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#
# 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
import sys
from pyspark import SparkContext
from pyspark.ml.classification import GBTClassifier
from pyspark.ml.feature import StringIndexer
from pyspark.ml.regression import GBTRegressor
from pyspark.mllib.evaluation import BinaryClassificationMetrics, RegressionMetrics
from pyspark.sql import Row, SQLContext
"""
A simple example demonstrating a Gradient Boosted Trees Classification/Regression Pipeline.
Note: GBTClassifier only supports binary classification currently
Run with:
bin/spark-submit examples/src/main/python/ml/gradient_boosted_trees.py
"""
def testClassification(train, test):
# Train a GradientBoostedTrees model.
rf = GBTClassifier(maxIter=30, maxDepth=4, labelCol="indexedLabel")
model = rf.fit(train)
predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \
.map(lambda x: (x.prediction, x.indexedLabel))
metrics = BinaryClassificationMetrics(predictionAndLabels)
print("AUC %.3f" % metrics.areaUnderROC)
def testRegression(train, test):
# Train a GradientBoostedTrees model.
rf = GBTRegressor(maxIter=30, maxDepth=4, labelCol="indexedLabel")
model = rf.fit(train)
predictionAndLabels = model.transform(test).select("prediction", "indexedLabel") \
.map(lambda x: (x.prediction, x.indexedLabel))
metrics = RegressionMetrics(predictionAndLabels)
print("rmse %.3f" % metrics.rootMeanSquaredError)
print("r2 %.3f" % metrics.r2)
print("mae %.3f" % metrics.meanAbsoluteError)
if __name__ == "__main__":
if len(sys.argv) > 1:
print("Usage: gradient_boosted_trees", file=sys.stderr)
exit(1)
sc = SparkContext(appName="PythonGBTExample")
sqlContext = SQLContext(sc)
# Load the data stored in LIBSVM format as a DataFrame.
df = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
# Map labels into an indexed column of labels in [0, numLabels)
stringIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel")
si_model = stringIndexer.fit(df)
td = si_model.transform(df)
[train, test] = td.randomSplit([0.7, 0.3])
testClassification(train, test)
testRegression(train, test)
sc.stop()
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