/**
* 用当前模型在测试集上进行测试 输出正确率
*
* @param testSet
*/
public void test(InstanceSet testSet) {
double err = 0;
double errorAll = 0;
int total = 0;
for (int i = 0; i < testSet.size(); i++) {
Instance inst = testSet.getInstance(i);
total += ((int[]) inst.getTarget()).length;
Results pred = (Results) msolver.getBest(inst, 1);
double l = loss.calc(pred.getPredAt(0), inst.getTarget());
if (l > 0) { // 预测错误
errorAll += 1.0;
err += l;
}
}
if (!simpleOutput) {
System.out.print("Test:\t");
System.out.print(total - err);
System.out.print('/');
System.out.print(total);
System.out.print("\tTag acc:");
} else {
System.out.print('\t');
}
System.out.print(1 - err / total);
if (!simpleOutput) {
System.out.print("\tSentence acc:");
System.out.println(1 - errorAll / testSet.size());
}
System.out.println();
}
public Results getBest(Instance inst, int n) {
Integer target = null;
if (isUseTarget) target = (Integer) inst.getTarget();
SparseVector fv = featureGen.getVector(inst);
// 每个类对应的内积
double[] sw = new double[alphabet.size()];
Callable<Double>[] c = new Multiplesolve[numClass];
Future<Double>[] f = new Future[numClass];
for (int i = 0; i < numClass; i++) {
c[i] = new Multiplesolve(fv, i);
f[i] = pool.submit(c[i]);
}
// 执行任务并获取Future对象
for (int i = 0; i < numClass; i++) {
try {
sw[i] = (Double) f[i].get();
} catch (Exception e) {
e.printStackTrace();
return null;
}
}
Results res = new Results(n);
if (target != null) {
res.buildOracle();
}
Iterator<Integer> it = leafs.iterator();
while (it.hasNext()) {
double score = 0.0;
Integer i = it.next();
if (tree != null) { // 计算含层次信息的内积
ArrayList<Integer> anc = tree.getPath(i);
for (int j = 0; j < anc.size(); j++) {
score += sw[anc.get(j)];
}
} else {
score = sw[i];
}
// 给定目标范围是，只计算目标范围的值
if (target != null && target.equals(i)) {
res.addOracle(score, i);
} else {
res.addPred(score, i);
}
}
return res;
}
/** 训练 */
public Linear train(InstanceSet trainingList, InstanceSet testList) {
int numSamples = trainingList.size();
count = 0;
for (int ii = 0; ii < trainingList.size(); ii++) {
Instance inst = trainingList.getInstance(ii);
count += ((int[]) inst.getTarget()).length;
}
System.out.println("Chars Number: " + count);
double oldErrorRate = Double.MAX_VALUE;
// 开始循环
long beginTime, endTime;
long beginTimeIter, endTimeIter;
beginTime = System.currentTimeMillis();
double pE = 0;
int iter = 0;
int frac = numSamples / 10;
while (iter++ < maxIter) {
if (!simpleOutput) {
System.out.print("iter:");
System.out.print(iter + "\t");
}
double err = 0;
double errorAll = 0;
beginTimeIter = System.currentTimeMillis();
int progress = frac;
for (int ii = 0; ii < numSamples; ii++) {
Instance inst = trainingList.getInstance(ii);
Results pred = (Results) msolver.getBest(inst, 1);
double l = loss.calc(pred.getPredAt(0), inst.getTarget());
if (l > 0) { // 预测错误，更新权重
errorAll += 1.0;
err += l;
update.update(inst, weights, pred.getPredAt(0), c);
} else {
if (pred.size() > 1) update.update(inst, weights, pred.getPredAt(1), c);
}
if (!simpleOutput && ii % progress == 0) { // 显示进度
System.out.print('.');
progress += frac;
}
}
double errRate = err / count;
endTimeIter = System.currentTimeMillis();
if (!simpleOutput) {
System.out.println("\ttime:" + (endTimeIter - beginTimeIter) / 1000.0 + "s");
System.out.print("Train:");
System.out.print("\tTag acc:");
}
System.out.print(1 - errRate);
if (!simpleOutput) {
System.out.print("\tSentence acc:");
System.out.print(1 - errorAll / numSamples);
System.out.println();
}
if (testList != null) {
test(testList);
}
if (Math.abs(errRate - oldErrorRate) < eps) {
System.out.println("Convergence!");
break;
}
oldErrorRate = errRate;
if (interim) {
Linear p = new Linear(msolver, trainingList.getAlphabetFactory());
try {
p.saveTo("tmp.model");
} catch (IOException e) {
System.err.println("write model error!");
}
}
if (isOptimized) { // 模型优化，去掉不显著的特征
int[] idx = MyArrays.getTop(weights.clone(), threshold, false);
System.out.print("Opt: weight numbers: " + MyArrays.countNoneZero(weights));
MyArrays.set(weights, idx, 0.0);
System.out.println(" -> " + MyArrays.countNoneZero(weights));
}
}
endTime = System.currentTimeMillis();
System.out.println("done!");
System.out.println("time escape:" + (endTime - beginTime) / 1000.0 + "s");
Linear p = new Linear(msolver, trainingList.getAlphabetFactory());
return p;
}