org.apache.mahout.classifier.naivebayes.ComplementaryNaiveBayesClassifier 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;


/** Implementation of the Naive Bayes Classifier Algorithm */
public class ComplementaryNaiveBayesClassifier extends AbstractNaiveBayesClassifier {
  public ComplementaryNaiveBayesClassifier(NaiveBayesModel model) {
    super(model);
  }

  @Override
  public double getScoreForLabelFeature(int label, int feature) {
    NaiveBayesModel model = getModel();
    double weight = computeWeight(model.featureWeight(feature), model.weight(label, feature),
        model.totalWeightSum(), model.labelWeight(label), model.alphaI(), model.numFeatures());
    // see http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf - Section 3.2, Weight Magnitude Errors
    return weight / model.thetaNormalizer(label);
  }

  // see http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf - Section 3.1, Skewed Data bias
  public static double computeWeight(double featureWeight, double featureLabelWeight,
      double totalWeight, double labelWeight, double alphaI, double numFeatures) {
    double numerator = featureWeight - featureLabelWeight + alphaI;
    double denominator = totalWeight - labelWeight + alphaI * numFeatures;
    return -Math.log(numerator / denominator);
  }
}