优雅解读 LightGBM

import com.microsoft.ml.lightgbm.*;

public class LightGBMDemo {
    public static void main(String[] args) {
        // 创建数据集
        SWIGTYPE_p_void dataset = LGBM_DatasetCreateFromFile("data.txt", "max_bin=255");
        
        // 设置参数（关键参数示例）
        String params = "objective=regression metric=rmse num_leaves=31 learning_rate=0.05";
        
        // 训练模型
        SWIGTYPE_p_void booster = LGBM_BoosterCreate(dataset, params);
        for (int i = 0; i < 100; i++) {
            LGBM_BoosterUpdateOneIter(booster);
            double[] loss = new double[1];
            LGBM_BoosterGetEval(booster, 0, loss);
            System.out.println("Iteration "+i+" : loss = "+loss[0]);
        }
        
        // 预测示例
        long numRows = 1;
        float[] data = {0.5f, 1.3f, 4.9f};
        double[] result = new double[1];
        LGBM_BoosterPredictForMat(
            booster, data, numRows, data.length, 1, 0, 0, 50, result);
        System.out.println("Prediction: "+result[0]);
    }
}

// 内存优化示例：启用直方图内存复用
String params = "histogram_pool_size=2048 ...";
// 并行计算优化
String params = "num_threads=8 ...";