Source code for pyspark.mllib.fpm

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import numpy
from numpy import array
from collections import namedtuple

from pyspark import SparkContext
from pyspark.rdd import ignore_unicode_prefix
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc

__all__ = ['FPGrowth', 'FPGrowthModel']

[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), ... """
[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. """ @classmethod
[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. """
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()