96c5eeec39
This reverts commit 25f574eb9a. After speaking to some users and developers, we realized that FP-growth doesn't meet the requirement for frequent sequence mining. PrefixSpan (SPARK-6487) would be the correct algorithm for it. feynmanliang
Author: Xiangrui Meng <meng@databricks.com>
Closes #7240 from mengxr/SPARK-7212.revert and squashes the following commits:
2b3d66b [Xiangrui Meng] Revert "[SPARK-7212] [MLLIB] Add sequence learning flag"
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import numpy
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from numpy import array
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from collections import namedtuple
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from pyspark import SparkContext
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from pyspark.rdd import ignore_unicode_prefix
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from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
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__all__ = ['FPGrowth', 'FPGrowthModel']
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@inherit_doc
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@ignore_unicode_prefix
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class FPGrowthModel(JavaModelWrapper):
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"""
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.. note:: Experimental
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A FP-Growth model for mining frequent itemsets
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using the Parallel FP-Growth algorithm.
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>>> data = [["a", "b", "c"], ["a", "b", "d", "e"], ["a", "c", "e"], ["a", "c", "f"]]
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>>> rdd = sc.parallelize(data, 2)
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>>> model = FPGrowth.train(rdd, 0.6, 2)
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>>> sorted(model.freqItemsets().collect())
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[FreqItemset(items=[u'a'], freq=4), FreqItemset(items=[u'c'], freq=3), ...
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"""
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def freqItemsets(self):
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"""
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Returns the frequent itemsets of this model.
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"""
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return self.call("getFreqItemsets").map(lambda x: (FPGrowth.FreqItemset(x[0], x[1])))
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class FPGrowth(object):
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"""
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.. note:: Experimental
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A Parallel FP-growth algorithm to mine frequent itemsets.
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"""
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@classmethod
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def train(cls, data, minSupport=0.3, numPartitions=-1):
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"""
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Computes an FP-Growth model that contains frequent itemsets.
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:param data: The input data set, each element contains a
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transaction.
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:param minSupport: The minimal support level (default: `0.3`).
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:param numPartitions: The number of partitions used by
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parallel FP-growth (default: same as input data).
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"""
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model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
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return FPGrowthModel(model)
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class FreqItemset(namedtuple("FreqItemset", ["items", "freq"])):
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"""
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Represents an (items, freq) tuple.
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"""
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def _test():
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import doctest
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import pyspark.mllib.fpm
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globs = pyspark.mllib.fpm.__dict__.copy()
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globs['sc'] = SparkContext('local[4]', 'PythonTest')
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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