Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/3095#discussion_r19846425
--- Diff: python/pyspark/mllib/recommendation.py ---
@@ -69,6 +69,14 @@ class MatrixFactorizationModel(JavaModelWrapper):
>>> latents = first_product[1]
>>> len(latents) == 4
True
+
+ >>> model = ALS.train(ratings, 1, nonnegative=True)
+ >>> model.predict(2,2) is not None
+ True
--- End diff --
The non-deterministic could be removed by have a fixed `seed`, but right
now we can not set seed in Python (it will be great if you could also fix it).
How about these:
```
>>> r1 = (1, 1, 1.0)
>>> r2 = (1, 2, 2.0)
>>> r3 = (2, 1, 2.0)
>>> ratings = sc.parallelize([r1, r2, r3])
>>> model = ALS.trainImplicit(ratings, 1)
>>> model.predict(2,2)
0.4473...
>>> testset = sc.parallelize([(1, 2), (1, 1)])
>>> model = ALS.train(ratings, 1)
>>> model.predictAll(testset).collect()
[Rating(1, 1, 1), Rating(1, 2, 1)]
>>> model = ALS.train(ratings, 4)
>>> model.userFeatures().collect()
[(2, array('d', [...])), (1, array('d', [...]))]
>>> model.productFeatures().collect()
[(2, array('d', [...])), (1, array('d', [...]))]
```
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