/**
Copyright 2016 Huawei Technologies Co. Ltd.

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 under the License.
**/

package model;

import java.io.*;
import java.util.*;
import java.text.DateFormat;
import java.text.SimpleDateFormat;

import org.apache.log4j.*;

import org.apache.commons.math3.stat.descriptive.moment.*;
import org.apache.commons.math3.distribution.NormalDistribution;

import weka.core.*;
import weka.classifiers.evaluation.*;
import weka.core.converters.*;

import input.ARFFReader;
import predictor.*;


public class Model implements ModelInterface {
	protected Logger logger = Logger.getLogger(Model.class);
	
	protected String datapath;
	protected Instances trainingInstances;
	protected Instances preprocessedInstances;
	protected ArrayList<PredictorInterface> predictors;

	protected static DateFormat dateFormat = new SimpleDateFormat("yyyyMMdd_HHmmss");
	protected static Date date = new Date();
	protected static String resultFilename = dateFormat.format(date);

	public Model(){
		this.datapath = "";
		this.trainingInstances = null;
		this.preprocessedInstances = null;
		this.predictors = new ArrayList<PredictorInterface>();
	}

	public Model (ModelInterface model) {
		this.datapath = model.getDatapath();
		this.trainingInstances = new Instances(model.getTrainingInstances());
		this.preprocessedInstances = new Instances(model.getPreprocessedInstances());
		this.predictors = new ArrayList<PredictorInterface>(model.getPredictors());
	}

	@Override
	public Instances getPreprocessedInstances() {
		return this.preprocessedInstances;
	}

	@Override
	public Instances getTrainingInstances() {
		return this.trainingInstances;
	}


	@Override
	public String getDatapath() {
		return this.datapath;
	}


	@Override
	public ArrayList<PredictorInterface> getPredictors() {
		return this.predictors;
	}


	@Override
	public void loadTrainingData(String path) {
		logger.debug("Reading training data from " + path);
		this.datapath = path;
		try {
			trainingInstances = ARFFReader.read(path);
			preprocessedInstances = trainingInstances;
			logger.debug("Training data is read");
			logger.trace(trainingInstances);
		} catch (Exception e) {
			logger.warn(e.toString());
		}
	}

	@Override
	public void loadRawLog(String path) {
		logger.debug("Reading logfile from " + path);
		try {
			BufferedReader in = new BufferedReader(new FileReader(path));
			String line = in.readLine();
			System.out.println(line);
		} catch (Exception e) {
			logger.warn(e.toString());
		}
	}



	@Override
	public void setPreprocessedInstances(Instances instances){
		this.preprocessedInstances=new Instances(instances);
	}

	@Override
	public void savePreprocessedInstances(String path) {
		ArffSaver saver = new ArffSaver();
		saver.setInstances(this.preprocessedInstances);
		try {
			saver.setFile(new File(path));
			saver.writeBatch();
		} catch (Exception e) {
			logger.error("Cannot save preprocessed instances to file " + path);
		}
	}

	@Override
	public void addPredictor(String shortName) {
		for (PredictorFactory.PredictionTechnique pTechnique : PredictorFactory.PredictionTechnique.values()) {
			if (pTechnique.getShortName().equals(shortName)) {
				predictors.add(PredictorFactory.createPredictor(pTechnique));
				logger.info("Added predictor: " + pTechnique.getName());
				return;
			}
		}
		logger.warn("Added predictor: None");
		logger.warn(shortName + " is not in the list.");
	}

	@Override
	public void selectTrainingMethod() {

	}

	@Override
	public void trainPredictors() throws Exception{
		if (preprocessedInstances == null) {
			throw new Exception("No training data");
		}
		if (predictors.size() == 0) {
			throw new Exception("No predictors selected");
		}
		for (PredictorInterface p : predictors) {
			try {
				logger.info("Training " + p.getName());
				p.train(preprocessedInstances);
				logger.debug(p.toString());
			} catch (Exception e) {
				logger.error(e.toString());
			}
		}
	}

	@Override
	public void crossValidatePredictors(int numFold) {
		long seed = 1;
		this.crossValidatePredictors(numFold, seed);
	}

	@Override
	public void crossValidatePredictors(int numFold, long seed) {
		for (PredictorInterface p : predictors) {
			try {
				logger.debug(numFold + "-fold cross-validating: " + p.getName());
				Random rand = new Random(seed);
				p.crossValidate(preprocessedInstances, numFold, rand);

				///* 
				ThresholdCurve tc = new ThresholdCurve();
				int classIndex = 1;
				Instances result = tc.getCurve(p.getEvaluationPredictions(), classIndex);


				// Save ROC

				BufferedWriter br = new BufferedWriter(new FileWriter(resultFilename + "_" + p.getName().replace(" ", "_") + "_ROC.arff"));
				br.write(result.toString());
				br.close();
			} catch (Exception e) {
				logger.error(e.toString());
			}
		}
	}

	@Override
	public void benchmark(int rounds, String filename) throws Exception {
		ArrayList<ArrayList<Double>> trainingTime = new ArrayList<ArrayList<Double>>(this.predictors.size());
		ArrayList<ArrayList<Double>> predictionTime = new ArrayList<ArrayList<Double>>(this.predictors.size());

		Runtime runtime = Runtime.getRuntime();

		for (int i=0; i<this.predictors.size(); i++) {
			trainingTime.add(new ArrayList<Double>(rounds));
			predictionTime.add(new ArrayList<Double>(rounds));
		}

		// Benchmark - using preprocessed instances
		long startTime;
		long endTime;
		double elapsedTime;
		for (int pIndex=0; pIndex<this.predictors.size(); pIndex++) {
			for (int rIndex=0; rIndex<rounds; rIndex++) {
				logger.debug("Benchmarking " + this.predictors.get(pIndex).getName() + " round " + rIndex);

				// Training time
				startTime = System.currentTimeMillis();
				this.predictors.get(pIndex).train(this.preprocessedInstances);
				endTime = System.currentTimeMillis();
				elapsedTime = ((double)(endTime - startTime))/1000;
				trainingTime.get(pIndex).add(elapsedTime);
				logger.debug("Training time = " + elapsedTime + " seconds");

				// Prediction time
				startTime = System.currentTimeMillis();
				this.predictors.get(pIndex).predict(this.preprocessedInstances);
				endTime = System.currentTimeMillis();
				elapsedTime = ((double)(endTime - startTime))/1000;
				predictionTime.get(pIndex).add(elapsedTime);
				logger.debug("Prediction time = " + elapsedTime + " seconds");
			}
		}

		// Save time results to file
		BufferedWriter br = new BufferedWriter(new FileWriter(filename+"_benchmark"));
		// Write header
		String header = "";
		for (int pIndex=0; pIndex<this.predictors.size(); pIndex++) {
			header += "\"" + this.predictors.get(pIndex).getName() + " Training\" ";
			header += "\"" + this.predictors.get(pIndex).getName() + " Prediction\" ";
		}
		header += "\n";
		br.write(header);

		for (int rIndex=0; rIndex<rounds; rIndex++) {
			String line = "";
			for (int pIndex=0; pIndex<this.predictors.size(); pIndex++) {
				line += trainingTime.get(pIndex).get(rIndex).toString() + " " + predictionTime.get(pIndex).get(rIndex).toString() + " ";
			}
			line += "\n";
			br.write(line);
		}
		br.close();

		// TODO: move to another function
		// refactor
		// Calculate and save summary results
		BufferedWriter brSummary = new BufferedWriter(new FileWriter(filename+"_benchmark_time_summary"));
		//header = "Algorithms tMean tError pMean pError\n";
		//brSummary.write(header);
		for (int pIndex=0; pIndex<this.predictors.size(); pIndex++) {
			String line = "\"" + this.predictors.get(pIndex).getName() + "\" ";
			// Calculate mean
			double [] training = new double[rounds];
			// Convert ArrayList to array for Mean.evaluate
			for (int rIndex=0; rIndex<rounds; rIndex++) {
				//System.out.println("pIndex = " + pIndex + " rIndex = " + rIndex);
				//System.out.println("array size = " + training.length);
				training[rIndex] = trainingTime.get(pIndex).get(rIndex);
			}
			double meanTraining = new Mean().evaluate(training);
			double varTraining = new Variance().evaluate(training);
			double stdTraining = Math.sqrt(varTraining);
			double errorTraining = new NormalDistribution().inverseCumulativeProbability(0.975)*(stdTraining/Math.sqrt(rounds));
			double lowerCITraining = meanTraining - errorTraining;
			double upperCITraining = meanTraining + errorTraining;
			line += meanTraining + " " + errorTraining + " ";
			//brSummary.write(line);

			//line = "\"" + this.predictors.get(pIndex).getName() + " Prediction\" ";
			// Calculate mean
			double [] prediction = new double[rounds];
			// Convert ArrayList to array for Mean.evaluate
			for (int rIndex=0; rIndex<rounds; rIndex++) {
				prediction[rIndex] = predictionTime.get(pIndex).get(rIndex);
			}
			double meanPrediction = new Mean().evaluate(prediction);
			double varPrediction = new Variance().evaluate(prediction);
			double stdPrediction = Math.sqrt(varPrediction);
			double errorPrediction = new NormalDistribution().inverseCumulativeProbability(0.975)*(stdPrediction/Math.sqrt(rounds));
			double lowerCIPrediction = meanPrediction - errorPrediction;
			double upperCIPrediction = meanPrediction + errorPrediction;
			line += meanPrediction + " " + errorPrediction + "\n";
			brSummary.write(line);
		}
		brSummary.close();
	}


	@Override
	public String getPredictorNames() {
		String str = "";
		for (PredictorInterface p : this.predictors) {
			str += p.getName() + "\n";
		}
		return str;
	}

	@Override
	public String toString() {
		String str = "";
		str += "Training data:\n" + this.datapath + "\n";
		str += "Training data summary:\n" + this.getTrainingInstances().toSummaryString() + "\n";
		str += "Preprocessed data summary:\n" + this.getPreprocessedInstances().toSummaryString() + "\n";
		str += "Predictors:\n" + this.getPredictorNames() + "\n";
		return str;
	}

	@Override
	public void saveSettings(String filename) throws Exception {
		BufferedWriter br = new BufferedWriter(new FileWriter(filename,true));
		br.write(this.toString());
		br.close();
	}

	@Override
	public void saveResults(String filename) throws Exception {
		BufferedWriter br = new BufferedWriter(new FileWriter(filename,true));

		for (PredictorInterface p : this.predictors) {
			logger.info(p.getEvaluationResults());
			br.write(p.getEvaluationResults());
		}

		br.close();
	}
}