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author | Ram Sriharsha <rsriharsha@hw11853.local> | 2015-06-02 18:53:04 -0700 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-06-02 18:53:04 -0700 |
commit | c3f4c3257194ba34ccd298d13ea1edcfc75f7552 (patch) | |
tree | dd1155697c003e0af5ab98c847473229bf071bab /examples/src/main/python/ml/cross_validator.py | |
parent | 5cd6a63d9692d153751747e0293dc030d73a6194 (diff) | |
download | spark-c3f4c3257194ba34ccd298d13ea1edcfc75f7552.tar.gz spark-c3f4c3257194ba34ccd298d13ea1edcfc75f7552.tar.bz2 spark-c3f4c3257194ba34ccd298d13ea1edcfc75f7552.zip |
[SPARK-7387] [ML] [DOC] CrossValidator example code in Python
Author: Ram Sriharsha <rsriharsha@hw11853.local>
Closes #6358 from harsha2010/SPARK-7387 and squashes the following commits:
63efda2 [Ram Sriharsha] more examples for classifier to distinguish mapreduce from spark properly
aeb6bb6 [Ram Sriharsha] Python Style Fix
54a500c [Ram Sriharsha] Merge branch 'master' into SPARK-7387
615e91c [Ram Sriharsha] cleanup
204c4e3 [Ram Sriharsha] Merge branch 'master' into SPARK-7387
7246d35 [Ram Sriharsha] [SPARK-7387][ml][doc] CrossValidator example code in Python
Diffstat (limited to 'examples/src/main/python/ml/cross_validator.py')
-rw-r--r-- | examples/src/main/python/ml/cross_validator.py | 96 |
1 files changed, 96 insertions, 0 deletions
diff --git a/examples/src/main/python/ml/cross_validator.py b/examples/src/main/python/ml/cross_validator.py new file mode 100644 index 0000000000..f0ca97c724 --- /dev/null +++ b/examples/src/main/python/ml/cross_validator.py @@ -0,0 +1,96 @@ +# +# 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 __future__ import print_function + +from pyspark import SparkContext +from pyspark.ml import Pipeline +from pyspark.ml.classification import LogisticRegression +from pyspark.ml.evaluation import BinaryClassificationEvaluator +from pyspark.ml.feature import HashingTF, Tokenizer +from pyspark.ml.tuning import CrossValidator, ParamGridBuilder +from pyspark.sql import Row, SQLContext + +""" +A simple example demonstrating model selection using CrossValidator. +This example also demonstrates how Pipelines are Estimators. +Run with: + + bin/spark-submit examples/src/main/python/ml/cross_validator.py +""" + +if __name__ == "__main__": + sc = SparkContext(appName="CrossValidatorExample") + sqlContext = SQLContext(sc) + + # Prepare training documents, which are labeled. + LabeledDocument = Row("id", "text", "label") + training = sc.parallelize([(0, "a b c d e spark", 1.0), + (1, "b d", 0.0), + (2, "spark f g h", 1.0), + (3, "hadoop mapreduce", 0.0), + (4, "b spark who", 1.0), + (5, "g d a y", 0.0), + (6, "spark fly", 1.0), + (7, "was mapreduce", 0.0), + (8, "e spark program", 1.0), + (9, "a e c l", 0.0), + (10, "spark compile", 1.0), + (11, "hadoop software", 0.0) + ]) \ + .map(lambda x: LabeledDocument(*x)).toDF() + + # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. + tokenizer = Tokenizer(inputCol="text", outputCol="words") + hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") + lr = LogisticRegression(maxIter=10) + pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) + + # We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance. + # This will allow us to jointly choose parameters for all Pipeline stages. + # A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. + # We use a ParamGridBuilder to construct a grid of parameters to search over. + # With 3 values for hashingTF.numFeatures and 2 values for lr.regParam, + # this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from. + paramGrid = ParamGridBuilder() \ + .addGrid(hashingTF.numFeatures, [10, 100, 1000]) \ + .addGrid(lr.regParam, [0.1, 0.01]) \ + .build() + + crossval = CrossValidator(estimator=pipeline, + estimatorParamMaps=paramGrid, + evaluator=BinaryClassificationEvaluator(), + numFolds=2) # use 3+ folds in practice + + # Run cross-validation, and choose the best set of parameters. + cvModel = crossval.fit(training) + + # Prepare test documents, which are unlabeled. + Document = Row("id", "text") + test = sc.parallelize([(4L, "spark i j k"), + (5L, "l m n"), + (6L, "mapreduce spark"), + (7L, "apache hadoop")]) \ + .map(lambda x: Document(*x)).toDF() + + # Make predictions on test documents. cvModel uses the best model found (lrModel). + prediction = cvModel.transform(test) + selected = prediction.select("id", "text", "probability", "prediction") + for row in selected.collect(): + print(row) + + sc.stop() |