diff options
Diffstat (limited to 'python/pyspark/mllib/recommendation.py')
-rw-r--r-- | python/pyspark/mllib/recommendation.py | 39 |
1 files changed, 39 insertions, 0 deletions
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index 4b7d17d64e..9c4647ddfd 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -65,6 +65,13 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): >>> model.userFeatures().collect() [(1, array('d', [...])), (2, array('d', [...]))] + >>> model.recommendUsers(1, 2) + [Rating(user=2, product=1, rating=1.9...), Rating(user=1, product=1, rating=1.0...)] + >>> model.recommendProducts(1, 2) + [Rating(user=1, product=2, rating=1.9...), Rating(user=1, product=1, rating=1.0...)] + >>> model.rank + 4 + >>> first_user = model.userFeatures().take(1)[0] >>> latents = first_user[1] >>> len(latents) == 4 @@ -105,9 +112,15 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): ... pass """ def predict(self, user, product): + """ + Predicts rating for the given user and product. + """ return self._java_model.predict(int(user), int(product)) def predictAll(self, user_product): + """ + Returns a list of predicted ratings for input user and product pairs. + """ assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)" first = user_product.first() assert len(first) == 2, "user_product should be RDD of (user, product)" @@ -115,11 +128,37 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): return self.call("predict", user_product) def userFeatures(self): + """ + Returns a paired RDD, where the first element is the user and the + second is an array of features corresponding to that user. + """ return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v)) def productFeatures(self): + """ + Returns a paired RDD, where the first element is the product and the + second is an array of features corresponding to that product. + """ return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v)) + def recommendUsers(self, product, num): + """ + Recommends the top "num" number of users for a given product and returns a list + of Rating objects sorted by the predicted rating in descending order. + """ + return list(self.call("recommendUsers", product, num)) + + def recommendProducts(self, user, num): + """ + Recommends the top "num" number of products for a given user and returns a list + of Rating objects sorted by the predicted rating in descending order. + """ + return list(self.call("recommendProducts", user, num)) + + @property + def rank(self): + return self.call("rank") + @classmethod def load(cls, sc, path): model = cls._load_java(sc, path) |