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# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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import sys
from pyspark import since, keyword_only
from pyspark.ml.util import *
from pyspark.ml.wrapper import JavaEstimator, JavaModel
from pyspark.ml.param.shared import *
from pyspark.ml.common import inherit_doc
__all__ = ['ALS', 'ALSModel']
[docs]@inherit_doc
class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, HasRegParam, HasSeed,
JavaMLWritable, JavaMLReadable):
"""
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",
<https://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.
>>> df = spark.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"])
>>> als = ALS(rank=10, maxIter=5, seed=0)
>>> model = als.fit(df)
>>> model.rank
10
>>> model.userFactors.orderBy("id").collect()
[Row(id=0, features=[...]), Row(id=1, ...), Row(id=2, ...)]
>>> test = spark.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=-0.13807615637779236)
>>> predictions[1]
Row(user=1, item=0, prediction=2.6258413791656494)
>>> predictions[2]
Row(user=2, item=0, prediction=-1.5018409490585327)
>>> user_recs = model.recommendForAllUsers(3)
>>> user_recs.where(user_recs.user == 0)\
.select("recommendations.item", "recommendations.rating").collect()
[Row(item=[0, 1, 2], rating=[3.910..., 1.992..., -0.138...])]
>>> item_recs = model.recommendForAllItems(3)
>>> item_recs.where(item_recs.item == 2)\
.select("recommendations.user", "recommendations.rating").collect()
[Row(user=[2, 1, 0], rating=[4.901..., 3.981..., -0.138...])]
>>> user_subset = df.where(df.user == 2)
>>> user_subset_recs = model.recommendForUserSubset(user_subset, 3)
>>> user_subset_recs.select("recommendations.item", "recommendations.rating").first()
Row(item=[2, 1, 0], rating=[4.901..., 1.056..., -1.501...])
>>> item_subset = df.where(df.item == 0)
>>> item_subset_recs = model.recommendForItemSubset(item_subset, 3)
>>> item_subset_recs.select("recommendations.user", "recommendations.rating").first()
Row(user=[0, 1, 2], rating=[3.910..., 2.625..., -1.501...])
>>> als_path = temp_path + "/als"
>>> als.save(als_path)
>>> als2 = ALS.load(als_path)
>>> als.getMaxIter()
5
>>> model_path = temp_path + "/als_model"
>>> model.save(model_path)
>>> model2 = ALSModel.load(model_path)
>>> model.rank == model2.rank
True
>>> sorted(model.userFactors.collect()) == sorted(model2.userFactors.collect())
True
>>> sorted(model.itemFactors.collect()) == sorted(model2.itemFactors.collect())
True
.. versionadded:: 1.4.0
"""
rank = Param(Params._dummy(), "rank", "rank of the factorization",
typeConverter=TypeConverters.toInt)
numUserBlocks = Param(Params._dummy(), "numUserBlocks", "number of user blocks",
typeConverter=TypeConverters.toInt)
numItemBlocks = Param(Params._dummy(), "numItemBlocks", "number of item blocks",
typeConverter=TypeConverters.toInt)
implicitPrefs = Param(Params._dummy(), "implicitPrefs", "whether to use implicit preference",
typeConverter=TypeConverters.toBoolean)
alpha = Param(Params._dummy(), "alpha", "alpha for implicit preference",
typeConverter=TypeConverters.toFloat)
userCol = Param(Params._dummy(), "userCol", "column name for user ids. Ids must be within " +
"the integer value range.", typeConverter=TypeConverters.toString)
itemCol = Param(Params._dummy(), "itemCol", "column name for item ids. Ids must be within " +
"the integer value range.", typeConverter=TypeConverters.toString)
ratingCol = Param(Params._dummy(), "ratingCol", "column name for ratings",
typeConverter=TypeConverters.toString)
nonnegative = Param(Params._dummy(), "nonnegative",
"whether to use nonnegative constraint for least squares",
typeConverter=TypeConverters.toBoolean)
intermediateStorageLevel = Param(Params._dummy(), "intermediateStorageLevel",
"StorageLevel for intermediate datasets. Cannot be 'NONE'.",
typeConverter=TypeConverters.toString)
finalStorageLevel = Param(Params._dummy(), "finalStorageLevel",
"StorageLevel for ALS model factors.",
typeConverter=TypeConverters.toString)
coldStartStrategy = Param(Params._dummy(), "coldStartStrategy", "strategy for dealing with " +
"unknown or new users/items at prediction time. This may be useful " +
"in cross-validation or production scenarios, for handling " +
"user/item ids the model has not seen in the training data. " +
"Supported values: 'nan', 'drop'.",
typeConverter=TypeConverters.toString)
@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=None,
ratingCol="rating", nonnegative=False, checkpointInterval=10,
intermediateStorageLevel="MEMORY_AND_DISK",
finalStorageLevel="MEMORY_AND_DISK", coldStartStrategy="nan"):
"""
__init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \
implicitPrefs=false, alpha=1.0, userCol="user", itemCol="item", seed=None, \
ratingCol="rating", nonnegative=false, checkpointInterval=10, \
intermediateStorageLevel="MEMORY_AND_DISK", \
finalStorageLevel="MEMORY_AND_DISK", coldStartStrategy="nan")
"""
super(ALS, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.recommendation.ALS", self.uid)
self._setDefault(rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item",
ratingCol="rating", nonnegative=False, checkpointInterval=10,
intermediateStorageLevel="MEMORY_AND_DISK",
finalStorageLevel="MEMORY_AND_DISK", coldStartStrategy="nan")
kwargs = self._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
@since("1.4.0")
def setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
ratingCol="rating", nonnegative=False, checkpointInterval=10,
intermediateStorageLevel="MEMORY_AND_DISK",
finalStorageLevel="MEMORY_AND_DISK", coldStartStrategy="nan"):
"""
setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \
implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None, \
ratingCol="rating", nonnegative=False, checkpointInterval=10, \
intermediateStorageLevel="MEMORY_AND_DISK", \
finalStorageLevel="MEMORY_AND_DISK", coldStartStrategy="nan")
Sets params for ALS.
"""
kwargs = self._input_kwargs
return self._set(**kwargs)
def _create_model(self, java_model):
return ALSModel(java_model)
[docs] @since("1.4.0")
def setRank(self, value):
"""
Sets the value of :py:attr:`rank`.
"""
return self._set(rank=value)
[docs] @since("1.4.0")
def getRank(self):
"""
Gets the value of rank or its default value.
"""
return self.getOrDefault(self.rank)
[docs] @since("1.4.0")
def setNumUserBlocks(self, value):
"""
Sets the value of :py:attr:`numUserBlocks`.
"""
return self._set(numUserBlocks=value)
[docs] @since("1.4.0")
def getNumUserBlocks(self):
"""
Gets the value of numUserBlocks or its default value.
"""
return self.getOrDefault(self.numUserBlocks)
[docs] @since("1.4.0")
def setNumItemBlocks(self, value):
"""
Sets the value of :py:attr:`numItemBlocks`.
"""
return self._set(numItemBlocks=value)
[docs] @since("1.4.0")
def getNumItemBlocks(self):
"""
Gets the value of numItemBlocks or its default value.
"""
return self.getOrDefault(self.numItemBlocks)
[docs] @since("1.4.0")
def setNumBlocks(self, value):
"""
Sets both :py:attr:`numUserBlocks` and :py:attr:`numItemBlocks` to the specific value.
"""
self._set(numUserBlocks=value)
return self._set(numItemBlocks=value)
[docs] @since("1.4.0")
def setImplicitPrefs(self, value):
"""
Sets the value of :py:attr:`implicitPrefs`.
"""
return self._set(implicitPrefs=value)
[docs] @since("1.4.0")
def getImplicitPrefs(self):
"""
Gets the value of implicitPrefs or its default value.
"""
return self.getOrDefault(self.implicitPrefs)
[docs] @since("1.4.0")
def setAlpha(self, value):
"""
Sets the value of :py:attr:`alpha`.
"""
return self._set(alpha=value)
[docs] @since("1.4.0")
def getAlpha(self):
"""
Gets the value of alpha or its default value.
"""
return self.getOrDefault(self.alpha)
[docs] @since("1.4.0")
def setUserCol(self, value):
"""
Sets the value of :py:attr:`userCol`.
"""
return self._set(userCol=value)
[docs] @since("1.4.0")
def getUserCol(self):
"""
Gets the value of userCol or its default value.
"""
return self.getOrDefault(self.userCol)
[docs] @since("1.4.0")
def setItemCol(self, value):
"""
Sets the value of :py:attr:`itemCol`.
"""
return self._set(itemCol=value)
[docs] @since("1.4.0")
def getItemCol(self):
"""
Gets the value of itemCol or its default value.
"""
return self.getOrDefault(self.itemCol)
[docs] @since("1.4.0")
def setRatingCol(self, value):
"""
Sets the value of :py:attr:`ratingCol`.
"""
return self._set(ratingCol=value)
[docs] @since("1.4.0")
def getRatingCol(self):
"""
Gets the value of ratingCol or its default value.
"""
return self.getOrDefault(self.ratingCol)
[docs] @since("1.4.0")
def setNonnegative(self, value):
"""
Sets the value of :py:attr:`nonnegative`.
"""
return self._set(nonnegative=value)
[docs] @since("1.4.0")
def getNonnegative(self):
"""
Gets the value of nonnegative or its default value.
"""
return self.getOrDefault(self.nonnegative)
[docs] @since("2.0.0")
def setFinalStorageLevel(self, value):
"""
Sets the value of :py:attr:`finalStorageLevel`.
"""
return self._set(finalStorageLevel=value)
[docs] @since("2.0.0")
def getFinalStorageLevel(self):
"""
Gets the value of finalStorageLevel or its default value.
"""
return self.getOrDefault(self.finalStorageLevel)
[docs] @since("2.2.0")
def setColdStartStrategy(self, value):
"""
Sets the value of :py:attr:`coldStartStrategy`.
"""
return self._set(coldStartStrategy=value)
[docs] @since("2.2.0")
def getColdStartStrategy(self):
"""
Gets the value of coldStartStrategy or its default value.
"""
return self.getOrDefault(self.coldStartStrategy)
[docs]class ALSModel(JavaModel, JavaMLWritable, JavaMLReadable):
"""
Model fitted by ALS.
.. versionadded:: 1.4.0
"""
@property
@since("1.4.0")
def rank(self):
"""rank of the matrix factorization model"""
return self._call_java("rank")
@property
@since("1.4.0")
def userFactors(self):
"""
a DataFrame that stores user factors in two columns: `id` and
`features`
"""
return self._call_java("userFactors")
@property
@since("1.4.0")
def itemFactors(self):
"""
a DataFrame that stores item factors in two columns: `id` and
`features`
"""
return self._call_java("itemFactors")
[docs] @since("2.2.0")
def recommendForAllUsers(self, numItems):
"""
Returns top `numItems` items recommended for each user, for all users.
:param numItems: max number of recommendations for each user
:return: a DataFrame of (userCol, recommendations), where recommendations are
stored as an array of (itemCol, rating) Rows.
"""
return self._call_java("recommendForAllUsers", numItems)
[docs] @since("2.2.0")
def recommendForAllItems(self, numUsers):
"""
Returns top `numUsers` users recommended for each item, for all items.
:param numUsers: max number of recommendations for each item
:return: a DataFrame of (itemCol, recommendations), where recommendations are
stored as an array of (userCol, rating) Rows.
"""
return self._call_java("recommendForAllItems", numUsers)
[docs] @since("2.3.0")
def recommendForUserSubset(self, dataset, numItems):
"""
Returns top `numItems` items recommended for each user id in the input data set. Note that
if there are duplicate ids in the input dataset, only one set of recommendations per unique
id will be returned.
:param dataset: a Dataset containing a column of user ids. The column name must match
`userCol`.
:param numItems: max number of recommendations for each user
:return: a DataFrame of (userCol, recommendations), where recommendations are
stored as an array of (itemCol, rating) Rows.
"""
return self._call_java("recommendForUserSubset", dataset, numItems)
[docs] @since("2.3.0")
def recommendForItemSubset(self, dataset, numUsers):
"""
Returns top `numUsers` users recommended for each item id in the input data set. Note that
if there are duplicate ids in the input dataset, only one set of recommendations per unique
id will be returned.
:param dataset: a Dataset containing a column of item ids. The column name must match
`itemCol`.
:param numUsers: max number of recommendations for each item
:return: a DataFrame of (itemCol, recommendations), where recommendations are
stored as an array of (userCol, rating) Rows.
"""
return self._call_java("recommendForItemSubset", dataset, numUsers)
if __name__ == "__main__":
import doctest
import pyspark.ml.recommendation
from pyspark.sql import SparkSession
globs = pyspark.ml.recommendation.__dict__.copy()
# The small batch size here ensures that we see multiple batches,
# even in these small test examples:
spark = SparkSession.builder\
.master("local[2]")\
.appName("ml.recommendation tests")\
.getOrCreate()
sc = spark.sparkContext
globs['sc'] = sc
globs['spark'] = spark
import tempfile
temp_path = tempfile.mkdtemp()
globs['temp_path'] = temp_path
try:
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
spark.stop()
finally:
from shutil import rmtree
try:
rmtree(temp_path)
except OSError:
pass
if failure_count:
sys.exit(-1)