/// <summary>
/// Calculates MAE of items placed to different buckets according to Number ofrating given to them.
/// </summary>
/// <param name="trainSource">An instance source</param>
/// <param name="mapping">Mapping which converts input data to RatingTriples</param>
/// <param name="predictionErrors">Absolute errors of predictions</param>
/// <returns></returns>
private static Dictionary <string, double> CreateItemPopularityPredictions(
SplitInstanceSource <string> trainSource,
IRecommenderMapping <SplitInstanceSource <string>, RatingTriple, string, Movie, NoFeatureSource, Vector> mapping,
Dictionary <Movie, List <double> > predictionErrors)
{
Rand.Restart(RandomSeed);
const int BucketCount = 4;
var trainingSetCounts = mapping.GetInstances(trainSource)
.GroupBy(i => i.Movie)
.ToDictionary(x => x.Key, x => x.Count());
var orderedItems = predictionErrors
.Keys
.Select(x => new { Item = x, Count = GetItemPopularityCount(x, trainingSetCounts) })
.OrderBy(x => x.Count)
.Select(x => x.Item)
.ToArray();
var result = new Dictionary <string, double>();
for (var bucket = 0; bucket < BucketCount; ++bucket)
{
var items = GetFixedItems(bucket, orderedItems, trainingSetCounts);
var bucketName = GetBucketName(items, trainingSetCounts);
var mae = ComputeMae(items, predictionErrors) / 2.0;
result.Add(bucketName, mae);
}
return(result);
}
