// Use model for single prediction
public static void UseModelForSinglePrediction(MLContext mlContext, ITransformer model)
{
// <SnippetPredictionEngine>
Console.WriteLine("=============== Making a prediction ===============");
var predictionEngine = mlContext.Model.CreatePredictionEngine <MovieRating, MovieRatingPrediction>(model);
// </SnippetPredictionEngine>
// Create test input & make single prediction
// <SnippetMakeSinglePrediction>
var testInput = new MovieRating {
userId = 6, movieId = 10
};
var movieRatingPrediction = predictionEngine.Predict(testInput);
// </SnippetMakeSinglePrediction>
// <SnippetPrintResults>
if (Math.Round(movieRatingPrediction.Score, 1) > 3.5)
{
Console.WriteLine("Movie " + testInput.movieId + " is recommended for user " + testInput.userId);
}
else
{
Console.WriteLine("Movie " + testInput.movieId + " is not recommended for user " + testInput.userId);
}
// </SnippetPrintResults>
}
public static async Task <IActionResult> Run(
[HttpTrigger(AuthorizationLevel.Function, "get", Route = null)] HttpRequest req,
ILogger log)
{
log.LogInformation("C# HTTP trigger function processed a request.");
string movieData = req.Query["movieData"];
if (String.IsNullOrWhiteSpace(movieData))
{
return(new BadRequestObjectResult("Please pass a name on the query string"));
}
// Create test input & make single prediction
String[] values = movieData.Split(":");
var movieRatingTestInput = new MovieRating
{
userId = Int32.Parse(values[0]),
movieId = Int32.Parse(values[1])
};
// Create MLContext to be shared across the model creation workflow objects
MLContext mlContext = new MLContext();
// Load data
(IDataView trainingDataView, IDataView testDataView) = LoadData(mlContext);
// Build & train model
ITransformer model = BuildAndTrainModel(mlContext, trainingDataView);
// Evaluate quality of model
EvaluateModel(mlContext, testDataView, model);
// Use model to try a single prediction (one row of data)
string result = UseModelForSinglePrediction(mlContext, model, movieRatingTestInput);
// Save model
SaveModel(mlContext, trainingDataView.Schema, model);
return((ActionResult) new OkObjectResult(result));
}
public static void UseModelForSinglePrediction(MLContext mlContext, ITransformer model)
{
Console.WriteLine("=============== Making a prediction ==============");
var predictionEngine = mlContext.Model.CreatePredictionEngine <MovieRating, MovieRatingPrediction>(model);
var testInput = new MovieRating {
userId = 6, movieId = 10
};
var movieRatingPrediction = predictionEngine.Predict(testInput);
Console.WriteLine("Score: " + movieRatingPrediction.Score.ToString());
if (Math.Round(movieRatingPrediction.Score, 1) > 3.5)
{
Console.WriteLine("Movie: " + testInput.movieId + " is recommended for user " + testInput.userId);
}
else
{
Console.WriteLine("Movie: " + testInput.movieId + " is not recommended for user " + testInput.userId);
}
}
public static void UseModelForSinglePrediction(MLContext mlcontext, ITransformer model)
{
Console.WriteLine("=============== Making a prediction ===============");
// PredictionEngine - a convenience API that performs prediction on single instance of data
var predictionEngine = mlcontext.Model.CreatePredictionEngine <MovieRating, MovieRatingPrediction>(model);
// instance of MovieRating called testInput, pass to Prediction Engine
// determine if movieId 10 should be recommended to userId 6
var testInput = new MovieRating {
userId = 6, movieId = 10
};
var movieRatingPrediction = predictionEngine.Predict(testInput);
if (Math.Round(movieRatingPrediction.Score, 1) > 3.5)
{
Console.WriteLine("Movie " + testInput.movieId + " is recommended for user " + testInput.userId);
}
else
{
Console.WriteLine("Movie " + testInput.movieId + " is not recommended for user " + testInput.userId);
}
}
// Use model for single prediction
public static String UseModelForSinglePrediction(MLContext mlContext, ITransformer model, MovieRating movieRatingTestInput)
{
String result = String.Empty;
Console.WriteLine("=============== Making a prediction ===============");
var predictionEngine = mlContext.Model.CreatePredictionEngine <MovieRating, MovieRatingPrediction>(model);
var movieRatingPrediction = predictionEngine.Predict(movieRatingTestInput);
if (Math.Round(movieRatingPrediction.Score, 1) > 3.5)
{
result = String.Concat(movieRatingTestInput.userId, ":", movieRatingTestInput.movieId, ":", true);
}
else
{
result = String.Concat(movieRatingTestInput.userId, ":", movieRatingTestInput.movieId, ":", false);
}
Console.WriteLine(result);
return(result);
}
