// Returns true if a normalizer was added. public static bool AddNormalizerIfNeeded(IHostEnvironment env, IChannel ch, ITrainer trainer, ref IDataView view, string featureColumn, NormalizeOption autoNorm) { Contracts.CheckValue(env, nameof(env)); env.CheckValue(ch, nameof(ch)); ch.CheckValue(trainer, nameof(trainer)); ch.CheckValue(view, nameof(view)); ch.CheckValueOrNull(featureColumn); ch.CheckUserArg(Enum.IsDefined(typeof(NormalizeOption), autoNorm), nameof(TrainCommand.Arguments.NormalizeFeatures), "Normalize option is invalid. Specify one of 'norm=No', 'norm=Warn', 'norm=Auto', or 'norm=Yes'."); if (autoNorm == NormalizeOption.No) { ch.Info("Not adding a normalizer."); return(false); } if (string.IsNullOrEmpty(featureColumn)) { return(false); } int featCol; var schema = view.Schema; if (schema.TryGetColumnIndex(featureColumn, out featCol)) { if (autoNorm != NormalizeOption.Yes) { DvBool isNormalized = DvBool.False; if (!trainer.Info.NeedNormalization || schema.IsNormalized(featCol)) { ch.Info("Not adding a normalizer."); return(false); } if (autoNorm == NormalizeOption.Warn) { ch.Warning("A normalizer is needed for this trainer. Either add a normalizing transform or use the 'norm=Auto', 'norm=Yes' or 'norm=No' options."); return(false); } } ch.Info("Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off."); IDataView ApplyNormalizer(IHostEnvironment innerEnv, IDataView input) => NormalizeTransform.CreateMinMaxNormalizer(innerEnv, input, featureColumn); if (view is IDataLoader loader) { view = CompositeDataLoader.ApplyTransform(env, loader, tag: null, creationArgs: null, ApplyNormalizer); } else { view = ApplyNormalizer(env, view); } return(true); } return(false); }
public static CommonOutputs.TransformOutput SupervisedBin(IHostEnvironment env, NormalizeTransform.SupervisedBinArguments input) { Contracts.CheckValue(env, nameof(env)); var host = env.Register("SupervisedBin"); host.CheckValue(input, nameof(input)); EntryPointUtils.CheckInputArgs(host, input); var xf = NormalizeTransform.Create(host, input, input.Data); return(new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }); }