/// <summary>
/// Initializes a new instance of the <see cref="NegativeDataGeneratorMapping{TInstanceSource,TInstance,TUser,TItem,TFeatureSource,TFeatureValues}"/>
/// class from a reader of a binary stream.
/// </summary>
/// <param name="reader">The binary reader to read the state of the negative data generation mapping from.</param>
/// <param name="mapping">The top level mapping.</param>
public NegativeDataGeneratorMapping(
IReader reader,
IRecommenderMapping <TInstanceSource, TInstance, TUser, TItem, TFeatureSource, TFeatureValues> mapping)
{
if (reader == null)
{
throw new ArgumentNullException(nameof(reader));
}
if (mapping == null)
{
throw new ArgumentNullException(nameof(mapping));
}
this.mapping = mapping;
reader.VerifySerializationGuid(
this.customSerializationGuid, "The binary stream does not contain the negative data generation mapping of an Infer.NET Matchbox recommender.");
int deserializedVersion = reader.ReadSerializationVersion(CustomSerializationVersion);
if (deserializedVersion == CustomSerializationVersion)
{
this.itemHistogramAdjustment = reader.ReadDouble();
}
}
WithGeneratedNegativeData <TInstanceSource, TInstance, TUser, TItem, TFeatureSource, TFeatureValues>(
this IRecommenderMapping <TInstanceSource, TInstance, TUser, TItem, TFeatureSource, TFeatureValues> mapping,
double itemHistogramAdjustment = 1.2)
{
return(new NegativeDataGeneratorMapping <TInstanceSource, TInstance, TUser, TItem, TFeatureSource, TFeatureValues>(
mapping, itemHistogramAdjustment));
}
/// <summary>
/// Initializes a new instance of the <see cref="NegativeDataGeneratorMapping{TInstanceSource,TInstance,TUser,TItem,TFeatureSource,TFeatureValues}"/> class.
/// </summary>
/// <param name="mapping">The top level mapping.</param>
/// <param name="itemHistogramAdjustment">
/// The degree to which each element of the item histogram is raised before sampling.
/// Should be different from 1.0 if it is believed that there is some quality
/// bar which drives popular items to be more generally liked.
/// </param>
public NegativeDataGeneratorMapping(
IRecommenderMapping <TInstanceSource, TInstance, TUser, TItem, TFeatureSource, TFeatureValues> mapping,
double itemHistogramAdjustment = DefaultItemHistogramAdjustment)
{
if (mapping == null)
{
throw new ArgumentNullException(nameof(mapping));
}
this.mapping = mapping;
this.itemHistogramAdjustment = itemHistogramAdjustment;
}
SplitToTrainTest <TInstanceSource, TInstance, TUser, TItem, TFeatureSource, TFeatureValues>(
this IRecommenderMapping <TInstanceSource, TInstance, TUser, TItem, TFeatureSource, TFeatureValues> mapping,
double trainingOnlyUserFraction,
double testUserRatingTrainingFraction,
double coldUserFraction = 0,
double coldItemFraction = 0,
double ignoredUserFraction = 0,
double ignoredItemFraction = 0,
bool removeOccasionalColdItems = false)
{
return(new TrainTestSplittingRecommenderMapping <TInstanceSource, TInstance, TUser, TItem, TFeatureSource, TFeatureValues>(
mapping, trainingOnlyUserFraction, testUserRatingTrainingFraction, coldUserFraction, coldItemFraction, ignoredUserFraction, ignoredItemFraction, removeOccasionalColdItems));
}
/// <summary>
/// Initializes a new instance of the
/// <see cref="TrainTestSplittingRecommenderMapping{TInstanceSource,TInstance,TUser,TItem,TFeatureSource,TFeatureValues}"/> class.
/// </summary>
/// <param name="mapping">The wrapped recommender mapping.</param>
/// <param name="trainingOnlyUserFraction">The fraction of users included in the training set only.</param>
/// <param name="testUserRatingTrainingFraction">The fraction of ratings in the training set for each user who is presented in both sets.</param>
/// <param name="coldUserFraction">The fraction of users included in the test set only.</param>
/// <param name="coldItemFraction">The fraction of items included in the test set only.</param>
/// <param name="ignoredUserFraction">The fraction of users not included in either the training or the test set.</param>
/// <param name="ignoredItemFraction">The fraction of items not included in either the training or the test set.</param>
/// <param name="removeOccasionalColdItems">Whether the occasionally produced cold items should be removed from the test set.</param>
public TrainTestSplittingRecommenderMapping(
IRecommenderMapping <TInstanceSource, TInstance, TUser, TItem, TFeatureSource, TFeatureValues> mapping,
double trainingOnlyUserFraction,
double testUserRatingTrainingFraction,
double coldUserFraction = 0,
double coldItemFraction = 0,
double ignoredUserFraction = 0,
double ignoredItemFraction = 0,
bool removeOccasionalColdItems = false)
{
if (mapping == null)
{
throw new ArgumentNullException("mapping");
}
CheckFraction(trainingOnlyUserFraction, "trainingOnlyUserFraction");
CheckFraction(testUserRatingTrainingFraction, "testUserRatingTrainingFraction");
CheckFraction(coldUserFraction, "coldUserFraction");
CheckFraction(coldItemFraction, "coldItemFraction");
CheckFraction(ignoredUserFraction, "ignoredUserFraction");
CheckFraction(ignoredItemFraction, "ignoredItemFraction");
if (ignoredUserFraction + coldUserFraction + trainingOnlyUserFraction > 1)
{
throw new ArgumentException("The total fraction of cold users, training-only users and ignored users can not be greater than 1.");
}
if (ignoredItemFraction + coldItemFraction > 1)
{
throw new ArgumentException("The total fraction of cold items and ignored items can not be greater than 1.");
}
this.mapping = mapping;
this.ignoredUserFraction = ignoredUserFraction;
this.ignoredItemFraction = ignoredItemFraction;
this.trainingOnlyUserFraction = trainingOnlyUserFraction;
this.testUserRatingTrainingFraction = testUserRatingTrainingFraction;
this.coldUserFraction = coldUserFraction;
this.coldItemFraction = coldItemFraction;
this.removeUndesiredColdItems = removeOccasionalColdItems;
}
