[SPARK-7333] [MLLIB] Add BinaryClassificationEvaluator to PySpark

This PR adds `BinaryClassificationEvaluator` to Python ML Pipelines API, which is a simple wrapper of the Scala implementation. oefirouz

Author: Xiangrui Meng <meng@databricks.com>

Closes #5885 from mengxr/SPARK-7333 and squashes the following commits:

25d7451 [Xiangrui Meng] fix tests in python 3
babdde7 [Xiangrui Meng] fix doc
cb51e6a [Xiangrui Meng] add BinaryClassificationEvaluator in PySpark

1 files changed, 16 insertions, 1 deletions

diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py
index 394f23c5e9..73741c4b40 100644
--- a/python/pyspark/ml/wrapper.py
+++ b/python/pyspark/ml/wrapper.py
@@ -20,7 +20,7 @@ from abc import ABCMeta
 from pyspark import SparkContext
 from pyspark.sql import DataFrame
 from pyspark.ml.param import Params
-from pyspark.ml.pipeline import Estimator, Transformer
+from pyspark.ml.pipeline import Estimator, Transformer, Evaluator
 from pyspark.mllib.common import inherit_doc
 
 
@@ -147,3 +147,18 @@ class JavaModel(JavaTransformer):
 
     def _java_obj(self):
         return self._java_model
+
+
+@inherit_doc
+class JavaEvaluator(Evaluator, JavaWrapper):
+    """
+    Base class for :py:class:`Evaluator`s that wrap Java/Scala
+    implementations.
+    """
+
+    __metaclass__ = ABCMeta
+
+    def evaluate(self, dataset, params={}):
+        java_obj = self._java_obj()
+        self._transfer_params_to_java(params, java_obj)
+        return java_obj.evaluate(dataset._jdf, self._empty_java_param_map())