#
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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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from pyspark import SparkContext
from pyspark.rdd import ignore_unicode_prefix
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
__all__ = ['FPGrowth', 'FPGrowthModel']
@inherit_doc
@ignore_unicode_prefix
class FPGrowthModel(JavaModelWrapper):
"""
.. note:: Experimental
A FP-Growth model for mining frequent itemsets
using the Parallel FP-Growth algorithm.
>>> 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())
[([u'a'], 4), ([u'c'], 3), ([u'c', u'a'], 3)]
"""
def freqItemsets(self):
"""
Get the frequent itemsets of this model
"""
return self.call("getFreqItemsets")
class FPGrowth(object):
"""
.. note:: Experimental
A Parallel FP-growth algorithm to mine frequent itemsets.
"""
@classmethod
def train(cls, data, minSupport=0.3, numPartitions=-1):
"""
Computes an FP-Growth model that contains frequent itemsets.
:param data: The input data set, each element
contains a transaction.
:param minSupport: The minimal support level
(default: `0.3`).
:param numPartitions: The number of partitions used by parallel
FP-growth (default: same as input data).
"""
model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
return FPGrowthModel(model)
def _test():
import doctest
import pyspark.mllib.fpm
globs = pyspark.mllib.fpm.__dict__.copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest')
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
