04e44b37cc
This PR update PySpark to support Python 3 (tested with 3.4). Known issue: unpickle array from Pyrolite is broken in Python 3, those tests are skipped. TODO: ec2/spark-ec2.py is not fully tested with python3. Author: Davies Liu <davies@databricks.com> Author: twneale <twneale@gmail.com> Author: Josh Rosen <joshrosen@databricks.com> Closes #5173 from davies/python3 and squashes the following commits: d7d6323 [Davies Liu] fix tests 6c52a98 [Davies Liu] fix mllib test 99e334f [Davies Liu] update timeout b716610 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 cafd5ec [Davies Liu] adddress comments from @mengxr bf225d7 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 179fc8d [Davies Liu] tuning flaky tests 8c8b957 [Davies Liu] fix ResourceWarning in Python 3 5c57c95 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 4006829 [Davies Liu] fix test 2fc0066 [Davies Liu] add python3 path 71535e9 [Davies Liu] fix xrange and divide 5a55ab4 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 125f12c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 ed498c8 [Davies Liu] fix compatibility with python 3 820e649 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 e8ce8c9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 ad7c374 [Davies Liu] fix mllib test and warning ef1fc2f [Davies Liu] fix tests 4eee14a [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 20112ff [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 59bb492 [Davies Liu] fix tests 1da268c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 ca0fdd3 [Davies Liu] fix code style 9563a15 [Davies Liu] add imap back for python 2 0b1ec04 [Davies Liu] make python examples work with Python 3 d2fd566 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 a716d34 [Davies Liu] test with python 3.4 f1700e8 [Davies Liu] fix test in python3 671b1db [Davies Liu] fix test in python3 692ff47 [Davies Liu] fix flaky test 7b9699f [Davies Liu] invalidate import cache for Python 3.3+ 9c58497 [Davies Liu] fix kill worker 309bfbf [Davies Liu] keep compatibility 5707476 [Davies Liu] cleanup, fix hash of string in 3.3+ 8662d5b [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 f53e1f0 [Davies Liu] fix tests 70b6b73 [Davies Liu] compile ec2/spark_ec2.py in python 3 a39167e [Davies Liu] support customize class in __main__ 814c77b [Davies Liu] run unittests with python 3 7f4476e [Davies Liu] mllib tests passed d737924 [Davies Liu] pass ml tests 375ea17 [Davies Liu] SQL tests pass 6cc42a9 [Davies Liu] rename 431a8de [Davies Liu] streaming tests pass 78901a7 [Davies Liu] fix hash of serializer in Python 3 24b2f2e [Davies Liu] pass all RDD tests 35f48fe [Davies Liu] run future again 1eebac2 [Davies Liu] fix conflict in ec2/spark_ec2.py 6e3c21d [Davies Liu] make cloudpickle work with Python3 2fb2db3 [Josh Rosen] Guard more changes behind sys.version; still doesn't run 1aa5e8f [twneale] Turned out `pickle.DictionaryType is dict` == True, so swapped it out 7354371 [twneale] buffer --> memoryview I'm not super sure if this a valid change, but the 2.7 docs recommend using memoryview over buffer where possible, so hoping it'll work. b69ccdf [twneale] Uses the pure python pickle._Pickler instead of c-extension _pickle.Pickler. It appears pyspark 2.7 uses the pure python pickler as well, so this shouldn't degrade pickling performance (?). f40d925 [twneale] xrange --> range e104215 [twneale] Replaces 2.7 types.InstsanceType with 3.4 `object`....could be horribly wrong depending on how types.InstanceType is used elsewhere in the package--see http://bugs.python.org/issue8206 79de9d0 [twneale] Replaces python2.7 `file` with 3.4 _io.TextIOWrapper 2adb42d [Josh Rosen] Fix up some import differences between Python 2 and 3 854be27 [Josh Rosen] Run `futurize` on Python code: 7c5b4ce [Josh Rosen] Remove Python 3 check in shell.py.
131 lines
4.7 KiB
Python
131 lines
4.7 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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from pyspark.rdd import ignore_unicode_prefix
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from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasNumFeatures
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from pyspark.ml.util import keyword_only
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from pyspark.ml.wrapper import JavaTransformer
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from pyspark.mllib.common import inherit_doc
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__all__ = ['Tokenizer', 'HashingTF']
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@inherit_doc
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@ignore_unicode_prefix
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class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol):
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"""
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A tokenizer that converts the input string to lowercase and then
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splits it by white spaces.
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>>> from pyspark.sql import Row
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>>> df = sc.parallelize([Row(text="a b c")]).toDF()
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>>> tokenizer = Tokenizer(inputCol="text", outputCol="words")
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>>> tokenizer.transform(df).head()
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Row(text=u'a b c', words=[u'a', u'b', u'c'])
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>>> # Change a parameter.
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>>> tokenizer.setParams(outputCol="tokens").transform(df).head()
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Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
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>>> # Temporarily modify a parameter.
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>>> tokenizer.transform(df, {tokenizer.outputCol: "words"}).head()
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Row(text=u'a b c', words=[u'a', u'b', u'c'])
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>>> tokenizer.transform(df).head()
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Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
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>>> # Must use keyword arguments to specify params.
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>>> tokenizer.setParams("text")
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Traceback (most recent call last):
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...
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TypeError: Method setParams forces keyword arguments.
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"""
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_java_class = "org.apache.spark.ml.feature.Tokenizer"
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@keyword_only
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def __init__(self, inputCol=None, outputCol=None):
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"""
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__init__(self, inputCol=None, outputCol=None)
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"""
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super(Tokenizer, self).__init__()
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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def setParams(self, inputCol=None, outputCol=None):
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"""
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setParams(self, inputCol="input", outputCol="output")
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Sets params for this Tokenizer.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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@inherit_doc
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class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures):
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"""
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Maps a sequence of terms to their term frequencies using the
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hashing trick.
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>>> from pyspark.sql import Row
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>>> df = sc.parallelize([Row(words=["a", "b", "c"])]).toDF()
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>>> hashingTF = HashingTF(numFeatures=10, inputCol="words", outputCol="features")
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>>> hashingTF.transform(df).head().features
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SparseVector(10, {7: 1.0, 8: 1.0, 9: 1.0})
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>>> hashingTF.setParams(outputCol="freqs").transform(df).head().freqs
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SparseVector(10, {7: 1.0, 8: 1.0, 9: 1.0})
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>>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"}
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>>> hashingTF.transform(df, params).head().vector
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SparseVector(5, {2: 1.0, 3: 1.0, 4: 1.0})
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"""
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_java_class = "org.apache.spark.ml.feature.HashingTF"
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@keyword_only
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def __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None):
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"""
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__init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None)
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"""
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super(HashingTF, self).__init__()
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self._setDefault(numFeatures=1 << 18)
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None):
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"""
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setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None)
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Sets params for this HashingTF.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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if __name__ == "__main__":
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import doctest
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from pyspark.context import SparkContext
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from pyspark.sql import SQLContext
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globs = globals().copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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sc = SparkContext("local[2]", "ml.feature tests")
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sqlContext = SQLContext(sc)
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globs['sc'] = sc
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globs['sqlContext'] = sqlContext
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(failure_count, test_count) = doctest.testmod(
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globs=globs, optionflags=doctest.ELLIPSIS)
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sc.stop()
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if failure_count:
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exit(-1)
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