Examples with LSBoost edu.neu.ccs.pyramid.regression.least_squares_boost.LSBoost used on opensource projects

Example 1 with LSBoost

use of edu.neu.ccs.pyramid.regression.least_squares_boost.LSBoost in project pyramid by cheng-li.

the class GBRegressor method test.

private static void test(Config config) throws Exception {
    String output = config.getString("output.folder");
    File serializedModel = new File(output, "model");
    LSBoost lsBoost = (LSBoost) Serialization.deserialize(serializedModel);
    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 testSet = TRECFormat.loadRegDataSet(config.getString("input.testData"), dataSetType, true);
    System.out.println("test RMSE = " + RMSE.rmse(lsBoost, testSet));
    File reportFile = new File(output, "test_predictions.txt");
    report(lsBoost, testSet, reportFile);
    System.out.println("predictions on the test set are written to " + reportFile.getAbsolutePath());
}

Example 2 with LSBoost

use of edu.neu.ccs.pyramid.regression.least_squares_boost.LSBoost in project pyramid by cheng-li.

the class LSLogisticBoostTest method test3.

private static void test3() throws Exception {
    RegDataSet train = TRECFormat.loadRegDataSet("/Users/chengli/Downloads/spam/train", DataSetType.REG_DENSE, true);
    RegDataSet test = TRECFormat.loadRegDataSet("/Users/chengli/Downloads/spam/test", DataSetType.REG_DENSE, true);
    LSLogisticBoost lsLogisticBoost = new LSLogisticBoost();
    RegTreeConfig regTreeConfig = new RegTreeConfig().setMaxNumLeaves(10);
    RegTreeFactory regTreeFactory = new RegTreeFactory(regTreeConfig);
    LSLogisticBoostOptimizer optimizer = new LSLogisticBoostOptimizer(lsLogisticBoost, train, regTreeFactory);
    optimizer.setShrinkage(1);
    optimizer.initialize();
    for (int i = 0; i < 200; i++) {
        System.out.println("training rmse = " + RMSE.rmse(lsLogisticBoost, train));
        System.out.println("test rmse = " + RMSE.rmse(lsLogisticBoost, test));
        optimizer.iterate();
    }
    for (int i = 0; i < test.getNumDataPoints(); i++) {
        System.out.println(test.getLabels()[i] + " " + lsLogisticBoost.predict(test.getRow(i)));
    }
    System.out.println("********************** LSBOOST **************");
    LSBoost lsBoost = new LSBoost();
    LSBoostOptimizer lsBoostOptimizer = new LSBoostOptimizer(lsBoost, train, regTreeFactory);
    lsBoostOptimizer.setShrinkage(0.1);
    lsBoostOptimizer.initialize();
    for (int i = 0; i < 100; i++) {
        System.out.println("training rmse = " + RMSE.rmse(lsBoost, train));
        System.out.println("test rmse = " + RMSE.rmse(lsBoost, test));
        lsBoostOptimizer.iterate();
    }
    for (int i = 0; i < test.getNumDataPoints(); i++) {
        System.out.println(test.getLabels()[i] + " " + lsBoost.predict(test.getRow(i)));
    }
}

Example 3 with LSBoost

use of edu.neu.ccs.pyramid.regression.least_squares_boost.LSBoost in project pyramid by cheng-li.

the class LSLogisticBoostTest method test2.

private static void test2() throws Exception {
    RegDataSet train = TRECFormat.loadRegDataSet("/Users/chengli/Downloads/spam/train", DataSetType.REG_DENSE, true);
    RegDataSet test = TRECFormat.loadRegDataSet("/Users/chengli/Downloads/spam/test", DataSetType.REG_DENSE, true);
    for (int i = 0; i < train.getNumDataPoints(); i++) {
        if (train.getLabels()[i] == 0) {
            train.setLabel(i, -1);
        } else {
            train.setLabel(i, 2);
        }
    }
    for (int i = 0; i < test.getNumDataPoints(); i++) {
        if (test.getLabels()[i] == 0) {
            test.setLabel(i, -1);
        } else {
            test.setLabel(i, 2);
        }
    }
    LSLogisticBoost lsLogisticBoost = new LSLogisticBoost();
    RegTreeConfig regTreeConfig = new RegTreeConfig().setMaxNumLeaves(5);
    RegTreeFactory regTreeFactory = new RegTreeFactory(regTreeConfig);
    LSLogisticBoostOptimizer optimizer = new LSLogisticBoostOptimizer(lsLogisticBoost, train, regTreeFactory);
    optimizer.initialize();
    for (int i = 0; i < 100; i++) {
        System.out.println("training rmse = " + RMSE.rmse(lsLogisticBoost, train));
        System.out.println("test rmse = " + RMSE.rmse(lsLogisticBoost, test));
        optimizer.iterate();
    }
    for (int i = 0; i < test.getNumDataPoints(); i++) {
        System.out.println(test.getLabels()[i] + " " + lsLogisticBoost.predict(test.getRow(i)));
    }
    // System.out.println(Arrays.toString(lsLogisticBoost.predict(train)));
    LSBoost lsBoost = new LSBoost();
    LSBoostOptimizer lsBoostOptimizer = new LSBoostOptimizer(lsBoost, train, regTreeFactory);
    System.out.println("LSBOOST");
    lsBoostOptimizer.initialize();
    for (int i = 0; i < 100; i++) {
        System.out.println("training rmse = " + RMSE.rmse(lsBoost, train));
        System.out.println("test rmse = " + RMSE.rmse(lsBoost, test));
        lsBoostOptimizer.iterate();
    }
}

Example 4 with LSBoost

use of edu.neu.ccs.pyramid.regression.least_squares_boost.LSBoost in project pyramid by cheng-li.

the class GBRegressor method train.

private static void train(Config config, Logger logger) 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++) {
        logger.info("iteration " + i);
        optimizer.iterate();
        if (config.getBoolean("train.showTrainProgress") && (i % progressInterval == 0 || i == numIterations)) {
            logger.info("training RMSE = " + RMSE.rmse(lsBoost, trainSet));
        }
        if (config.getBoolean("train.showTestProgress") && (i % progressInterval == 0 || i == numIterations)) {
            logger.info("test RMSE = " + RMSE.rmse(lsBoost, testSet));
        }
    }
    logger.info("training done!");
    String output = config.getString("output.folder");
    new File(output).mkdirs();
    File serializedModel = new File(output, "model");
    Serialization.serialize(lsBoost, serializedModel);
    logger.info("model saved to " + serializedModel.getAbsolutePath());
    if (config.getBoolean("output.generatePMML")) {
        File pmmlModel = new File(output, "model.pmml");
        PMMLConverter.savePMML(lsBoost, pmmlModel);
        logger.info("PMML model saved to " + pmmlModel.getAbsolutePath());
    }
    String trainReportName = config.getString("output.trainReportFolderName");
    File reportFile = Paths.get(output, trainReportName, "train_predictions.txt").toFile();
    report(lsBoost, trainSet, reportFile);
    logger.info("predictions on the training set are written to " + reportFile.getAbsolutePath());
}

Example 5 with LSBoost

use of edu.neu.ccs.pyramid.regression.least_squares_boost.LSBoost 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;
}