public void FeatureContributionEstimatorWorkout()
{
var data = GetSparseDataset();
var model = ML.Regression.Trainers.OrdinaryLeastSquares().Fit(data);
var estPipe = new FeatureContributionCalculatingEstimator(ML, model.Model, model.FeatureColumn)
.Append(new FeatureContributionCalculatingEstimator(ML, model.Model, model.FeatureColumn, normalize: false))
.Append(new FeatureContributionCalculatingEstimator(ML, model.Model, model.FeatureColumn, numPositiveContributions: 0))
.Append(new FeatureContributionCalculatingEstimator(ML, model.Model, model.FeatureColumn, numNegativeContributions: 0))
.Append(new FeatureContributionCalculatingEstimator(ML, model.Model, model.FeatureColumn, numPositiveContributions: 0, numNegativeContributions: 0));
TestEstimatorCore(estPipe, data);
Done();
}
private void TestFeatureContribution(
ITrainerEstimator <ISingleFeaturePredictionTransformer <IPredictor>, IPredictor> trainer,
IDataView data,
string testFile,
int precision = 6)
{
// Train the model.
var model = trainer.Fit(data);
// Extract the predictor, check that it supports feature contribution.
var predictor = model.Model as ICalculateFeatureContribution;
Assert.NotNull(predictor);
// Calculate feature contributions.
var est = new FeatureContributionCalculatingEstimator(ML, predictor, "Features", numPositiveContributions: 3, numNegativeContributions: 0)
.Append(new FeatureContributionCalculatingEstimator(ML, predictor, "Features", numPositiveContributions: 0, numNegativeContributions: 3))
.Append(new FeatureContributionCalculatingEstimator(ML, predictor, "Features", numPositiveContributions: 1, numNegativeContributions: 1))
.Append(new FeatureContributionCalculatingEstimator(ML, predictor, "Features", numPositiveContributions: 1, numNegativeContributions: 1, normalize: false));
TestEstimatorCore(est, data);
// Verify output.
var outputPath = GetOutputPath("FeatureContribution", testFile + ".tsv");
using (var ch = Env.Start("save"))
{
var saver = new TextSaver(ML, new TextSaver.Arguments {
Silent = true, OutputHeader = false
});
IDataView savedData = TakeFilter.Create(ML, est.Fit(data).Transform(data), 4);
using (var fs = File.Create(outputPath))
DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true);
}
CheckEquality("FeatureContribution", testFile + ".tsv", digitsOfPrecision: precision);
Done();
}
