/*
*
*/
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) {
}
}
}
Datasets
Models
