org.apache.mahout.df.mapreduce.partial.PartialSequentialBuilder.java Source code

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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.
 */

package org.apache.mahout.df.mapreduce.partial;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

import org.apache.commons.lang.ArrayUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.JobID;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.TaskAttemptID;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.mahout.df.DFUtils;
import org.apache.mahout.df.DecisionForest;
import org.apache.mahout.df.builder.TreeBuilder;
import org.apache.mahout.df.callback.PredictionCallback;
import org.apache.mahout.df.data.Dataset;
import org.apache.mahout.df.mapreduce.Builder;
import org.apache.mahout.df.mapreduce.MapredOutput;
import org.apache.mahout.df.node.Node;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * Simulates the Partial mapreduce implementation in a sequential manner. Must
 * receive a seed
 */
public class PartialSequentialBuilder extends PartialBuilder {

    private static final Logger log = LoggerFactory.getLogger(PartialSequentialBuilder.class);

    private MockContext firstOutput;

    private MockContext secondOutput;

    private final Dataset dataset;

    /** first instance id in hadoop's order */
    private int[] firstIds;

    /** partitions' sizes in hadoop order */
    private int[] sizes;

    public PartialSequentialBuilder(TreeBuilder treeBuilder, Path dataPath, Dataset dataset, long seed,
            Configuration conf) {
        super(treeBuilder, dataPath, new Path("notUsed"), seed, conf);
        this.dataset = dataset;
    }

    public PartialSequentialBuilder(TreeBuilder treeBuilder, Path dataPath, Dataset dataset, long seed) {
        this(treeBuilder, dataPath, dataset, seed, new Configuration());
    }

    @Override
    protected void configureJob(Job job, int nbTrees, boolean oobEstimate) throws IOException {
        Configuration conf = job.getConfiguration();

        int num = conf.getInt("mapred.map.tasks", -1);

        super.configureJob(job, nbTrees, oobEstimate);

        // PartialBuilder sets the number of maps to 1 if we are running in 'local'
        conf.setInt("mapred.map.tasks", num);
    }

    @Override
    protected boolean runJob(Job job) throws IOException, InterruptedException {
        Configuration conf = job.getConfiguration();

        // retrieve the splits
        TextInputFormat input = new TextInputFormat();
        List<InputSplit> splits = input.getSplits(job);

        int nbSplits = splits.size();
        log.debug("Nb splits : {}", nbSplits);

        InputSplit[] sorted = new InputSplit[nbSplits];
        splits.toArray(sorted);
        Builder.sortSplits(sorted);

        int numTrees = Builder.getNbTrees(conf); // total number of trees

        TaskAttemptContext task = new TaskAttemptContext(conf, new TaskAttemptID());

        firstOutput = new MockContext(new Step1Mapper(), conf, task.getTaskAttemptID(), numTrees);

        firstIds = new int[nbSplits];
        sizes = new int[nbSplits];

        // to compute firstIds, process the splits in file order
        long slowest = 0; // duration of slowest map
        int firstId = 0;
        for (int p = 0; p < nbSplits; p++) {
            InputSplit split = splits.get(p);
            int hp = ArrayUtils.indexOf(sorted, split); // hadoop's partition

            RecordReader<LongWritable, Text> reader = input.createRecordReader(split, task);
            reader.initialize(split, task);

            Step1Mapper mapper = new MockStep1Mapper(getTreeBuilder(), dataset, getSeed(), hp, nbSplits, numTrees);

            long time = System.currentTimeMillis();

            firstIds[hp] = firstId;

            while (reader.nextKeyValue()) {
                mapper.map(reader.getCurrentKey(), reader.getCurrentValue(), firstOutput);
                firstId++;
                sizes[hp]++;
            }

            mapper.cleanup(firstOutput);

            time = System.currentTimeMillis() - time;
            log.info("Duration : {}", DFUtils.elapsedTime(time));

            if (time > slowest) {
                slowest = time;
            }
        }

        log.info("Longest duration : {}", DFUtils.elapsedTime(slowest));
        return true;
    }

    @Override
    protected DecisionForest parseOutput(Job job, PredictionCallback callback)
            throws IOException, InterruptedException {
        Configuration conf = job.getConfiguration();

        DecisionForest forest = processOutput(firstOutput.getKeys(), firstOutput.getValues(), callback);

        if (isStep2(conf)) {
            Path forestPath = new Path(getOutputPath(conf), "step1.inter");
            FileSystem fs = forestPath.getFileSystem(conf);

            Node[] trees = new Node[forest.getTrees().size()];
            forest.getTrees().toArray(trees);
            InterResults.store(fs, forestPath, firstOutput.getKeys(), trees, sizes);

            log.info("***********");
            log.info("Second Step");
            log.info("***********");
            secondStep(conf, forestPath, callback);

            processOutput(secondOutput.getKeys(), secondOutput.getValues(), callback);
        }

        return forest;
    }

    /**
     * extract the decision forest and call the callback after correcting the instance ids
     */
    protected DecisionForest processOutput(TreeID[] keys, MapredOutput[] values, PredictionCallback callback) {
        List<Node> trees = new ArrayList<Node>();

        for (int index = 0; index < keys.length; index++) {
            TreeID key = keys[index];
            MapredOutput value = values[index];

            trees.add(value.getTree());

            int[] predictions = value.getPredictions();
            for (int id = 0; id < predictions.length; id++) {
                callback.prediction(key.treeId(), firstIds[key.partition()] + id, predictions[id]);
            }
        }

        return new DecisionForest(trees);
    }

    /**
     * The second step uses the trees to predict the rest of the instances outside
     * their own partition
     */
    protected void secondStep(Configuration conf, Path forestPath, PredictionCallback callback)
            throws IOException, InterruptedException {
        JobContext jobContext = new JobContext(conf, new JobID());

        // retrieve the splits
        TextInputFormat input = new TextInputFormat();
        List<InputSplit> splits = input.getSplits(jobContext);

        int nbSplits = splits.size();
        log.debug("Nb splits : {}", nbSplits);

        InputSplit[] sorted = new InputSplit[nbSplits];
        splits.toArray(sorted);
        Builder.sortSplits(sorted);

        int numTrees = Builder.getNbTrees(conf); // total number of trees

        // compute the expected number of outputs
        int total = 0;
        for (int p = 0; p < nbSplits; p++) {
            total += Step2Mapper.nbConcerned(nbSplits, numTrees, p);
        }

        TaskAttemptContext task = new TaskAttemptContext(conf, new TaskAttemptID());

        secondOutput = new MockContext(new Step2Mapper(), conf, task.getTaskAttemptID(), numTrees);
        long slowest = 0; // duration of slowest map

        for (int partition = 0; partition < nbSplits; partition++) {

            InputSplit split = sorted[partition];
            RecordReader<LongWritable, Text> reader = input.createRecordReader(split, task);

            // load the output of the 1st step
            int nbConcerned = Step2Mapper.nbConcerned(nbSplits, numTrees, partition);
            TreeID[] fsKeys = new TreeID[nbConcerned];
            Node[] fsTrees = new Node[nbConcerned];

            FileSystem fs = forestPath.getFileSystem(conf);
            int numInstances = InterResults.load(fs, forestPath, nbSplits, numTrees, partition, fsKeys, fsTrees);

            Step2Mapper mapper = new Step2Mapper();
            mapper.configure(partition, dataset, fsKeys, fsTrees, numInstances);

            long time = System.currentTimeMillis();

            while (reader.nextKeyValue()) {
                mapper.map(reader.getCurrentKey(), reader.getCurrentValue(), secondOutput);
            }

            mapper.cleanup(secondOutput);

            time = System.currentTimeMillis() - time;
            log.info("Duration : {}", DFUtils.elapsedTime(time));

            if (time > slowest) {
                slowest = time;
            }
        }

        log.info("Longest duration : {}", DFUtils.elapsedTime(slowest));
    }

    /**
     * Special Step1Mapper that can be configured without using a Configuration
     * 
     */
    private static class MockStep1Mapper extends Step1Mapper {
        protected MockStep1Mapper(TreeBuilder treeBuilder, Dataset dataset, Long seed, int partition,
                int numMapTasks, int numTrees) {
            configure(false, true, treeBuilder, dataset);
            configure(seed, partition, numMapTasks, numTrees);
        }

    }

}