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));
}
public void TestOvaMacroWithUncalibratedLearner()
{
var dataPath = GetDataPath(@"iris.txt");
var env = new MLContext(42);
// Specify subgraph for OVA
var subGraph = env.CreateExperiment();
var learnerInput = new Legacy.Trainers.AveragedPerceptronBinaryClassifier {
Shuffle = false
};
var learnerOutput = subGraph.Add(learnerInput);
// Create pipeline with OVA and multiclass scoring.
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
importInput.Arguments.Column = new TextLoaderColumn[]
{
new TextLoaderColumn {
Name = "Label", Source = new[] { new TextLoaderRange(0) }
},
new TextLoaderColumn {
Name = "Features", Source = new[] { new TextLoaderRange(1, 4) }
}
};
var importOutput = experiment.Add(importInput);
var oneVersusAll = new Legacy.Models.OneVersusAll
{
TrainingData = importOutput.Data,
Nodes = subGraph,
UseProbabilities = true,
};
var ovaOutput = experiment.Add(oneVersusAll);
var scoreInput = new Legacy.Transforms.DatasetScorer
{
Data = importOutput.Data,
PredictorModel = ovaOutput.PredictorModel
};
var scoreOutput = experiment.Add(scoreInput);
var evalInput = new Legacy.Models.ClassificationEvaluator
{
Data = scoreOutput.ScoredData
};
var evalOutput = experiment.Add(evalInput);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(evalOutput.OverallMetrics);
var schema = data.Schema;
var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == accCol))
{
var getter = cursor.GetGetter <double>(accCol);
b = cursor.MoveNext();
Assert.True(b);
double acc = 0;
getter(ref acc);
Assert.Equal(0.71, acc, 2);
b = cursor.MoveNext();
Assert.False(b);
}
}
