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author | Yu ISHIKAWA <yuu.ishikawa@gmail.com> | 2015-09-16 04:34:14 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-09-16 04:34:14 -0700 |
commit | d9b7f3e4dbceb91ea4d1a1fed3ab847335f8588b (patch) | |
tree | b66b1e7eabf64c404dbcbc29e4b134596bd73353 | |
parent | 1894653edce718e874d1ddc9ba442bce43cbc082 (diff) | |
download | spark-d9b7f3e4dbceb91ea4d1a1fed3ab847335f8588b.tar.gz spark-d9b7f3e4dbceb91ea4d1a1fed3ab847335f8588b.tar.bz2 spark-d9b7f3e4dbceb91ea4d1a1fed3ab847335f8588b.zip |
[SPARK-10276] [MLLIB] [PYSPARK] Add @since annotation to pyspark.mllib.recommendation
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes #8677 from yu-iskw/SPARK-10276.
-rw-r--r-- | python/pyspark/mllib/recommendation.py | 36 |
1 files changed, 35 insertions, 1 deletions
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index 506ca2151c..95047b5b7b 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -18,7 +18,7 @@ import array from collections import namedtuple -from pyspark import SparkContext +from pyspark import SparkContext, since from pyspark.rdd import RDD from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc from pyspark.mllib.util import JavaLoader, JavaSaveable @@ -36,6 +36,8 @@ class Rating(namedtuple("Rating", ["user", "product", "rating"])): (1, 2, 5.0) >>> (r[0], r[1], r[2]) (1, 2, 5.0) + + .. versionadded:: 1.2.0 """ def __reduce__(self): @@ -111,13 +113,17 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): ... rmtree(path) ... except OSError: ... pass + + .. versionadded:: 0.9.0 """ + @since("0.9.0") def predict(self, user, product): """ Predicts rating for the given user and product. """ return self._java_model.predict(int(user), int(product)) + @since("0.9.0") def predictAll(self, user_product): """ Returns a list of predicted ratings for input user and product pairs. @@ -128,6 +134,7 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): user_product = user_product.map(lambda u_p: (int(u_p[0]), int(u_p[1]))) return self.call("predict", user_product) + @since("1.2.0") def userFeatures(self): """ Returns a paired RDD, where the first element is the user and the @@ -135,6 +142,7 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): """ return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v)) + @since("1.2.0") def productFeatures(self): """ Returns a paired RDD, where the first element is the product and the @@ -142,6 +150,7 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): """ return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v)) + @since("1.4.0") def recommendUsers(self, product, num): """ Recommends the top "num" number of users for a given product and returns a list @@ -149,6 +158,7 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): """ return list(self.call("recommendUsers", product, num)) + @since("1.4.0") def recommendProducts(self, user, num): """ Recommends the top "num" number of products for a given user and returns a list @@ -157,17 +167,25 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): return list(self.call("recommendProducts", user, num)) @property + @since("1.4.0") def rank(self): + """Rank for the features in this model""" return self.call("rank") @classmethod + @since("1.3.1") def load(cls, sc, path): + """Load a model from the given path""" model = cls._load_java(sc, path) wrapper = sc._jvm.MatrixFactorizationModelWrapper(model) return MatrixFactorizationModel(wrapper) class ALS(object): + """Alternating Least Squares matrix factorization + + .. versionadded:: 0.9.0 + """ @classmethod def _prepare(cls, ratings): @@ -188,15 +206,31 @@ class ALS(object): return ratings @classmethod + @since("0.9.0") def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False, seed=None): + """ + Train a matrix factorization model given an RDD of ratings given by users to some products, + in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the + product of two lower-rank matrices of a given rank (number of features). To solve for these + features, we run a given number of iterations of ALS. This is done using a level of + parallelism given by `blocks`. + """ model = callMLlibFunc("trainALSModel", cls._prepare(ratings), rank, iterations, lambda_, blocks, nonnegative, seed) return MatrixFactorizationModel(model) @classmethod + @since("0.9.0") def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01, nonnegative=False, seed=None): + """ + Train a matrix factorization model given an RDD of 'implicit preferences' given by users + to some products, in the form of (userID, productID, preference) pairs. We approximate the + ratings matrix as the product of two lower-rank matrices of a given rank (number of + features). To solve for these features, we run a given number of iterations of ALS. + This is done using a level of parallelism given by `blocks`. + """ model = callMLlibFunc("trainImplicitALSModel", cls._prepare(ratings), rank, iterations, lambda_, blocks, alpha, nonnegative, seed) return MatrixFactorizationModel(model) |