public void LossSquared()
{
SquaredLoss loss = new SquaredLoss();
TestHelper(loss, 3, 2, 1, 2);
TestHelper(loss, 3, 4, 1, -2);
TestHelper(loss, 3, 5, 4, -4);
TestHelper(loss, 0, -3, 9, 6);
TestHelper(loss, -3, -5, 4, 4);
}
public void OnlineGradientDescent()
{
var env = new MLContext(seed: 0);
var dataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
var dataSource = new MultiFileSource(dataPath);
var ctx = new RegressionContext(env);
var reader = TextLoader.CreateReader(env,
c => (label: c.LoadFloat(11), features: c.LoadFloat(0, 10)),
separator: ';', hasHeader: true);
LinearRegressionModelParameters pred = null;
var loss = new SquaredLoss();
var est = reader.MakeNewEstimator()
.Append(r => (r.label, score: ctx.Trainers.OnlineGradientDescent(r.label, r.features,
lossFunction: loss,
onFit: (p) => { pred = p; })));
var pipe = reader.Append(est);
Assert.Null(pred);
var model = pipe.Fit(dataSource);
Assert.NotNull(pred);
// 11 input features, so we ought to have 11 weights.
VBuffer <float> weights = new VBuffer <float>();
pred.GetFeatureWeights(ref weights);
Assert.Equal(11, weights.Length);
var data = model.Read(dataSource);
var metrics = ctx.Evaluate(data, r => r.label, r => r.score, new PoissonLoss());
// Run a sanity check against a few of the metrics.
Assert.InRange(metrics.L1, 0, double.PositiveInfinity);
Assert.InRange(metrics.L2, 0, double.PositiveInfinity);
Assert.InRange(metrics.Rms, 0, double.PositiveInfinity);
Assert.Equal(metrics.Rms * metrics.Rms, metrics.L2, 5);
Assert.InRange(metrics.LossFn, 0, double.PositiveInfinity);
}
