# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # 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 # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import sys from collections import namedtuple from pyspark import since from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc from pyspark.mllib.util import JavaSaveable, JavaLoader, inherit_doc __all__ = ['FPGrowth', 'FPGrowthModel', 'PrefixSpan', 'PrefixSpanModel'] [docs]@inherit_doc class FPGrowthModel(JavaModelWrapper, JavaSaveable, JavaLoader): """ 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.save(sc, 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): """ Returns the frequent itemsets of this model. """ return self.call("getFreqItemsets").map(lambda x: (FPGrowth.FreqItemset(x[0], x[1]))) [docs] @classmethod @since("2.0.0") def load(cls, sc, path): """ Load a model from the given path. """ model = cls._load_java(sc, path) wrapper = sc._jvm.org.apache.spark.mllib.api.python.FPGrowthModelWrapper(model) return FPGrowthModel(wrapper) [docs]class FPGrowth(object): """ A Parallel FP-growth algorithm to mine frequent itemsets. .. versionadded:: 1.4.0 """ [docs] @classmethod def train(cls, data, minSupport=0.3, numPartitions=-1): """ 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("FreqItemset", ["items", "freq"])): """ Represents an (items, freq) tuple. .. versionadded:: 1.4.0 """ [docs]@inherit_doc class PrefixSpanModel(JavaModelWrapper): """ 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): """Gets frequent sequences""" return self.call("getFreqSequences").map(lambda x: PrefixSpan.FreqSequence(x[0], x[1])) [docs]class PrefixSpan(object): """ 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: https://doi.org/10.1109/ICDE.2001.914830 """ [docs] @classmethod 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. .. 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("FreqSequence", ["sequence", "freq"])): """ Represents a (sequence, freq) tuple. .. versionadded:: 1.6.0 """ def _test(): 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()