public static void Run()
{
// load data
var context = new MLContext();
var columnInference = context.Data.InferColumns(TrainDataPath, Label, true);
var textLoader = context.Data.CreateTextReader(columnInference);
var data = textLoader.Read(TrainDataPath);
// get trainers & transforms
var transforms = TransformInferenceApi.InferTransforms(context, data, Label);
var availableTrainers = RecipeInference.AllowedTrainers(context, TaskKind.BinaryClassification, 4);
// get next pipeline loop
var history = new List <PipelineRunResult>();
for (var i = 0; i < 100; i++)
{
// get next pipeline
var pipeline = PipelineSuggester.GetNextPipeline(history, transforms, availableTrainers);
if (pipeline == null)
{
break;
}
Console.WriteLine($"{i}\t{pipeline}");
// mock pipeline run
var pipelineScore = AutoMlUtils.Random.NextDouble();
var result = new PipelineRunResult(null, null, pipeline, pipelineScore, null);
history.Add(result);
}
Console.ReadLine();
}
private static void TransformPostTrainerInferenceTestCore(
TaskKind task,
DatasetColumnInfo[] columns,
string expectedJson)
{
var transforms = TransformInferenceApi.InferTransformsPostTrainer(new MLContext(1), task, columns);
var pipelineNodes = transforms.Select(t => t.PipelineNode);
Util.AssertObjectMatchesJson(expectedJson, pipelineNodes);
}
private static void TransformInferenceTestCore(
DatasetColumnInfo[] columns,
string expectedJson,
TaskKind task = TaskKind.BinaryClassification)
{
var transforms = TransformInferenceApi.InferTransforms(new MLContext(), task, columns);
TestApplyTransformsToRealDataView(transforms, columns);
var pipelineNodes = transforms.Select(t => t.PipelineNode);
Util.AssertObjectMatchesJson(expectedJson, pipelineNodes);
}
