Examples with StopWatch org.apache.commons.lang3.time.StopWatch used on opensource projects

Example 61 with StopWatch

use of org.apache.commons.lang3.time.StopWatch in project pyramid by cheng-li.

the class GBCBMOptimizer method updateBinaryClassifier.

@Override
protected void updateBinaryClassifier(int component, int label, MultiLabelClfDataSet activeDataset, double[] activeGammas) {
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    if (cbm.binaryClassifiers[component][label] == null || cbm.binaryClassifiers[component][label] instanceof PriorProbClassifier) {
        cbm.binaryClassifiers[component][label] = new LKBoost(2);
    }
    int[] binaryLabels = DataSetUtil.toBinaryLabels(activeDataset.getMultiLabels(), label);
    double[][] targetsDistributions = DataSetUtil.labelsToDistributions(binaryLabels, 2);
    LKBoost boost = (LKBoost) this.cbm.binaryClassifiers[component][label];
    RegTreeConfig regTreeConfig = new RegTreeConfig().setMaxNumLeaves(numLeaves);
    RegTreeFactory regTreeFactory = new RegTreeFactory(regTreeConfig);
    regTreeFactory.setLeafOutputCalculator(new LKBOutputCalculator(2));
    LKBoostOptimizer optimizer = new LKBoostOptimizer(boost, activeDataset, regTreeFactory, activeGammas, targetsDistributions);
    optimizer.setShrinkage(shrinkage);
    optimizer.initialize();
    optimizer.iterate(binaryUpdatesPerIter);
    if (logger.isDebugEnabled()) {
        logger.debug("time spent on updating component " + component + " label " + label + " = " + stopWatch);
    }
}

Example 62 with StopWatch

use of org.apache.commons.lang3.time.StopWatch in project pyramid by cheng-li.

the class SparseCBMOptimzer method updateBinaryLogisticRegression.

private void updateBinaryLogisticRegression(int componentIndex, int labelIndex) {
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    double effectivePositives = effectivePositives(componentIndex, labelIndex);
    StringBuilder sb = new StringBuilder();
    sb.append("for component ").append(componentIndex).append(", label ").append(labelIndex);
    sb.append(", effective positives = ").append(effectivePositives);
    if (effectivePositives <= 1) {
        double positiveProb = prior(componentIndex, labelIndex);
        double[] probs = { 1 - positiveProb, positiveProb };
        cbm.binaryClassifiers[componentIndex][labelIndex] = new PriorProbClassifier(probs);
        sb.append(", skip, use prior = ").append(positiveProb);
        sb.append(", time spent = " + stopWatch.toString());
        System.out.println(sb.toString());
        return;
    }
    if (cbm.binaryClassifiers[componentIndex][labelIndex] == null || cbm.binaryClassifiers[componentIndex][labelIndex] instanceof PriorProbClassifier) {
        cbm.binaryClassifiers[componentIndex][labelIndex] = new LogisticRegression(2, dataSet.getNumFeatures());
    }
    RidgeLogisticOptimizer ridgeLogisticOptimizer;
    int[] binaryLabels = DataSetUtil.toBinaryLabels(dataSet.getMultiLabels(), labelIndex);
    // no parallelism
    ridgeLogisticOptimizer = new RidgeLogisticOptimizer((LogisticRegression) cbm.binaryClassifiers[componentIndex][labelIndex], dataSet, binaryLabels, activeGammas, priorVarianceBinary, false);
    //TODO maximum iterations
    ridgeLogisticOptimizer.getOptimizer().getTerminator().setMaxIteration(numBinaryUpdates);
    ridgeLogisticOptimizer.optimize();
    sb.append(", time spent = " + stopWatch.toString());
    System.out.println(sb.toString());
}

Example 63 with StopWatch

use of org.apache.commons.lang3.time.StopWatch in project pyramid by cheng-li.

the class IMLGradientBoostingTest method test2_build.

/**
     * add a fake label in spam data set, if x=spam and x_0<0.1, also label it as 2
     * @throws Exception
     */
static void test2_build() throws Exception {
    ClfDataSet singleLabeldataSet = TRECFormat.loadClfDataSet(new File(DATASETS, "/spam/trec_data/train.trec"), DataSetType.CLF_DENSE, true);
    int numDataPoints = singleLabeldataSet.getNumDataPoints();
    int numFeatures = singleLabeldataSet.getNumFeatures();
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numDataPoints(numDataPoints).numFeatures(numFeatures).numClasses(3).build();
    int[] labels = singleLabeldataSet.getLabels();
    for (int i = 0; i < numDataPoints; i++) {
        dataSet.addLabel(i, labels[i]);
        if (labels[i] == 1 && singleLabeldataSet.getRow(i).get(0) < 0.1) {
            dataSet.addLabel(i, 2);
        }
        for (int j = 0; j < numFeatures; j++) {
            double value = singleLabeldataSet.getRow(i).get(j);
            dataSet.setFeatureValue(i, j, value);
        }
    }
    IMLGradientBoosting boosting = new IMLGradientBoosting(3);
    IMLGBConfig trainConfig = new IMLGBConfig.Builder(dataSet).numLeaves(60).learningRate(0.1).numSplitIntervals(1000).minDataPerLeaf(2).dataSamplingRate(1).featureSamplingRate(1).build();
    System.out.println(Arrays.toString(trainConfig.getActiveFeatures()));
    IMLGBTrainer trainer = new IMLGBTrainer(trainConfig, boosting);
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    for (int round = 0; round < 20; round++) {
        System.out.println("round=" + round);
        trainer.iterate();
        System.out.println("accuracy=" + Accuracy.accuracy(boosting, dataSet));
    //            System.out.println(Arrays.toString(boosting.getGradients(0)));
    //            System.out.println(Arrays.toString(boosting.getGradients(1)));
    }
    stopWatch.stop();
    System.out.println(stopWatch);
    System.out.println(boosting);
    for (int i = 0; i < numDataPoints; i++) {
        Vector featureRow = dataSet.getRow(i);
        MultiLabel label = dataSet.getMultiLabels()[i];
        MultiLabel prediction = boosting.predict(featureRow);
    //            System.out.println("label="+label);
    //            System.out.println(boosting.calAssignmentScore(featureRow,assignments.get(0)));
    //            System.out.println(boosting.calAssignmentScore(featureRow,assignments.get(1)));
    //            System.out.println("prediction="+prediction);
    //            if (!MultiLabel.equivalent(label,prediction)){
    //                System.out.println(i);
    //                System.out.println("label="+label);
    //                System.out.println("prediction="+prediction);
    //            }
    }
    System.out.println("accuracy");
    System.out.println(Accuracy.accuracy(boosting, dataSet));
    boosting.serialize(new File(TMP, "imlgb/boosting.ser"));
}

Example 64 with StopWatch

use of org.apache.commons.lang3.time.StopWatch in project pyramid by cheng-li.

the class IMLGradientBoostingTest method test3_build.

/**
     * add 2 fake labels in spam data set,
     * if x=spam and x_0<0.1, also label it as 2
     * if x=spam and x_1<0.1, also label it as 3
     * @throws Exception
     */
static void test3_build() throws Exception {
    ClfDataSet singleLabeldataSet = TRECFormat.loadClfDataSet(new File(DATASETS, "spam/trec_data/train.trec"), DataSetType.CLF_DENSE, true);
    int numDataPoints = singleLabeldataSet.getNumDataPoints();
    int numFeatures = singleLabeldataSet.getNumFeatures();
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numDataPoints(numDataPoints).numFeatures(numFeatures).numClasses(4).build();
    int[] labels = singleLabeldataSet.getLabels();
    for (int i = 0; i < numDataPoints; i++) {
        dataSet.addLabel(i, labels[i]);
        if (labels[i] == 1 && singleLabeldataSet.getRow(i).get(0) < 0.1) {
            dataSet.addLabel(i, 2);
        }
        if (labels[i] == 1 && singleLabeldataSet.getRow(i).get(1) < 0.1) {
            dataSet.addLabel(i, 3);
        }
        for (int j = 0; j < numFeatures; j++) {
            double value = singleLabeldataSet.getRow(i).get(j);
            dataSet.setFeatureValue(i, j, value);
        }
    }
    IMLGradientBoosting boosting = new IMLGradientBoosting(4);
    List<MultiLabel> assignments = DataSetUtil.gatherMultiLabels(dataSet);
    boosting.setAssignments(assignments);
    IMLGBConfig trainConfig = new IMLGBConfig.Builder(dataSet).numLeaves(10).learningRate(0.1).numSplitIntervals(1000).minDataPerLeaf(2).dataSamplingRate(1).featureSamplingRate(1).build();
    System.out.println(Arrays.toString(trainConfig.getActiveFeatures()));
    IMLGBTrainer trainer = new IMLGBTrainer(trainConfig, boosting);
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    for (int round = 0; round < 100; round++) {
        System.out.println("round=" + round);
        trainer.iterate();
        System.out.println("accuracy=" + Accuracy.accuracy(boosting, dataSet));
    //            System.out.println(Arrays.toString(boosting.getGradients(0)));
    //            System.out.println(Arrays.toString(boosting.getGradients(1)));
    //            System.out.println(Arrays.toString(boosting.getGradients(2)));
    //            System.out.println(Arrays.toString(boosting.getGradients(3)));
    }
    stopWatch.stop();
    System.out.println(stopWatch);
    //        System.out.println(boosting);
    for (int i = 0; i < numDataPoints; i++) {
        Vector featureRow = dataSet.getRow(i);
        MultiLabel label = dataSet.getMultiLabels()[i];
        MultiLabel prediction = boosting.predict(featureRow);
        //            System.out.println("prediction="+prediction);
        if (!label.equals(prediction)) {
            System.out.println(i);
            System.out.println("label=" + label);
            System.out.println("prediction=" + prediction);
        }
    }
    System.out.println("accuracy");
    System.out.println(Accuracy.accuracy(boosting, dataSet));
    System.out.println("overlap = " + Overlap.overlap(boosting, dataSet));
    boosting.serialize(new File(TMP, "/imlgb/boosting.ser"));
}

Example 65 with StopWatch

use of org.apache.commons.lang3.time.StopWatch in project pyramid by cheng-li.

the class IMLLogisticRegressionTest method test1.

static void test1() throws Exception {
    MultiLabelClfDataSet dataSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "ohsumed/3/train.trec"), DataSetType.ML_CLF_SPARSE, true);
    MultiLabelClfDataSet testSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "ohsumed/3/test.trec"), DataSetType.ML_CLF_SPARSE, true);
    List<MultiLabel> assignments = DataSetUtil.gatherMultiLabels(dataSet);
    IMLLogisticTrainer trainer = IMLLogisticTrainer.getBuilder().setEpsilon(0.01).setGaussianPriorVariance(1).setHistory(5).build();
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    IMLLogisticRegression logisticRegression = trainer.train(dataSet, assignments);
    System.out.println(stopWatch);
    System.out.println("training accuracy=" + Accuracy.accuracy(logisticRegression, dataSet));
    System.out.println("training overlap = " + Overlap.overlap(logisticRegression, dataSet));
    System.out.println("test accuracy=" + Accuracy.accuracy(logisticRegression, testSet));
    System.out.println("test overlap = " + Overlap.overlap(logisticRegression, testSet));
}