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)); }