From 84bf931f36edf1f319c9116f7f326959a6118991 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Fri, 8 May 2015 17:24:32 -0700 Subject: [SPARK-7488] [ML] Feature Parity in PySpark for ml.recommendation Adds Python Api for `ALS` under `ml.recommendation` in PySpark. Also adds seed as a settable parameter in the Scala Implementation of ALS. Author: Burak Yavuz Closes #6015 from brkyvz/ml-rec and squashes the following commits: be6e931 [Burak Yavuz] addressed comments eaed879 [Burak Yavuz] readd numFeatures 0bd66b1 [Burak Yavuz] fixed seed 7f6d964 [Burak Yavuz] merged master 52e2bda [Burak Yavuz] added ALS --- python/pyspark/ml/recommendation.py | 279 ++++++++++++++++++++++++++++++++++++ 1 file changed, 279 insertions(+) create mode 100644 python/pyspark/ml/recommendation.py (limited to 'python/pyspark/ml/recommendation.py') diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py new file mode 100644 index 0000000000..4846b907e8 --- /dev/null +++ b/python/pyspark/ml/recommendation.py @@ -0,0 +1,279 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from pyspark.ml.util import keyword_only +from pyspark.ml.wrapper import JavaEstimator, JavaModel +from pyspark.ml.param.shared import * +from pyspark.mllib.common import inherit_doc + + +__all__ = ['ALS', 'ALSModel'] + + +@inherit_doc +class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, HasRegParam, HasSeed): + """ + Alternating Least Squares (ALS) matrix factorization. + + ALS attempts to estimate the ratings matrix `R` as the product of + two lower-rank matrices, `X` and `Y`, i.e. `X * Yt = R`. Typically + these approximations are called 'factor' matrices. The general + approach is iterative. During each iteration, one of the factor + matrices is held constant, while the other is solved for using least + squares. The newly-solved factor matrix is then held constant while + solving for the other factor matrix. + + This is a blocked implementation of the ALS factorization algorithm + that groups the two sets of factors (referred to as "users" and + "products") into blocks and reduces communication by only sending + one copy of each user vector to each product block on each + iteration, and only for the product blocks that need that user's + feature vector. This is achieved by pre-computing some information + about the ratings matrix to determine the "out-links" of each user + (which blocks of products it will contribute to) and "in-link" + information for each product (which of the feature vectors it + receives from each user block it will depend on). This allows us to + send only an array of feature vectors between each user block and + product block, and have the product block find the users' ratings + and update the products based on these messages. + + For implicit preference data, the algorithm used is based on + "Collaborative Filtering for Implicit Feedback Datasets", available + at `http://dx.doi.org/10.1109/ICDM.2008.22`, adapted for the blocked + approach used here. + + Essentially instead of finding the low-rank approximations to the + rating matrix `R`, this finds the approximations for a preference + matrix `P` where the elements of `P` are 1 if r > 0 and 0 if r <= 0. + The ratings then act as 'confidence' values related to strength of + indicated user preferences rather than explicit ratings given to + items. + + >>> als = ALS(rank=10, maxIter=5) + >>> model = als.fit(df) + >>> test = sqlContext.createDataFrame([(0, 2), (1, 0), (2, 0)], ["user", "item"]) + >>> predictions = sorted(model.transform(test).collect(), key=lambda r: r[0]) + >>> predictions[0] + Row(user=0, item=2, prediction=0.39...) + >>> predictions[1] + Row(user=1, item=0, prediction=3.19...) + >>> predictions[2] + Row(user=2, item=0, prediction=-1.15...) + """ + _java_class = "org.apache.spark.ml.recommendation.ALS" + # a placeholder to make it appear in the generated doc + rank = Param(Params._dummy(), "rank", "rank of the factorization") + numUserBlocks = Param(Params._dummy(), "numUserBlocks", "number of user blocks") + numItemBlocks = Param(Params._dummy(), "numItemBlocks", "number of item blocks") + implicitPrefs = Param(Params._dummy(), "implicitPrefs", "whether to use implicit preference") + alpha = Param(Params._dummy(), "alpha", "alpha for implicit preference") + userCol = Param(Params._dummy(), "userCol", "column name for user ids") + itemCol = Param(Params._dummy(), "itemCol", "column name for item ids") + ratingCol = Param(Params._dummy(), "ratingCol", "column name for ratings") + nonnegative = Param(Params._dummy(), "nonnegative", + "whether to use nonnegative constraint for least squares") + + @keyword_only + def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, + implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0, + ratingCol="rating", nonnegative=False, checkpointInterval=10): + """ + __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, + implicitPrefs=false, alpha=1.0, userCol="user", itemCol="item", seed=0, + ratingCol="rating", nonnegative=false, checkpointInterval=10) + """ + super(ALS, self).__init__() + self.rank = Param(self, "rank", "rank of the factorization") + self.numUserBlocks = Param(self, "numUserBlocks", "number of user blocks") + self.numItemBlocks = Param(self, "numItemBlocks", "number of item blocks") + self.implicitPrefs = Param(self, "implicitPrefs", "whether to use implicit preference") + self.alpha = Param(self, "alpha", "alpha for implicit preference") + self.userCol = Param(self, "userCol", "column name for user ids") + self.itemCol = Param(self, "itemCol", "column name for item ids") + self.ratingCol = Param(self, "ratingCol", "column name for ratings") + self.nonnegative = Param(self, "nonnegative", + "whether to use nonnegative constraint for least squares") + self._setDefault(rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, + implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0, + ratingCol="rating", nonnegative=False, checkpointInterval=10) + kwargs = self.__init__._input_kwargs + self.setParams(**kwargs) + + @keyword_only + def setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, + implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0, + ratingCol="rating", nonnegative=False, checkpointInterval=10): + """ + setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, + implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0, + ratingCol="rating", nonnegative=False, checkpointInterval=10) + Sets params for ALS. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def _create_model(self, java_model): + return ALSModel(java_model) + + def setRank(self, value): + """ + Sets the value of :py:attr:`rank`. + """ + self.paramMap[self.rank] = value + return self + + def getRank(self): + """ + Gets the value of rank or its default value. + """ + return self.getOrDefault(self.rank) + + def setNumUserBlocks(self, value): + """ + Sets the value of :py:attr:`numUserBlocks`. + """ + self.paramMap[self.numUserBlocks] = value + return self + + def getNumUserBlocks(self): + """ + Gets the value of numUserBlocks or its default value. + """ + return self.getOrDefault(self.numUserBlocks) + + def setNumItemBlocks(self, value): + """ + Sets the value of :py:attr:`numItemBlocks`. + """ + self.paramMap[self.numItemBlocks] = value + return self + + def getNumItemBlocks(self): + """ + Gets the value of numItemBlocks or its default value. + """ + return self.getOrDefault(self.numItemBlocks) + + def setNumBlocks(self, value): + """ + Sets both :py:attr:`numUserBlocks` and :py:attr:`numItemBlocks` to the specific value. + """ + self.paramMap[self.numUserBlocks] = value + self.paramMap[self.numItemBlocks] = value + + def setImplicitPrefs(self, value): + """ + Sets the value of :py:attr:`implicitPrefs`. + """ + self.paramMap[self.implicitPrefs] = value + return self + + def getImplicitPrefs(self): + """ + Gets the value of implicitPrefs or its default value. + """ + return self.getOrDefault(self.implicitPrefs) + + def setAlpha(self, value): + """ + Sets the value of :py:attr:`alpha`. + """ + self.paramMap[self.alpha] = value + return self + + def getAlpha(self): + """ + Gets the value of alpha or its default value. + """ + return self.getOrDefault(self.alpha) + + def setUserCol(self, value): + """ + Sets the value of :py:attr:`userCol`. + """ + self.paramMap[self.userCol] = value + return self + + def getUserCol(self): + """ + Gets the value of userCol or its default value. + """ + return self.getOrDefault(self.userCol) + + def setItemCol(self, value): + """ + Sets the value of :py:attr:`itemCol`. + """ + self.paramMap[self.itemCol] = value + return self + + def getItemCol(self): + """ + Gets the value of itemCol or its default value. + """ + return self.getOrDefault(self.itemCol) + + def setRatingCol(self, value): + """ + Sets the value of :py:attr:`ratingCol`. + """ + self.paramMap[self.ratingCol] = value + return self + + def getRatingCol(self): + """ + Gets the value of ratingCol or its default value. + """ + return self.getOrDefault(self.ratingCol) + + def setNonnegative(self, value): + """ + Sets the value of :py:attr:`nonnegative`. + """ + self.paramMap[self.nonnegative] = value + return self + + def getNonnegative(self): + """ + Gets the value of nonnegative or its default value. + """ + return self.getOrDefault(self.nonnegative) + + +class ALSModel(JavaModel): + """ + Model fitted by ALS. + """ + + +if __name__ == "__main__": + import doctest + from pyspark.context import SparkContext + from pyspark.sql import SQLContext + globs = globals().copy() + # The small batch size here ensures that we see multiple batches, + # even in these small test examples: + sc = SparkContext("local[2]", "ml.recommendation tests") + sqlContext = SQLContext(sc) + globs['sc'] = sc + globs['sqlContext'] = sqlContext + globs['df'] = sqlContext.createDataFrame([(0, 0, 4.0), (0, 1, 2.0), (1, 1, 3.0), (1, 2, 4.0), + (2, 1, 1.0), (2, 2, 5.0)], ["user", "item", "rating"]) + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) + sc.stop() + if failure_count: + exit(-1) -- cgit v1.2.3