Source code for pyspark.mllib.fpm

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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']

[docs]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")
[docs] def freqItemsets(self): """ Returns the frequent itemsets of this model. """ return"getFreqItemsets").map(lambda x: (FPGrowth.FreqItemset(x[0], x[1])))
[docs]class FPGrowth(object): """ .. note:: Experimental A Parallel FP-growth algorithm to mine frequent itemsets. .. versionadded:: 1.4.0 """ @classmethod @since("1.4.0")
[docs] 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)
[docs] class FreqItemset(namedtuple("FreqItemset", ["items", "freq"])): """ Represents an (items, freq) tuple. .. versionadded:: 1.4.0 """
@inherit_doc @ignore_unicode_prefix
[docs]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")
[docs] def freqSequences(self): """Gets frequence sequences""" return"getFreqSequences").map(lambda x: PrefixSpan.FreqSequence(x[0], x[1]))
[docs]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 ([[]]). .. versionadded:: 1.6.0 """ @classmethod @since("1.6.0")
[docs] 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)
[docs] 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()