org.apache.mahout.classifier.naivebayes.NaiveBayesModel Maven / Gradle / Ivy

/**
 * 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.classifier.naivebayes;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.SparseRowMatrix;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;

import com.google.common.base.Preconditions;

/** NaiveBayesModel holds the weight matrix, the feature and label sums and the weight normalizer vectors.*/
public class NaiveBayesModel {

  private final Vector weightsPerLabel;
  private final Vector perlabelThetaNormalizer;
  private final Vector weightsPerFeature;
  private final Matrix weightsPerLabelAndFeature;
  private final float alphaI;
  private final double numFeatures;
  private final double totalWeightSum;
  private final boolean isComplementary;  
   
  public final static String COMPLEMENTARY_MODEL = "COMPLEMENTARY_MODEL";

  public NaiveBayesModel(Matrix weightMatrix, Vector weightsPerFeature, Vector weightsPerLabel, Vector thetaNormalizer,
                         float alphaI, boolean isComplementary) {
    this.weightsPerLabelAndFeature = weightMatrix;
    this.weightsPerFeature = weightsPerFeature;
    this.weightsPerLabel = weightsPerLabel;
    this.perlabelThetaNormalizer = thetaNormalizer;
    this.numFeatures = weightsPerFeature.getNumNondefaultElements();
    this.totalWeightSum = weightsPerLabel.zSum();
    this.alphaI = alphaI;
    this.isComplementary=isComplementary;
  }

  public double labelWeight(int label) {
    return weightsPerLabel.getQuick(label);
  }

  public double thetaNormalizer(int label) {
    return perlabelThetaNormalizer.get(label); 
  }

  public double featureWeight(int feature) {
    return weightsPerFeature.getQuick(feature);
  }

  public double weight(int label, int feature) {
    return weightsPerLabelAndFeature.getQuick(label, feature);
  }

  public float alphaI() {
    return alphaI;
  }

  public double numFeatures() {
    return numFeatures;
  }

  public double totalWeightSum() {
    return totalWeightSum;
  }
  
  public int numLabels() {
    return weightsPerLabel.size();
  }

  public Vector createScoringVector() {
    return weightsPerLabel.like();
  }
  
  public boolean isComplemtary(){
      return isComplementary;
  }
  
  public static NaiveBayesModel materialize(Path output, Configuration conf) throws IOException {
    FileSystem fs = output.getFileSystem(conf);

    Vector weightsPerLabel;
    Vector perLabelThetaNormalizer = null;
    Vector weightsPerFeature;
    Matrix weightsPerLabelAndFeature;
    float alphaI;
    boolean isComplementary;

    try (FSDataInputStream in = fs.open(new Path(output, "naiveBayesModel.bin"))) {
      alphaI = in.readFloat();
      isComplementary = in.readBoolean();
      weightsPerFeature = VectorWritable.readVector(in);
      weightsPerLabel = new DenseVector(VectorWritable.readVector(in));
      if (isComplementary){
        perLabelThetaNormalizer = new DenseVector(VectorWritable.readVector(in));
      }
      weightsPerLabelAndFeature = new SparseRowMatrix(weightsPerLabel.size(), weightsPerFeature.size());
      for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) {
        weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in));
      }
    }

    NaiveBayesModel model = new NaiveBayesModel(weightsPerLabelAndFeature, weightsPerFeature, weightsPerLabel,
        perLabelThetaNormalizer, alphaI, isComplementary);
    model.validate();
    return model;
  }

  public void serialize(Path output, Configuration conf) throws IOException {
    FileSystem fs = output.getFileSystem(conf);
    try (FSDataOutputStream out = fs.create(new Path(output, "naiveBayesModel.bin"))) {
      out.writeFloat(alphaI);
      out.writeBoolean(isComplementary);
      VectorWritable.writeVector(out, weightsPerFeature);
      VectorWritable.writeVector(out, weightsPerLabel); 
      if (isComplementary){
        VectorWritable.writeVector(out, perlabelThetaNormalizer);
      }
      for (int row = 0; row < weightsPerLabelAndFeature.numRows(); row++) {
        VectorWritable.writeVector(out, weightsPerLabelAndFeature.viewRow(row));
      }
    }
  }
  
  public void validate() {
    Preconditions.checkState(alphaI > 0, "alphaI has to be greater than 0!");
    Preconditions.checkArgument(numFeatures > 0, "the vocab count has to be greater than 0!");
    Preconditions.checkArgument(totalWeightSum > 0, "the totalWeightSum has to be greater than 0!");
    Preconditions.checkNotNull(weightsPerLabel, "the number of labels has to be defined!");
    Preconditions.checkArgument(weightsPerLabel.getNumNondefaultElements() > 0,
        "the number of labels has to be greater than 0!");
    Preconditions.checkNotNull(weightsPerFeature, "the feature sums have to be defined");
    Preconditions.checkArgument(weightsPerFeature.getNumNondefaultElements() > 0,
        "the feature sums have to be greater than 0!");
    if (isComplementary){
        Preconditions.checkArgument(perlabelThetaNormalizer != null, "the theta normalizers have to be defined");
        Preconditions.checkArgument(perlabelThetaNormalizer.getNumNondefaultElements() > 0,
            "the number of theta normalizers has to be greater than 0!");    
        Preconditions.checkArgument(Math.signum(perlabelThetaNormalizer.minValue()) 
                == Math.signum(perlabelThetaNormalizer.maxValue()), 
           "Theta normalizers do not all have the same sign");            
        Preconditions.checkArgument(perlabelThetaNormalizer.getNumNonZeroElements() 
                == perlabelThetaNormalizer.size(), 
           "Theta normalizers can not have zero value.");
    }
    
  }
}