public LearningPipelineDebugProxy(LearningPipeline pipeline)
{
if (pipeline == null)
{
throw new ArgumentNullException(nameof(pipeline));
}
_pipeline = new LearningPipeline();
// use a ConcurrencyFactor of 1 so other threads don't need to run in the debugger
_environment = new MLContext(conc: 1);
foreach (ILearningPipelineItem item in pipeline)
{
_pipeline.Add(item);
if (item is ILearningPipelineLoader loaderItem)
{
// add a take filter to any loaders, so it returns in a reasonable
// amount of time
_pipeline.Add(new RowTakeFilter()
{
Count = MaxLoaderRows
});
}
}
}
public void TestEP_Q_KMeansEntryPointAPI_04()
{
var iris = FileHelper.GetTestFile("iris.txt");
var pipeline = new Legacy.LearningPipeline();
pipeline.Add(new Legacy.Data.TextLoader(iris).CreateFrom <IrisObservation>(separator: '\t', useHeader: true));
pipeline.Add(new Legacy.Transforms.ColumnConcatenator("Features", "Sepal_length", "Sepal_width"));
pipeline.Add(new Legacy.Trainers.KMeansPlusPlusClusterer());
var model = pipeline.Train <IrisObservation, IrisPrediction>();
var obs = new IrisObservation()
{
Sepal_length = 3.3f,
Sepal_width = 1.6f,
Petal_length = 0.2f,
Petal_width = 5.1f,
};
var predictions = model.Predict(obs);
Assert.IsTrue(predictions.PredictedLabel != 0);
}