Source code for pyspark.mllib.fpm

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import sys

from typing import Any, Generic, List, NamedTuple, TypeVar

from pyspark import since, SparkContext
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
from pyspark.mllib.util import JavaSaveable, JavaLoader, inherit_doc
from pyspark.rdd import RDD

__all__ = ["FPGrowth", "FPGrowthModel", "PrefixSpan", "PrefixSpanModel"]

T = TypeVar("T")

[docs]@inherit_doc class FPGrowthModel(JavaModelWrapper, JavaSaveable, JavaLoader["FPGrowthModel"]): """ 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_path) >>> sameModel = FPGrowthModel.load(sc, model_path) >>> sorted(model.freqItemsets().collect()) == sorted(sameModel.freqItemsets().collect()) True """
[docs] @since("1.4.0") def freqItemsets(self) -> RDD["FPGrowth.FreqItemset"]: """ Returns the frequent itemsets of this model. """ return"getFreqItemsets").map(lambda x: (FPGrowth.FreqItemset(x[0], x[1])))
[docs] @classmethod @since("2.0.0") def load(cls, sc: SparkContext, path: str) -> "FPGrowthModel": """ Load a model from the given path. """ model = cls._load_java(sc, path) assert sc._jvm is not None wrapper = return FPGrowthModel(wrapper)
[docs]class FPGrowth: """ A Parallel FP-growth algorithm to mine frequent itemsets. .. versionadded:: 1.4.0 """
[docs] @classmethod def train( cls, data: RDD[List[T]], minSupport: float = 0.3, numPartitions: int = -1 ) -> "FPGrowthModel": """ 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): """ Represents an (items, freq) tuple. .. versionadded:: 1.4.0 """ items: List[Any] freq: int
[docs]@inherit_doc class PrefixSpanModel(JavaModelWrapper, Generic[T]): """ 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) -> RDD["PrefixSpan.FreqSequence"]: """Gets frequent sequences""" return"getFreqSequences").map(lambda x: PrefixSpan.FreqSequence(x[0], x[1]))
[docs]class PrefixSpan: """ 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: """
[docs] @classmethod def train( cls, data: RDD[List[List[T]]], minSupport: float = 0.1, maxPatternLength: int = 10, maxLocalProjDBSize: int = 32000000, ) -> PrefixSpanModel[T]: """ 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): """ Represents a (sequence, freq) tuple. .. versionadded:: 1.6.0 """ sequence: List[List[Any]] freq: int
def _test() -> None: 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()