package de.ugoe.cs.cpdp.execution;

import java.io.File;
import java.util.Collections;
import java.util.LinkedList;
import java.util.List;
import java.util.logging.Level;

import org.apache.commons.collections4.list.SetUniqueList;

import weka.core.Instances;
import de.ugoe.cs.cpdp.ExperimentConfiguration;
import de.ugoe.cs.cpdp.dataprocessing.IProcessesingStrategy;
import de.ugoe.cs.cpdp.dataprocessing.ISetWiseProcessingStrategy;
import de.ugoe.cs.cpdp.dataselection.IPointWiseDataselectionStrategy;
import de.ugoe.cs.cpdp.dataselection.ISetWiseDataselectionStrategy;
import de.ugoe.cs.cpdp.eval.IEvaluationStrategy;
import de.ugoe.cs.cpdp.loader.IVersionLoader;
import de.ugoe.cs.cpdp.training.ISetWiseTrainingStrategy;
import de.ugoe.cs.cpdp.training.ITrainer;
import de.ugoe.cs.cpdp.training.ITrainingStrategy;
import de.ugoe.cs.cpdp.versions.IVersionFilter;
import de.ugoe.cs.cpdp.versions.SoftwareVersion;
import de.ugoe.cs.util.console.Console;

/**
 * Class responsible for executing an experiment according to an {@link ExperimentConfiguration}. The steps of an experiment are as follows:
 * <ul>
 *  <li>load the data from the provided data path</li>
 *  <li>filter the data sets according to the provided version filters</li>
 *  <li>execute the following steps for each data sets as test data that is not ignored through the test version filter:
 *  <ul>
 *   <li>filter the data sets to setup the candidate training data:
 *   <ul>
 *    <li>remove all data sets from the same project</li>
 *    <li>filter all data sets according to the training data filter
 *   </ul></li>
 *   <li>apply the setwise preprocessors</li>
 *   <li>apply the setwise data selection algorithms</li>
 *   <li>apply the setwise postprocessors</li>
 *   <li>train the setwise training classifiers</li>
 *   <li>unify all remaining training data into one data set</li>
 *   <li>apply the preprocessors</li>
 *   <li>apply the pointwise data selection algorithms</li>
 *   <li>apply the postprocessors</li>
 *   <li>train the normal classifiers</li>
 *   <li>evaluate the results for all trained classifiers on the training data</li>
 *  </ul></li>
 * </ul>
 * 
 * Note that this class implements {@link Runnable}, i.e., each experiment can be started in its own thread.
 * @author Steffen Herbold
 */
public class CrossProjectExperiment implements IExecutionStrategy {

	/**
	 * configuration of the experiment
	 */
	private final ExperimentConfiguration config;
	
	/**
	 * Constructor. Creates a new experiment based on a configuration.
	 * @param config configuration of the experiment
	 */
	public CrossProjectExperiment(ExperimentConfiguration config) {
		this.config = config;
	}
	
	/**
	 * Executes the experiment with the steps as described in the class comment.
	 * @see Runnable#run() 
	 */
	@Override
	public void run() {
		final List<SoftwareVersion> versions = new LinkedList<>();
		
		for(IVersionLoader loader : config.getLoaders()) {
			versions.addAll(loader.load());
		}
		
		for( IVersionFilter filter : config.getVersionFilters() ) {
			filter.apply(versions);
		}
		boolean writeHeader = true;
		int versionCount = 1;
		int testVersionCount = 0;
		
		for( SoftwareVersion testVersion : versions ) {
			if( isVersion(testVersion, config.getTestVersionFilters()) ) {
				testVersionCount++;
			}
		}
		
		// sort versions
		Collections.sort(versions);
		
		for( SoftwareVersion testVersion : versions ) {
			if( isVersion(testVersion, config.getTestVersionFilters()) ) {
				Console.traceln(Level.INFO, String.format("[%s] [%02d/%02d] %s: starting", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion()));
				
				// Setup testdata and training data
				Instances testdata = testVersion.getInstances();
				String testProject = testVersion.getProject();
				SetUniqueList<Instances> traindataSet = SetUniqueList.setUniqueList(new LinkedList<Instances>());
				for( SoftwareVersion trainingVersion : versions ) {
					if( isVersion(trainingVersion, config.getTrainingVersionFilters()) ) {
						if( trainingVersion!=testVersion ) {
							if( !trainingVersion.getProject().equals(testProject) ) {
								traindataSet.add(trainingVersion.getInstances());
							}
						}
					}
				}
				
				for( ISetWiseProcessingStrategy processor : config.getSetWisePreprocessors() ) {
					Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying setwise preprocessor %s", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion(), processor.getClass().getName()));
					processor.apply(testdata, traindataSet);
				}
				for( ISetWiseDataselectionStrategy dataselector : config.getSetWiseSelectors() ) {
					Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying setwise selection %s", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion(), dataselector.getClass().getName()));
					dataselector.apply(testdata, traindataSet);
				}
				for( ISetWiseProcessingStrategy processor : config.getSetWisePostprocessors() ) {
					Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying setwise postprocessor %s", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion(), processor.getClass().getName()));
					processor.apply(testdata, traindataSet);
				}
				for( ISetWiseTrainingStrategy setwiseTrainer : config.getSetWiseTrainers() ) {
					Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying setwise trainer %s", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion(), setwiseTrainer.getName()));
					setwiseTrainer.apply(traindataSet);
				}
				Instances traindata = makeSingleTrainingSet(traindataSet);
				for( IProcessesingStrategy processor : config.getPreProcessors() ) {
					Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying preprocessor %s", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion(), processor.getClass().getName()));
					processor.apply(testdata, traindata);
				}
				for( IPointWiseDataselectionStrategy dataselector : config.getPointWiseSelectors() ) {
					Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying pointwise selection %s", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion(), dataselector.getClass().getName()));
					traindata = dataselector.apply(testdata, traindata);
				}
				for( IProcessesingStrategy processor : config.getPostProcessors() ) {
					Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying setwise postprocessor %s", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion(), processor.getClass().getName()));
					processor.apply(testdata, traindata);
				}
				for( ITrainingStrategy trainer : config.getTrainers() ) {
					Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying trainer %s", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion(), trainer.getName()));
					trainer.apply(traindata);
				}
				File resultsDir = new File(config.getResultsPath());
				if (!resultsDir.exists()) {
					resultsDir.mkdir();
				}
				for( IEvaluationStrategy evaluator : config.getEvaluators() ) {
					Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying evaluator %s", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion(), evaluator.getClass().getName()));
					List<ITrainer> allTrainers = new LinkedList<>();
					for( ISetWiseTrainingStrategy setwiseTrainer : config.getSetWiseTrainers() ) {
						allTrainers.add(setwiseTrainer);
					}
					for( ITrainingStrategy trainer : config.getTrainers() ) {
						allTrainers.add(trainer);
					}
					if( writeHeader ) {
						evaluator.setParameter(config.getResultsPath() + "/" + config.getExperimentName() + ".csv");
					}
					evaluator.apply(testdata, traindata, allTrainers, writeHeader);
					writeHeader = false;
				}
				Console.traceln(Level.INFO, String.format("[%s] [%02d/%02d] %s: finished", config.getExperimentName(), versionCount, testVersionCount, testVersion.getVersion()));
				versionCount++;
			}
		}
	}
	
	/**
	 * Helper method that checks if a version passes all filters.
	 * @param version version that is checked
	 * @param filters list of the filters
	 * @return true, if the version passes all filters, false otherwise
	 */
	private boolean isVersion(SoftwareVersion version, List<IVersionFilter> filters) {
		boolean result = true;
		for( IVersionFilter filter : filters) {
			result &= !filter.apply(version);
		}
		return result;
	}

	/**
	 * Helper method that combines a set of Weka {@link Instances} sets into a single {@link Instances} set.
	 * @param traindataSet set of {@link Instances} to be combines
	 * @return single {@link Instances} set
	 */
	public static Instances makeSingleTrainingSet(SetUniqueList<Instances> traindataSet) {
		Instances traindataFull = null;
		for( Instances traindata : traindataSet) {
			if( traindataFull==null ) {
				traindataFull = new Instances(traindata);
			} else {
				for( int i=0 ; i<traindata.numInstances() ; i++ ) {
					traindataFull.add(traindata.instance(i));
				}
			}
		}
		return traindataFull;
	}
}
