org.apache.mahout.classifier.naivebayes.training.TrainNaiveBayesJob Maven / Gradle / Ivy

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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.mahout.classifier.naivebayes.training;

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

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.classifier.naivebayes.BayesUtils;
import org.apache.mahout.classifier.naivebayes.NaiveBayesModel;
import org.apache.mahout.common.AbstractJob;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.Pair;
import org.apache.mahout.common.commandline.DefaultOptionCreator;
import org.apache.mahout.common.iterator.sequencefile.PathFilters;
import org.apache.mahout.common.iterator.sequencefile.PathType;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirIterable;
import org.apache.mahout.common.mapreduce.VectorSumReducer;
import org.apache.mahout.math.VectorWritable;

import com.google.common.base.Splitter;

/** Trains a Naive Bayes Classifier (parameters for both Naive Bayes and Complementary Naive Bayes) */
public final class TrainNaiveBayesJob extends AbstractJob {
  private static final String TRAIN_COMPLEMENTARY = "trainComplementary";
  private static final String ALPHA_I = "alphaI";
  private static final String LABEL_INDEX = "labelIndex";
  public static final String WEIGHTS_PER_FEATURE = "__SPF";
  public static final String WEIGHTS_PER_LABEL = "__SPL";
  public static final String LABEL_THETA_NORMALIZER = "_LTN";
  public static final String SUMMED_OBSERVATIONS = "summedObservations";
  public static final String WEIGHTS = "weights";
  public static final String THETAS = "thetas";

  public static void main(String[] args) throws Exception {
    ToolRunner.run(new Configuration(), new TrainNaiveBayesJob(), args);
  }

  @Override
  public int run(String[] args) throws Exception {

    addInputOption();
    addOutputOption();

    addOption(ALPHA_I, "a", "smoothing parameter", String.valueOf(1.0f));
    addOption(buildOption(TRAIN_COMPLEMENTARY, "c", "train complementary?", false, false, String.valueOf(false)));
    addOption(LABEL_INDEX, "li", "The path to store the label index in", false);
    addOption(DefaultOptionCreator.overwriteOption().create());

    Map> parsedArgs = parseArguments(args);
    if (parsedArgs == null) {
      return -1;
    }
    if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {
      HadoopUtil.delete(getConf(), getOutputPath());
      HadoopUtil.delete(getConf(), getTempPath());
    }
    Path labPath;
    String labPathStr = getOption(LABEL_INDEX);
    if (labPathStr != null) {
      labPath = new Path(labPathStr);
    } else {
      labPath = getTempPath(LABEL_INDEX);
    }
    long labelSize = createLabelIndex(labPath);
    float alphaI = Float.parseFloat(getOption(ALPHA_I));
    boolean trainComplementary = hasOption(TRAIN_COMPLEMENTARY);

    HadoopUtil.setSerializations(getConf());
    HadoopUtil.cacheFiles(labPath, getConf());

    // Add up all the vectors with the same labels, while mapping the labels into our index
    Job indexInstances = prepareJob(getInputPath(),
                                    getTempPath(SUMMED_OBSERVATIONS),
                                    SequenceFileInputFormat.class,
                                    IndexInstancesMapper.class,
                                    IntWritable.class,
                                    VectorWritable.class,
                                    VectorSumReducer.class,
                                    IntWritable.class,
                                    VectorWritable.class,
                                    SequenceFileOutputFormat.class);
    indexInstances.setCombinerClass(VectorSumReducer.class);
    boolean succeeded = indexInstances.waitForCompletion(true);
    if (!succeeded) {
      return -1;
    }
    // Sum up all the weights from the previous step, per label and per feature
    Job weightSummer = prepareJob(getTempPath(SUMMED_OBSERVATIONS),
                                  getTempPath(WEIGHTS),
                                  SequenceFileInputFormat.class,
                                  WeightsMapper.class,
                                  Text.class,
                                  VectorWritable.class,
                                  VectorSumReducer.class,
                                  Text.class,
                                  VectorWritable.class,
                                  SequenceFileOutputFormat.class);
    weightSummer.getConfiguration().set(WeightsMapper.NUM_LABELS, String.valueOf(labelSize));
    weightSummer.setCombinerClass(VectorSumReducer.class);
    succeeded = weightSummer.waitForCompletion(true);
    if (!succeeded) {
      return -1;
    }

    // Put the per label and per feature vectors into the cache
    HadoopUtil.cacheFiles(getTempPath(WEIGHTS), getConf());

    if (trainComplementary){
      // Calculate the per label theta normalizers, write out to LABEL_THETA_NORMALIZER vector
      // see http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf - Section 3.2, Weight Magnitude Errors
      Job thetaSummer = prepareJob(getTempPath(SUMMED_OBSERVATIONS),
                                   getTempPath(THETAS),
                                   SequenceFileInputFormat.class,
                                   ThetaMapper.class,
                                   Text.class,
                                   VectorWritable.class,
                                   VectorSumReducer.class,
                                   Text.class,
                                   VectorWritable.class,
                                   SequenceFileOutputFormat.class);
      thetaSummer.setCombinerClass(VectorSumReducer.class);
      thetaSummer.getConfiguration().setFloat(ThetaMapper.ALPHA_I, alphaI);
      thetaSummer.getConfiguration().setBoolean(ThetaMapper.TRAIN_COMPLEMENTARY, trainComplementary);
      succeeded = thetaSummer.waitForCompletion(true);
      if (!succeeded) {
        return -1;
      }
    }
    
    // Put the per label theta normalizers into the cache
    HadoopUtil.cacheFiles(getTempPath(THETAS), getConf());
    
    // Validate our model and then write it out to the official output
    getConf().setFloat(ThetaMapper.ALPHA_I, alphaI);
    getConf().setBoolean(NaiveBayesModel.COMPLEMENTARY_MODEL, trainComplementary);
    NaiveBayesModel naiveBayesModel = BayesUtils.readModelFromDir(getTempPath(), getConf());
    naiveBayesModel.validate();
    naiveBayesModel.serialize(getOutputPath(), getConf());

    return 0;
  }

  private long createLabelIndex(Path labPath) throws IOException {
    long labelSize = 0;
    Iterable> iterable =
      new SequenceFileDirIterable(getInputPath(),
                                                     PathType.LIST,
                                                     PathFilters.logsCRCFilter(),
                                                     getConf());
    labelSize = BayesUtils.writeLabelIndex(getConf(), labPath, iterable);
    return labelSize;
  }
}