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Dongjoon Hyun authored
[SPARK-15134][EXAMPLE] Indent SparkSession builder patterns and update binary_classification_metrics_example.py ## What changes were proposed in this pull request? This issue addresses the comments in SPARK-15031 and also fix java-linter errors. - Use multiline format in SparkSession builder patterns. - Update `binary_classification_metrics_example.py` to use `SparkSession`. - Fix Java Linter errors (in SPARK-13745, SPARK-15031, and so far) ## How was this patch tested? After passing the Jenkins tests and run `dev/lint-java` manually. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12911 from dongjoon-hyun/SPARK-15134.
Dongjoon Hyun authored[SPARK-15134][EXAMPLE] Indent SparkSession builder patterns and update binary_classification_metrics_example.py ## What changes were proposed in this pull request? This issue addresses the comments in SPARK-15031 and also fix java-linter errors. - Use multiline format in SparkSession builder patterns. - Update `binary_classification_metrics_example.py` to use `SparkSession`. - Fix Java Linter errors (in SPARK-13745, SPARK-15031, and so far) ## How was this patch tested? After passing the Jenkins tests and run `dev/lint-java` manually. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12911 from dongjoon-hyun/SPARK-15134.
decision_tree_regression_example.py 2.64 KiB
#
# 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
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#
"""
Decision Tree Regression Example.
"""
from __future__ import print_function
# $example on$
from pyspark.ml import Pipeline
from pyspark.ml.regression import DecisionTreeRegressor
from pyspark.ml.feature import VectorIndexer
from pyspark.ml.evaluation import RegressionEvaluator
# $example off$
from pyspark.sql import SparkSession
if __name__ == "__main__":
spark = SparkSession\
.builder\
.appName("decision_tree_classification_example")\
.getOrCreate()
# $example on$
# Load the data stored in LIBSVM format as a DataFrame.
data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
# Automatically identify categorical features, and index them.
# We specify 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 DecisionTree model.
dt = DecisionTreeRegressor(featuresCol="indexedFeatures")
# Chain indexer and tree in a Pipeline
pipeline = Pipeline(stages=[featureIndexer, dt])
# Train model. This also runs the indexer.
model = pipeline.fit(trainingData)
# Make predictions.
predictions = model.transform(testData)
# Select example rows to display.
predictions.select("prediction", "label", "features").show(5)
# Select (prediction, true label) and compute test error
evaluator = RegressionEvaluator(
labelCol="label", predictionCol="prediction", metricName="rmse")
rmse = evaluator.evaluate(predictions)
print("Root Mean Squared Error (RMSE) on test data = %g" % rmse)
treeModel = model.stages[1]
# summary only
print(treeModel)
# $example off$
spark.stop()