/// <summary>
/// Predict matrix entry using matrix factorization
/// </summary>
/// <typeparam name="T">The type of physical value of matrix's row and column index. It must be an integer type such as uint.</typeparam>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label variable.</param>
/// <param name="matrixColumnIndex">The column index of the considered matrix.</param>
/// <param name="matrixRowIndex">The row index of the considered matrix.</param>
/// <param name="options">Advanced algorithm settings.</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> MatrixFactorization <T>(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Key <T> matrixColumnIndex, Key <T> matrixRowIndex,
MatrixFactorizationTrainer.Options options,
Action <MatrixFactorizationPredictor> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(matrixColumnIndex, nameof(matrixColumnIndex));
Contracts.CheckValue(matrixRowIndex, nameof(matrixRowIndex));
Contracts.CheckValue(options, nameof(options));
Contracts.CheckValueOrNull(onFit);
var rec = new MatrixFactorizationReconciler <T>((env, labelColName, matrixColumnIndexColName, matrixRowIndexColName) =>
{
options.MatrixColumnIndexColumnName = matrixColumnIndexColName;
options.MatrixRowIndexColumnName = matrixRowIndexColName;
options.LabelColumnName = labelColName;
var trainer = new MatrixFactorizationTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, matrixColumnIndex, matrixRowIndex);
return(rec.Output);
}
/// <summary>
/// Predict matrix entry using matrix factorization
/// </summary>
/// <typeparam name="T">The type of physical value of matrix's row and column index. It must be an integer type such as uint.</typeparam>
/// <param name="ctx">The regression context trainer object.</param>
/// <param name="label">The label variable.</param>
/// <param name="matrixColumnIndex">The column index of the considered matrix.</param>
/// <param name="matrixRowIndex">The row index of the considered matrix.</param>
/// <param name="regularizationCoefficient">The frobenius norms of factor matrices.</param>
/// <param name="approximationRank">Rank of the two factor matrices whose product is used to approximate the consdered matrix</param>
/// <param name="learningRate">Initial learning rate.</param>
/// <param name="numIterations">Number of training iterations.</param>
/// <param name="advancedSettings">A delegate to set more settings.</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> MatrixFactorization <T>(this RegressionContext.RegressionTrainers ctx,
Scalar <float> label, Key <T> matrixColumnIndex, Key <T> matrixRowIndex,
float regularizationCoefficient = 0.1f,
int approximationRank = 8,
float learningRate = 0.1f,
int numIterations = 20,
Action <MatrixFactorizationTrainer.Arguments> advancedSettings = null,
Action <MatrixFactorizationPredictor> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(matrixColumnIndex, nameof(matrixColumnIndex));
Contracts.CheckValue(matrixRowIndex, nameof(matrixRowIndex));
Contracts.CheckParam(regularizationCoefficient >= 0, nameof(regularizationCoefficient), "Must be non-negative");
Contracts.CheckParam(approximationRank > 0, nameof(approximationRank), "Must be positive");
Contracts.CheckParam(learningRate > 0, nameof(learningRate), "Must be positive");
Contracts.CheckParam(numIterations > 0, nameof(numIterations), "Must be positive");
Contracts.CheckValueOrNull(advancedSettings);
Contracts.CheckValueOrNull(onFit);
var rec = new MatrixFactorizationReconciler <T>((env, labelColName, matrixColumnIndexColName, matrixRowIndexColName) =>
{
var trainer = new MatrixFactorizationTrainer(env, labelColName, matrixColumnIndexColName, matrixRowIndexColName, advancedSettings:
args =>
{
args.Lambda = regularizationCoefficient;
args.K = approximationRank;
args.Eta = learningRate;
args.NumIterations = numIterations;
// The previous settings may be overwritten by the line below.
advancedSettings?.Invoke(args);
});
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, matrixColumnIndex, matrixRowIndex);
return(rec.Output);
}
public Impl(MatrixFactorizationReconciler <T> rec) : base(rec, rec.Inputs)
{
}
