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import sys
from collections import namedtuple
from pyspark import since
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
from pyspark.mllib.util import JavaSaveable, JavaLoader, inherit_doc
__all__ = ['FPGrowth', 'FPGrowthModel', 'PrefixSpan', 'PrefixSpanModel']
[docs]@inherit_doc
class FPGrowthModel(JavaModelWrapper, JavaSaveable, JavaLoader):
"""
A FP-Growth model for mining frequent itemsets
using the Parallel FP-Growth algorithm.
.. versionadded:: 1.4.0
Examples
--------
>>> data = [["a", "b", "c"], ["a", "b", "d", "e"], ["a", "c", "e"], ["a", "c", "f"]]
>>> rdd = sc.parallelize(data, 2)
>>> model = FPGrowth.train(rdd, 0.6, 2)
>>> sorted(model.freqItemsets().collect())
[FreqItemset(items=['a'], freq=4), FreqItemset(items=['c'], freq=3), ...
>>> model_path = temp_path + "/fpm"
>>> model.save(sc, model_path)
>>> sameModel = FPGrowthModel.load(sc, model_path)
>>> sorted(model.freqItemsets().collect()) == sorted(sameModel.freqItemsets().collect())
True
"""
[docs] @since("1.4.0")
def freqItemsets(self):
"""
Returns the frequent itemsets of this model.
"""
return self.call("getFreqItemsets").map(lambda x: (FPGrowth.FreqItemset(x[0], x[1])))
[docs] @classmethod
@since("2.0.0")
def load(cls, sc, path):
"""
Load a model from the given path.
"""
model = cls._load_java(sc, path)
wrapper = sc._jvm.org.apache.spark.mllib.api.python.FPGrowthModelWrapper(model)
return FPGrowthModel(wrapper)
[docs]class FPGrowth(object):
"""
A Parallel FP-growth algorithm to mine frequent itemsets.
.. versionadded:: 1.4.0
"""
[docs] @classmethod
def train(cls, data, minSupport=0.3, numPartitions=-1):
"""
Computes an FP-Growth model that contains frequent itemsets.
.. versionadded:: 1.4.0
Parameters
----------
data : :py:class:`pyspark.RDD`
The input data set, each element contains a transaction.
minSupport : float, optional
The minimal support level.
(default: 0.3)
numPartitions : int, optional
The number of partitions used by parallel FP-growth. A value
of -1 will use the same number as input data.
(default: -1)
"""
model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
return FPGrowthModel(model)
class FreqItemset(namedtuple("FreqItemset", ["items", "freq"])):
"""
Represents an (items, freq) tuple.
.. versionadded:: 1.4.0
"""
[docs]@inherit_doc
class PrefixSpanModel(JavaModelWrapper):
"""
Model fitted by PrefixSpan
.. versionadded:: 1.6.0
Examples
--------
>>> data = [
... [["a", "b"], ["c"]],
... [["a"], ["c", "b"], ["a", "b"]],
... [["a", "b"], ["e"]],
... [["f"]]]
>>> rdd = sc.parallelize(data, 2)
>>> model = PrefixSpan.train(rdd)
>>> sorted(model.freqSequences().collect())
[FreqSequence(sequence=[['a']], freq=3), FreqSequence(sequence=[['a'], ['a']], freq=1), ...
"""
[docs] @since("1.6.0")
def freqSequences(self):
"""Gets frequent sequences"""
return self.call("getFreqSequences").map(lambda x: PrefixSpan.FreqSequence(x[0], x[1]))
[docs]class PrefixSpan(object):
"""
A parallel PrefixSpan algorithm to mine frequent sequential patterns.
The PrefixSpan algorithm is described in Jian Pei et al (2001) [1]_
.. versionadded:: 1.6.0
.. [1] Jian Pei et al.,
"PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth,"
Proceedings 17th International Conference on Data Engineering, Heidelberg,
Germany, 2001, pp. 215-224,
doi: https://doi.org/10.1109/ICDE.2001.914830
"""
[docs] @classmethod
def train(cls, data, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000):
"""
Finds the complete set of frequent sequential patterns in the
input sequences of itemsets.
.. versionadded:: 1.6.0
Parameters
----------
data : :py:class:`pyspark.RDD`
The input data set, each element contains a sequence of
itemsets.
minSupport : float, optional
The minimal support level of the sequential pattern, any
pattern that appears more than (minSupport *
size-of-the-dataset) times will be output.
(default: 0.1)
maxPatternLength : int, optional
The maximal length of the sequential pattern, any pattern
that appears less than maxPatternLength will be output.
(default: 10)
maxLocalProjDBSize : int, optional
The maximum number of items (including delimiters used in the
internal storage format) allowed in a projected database before
local processing. If a projected database exceeds this size,
another iteration of distributed prefix growth is run.
(default: 32000000)
"""
model = callMLlibFunc("trainPrefixSpanModel",
data, minSupport, maxPatternLength, maxLocalProjDBSize)
return PrefixSpanModel(model)
class FreqSequence(namedtuple("FreqSequence", ["sequence", "freq"])):
"""
Represents a (sequence, freq) tuple.
.. versionadded:: 1.6.0
"""
def _test():
import doctest
from pyspark.sql import SparkSession
import pyspark.mllib.fpm
globs = pyspark.mllib.fpm.__dict__.copy()
spark = SparkSession.builder\
.master("local[4]")\
.appName("mllib.fpm tests")\
.getOrCreate()
globs['sc'] = spark.sparkContext
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)
if __name__ == "__main__":
_test()