private void trainModelWithClasses(double param) {
int wayListSizeWithoutUnclassified = wayList.size();
int u = 0;
System.out.println("trainList size: " + wayListSizeWithoutUnclassified);
// set classes for each osm instance
int sizeToBeAddedToArray =
0; // this will be used to proper init the features array, adding the multiple vectors size
// int lalala = 0;
// setProgress(trainProgress-10);
for (OSMWay way : wayList) {
// setProgress(trainProgress++);
OSMClassification classifyInstances = new OSMClassification();
classifyInstances.calculateClasses(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
if (way.getClassIDs().isEmpty()) {
wayListSizeWithoutUnclassified -= 1;
u++;
} else {
sizeToBeAddedToArray = sizeToBeAddedToArray + way.getClassIDs().size() - 1;
}
}
double C = param;
double eps = 0.001;
double[] GROUPS_ARRAY2 =
new double[wayListSizeWithoutUnclassified + sizeToBeAddedToArray]; // new double[117558];//
FeatureNode[][] trainingSetWithUnknown2 =
new FeatureNode[wayListSizeWithoutUnclassified + sizeToBeAddedToArray]
[numberOfFeatures + 1422]; // working[3812];
int k = 0;
// setProgress(trainProgress+5);
for (OSMWay way : wayList) {
// adding multiple vectors
int id;
// if(USE_CLASS_FEATURES){
ClassFeatures class_vector = new ClassFeatures();
class_vector.createClassFeatures(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
id = 1422;
// }
// else{
// id = 1;
// }
// pass id also: 1422 if using classes, 1 if not
GeometryFeatures geometryFeatures = new GeometryFeatures(id);
geometryFeatures.createGeometryFeatures(way);
id = geometryFeatures.getLastID();
// id after geometry, cases: all geometry features with mean-variance boolean intervals:
// id = 1526
if (USE_RELATION_FEATURES) {
RelationFeatures relationFeatures = new RelationFeatures(id);
relationFeatures.createRelationFeatures(way, relationList);
id = relationFeatures.getLastID();
} else {
id = geometryFeatures.getLastID();
}
// id 1531
TextualFeatures textualFeatures;
if (USE_TEXTUAL_FEATURES) {
textualFeatures = new TextualFeatures(id, namesList, languageDetector);
textualFeatures.createTextualFeatures(way);
}
List<FeatureNode> featureNodeList = way.getFeatureNodeList();
FeatureNode[] featureNodeArray = new FeatureNode[featureNodeList.size()];
if (!way.getClassIDs().isEmpty()) {
int i = 0;
for (FeatureNode featureNode : featureNodeList) {
featureNodeArray[i] = featureNode;
i++;
}
for (int classID : way.getClassIDs()) {
// lalala++;
trainingSetWithUnknown2[k] = featureNodeArray;
GROUPS_ARRAY2[k] = classID;
k++;
}
}
}
// Linear.enableDebugOutput();
Problem problem = new Problem();
problem.l =
wayListSizeWithoutUnclassified
+ sizeToBeAddedToArray; // wayListSizeWithoutUnclassified;//wayList.size();
problem.n =
numberOfFeatures
+ 1422; // 3797; // number of features //the largest index of all features //3811;//3812
// //1812 with classes
problem.x = trainingSetWithUnknown2; // feature nodes
problem.y = GROUPS_ARRAY2; // target values
// SolverType solver = SolverType.MCSVM_CS; //Cramer and Singer for multiclass classification -
// equivalent of SVMlight
SolverType solver2 =
SolverType.getById(2); // 2 -- L2-regularized L2-loss support vector classification (primal)
Parameter parameter = new Parameter(solver2, C, eps);
// System.out.println("param set ok");
// System.out.println("number of features: " + vc.getNumOfFeatures());
long start = System.nanoTime();
System.out.println("training...");
PrintStream original = System.out;
System.setOut(
new PrintStream(
new OutputStream() {
@Override
public void write(int arg0) throws IOException {}
}));
Model model = Linear.train(problem, parameter);
long end = System.nanoTime();
Long elapsedTime = end - start;
System.setOut(original);
System.out.println(
"training process completed in: " + NANOSECONDS.toSeconds(elapsedTime) + " seconds.");
// System.out.println("trained");
// decide model path and naming and/or way of deleting/creating 1 or more models.
// File inFile = new File(inputFilePath).getParentFile();
File modelFile = new File(modelDirectory.getAbsolutePath() + "/model_with_classes");
File customModelFile;
if (topKIsSelected) {
customModelFile =
new File(
modelDirectory.getAbsolutePath()
+ "/"
+ inputFileName
+ "_model"
+ "_c"
+ param
+ "_topK"
+ topK
+ ".1");
} else {
customModelFile =
new File(
modelDirectory.getAbsolutePath()
+ "/"
+ inputFileName
+ "_model_c"
+ param
+ "_maxF"
+ frequency
+ ".1");
}
if (customModelFile.exists()) {
customModelFile.delete();
}
if (modelFile.exists()) {
modelFile.delete();
}
try {
// System.out.println("file created");
model.save(modelFile);
model.save(customModelFile);
System.out.println("model with classes saved at: " + modelFile);
System.out.println("custom model with classes saved at: " + modelFile);
} catch (IOException ex) {
Logger.getLogger(TrainWorker.class.getName()).log(Level.SEVERE, null, ex);
}
}
public void crossValidateFold(int a, int b, int c, int d, boolean skip, double param) {
System.out.println("Starting cross validation");
int testSize = wayList.size() / 5;
List<OSMWay> trainList = new ArrayList<>();
for (int g = a * testSize; g < b * testSize; g++) { // 0~~1~~2~~3~~4~~5
if (skip) {
if (g == (c) * testSize) {
g = (c + 1) * testSize;
}
}
trainList.add(wayList.get(g));
}
int wayListSizeWithoutUnclassified = trainList.size();
int u = 0;
System.out.println("trainList size: " + wayListSizeWithoutUnclassified);
// set classes for each osm instance
int sizeToBeAddedToArray =
0; // this will be used to proper init the features array, adding the multiple vectors size
int lalala = 0;
for (OSMWay way : trainList) {
OSMClassification classifyInstances = new OSMClassification();
classifyInstances.calculateClasses(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
if (way.getClassIDs().isEmpty()) {
wayListSizeWithoutUnclassified -= 1;
u++;
} else {
sizeToBeAddedToArray = sizeToBeAddedToArray + way.getClassIDs().size() - 1;
}
}
double C = param;
double eps = 0.001;
double[] GROUPS_ARRAY2 =
new double[wayListSizeWithoutUnclassified + sizeToBeAddedToArray]; // new double[117558];//
FeatureNode[][] trainingSetWithUnknown2 =
new FeatureNode[wayListSizeWithoutUnclassified + sizeToBeAddedToArray]
[numberOfFeatures]; // working[3812];
int k = 0;
for (OSMWay way : trainList) {
// adding multiple vectors
int id;
if (USE_CLASS_FEATURES) {
ClassFeatures class_vector = new ClassFeatures();
class_vector.createClassFeatures(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
id = 1422;
} else {
id = 1;
}
// pass id also: 1422 if using classes, 1 if not
GeometryFeatures geometryFeatures = new GeometryFeatures(id);
geometryFeatures.createGeometryFeatures(way);
id = geometryFeatures.getLastID();
// id after geometry, cases: all geometry features with mean-variance boolean intervals:
// id = 1526
if (USE_RELATION_FEATURES) {
RelationFeatures relationFeatures = new RelationFeatures(id);
relationFeatures.createRelationFeatures(way, relationList);
id = relationFeatures.getLastID();
} else {
id = geometryFeatures.getLastID();
}
// id 1531
TextualFeatures textualFeatures;
if (USE_TEXTUAL_FEATURES) {
textualFeatures = new TextualFeatures(id, namesList, languageDetector);
textualFeatures.createTextualFeatures(way);
}
List<FeatureNode> featureNodeList = way.getFeatureNodeList();
FeatureNode[] featureNodeArray = new FeatureNode[featureNodeList.size()];
if (!way.getClassIDs().isEmpty()) {
int i = 0;
for (FeatureNode featureNode : featureNodeList) {
featureNodeArray[i] = featureNode;
i++;
}
for (int classID : way.getClassIDs()) {
lalala++;
trainingSetWithUnknown2[k] = featureNodeArray;
GROUPS_ARRAY2[k] = classID;
k++;
}
}
}
// Linear.enableDebugOutput();
Problem problem = new Problem();
problem.l =
wayListSizeWithoutUnclassified
+ sizeToBeAddedToArray; // wayListSizeWithoutUnclassified;//wayList.size();
problem.n =
numberOfFeatures; // (geometry 105 + textual //3797; // number of features //the
// largest index of all features //3811;//3812 //1812 with classes
problem.x = trainingSetWithUnknown2; // feature nodes
problem.y = GROUPS_ARRAY2; // target values
// SolverType solver = SolverType.MCSVM_CS; //Cramer and Singer for multiclass classification -
// equivalent of SVMlight
SolverType solver2 =
SolverType.getById(2); // 2 -- L2-regularized L2-loss support vector classification (primal)
Parameter parameter = new Parameter(solver2, C, eps);
// System.out.println("param set ok");
// System.out.println("number of features: " + vc.getNumOfFeatures());
long start = System.nanoTime();
System.out.println("training...");
PrintStream original = System.out;
System.setOut(
new PrintStream(
new OutputStream() {
@Override
public void write(int arg0) throws IOException {}
}));
Model model = Linear.train(problem, parameter);
long end = System.nanoTime();
Long elapsedTime = end - start;
System.setOut(original);
System.out.println(
"training process completed in: " + NANOSECONDS.toSeconds(elapsedTime) + " seconds.");
// System.out.println("trained");
// decide model path and naming and/or way of deleting/creating 1 or more models.
// File inFile = new File(inputFilePath).getParentFile();
File modelFile;
if (USE_CLASS_FEATURES) {
modelFile = new File(modelDirectory.getAbsolutePath() + "/model_with_classes_c=" + param);
} else {
modelFile =
new File(modelDirectory.getAbsolutePath() + "/model_geometries_textual_c=" + param);
}
if (modelFile.exists()) {
modelFile.delete();
}
try {
// System.out.println("file created");
model.save(modelFile);
System.out.println("model saved at: " + modelFile);
} catch (IOException ex) {
Logger.getLogger(TrainWorker.class.getName()).log(Level.SEVERE, null, ex);
}
// end of evaluation training
// test set
List<OSMWay> testList = new ArrayList<>();
for (int g = c * testSize; g < d * testSize; g++) {
testList.add(wayList.get(g));
// liblinear test
}
System.out.println("testList size: " + testList.size());
int succededInstances = 0;
int succededInstances5 = 0;
int succededInstances10 = 0;
try {
model = Model.load(modelFile);
} catch (IOException ex) {
Logger.getLogger(TrainWorker.class.getName()).log(Level.SEVERE, null, ex);
}
int modelLabelSize = model.getLabels().length;
int[] labels = model.getLabels();
Map<Integer, Integer> mapLabelsToIDs = new HashMap<>();
for (int h = 0; h < model.getLabels().length; h++) {
mapLabelsToIDs.put(labels[h], h);
// System.out.println(h + " <-> " + labels[h]);
}
int wayListSizeWithoutUnclassified2 = testList.size();
for (OSMWay way : testList) {
OSMClassification classifyInstances = new OSMClassification();
classifyInstances.calculateClasses(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
if (way.getClassIDs().isEmpty()) {
// System.out.println("found unclassified" + way.getClassIDs() + "class: "
// +way.getClassID());
wayListSizeWithoutUnclassified2 -= 1;
// u++;
}
}
FeatureNode[] testInstance2;
for (OSMWay way : testList) {
int id;
if (USE_CLASS_FEATURES) {
ClassFeatures class_vector = new ClassFeatures();
class_vector.createClassFeatures(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
id = 1422;
} else {
id = 1;
}
// pass id also: 1422 if using classes, 1 if not
GeometryFeatures geometryFeatures = new GeometryFeatures(id);
geometryFeatures.createGeometryFeatures(way);
id = geometryFeatures.getLastID();
// id after geometry, cases: all geometry features with mean-variance boolean intervals:
// id = 1526
// System.out.println("id 1526 -> " + geometryFeatures.getLastID());
if (USE_RELATION_FEATURES) {
RelationFeatures relationFeatures = new RelationFeatures(id);
relationFeatures.createRelationFeatures(way, relationList);
id = relationFeatures.getLastID();
} else {
id = geometryFeatures.getLastID();
// System.out.println("geom feat " + id);
}
// id 1531
// System.out.println("id 1532 -> " + relationFeatures.getLastID());
if (USE_TEXTUAL_FEATURES) {
TextualFeatures textualFeatures = new TextualFeatures(id, namesList, languageDetector);
textualFeatures.createTextualFeatures(way);
// System.out.println("last textual id: " + textualFeatures.getLastID());
// System.out.println("full: " + way.getFeatureNodeList());
} else {
}
List<FeatureNode> featureNodeList = way.getFeatureNodeList();
FeatureNode[] featureNodeArray = new FeatureNode[featureNodeList.size()];
int i = 0;
for (FeatureNode featureNode : featureNodeList) {
featureNodeArray[i] = featureNode;
i++;
}
testInstance2 = featureNodeArray;
// double prediction = Linear.predict(model, testInstance2);
// System.out.println("test prediction: " + prediction);
double[] scores = new double[modelLabelSize];
Linear.predictValues(model, testInstance2, scores);
// find index of max values in scores array: predicted classes are the elements of these
// indexes from array model.getlabels
// iter scores and find 10 max values with their indexes first. then ask those indexes from
// model.getlabels
Map<Double, Integer> scoresValues = new HashMap<>();
for (int h = 0; h < scores.length; h++) {
scoresValues.put(scores[h], h);
// System.out.println(h + " <-> " + scores[h]);
}
Arrays.sort(scores);
// System.out.println("max value: " + scores[scores.length-1] + " second max: " +
// scores[scores.length-2]);
// System.out.println("ask this index from labels: " +
// scoresValues.get(scores[scores.length-1]));
// System.out.println("got from labels: " +
// labels[scoresValues.get(scores[scores.length-1])]);
// System.out.println("next prediction: " +
// labels[scoresValues.get(scores[scores.length-2])]);
// System.out.println("way labels: " + way.getClassIDs());
// System.out.println("test prediction: " + prediction);
if (way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 1])])) {
succededInstances++;
}
if (way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 1])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 2])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 3])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 4])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 5])])) {
succededInstances5++;
}
if (way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 1])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 2])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 3])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 4])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 5])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 6])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 7])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 8])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 9])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 10])])) {
succededInstances10++;
}
// System.out.println("labels: " + Arrays.toString(model.getLabels()));
// System.out.println("label[0]: " + model.getLabels()[0]);
}
System.out.println(
"Succeeded "
+ succededInstances
+ " of "
+ testList.size()
+ " total (1 class prediction)");
double precision1 = succededInstances / (double) wayListSizeWithoutUnclassified2;
score1 = precision1;
System.out.println(precision1);
System.out.println(
"Succeeded "
+ succededInstances5
+ " of "
+ testList.size()
+ " total (5 class prediction)");
double precision5 = succededInstances5 / (double) wayListSizeWithoutUnclassified2;
score5 = precision5;
System.out.println(precision5);
System.out.println(
"Succeeded "
+ succededInstances10
+ " of "
+ testList.size()
+ " total (10 class prediction)");
double precision10 = succededInstances10 / (double) wayListSizeWithoutUnclassified2;
score10 = precision10;
System.out.println(precision10);
}