IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input, out IDataView sourceCtx)
{
sourceCtx = input;
Contracts.CheckValue(env, "env");
env.CheckValue(args, "args");
env.CheckValue(input, "input");
env.CheckValue(args.tag, "tag is empty");
env.CheckValue(args.trainer, "trainer",
"Trainer cannot be null. If your model is already trained, please use ScoreTransform instead.");
var views = TagHelper.EnumerateTaggedView(true, input).Where(c => c.Item1 == args.tag);
if (views.Any())
{
throw env.Except("Tag '{0}' is already used.", args.tag);
}
var host = env.Register("TagTrainOrScoreTransform");
using (var ch = host.Start("Train"))
{
ch.Trace("Constructing trainer");
var trainerSett = ScikitSubComponent <ITrainer, SignatureTrainer> .AsSubComponent(args.trainer);
ITrainer trainer = trainerSett.CreateInstance(host);
var customCols = TrainUtils.CheckAndGenerateCustomColumns(env, args.CustomColumns);
string feat;
string group;
var data = CreateDataFromArgs(_host, ch, new OpaqueDataView(input), args, out feat, out group);
ICalibratorTrainer calibrator = args.calibrator == null
? null
: ScikitSubComponent <ICalibratorTrainer, SignatureCalibrator> .AsSubComponent(args.calibrator).CreateInstance(host);
var nameTrainer = args.trainer.ToString().Replace("{", "").Replace("}", "").Replace(" ", "").Replace("=", "").Replace("+", "Y").Replace("-", "N");
var extTrainer = new ExtendedTrainer(trainer, nameTrainer);
_predictor = extTrainer.Train(host, ch, data, null, calibrator, args.maxCalibrationExamples);
if (!string.IsNullOrEmpty(args.outputModel))
{
ch.Info("Saving model into '{0}'", args.outputModel);
using (var fs = File.Create(args.outputModel))
TrainUtils.SaveModel(env, ch, fs, _predictor, data);
ch.Info("Done.");
}
if (_cali != null)
{
throw ch.ExceptNotImpl("Calibrator is not implemented yet.");
}
ch.Trace("Scoring");
if (_args.scorer != null)
{
var mapper = new SchemaBindablePredictorWrapper(_predictor);
var roles = new RoleMappedSchema(input.Schema, null, feat, group: group);
var bound = (mapper as ISchemaBindableMapper).Bind(_host, roles);
var scorPars = ScikitSubComponent <IDataScorerTransform, SignatureDataScorer> .AsSubComponent(_args.scorer);
_scorer = scorPars.CreateInstance(_host, input, bound, roles);
}
else
{
_scorer = PredictorHelper.CreateDefaultScorer(_host, input, feat, group, _predictor);
}
ch.Info("Tagging with tag '{0}'.", args.tag);
var ar = new TagViewTransform.Arguments {
tag = args.tag
};
var res = new TagViewTransform(env, ar, _scorer, _predictor);
return(res);
}
}
public IEnumerable <Tuple <string, ITaggedDataView> > EnumerateTaggedView(bool recursive = true)
{
return(TagHelper.EnumerateTaggedView(recursive, this));
}
public TagTrainOrScoreTransform(IHost host, ModelLoadContext ctx, IDataView input) :
base(host, ctx, input, LoaderSignature)
{
_args = new Arguments();
_args.Read(ctx, _host);
bool hasPredictor = ctx.Reader.ReadByte() == 1;
bool hasCali = ctx.Reader.ReadByte() == 1;
bool hasScorer = ctx.Reader.ReadByte() == 1;
if (hasPredictor)
{
ctx.LoadModel <IPredictor, SignatureLoadModel>(host, out _predictor, "predictor");
}
else
{
_predictor = null;
}
using (var ch = _host.Start("TagTrainOrScoreTransform loading"))
{
var views = TagHelper.EnumerateTaggedView(true, input).Where(c => c.Item1 == _args.tag);
if (views.Any())
{
throw _host.Except("Tag '{0}' is already used.", _args.tag);
}
var customCols = TrainUtils.CheckAndGenerateCustomColumns(_host, _args.CustomColumns);
string feat;
string group;
var data = CreateDataFromArgs(_host, ch, new OpaqueDataView(input), _args, out feat, out group);
if (hasCali)
{
ctx.LoadModel <ICalibratorTrainer, SignatureLoadModel>(host, out _cali, "calibrator", _predictor);
}
else
{
_cali = null;
}
if (_cali != null)
{
throw ch.ExceptNotImpl("Calibrator is not implemented yet.");
}
if (hasScorer)
{
ctx.LoadModel <IDataScorerTransform, SignatureLoadDataTransform>(host, out _scorer, "scorer", data.Data);
}
else
{
_scorer = null;
}
ch.Info("Tagging with tag '{0}'.", _args.tag);
var ar = new TagViewTransform.Arguments {
tag = _args.tag
};
var res = new TagViewTransform(_host, ar, _scorer, _predictor);
_sourcePipe = res;
}
}