package com.yahoo.labs.samoa.learners.classifiers.ensemble;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2014 - 2015 Apache Software Foundation
 * %%
 * Licensed 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.
 * #L%
 */

/**
 * License
 */

import com.google.common.collect.ImmutableSet;
import java.util.Set;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.github.javacliparser.ClassOption;
import com.github.javacliparser.Configurable;
import com.github.javacliparser.IntOption;
import com.yahoo.labs.samoa.core.Processor;
import com.yahoo.labs.samoa.instances.Instances;
import com.yahoo.labs.samoa.learners.AdaptiveLearner;
import com.yahoo.labs.samoa.learners.Learner;
import com.yahoo.labs.samoa.learners.classifiers.trees.VerticalHoeffdingTree;
import com.yahoo.labs.samoa.moa.classifiers.core.driftdetection.ADWINChangeDetector;
import com.yahoo.labs.samoa.moa.classifiers.core.driftdetection.ChangeDetector;
import com.yahoo.labs.samoa.topology.Stream;
import com.yahoo.labs.samoa.topology.TopologyBuilder;

/**
 * The Bagging Classifier by Oza and Russell.
 */
public class AdaptiveBagging implements Learner, Configurable {

  /** Logger */
  private static final Logger logger = LoggerFactory.getLogger(AdaptiveBagging.class);

  /** The Constant serialVersionUID. */
  private static final long serialVersionUID = -2971850264864952099L;

  /** The base learner option. */
  public ClassOption baseLearnerOption = new ClassOption("baseLearner", 'l',
      "Classifier to train.", Learner.class, VerticalHoeffdingTree.class.getName());

  /** The ensemble size option. */
  public IntOption ensembleSizeOption = new IntOption("ensembleSize", 's',
      "The number of models in the bag.", 10, 1, Integer.MAX_VALUE);

  public ClassOption driftDetectionMethodOption = new ClassOption("driftDetectionMethod", 'd',
      "Drift detection method to use.", ChangeDetector.class, ADWINChangeDetector.class.getName());

  /** The distributor processor. */
  private BaggingDistributorProcessor distributorP;

  /** The result stream. */
  protected Stream resultStream;

  /** The dataset. */
  private Instances dataset;

  protected Learner classifier;

  protected int parallelism;

  /**
   * Sets the layout.
   */
  protected void setLayout() {

    int sizeEnsemble = this.ensembleSizeOption.getValue();

    distributorP = new BaggingDistributorProcessor();
    distributorP.setSizeEnsemble(sizeEnsemble);
    this.builder.addProcessor(distributorP, 1);

    // instantiate classifier
    classifier = this.baseLearnerOption.getValue();
    if (classifier instanceof AdaptiveLearner) {
      // logger.info("Building an AdaptiveLearner {}",
      // classifier.getClass().getName());
      AdaptiveLearner ada = (AdaptiveLearner) classifier;
      ada.setChangeDetector((ChangeDetector) this.driftDetectionMethodOption.getValue());
    }
    classifier.init(builder, this.dataset, sizeEnsemble);

    PredictionCombinerProcessor predictionCombinerP = new PredictionCombinerProcessor();
    predictionCombinerP.setSizeEnsemble(sizeEnsemble);
    this.builder.addProcessor(predictionCombinerP, 1);

    // Streams
    resultStream = this.builder.createStream(predictionCombinerP);
    predictionCombinerP.setOutputStream(resultStream);

    for (Stream subResultStream : classifier.getResultStreams()) {
      this.builder.connectInputKeyStream(subResultStream, predictionCombinerP);
    }

    /* The training stream. */
    Stream testingStream = this.builder.createStream(distributorP);
    this.builder.connectInputKeyStream(testingStream, classifier.getInputProcessor());

    /* The prediction stream. */
    Stream predictionStream = this.builder.createStream(distributorP);
    this.builder.connectInputKeyStream(predictionStream, classifier.getInputProcessor());

    distributorP.setOutputStream(testingStream);
    distributorP.setPredictionStream(predictionStream);
  }

  /** The builder. */
  private TopologyBuilder builder;

  @Override
  public void init(TopologyBuilder builder, Instances dataset, int parallelism) {
    this.builder = builder;
    this.dataset = dataset;
    this.parallelism = parallelism;
    this.setLayout();
  }

  @Override
  public Processor getInputProcessor() {
    return distributorP;
  }

  /*
   * (non-Javadoc)
   * 
   * @see samoa.learners.Learner#getResultStreams()
   */
  @Override
  public Set<Stream> getResultStreams() {
    return ImmutableSet.of(this.resultStream);
  }
}