Spark機器學習示例

1. Java代碼

/*
 * 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 com.XXX.YYY.hello;

import java.util.regex.Pattern;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;

import org.apache.spark.mllib.classification.LogisticRegressionWithSGD;
import org.apache.spark.mllib.classification.LogisticRegressionModel;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.linalg.DenseVector;
/**
 * Logistic regression based classification using ML Lib.
 */
public final class JavaLR {

  static class ParsePoint implements Function<String, LabeledPoint> {
    private static final Pattern COMMA = Pattern.compile(",");
    private static final Pattern SPACE = Pattern.compile(" ");

    @Override
    public LabeledPoint call(String line) {
      String[] parts = COMMA.split(line);
      double y = Double.parseDouble(parts[0]);
      String[] tok = SPACE.split(parts[1]);
      double[] x = new double[tok.length];
      for (int i = 0; i < tok.length; ++i) {
        x[i] = Double.parseDouble(tok[i]);
      }
      return new LabeledPoint(y, Vectors.dense(x));
    }
  }

  public static void main(String[] args) {
    if (args.length != 3) {
      System.err.println("Usage: JavaLR <input_dir> <step_size> <niters>");
      System.exit(1);
    }
    SparkConf sparkConf = new SparkConf().setAppName("JavaLR");
    JavaSparkContext sc = new JavaSparkContext(sparkConf);
    JavaRDD<String> lines = sc.textFile(args[0]);
    JavaRDD<LabeledPoint> points = lines.map(new ParsePoint()).cache();
    double stepSize = Double.parseDouble(args[1]);
    int iterations = Integer.parseInt(args[2]);

    // Another way to configure LogisticRegression
    //
    // LogisticRegressionWithSGD lr = new LogisticRegressionWithSGD();
    // lr.optimizer().setNumIterations(iterations)
    //               .setStepSize(stepSize)
    //               .setMiniBatchFraction(1.0);
    // lr.setIntercept(true);
    // LogisticRegressionModel model = lr.train(points.rdd());

    LogisticRegressionModel model = LogisticRegressionWithSGD.train(points.rdd(),
      iterations, stepSize);

    System.out.print("Final w: " + model.weights() + "and intercept is " + model.intercept() + "\n");
    double[] point = new double[2];
    point[0] = 8;
    point[1] = 8;
    double label = model.predictPoint(new DenseVector(point), model.weights(), model.intercept());
    System.out.print("label for [" + point[0] + " "  + point[1] + "] is " + label + "\n");
    sc.stop();
  }
}

2. 數據文件

0,0 0
0,1 2
0,1 3
0,2 1
0,3 1
0,2 2
1,6 5
1,7 6
1,8 6
1,6 7

3. 執行命令

# spark-submit --class com.XXX.YYY.hello.JavaLR --master yarn --deploy-mode cluster ./hello-1.0-SNAPSHOT-jar-with-dependencies.jar /lr.training.txt 0.2 100

/lr.training.txt放在hadoop的根目錄

4. 執行結果

Final w: [0.1618320065279109,0.03974871803971457]and intercept is 0.0
label for [8.0 8.0] is 1.0