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# The ASF licenses this file to You under the Apache License, Version 2.0
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import numpy
from numpy import array
from collections import namedtuple
from pyspark import SparkContext, since
from pyspark.rdd import ignore_unicode_prefix
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
__all__ = ['FPGrowth', 'FPGrowthModel', 'PrefixSpan', 'PrefixSpanModel']
@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())
[FreqItemset(items=[u'a'], freq=4), FreqItemset(items=[u'c'], freq=3), ...
.. versionadded:: 1.4.0
"""
@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])))
class FPGrowth(object):
"""
.. note:: Experimental
A Parallel FP-growth algorithm to mine frequent itemsets.
.. versionadded:: 1.4.0
"""
@classmethod
@since("1.4.0")
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)
class FreqItemset(namedtuple("FreqItemset", ["items", "freq"])):
"""
Represents an (items, freq) tuple.
.. versionadded:: 1.4.0
"""
@inherit_doc
@ignore_unicode_prefix
class PrefixSpanModel(JavaModelWrapper):
"""
.. note:: Experimental
Model fitted by PrefixSpan
>>> 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=[[u'a']], freq=3), FreqSequence(sequence=[[u'a'], [u'a']], freq=1), ...
.. versionadded:: 1.6.0
"""
@since("1.6.0")
def freqSequences(self):
"""Gets frequence sequences"""
return self.call("getFreqSequences").map(lambda x: PrefixSpan.FreqSequence(x[0], x[1]))
class PrefixSpan(object):
"""
.. note:: Experimental
A parallel PrefixSpan algorithm to mine frequent sequential patterns.
The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan:
Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth
([[http://doi.org/10.1109/ICDE.2001.914830]]).
.. versionadded:: 1.6.0
"""
@classmethod
@since("1.6.0")
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.
:param data: The input data set, each element contains a sequnce of itemsets.
:param minSupport: the minimal support level of the sequential pattern, any pattern appears
more than (minSupport * size-of-the-dataset) times will be output (default: `0.1`)
:param maxPatternLength: the maximal length of the sequential pattern, any pattern appears
less than maxPatternLength will be output. (default: `10`)
:param maxLocalProjDBSize: 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
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()
