private static void MakePredictionsOnTestFileAndCalculateError(MovieScorePredictor predictor, UserCache testingSetCache)
{
// Get the list of users to make predictions on
IReadOnlyDictionary <int, UserCache.UserRatingsCache> listOfUsersToPredictScoresOn = testingSetCache.GetAllUsersAndMovieRatings();
Console.WriteLine("Making predictions...");
// Predict ratings for all the users in parallel
List <MoviePrediction> predictions = listOfUsersToPredictScoresOn.AsParallel().Select(l =>
{
// Make the prediction for this users movies
var returnValue = predictor.PredictAllScores(l.Key, l.Value.GetMovieRatings(), K);
// This is simply to update the console on the current progress
l.Value.Predicted = true;
int predicted = listOfUsersToPredictScoresOn.Values.Count(n => n.Predicted);
Console.Write("\r{0}/{1}", predicted, listOfUsersToPredictScoresOn.Count);
// Return the prediction
return(returnValue);
}).SelectMany(s => s.Values).ToList();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Calculating errors...");
var rootMeanSquareError = RootMeanSquareError.Calculate(predictions);
var meanAbsoluteError = MeanAbsoluteError.Calculate(predictions);
Console.WriteLine("=========================================");
Console.WriteLine("Root mean square error: {0}", rootMeanSquareError);
Console.WriteLine("Mean absolute error: {0}", meanAbsoluteError);
}
private static void MakeMovieRecommendationsForUser999999InTrainingSet(MovieScorePredictor predictor, UserCache trainingSetCache, IDictionary <int, string> movieTitles)
{
// Make recommendations
Console.WriteLine("Making movie recommendations...");
if (!trainingSetCache.GetAllUsersAndMovieRatings().ContainsKey(999999))
{
Console.WriteLine("User 999999 must be present in traning set to be able to make recommendations. Please add your ratings as that user");
return;
}
// To make recommendations, we will predict all movies for this user. Once we do that prediction, we will obtain the
// movies with the top score, and display them here
IReadOnlyDictionary <int, MoviePrediction> movieScores = predictor.PredictAllScores(999999, null, K);
var bestPredictionsFiltered = movieScores.Where(n => !trainingSetCache.GetUserMovieRatings(999999).ContainsKey(n.Key));
var bestPredictions = bestPredictionsFiltered.Select(n => new
{
MovieId = n.Key,
MovieScore = n.Value.Prediction
}).OrderByDescending(n => n.MovieScore).Take(10).ToList();
Console.WriteLine("For user {0}, our best predictions are...");
for (int index = 0; index < bestPredictions.Count; index++)
{
var bestPrediction = bestPredictions[index];
Console.WriteLine("{0}: Id:'{1}', {2}", index, bestPrediction.MovieId, movieTitles[bestPrediction.MovieId]);
}
Console.WriteLine();
}
// For recommendations, the following movies were used
//17622, 999999, 5.0
//10362, 999999, 5.0
//5448, 999999, 5.0
//14, 999999, 4.0
//21, 999999, 1.0
//69, 999999, 2.0
//68, 999999, 4.0
//209, 999999, 1.0
//215, 999999, 5.0
//731, 999999, 1.0
//6205, 999999, 5.0
//12293, 999999, 5.0
//12298, 999999, 1.0
//12311, 999999, 1.0
//12954, 999999, 1.0
//11726, 999999, 5.0
//13501, 999999, 5.0
//13595, 999999, 1.0
//13617, 999999, 1.0
//13638, 999999, 2.0
//7624, 999999, 4.0
// NOTE: Only used for testing
private void RunLocalTests(MovieScorePredictor predictor)
{
List <UserRating> testingUserRatings = CsvParserUtils.ParseCsvAsList <UserRating>(UserRatingsTestPath);
double prediction = predictor.PredictScore(testingUserRatings[0].UserId, testingUserRatings[0].MovieId);
UserCache testingSetCache = UserCache.BuildUserCache(UserRatingsTestPath);
var allScores = predictor.PredictAllScores(testingUserRatings[0].UserId,
testingSetCache.GetUserMovieRatings(testingUserRatings[0].UserId), K);
var allErrors = new List <double>();
foreach (var keyValuePair in allScores)
{
var realScore = testingSetCache.GetUserMovieRatings(testingUserRatings[0].UserId)[keyValuePair.Key];
double error = Math.Abs(keyValuePair.Value.Prediction - realScore);
allErrors.Add(error);
}
}