Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/4151#discussion_r23656902
--- Diff: python/pyspark/ml/feature.py ---
@@ -0,0 +1,88 @@
+#
+# 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.sql import inherit_doc
+from pyspark.ml import JavaTransformer
+from pyspark.ml.param.shared import HasInputCol, HasOutputCol,
HasNumFeatures
+
+
+@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).first()
+ Row(text=u'a b c', words=[u'a', u'b', u'c'])
+ >>> print tokenizer.transform(dataset, {tokenizer.outputCol:
"tokens"}).first()
+ Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
+ """
+
+ def __init__(self):
+ super(Tokenizer, self).__init__()
+
+ @property
+ def _java_class(self):
+ return "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).first().features
+ (10,[7,8,9],[1.0,1.0,1.0])
+ >>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"}
+ >>> print hashingTF.transform(dataset, params).first().vector
+ (5,[2,3,4],[1.0,1.0,1.0])
+ """
+
+ def __init__(self):
+ super(HashingTF, self).__init__()
--- End diff --
This is the default behavior, I'd like to remove these.
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