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author | Xusen Yin <yinxusen@gmail.com> | 2015-11-17 23:44:06 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-11-17 23:44:06 -0800 |
commit | 9154f89befb7a33d4853cea95efd7dc6b25d033b (patch) | |
tree | 8eb6da0ff09ba6c3b2fe34859077e5a55c5ed3df /examples/src/main/python/ml/random_forest_classifier_example.py | |
parent | 2f191c66b668fc97f82f44fd8336b6a4488c2f5d (diff) | |
download | spark-9154f89befb7a33d4853cea95efd7dc6b25d033b.tar.gz spark-9154f89befb7a33d4853cea95efd7dc6b25d033b.tar.bz2 spark-9154f89befb7a33d4853cea95efd7dc6b25d033b.zip |
[SPARK-11728] Replace example code in ml-ensembles.md using include_example
JIRA issue https://issues.apache.org/jira/browse/SPARK-11728.
The ml-ensembles.md file contains `OneVsRestExample`. Instead of writing new code files of two `OneVsRestExample`s, I use two existing files in the examples directory, they are `OneVsRestExample.scala` and `JavaOneVsRestExample.scala`.
Author: Xusen Yin <yinxusen@gmail.com>
Closes #9716 from yinxusen/SPARK-11728.
Diffstat (limited to 'examples/src/main/python/ml/random_forest_classifier_example.py')
-rw-r--r-- | examples/src/main/python/ml/random_forest_classifier_example.py | 77 |
1 files changed, 77 insertions, 0 deletions
diff --git a/examples/src/main/python/ml/random_forest_classifier_example.py b/examples/src/main/python/ml/random_forest_classifier_example.py new file mode 100644 index 0000000000..b3530d4f41 --- /dev/null +++ b/examples/src/main/python/ml/random_forest_classifier_example.py @@ -0,0 +1,77 @@ +# +# 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. +# + +""" +Random Forest Classifier Example. +""" +from __future__ import print_function + +import sys + +from pyspark import SparkContext, SQLContext +# $example on$ +from pyspark.ml import Pipeline +from pyspark.ml.classification import RandomForestClassifier +from pyspark.ml.feature import StringIndexer, VectorIndexer +from pyspark.ml.evaluation import MulticlassClassificationEvaluator +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="random_forest_classifier_example") + sqlContext = SQLContext(sc) + + # $example on$ + # Load and parse the data file, converting it to a DataFrame. + data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + # Index labels, adding metadata to the label column. + # Fit on whole dataset to include all labels in index. + labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) + # Automatically identify categorical features, and index them. + # Set maxCategories so features with > 4 distinct values are treated as continuous. + featureIndexer =\ + VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) + + # Split the data into training and test sets (30% held out for testing) + (trainingData, testData) = data.randomSplit([0.7, 0.3]) + + # Train a RandomForest model. + rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") + + # Chain indexers and forest in a Pipeline + pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf]) + + # Train model. This also runs the indexers. + model = pipeline.fit(trainingData) + + # Make predictions. + predictions = model.transform(testData) + + # Select example rows to display. + predictions.select("prediction", "indexedLabel", "features").show(5) + + # Select (prediction, true label) and compute test error + evaluator = MulticlassClassificationEvaluator( + labelCol="indexedLabel", predictionCol="prediction", metricName="precision") + accuracy = evaluator.evaluate(predictions) + print("Test Error = %g" % (1.0 - accuracy)) + + rfModel = model.stages[2] + print(rfModel) # summary only + # $example off$ + + sc.stop() |