4ad9bfd53b
### What changes were proposed in this pull request? This PR aims to drop Python 2.7, 3.4 and 3.5. Roughly speaking, it removes all the widely known Python 2 compatibility workarounds such as `sys.version` comparison, `__future__`. Also, it removes the Python 2 dedicated codes such as `ArrayConstructor` in Spark. ### Why are the changes needed? 1. Unsupport EOL Python versions 2. Reduce maintenance overhead and remove a bit of legacy codes and hacks for Python 2. 3. PyPy2 has a critical bug that causes a flaky test, SPARK-28358 given my testing and investigation. 4. Users can use Python type hints with Pandas UDFs without thinking about Python version 5. Users can leverage one latest cloudpickle, https://github.com/apache/spark/pull/28950. With Python 3.8+ it can also leverage C pickle. ### Does this PR introduce _any_ user-facing change? Yes, users cannot use Python 2.7, 3.4 and 3.5 in the upcoming Spark version. ### How was this patch tested? Manually tested and also tested in Jenkins. Closes #28957 from HyukjinKwon/SPARK-32138. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
202 lines
6.4 KiB
Python
202 lines
6.4 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 sys
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from collections import namedtuple
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from pyspark import since
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from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
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from pyspark.mllib.util import JavaSaveable, JavaLoader, inherit_doc
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__all__ = ['FPGrowth', 'FPGrowthModel', 'PrefixSpan', 'PrefixSpanModel']
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@inherit_doc
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class FPGrowthModel(JavaModelWrapper, JavaSaveable, JavaLoader):
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"""
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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=['a'], freq=4), FreqItemset(items=['c'], freq=3), ...
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>>> model_path = temp_path + "/fpm"
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>>> model.save(sc, model_path)
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>>> sameModel = FPGrowthModel.load(sc, model_path)
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>>> sorted(model.freqItemsets().collect()) == sorted(sameModel.freqItemsets().collect())
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True
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.. versionadded:: 1.4.0
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"""
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@since("1.4.0")
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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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@classmethod
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@since("2.0.0")
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def load(cls, sc, path):
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"""
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Load a model from the given path.
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"""
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model = cls._load_java(sc, path)
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wrapper = sc._jvm.org.apache.spark.mllib.api.python.FPGrowthModelWrapper(model)
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return FPGrowthModel(wrapper)
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class FPGrowth(object):
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"""
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A Parallel FP-growth algorithm to mine frequent itemsets.
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.. versionadded:: 1.4.0
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"""
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@classmethod
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@since("1.4.0")
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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:
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The input data set, each element contains a transaction.
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:param minSupport:
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The minimal support level.
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(default: 0.3)
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:param numPartitions:
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The number of partitions used by parallel FP-growth. A value
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of -1 will use the same number as input data.
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(default: -1)
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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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.. versionadded:: 1.4.0
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"""
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@inherit_doc
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class PrefixSpanModel(JavaModelWrapper):
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"""
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Model fitted by PrefixSpan
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>>> data = [
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... [["a", "b"], ["c"]],
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... [["a"], ["c", "b"], ["a", "b"]],
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... [["a", "b"], ["e"]],
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... [["f"]]]
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>>> rdd = sc.parallelize(data, 2)
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>>> model = PrefixSpan.train(rdd)
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>>> sorted(model.freqSequences().collect())
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[FreqSequence(sequence=[['a']], freq=3), FreqSequence(sequence=[['a'], ['a']], freq=1), ...
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.. versionadded:: 1.6.0
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"""
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@since("1.6.0")
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def freqSequences(self):
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"""Gets frequent sequences"""
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return self.call("getFreqSequences").map(lambda x: PrefixSpan.FreqSequence(x[0], x[1]))
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class PrefixSpan(object):
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"""
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A parallel PrefixSpan algorithm to mine frequent sequential patterns.
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The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan:
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Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth
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([[https://doi.org/10.1109/ICDE.2001.914830]]).
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.. versionadded:: 1.6.0
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"""
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@classmethod
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@since("1.6.0")
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def train(cls, data, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000):
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"""
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Finds the complete set of frequent sequential patterns in the
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input sequences of itemsets.
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:param data:
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The input data set, each element contains a sequence of
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itemsets.
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:param minSupport:
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The minimal support level of the sequential pattern, any
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pattern that appears more than (minSupport *
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size-of-the-dataset) times will be output.
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(default: 0.1)
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:param maxPatternLength:
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The maximal length of the sequential pattern, any pattern
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that appears less than maxPatternLength will be output.
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(default: 10)
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:param maxLocalProjDBSize:
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The maximum number of items (including delimiters used in the
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internal storage format) allowed in a projected database before
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local processing. If a projected database exceeds this size,
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another iteration of distributed prefix growth is run.
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(default: 32000000)
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"""
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model = callMLlibFunc("trainPrefixSpanModel",
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data, minSupport, maxPatternLength, maxLocalProjDBSize)
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return PrefixSpanModel(model)
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class FreqSequence(namedtuple("FreqSequence", ["sequence", "freq"])):
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"""
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Represents a (sequence, freq) tuple.
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.. versionadded:: 1.6.0
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"""
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def _test():
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import doctest
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from pyspark.sql import SparkSession
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import pyspark.mllib.fpm
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globs = pyspark.mllib.fpm.__dict__.copy()
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spark = SparkSession.builder\
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.master("local[4]")\
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.appName("mllib.fpm tests")\
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.getOrCreate()
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globs['sc'] = spark.sparkContext
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import tempfile
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temp_path = tempfile.mkdtemp()
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globs['temp_path'] = temp_path
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try:
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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spark.stop()
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finally:
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from shutil import rmtree
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try:
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rmtree(temp_path)
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except OSError:
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pass
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if failure_count:
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sys.exit(-1)
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if __name__ == "__main__":
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_test()
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