Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/4151#discussion_r23655542
--- Diff:
examples/src/main/python/ml/simple_text_classification_pipeline.py ---
@@ -0,0 +1,79 @@
+#
+# 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 import SparkContext
+from pyspark.sql import SQLContext, Row
+from pyspark.ml import Pipeline
+from pyspark.ml.feature import HashingTF, Tokenizer
+from pyspark.ml.classification import LogisticRegression
+
+
+"""
+A simple text classification pipeline that recognizes "spark" from
+input text. This is to show how to create and configure a Spark ML
+pipeline in Python. Run with:
+
+ bin/spark-submit
examples/src/main/python/ml/simple_text_classification_pipeline.py
+"""
+
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="SimpleTextClassificationPipeline")
+ sqlCtx = SQLContext(sc)
+
+ # Prepare training documents, which are labeled.
+ LabeledDocument = Row('id', 'text', 'label')
+ training = sqlCtx.inferSchema(
+ sc.parallelize([(0L, "a b c d e spark", 1.0),
+ (1L, "b d", 0.0),
+ (2L, "spark f g h", 1.0),
+ (3L, "hadoop mapreduce", 0.0)])
+ .map(lambda x: LabeledDocument(*x)))
+
+ # Configure an ML pipeline, which consists of tree stages: tokenizer,
hashingTF, and lr.
+ tokenizer = Tokenizer() \
+ .setInputCol("text") \
+ .setOutputCol("words")
+ hashingTF = HashingTF() \
+ .setInputCol(tokenizer.getOutputCol()) \
+ .setOutputCol("features")
+ lr = LogisticRegression() \
+ .setMaxIter(10) \
+ .setRegParam(0.01)
+ pipeline = Pipeline() \
+ .setStages([tokenizer, hashingTF, lr])
--- End diff --
This looks very Java style, verbose and many lines, imaged that could be
simplified as :
```
tokenizer = Tokenizer("text", "words")
hashingTF = HashingTF("words", "features")
lr = LogisticRegression(maxIter=10, regParam=0.01)
pipeline = Pipeline([tokenizer, hashingTF, lr])
```
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
