package org.apache.samoa.tasks;

/*
 * #%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.text.SimpleDateFormat;
import java.util.Date;

import org.apache.samoa.evaluation.ClusteringEvaluatorProcessor;
import org.apache.samoa.learners.Learner;
import org.apache.samoa.learners.clusterers.simple.ClusteringDistributorProcessor;
import org.apache.samoa.learners.clusterers.simple.DistributedClusterer;
import org.apache.samoa.moa.streams.InstanceStream;
import org.apache.samoa.moa.streams.clustering.ClusteringStream;
import org.apache.samoa.moa.streams.clustering.RandomRBFGeneratorEvents;
import org.apache.samoa.streams.ClusteringEntranceProcessor;
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.FloatOption;
import com.github.javacliparser.IntOption;
import com.github.javacliparser.StringOption;

/**
 * A task that runs and evaluates a distributed clustering algorithm.
 * 
 */
public class ClusteringEvaluation implements Task, Configurable {

  private static final long serialVersionUID = -8246537378371580550L;

  private static final int DISTRIBUTOR_PARALLELISM = 1;

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

  public ClassOption learnerOption = new ClassOption("learner", 'l', "Clustering to run.", Learner.class,
      DistributedClusterer.class.getName());

  public ClassOption streamTrainOption = new ClassOption("streamTrain", 's', "Input stream.", InstanceStream.class,
      RandomRBFGeneratorEvents.class.getName());

  public IntOption instanceLimitOption = new IntOption("instanceLimit", 'i',
      "Maximum number of instances to test/train on  (-1 = no limit).", 100000, -1,
      Integer.MAX_VALUE);

  public IntOption measureCollectionTypeOption = new IntOption("measureCollectionType", 'm',
      "Type of measure collection", 0, 0, 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.", 1000, 0,
      Integer.MAX_VALUE);

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

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

  public FloatOption samplingThresholdOption = new FloatOption("samplingThreshold", 'a',
      "Ratio of instances sampled that will be used for evaluation.", 0.5,
      0.0, 1.0);

  private ClusteringEntranceProcessor source;
  private InstanceStream streamTrain;
  private ClusteringDistributorProcessor distributor;
  private Stream distributorStream;
  private Stream evaluationStream;

  // Default=0: no delay/waiting
  public IntOption sourceDelayOption = new IntOption("sourceDelay", 'w',
      "How many miliseconds between injections of two instances.", 0, 0, Integer.MAX_VALUE);

  private Learner learner;
  private ClusteringEvaluatorProcessor evaluator;

  private Topology topology;
  private TopologyBuilder builder;

  public void getDescription(StringBuilder sb) {
    sb.append("Clustering evaluation");
  }

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

    if (builder == null) {
      logger.warn("Builder was not initialized, initializing it from the Task");

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

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

    // instantiate ClusteringEntranceProcessor and its output stream
    // (sourceStream)
    source = new ClusteringEntranceProcessor();
    streamTrain = this.streamTrainOption.getValue();
    source.setStreamSource(streamTrain);
    builder.addEntranceProcessor(source);
    source.setSamplingThreshold(samplingThresholdOption.getValue());
    source.setMaxNumInstances(instanceLimitOption.getValue());
    logger.debug("Successfully instantiated ClusteringEntranceProcessor");

    Stream sourceStream = builder.createStream(source);
    // starter.setInputStream(sourcePiOutputStream); // FIXME set stream in the
    // EntrancePI

    // distribution of instances and sampling for evaluation
    distributor = new ClusteringDistributorProcessor();
    builder.addProcessor(distributor, DISTRIBUTOR_PARALLELISM);
    builder.connectInputShuffleStream(sourceStream, distributor);
    distributorStream = builder.createStream(distributor);
    distributor.setOutputStream(distributorStream);
    evaluationStream = builder.createStream(distributor);
    distributor.setEvaluationStream(evaluationStream); // passes evaluation events along
    logger.debug("Successfully instantiated Distributor");

    // instantiate learner and connect it to distributorStream
    learner = this.learnerOption.getValue();
    learner.init(builder, source.getDataset(), 1);
    builder.connectInputShuffleStream(distributorStream, learner.getInputProcessor());
    logger.debug("Successfully instantiated Learner");

    evaluator = new ClusteringEvaluatorProcessor.Builder(
        sampleFrequencyOption.getValue()).dumpFile(dumpFileOption.getFile())
        .decayHorizon(((ClusteringStream) streamTrain).getDecayHorizon()).build();

    builder.addProcessor(evaluator);
    for (Stream evaluatorPiInputStream : learner.getResultStreams()) {
      builder.connectInputShuffleStream(evaluatorPiInputStream, evaluator);
    }
    builder.connectInputAllStream(evaluationStream, evaluator);
    logger.debug("Successfully instantiated EvaluatorProcessor");

    topology = builder.build();
    logger.debug("Successfully built 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 instantiated TopologyBuilder");

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

  }

  public Topology getTopology() {
    return topology;
  }
}