static void Main(string[] args)
{
//STEP 1: Create MLContext to be shared across the model creation workflow objects
MLContext mlcontext = new MLContext();
//STEP 2: Read the training data which will be used to train the movie recommendation model
IDataView trainingDataView = mlcontext.Data.LoadFromTextFile <MovieRating>(TrainingDataLocation, hasHeader: true, separatorChar: ',');
//STEP 3: Transform your data by encoding the two features userId and movieID. These encoded features will be provided as input
// to our MatrixFactorizationTrainer.
var dataProcessingPipeline = mlcontext.Transforms.Conversion.MapValueToKey(outputColumnName: "userIdEncoded", inputColumnName: nameof(MovieRating.userId))
.Append(mlcontext.Transforms.Conversion.MapValueToKey(outputColumnName: "movieIdEncoded", inputColumnName: nameof(MovieRating.movieId)));
//Specify the options for MatrixFactorization trainer
MatrixFactorizationTrainer.Options options = new MatrixFactorizationTrainer.Options();
options.MatrixColumnIndexColumnName = "userIdEncoded";
options.MatrixRowIndexColumnName = "movieIdEncoded";
options.LabelColumnName = "Label";
options.NumberOfIterations = 20;
options.ApproximationRank = 100;
//STEP 4: Create the training pipeline
var trainingPipeLine = dataProcessingPipeline.Append(mlcontext.Recommendation().Trainers.MatrixFactorization(options));
//STEP 5: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
ITransformer model = trainingPipeLine.Fit(trainingDataView);
//STEP 6: Evaluate the model performance
Console.WriteLine("=============== Evaluating the model ===============");
IDataView testDataView = mlcontext.Data.LoadFromTextFile <MovieRating>(TestDataLocation, hasHeader: true, separatorChar: ',');
var prediction = model.Transform(testDataView);
var metrics = mlcontext.Regression.Evaluate(prediction, labelColumnName: "Label", scoreColumnName: "Score");
Console.WriteLine("The model evaluation metrics RootMeanSquaredError:" + metrics.RootMeanSquaredError);
//STEP 7: Try/test a single prediction by predicting a single movie rating for a specific user
var predictionengine = mlcontext.Model.CreatePredictionEngine <MovieRating, MovieRatingPrediction>(model);
/* Make a single movie rating prediction, the scores are for a particular user and will range from 1 - 5.*/
var movieratingprediction = predictionengine.Predict(
new MovieRating()
{
userId = predictionuserId,
movieId = predictionmovieId
}
);
Movie movieService = new Movie();
Console.WriteLine(" movie rating prediction (1 - 5 stars) for movie:" + movieService.Get(predictionmovieId).movieTitle + " is:" + Math.Round(movieratingprediction.Score, 1));
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadLine();
}
static void Main(string[] args)
{
//STEP 1: Create MLContext to be shared across the model creation workflow objects
var mlcontext = new MLContext();
//STEP 2: Create a reader by defining the schema for reading the movie recommendation datasets
var reader = mlcontext.Data.CreateTextReader(new TextLoader.Arguments()
{
Separator = ",",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("userId", DataKind.R4, 0),
new TextLoader.Column("movieId", DataKind.R4, 1),
new TextLoader.Column("Label", DataKind.R4, 2)
}
});
//STEP 3: Read the training data which will be used to train the movie recommendation model
IDataView trainingDataView = reader.Read(TrainingDataLocation);
//STEP 4: Transform your data by encoding the two features userId and movieID. These encoded features will be provided as input
// to our MatrixFactorizationTrainer.
var pipeline = mlcontext.Transforms.Conversion.MapValueToKey("userId", "userIdEncoded")
.Append(mlcontext.Transforms.Conversion.MapValueToKey("movieId", "movieIdEncoded"))
.Append(mlcontext.Recommendation().Trainers.MatrixFactorization("userIdEncoded", "movieIdEncoded", "Label",
advancedSettings: s => { s.NumIterations = 20; s.K = 100; }));
//STEP 5: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
var model = pipeline.Fit(trainingDataView);
//STEP 6: Evaluate the model performance
Console.WriteLine("=============== Evaluating the model ===============");
IDataView testDataView = reader.Read(TestDataLocation);
var prediction = model.Transform(testDataView);
var metrics = mlcontext.Regression.Evaluate(prediction, label: "Label", score: "Score");
//Console.WriteLine("The model evaluation metrics rms:" + Math.Round(float.Parse(metrics.Rms.ToString()), 1));
//STEP 7: Try/test a single prediction by predicting a single movie rating for a specific user
var predictionengine = model.CreatePredictionEngine <MovieRating, MovieRatingPrediction>(mlcontext);
/* Make a single movie rating prediction, the scores are for a particular user and will range from 1 - 5.
* The higher the score the higher the likelyhood of a user liking a particular movie.
* You can recommend a movie to a user if say rating > 3.5.*/
var movieratingprediction = predictionengine.Predict(
new MovieRating()
{
//Example rating prediction for userId = 6, movieId = 10 (GoldenEye)
userId = predictionuserId,
movieId = predictionmovieId
}
);
Movie movieService = new Movie();
Console.WriteLine("For userId:" + predictionuserId + " movie rating prediction (1 - 5 stars) for movie:" + movieService.Get(predictionmovieId).movieTitle + " is:" + Math.Round(movieratingprediction.Score, 1));
}
static void Main(string[] args)
{
//STEP 1: Create MLContext to be shared across the model creation workflow objects
MLContext mlcontext = new MLContext();
//STEP 2: Read the training data which will be used to train the movie recommendation model
//The schema for training data is defined by type 'TInput' in ReadFromTextFile<TInput>() method.
IDataView trainingDataView = mlcontext.Data.ReadFromTextFile <MovieRating>(TrainingDataLocation, hasHeader: true, separatorChar: ',');
//STEP 3: Transform your data by encoding the two features userId and movieID. These encoded features will be provided as input
// to our MatrixFactorizationTrainer.
var dataProcessingPipeline = mlcontext.Transforms.Conversion.MapValueToKey(outputColumnName: userIdEncoded, inputColumnName: nameof(MovieRating.userId))
.Append(mlcontext.Transforms.Conversion.MapValueToKey(outputColumnName: movieIdEncoded, inputColumnName: nameof(MovieRating.movieId)));
//Specify the options for MatrixFactorization trainer
MatrixFactorizationTrainer.Options options = new MatrixFactorizationTrainer.Options();
options.MatrixColumnIndexColumnName = userIdEncoded;
options.MatrixRowIndexColumnName = movieIdEncoded;
options.LabelColumnName = DefaultColumnNames.Label;
options.NumIterations = 20;
options.K = 100;
//STEP 4: Create the training pipeline
var trainingPipeLine = dataProcessingPipeline.Append(mlcontext.Recommendation().Trainers.MatrixFactorization(options));
//STEP 5: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
ITransformer model = trainingPipeLine.Fit(trainingDataView);
//STEP 6: Evaluate the model performance
Console.WriteLine("=============== Evaluating the model ===============");
IDataView testDataView = mlcontext.Data.ReadFromTextFile <MovieRating>(TestDataLocation, hasHeader: true, separatorChar: ',');
var prediction = model.Transform(testDataView);
var metrics = mlcontext.Regression.Evaluate(prediction, label: DefaultColumnNames.Label, score: DefaultColumnNames.Score);
Console.WriteLine("The model evaluation metrics rms:" + metrics.Rms);
//STEP 7: Try/test a single prediction by predicting a single movie rating for a specific user
var predictionengine = model.CreatePredictionEngine <MovieRating, MovieRatingPrediction>(mlcontext);
/* Make a single movie rating prediction, the scores are for a particular user and will range from 1 - 5.
* The higher the score the higher the likelyhood of a user liking a particular movie.
* You can recommend a movie to a user if say rating > 3.5.*/
var movieratingprediction = predictionengine.Predict(
new MovieRating()
{
//Example rating prediction for userId = 6, movieId = 10 (GoldenEye)
userId = predictionuserId,
movieId = predictionmovieId
}
);
Movie movieService = new Movie();
Console.WriteLine("For userId:" + predictionuserId + " movie rating prediction (1 - 5 stars) for movie:" + movieService.Get(predictionmovieId).movieTitle + " is:" + Math.Round(movieratingprediction.Score, 1));
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadLine();
}
static void Main(string[] args)
{
var mlcontext = new MLContext();
#region build_model
var reader = mlcontext.Data.CreateTextReader(new TextLoader.Arguments()
{
Separator = ",",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("userId", DataKind.R4, 0),
new TextLoader.Column("movieId", DataKind.R4, 1),
new TextLoader.Column("Label", DataKind.R4, 2)
}
});
var trainingDataView = reader.Read(TrainingDataLocation);
var pipeline = mlcontext.Transforms.Conversion.MapValueToKey("userId", "userIdEncoded")
.Append(mlcontext.Transforms.Conversion.MapValueToKey("movieId", "movieIdEncoded"))
.Append(mlcontext.Recommendation().Trainers.MatrixFactorization("userIdEncoded", "movieIdEncoded", "Label", advancedSettings: s => { s.NumIterations = 20; s.K = 100; }));
#endregion
Console.WriteLine("=============== Training the model ===============");
#region train_model
var model = pipeline.Fit(trainingDataView);
#endregion
Console.WriteLine("=============== Evaluating the model ===============");
#region evaluate_model
var testDataView = reader.Read(TestDataLocation);
var prediction = model.Transform(testDataView);
var metrics = mlcontext.Regression.Evaluate(prediction, label: "Label", score: "Score");
Console.WriteLine($"The model evaluation metrics rms: {Math.Round(metrics.Rms, 1)}");
#endregion
#region prediction
var userId = 6;
var movieId = 10;
var predictionengine = model.CreatePredictionEngine <MovieRating, MovieRatingPrediction>(mlcontext);
/* Make a single movie rating prediction, the scores are for a particular user and will range from 1 - 5.
* The higher the score the higher the likelihood of a user liking a particular movie.
* You can recommend a movie to a user if say rating > 3.5.*/
var movieratingprediction = predictionengine.Predict(
new MovieRating()
{
//Example rating prediction for userId = 6, movieId = 10 (GoldenEye)
userId = userId,
movieId = movieId
}
);
var movieService = new Movie();
Console.WriteLine($"For userId: {userId} movie rating prediction (1 - 5 stars) for movie: {movieService.Get(movieId).movieTitle} is: {Math.Round(movieratingprediction.Score, 0, MidpointRounding.ToEven)}");
#endregion
}