OSCL-LXR spark-3.0.0/​python/​pyspark/​mllib/​fpm.py	Source navigation Diff markup Identifier search General search
	Version: spark-3.0.0 hadoop-3.2.0-src hadoop-3.3.0-src spark-2.4.7 linux-4.15.13 Revert   Change

 #
 # 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.rdd import ignore_unicode_prefix
 from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
 from pyspark.mllib.util import JavaSaveable, JavaLoader, inherit_doc
 
 __all__ = ['FPGrowth', 'FPGrowthModel', 'PrefixSpan', 'PrefixSpanModel']
 
 
 @inherit_doc
 @ignore_unicode_prefix
 class FPGrowthModel(JavaModelWrapper, JavaSaveable, JavaLoader):
     """
     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), ...
     >>> 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
 
     .. 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])))
 
     @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)
 
 
 class FPGrowth(object):
     """
     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. 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
         """
 
 
 @inherit_doc
 @ignore_unicode_prefix
 class PrefixSpanModel(JavaModelWrapper):
     """
     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 frequent sequences"""
         return self.call("getFreqSequences").map(lambda x: PrefixSpan.FreqSequence(x[0], x[1]))
 
 
 class PrefixSpan(object):
     """
     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
     ([[https://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 sequence of
           itemsets.
         :param minSupport:
           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)
         :param maxPatternLength:
           The maximal length of the sequential pattern, any pattern
           that 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
     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()

This page was automatically generated by the 2.1.0 LXR engine. The LXR team