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examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala
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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. | ||
*/ | ||
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package org.apache.spark.examples.mllib | ||
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import org.apache.spark.mllib.linalg.Vectors | ||
import org.apache.spark.mllib.util.MLUtils | ||
import org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD | ||
import org.apache.spark.SparkConf | ||
import org.apache.spark.streaming.{Seconds, StreamingContext} | ||
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/** | ||
* Train a linear regression model on one stream of data and make predictions | ||
* on another stream, where the data streams arrive as text files | ||
* into two different directories. | ||
* | ||
* The rows of the text files must be labeled data points in the form | ||
* `(y,[x1,x2,x3,...,xn])` | ||
* Where n is the number of features. n must be the same for train and test. | ||
* | ||
* Usage: StreamingLinearRegression <trainingDir> <testDir> <batchDuration> <numFeatures> | ||
* | ||
* To run on your local machine using the two directories `trainingDir` and `testDir`, | ||
* with updates every 5 seconds, and 2 features per data point, call: | ||
* $ bin/run-example \ | ||
* org.apache.spark.examples.mllib.StreamingLinearRegression trainingDir testDir 5 2 | ||
* | ||
* As you add text files to `trainingDir` the model will continuously update. | ||
* Anytime you add text files to `testDir`, you'll see predictions from the current model. | ||
* | ||
*/ | ||
object StreamingLinearRegression { | ||
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def main(args: Array[String]) { | ||
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if (args.length != 4) { | ||
System.err.println( | ||
"Usage: StreamingLinearRegression <trainingDir> <testDir> <batchDuration> <numFeatures>") | ||
System.exit(1) | ||
} | ||
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val conf = new SparkConf().setMaster("local").setAppName("StreamingLinearRegression") | ||
val ssc = new StreamingContext(conf, Seconds(args(2).toLong)) | ||
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val trainingData = MLUtils.loadStreamingLabeledPoints(ssc, args(0)) | ||
val testData = MLUtils.loadStreamingLabeledPoints(ssc, args(1)) | ||
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val model = new StreamingLinearRegressionWithSGD() | ||
.setInitialWeights(Vectors.dense(Array.fill[Double](args(3).toInt)(0))) | ||
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model.trainOn(trainingData) | ||
model.predictOn(testData).print() | ||
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ssc.start() | ||
ssc.awaitTermination() | ||
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} | ||
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} |
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