python/ml/random_forest_regressor_example.py

0001 #
0002 # Licensed to the Apache Software Foundation (ASF) under one or more
0003 # contributor license agreements.  See the NOTICE file distributed with
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0005 # The ASF licenses this file to You under the Apache License, Version 2.0
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0007 # the License.  You may obtain a copy of the License at
0008 #
0009 #    http://www.apache.org/licenses/LICENSE-2.0
0010 #
0011 # Unless required by applicable law or agreed to in writing, software
0012 # distributed under the License is distributed on an "AS IS" BASIS,
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0014 # See the License for the specific language governing permissions and
0015 # limitations under the License.
0016 #
0017
0018 """
0019 Random Forest Regressor Example.
0020 """
0021 from __future__ import print_function
0022
0023 # $example on$
0024 from pyspark.ml import Pipeline
0025 from pyspark.ml.regression import RandomForestRegressor
0026 from pyspark.ml.feature import VectorIndexer
0027 from pyspark.ml.evaluation import RegressionEvaluator
0028 # $example off$
0029 from pyspark.sql import SparkSession
0030
0031 if __name__ == "__main__":
0032     spark = SparkSession\
0033         .builder\
0034         .appName("RandomForestRegressorExample")\
0035         .getOrCreate()
0036
0037     # $example on$
0038     # Load and parse the data file, converting it to a DataFrame.
0039     data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
0040
0041     # Automatically identify categorical features, and index them.
0042     # Set maxCategories so features with > 4 distinct values are treated as continuous.
0043     featureIndexer =\
0044         VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
0045
0046     # Split the data into training and test sets (30% held out for testing)
0047     (trainingData, testData) = data.randomSplit([0.7, 0.3])
0048
0049     # Train a RandomForest model.
0050     rf = RandomForestRegressor(featuresCol="indexedFeatures")
0051
0052     # Chain indexer and forest in a Pipeline
0053     pipeline = Pipeline(stages=[featureIndexer, rf])
0054
0055     # Train model.  This also runs the indexer.
0056     model = pipeline.fit(trainingData)
0057
0058     # Make predictions.
0059     predictions = model.transform(testData)
0060
0061     # Select example rows to display.
0062     predictions.select("prediction", "label", "features").show(5)
0063
0064     # Select (prediction, true label) and compute test error
0065     evaluator = RegressionEvaluator(
0066         labelCol="label", predictionCol="prediction", metricName="rmse")
0067     rmse = evaluator.evaluate(predictions)
0068     print("Root Mean Squared Error (RMSE) on test data = %g" % rmse)
0069
0070     rfModel = model.stages[1]
0071     print(rfModel)  # summary only
0072     # $example off$
0073
0074     spark.stop()