| # |
| # 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. |
| # |
| |
| """ |
| Streaming Linear Regression Example. |
| """ |
| from __future__ import print_function |
| |
| # $example on$ |
| import sys |
| # $example off$ |
| |
| from pyspark import SparkContext |
| from pyspark.streaming import StreamingContext |
| # $example on$ |
| from pyspark.mllib.linalg import Vectors |
| from pyspark.mllib.regression import LabeledPoint |
| from pyspark.mllib.regression import StreamingLinearRegressionWithSGD |
| # $example off$ |
| |
| if __name__ == "__main__": |
| if len(sys.argv) != 3: |
| print("Usage: streaming_linear_regression_example.py <trainingDir> <testDir>", |
| file=sys.stderr) |
| exit(-1) |
| |
| sc = SparkContext(appName="PythonLogisticRegressionWithLBFGSExample") |
| ssc = StreamingContext(sc, 1) |
| |
| # $example on$ |
| def parse(lp): |
| label = float(lp[lp.find('(') + 1: lp.find(',')]) |
| vec = Vectors.dense(lp[lp.find('[') + 1: lp.find(']')].split(',')) |
| return LabeledPoint(label, vec) |
| |
| trainingData = ssc.textFileStream(sys.argv[1]).map(parse).cache() |
| testData = ssc.textFileStream(sys.argv[2]).map(parse) |
| |
| numFeatures = 3 |
| model = StreamingLinearRegressionWithSGD() |
| model.setInitialWeights([0.0, 0.0, 0.0]) |
| |
| model.trainOn(trainingData) |
| print(model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features)))) |
| |
| ssc.start() |
| ssc.awaitTermination() |
| # $example off$ |