/*

 *

 */

package biodiscml;

import java.io.FileWriter;

import java.io.PrintWriter;

import java.util.HashMap;

/**

 *

 * @author mik

 */

public class demo {

    // public static String folder = "/home/mickael/ownCloud/";

    public static String folder = "E:\\cloud\\";

    // public static String folder = "C:\\Users\\Mickael\\ownCloud\\";

    public static void main(String[] args) {

        System.out.println("=== Demo mode ===");

        //trainingExecution();

        //testingExecution();

        bestModel();

        //benchmark();

    }

    private static void trainingExecution() {

        try {

            //String s[] = {"-config " + folder + "Data\\TCGA_PRAD\\datamining\\config.conf -train"};

            //String s[] = {"-config " + folder + "Data\\TCGA_PRAD\\datamining\\time\\config.conf -train"};

            //String s[] = {"-config " + folder + "Projects/loreal/VESPA/datamining//config_vespa.conf -train"};

            //String s[] = {"-config " + folder + "/Projects/Benjamin/Collaboration-CHUL-Quebec/1_Prostate/READY_TO_USE_for_Brute_force_X/datamining/2_Genes+clinic/config.conf -train"};

            //String s[] = {"-config config_example_2class.conf -train"};

            String s[] = {"-config " + folder + "Code\\BruteForceML\\benchmark\\CNS_test/config.conf -train"};

            //String s[] = {"-config " + folder + "Code/BruteForceML/benchmark/Benjamin_signature/config.conf -train"};

            //String s[] = {"-config " + folder + "Projects\\bacteria\\datamining\\config.conf -train"};

            Main.main(s);

        } catch (Exception e) {

            e.printStackTrace();

        }

    }

    public static void bestModel() {

        Main m = new Main();

        //demo Benjamin

//        m.wd = folder + "Projects\\Benjamin\\Collaboration - CHUL - Quebec\\1_Prostate\\READY_TO_USE_for_Brute_force_X\\datamining\\";

//        m.configFile = m.wd + "config.conf";

//        m.setConfiguration();

//        m.wd = folder + "Projects\\Benjamin\\Collaboration - CHUL - Quebec\\1_Prostate\\READY_TO_USE_for_Brute_force_X\\datamining\\";

//        String CLASSIFICATION_FILE = m.wd + m.project + "a.classification.data_to_train.csv"; // output of Training(), models performances

//        String TRAINING_RESULTS_FILE = m.wd + m.project + "c.classification.results.csv"; // output of Training(), models performances

//        String FEATURE_SELECTION_FILE = m.wd + m.project + "b.featureSelection.infoGain.csv"; // output of Training(), feature selection result

//        BestModelSelectionAndReport b = new BestModelSelectionAndReport(CLASSIFICATION_FILE, FEATURE_SELECTION_FILE, TRAINING_RESULTS_FILE,

//                "classification");

//        //mint

//        m.wd = folder + "Code/BruteForceML/benchmark/mint/";

//        m.configFile = m.wd + "config.conf";

//        m.setConfiguration();

//        m.wd = folder + "Code/BruteForceML/benchmark/mint/";

//        m.hmTrainingBestModelList.put("trees.RandomForest_AUC_BF_16_0.9571_77", "1");

//        String CLASSIFICATION_FILE = m.wd + m.project + "a.classification.data_to_train.csv"; // output of Training(), models performances

//        String TRAINING_RESULTS_FILE = m.wd + m.project + "c.classification.results.csv"; // output of Training(), models performances

//        String FEATURE_SELECTION_FILE = m.wd + m.project + "b.featureSelection.infoGain.csv"; // output of Training(), feature selection result

//        BestModelSelectionAndReport b = new BestModelSelectionAndReport(CLASSIFICATION_FILE, FEATURE_SELECTION_FILE, TRAINING_RESULTS_FILE,

//                "classification");

        //        //mint

        m.wd = folder + "Code/BruteForceML/benchmark/CNS_test/";

        m.configFile = m.wd + "config.conf";

        m.setConfiguration();

        m.wd = folder + "Code/BruteForceML/benchmark/CNS_test/";

        String CLASSIFICATION_FILE = m.wd + m.project + "a.regression.data_to_train.csv"; // output of Training(), models performances

        String TRAINING_RESULTS_FILE = m.wd + m.project + "c.regression.results.csv"; // output of Training(), models performances

        String FEATURE_SELECTION_FILE = m.wd + m.project + "b.featureSelection.RELIEFF.csv"; // output of Training(), feature selection result

        BestModelSelectionAndReport b = new BestModelSelectionAndReport(CLASSIFICATION_FILE, FEATURE_SELECTION_FILE, TRAINING_RESULTS_FILE,

                "regression");

        //bacteria

//        m.wd = folder + "Projects\\bacteria\\datamining\\";

//        m.configFile = m.wd + "config.conf";

//        m.setConfiguration();

//        m.wd = folder + "Projects\\bacteria\\datamining\\";

//        m.hmTrainingBestModelList.put("trees.RandomForest_AUC_B_25_0.9531_907", "1");

//        String CLASSIFICATION_FILE = m.wd + m.project + "a.classification.data_to_train.csv"; // output of Training(), models performances

//        String TRAINING_RESULTS_FILE = m.wd + m.project + "c.classification.results.csv"; // output of Training(), models performances

//        String FEATURE_SELECTION_FILE = m.wd + m.project + "b.featureSelection.infoGain.csv"; // output of Training(), feature selection result

//        BestModelSelectionAndReport b = new BestModelSelectionAndReport(CLASSIFICATION_FILE, FEATURE_SELECTION_FILE, TRAINING_RESULTS_FILE,

//                "classification");

//        //benjamin

//        m.wd = folder + "Code/BruteForceML/benchmark/Benjamin_prostate/";

//        m.configFile = m.wd + "config.conf";

//        m.setConfiguration();

//        m.wd = folder + "Code/BruteForceML/benchmark/Benjamin_prostate/";

//        String CLASSIFICATION_FILE = m.wd + m.project + "a.classification.data_to_train.csv"; // output of Training(), models performances

//        String TRAINING_RESULTS_FILE = m.wd + m.project + "c.classification.results.csv"; // output of Training(), models performances

//        String FEATURE_SELECTION_FILE = m.wd + m.project + "b.featureSelection.infoGain.csv"; // output of Training(), feature selection result

//        BestModelSelectionAndReport b = new BestModelSelectionAndReport(CLASSIFICATION_FILE, FEATURE_SELECTION_FILE, TRAINING_RESULTS_FILE,

//                "classification");

//       //golub

//        m.wd = folder + "Code/BruteForceML/benchmark/brain/";

//        m.configFile = m.wd + "config.conf";

//        m.setConfiguration();

//        m.wd = folder + "Code/BruteForceML/benchmark/brain/";

//        String CLASSIFICATION_FILE = m.wd + m.project + "a.classification.data_to_train.csv"; // output of Training(), models performances

//        String TRAINING_RESULTS_FILE = m.wd + m.project + "c.classification.results.csv"; // output of Training(), models performances

//        String FEATURE_SELECTION_FILE = m.wd + m.project + "b.featureSelection.infoGain.csv"; // output of Training(), feature selection result

//        BestModelSelectionAndReport b = new BestModelSelectionAndReport(CLASSIFICATION_FILE, FEATURE_SELECTION_FILE, TRAINING_RESULTS_FILE,

//                "classification");

//        //demo dreamchallenge

//        m.wd = folder + "Projects\\dreamchallenge\\proteogenomics\\SUB2_ML\\";

//        m.configFile = m.wd + "config.conf";

//        m.setConfiguration();

//        m.wd = folder + "Projects\\dreamchallenge\\proteogenomics\\SUB2_ML\\";

//        String CLASSIFICATION_FILE = m.wd + m.project + "a.regression.data_to_train.csv"; // output of Training(), models performances

//        String TRAINING_RESULTS_FILE = m.wd + m.project + "c.regression.results.csv"; // output of Training(), models performances

//        String FEATURE_SELECTION_FILE = m.wd + m.project + "b.featureSelection.RELIEFF.csv"; // output of Training(), feature selection result

//        BestModelSelectionAndReport b = new BestModelSelectionAndReport(CLASSIFICATION_FILE, FEATURE_SELECTION_FILE, TRAINING_RESULTS_FILE,

//                "regression");

        //demo vespa

//        m.wd = folder + "Projects\\loreal\\VESPA\\datamining\\";

//        m.configFile = m.wd + "config_vespa.conf";

//        m.setConfiguration();

//        m.wd = folder + "Projects\\loreal\\VESPA\\datamining\\";

//        String CLASSIFICATION_FILE = m.wd + m.project + "a.classification.data_to_train.csv"; // output of Training(), models performances

//        String TRAINING_RESULTS_FILE = m.wd + m.project + "c.classification.results.csv"; // output of Training(), models performances

//        String FEATURE_SELECTION_FILE = m.wd + m.project + "b.featureSelection.infoGain.csv"; // output of Training(), feature selection result

//        BestModelSelectionAndReport b = new BestModelSelectionAndReport(CLASSIFICATION_FILE, FEATURE_SELECTION_FILE, TRAINING_RESULTS_FILE,

//                "classification");

        //demo DATA

//        m.configFile = "config_example_2class.conf";

//        m.setConfiguration();

//        m.wd = "";

//        String CLASSIFICATION_FILE = m.wd + m.project + "a.classification.data_to_train.csv"; // output of Training(), models performances

//        String TRAINING_RESULTS_FILE = m.wd + m.project + "c.classification.results.csv"; // output of Training(), models performances

//        String FEATURE_SELECTION_FILE = m.wd + m.project + "b.featureSelection.infoGain.csv"; // output of Training(), feature selection result

//        BestModelSelectionAndReport b = new BestModelSelectionAndReport(CLASSIFICATION_FILE, FEATURE_SELECTION_FILE, TRAINING_RESULTS_FILE,

//                "classification");

    }

    public static void testingExecution() {

        try {

//            String s[] = {"-test -model gdx_data_.misc.VFI_-B0.6.txt.model "

//                + "-testfiles gdx.545patients.clinical.csv gdx.1742patients.expr.csv "

//                //+ "-prefixes clin expr "

//                + "-mergingID patient -separator \\t -classification -keyword BCR_sensor"};

            String s[] = {"-test -model " + folder + "Data\\TCGA_PRAD\\datamining\\TCGA_BCR_.misc.VFI_-B0.6.txt.model "

                + "-testfiles " + folder + "Data\\TCGA_PRAD\\datamining\\geneExpression.log2RUVg.csv"

                + " " + folder + "Data\\TCGA_PRAD\\datamining\\clinical_test.csv "

                + "-mergingID Patient -separator \\t -classification -keyword BCR_sensor"};

            for (String s1 : s) {

                System.out.print(s1);

            }

            System.out.println("");

            Main.main(s);

        } catch (Exception e) {

            e.printStackTrace();

        }

    }

    private static void benchmark() {

        try {

            PrintWriter pw = new PrintWriter(new FileWriter("benchmark_3.txt"));

            pw.println("FeaturesLimit\tAUC_Train\tAUC_Test");

            for (int i = 5; i <= 200; i = i + 5) {

                //train

                Main.hmTrainFiles = new HashMap<>();

                Main.needConfigFile = true;

                Main.testing = false;

                Main.training = true;

                System.out.println("\n-------------\nTRAIN " + i);

                String s[] = {"-config " + folder + "Data\\TCGA_PRAD\\datamining\\config_opt.conf -train"};

                Main.maxNumberOfFeaturesInModel = i;

                Main.main(s);

                String train = Main.bench_AUC;

                //test

                Main.hmTrainFiles = new HashMap<>();

                Main.configFile = "";

                Main.needConfigFile = false;

                Main.testing = true;

                Main.training = false;

                Main.project = "outfile";

                System.out.println("\n-------------\nTEST " + i);

                String s2[] = {"-test -model " + folder + "Data\\TCGA_PRAD\\datamining\\bench_.misc.VFI_-B0.6.txt.model "

                    + "-testfiles " + folder + "Data\\TCGA_PRAD\\datamining\\geneExpression.log2RUVg.csv"

                    + " " + folder + "Data\\TCGA_PRAD\\datamining\\clinical_test.csv "

                    + "-mergingID Patient -separator \\t -classification -keyword BCR_sensor"};

                Main.main(s2);

                String test = Main.bench_AUC;

                pw.println(i + "\t" + train + "\t" + test);

                pw.flush();

            }

        } catch (Exception e) {

        }

    }

}