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author | Michelangelo D'Agostino <mdagostino@civisanalytics.com> | 2014-11-07 22:53:01 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2014-11-07 22:53:01 -0800 |
commit | 7e9d975676d56ace0e84c2200137e4cd4eba074a (patch) | |
tree | 59f03936200f7a6a5502a5bf70e94631c03c63c7 | |
parent | 7779109796c90d789464ab0be35917f963bbe867 (diff) | |
download | spark-7e9d975676d56ace0e84c2200137e4cd4eba074a.tar.gz spark-7e9d975676d56ace0e84c2200137e4cd4eba074a.tar.bz2 spark-7e9d975676d56ace0e84c2200137e4cd4eba074a.zip |
[MLLIB] [PYTHON] SPARK-4221: Expose nonnegative ALS in the python API
SPARK-1553 added alternating nonnegative least squares to MLLib, however it's not possible to access it via the python API. This pull request resolves that.
Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com>
Closes #3095 from mdagost/python_nmf and squashes the following commits:
a6743ad [Michelangelo D'Agostino] Use setters instead of static methods in PythonMLLibAPI. Remove the new static methods I added. Set seed in tests. Change ratings to ratingsRDD in both train and trainImplicit for consistency.
7cffd39 [Michelangelo D'Agostino] Swapped nonnegative and seed in a few more places.
3fdc851 [Michelangelo D'Agostino] Moved seed to the end of the python parameter list.
bdcc154 [Michelangelo D'Agostino] Change seed type to java.lang.Long so that it can handle null.
cedf043 [Michelangelo D'Agostino] Added in ability to set the seed from python and made that play nice with the nonnegative changes. Also made the python ALS tests more exact.
a72fdc9 [Michelangelo D'Agostino] Expose nonnegative ALS in the python API.
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala | 39 | ||||
-rw-r--r-- | python/pyspark/mllib/recommendation.py | 40 |
2 files changed, 58 insertions, 21 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index d832ae34b5..70d7138e30 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -275,12 +275,25 @@ class PythonMLLibAPI extends Serializable { * the Py4J documentation. */ def trainALSModel( - ratings: JavaRDD[Rating], + ratingsJRDD: JavaRDD[Rating], rank: Int, iterations: Int, lambda: Double, - blocks: Int): MatrixFactorizationModel = { - new MatrixFactorizationModelWrapper(ALS.train(ratings.rdd, rank, iterations, lambda, blocks)) + blocks: Int, + nonnegative: Boolean, + seed: java.lang.Long): MatrixFactorizationModel = { + + val als = new ALS() + .setRank(rank) + .setIterations(iterations) + .setLambda(lambda) + .setBlocks(blocks) + .setNonnegative(nonnegative) + + if (seed != null) als.setSeed(seed) + + val model = als.run(ratingsJRDD.rdd) + new MatrixFactorizationModelWrapper(model) } /** @@ -295,9 +308,23 @@ class PythonMLLibAPI extends Serializable { iterations: Int, lambda: Double, blocks: Int, - alpha: Double): MatrixFactorizationModel = { - new MatrixFactorizationModelWrapper( - ALS.trainImplicit(ratingsJRDD.rdd, rank, iterations, lambda, blocks, alpha)) + alpha: Double, + nonnegative: Boolean, + seed: java.lang.Long): MatrixFactorizationModel = { + + val als = new ALS() + .setImplicitPrefs(true) + .setRank(rank) + .setIterations(iterations) + .setLambda(lambda) + .setBlocks(blocks) + .setAlpha(alpha) + .setNonnegative(nonnegative) + + if (seed != null) als.setSeed(seed) + + val model = als.run(ratingsJRDD.rdd) + new MatrixFactorizationModelWrapper(model) } /** diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index e8b998414d..e26b152e0c 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -44,31 +44,39 @@ class MatrixFactorizationModel(JavaModelWrapper): >>> 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) is not None - True + >>> model = ALS.trainImplicit(ratings, 1, seed=10) + >>> model.predict(2,2) + 0.4473... >>> testset = sc.parallelize([(1, 2), (1, 1)]) - >>> model = ALS.train(ratings, 1) - >>> model.predictAll(testset).count() == 2 - True + >>> model = ALS.train(ratings, 1, seed=10) + >>> model.predictAll(testset).collect() + [Rating(1, 1, 1), Rating(1, 2, 1)] - >>> model = ALS.train(ratings, 4) - >>> model.userFeatures().count() == 2 - True + >>> model = ALS.train(ratings, 4, seed=10) + >>> model.userFeatures().collect() + [(2, array('d', [...])), (1, array('d', [...]))] >>> first_user = model.userFeatures().take(1)[0] >>> latents = first_user[1] >>> len(latents) == 4 True - >>> model.productFeatures().count() == 2 - True + >>> model.productFeatures().collect() + [(2, array('d', [...])), (1, 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) + 3.735... + + >>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10) + >>> model.predict(2,2) + 0.4473... """ def predict(self, user, product): return self._java_model.predict(user, product) @@ -101,15 +109,17 @@ class ALS(object): return _to_java_object_rdd(ratings, True) @classmethod - def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1): + 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) + lambda_, blocks, nonnegative, seed) return MatrixFactorizationModel(model) @classmethod - def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01): + 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) + iterations, lambda_, blocks, alpha, nonnegative, seed) return MatrixFactorizationModel(model) |