private static IPredictor TrainCore(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, string name, RoleMappedData validData,
ICalibratorTrainer calibrator, int maxCalibrationExamples, bool?cacheData, IPredictor inputPredictor = null)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ch, nameof(ch));
ch.CheckValue(data, nameof(data));
ch.CheckValue(trainer, nameof(trainer));
ch.CheckNonEmpty(name, nameof(name));
ch.CheckValueOrNull(validData);
ch.CheckValueOrNull(inputPredictor);
AddCacheIfWanted(env, ch, trainer, ref data, cacheData);
ch.Trace("Training");
if (validData != null)
{
AddCacheIfWanted(env, ch, trainer, ref validData, cacheData);
}
if (inputPredictor != null && !trainer.Info.SupportsIncrementalTraining)
{
ch.Warning("Ignoring " + nameof(TrainCommand.Arguments.InputModelFile) +
": Trainer does not support incremental training.");
inputPredictor = null;
}
ch.Assert(validData == null || trainer.Info.SupportsValidation);
var predictor = trainer.Train(new TrainContext(data, validData, inputPredictor));
return(CalibratorUtils.TrainCalibratorIfNeeded(env, ch, calibrator, maxCalibrationExamples, trainer, predictor, data));
}
private static IPredictor TrainCore(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, string name, RoleMappedData validData,
ICalibratorTrainer calibrator, int maxCalibrationExamples, bool?cacheData, IPredictor inpPredictor = null)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ch, nameof(ch));
ch.CheckValue(data, nameof(data));
ch.CheckValue(trainer, nameof(trainer));
ch.CheckNonEmpty(name, nameof(name));
ch.CheckValueOrNull(validData);
ch.CheckValueOrNull(inpPredictor);
var trainerRmd = trainer as ITrainer <RoleMappedData>;
if (trainerRmd == null)
{
throw ch.ExceptUserArg(nameof(TrainCommand.Arguments.Trainer), "Trainer '{0}' does not accept known training data type", name);
}
Action <IChannel, ITrainer, Action <object>, object, object, object> trainCoreAction = TrainCore;
IPredictor predictor;
AddCacheIfWanted(env, ch, trainer, ref data, cacheData);
ch.Trace("Training");
if (validData != null)
{
AddCacheIfWanted(env, ch, trainer, ref validData, cacheData);
}
var genericExam = trainCoreAction.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(
typeof(RoleMappedData),
inpPredictor != null ? inpPredictor.GetType() : typeof(IPredictor));
Action <RoleMappedData> trainExam = trainerRmd.Train;
genericExam.Invoke(null, new object[] { ch, trainerRmd, trainExam, data, validData, inpPredictor });
ch.Trace("Constructing predictor");
predictor = trainerRmd.CreatePredictor();
return(CalibratorUtils.TrainCalibratorIfNeeded(env, ch, calibrator, maxCalibrationExamples, trainer, predictor, data));
}
/// <summary>
/// Trains a model.
/// </summary>
/// <param name="env">host</param>
/// <param name="ch">channel</param>
/// <param name="data">traing data</param>
/// <param name="validData">validation data</param>
/// <param name="calibrator">calibrator</param>
/// <param name="maxCalibrationExamples">number of examples used to calibrate</param>
/// <param name="cacheData">cache training data</param>
/// <param name="inputPredictor">for continuous training, initial state</param>
/// <returns>predictor</returns>
public IPredictor Train(IHostEnvironment env, IChannel ch, RoleMappedData data, RoleMappedData validData = null,
ICalibratorTrainer calibrator = null, int maxCalibrationExamples = 0,
bool?cacheData = null, IPredictor inputPredictor = null)
{
/*
* return TrainUtils.Train(env, ch, data, Trainer, LoadName, validData, calibrator, maxCalibrationExamples,
* cacheData, inpPredictor);
*/
var trainer = Trainer;
var name = LoadName;
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ch, nameof(ch));
ch.CheckValue(data, nameof(data));
ch.CheckValue(trainer, nameof(trainer));
ch.CheckNonEmpty(name, nameof(name));
ch.CheckValueOrNull(validData);
ch.CheckValueOrNull(inputPredictor);
AddCacheIfWanted(env, ch, trainer, ref data, cacheData);
ch.Trace(MessageSensitivity.None, "Training");
if (validData != null)
{
AddCacheIfWanted(env, ch, trainer, ref validData, cacheData);
}
if (inputPredictor != null && !trainer.Info.SupportsIncrementalTraining)
{
ch.Warning(MessageSensitivity.None, "Ignoring " + nameof(TrainCommand.Arguments.InputModelFile) +
": Trainer does not support incremental training.");
inputPredictor = null;
}
ch.Assert(validData == null || trainer.Info.SupportsValidation);
var predictor = trainer.Train(new TrainContext(data, validData, null, inputPredictor));
return(CalibratorUtils.TrainCalibratorIfNeeded(env, ch, calibrator, maxCalibrationExamples, trainer, predictor, data));
}