public static CommonOutputs.AnomalyDetectionOutput CreateAnomalyPipelineEnsemble(IHostEnvironment env, PipelineAnomalyInput input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("CombineModels");
host.CheckValue(input, nameof(input));
host.CheckNonEmpty(input.Models, nameof(input.Models));
IRegressionOutputCombiner combiner;
switch (input.ModelCombiner)
{
case ScoreCombiner.Median:
combiner = new Median(host);
break;
case ScoreCombiner.Average:
combiner = new Average(host);
break;
default:
throw host.Except("Unknown combiner kind");
}
var ensemble = SchemaBindablePipelineEnsembleBase.Create(host, input.Models, combiner, MetadataUtils.Const.ScoreColumnKind.AnomalyDetection);
return(CreatePipelineEnsemble <CommonOutputs.AnomalyDetectionOutput>(host, input.Models, ensemble));
}
public static CommonOutputs.MulticlassClassificationOutput CreateMultiClassPipelineEnsemble(IHostEnvironment env, PipelineClassifierInput input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("CombineModels");
host.CheckValue(input, nameof(input));
host.CheckNonEmpty(input.Models, nameof(input.Models));
IOutputCombiner <VBuffer <Single> > combiner;
switch (input.ModelCombiner)
{
case ClassifierCombiner.Median:
combiner = new MultiMedian(host, new MultiMedian.Arguments()
{
Normalize = true
});
break;
case ClassifierCombiner.Average:
combiner = new MultiAverage(host, new MultiAverage.Arguments()
{
Normalize = true
});
break;
case ClassifierCombiner.Vote:
combiner = new MultiVoting(host);
break;
default:
throw host.Except("Unknown combiner kind");
}
var ensemble = SchemaBindablePipelineEnsembleBase.Create(host, input.Models, combiner, MetadataUtils.Const.ScoreColumnKind.MultiClassClassification);
return(CreatePipelineEnsemble <CommonOutputs.MulticlassClassificationOutput>(host, input.Models, ensemble));
}
private static TOut CreatePipelineEnsemble <TOut>(IHostEnvironment env, IPredictorModel[] predictors, SchemaBindablePipelineEnsembleBase ensemble)
where TOut : CommonOutputs.TrainerOutput, new()
{
var inputSchema = predictors[0].TransformModel.InputSchema;
var dv = new EmptyDataView(env, inputSchema);
// The role mappings are specific to the individual predictors.
var rmd = new RoleMappedData(dv);
var predictorModel = new PredictorModel(env, rmd, dv, ensemble);
var output = new TOut {
PredictorModel = predictorModel
};
return(output);
}
