.0, 1.0, 1.0])
     True
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction