content: /*  XValSpectrumPADFeatureExtractor.java
  
      Copyright 2009-2014 Andrew Rosenberg
  
    This file is part of the AuToBI prosodic analysis package.
  
    AuToBI is free software: you can redistribute it and/or modify
    it under the terms of the Apache License (see boilerplate below)
  
   ***********************************************************************************************************************
   *
   * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
   * the License. You should have received a copy of the Apache 2.0 License along with AuToBI.
   * 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 edu.cuny.qc.speech.AuToBI.featureextractor;
  
  import edu.cuny.qc.speech.AuToBI.classifier.AuToBIClassifier;
  import edu.cuny.qc.speech.AuToBI.classifier.WekaClassifier;
  import edu.cuny.qc.speech.AuToBI.core.*;
  import edu.cuny.qc.speech.AuToBI.util.PartitionUtils;
  import weka.classifiers.trees.J48;
  
  import java.util.List;
  import java.util.ArrayList;
  
  /**
   * The XValSpectrumPADFeatureExtractor is used to generate cross vaidated predictions of spectral pitch accent detection
   * (PAD) hypotheses.  These xval hypotheses are used in training correction classfiers.
   * <p/>
   * This has been deprecated because it does not conform to the AuToBI v1.4 feature naming conventions
   */
  @SuppressWarnings("unchecked")
  @Deprecated
  public class XValSpectrumPADFeatureExtractor extends FeatureExtractor {
    public static final String moniker = "barkPred,barkPredConf,barkPredConfAcc";
  
    private final String ACCENTED_VALUE = "ACCENTED";  // the accented value
    private final String FOLD_ASSIGNMENT_FEATURE = "FOLD_ASSIGNMENT";
    // an feature to store fold assignment information on
    private int low;  // the bottom of the frequency region
    private int high; // the top of the frequency region
    private int num_folds;  // the number of folds used in the hypothesis generation.
    private FeatureSet fs;  // a description of the featureset used in the prediction
  
    /**
     * Constructs a new XValSpectrumFeatureExtractor for a specific spectral region.
     *
     * @param low       the bottom of the spectral region.
     * @param high      the top of the spectral region.
     * @param num_folds the number of folds to be used in the generation
     * @param fs        a feature set used in the xval experiments
     */
    public XValSpectrumPADFeatureExtractor(int low, int high, int num_folds, FeatureSet fs) {
      this.low = low;
      this.high = high;
      this.num_folds = num_folds;
      this.fs = fs;
  
      extracted_features.add("nominal_bark_" + low + "_" + high + "__prediction");
      extracted_features.add("bark_" + low + "_" + high + "__prediction_confidence");
      extracted_features.add("bark_" + low + "_" + high + "__prediction_confidence_accented");
  
      required_features.addAll(fs.getRequiredFeatures());
      required_features.add(fs.getClassAttribute());
    }
  
    /**
     * Generates cross validated pitch accent detection hypotheses using energy information drawn from the assigned
     * spectral region.
     *
     * @param regions The regions to extract features from.
     * @throws FeatureExtractorException if something goes wrong.
     */
    public void extractFeatures(List regions) throws FeatureExtractorException {
      // Construct a feature set.
      FeatureSet feature_set = fs.newInstance();
  
      // Extract spectrum features.
      feature_set.setDataPoints((List<Word>) regions);
      try {
        PartitionUtils
            .assignStratifiedFoldNum((List<Word>) regions, FOLD_ASSIGNMENT_FEATURE, num_folds,
                feature_set.getClassAttribute());
      } catch (AuToBIException e) {
        throw new FeatureExtractorException(e.getMessage());
      }
  
      // Train n-fold cross validated prediction features.
      for (int fold_num = 0; fold_num < num_folds; ++fold_num) {
        AuToBIClassifier classifier = new WekaClassifier(new J48());
  
        FeatureSet training_fs = fs.newInstance();
  
        List<Word> training_regions = new ArrayList<Word>();
        List<Word> testing_regions = new ArrayList<Word>();
        try {
          PartitionUtils
              .splitData((List<Word>) regions, training_regions, testing_regions, fold_num, FOLD_ASSIGNMENT_FEATURE);
        } catch (AuToBIException e) {
          // This should never happen.  If there is a problem with splitData it would have thrown
          // an exception during the fold assignment method assignStratifiedFoldNum
          throw new FeatureExtractorException(e.getMessage());
        }
        training_fs.setDataPoints(training_regions);
        training_fs.constructFeatures();
        try {
          classifier.train(training_fs);
        } catch (Exception e) {
          throw new FeatureExtractorException(e.getMessage());
        }
  
        for (Word w : testing_regions) {
          try {
            Distribution result = classifier.distributionForInstance(w);
  
            w.setAttribute("nominal_bark_" + low + "_" + high + "__prediction", result.getKeyWithMaximumValue());
            w.setAttribute("bark_" + low + "_" + high + "__prediction_confidence",
                result.get(result.getKeyWithMaximumValue()));
            w.setAttribute("bark_" + low + "_" + high + "__prediction_confidence_accented", result.get(ACCENTED_VALUE));
          } catch (Exception e) {
            throw new FeatureExtractorException(e.getMessage());
          }
        }
      }
    }
  }