[SPARK-5536] replace old ALS implementation by the new one

The only issue is that `analyzeBlock` is removed, which was marked as a developer API. I didn't change other tests in the ALSSuite under `spark.mllib` to ensure that the implementation is correct.

CC: srowen coderxiang

Author: Xiangrui Meng <meng@databricks.com>

Closes #4321 from mengxr/SPARK-5536 and squashes the following commits:

5a3cee8 [Xiangrui Meng] update python tests that are too strict
e840acf [Xiangrui Meng] ignore scala style check for ALS.train
e9a721c [Xiangrui Meng] update mima excludes
9ee6a36 [Xiangrui Meng] merge master
9a8aeac [Xiangrui Meng] update tests
d8c3271 [Xiangrui Meng] remove analyzeBlocks
d68eee7 [Xiangrui Meng] add checkpoint to new ALS
22a56f8 [Xiangrui Meng] wrap old ALS
c387dff [Xiangrui Meng] support random seed
3bdf24b [Xiangrui Meng] make storage level configurable in the new ALS

1 files changed, 8 insertions, 8 deletions

diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 97ec74eda0..0d99e6dedf 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -49,17 +49,17 @@ class MatrixFactorizationModel(JavaModelWrapper):
     >>> r3 = (2, 1, 2.0)
     >>> ratings = sc.parallelize([r1, r2, r3])
     >>> model = ALS.trainImplicit(ratings, 1, seed=10)
-    >>> model.predict(2,2)
-    0.4473...
+    >>> model.predict(2, 2)
+    0.43...
 
     >>> testset = sc.parallelize([(1, 2), (1, 1)])
-    >>> model = ALS.train(ratings, 1, seed=10)
+    >>> model = ALS.train(ratings, 2, seed=0)
     >>> model.predictAll(testset).collect()
-    [Rating(user=1, product=1, rating=1.0471...), Rating(user=1, product=2, rating=1.9679...)]
+    [Rating(user=1, product=1, rating=1.0...), Rating(user=1, product=2, rating=1.9...)]
 
     >>> model = ALS.train(ratings, 4, seed=10)
     >>> model.userFeatures().collect()
-    [(2, array('d', [...])), (1, array('d', [...]))]
+    [(1, array('d', [...])), (2, array('d', [...]))]
 
     >>> first_user = model.userFeatures().take(1)[0]
     >>> latents = first_user[1]
@@ -67,7 +67,7 @@ class MatrixFactorizationModel(JavaModelWrapper):
     True
 
     >>> model.productFeatures().collect()
-    [(2, array('d', [...])), (1, array('d', [...]))]
+    [(1, array('d', [...])), (2, array('d', [...]))]
 
     >>> first_product = model.productFeatures().take(1)[0]
     >>> latents = first_product[1]
@@ -76,11 +76,11 @@ class MatrixFactorizationModel(JavaModelWrapper):
 
     >>> model = ALS.train(ratings, 1, nonnegative=True, seed=10)
     >>> model.predict(2,2)
-    3.735...
+    3.8...
 
     >>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10)
     >>> model.predict(2,2)
-    0.4473...
+    0.43...
     """
     def predict(self, user, product):
         return self._java_model.predict(int(user), int(product))