public void FieldAwareFactorizationMachine_Estimator()
{
var data = new TextLoader(Env, GetFafmBCLoaderArgs())
.Load(GetDataPath(TestDatasets.breastCancer.trainFilename));
var ffmArgs = new FieldAwareFactorizationMachineBinaryClassificationTrainer.Options {
FeatureColumnName = "Feature1", // Features from the 1st field.
ExtraFeatureColumns = new[] { "Feature2", "Feature3", "Feature4" }, // 2nd field's feature column, 3rd field's feature column, 4th field's feature column.
Shuffle = false,
NumberOfIterations = 3,
LatentDimension = 7,
};
var est = ML.BinaryClassification.Trainers.FieldAwareFactorizationMachine(ffmArgs);
TestEstimatorCore(est, data);
var model = est.Fit(data);
var anotherModel = est.Fit(data, data, model.Model);
Done();
}
public void FfmBinaryClassificationWithAdvancedArguments()
{
var mlContext = new MLContext(seed: 0);
var data = DatasetUtils.GenerateFfmSamples(500);
var dataView = mlContext.Data.LoadFromEnumerable(data);
var ffmArgs = new FieldAwareFactorizationMachineBinaryClassificationTrainer.Options();
// Customized the field names.
ffmArgs.FeatureColumnName = nameof(DatasetUtils.FfmExample.Field0); // First field.
ffmArgs.ExtraFeatureColumns = new[] { nameof(DatasetUtils.FfmExample.Field1), nameof(DatasetUtils.FfmExample.Field2) };
var pipeline = mlContext.BinaryClassification.Trainers.FieldAwareFactorizationMachine(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.AreaUnderRocCurve, 0.9, 1);
Assert.InRange(metrics.AreaUnderPrecisionRecallCurve, 0.9, 1);
}
/// <summary>
/// Predict a target using a field-aware factorization machine algorithm.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static FieldAwareFactorizationMachineBinaryClassificationTrainer FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
FieldAwareFactorizationMachineBinaryClassificationTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FieldAwareFactorizationMachineBinaryClassificationTrainer(env, options));
}
/// <summary>
/// Predict a target using a field-aware factorization machine.
/// </summary>
/// <param name="catalog">The binary classifier catalog trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this.
/// This delegate will receive the model that was trained. The type of the model is <see cref="FieldAwareFactorizationMachineModelParameters"/>.
/// Note that this action cannot change the result in any way; it is only a way for the caller to
/// be informed about what was learnt.</param>
/// <returns>The predicted output.</returns>
public static (Scalar <float> score, Scalar <bool> predictedLabel) FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float>[] features,
FieldAwareFactorizationMachineBinaryClassificationTrainer.Options options,
Action <FieldAwareFactorizationMachineModelParameters> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckNonEmpty(features, nameof(features));
Contracts.CheckValueOrNull(options);
Contracts.CheckValueOrNull(onFit);
var rec = new CustomReconciler((env, labelCol, featureCols) =>
{
var trainer = new FieldAwareFactorizationMachineBinaryClassificationTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features);
return(rec.Output);
}
