Examples with RegTreeConfig edu.neu.ccs.pyramid.regression.regression_tree.RegTreeConfig used on opensource projects

Example 16 with RegTreeConfig

use of edu.neu.ccs.pyramid.regression.regression_tree.RegTreeConfig in project pyramid by cheng-li.

the class Calibration method trainCalibrator.

private static LSBoost trainCalibrator(RegDataSet calib, RegDataSet valid, int[] monotonicity) {
    LSBoost lsBoost = new LSBoost();
    RegTreeConfig regTreeConfig = new RegTreeConfig().setMaxNumLeaves(10).setMinDataPerLeaf(5);
    RegTreeFactory regTreeFactory = new RegTreeFactory(regTreeConfig);
    LSBoostOptimizer optimizer = new LSBoostOptimizer(lsBoost, calib, regTreeFactory, calib.getLabels());
    if (true) {
        int[][] mono = new int[1][calib.getNumFeatures()];
        mono[0] = monotonicity;
        optimizer.setMonotonicity(mono);
    }
    optimizer.setShrinkage(0.1);
    optimizer.initialize();
    EarlyStopper earlyStopper = new EarlyStopper(EarlyStopper.Goal.MINIMIZE, 5);
    LSBoost bestModel = null;
    for (int i = 1; i < 1000; i++) {
        optimizer.iterate();
        if (i % 10 == 0) {
            double mse = MSE.mse(lsBoost, valid);
            earlyStopper.add(i, mse);
            if (earlyStopper.getBestIteration() == i) {
                try {
                    bestModel = (LSBoost) Serialization.deepCopy(lsBoost);
                } catch (IOException e) {
                    e.printStackTrace();
                } catch (ClassNotFoundException e) {
                    e.printStackTrace();
                }
            }
            if (earlyStopper.shouldStop()) {
                break;
            }
        }
    }
    return bestModel;
}

Example 17 with RegTreeConfig

use of edu.neu.ccs.pyramid.regression.regression_tree.RegTreeConfig in project pyramid by cheng-li.

the class GBRegressor method train.

private static void train(Config config) throws Exception {
    String sparsity = config.getString("input.matrixType");
    DataSetType dataSetType = null;
    switch(sparsity) {
        case "dense":
            dataSetType = DataSetType.REG_DENSE;
            break;
        case "sparse":
            dataSetType = DataSetType.REG_SPARSE;
            break;
        default:
            throw new IllegalArgumentException("input.matrixType should be dense or sparse");
    }
    RegDataSet trainSet = TRECFormat.loadRegDataSet(config.getString("input.trainData"), dataSetType, true);
    RegDataSet testSet = null;
    if (config.getBoolean("train.showTestProgress")) {
        testSet = TRECFormat.loadRegDataSet(config.getString("input.testData"), dataSetType, true);
    }
    LSBoost lsBoost = new LSBoost();
    RegTreeConfig regTreeConfig = new RegTreeConfig().setMaxNumLeaves(config.getInt("train.numLeaves"));
    RegTreeFactory regTreeFactory = new RegTreeFactory(regTreeConfig);
    LSBoostOptimizer optimizer = new LSBoostOptimizer(lsBoost, trainSet, regTreeFactory);
    optimizer.setShrinkage(config.getDouble("train.shrinkage"));
    optimizer.initialize();
    int progressInterval = config.getInt("train.showProgress.interval");
    int numIterations = config.getInt("train.numIterations");
    for (int i = 1; i <= numIterations; i++) {
        System.out.println("iteration " + i);
        optimizer.iterate();
        if (config.getBoolean("train.showTrainProgress") && (i % progressInterval == 0 || i == numIterations)) {
            System.out.println("training RMSE = " + RMSE.rmse(lsBoost, trainSet));
        }
        if (config.getBoolean("train.showTestProgress") && (i % progressInterval == 0 || i == numIterations)) {
            System.out.println("test RMSE = " + RMSE.rmse(lsBoost, testSet));
        }
    }
    System.out.println("training done!");
    String output = config.getString("output.folder");
    new File(output).mkdirs();
    File serializedModel = new File(output, "model");
    Serialization.serialize(lsBoost, serializedModel);
    System.out.println("model saved to " + serializedModel.getAbsolutePath());
    File reportFile = new File(output, "train_predictions.txt");
    report(lsBoost, trainSet, reportFile);
    System.out.println("predictions on the training set are written to " + reportFile.getAbsolutePath());
}

Example 18 with RegTreeConfig

use of edu.neu.ccs.pyramid.regression.regression_tree.RegTreeConfig in project pyramid by cheng-li.

the class RerankerTrainer method train.

public Reranker train(RegDataSet regDataSet, double[] instanceWeights, MultiLabelClassifier.ClassProbEstimator classProbEstimator, PredictionFeatureExtractor predictionFeatureExtractor, LabelCalibrator labelCalibrator, RegDataSet validation) {
    LSBoost lsBoost = new LSBoost();
    RegTreeConfig regTreeConfig = new RegTreeConfig().setMaxNumLeaves(numLeaves).setMinDataPerLeaf(minDataPerLeaf).setMonotonicityType(monotonicityType);
    RegTreeFactory regTreeFactory = new RegTreeFactory(regTreeConfig);
    LSBoostOptimizer optimizer = new LSBoostOptimizer(lsBoost, regDataSet, regTreeFactory, instanceWeights, regDataSet.getLabels());
    if (!monotonicityType.equals("none")) {
        int[][] mono = new int[1][regDataSet.getNumFeatures()];
        mono[0] = predictionFeatureExtractor.featureMonotonicity();
        optimizer.setMonotonicity(mono);
    }
    optimizer.setShrinkage(shrinkage);
    optimizer.initialize();
    EarlyStopper earlyStopper = new EarlyStopper(EarlyStopper.Goal.MINIMIZE, 5);
    LSBoost bestModel = null;
    for (int i = 1; i <= maxIter; i++) {
        optimizer.iterate();
        if (i % 10 == 0 || i == maxIter) {
            double mse = MSE.mse(lsBoost, validation);
            earlyStopper.add(i, mse);
            if (earlyStopper.getBestIteration() == i) {
                try {
                    bestModel = (LSBoost) Serialization.deepCopy(lsBoost);
                } catch (IOException e) {
                    e.printStackTrace();
                } catch (ClassNotFoundException e) {
                    e.printStackTrace();
                }
            }
            if (earlyStopper.shouldStop()) {
                break;
            }
        }
    }
    // System.out.println("best iteration = "+earlyStopper.getBestIteration());
    return new Reranker(bestModel, classProbEstimator, numCandidates, predictionFeatureExtractor, labelCalibrator);
}

Example 19 with RegTreeConfig

use of edu.neu.ccs.pyramid.regression.regression_tree.RegTreeConfig in project pyramid by cheng-li.

the class HMLGBTrainer method fitClassK.

/**
 * parallel
 * find the best regression tree for class k
 * apply newton step and learning rate
 * @param k class index
 * @return regressionTreeLk, shrunk
 * @throws Exception
 */
private RegressionTree fitClassK(int k) {
    double[] gradients = gradientMatrix.getGradientsForClass(k);
    int numClasses = this.config.getDataSet().getNumClasses();
    double learningRate = this.config.getLearningRate();
    LeafOutputCalculator leafOutputCalculator = new HMLGBLeafOutputCalculator(numClasses);
    RegTreeConfig regTreeConfig = new RegTreeConfig();
    regTreeConfig.setMaxNumLeaves(this.config.getNumLeaves());
    regTreeConfig.setMinDataPerLeaf(this.config.getMinDataPerLeaf());
    regTreeConfig.setNumSplitIntervals(this.config.getNumSplitIntervals());
    RegressionTree regressionTree = RegTreeTrainer.fit(regTreeConfig, this.config.getDataSet(), gradients, leafOutputCalculator);
    regressionTree.shrink(learningRate);
    return regressionTree;
}

Example 20 with RegTreeConfig

use of edu.neu.ccs.pyramid.regression.regression_tree.RegTreeConfig 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);
    }
}