e80dc1c5a8
This PR adds Python API for ML pipeline and parameters. The design doc can be found on the JIRA page. It includes transformers and an estimator to demo the simple text classification example code. TODO: - [x] handle parameters in LRModel - [x] unit tests - [x] missing some docs CC: davies jkbradley Author: Xiangrui Meng <meng@databricks.com> Author: Davies Liu <davies@databricks.com> Closes #4151 from mengxr/SPARK-4586 and squashes the following commits: 415268e [Xiangrui Meng] remove inherit_doc from __init__ edbd6fe [Xiangrui Meng] move Identifiable to ml.util 44c2405 [Xiangrui Meng] Merge pull request #2 from davies/ml dd1256b [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4586 14ae7e2 [Davies Liu] fix docs 54ca7df [Davies Liu] fix tests 78638df [Davies Liu] Merge branch 'SPARK-4586' of github.com:mengxr/spark into ml fc59a02 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4586 1dca16a [Davies Liu] refactor 090b3a3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into ml 0882513 [Xiangrui Meng] update doc style a4f4dbf [Xiangrui Meng] add unit test for LR 7521d1c [Xiangrui Meng] add unit tests to HashingTF and Tokenizer ba0ba1e [Xiangrui Meng] add unit tests for pipeline 0586c7b [Xiangrui Meng] add more comments to the example 5153cff [Xiangrui Meng] simplify java models 036ca04 [Xiangrui Meng] gen numFeatures 46fa147 [Xiangrui Meng] update mllib/pom.xml to include python files in the assembly 1dcc17e [Xiangrui Meng] update code gen and make param appear in the doc f66ba0c [Xiangrui Meng] make params a property d5efd34 [Xiangrui Meng] update doc conf and move embedded param map to instance attribute f4d0fe6 [Xiangrui Meng] use LabeledDocument and Document in example 05e3e40 [Xiangrui Meng] update example d3e8dbe [Xiangrui Meng] more docs optimize pipeline.fit impl 56de571 [Xiangrui Meng] fix style d0c5bb8 [Xiangrui Meng] a working copy bce72f4 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4586 17ecfb9 [Xiangrui Meng] code gen for shared params d9ea77c [Xiangrui Meng] update doc c18dca1 [Xiangrui Meng] make the example working dadd84e [Xiangrui Meng] add base classes and docs a3015cf [Xiangrui Meng] add Estimator and Transformer 46eea43 [Xiangrui Meng] a pipeline in python 33b68e0 [Xiangrui Meng] a working LR
83 lines
2.9 KiB
Python
83 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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from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasNumFeatures
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from pyspark.ml.util import inherit_doc
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from pyspark.ml.wrapper import JavaTransformer
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__all__ = ['Tokenizer', 'HashingTF']
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@inherit_doc
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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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>>> dataset = sqlCtx.inferSchema(sc.parallelize([Row(text="a b c")]))
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>>> tokenizer = Tokenizer() \
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.setInputCol("text") \
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.setOutputCol("words")
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>>> print tokenizer.transform(dataset).head()
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Row(text=u'a b c', words=[u'a', u'b', u'c'])
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>>> print tokenizer.transform(dataset, {tokenizer.outputCol: "tokens"}).head()
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Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
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"""
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_java_class = "org.apache.spark.ml.feature.Tokenizer"
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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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>>> dataset = sqlCtx.inferSchema(sc.parallelize([Row(words=["a", "b", "c"])]))
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>>> hashingTF = HashingTF() \
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.setNumFeatures(10) \
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.setInputCol("words") \
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.setOutputCol("features")
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>>> print hashingTF.transform(dataset).head().features
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(10,[7,8,9],[1.0,1.0,1.0])
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>>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"}
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>>> print hashingTF.transform(dataset, params).head().vector
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(5,[2,3,4],[1.0,1.0,1.0])
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"""
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_java_class = "org.apache.spark.ml.feature.HashingTF"
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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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sqlCtx = SQLContext(sc)
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globs['sc'] = sc
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globs['sqlCtx'] = sqlCtx
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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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