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[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 <brkyvz@gmail.com> Closes apache#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
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| # | ||
| # 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. | ||
| # | ||
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| 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 | ||
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| __all__ = ['ALS', 'ALSModel'] | ||
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| @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") | ||
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| @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) | ||
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| @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) | ||
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| def _create_model(self, java_model): | ||
| return ALSModel(java_model) | ||
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| def setRank(self, value): | ||
| """ | ||
| Sets the value of :py:attr:`rank`. | ||
| """ | ||
| self.paramMap[self.rank] = value | ||
| return self | ||
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| def getRank(self): | ||
| """ | ||
| Gets the value of rank or its default value. | ||
| """ | ||
| return self.getOrDefault(self.rank) | ||
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| def setNumUserBlocks(self, value): | ||
| """ | ||
| Sets the value of :py:attr:`numUserBlocks`. | ||
| """ | ||
| self.paramMap[self.numUserBlocks] = value | ||
| return self | ||
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| def getNumUserBlocks(self): | ||
| """ | ||
| Gets the value of numUserBlocks or its default value. | ||
| """ | ||
| return self.getOrDefault(self.numUserBlocks) | ||
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| def setNumItemBlocks(self, value): | ||
| """ | ||
| Sets the value of :py:attr:`numItemBlocks`. | ||
| """ | ||
| self.paramMap[self.numItemBlocks] = value | ||
| return self | ||
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| def getNumItemBlocks(self): | ||
| """ | ||
| Gets the value of numItemBlocks or its default value. | ||
| """ | ||
| return self.getOrDefault(self.numItemBlocks) | ||
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| 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 | ||
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| def setImplicitPrefs(self, value): | ||
| """ | ||
| Sets the value of :py:attr:`implicitPrefs`. | ||
| """ | ||
| self.paramMap[self.implicitPrefs] = value | ||
| return self | ||
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| def getImplicitPrefs(self): | ||
| """ | ||
| Gets the value of implicitPrefs or its default value. | ||
| """ | ||
| return self.getOrDefault(self.implicitPrefs) | ||
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| def setAlpha(self, value): | ||
| """ | ||
| Sets the value of :py:attr:`alpha`. | ||
| """ | ||
| self.paramMap[self.alpha] = value | ||
| return self | ||
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| def getAlpha(self): | ||
| """ | ||
| Gets the value of alpha or its default value. | ||
| """ | ||
| return self.getOrDefault(self.alpha) | ||
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| def setUserCol(self, value): | ||
| """ | ||
| Sets the value of :py:attr:`userCol`. | ||
| """ | ||
| self.paramMap[self.userCol] = value | ||
| return self | ||
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| def getUserCol(self): | ||
| """ | ||
| Gets the value of userCol or its default value. | ||
| """ | ||
| return self.getOrDefault(self.userCol) | ||
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| def setItemCol(self, value): | ||
| """ | ||
| Sets the value of :py:attr:`itemCol`. | ||
| """ | ||
| self.paramMap[self.itemCol] = value | ||
| return self | ||
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| def getItemCol(self): | ||
| """ | ||
| Gets the value of itemCol or its default value. | ||
| """ | ||
| return self.getOrDefault(self.itemCol) | ||
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| def setRatingCol(self, value): | ||
| """ | ||
| Sets the value of :py:attr:`ratingCol`. | ||
| """ | ||
| self.paramMap[self.ratingCol] = value | ||
| return self | ||
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| def getRatingCol(self): | ||
| """ | ||
| Gets the value of ratingCol or its default value. | ||
| """ | ||
| return self.getOrDefault(self.ratingCol) | ||
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| def setNonnegative(self, value): | ||
| """ | ||
| Sets the value of :py:attr:`nonnegative`. | ||
| """ | ||
| self.paramMap[self.nonnegative] = value | ||
| return self | ||
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| def getNonnegative(self): | ||
| """ | ||
| Gets the value of nonnegative or its default value. | ||
| """ | ||
| return self.getOrDefault(self.nonnegative) | ||
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| class ALSModel(JavaModel): | ||
| """ | ||
| Model fitted by ALS. | ||
| """ | ||
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| 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) |