/** 
 * Copyright (c) 2012, Regents of the University of Colorado 
 * All rights reserved.
 * 
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 * 
 * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 
 * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 
 * Neither the name of the University of Colorado at Boulder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. 
 * 
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
 * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
 * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
 * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 * POSSIBILITY OF SUCH DAMAGE. 
 */

package org.cleartk.ml.feature.transform.extractor;

import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.net.URI;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Locale;
import java.util.Set;

import org.apache.uima.jcas.JCas;
import org.apache.uima.jcas.tcas.Annotation;
import org.cleartk.ml.Feature;
import org.cleartk.ml.Instance;
import org.cleartk.ml.feature.extractor.CleartkExtractorException;
import org.cleartk.ml.feature.extractor.FeatureExtractor1;
import org.cleartk.ml.feature.transform.OneToOneTrainableExtractor_ImplBase;
import org.cleartk.ml.feature.transform.TransformableFeature;

import com.google.common.collect.LinkedHashMultiset;
import com.google.common.collect.Multiset;

/**
 * Transforms count features produced by its subextractor into TF*IDF values
 * <p>
 * 
 * <br>
 * Copyright (c) 2012, Regents of the University of Colorado <br>
 * All rights reserved.
 * 
 * @author Lee Becker
 * 
 */
public class TfidfExtractor<OUTCOME_T, FOCUS_T extends Annotation> extends
    OneToOneTrainableExtractor_ImplBase<OUTCOME_T> implements FeatureExtractor1<FOCUS_T> {

  protected FeatureExtractor1<FOCUS_T> subExtractor;

  protected boolean isTrained;

  protected IDFMap idfMap;

  public TfidfExtractor(String name) {
    this(name, null);
  }

  /**
   * 
   * @param extractor
   *          - This assumes that any extractors passed in will produce counts of some variety
   */
  public TfidfExtractor(String name, FeatureExtractor1<FOCUS_T> extractor) {
    super(name);
    this.subExtractor = extractor;
    this.isTrained = false;
    this.idfMap = new IDFMap();
  }

  @Override
  protected Feature transform(Feature feature) {
    int tf = (Integer) feature.getValue();
    double tfidf = tf * this.idfMap.getIDF(feature.getName());
    return new Feature("TF-IDF_" + feature.getName(), tfidf);
  }

  @Override
  public List<Feature> extract(JCas view, FOCUS_T focusAnnotation) throws CleartkExtractorException {

    List<Feature> extracted = this.subExtractor.extract(view, focusAnnotation);
    List<Feature> result = new ArrayList<Feature>();
    if (this.isTrained) {
      // We have trained / loaded a tf*idf model, so now fix up the values
      for (Feature feature : extracted) {
        result.add(this.transform(feature));
      }
    } else {
      // We haven't trained this extractor yet, so just mark the existing features
      // for future modification, by creating one mega container feature
      result.add(new TransformableFeature(this.name, extracted));
    }

    return result;
  }

  protected IDFMap createIdfMap(Iterable<Instance<OUTCOME_T>> instances) {
    IDFMap newIdfMap = new IDFMap();

    // Add instance's term frequencies to the global counts
    for (Instance<OUTCOME_T> instance : instances) {

      Set<String> featureNames = new HashSet<String>();
      // Grab the matching tf*idf features from the set of all features in an instance
      for (Feature feature : instance.getFeatures()) {
        if (this.isTransformable(feature)) {
          // tf*idf features contain a list of features, these are actually what get added
          // to our document frequency map
          for (Feature untransformedFeature : ((TransformableFeature) feature).getFeatures()) {
            featureNames.add(untransformedFeature.getName());
          }
        }
      }

      for (String featureName : featureNames) {
        newIdfMap.add(featureName);
      }
      newIdfMap.incTotalDocumentCount();

    }
    return newIdfMap;
  }

  @Override
  public void train(Iterable<Instance<OUTCOME_T>> instances) {
    this.idfMap = this.createIdfMap(instances);
    this.isTrained = true;
  }

  @Override
  public void save(URI documentFreqDataURI) throws IOException {
    this.idfMap.save(documentFreqDataURI);
  }

  @Override
  public void load(URI documentFreqDataURI) throws IOException {
    this.idfMap.load(documentFreqDataURI);
    this.isTrained = true;
  }

  protected static class IDFMap {
    private Multiset<String> documentFreqMap;

    private int totalDocumentCount;

    public IDFMap() {
      this.documentFreqMap = LinkedHashMultiset.create();
      this.totalDocumentCount = 0;
    }

    public void add(String term) {
      this.documentFreqMap.add(term);
    }

    public void incTotalDocumentCount() {
      this.totalDocumentCount++;
    }

    public int getTotalDocumentCount() {
      return this.totalDocumentCount;
    }

    public int getDF(String term) {
      return this.documentFreqMap.count(term);
    }

    public double getIDF(String term) {
      int df = this.getDF(term);

      // see issue 396 for discussion about the ratio here:
      // https://code.google.com/p/cleartk/issues/detail?id=396
      // In short, we add 1 to the document frequency for smoothing purposes so that unseen words
      // will not generate a NaN. We add 2 to the numerator to make sure that we get a positive
      // value for words that appear in every document (i.e. when df == totalDocumentCount)
      return Math.log((this.totalDocumentCount + 2) / (double) (df + 1));
    }

    public void save(URI outputURI) throws IOException {
      File out = new File(outputURI);
      BufferedWriter writer = null;
      writer = new BufferedWriter(new FileWriter(out));
      writer.append(String.format(Locale.ROOT, "NUM DOCUMENTS\t%d\n", this.totalDocumentCount));
      for (Multiset.Entry<String> entry : this.documentFreqMap.entrySet()) {
        writer.append(String.format(Locale.ROOT, "%s\t%d\n", entry.getElement(), entry.getCount()));
      }
      writer.close();
    }

    public void load(URI inputURI) throws IOException {
      File in = new File(inputURI);
      BufferedReader reader = null;
      // this.documentFreqMap = LinkedHashMultiset.create();
      reader = new BufferedReader(new FileReader(in));
      // First line specifies the number of documents
      String firstLine = reader.readLine();
      String[] keyValuePair = firstLine.split("\\t");
      this.totalDocumentCount = Integer.parseInt(keyValuePair[1]);

      // The rest of the lines are the term counts
      String line = null;
      while ((line = reader.readLine()) != null) {
        String[] termFreqPair = line.split("\\t");
        this.documentFreqMap.add(termFreqPair[0], Integer.parseInt(termFreqPair[1]));
      }
      reader.close();
    }
  }
}