Source code for pyspark.mllib.recommendation

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from collections import namedtuple

from pyspark import SparkContext
from pyspark.rdd import RDD
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
from pyspark.mllib.util import JavaLoader, JavaSaveable

__all__ = ['MatrixFactorizationModel', 'ALS', 'Rating']


[docs]class Rating(namedtuple("Rating", ["user", "product", "rating"])): """ Represents a (user, product, rating) tuple. >>> r = Rating(1, 2, 5.0) >>> (r.user, r.product, r.rating) (1, 2, 5.0) >>> (r[0], r[1], r[2]) (1, 2, 5.0) """ def __reduce__(self): return Rating, (int(self.user), int(self.product), float(self.rating))
@inherit_doc
[docs]class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): """A matrix factorisation model trained by regularized alternating least-squares. >>> 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, seed=10) >>> model.predict(2, 2) 0.43... >>> testset = sc.parallelize([(1, 2), (1, 1)]) >>> model = ALS.train(ratings, 2, seed=0) >>> model.predictAll(testset).collect() [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() [(1, array('d', [...])), (2, array('d', [...]))] >>> first_user = model.userFeatures().take(1)[0] >>> latents = first_user[1] >>> len(latents) == 4 True >>> model.productFeatures().collect() [(1, array('d', [...])), (2, array('d', [...]))] >>> first_product = model.productFeatures().take(1)[0] >>> latents = first_product[1] >>> len(latents) == 4 True >>> model = ALS.train(ratings, 1, nonnegative=True, seed=10) >>> model.predict(2,2) 3.8... >>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10) >>> model.predict(2,2) 0.43... >>> import os, tempfile >>> path = tempfile.mkdtemp() >>> model.save(sc, path) >>> sameModel = MatrixFactorizationModel.load(sc, path) >>> sameModel.predict(2,2) 0.43... >>> try: ... os.removedirs(path) ... except OSError: ... pass """
[docs] def predict(self, user, product): return self._java_model.predict(int(user), int(product))
[docs] def predictAll(self, user_product): 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)" user_product = user_product.map(lambda (u, p): (int(u), int(p))) return self.call("predict", user_product)
[docs] def userFeatures(self): return self.call("getUserFeatures")
[docs] def productFeatures(self): return self.call("getProductFeatures")
[docs]class ALS(object): @classmethod def _prepare(cls, ratings): assert isinstance(ratings, RDD), "ratings should be RDD" first = ratings.first() if not isinstance(first, Rating): if isinstance(first, (tuple, list)): ratings = ratings.map(lambda x: Rating(*x)) else: raise ValueError("rating should be RDD of Rating or tuple/list") return ratings @classmethod
[docs] def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False, seed=None): model = callMLlibFunc("trainALSModel", cls._prepare(ratings), rank, iterations, lambda_, blocks, nonnegative, seed) return MatrixFactorizationModel(model)
@classmethod
[docs] def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01, nonnegative=False, seed=None): model = callMLlibFunc("trainImplicitALSModel", cls._prepare(ratings), rank, iterations, lambda_, blocks, alpha, nonnegative, seed) return MatrixFactorizationModel(model)
def _test(): import doctest import pyspark.mllib.recommendation globs = pyspark.mllib.recommendation.__dict__.copy() globs['sc'] = SparkContext('local[4]', 'PythonTest') (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) if __name__ == "__main__": _test()