77 references to MatrixFactorizationTrainer
Microsoft.ML.AutoML (12)
TrainerExtensions\RecommendationTrainerExtensions.cs (3)
17var options = TrainerExtensionUtil.CreateOptions<MatrixFactorizationTrainer.Options>(sweepParams); 28property.Add(nameof(MatrixFactorizationTrainer.Options.MatrixColumnIndexColumnName), columnInfo.UserIdColumnName); 29property.Add(nameof(MatrixFactorizationTrainer.Options.MatrixRowIndexColumnName), columnInfo.ItemIdColumnName);
TrainerExtensions\SweepableParams.cs (9)
124new SweepableDiscreteParam(nameof(MatrixFactorizationTrainer.Options.NumberOfIterations), new object[] { 10, 20, 40 }), 125new SweepableDiscreteParam(nameof(MatrixFactorizationTrainer.Options.LearningRate), new object[] { 0.001f, 0.01f, 0.1f }), 126new SweepableDiscreteParam(nameof(MatrixFactorizationTrainer.Options.ApproximationRank), new object[] { 8, 16, 64, 128 }), 127new SweepableDiscreteParam(nameof(MatrixFactorizationTrainer.Options.Lambda), new object[] { 0.01f, 0.05f, 0.1f, 0.5f, 1f }), 128new SweepableDiscreteParam(nameof(MatrixFactorizationTrainer.Options.LossFunction), new object[] { MatrixFactorizationTrainer.LossFunctionType.SquareLossRegression, MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass }), 129new SweepableDiscreteParam(nameof(MatrixFactorizationTrainer.Options.Alpha), new object[] { 1f, 0.01f, 0.0001f, 0.000001f }), 130new SweepableDiscreteParam(nameof(MatrixFactorizationTrainer.Options.C), new object[] { 0.000001f, 0.0001f, 0.01f }),
Microsoft.ML.CodeGenerator.Tests (16)
ApprovalTests\ConsoleCodeGeneratorTests.cs (8)
550{nameof(MatrixFactorizationTrainer.Options.NumberOfIterations), 10 }, 551{nameof(MatrixFactorizationTrainer.Options.LearningRate), 0.01f }, 552{nameof(MatrixFactorizationTrainer.Options.ApproximationRank), 8 }, 553{nameof(MatrixFactorizationTrainer.Options.Lambda), 0.01f }, 554{nameof(MatrixFactorizationTrainer.Options.LossFunction), MatrixFactorizationTrainer.LossFunctionType.SquareLossRegression }, 555{nameof(MatrixFactorizationTrainer.Options.Alpha), 1f }, 556{nameof(MatrixFactorizationTrainer.Options.C), 0.00001f },
TrainerGeneratorTests.cs (8)
305{nameof(MatrixFactorizationTrainer.Options.NumberOfIterations), 10 }, 306{nameof(MatrixFactorizationTrainer.Options.LearningRate), 0.01f }, 307{nameof(MatrixFactorizationTrainer.Options.ApproximationRank), 8 }, 308{nameof(MatrixFactorizationTrainer.Options.Lambda), 0.01f }, 309{nameof(MatrixFactorizationTrainer.Options.LossFunction), MatrixFactorizationTrainer.LossFunctionType.SquareLossRegression }, 310{nameof(MatrixFactorizationTrainer.Options.Alpha), 1f }, 311{nameof(MatrixFactorizationTrainer.Options.C), 0.00001f },
Microsoft.ML.IntegrationTests (2)
Evaluation.cs (2)
237var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization( 238new MatrixFactorizationTrainer.Options
Microsoft.ML.Recommender (22)
MatrixFactorizationPredictor.cs (2)
27/// Model parameters for <see cref="MatrixFactorizationTrainer"/>. 440/// <param name="model">The model trained by one of the training functions in <see cref="MatrixFactorizationTrainer"/></param>
MatrixFactorizationTrainer.cs (9)
19[assembly: LoadableClass(MatrixFactorizationTrainer.Summary, typeof(MatrixFactorizationTrainer), typeof(MatrixFactorizationTrainer.Options), 21"Matrix Factorization", MatrixFactorizationTrainer.LoadNameValue, "libmf", "mf")] 111/// <seealso cref="Microsoft.ML.RecommendationCatalog.RecommendationTrainers.MatrixFactorization(MatrixFactorizationTrainer.Options)"/> 139/// Options for the <see cref="MatrixFactorizationTrainer"/> as used in [MatrixFactorization(Options)](xref:Microsoft.ML.RecommendationCatalog.RecommendationTrainers.MatrixFactorization(Microsoft.ML.Trainers.MatrixFactorizationTrainer.Options)). 340/// Initializes a new instance of <see cref="MatrixFactorizationTrainer"/> through the <see cref="Options"/> class. 378/// Initializes a new instance of <see cref="MatrixFactorizationTrainer"/>. 521/// Trains a <see cref="MatrixFactorizationTrainer"/> using both training and validation data, returns a <see cref="MatrixFactorizationPredictionTransformer"/>.
RecommenderCatalog.cs (11)
46/// Create <see cref="MatrixFactorizationTrainer"/>, which predicts element values in a matrix using matrix factorization. 68public MatrixFactorizationTrainer MatrixFactorization( 72int approximationRank = MatrixFactorizationTrainer.Defaults.ApproximationRank, 73double learningRate = MatrixFactorizationTrainer.Defaults.LearningRate, 74int numberOfIterations = MatrixFactorizationTrainer.Defaults.NumIterations) 79/// Create <see cref="MatrixFactorizationTrainer"/> with advanced options, which predicts element values in a matrix using matrix factorization. 84/// and the value at the location specified by the two indexes. The training configuration is encoded in <see cref="MatrixFactorizationTrainer.Options"/>. 85/// To invoke one-class matrix factorization, user needs to specify <see cref="MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass"/>. 86/// The default setting <see cref="MatrixFactorizationTrainer.LossFunctionType.SquareLossRegression"/> is for standard matrix factorization problem. 97public MatrixFactorizationTrainer MatrixFactorization( 98MatrixFactorizationTrainer.Options options)
Microsoft.ML.Samples (6)
Dynamic\Trainers\Recommendation\MatrixFactorization.cs (1)
34var pipeline = mlContext.Recommendation().Trainers.
Dynamic\Trainers\Recommendation\MatrixFactorizationWithOptions.cs (2)
35var options = new MatrixFactorizationTrainer.Options 60var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(
Dynamic\Trainers\Recommendation\OneClassMatrixFactorizationWithOptions.cs (3)
44var options = new MatrixFactorizationTrainer.Options 62LossFunction = MatrixFactorizationTrainer.LossFunctionType 66var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(
Microsoft.ML.Tests (19)
TrainerEstimators\MatrixFactorizationTests.cs (19)
39var options = new MatrixFactorizationTrainer.Options 49var est = ML.Recommendation().Trainers.MatrixFactorization(options); 74var options = new MatrixFactorizationTrainer.Options 84var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options); 218var options = new MatrixFactorizationTrainer.Options 228var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options); 329var options = new MatrixFactorizationTrainer.Options 340var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options); 449var options = new MatrixFactorizationTrainer.Options 454LossFunction = MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass, 463var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options); 593var options = new MatrixFactorizationTrainer.Options 598LossFunction = MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass, 607var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options); 658var options = new MatrixFactorizationTrainer.Options 670LossFunction = MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass 673var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options); 776var options = new MatrixFactorizationTrainer.Options 787var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options);