use of edu.neu.ccs.pyramid.regression.least_squares_boost.LSBoostOptimizer 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());
}
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