/// <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); }