public void FieldAwareFactorizationMachine_Estimator()
{
var data = new TextLoader(Env, GetFafmBCLoaderArgs())
.Read(GetDataPath(TestDatasets.breastCancer.trainFilename));
var est = new FieldAwareFactorizationMachineTrainer(Env, new[] { "Feature1", "Feature2", "Feature3", "Feature4" }, "Label",
advancedSettings: s =>
{
s.Shuffle = false;
s.Iters = 3;
s.LatentDim = 7;
});
TestEstimatorCore(est, data);
var model = est.Fit(data);
var anotherModel = est.Train(data, data, model.Model);
Done();
}
public void FfmBinaryClassificationWithAdvancedArguments()
{
var mlContext = new MLContext(seed: 0);
var data = DatasetUtils.GenerateFfmSamples(500);
var dataView = ComponentCreation.CreateDataView(mlContext, data.ToList());
var ffmArgs = new FieldAwareFactorizationMachineTrainer.Arguments();
// Customized the field names.
ffmArgs.FeatureColumn = nameof(DatasetUtils.FfmExample.Field0); // First field.
ffmArgs.ExtraFeatureColumns = new[] { nameof(DatasetUtils.FfmExample.Field1), nameof(DatasetUtils.FfmExample.Field2) };
var pipeline = new FieldAwareFactorizationMachineTrainer(mlContext, ffmArgs);
var model = pipeline.Fit(dataView);
var prediction = model.Transform(dataView);
var metrics = mlContext.BinaryClassification.Evaluate(prediction);
// Run a sanity check against a few of the metrics.
Assert.InRange(metrics.Accuracy, 0.9, 1);
Assert.InRange(metrics.Auc, 0.9, 1);
Assert.InRange(metrics.Auprc, 0.9, 1);
}