/// <summary>
/// Given a predictor type returns a set of all permissible learners (with their sweeper params, if defined).
/// </summary>
/// <returns>Array of viable learners.</returns>
public static SuggestedRecipe.SuggestedLearner[] AllowedLearners(IHostEnvironment env, MacroUtils.TrainerKinds trainerKind)
{
//not all learners advertised in the API are available in CORE.
var catalog = ModuleCatalog.CreateInstance(env);
var availableLearnersList = catalog.AllEntryPoints().Where(
x => x.InputKinds?.FirstOrDefault(i => i == typeof(CommonInputs.ITrainerInput)) != null);
var learners = new List <SuggestedRecipe.SuggestedLearner>();
var type = typeof(CommonInputs.ITrainerInput);
var trainerTypes = typeof(Experiment).Assembly.GetTypes()
.Where(p => type.IsAssignableFrom(p) &&
MacroUtils.IsTrainerOfKind(p, trainerKind));
foreach (var tt in trainerTypes)
{
var sweepParams = AutoMlUtils.GetSweepRanges(tt);
var epInputObj = (CommonInputs.ITrainerInput)tt.GetConstructor(Type.EmptyTypes)?.Invoke(new object[] { });
var sl = new SuggestedRecipe.SuggestedLearner
{
PipelineNode = new TrainerPipelineNode(epInputObj, sweepParams),
LearnerName = tt.Name
};
if (sl.PipelineNode != null && availableLearnersList.FirstOrDefault(l => l.Name.Equals(sl.PipelineNode.GetEpName())) != null)
{
learners.Add(sl);
}
}
return(learners.ToArray());
}
protected override IEnumerable <SuggestedRecipe> ApplyCore(Type predictorType,
TransformInference.SuggestedTransform[] transforms)
{
SuggestedRecipe.SuggestedLearner learner = new SuggestedRecipe.SuggestedLearner();
learner.LoadableClassInfo =
ComponentCatalog.GetLoadableClassInfo <SignatureTrainer>(Learners.MultiClassNaiveBayesTrainer.LoadName);
learner.Settings = "";
var epInput = new Legacy.Trainers.NaiveBayesClassifier();
learner.PipelineNode = new TrainerPipelineNode(epInput);
yield return(new SuggestedRecipe(ToString(), transforms, new[] { learner }));
}
protected override IEnumerable <SuggestedRecipe> ApplyCore(Type predictorType,
TransformInference.SuggestedTransform[] transforms)
{
SuggestedRecipe.SuggestedLearner learner = new SuggestedRecipe.SuggestedLearner();
if (predictorType == typeof(SignatureMultiClassClassifierTrainer))
{
learner.LoadableClassInfo =
ComponentCatalog.GetLoadableClassInfo <SignatureTrainer>(Learners.SdcaMultiClassTrainer.LoadNameValue);
}
else
{
learner.LoadableClassInfo =
ComponentCatalog.GetLoadableClassInfo <SignatureTrainer>(Learners.LinearClassificationTrainer.LoadNameValue);
var epInput = new Legacy.Trainers.StochasticDualCoordinateAscentBinaryClassifier();
learner.PipelineNode = new TrainerPipelineNode(epInput);
}
learner.Settings = "";
yield return(new SuggestedRecipe(ToString(), transforms, new[] { learner }));
}
protected override IEnumerable <SuggestedRecipe> ApplyCore(Type predictorType,
TransformInference.SuggestedTransform[] transforms)
{
SuggestedRecipe.SuggestedLearner learner = new SuggestedRecipe.SuggestedLearner();
if (predictorType == typeof(SignatureMultiClassClassifierTrainer))
{
learner.LoadableClassInfo = Host.ComponentCatalog.GetLoadableClassInfo <SignatureTrainer>("OVA");
learner.Settings = "p=FastTreeBinaryClassification";
}
else
{
learner.LoadableClassInfo =
Host.ComponentCatalog.GetLoadableClassInfo <SignatureTrainer>(FastTreeBinaryClassificationTrainer.LoadNameValue);
learner.Settings = "";
var epInput = new Legacy.Trainers.FastTreeBinaryClassifier();
learner.PipelineNode = new TrainerPipelineNode(epInput);
}
yield return(new SuggestedRecipe(ToString(), transforms, new[] { learner }));
}
protected override IEnumerable <SuggestedRecipe> ApplyCore(Type predictorType,
TransformInference.SuggestedTransform[] transforms)
{
SuggestedRecipe.SuggestedLearner learner = new SuggestedRecipe.SuggestedLearner();
if (predictorType == typeof(SignatureMultiClassClassifierTrainer))
{
learner.LoadableClassInfo = ComponentCatalog.GetLoadableClassInfo <SignatureTrainer>("OVA");
learner.Settings = "p=AveragedPerceptron{iter=10}";
}
else
{
learner.LoadableClassInfo = ComponentCatalog.GetLoadableClassInfo <SignatureTrainer>(Learners.AveragedPerceptronTrainer.LoadNameValue);
learner.Settings = "iter=10";
var epInput = new Legacy.Trainers.AveragedPerceptronBinaryClassifier
{
NumIterations = 10
};
learner.PipelineNode = new TrainerPipelineNode(epInput);
}
yield return
(new SuggestedRecipe(ToString(), transforms, new[] { learner }, Int32.MaxValue));
}
