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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.
#
# $example on$
from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD
from pyspark.mllib.evaluation import RegressionMetrics
from pyspark.mllib.linalg import DenseVector
# $example off$
from pyspark import SparkContext
if __name__ == "__main__":
sc = SparkContext(appName="Regression Metrics Example")
# $example on$
# Load and parse the data
def parsePoint(line):
values = line.split()
return LabeledPoint(float(values[0]),
DenseVector([float(x.split(':')[1]) for x in values[1:]]))
data = sc.textFile("data/mllib/sample_linear_regression_data.txt")
parsedData = data.map(parsePoint)
# Build the model
model = LinearRegressionWithSGD.train(parsedData)
# Get predictions
valuesAndPreds = parsedData.map(lambda p: (float(model.predict(p.features)), p.label))
# Instantiate metrics object
metrics = RegressionMetrics(valuesAndPreds)
# Squared Error
print("MSE = %s" % metrics.meanSquaredError)
print("RMSE = %s" % metrics.rootMeanSquaredError)
# R-squared
print("R-squared = %s" % metrics.r2)
# Mean absolute error
print("MAE = %s" % metrics.meanAbsoluteError)
# Explained variance
print("Explained variance = %s" % metrics.explainedVariance)
# $example off$
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