public Predictors <TOutput> Train(Datasets data
, Func <int, double> learningRateSchedule = null // optional: learning rate as a function of iteration (zero-based)
)
{
// For multi class, the number of labels is required.
if (!(PredictionKind != PredictionKind.MultiClassClassification || Objective.NumClass > 1))
{
throw new Exception("LightGBM requires the number of classes to be specified in the parameters for multi-class classification.");
}
if (PredictionKind == PredictionKind.Ranking)
{
if (data.Training.GetGroups() == null)
{
throw new Exception("Require Groups training data for ObjectiveType.LambdaRank");
}
if (data.Validation != null && data.Validation.GetGroups() == null)
{
throw new Exception("Require Groups validation data for ObjectiveType.LambdaRank");
}
}
TrainMetrics.Clear();
ValidMetrics.Clear();
Booster?.Dispose();
Booster = null;
Datasets = data;
var args = GetParameters(data);
Booster = Train(args, data.Training, data.Validation, TrainMetrics, ValidMetrics, learningRateSchedule);
(var model, var argsout) = Booster.GetModel();
TrainedEnsemble = model;
FeatureCount = data.Training.NumFeatures;
// check parameter strings
if (learningRateSchedule != null)
{
argsout.Learning.LearningRate = args.Learning.LearningRate;
}
var strIn = args.ToString();
var strOut = argsout.ToString();
if (strIn != strOut)
{
throw new Exception($"Parameters differ:\n{strIn}\n{strOut}");
}
var managed = CreateManagedPredictor();
var native = CreateNativePredictor();
return(new Predictors <TOutput>(managed, native));
}
private Parameters GetParameters(Datasets data)
{
var args = new Parameters
{
Common = data.Common,
Dataset = data.Dataset,
Objective = Objective,
Learning = Learning
};
return(args);
}
/// <summary>
/// Generates files that can be used to run training with lightgbm.exe.
/// - train.conf: contains training parameters
/// - train.bin: training data
/// - valid.bin: validation data (if provided)
/// Command line: lightgbm.exe config=train.conf
/// </summary>
/// <param name="data"></param>
public void ToCommandLineFiles(Datasets data, string destinationDir = @"c:\temp")
{
var pms = GetParameters(data);
var kvs = pms.ToDict();
kvs.Add("output_model", Path.Combine(destinationDir, "LightGBM_model.txt"));
var datafile = Path.Combine(destinationDir, "train.bin");
if (File.Exists(datafile))
{
File.Delete(datafile);
}
data.Training.SaveBinary(datafile);
kvs.Add("data", datafile);
if (data.Validation != null)
{
datafile = Path.Combine(destinationDir, "valid.bin");
if (File.Exists(datafile))
{
File.Delete(datafile);
}
data.Validation.SaveBinary(datafile);
kvs.Add("valid", datafile);
}
using (var file = new StreamWriter(Path.Combine(destinationDir, "train.conf")))
{
foreach (var kv in kvs)
{
file.WriteLine($"{kv.Key} = {kv.Value}");
}
}
}
