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, AnnotationUtils.Const.ScoreColumnKind.AnomalyDetection);
return(CreatePipelineEnsemble <CommonOutputs.AnomalyDetectionOutput>(host, input.Models, ensemble));
}
public static CommonOutputs.BinaryClassificationOutput CreateBinaryPipelineEnsemble(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));
IBinaryOutputCombiner combiner;
switch (input.ModelCombiner)
{
case ClassifierCombiner.Median:
combiner = new Median(host);
break;
case ClassifierCombiner.Average:
combiner = new Average(host);
break;
case ClassifierCombiner.Vote:
combiner = new Voting(host);
break;
default:
throw host.Except("Unknown combiner kind");
}
var ensemble = SchemaBindablePipelineEnsembleBase.Create(host, input.Models, combiner, MetadataUtils.Const.ScoreColumnKind.BinaryClassification);
return(CreatePipelineEnsemble <CommonOutputs.BinaryClassificationOutput>(host, input.Models, ensemble));
}
private EnsembleDistributionModelParameters(IHostEnvironment env, ModelLoadContext ctx)
: base(env, RegistrationName, ctx)
{
PredictionKind = (PredictionKind)ctx.Reader.ReadInt32();
_probabilityCombiner = new Median(env);
_inputType = InitializeMappers(out _mappers);
ComputeAveragedWeights(out _averagedWeights);
}
/// <summary>
/// Instantiate new ensemble model from existing sub-models.
/// </summary>
/// <param name="env">The host environment.</param>
/// <param name="kind">The prediction kind <see cref="PredictionKind"/></param>
/// <param name="models">Array of sub-models that you want to ensemble together.</param>
/// <param name="combiner">The combiner class to use to ensemble the models.</param>
/// <param name="weights">The weights assigned to each model to be ensembled.</param>
internal EnsembleDistributionModelParameters(IHostEnvironment env, PredictionKind kind,
FeatureSubsetModel <float>[] models, IOutputCombiner <Single> combiner, Single[] weights = null)
: base(env, RegistrationName, models, combiner, weights)
{
PredictionKind = kind;
_probabilityCombiner = new Median(env);
_inputType = InitializeMappers(out _mappers);
ComputeAveragedWeights(out _averagedWeights);
}
