package com.yahoo.labs.samoa.evaluation;

/*
 * #%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%
 */

import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.PrintStream;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.TimeUnit;

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

import com.yahoo.labs.samoa.core.ContentEvent;
import com.yahoo.labs.samoa.core.Processor;
import com.yahoo.labs.samoa.evaluation.measures.SSQ;
import com.yahoo.labs.samoa.evaluation.measures.StatisticalCollection;
import com.yahoo.labs.samoa.moa.cluster.Clustering;
import com.yahoo.labs.samoa.moa.clusterers.KMeans;
import com.yahoo.labs.samoa.moa.core.DataPoint;
import com.yahoo.labs.samoa.moa.core.Measurement;
import com.yahoo.labs.samoa.moa.evaluation.LearningCurve;
import com.yahoo.labs.samoa.moa.evaluation.LearningEvaluation;
import com.yahoo.labs.samoa.moa.evaluation.MeasureCollection;

public class ClusteringEvaluatorProcessor implements Processor {

  /**
	 * 
	 */
  private static final long serialVersionUID = -2778051819116753612L;

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

  private static final String ORDERING_MEASUREMENT_NAME = "evaluation instances";

  private final int samplingFrequency;
  private final int decayHorizon;
  private final File dumpFile;
  private transient PrintStream immediateResultStream = null;
  private transient boolean firstDump = true;

  private long totalCount = 0;
  private long experimentStart = 0;

  private LearningCurve learningCurve;

  private MeasureCollection[] measures;

  private int id;

  protected Clustering gtClustering;

  protected ArrayList<DataPoint> points;

  private ClusteringEvaluatorProcessor(Builder builder) {
    this.samplingFrequency = builder.samplingFrequency;
    this.dumpFile = builder.dumpFile;
    this.points = new ArrayList<>();
    this.decayHorizon = builder.decayHorizon;
  }

  @Override
  public boolean process(ContentEvent event) {
    boolean ret = false;
    if (event instanceof ClusteringResultContentEvent) {
      ret = process((ClusteringResultContentEvent) event);
    }
    if (event instanceof ClusteringEvaluationContentEvent) {
      ret = process((ClusteringEvaluationContentEvent) event);
    }
    return ret;
  }

  private boolean process(ClusteringResultContentEvent result) {
    // evaluate
    Clustering clustering = KMeans.gaussianMeans(gtClustering, result.getClustering());
    for (MeasureCollection measure : measures) {
      try {
        measure.evaluateClusteringPerformance(clustering, gtClustering, points);
      } catch (Exception ex) {
        ex.printStackTrace();
      }
    }

    this.addMeasurement();

    if (result.isLastEvent()) {
      this.concludeMeasurement();
      return true;
    }

    totalCount += 1;

    if (totalCount == 1) {
      experimentStart = System.nanoTime();
    }

    return false;
  }

  private boolean process(ClusteringEvaluationContentEvent result) {
    boolean ret = false;
    if (result.getGTClustering() != null) {
      gtClustering = result.getGTClustering();
      ret = true;
    }
    if (result.getDataPoint() != null) {
      points.add(result.getDataPoint());
      if (points.size() > this.decayHorizon) {
        points.remove(0);
      }
      ret = true;
    }
    return ret;
  }

  @Override
  public void onCreate(int id) {
    this.id = id;
    this.learningCurve = new LearningCurve(ORDERING_MEASUREMENT_NAME);
    // create the measure collection
    measures = getMeasures(getMeasureSelection());

    if (this.dumpFile != null) {
      try {
        if (dumpFile.exists()) {
          this.immediateResultStream = new PrintStream(new FileOutputStream(dumpFile, true), true);
        } else {
          this.immediateResultStream = new PrintStream(new FileOutputStream(dumpFile), true);
        }

      } catch (FileNotFoundException e) {
        this.immediateResultStream = null;
        logger.error("File not found exception for {}:{}", this.dumpFile.getAbsolutePath(), e.toString());

      } catch (Exception e) {
        this.immediateResultStream = null;
        logger.error("Exception when creating {}:{}", this.dumpFile.getAbsolutePath(), e.toString());
      }
    }

    this.firstDump = true;
  }

  private static ArrayList<Class> getMeasureSelection() {
    ArrayList<Class> mclasses = new ArrayList<>();
    // mclasses.add(EntropyCollection.class);
    // mclasses.add(F1.class);
    // mclasses.add(General.class);
    // *mclasses.add(CMM.class);
    mclasses.add(SSQ.class);
    // *mclasses.add(SilhouetteCoefficient.class);
    mclasses.add(StatisticalCollection.class);
    // mclasses.add(Separation.class);

    return mclasses;
  }

  private static MeasureCollection[] getMeasures(ArrayList<Class> measure_classes) {
    MeasureCollection[] measures = new MeasureCollection[measure_classes.size()];
    for (int i = 0; i < measure_classes.size(); i++) {
      try {
        MeasureCollection m = (MeasureCollection) measure_classes.get(i).newInstance();
        measures[i] = m;

      } catch (Exception ex) {
        java.util.logging.Logger.getLogger("Couldn't create Instance for " + measure_classes.get(i).getName());
        ex.printStackTrace();
      }
    }
    return measures;
  }

  @Override
  public Processor newProcessor(Processor p) {
    ClusteringEvaluatorProcessor originalProcessor = (ClusteringEvaluatorProcessor) p;
    ClusteringEvaluatorProcessor newProcessor = new ClusteringEvaluatorProcessor.Builder(originalProcessor).build();

    if (originalProcessor.learningCurve != null) {
      newProcessor.learningCurve = originalProcessor.learningCurve;
    }

    return newProcessor;
  }

  @Override
  public String toString() {
    StringBuilder report = new StringBuilder();

    report.append(EvaluatorProcessor.class.getCanonicalName());
    report.append("id = ").append(this.id);
    report.append('\n');

    if (learningCurve.numEntries() > 0) {
      report.append(learningCurve.toString());
      report.append('\n');
    }
    return report.toString();
  }

  private void addMeasurement() {
    // printMeasures();
    List<Measurement> measurements = new ArrayList<>();
    measurements.add(new Measurement(ORDERING_MEASUREMENT_NAME, totalCount * this.samplingFrequency));

    addClusteringPerformanceMeasurements(measurements);
    Measurement[] finalMeasurements = measurements.toArray(new Measurement[measurements.size()]);

    LearningEvaluation learningEvaluation = new LearningEvaluation(finalMeasurements);
    learningCurve.insertEntry(learningEvaluation);
    logger.debug("evaluator id = {}", this.id);
    // logger.info(learningEvaluation.toString());

    if (immediateResultStream != null) {
      if (firstDump) {
        immediateResultStream.println(learningCurve.headerToString());
        firstDump = false;
      }

      immediateResultStream.println(learningCurve.entryToString(learningCurve.numEntries() - 1));
      immediateResultStream.flush();
    }
  }

  private void addClusteringPerformanceMeasurements(List<Measurement> measurements) {
    for (MeasureCollection measure : measures) {
      for (int j = 0; j < measure.getNumMeasures(); j++) {
        Measurement measurement = new Measurement(measure.getName(j), measure.getLastValue(j));
        measurements.add(measurement);
      }
    }
  }

  private void concludeMeasurement() {
    logger.info("last event is received!");
    logger.info("total count: {}", this.totalCount);

    String learningCurveSummary = this.toString();
    logger.info(learningCurveSummary);

    long experimentEnd = System.nanoTime();
    long totalExperimentTime = TimeUnit.SECONDS.convert(experimentEnd - experimentStart, TimeUnit.NANOSECONDS);
    logger.info("total evaluation time: {} seconds for {} instances", totalExperimentTime, totalCount);
    // logger.info("average throughput rate: {} instances/seconds",
    // (totalCount/totalExperimentTime));
  }

  private void printMeasures() {
    StringBuilder sb = new StringBuilder();
    for (MeasureCollection measure : measures) {

      sb.append("Mean ").append(measure.getClass().getSimpleName()).append(":").append(measure.getNumMeasures())
          .append("\n");
      for (int j = 0; j < measure.getNumMeasures(); j++) {
        sb.append("[").append(measure.getName(j)).append("=").append(measure.getLastValue(j)).append("] \n");

      }
      sb.append("\n");
    }

    logger.debug("\n MEASURES: \n\n {}", sb.toString());
    System.out.println(sb.toString());
  }

  public static class Builder {

    private int samplingFrequency = 1000;
    private File dumpFile = null;
    private int decayHorizon = 1000;

    public Builder(int samplingFrequency) {
      this.samplingFrequency = samplingFrequency;
    }

    public Builder(ClusteringEvaluatorProcessor oldProcessor) {
      this.samplingFrequency = oldProcessor.samplingFrequency;
      this.dumpFile = oldProcessor.dumpFile;
      this.decayHorizon = oldProcessor.decayHorizon;
    }

    public Builder samplingFrequency(int samplingFrequency) {
      this.samplingFrequency = samplingFrequency;
      return this;
    }

    public Builder decayHorizon(int decayHorizon) {
      this.decayHorizon = decayHorizon;
      return this;
    }

    public Builder dumpFile(File file) {
      this.dumpFile = file;
      return this;
    }

    public ClusteringEvaluatorProcessor build() {
      return new ClusteringEvaluatorProcessor(this);
    }
  }
}