[SPARK-4324] [PySpark] [MLlib] support numpy.array for all MLlib API

This PR check all of the existing Python MLlib API to make sure that numpy.array is supported as Vector (also RDD of numpy.array).

It also improve some docstring and doctest.

cc mateiz mengxr

Author: Davies Liu <davies@databricks.com>

Closes #3189 from davies/numpy and squashes the following commits:

d5057c4 [Davies Liu] fix tests
6987611 [Davies Liu] support numpy.array for all MLlib API

1 files changed, 3 insertions, 3 deletions

diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index e26b152e0c..41bbd9a779 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -32,7 +32,7 @@ class Rating(object):
         return Rating, (self.user, self.product, self.rating)
 
     def __repr__(self):
-        return "Rating(%d, %d, %d)" % (self.user, self.product, self.rating)
+        return "Rating(%d, %d, %s)" % (self.user, self.product, self.rating)
 
 
 class MatrixFactorizationModel(JavaModelWrapper):
@@ -51,7 +51,7 @@ class MatrixFactorizationModel(JavaModelWrapper):
     >>> testset = sc.parallelize([(1, 2), (1, 1)])
     >>> model = ALS.train(ratings, 1, seed=10)
     >>> model.predictAll(testset).collect()
-    [Rating(1, 1, 1), Rating(1, 2, 1)]
+    [Rating(1, 1, 1.0471...), Rating(1, 2, 1.9679...)]
 
     >>> model = ALS.train(ratings, 4, seed=10)
     >>> model.userFeatures().collect()
@@ -79,7 +79,7 @@ class MatrixFactorizationModel(JavaModelWrapper):
     0.4473...
     """
     def predict(self, user, product):
-        return self._java_model.predict(user, product)
+        return self._java_model.predict(int(user), int(product))
 
     def predictAll(self, user_product):
         assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)"