public static PredictionEngine <TIn, TOut> MatrixFactorization <TIn, TOut>(IEnumerable <TIn> trainDataset, string rowIndexColumnName, string columnIndexColumnName, int iteration = 20, int approximationRank = 100, double learningRate = 0.2, MatrixFactorizationTrainer.LossFunctionType lossFunctionType = MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass, bool suppressDetail = true, bool forceNonNegative = true, params string[] excludedColumns)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessing = Preprocessing.KeyToValueMapping(context, properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var options = new MatrixFactorizationTrainer.Options
{
MatrixColumnIndexColumnName = $@"{columnIndexColumnName}_encoded",
MatrixRowIndexColumnName = $@"{rowIndexColumnName}_encoded",
LabelColumnName = "Label",
NumberOfIterations = iteration,
ApproximationRank = approximationRank,
LearningRate = learningRate,
LossFunction = lossFunctionType,
Quiet = suppressDetail,
NonNegative = forceNonNegative
};
var pipeline = preprocessing.KeyToValueMappingEstimator.Append(context.Recommendation().Trainers.MatrixFactorization(options));
var model = pipeline.Fit(trainDataframe);
var engine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
return(engine);
}