protected override bool ReleaseHandle()
{
return(BoosterMethods.LGBM_BoosterFree(this.handle) == 0);
}
static void Main(string[] args)
{
string model_path = "../../model/regression_model.txt";
string data_path = "../../../data/regression/regression.test";
string out_filepath = "prediction_result_cs.csv";
string parameters = "";
int ret;
var sw = new System.Diagnostics.Stopwatch();
sw.Start();
ret = BoosterMethods.LGBM_BoosterCreateFromModelfile(model_path, out int num_models, out BoosterHandle booster);
sw.Stop();
if (ret != 0)
{
var err = Marshal.PtrToStringAnsi(NativeMethods.LGBM_GetLastError());
throw new Exception(err);
}
else
{
Console.WriteLine($"Successfully load model from {model_path}");
Console.WriteLine($"Elapsed {sw.ElapsedMilliseconds}ms");
}
DatasetHandle reference = null;
reference = reference ?? DatasetHandle.Zero;
ret = DatasetMethods.LGBM_DatasetCreateFromFile(data_path, parameters, reference, out DatasetHandle x_test);
if (ret != 0)
{
var err = Marshal.PtrToStringAnsi(NativeMethods.LGBM_GetLastError());
throw new Exception(err);
}
else
{
Console.WriteLine($"Successfully load data from {data_path}");
//Console.WriteLine($"Elapsed {sw.ElapsedMilliseconds}ms");
}
DatasetMethods.LGBM_DatasetGetNumData(x_test, out int num_rows);
DatasetMethods.LGBM_DatasetGetNumFeature(x_test, out int num_cols);
BoosterMethods.LGBM_BoosterCalcNumPredict(booster, num_rows, LGBMPredictType.PredictNormal, num_models, out int num_predicts);
var result = new double[num_predicts];
sw.Restart();
ret = BoosterMethods.LGBM_BoosterPredictForFile(booster, data_path, false, LGBMPredictType.PredictNormal, num_models, parameters, out_filepath);
sw.Stop();
if (ret != 0)
{
var err = Marshal.PtrToStringAnsi(NativeMethods.LGBM_GetLastError());
throw new Exception(err);
}
else
{
Console.WriteLine($"Successfully predict(from file) result is saved to {out_filepath}");
Console.WriteLine($"Elapsed {sw.ElapsedMilliseconds}ms");
}
sw.Restart();
ret = BoosterMethods.LGBM_BoosterPredictForMat(booster, x_test.DangerousGetHandle(), LGBMDataType.Float32, num_rows, num_cols,
0, LGBMPredictType.PredictNormal, num_models, parameters,
out int outputlen, result);
sw.Stop();
if (ret != 0)
{
var err = Marshal.PtrToStringAnsi(NativeMethods.LGBM_GetLastError());
throw new Exception(err);
}
else
{
Console.WriteLine($"Successfully predict(from mat)");
Console.WriteLine($"Elapsed {sw.ElapsedMilliseconds}ms");
}
Console.ReadLine();
booster.Close();
x_test.Close();
}