core

type CrossValidateResult struct {
	TestScore []float64
	TestCosts []float64
}

type CrossValidationEvaluator func(estimator ModelInterface, testSet, trainSet DataSetInterface) (scores, costs []float64)

type DataSet struct {
	// contains filtered or unexported fields
}

type DataSetInterface interface {
	// GlobalMean returns the global mean of ratings in the dataset.
	GlobalMean() float64
	// Count returns the number of ratings in the dataset.
	Count() int
	// UserCount returns the number of users in the dataset.
	UserCount() int
	// ItemCount returns the number of items in the dataset.
	ItemCount() int
	// FeatureCount returns the number of additional features.
	FeatureCount() int
	// Get i-th rating by (user ID, item ID, rating).
	Get(i int) (string, string, float64)
	// GetWithIndex gets i-th rating by (user index, item index, rating).
	GetWithIndex(i int) (int, int, float64)
	// UserIndexer returns the user indexer.
	UserIndexer() *base.Indexer
	// ItemIndexer returns the item indexer.
	ItemIndexer() *base.Indexer
	// SubSet gets a subset of current dataset.
	SubSet(subset []int) DataSetInterface
	// Users returns subsets of users.
	Users() []*base.MarginalSubSet
	// Items returns subsets of items.
	Items() []*base.MarginalSubSet
	// UserFeatures returns additional features of users.
	UserFeatures() []*base.SparseVector
	// ItemFeatures returns additional features of items.
	ItemFeatures() []*base.SparseVector
	// User returns the subset of a user.
	User(userId string) *base.MarginalSubSet
	// Item returns the subset of a item.
	Item(itemId string) *base.MarginalSubSet
	// UserByIndex returns the subset of a user by the index.
	UserByIndex(userIndex int) *base.MarginalSubSet
	// ItemByIndex returns the subset of a item by the index.
	ItemByIndex(itemIndex int) *base.MarginalSubSet
}

type ModelInterface interface {
	// Set parameters.
	SetParams(params base.Params)
	// Get parameters.
	GetParams() base.Params
	// Predict the rating given by a user (userId) to a item (itemId).
	Predict(userId, itemId string) float64
	// Fit a model with a train set and parameters.
	Fit(trainSet DataSetInterface, options *base.RuntimeOptions)
}

type ModelSelectionResult struct {
	BestScore  float64
	BestCost   float64
	BestParams base.Params
	BestIndex  int
	CVResults  []CrossValidateResult
	AllParams  []base.Params
}

type ParameterGrid map[base.ParamName][]interface{}

type RankMetric func(targetSet *base.MarginalSubSet, rankList []string) float64

type RatingMetric func(groundTruth []float64, prediction []float64) float64

type Splitter func(set DataSetInterface, seed int64) ([]DataSetInterface, []DataSetInterface)

type SubSet struct {
	*DataSet // the existed dataset.
	// contains filtered or unexported fields
}