@Test
public void saveBinary_loadBinary() throws Exception {
final Model model = createModel();
final ByteArrayOutputStream stream = new ByteArrayOutputStream();
model.saveBinary(stream);
final Model newModel = Model.loadBinary(new ByteArrayInputStream(stream.toByteArray()));
Assert.assertEquals(model.getBias(), newModel.getBias(), 0.001);
Assert.assertEquals(model.getNrFeature(), newModel.getNrFeature());
Assert.assertArrayEquals(model.getLabels(), newModel.getLabels());
Assert.assertEquals(model.getNrClass(), newModel.getNrClass());
Assert.assertArrayEquals(model.w, newModel.w, 0.001);
}
@Test
public void immutability() {
Model other = new Model(modelUpdated);
other.getIssues().add(new TurboIssue(REPO, 11, ""));
assertEquals(modelUpdated, other);
other = new Model(modelUpdated);
other.getLabels().add(new TurboLabel(REPO, "aksdjl"));
assertEquals(modelUpdated, other);
other = new Model(modelUpdated);
other.getMilestones().add(new TurboMilestone(REPO, 11, ""));
assertEquals(modelUpdated, other);
other = new Model(modelUpdated);
other.getUsers().add(new TurboUser(REPO, ""));
assertEquals(modelUpdated, other);
}
/** <b>Note: The streams are NOT closed</b> */
static void doPredict(BufferedReader reader, Writer writer, Model model) throws IOException {
int correct = 0;
int total = 0;
double error = 0;
double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;
int nr_class = model.getNrClass();
double[] prob_estimates = null;
int n;
int nr_feature = model.getNrFeature();
if (model.bias >= 0) n = nr_feature + 1;
else n = nr_feature;
if (flag_predict_probability && !model.isProbabilityModel()) {
throw new IllegalArgumentException(
"probability output is only supported for logistic regression");
}
Formatter out = new Formatter(writer);
if (flag_predict_probability) {
int[] labels = model.getLabels();
prob_estimates = new double[nr_class];
printf(out, "labels");
for (int j = 0; j < nr_class; j++) printf(out, " %d", labels[j]);
printf(out, "\n");
}
String line = null;
while ((line = reader.readLine()) != null) {
List<Feature> x = new ArrayList<Feature>();
StringTokenizer st = new StringTokenizer(line, " \t\n");
double target_label;
try {
String label = st.nextToken();
target_label = atof(label);
} catch (NoSuchElementException e) {
throw new RuntimeException("Wrong input format at line " + (total + 1), e);
}
while (st.hasMoreTokens()) {
String[] split = COLON.split(st.nextToken(), 2);
if (split == null || split.length < 2) {
throw new RuntimeException("Wrong input format at line " + (total + 1));
}
try {
int idx = atoi(split[0]);
double val = atof(split[1]);
// feature indices larger than those in training are not used
if (idx <= nr_feature) {
Feature node = new FeatureNode(idx, val);
x.add(node);
}
} catch (NumberFormatException e) {
throw new RuntimeException("Wrong input format at line " + (total + 1), e);
}
}
if (model.bias >= 0) {
Feature node = new FeatureNode(n, model.bias);
x.add(node);
}
Feature[] nodes = new Feature[x.size()];
nodes = x.toArray(nodes);
double predict_label;
if (flag_predict_probability) {
assert prob_estimates != null;
predict_label = Linear.predictProbability(model, nodes, prob_estimates);
printf(out, "%g", predict_label);
for (int j = 0; j < model.nr_class; j++) printf(out, " %g", prob_estimates[j]);
printf(out, "\n");
} else {
predict_label = Linear.predict(model, nodes);
printf(out, "%g\n", predict_label);
}
if (predict_label == target_label) {
++correct;
}
error += (predict_label - target_label) * (predict_label - target_label);
sump += predict_label;
sumt += target_label;
sumpp += predict_label * predict_label;
sumtt += target_label * target_label;
sumpt += predict_label * target_label;
++total;
}
if (model.solverType.isSupportVectorRegression()) //
{
info("Mean squared error = %g (regression)%n", error / total);
info(
"Squared correlation coefficient = %g (regression)%n", //
((total * sumpt - sump * sumt) * (total * sumpt - sump * sumt))
/ ((total * sumpp - sump * sump) * (total * sumtt - sumt * sumt)));
} else {
info("Accuracy = %g%% (%d/%d)%n", (double) correct / total * 100, correct, total);
}
}
private void mainClassifierFunction(int option, String trainFile, String testFile, String ddgFile)
throws IOException {
// SentimentClassifierHindi this = new SentimentClassifierHindi();
// int finalSize = this.SentimentClassifierHindi();
int finalSize = this.generateFeature(option, trainFile, testFile, ddgFile);
System.out.println("Hello aspectCategorizationSemEval2016!");
// Create features
Problem problem = new Problem();
// Save X to problem
double a[] = new double[this.trainingFeature.size()];
File file = new File(rootDirectory + "\\dataset\\trainingLabels.txt");
BufferedReader reader = new BufferedReader(new FileReader(file));
String read;
int count = 0;
while ((read = reader.readLine()) != null) {
// System.out.println(read);
a[count++] = Double.parseDouble(read.toString());
}
// Feature[][] f = new Feature[][]{ {}, {}, {}, {}, {}, {} };
// trainingFeature = trainingObject.getList();
Feature[][] trainFeatureVector = new Feature[trainingFeature.size()][finalSize];
System.out.println("Training Instances: " + trainingFeature.size());
System.out.println("Feature Length: " + finalSize);
System.out.println("Test Instances: " + testFeature.size());
for (int i = 0; i < trainingFeature.size(); i++) {
// System.out.println();
// System.out.println(trainingFeature.get(i));
System.out.println(i + " trained.");
for (int j = 0; j < finalSize; j++) {
// System.out.print(trainingFeature.get(i).get(j + 1)+" ");
// trainingFeature.get(i).
if (trainingFeature.get(i).containsKey(j + 1)) {
// System.out.print(j + 1 + ", ");
trainFeatureVector[i][j] = new FeatureNode(j + 1, trainingFeature.get(i).get(j + 1));
} else {
trainFeatureVector[i][j] = new FeatureNode(j + 1, 0.0);
}
}
// System.out.println();
}
problem.l = trainingFeature.size(); // number of training examples
problem.n = finalSize; // number of features
problem.x = trainFeatureVector; // feature nodes
problem.y = a; // target values ----
BasicParser bp = new BasicParser();
SolverType solver = SolverType.L2R_LR; // -s 7
double C = 0.75; // cost of constraints violation
double eps = 0.0001; // stopping criteria
Parameter parameter = new Parameter(solver, C, eps);
Model model = Linear.train(problem, parameter);
File modelFile = new File("model");
model.save(modelFile);
// PrintWriter write = new PrintWriter(new BufferedWriter(new FileWriter(rootDirectory +
// "\\dataset\\predictedLabels.txt")));
PrintWriter write =
new PrintWriter(
new BufferedWriter(
new FileWriter(
rootDirectory
+ "\\dataset\\dataset_aspectCategorization\\predictedHotelsLabels.txt")));
if (option == 1) {
BufferedReader trainReader =
new BufferedReader(
new FileReader(
new File(
rootDirectory + "\\dataset\\dataset_aspectCategorization\\" + trainFile)));
HashMap<String, Integer> id = new HashMap<String, Integer>();
HashMap<String, String> review = new HashMap<String, String>();
double[] val = new double[trainingFeature.size()];
double[] tempVal = new double[trainingFeature.size()];
LinearCopy.crossValidation(problem, parameter, 5, val, tempVal);
for (int i = 0; i < trainingFeature.size(); i++) {
int flag = 0;
String tokens[] = trainReader.readLine().split("\\|");
if (id.containsKey(tokens[1]) == true || tokens[2].compareToIgnoreCase("True") == 0) {
} else {
// System.out.println(tokens[1]);
/*int max = -1;
double probMax = -1.0;
for(int j=0; j<13; j++){
if(probMax<val[i][j]){
probMax = val[i][j];
max = j;
}
}*/
// System.out.println(tempVal[i]);
write.println((int) (val[i]));
write.println("next");
id.put(tokens[1], 1);
System.out.println(tokens[1] + "\t" + (int) (val[i]));
if (review.containsKey(tokens[1])) {
System.out.println(tokens[3]);
System.out.println(review.get(tokens[1]));
} else {
review.put(tokens[1], tokens[3]);
}
} /*else{
for (int j = 0; j < 13; j++) {
//System.out.print(val[i][j]+", ");
if (val[i] >= 0.185) {
flag = 1;
//System.out.println("i");
write.println(j + 1);
}
}
if (flag == 1) {
write.println("next");
} else {
write.println("-1");
write.println("next");
}
//write.println(prediction);
id.put(tokens[1], 1);
//System.out.println();
}*/
}
write.close();
return;
}
if (option == 3) {
System.out.println(rootDirectory);
BufferedReader testReader =
new BufferedReader(
new FileReader(
new File(
rootDirectory + "\\dataset\\dataset_aspectCategorization\\" + testFile)));
HashMap<String, Integer> id = new HashMap<String, Integer>();
model = Model.load(modelFile);
int countNext = 0;
for (int i = 0; i < testFeature.size(); i++) {
// System.out.println(i+", "+testFeature.size()+", "+testFeature.get(i).size());
Feature[] instance = new Feature[testFeature.get(i).size()];
int j = 0;
for (Map.Entry<Integer, Double> entry : testFeature.get(i).entrySet()) {
// System.out.print(entry.getKey() + ": " + entry.getValue() + "; ");
// listOfMaps.get(i).put(start + entry.getKey(), entry.getValue());
// do stuff
instance[j++] = new FeatureNode(entry.getKey(), entry.getValue());
}
// double d = LinearCopy.predict(model, instance);
double[] predict = new double[85];
double prediction = LinearCopy.predictProbability(model, instance, predict);
int labelMap[] = new int[13];
labelMap = model.getLabels();
for (int ar = 0; ar < labelMap.length; ar++) {
System.out.println("********************** " + ar + ": " + labelMap[ar]);
}
// System.out.println(prediction);
// Arrays.sort(predict, Collections.reverseOrder());
// System.out.println();
// double prediction = LinearCopy.predict(model, instance);
String tokens[] = testReader.readLine().split("\\|");
// System.out.println(tokens[1]);
int flag = -1;
if (id.containsKey(tokens[1]) == true || tokens[2].compareToIgnoreCase("True") == 0) {
flag = 4;
// System.out.println("OutofScope: "+tokens[1]);
} else if (tokens[3].compareToIgnoreCase("abc") == 0) {
flag = 2;
System.out.println(tokens[1]);
write.println("-1");
write.println("next");
countNext++;
id.put(tokens[1], 1);
} else {
flag = 0;
for (int p = 0; p < 85; p++) {
if (predict[p] >= 0.128) {
flag = 1;
write.println(labelMap[p]);
}
}
if (flag == 1) {
countNext++;
write.println("next");
} else {
countNext++;
write.println("-1");
write.println("next");
}
// write.println((int)d);
// write.println("next");
/*write.println(prediction);
write.println("next");*/
id.put(tokens[1], 1);
}
if (flag == -1) {
System.out.println("-1, " + tokens[1]);
}
}
write.close();
System.out.println("count " + countNext);
}
write.close();
}
@Test
public void equality() {
assertEquals(modelUpdated, modelUpdated);
assertNotEquals(modelUpdated, null);
assertNotEquals(modelUpdated, 1);
// Empty signature
assertEquals(modelEmptySig, modelEmptySig2);
// Copy correctness
assertEquals(modelCopyUpdated, modelUpdated);
assertEquals(modelCopyNotUpdated, modelEmptySig);
// Update signature
// Tested by changing one element in the model and ensuring inequality
assertEquals(modelEmptySig.hashCode(), modelEmptySig2.hashCode());
assertEquals(modelCopyUpdated.hashCode(), modelUpdated.hashCode());
assertNotEquals(modelEmptySig.hashCode(), modelUpdated.hashCode());
Model model =
new Model(
"something",
modelUpdated.getIssues(),
modelUpdated.getLabels(),
modelUpdated.getMilestones(),
modelUpdated.getUsers(),
modelUpdated.getUpdateSignature());
assertNotEquals(model.hashCode(), modelUpdated.hashCode());
assertNotEquals(model, modelUpdated);
List<TurboIssue> issues = new ArrayList<>(modelUpdated.getIssues());
issues.add(new TurboIssue(REPO, 11, "something"));
model =
new Model(
REPO,
issues,
modelUpdated.getLabels(),
modelUpdated.getMilestones(),
modelUpdated.getUsers(),
modelUpdated.getUpdateSignature());
assertNotEquals(model.hashCode(), modelUpdated.hashCode());
assertNotEquals(model, modelUpdated);
List<TurboLabel> labels = new ArrayList<>(modelUpdated.getLabels());
labels.add(new TurboLabel(REPO, "Label 11"));
model =
new Model(
REPO,
modelUpdated.getIssues(),
labels,
modelUpdated.getMilestones(),
modelUpdated.getUsers(),
modelUpdated.getUpdateSignature());
assertNotEquals(model.hashCode(), modelUpdated.hashCode());
assertNotEquals(model, modelUpdated);
List<TurboMilestone> milestones = new ArrayList<>(modelUpdated.getMilestones());
milestones.add(new TurboMilestone(REPO, 11, "something"));
model =
new Model(
REPO,
modelUpdated.getIssues(),
modelUpdated.getLabels(),
milestones,
modelUpdated.getUsers(),
modelUpdated.getUpdateSignature());
assertNotEquals(model.hashCode(), modelUpdated.hashCode());
assertNotEquals(model, modelUpdated);
List<TurboUser> users = new ArrayList<>(modelUpdated.getUsers());
users.add(new TurboUser(REPO, "someone"));
model =
new Model(
REPO,
modelUpdated.getIssues(),
modelUpdated.getLabels(),
modelUpdated.getMilestones(),
users,
modelUpdated.getUpdateSignature());
assertNotEquals(model.hashCode(), modelUpdated.hashCode());
assertNotEquals(model, modelUpdated);
}
@Test
public void getters() {
// ID
assertEquals(REPO, modelUpdated.getRepoId());
assertEquals(modelUpdated.getRepoId(), modelUpdated.getRepoId());
// Signature
assertEquals(true, modelEmptySig.getUpdateSignature().isEmpty());
assertEquals(modelEmptySig.getUpdateSignature(), UpdateSignature.EMPTY);
assertEquals(modelEmptySig.getUpdateSignature(), modelEmptySig2.getUpdateSignature());
// Resources
// Issues
ArrayList<Integer> issueIds = new ArrayList<>();
for (int i = 1; i <= DummyRepoState.NO_OF_DUMMY_ISSUES; i++) {
issueIds.add(i);
}
Collections.sort(issueIds); // 1, 2..10
int issueCount = 1;
for (TurboIssue issue : modelUpdated.getIssues()) {
assertEquals(issueCount, modelUpdated.getIssueById(issueCount).get().getId());
assertEquals(issueIds.get(issueCount - 1).intValue(), issue.getId());
issueCount++;
}
// Labels
ArrayList<String> labelNames = new ArrayList<>();
for (int i = 1; i <= DummyRepoState.NO_OF_DUMMY_ISSUES; i++) {
labelNames.add("Label " + i);
}
Collections.sort(labelNames); // Label 1, Label 10..12, Label 2..9
int labelCount = 1;
for (TurboLabel label : modelUpdated.getLabels()) {
if (label.getFullName().startsWith("Label")) {
assertEquals(labelNames.get(labelCount - 1), label.getFullName());
assertEquals(
"Label " + labelCount,
modelUpdated.getLabelByActualName("Label " + labelCount).get().getFullName());
labelCount++;
}
}
// Milestones
ArrayList<Integer> milestoneIds = new ArrayList<>();
for (int i = 1; i <= DummyRepoState.NO_OF_DUMMY_ISSUES; i++) {
milestoneIds.add(i);
}
Collections.sort(milestoneIds); // 1, 2..10
int milestoneCount = 1;
for (TurboMilestone milestone : modelUpdated.getMilestones()) {
assertEquals(milestoneCount, milestone.getId());
assertEquals(
milestoneIds.get(milestoneCount - 1).intValue(),
modelUpdated.getMilestoneById(milestoneCount).get().getId());
assertEquals(
"Milestone " + milestoneCount,
modelUpdated.getMilestoneByTitle("Milestone " + milestoneCount).get().getTitle());
milestoneCount++;
}
// Users
ArrayList<String> userLogins = new ArrayList<>();
for (int i = 1; i <= DummyRepoState.NO_OF_DUMMY_ISSUES; i++) {
userLogins.add("User " + i);
}
Collections.sort(userLogins); // User 1, User 10, User 2..9
int userCount = 1;
for (TurboUser user : modelUpdated.getUsers()) {
assertEquals(userLogins.get(userCount - 1), user.getLoginName());
assertEquals(
"User " + userCount,
modelUpdated.getUserByLogin("User " + userCount).get().getLoginName());
userCount++;
}
}