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 * 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
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package org.apache.samoa.tasks;

import java.text.SimpleDateFormat;
import java.util.Date;

import org.apache.samoa.evaluation.BasicClassificationPerformanceEvaluator;
import org.apache.samoa.evaluation.BasicRegressionPerformanceEvaluator;
import org.apache.samoa.evaluation.ClassificationPerformanceEvaluator;
import org.apache.samoa.evaluation.EvaluatorProcessor;
import org.apache.samoa.evaluation.PerformanceEvaluator;
import org.apache.samoa.evaluation.RegressionPerformanceEvaluator;
import org.apache.samoa.learners.ClassificationLearner;
import org.apache.samoa.learners.Learner;
import org.apache.samoa.learners.RegressionLearner;
import org.apache.samoa.learners.classifiers.trees.VerticalHoeffdingTree;
import org.apache.samoa.streams.InstanceStream;
import org.apache.samoa.streams.PrequentialSourceProcessor;
import org.apache.samoa.streams.generators.RandomTreeGenerator;
import org.apache.samoa.topology.ComponentFactory;
import org.apache.samoa.topology.Stream;
import org.apache.samoa.topology.Topology;
import org.apache.samoa.topology.TopologyBuilder;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.github.javacliparser.ClassOption;
import com.github.javacliparser.Configurable;
import com.github.javacliparser.FileOption;
import com.github.javacliparser.IntOption;
import com.github.javacliparser.StringOption;

/**
 * Prequential Evaluation task is a scheme in evaluating performance of online classifiers which uses each instance for
 * testing online classifiers model and then it further uses the same instance for training the model(Test-then-train)
 * 
 * @author Arinto Murdopo
 * 
 */
public class PrequentialEvaluation implements Task, Configurable {

  private static final long serialVersionUID = -8246537378371580550L;

  private static Logger logger = LoggerFactory.getLogger(PrequentialEvaluation.class);

  public ClassOption learnerOption = new ClassOption("learner", 'l', "Classifier to train.", Learner.class,
      VerticalHoeffdingTree.class.getName());

  public ClassOption streamTrainOption = new ClassOption("trainStream", 's', "Stream to learn from.",
      InstanceStream.class,
      RandomTreeGenerator.class.getName());

  public ClassOption evaluatorOption = new ClassOption("evaluator", 'e',
      "Classification performance evaluation method.",
      PerformanceEvaluator.class, BasicClassificationPerformanceEvaluator.class.getName());

  public IntOption instanceLimitOption = new IntOption("instanceLimit", 'i',
      "Maximum number of instances to test/train on  (-1 = no limit).", 1000000, -1,
      Integer.MAX_VALUE);

  public IntOption timeLimitOption = new IntOption("timeLimit", 't',
      "Maximum number of seconds to test/train for (-1 = no limit).", -1, -1,
      Integer.MAX_VALUE);

  public IntOption sampleFrequencyOption = new IntOption("sampleFrequency", 'f',
      "How many instances between samples of the learning performance.", 100000,
      0, Integer.MAX_VALUE);

  // The frequency of saving model output e.g. predicted class and votes made for individual classes to a file 
  // The name of the actual file to which model output will be saved is defined through resultFileOption
  public IntOption labelSampleFrequencyOption = new IntOption("labelSampleFrequency", 'h',
      "How many instances between samples of predicted labels and votes.", 1,
      0, Integer.MAX_VALUE);

  public StringOption evaluationNameOption = new StringOption("evaluationName", 'n', "Identifier of the evaluation",
      "Prequential_"
          + new SimpleDateFormat("yyyyMMddHHmmss").format(new Date()));

  public FileOption dumpFileOption = new FileOption("dumpFile", 'd', "File to append intermediate csv results to",
      null, "csv", true);

  // The name of the CSV file in which model output (and in the case of classification also votes for individual classes)
  // will be saved
  public FileOption resultFileOption = new FileOption("resultFile", 'g', "File to append intermediate model output to",
      null, "csv", true);

  // Default=0: no delay/waiting
  public IntOption sourceDelayOption = new IntOption("sourceDelay", 'w',
      "How many microseconds between injections of two instances.", 0, 0, Integer.MAX_VALUE);
  // Batch size to delay the incoming stream: delay of x milliseconds after each
  // batch
  public IntOption batchDelayOption = new IntOption("delayBatchSize", 'b',
      "The delay batch size: delay of x milliseconds after each batch ", 1, 1, Integer.MAX_VALUE);

  protected PrequentialSourceProcessor preqSource;

  // private PrequentialSourceTopologyStarter preqStarter;

  // private EntranceProcessingItem sourcePi;

  protected Stream sourcePiOutputStream;

  private Learner classifier;

  private EvaluatorProcessor evaluator;

  // private ProcessingItem evaluatorPi;

  // private Stream evaluatorPiInputStream;

  protected Topology prequentialTopology;

  protected TopologyBuilder builder;

  public void getDescription(StringBuilder sb, int indent) {
    sb.append("Prequential evaluation");
  }

  @Override
  public void init() {
    // TODO remove the if statement
    // theoretically, dynamic binding will work here!
    // test later!
    // for now, the if statement is used by Storm

    if (builder == null) {
      builder = new TopologyBuilder();
      logger.debug("Successfully instantiating TopologyBuilder");

      builder.initTopology(evaluationNameOption.getValue());
      logger.debug("Successfully initializing SAMOA topology with name {}", evaluationNameOption.getValue());
    }

    // instantiate PrequentialSourceProcessor and its output stream
    // (sourcePiOutputStream)
    preqSource = new PrequentialSourceProcessor();
    preqSource.setStreamSource((InstanceStream) this.streamTrainOption.getValue());
    preqSource.setMaxNumInstances(instanceLimitOption.getValue());
    preqSource.setSourceDelay(sourceDelayOption.getValue());
    preqSource.setDelayBatchSize(batchDelayOption.getValue());
    builder.addEntranceProcessor(preqSource);
    logger.debug("Successfully instantiating PrequentialSourceProcessor");

    // preqStarter = new PrequentialSourceTopologyStarter(preqSource,
    // instanceLimitOption.getValue());
    // sourcePi = builder.createEntrancePi(preqSource, preqStarter);
    // sourcePiOutputStream = builder.createStream(sourcePi);

    sourcePiOutputStream = builder.createStream(preqSource);
    // preqStarter.setInputStream(sourcePiOutputStream);

    // instantiate classifier and connect it to sourcePiOutputStream
    classifier = this.learnerOption.getValue();
    classifier.init(builder, preqSource.getDataset(), 1);
    builder.connectInputShuffleStream(sourcePiOutputStream, classifier.getInputProcessor());
    logger.debug("Successfully instantiating Classifier");

    PerformanceEvaluator evaluatorOptionValue = this.evaluatorOption.getValue();
    if (!PrequentialEvaluation.isLearnerAndEvaluatorCompatible(classifier, evaluatorOptionValue)) {
      evaluatorOptionValue = getDefaultPerformanceEvaluatorForLearner(classifier);
    }
    evaluator = new EvaluatorProcessor.Builder(evaluatorOptionValue)
        .samplingFrequency(sampleFrequencyOption.getValue()).dumpFile(dumpFileOption.getFile())
        .predictionFile(resultFileOption.getFile()).labelSamplingFrequency(labelSampleFrequencyOption.getValue())
        .build();

    // evaluatorPi = builder.createPi(evaluator);
    // evaluatorPi.connectInputShuffleStream(evaluatorPiInputStream);
    builder.addProcessor(evaluator);
    for (Stream evaluatorPiInputStream : classifier.getResultStreams()) {
      builder.connectInputShuffleStream(evaluatorPiInputStream, evaluator);
    }

    logger.debug("Successfully instantiating EvaluatorProcessor");

    prequentialTopology = builder.build();
    logger.debug("Successfully building the topology");
  }

  @Override
  public void setFactory(ComponentFactory factory) {
    // TODO unify this code with init()
    // for now, it's used by S4 App
    // dynamic binding theoretically will solve this problem
    builder = new TopologyBuilder(factory);
    logger.debug("Successfully instantiating TopologyBuilder");

    builder.initTopology(evaluationNameOption.getValue());
    logger.debug("Successfully initializing SAMOA topology with name {}", evaluationNameOption.getValue());

  }

  public Topology getTopology() {
    return prequentialTopology;
  }

  //
  // @Override
  // public TopologyStarter getTopologyStarter() {
  // return this.preqStarter;
  // }

  protected static boolean isLearnerAndEvaluatorCompatible(Learner learner, PerformanceEvaluator evaluator) {
    return (learner instanceof RegressionLearner && evaluator instanceof RegressionPerformanceEvaluator) ||
        (learner instanceof ClassificationLearner && evaluator instanceof ClassificationPerformanceEvaluator);
  }

  protected static PerformanceEvaluator getDefaultPerformanceEvaluatorForLearner(Learner learner) {
    if (learner instanceof RegressionLearner) {
      return new BasicRegressionPerformanceEvaluator();
    }
    // Default to BasicClassificationPerformanceEvaluator for all other cases
    return new BasicClassificationPerformanceEvaluator();
  }
}