diff options
Diffstat (limited to 'examples')
2 files changed, 74 insertions, 2 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala index c5bd5b0b17..1a95048bbf 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala @@ -35,8 +35,7 @@ import org.apache.spark.streaming.{Seconds, StreamingContext} * * 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 + * $ bin/run-example 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. diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala new file mode 100644 index 0000000000..e1998099c2 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala @@ -0,0 +1,73 @@ +/* + * 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. + */ + +package org.apache.spark.examples.mllib + +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD +import org.apache.spark.SparkConf +import org.apache.spark.streaming.{Seconds, StreamingContext} + +/** + * Train a logistic 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, y is a binary label, and + * n must be the same for train and test. + * + * Usage: StreamingLogisticRegression <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 mllib.StreamingLogisticRegression 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 StreamingLogisticRegression { + + def main(args: Array[String]) { + + if (args.length != 4) { + System.err.println( + "Usage: StreamingLogisticRegression <trainingDir> <testDir> <batchDuration> <numFeatures>") + System.exit(1) + } + + val conf = new SparkConf().setMaster("local").setAppName("StreamingLogisticRegression") + val ssc = new StreamingContext(conf, Seconds(args(2).toLong)) + + val trainingData = ssc.textFileStream(args(0)).map(LabeledPoint.parse) + val testData = ssc.textFileStream(args(1)).map(LabeledPoint.parse) + + val model = new StreamingLogisticRegressionWithSGD() + .setInitialWeights(Vectors.zeros(args(3).toInt)) + + model.trainOn(trainingData) + model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print() + + ssc.start() + ssc.awaitTermination() + + } + +} |