package MOEA_GP;

import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.ObjectOutputStream;
import java.io.OutputStream;
import java.util.Properties;

import org.jfree.data.xy.XYSeries;
import org.jfree.data.xy.XYSeriesCollection;
import org.jfree.chart.ChartFactory;
import org.jfree.chart.ChartUtilities;
import org.jfree.chart.JFreeChart;
import org.jfree.chart.plot.PlotOrientation;
import org.moeaframework.core.Solution;
import org.moeaframework.util.tree.Node;

import core.ConfusionMatrix;
import core.DataFrame;
import core.GraphViz;

public class Test_Single_Model {


    private DataFrame test_data ;
    Run_MOGP train_mogp ;
    Object[] true_test_labels ;

    public Test_Single_Model(DataFrame test_data,Run_MOGP train_mogp)
    {
        this.test_data = test_data ;
        this.train_mogp = train_mogp ;
        this.true_test_labels = test_data.get_column_data(this.train_mogp.topredict) ;
    }

    public Boolean [] predict(Solution model,DataFrame data)
    {

        Boolean [] test_predictions = Gp_try.compute_predictions(model,data.getData(),train_mogp.features.keySet()) ;
        return test_predictions ;
    }
    public void save(String path)
    {
        int i = 0 ;
        String model_path ;
        OutputStream train_confusion_matrix ;
        OutputStream test_confusion_matrix ;

        ConfusionMatrix train_matrix ;
        ConfusionMatrix test_matrix ;
        XYSeries train_series = new XYSeries("model train perforamnce Data");
        XYSeries test_series = new XYSeries("model test perforamnce Data");
        XYSeriesCollection data = new XYSeriesCollection() ;

        try
        {
            OutputStream chart_stream = new FileOutputStream(path+"/tpr_tnr.png");
            ObjectOutputStream parameters = new ObjectOutputStream(new FileOutputStream(path+"/parameters.txt")) ;
            parameters.writeChars(train_mogp.run_configs.toString());
            parameters.close();
            //System.out.println(train_mogp.solutions.size());
            for (Solution sol : train_mogp.solutions)
            {
                Properties Train_props = new Properties() ;
                Properties Test_props = new Properties() ;
                model_path = path+"/model" + ++i ;
                File dir = new File(model_path);
                dir.mkdir();
                train_confusion_matrix = new FileOutputStream(model_path+"/train_confusion_matrix.xml") ;
                test_confusion_matrix = new FileOutputStream(model_path+"/test_confusion_matrix.xml") ;
                Boolean[] train_predictions =  predict(sol,train_mogp.train_data) ;
                Boolean[] test_predictions =  predict(sol,this.test_data) ;

                train_matrix = new ConfusionMatrix(train_mogp.true_train_labels,train_predictions) ;
                test_matrix = new ConfusionMatrix(true_test_labels,test_predictions) ;

                train_series.add(train_matrix.sensitivity(),train_matrix.specificity());
                test_series.add(test_matrix.sensitivity(),test_matrix.specificity());


                //train_confusion_matrix.writeChars(train_matrix.get_statistics().toString());
                //test_confusion_matrix.writeChars(test_matrix.get_statistics().toString());

                GraphViz.createDotGraph(((Node)sol.getVariable(0)).getNodeAt(1).todot(1), model_path+"/tree");


                Train_props.putAll(train_matrix.get_statistics());
                Test_props.putAll(test_matrix.get_statistics());

                //System.out.println(Test_props) ;
                //System.out.println(Train_props) ;

                Train_props.storeToXML(train_confusion_matrix, "train performance");
                Test_props.storeToXML(test_confusion_matrix, "test performance");

                train_confusion_matrix.close();
                test_confusion_matrix.close();

            }
            data.addSeries(train_series);
            data.addSeries(test_series);
            JFreeChart chart = ChartFactory.createScatterPlot(
                    "Algorithm : "+this.train_mogp.run_configs.get("algorithm") +  " population size:" + this.train_mogp.run_configs.get("populationsize") + " max generation number: " + this.train_mogp.run_configs.get("maxgeneration") + " crossover rate: "+this.train_mogp.run_configs.get("crossover_rate") + " mutation rate: "+this.train_mogp.run_configs.get("mutation_rate") ,
                    "TPR",
                    "TNR",
                    data,
                    PlotOrientation.VERTICAL,
                    true,
                    true,
                    false
                     );
            ChartUtilities.writeChartAsPNG(chart_stream,
                    chart,
                    500,
                    300);

        }
        catch (FileNotFoundException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }
}