/// <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,
FieldAwareFactorizationMachineTrainer.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 FieldAwareFactorizationMachineTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features);
return(rec.Output);
}
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);
Done();
}
/// <summary>
/// Predict a target using a field-aware factorization machine.
/// </summary>
/// <param name="ctx">The binary classifier context trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="learningRate">Initial learning rate.</param>
/// <param name="numIterations">Number of training iterations.</param>
/// <param name="numLatentDimensions">Latent space dimensions.</param>
/// <param name="advancedSettings">A delegate to set more settings.
/// The settings here will override the ones provided in the direct method signature,
/// if both are present and have different values.
/// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</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. 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 BinaryClassificationContext.BinaryClassificationTrainers ctx,
Scalar <bool> label, Vector <float>[] features,
float learningRate = 0.1f,
int numIterations = 5,
int numLatentDimensions = 20,
Action <FieldAwareFactorizationMachineTrainer.Arguments> advancedSettings = null,
Action <FieldAwareFactorizationMachinePredictor> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckNonEmpty(features, nameof(features));
Contracts.CheckParam(learningRate > 0, nameof(learningRate), "Must be positive");
Contracts.CheckParam(numIterations > 0, nameof(numIterations), "Must be positive");
Contracts.CheckParam(numLatentDimensions > 0, nameof(numLatentDimensions), "Must be positive");
Contracts.CheckValueOrNull(advancedSettings);
Contracts.CheckValueOrNull(onFit);
var rec = new CustomReconciler((env, labelCol, featureCols) =>
{
var trainer = new FieldAwareFactorizationMachineTrainer(env, featureCols, labelCol, advancedSettings:
args =>
{
args.LearningRate = learningRate;
args.Iters = numIterations;
args.LatentDim = numLatentDimensions;
advancedSettings?.Invoke(args);
});
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features);
return(rec.Output);
}
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);
}
