public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnvironment env, ModelOperations.CombineOvaPredictorModelsInput input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("CombineOvaModels");
host.CheckValue(input, nameof(input));
EntryPointUtils.CheckInputArgs(host, input);
host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));
// Something tells me we should put normalization as part of macro expansion, but since i get
// subgraph instead of learner it's a bit tricky to get learner and decide should we add
// normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
using (var ch = host.Start("CombineOvaModels"))
{
ISchema schema = normalizedView.Schema;
var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
input.LabelColumn,
DefaultColumnNames.Label);
var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
input.FeatureColumn, DefaultColumnNames.Features);
var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
input.WeightColumn, DefaultColumnNames.Weight);
var data = new RoleMappedData(normalizedView, label, feature, null, weight);
return(new ModelOperations.PredictorModelOutput
{
PredictorModel = new PredictorModel(env, data, input.TrainingData,
Create(host, input.UseProbabilities,
input.ModelArray.Select(p => p.Predictor as IPredictorProducing <float>).ToArray()))
});
}
}
public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnvironment env, ModelOperations.CombineOvaPredictorModelsInput input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("CombineOvaModels");
host.CheckValue(input, nameof(input));
EntryPointUtils.CheckInputArgs(host, input);
host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));
using (var ch = host.Start("CombineOvaModels"))
{
ISchema schema = input.TrainingData.Schema;
var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
input.LabelColumn,
DefaultColumnNames.Label);
var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
input.FeatureColumn, DefaultColumnNames.Features);
var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
input.WeightColumn, DefaultColumnNames.Weight);
var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
return(new ModelOperations.PredictorModelOutput
{
PredictorModel = new PredictorModel(env, data, input.TrainingData,
Create(host, input.UseProbabilities,
input.ModelArray.Select(p => p.Predictor as IPredictorProducing <float>).ToArray()))
});
}
}
