public void TrainAndEvaluateRanking()
{
var mlContext = new MLContext(seed: 1, conc: 1);
var data = Iris.LoadAsRankingProblem(mlContext,
GetDataPath(TestDatasets.iris.trainFilename),
hasHeader: TestDatasets.iris.fileHasHeader,
separatorChar: TestDatasets.iris.fileSeparator);
// Create a training pipeline.
var pipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
.Append(mlContext.Ranking.Trainers.FastTree(new FastTreeRankingTrainer.Options {
NumThreads = 1
}));
// Train the model.
var model = pipeline.Fit(data);
// Evaluate the model.
var scoredData = model.Transform(data);
var metrics = mlContext.Ranking.Evaluate(scoredData, label: "Label", groupId: "GroupId");
// Check that the metrics returned are valid.
Common.AssertMetrics(metrics);
}
private IDataView GetScoredDataForRankingEvaluation(MLContext mlContext)
{
var data = Iris.LoadAsRankingProblem(mlContext,
TestCommon.GetDataPath(DataDir, TestDatasets.iris.trainFilename),
hasHeader: TestDatasets.iris.fileHasHeader,
separatorChar: TestDatasets.iris.fileSeparator);
// Create a training pipeline.
var pipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
.Append(mlContext.Ranking.Trainers.FastTree(new FastTreeRankingTrainer.Options {
NumberOfThreads = 1
}));
// Train the model.
var model = pipeline.Fit(data);
// Evaluate the model.
var scoredData = model.Transform(data);
return(scoredData);
}
