private static void TrainCore(IHost host, IDataView input, Arguments args, ref VBuffer <Single> scores)
{
Contracts.AssertValue(host);
host.AssertValue(args);
host.AssertValue(input);
host.Assert(args.Threshold.HasValue != args.NumSlotsToKeep.HasValue);
using (var ch = host.Start("Train"))
{
ch.Trace("Constructing trainer");
ITrainer trainer = args.Filter.CreateComponent(host);
IDataView view = input;
ISchema schema = view.Schema;
var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(args.LabelColumn), args.LabelColumn, DefaultColumnNames.Label);
var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(args.FeatureColumn), args.FeatureColumn, DefaultColumnNames.Features);
var group = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(args.GroupColumn), args.GroupColumn, DefaultColumnNames.GroupId);
var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(args.WeightColumn), args.WeightColumn, DefaultColumnNames.Weight);
var name = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(args.NameColumn), args.NameColumn, DefaultColumnNames.Name);
TrainUtils.AddNormalizerIfNeeded(host, ch, trainer, ref view, feature, args.NormalizeFeatures);
ch.Trace("Binding columns");
var customCols = TrainUtils.CheckAndGenerateCustomColumns(ch, args.CustomColumn);
var data = new RoleMappedData(view, label, feature, group, weight, name, customCols);
var predictor = TrainUtils.Train(host, ch, data, trainer, null,
null, 0, args.CacheData);
var rfs = predictor as IPredictorWithFeatureWeights <Single>;
Contracts.AssertValue(rfs);
rfs.GetFeatureWeights(ref scores);
}
}
public static TOut Train <TArg, TOut>(IHost host, TArg input,
Func <ITrainer> createTrainer,
Func <string> getLabel = null,
Func <string> getWeight = null,
Func <string> getGroup = null,
Func <string> getName = null,
Func <IEnumerable <KeyValuePair <RoleMappedSchema.ColumnRole, string> > > getCustom = null,
ICalibratorTrainerFactory calibrator = null,
int maxCalibrationExamples = 0)
where TArg : LearnerInputBase
where TOut : CommonOutputs.TrainerOutput, new()
{
using (var ch = host.Start("Training"))
{
var schema = input.TrainingData.Schema;
var feature = FindColumn(ch, schema, input.FeatureColumn);
var label = getLabel?.Invoke();
var weight = getWeight?.Invoke();
var group = getGroup?.Invoke();
var name = getName?.Invoke();
var custom = getCustom?.Invoke();
var trainer = createTrainer();
IDataView view = input.TrainingData;
TrainUtils.AddNormalizerIfNeeded(host, ch, trainer, ref view, feature, input.NormalizeFeatures);
ch.Trace("Binding columns");
var roleMappedData = new RoleMappedData(view, label, feature, group, weight, name, custom);
RoleMappedData cachedRoleMappedData = roleMappedData;
Cache.CachingType?cachingType = null;
switch (input.Caching)
{
case CachingOptions.Memory:
{
cachingType = Cache.CachingType.Memory;
break;
}
case CachingOptions.Disk:
{
cachingType = Cache.CachingType.Disk;
break;
}
case CachingOptions.Auto:
{
// REVIEW: we should switch to hybrid caching in future.
if (!(input.TrainingData is BinaryLoader) && trainer.Info.WantCaching)
{
// default to Memory so mml is on par with maml
cachingType = Cache.CachingType.Memory;
}
break;
}
case CachingOptions.None:
break;
default:
throw ch.ExceptParam(nameof(input.Caching), "Unknown option for caching: '{0}'", input.Caching);
}
if (cachingType.HasValue)
{
var cacheView = Cache.CacheData(host, new Cache.CacheInput()
{
Data = roleMappedData.Data,
Caching = cachingType.Value
}).OutputData;
cachedRoleMappedData = new RoleMappedData(cacheView, roleMappedData.Schema.GetColumnRoleNames());
}
var predictor = TrainUtils.Train(host, ch, cachedRoleMappedData, trainer, calibrator, maxCalibrationExamples);
return(new TOut()
{
PredictorModel = new PredictorModelImpl(host, roleMappedData, input.TrainingData, predictor)
});
}
}