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#
# 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.
#
from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasNumFeatures
from pyspark.ml.util import inherit_doc
from pyspark.ml.wrapper import JavaTransformer
__all__ = ['Tokenizer', 'HashingTF']
@inherit_doc
class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol):
"""
A tokenizer that converts the input string to lowercase and then
splits it by white spaces.
>>> from pyspark.sql import Row
>>> dataset = sqlCtx.inferSchema(sc.parallelize([Row(text="a b c")]))
>>> tokenizer = Tokenizer() \
.setInputCol("text") \
.setOutputCol("words")
>>> print tokenizer.transform(dataset).head()
Row(text=u'a b c', words=[u'a', u'b', u'c'])
>>> print tokenizer.transform(dataset, {tokenizer.outputCol: "tokens"}).head()
Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
"""
_java_class = "org.apache.spark.ml.feature.Tokenizer"
@inherit_doc
class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures):
"""
Maps a sequence of terms to their term frequencies using the
hashing trick.
>>> from pyspark.sql import Row
>>> dataset = sqlCtx.inferSchema(sc.parallelize([Row(words=["a", "b", "c"])]))
>>> hashingTF = HashingTF() \
.setNumFeatures(10) \
.setInputCol("words") \
.setOutputCol("features")
>>> print hashingTF.transform(dataset).head().features
(10,[7,8,9],[1.0,1.0,1.0])
>>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"}
>>> print hashingTF.transform(dataset, params).head().vector
(5,[2,3,4],[1.0,1.0,1.0])
"""
_java_class = "org.apache.spark.ml.feature.HashingTF"
if __name__ == "__main__":
import doctest
from pyspark.context import SparkContext
from pyspark.sql import SQLContext
globs = globals().copy()
# The small batch size here ensures that we see multiple batches,
# even in these small test examples:
sc = SparkContext("local[2]", "ml.feature tests")
sqlCtx = SQLContext(sc)
globs['sc'] = sc
globs['sqlCtx'] = sqlCtx
(failure_count, test_count) = doctest.testmod(
globs=globs, optionflags=doctest.ELLIPSIS)
sc.stop()
if failure_count:
exit(-1)
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