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ctr

package

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Versions in this module

v0

v0.5.4

Feb 2, 2026

v0.5.3

Jan 31, 2026

v0.5.2

Jan 17, 2026

Changes in this version

+ type AFM struct

+ A []nn.Layer

+ B *nn.Tensor

+ E []nn.Layer

+ V nn.Layer

+ W nn.Layer

+ func NewAFM(params model.Params) *AFM

+ func (fm *AFM) BatchInternalPredict(x []lo.Tuple2[[]int32, []float32], e [][][]float32, jobs int) []float32

+ func (fm *AFM) BatchPredict(inputs []lo.Tuple4[string, string, []Label, []Label], embeddings [][]Embedding, ...) []float32

+ func (fm *AFM) Clear()

+ func (fm *AFM) Fit(ctx context.Context, trainSet, testSet dataset.CTRSplit, config *FitConfig) Score

+ func (fm *AFM) Forward(indices, values *nn.Tensor, embeddings []*nn.Tensor, jobs int) *nn.Tensor

+ func (fm *AFM) Init(trainSet dataset.CTRSplit)

+ func (fm *AFM) InternalPredict(_ []int32, _ []float32) float32

+ func (fm *AFM) Invalid() bool

+ func (fm *AFM) Marshal(w io.Writer) error

+ func (fm *AFM) Parameters() []*nn.Tensor

+ func (fm *AFM) Predict(_, _ string, _, _ []Label) float32

+ func (fm *AFM) SetParams(params model.Params)

+ func (fm *AFM) SuggestParams(trial goptuna.Trial) model.Params

+ func (fm *AFM) Unmarshal(r io.Reader) error

type Dataset

+ func (dataset *Dataset) GetItemEmbeddingDim() []int

+ func (dataset *Dataset) GetItemEmbeddingIndex() *dataset.Index

v0.5.1

Dec 20, 2025

v0.5.0

Dec 15, 2025

Changes in this version

+ func AUC(posPrediction, negPrediction []float32) float32

+ func Accuracy(posPrediction, negPrediction []float32) float32

+ func LoadLibFMFile(path string) (features [][]lo.Tuple2[int32, float32], targets []float32, maxLabel int32, ...)

+ func MarshalModel(w io.Writer, m FactorizationMachines) error

+ func Precision(posPrediction, negPrediction []float32) float32

+ func Recall(posPrediction, _ []float32) float32

+ type BaseFactorizationMachines struct

+ Index dataset.UnifiedIndex

+ func (b *BaseFactorizationMachines) Init(trainSet dataset.CTRSplit)

+ type BatchInference interface

+ BatchInternalPredict func(x []lo.Tuple2[[]int32, []float32], jobs int) []float32

+ BatchPredict func(inputs []lo.Tuple4[string, string, []Label, []Label], jobs int) []float32

+ type Dataset struct

+ ContextLabels [][]lo.Tuple2[int32, float32]

+ Index dataset.UnifiedIndex

+ ItemEmbeddingDimension []int

+ ItemEmbeddingIndex *dataset.Index

+ ItemEmbeddings [][][]float32

+ ItemLabels [][]lo.Tuple2[int32, float32]

+ Items []int32

+ NegativeCount int

+ PositiveCount int

+ Target []float32

+ UserLabels [][]lo.Tuple2[int32, float32]

+ Users []int32

+ func LoadDataFromBuiltIn(name string) (train, test *Dataset, err error)

+ func (dataset *Dataset) Count() int

+ func (dataset *Dataset) CountContextLabels() int

+ func (dataset *Dataset) CountItemLabels() int

+ func (dataset *Dataset) CountItems() int

+ func (dataset *Dataset) CountNegative() int

+ func (dataset *Dataset) CountPositive() int

+ func (dataset *Dataset) CountUserLabels() int

+ func (dataset *Dataset) CountUsers() int

+ func (dataset *Dataset) Get(i int) ([]int32, []float32, float32)

+ func (dataset *Dataset) GetIndex() dataset.UnifiedIndex

+ func (dataset *Dataset) GetTarget(i int) float32

+ func (dataset *Dataset) Split(ratio float32, seed int64) (*Dataset, *Dataset)

+ type Embedding struct

+ Name string

+ Value []float32

+ func ConvertEmbeddings(o any) []Embedding

+ type FMV2 struct

+ B *nn.Tensor

+ V nn.Layer

+ W nn.Layer

+ func NewFMV2(params model.Params) *FMV2

+ func (fm *FMV2) BatchInternalPredict(x []lo.Tuple2[[]int32, []float32], jobs int) []float32

+ func (fm *FMV2) BatchPredict(inputs []lo.Tuple4[string, string, []Label, []Label], jobs int) []float32

+ func (fm *FMV2) Clear()

+ func (fm *FMV2) Fit(ctx context.Context, trainSet, testSet dataset.CTRSplit, config *FitConfig) Score

+ func (fm *FMV2) Forward(indices, values *nn.Tensor, jobs int) *nn.Tensor

+ func (fm *FMV2) Init(trainSet dataset.CTRSplit)

+ func (fm *FMV2) InternalPredict(_ []int32, _ []float32) float32

+ func (fm *FMV2) Invalid() bool

+ func (fm *FMV2) Marshal(w io.Writer) error

+ func (fm *FMV2) Parameters() []*nn.Tensor

+ func (fm *FMV2) Predict(_, _ string, _, _ []Label) float32

+ func (fm *FMV2) SetParams(params model.Params)

+ func (fm *FMV2) SuggestParams(trial goptuna.Trial) model.Params

+ func (fm *FMV2) Unmarshal(r io.Reader) error

+ type FactorizationMachineCloner interface

+ Clone func() FactorizationMachines

+ type FactorizationMachineSpawner interface

+ Spawn func() FactorizationMachines

+ type FactorizationMachines interface

+ Fit func(ctx context.Context, trainSet, testSet dataset.CTRSplit, config *FitConfig) Score

+ InternalPredict func(x []int32, values []float32) float32

+ Marshal func(w io.Writer) error

+ Predict func(userId, itemId string, userFeatures, itemFeatures []Label) float32

+ func Spawn(m FactorizationMachines) FactorizationMachines

+ func UnmarshalModel(r io.Reader) (FactorizationMachines, error)

+ type FitConfig struct

+ Jobs int

+ Patience int

+ Verbose int

+ func NewFitConfig() *FitConfig

+ func (config *FitConfig) LoadDefaultIfNil() *FitConfig

+ func (config *FitConfig) SetJobs(jobs int) *FitConfig

+ func (config *FitConfig) SetPatience(patience int) *FitConfig

+ func (config *FitConfig) SetVerbose(verbose int) *FitConfig

+ type Label struct

+ Name string

+ Value float32

+ func ConvertLabels(o any) []Label

+ type ModelCreator func() FactorizationMachines

+ type ModelSearch struct

+ func NewModelSearch(models map[string]ModelCreator, trainSet, testSet dataset.CTRSplit, ...) *ModelSearch

+ func (ms *ModelSearch) Objective(trial goptuna.Trial) (float64, error)

+ func (ms *ModelSearch) Result() meta.Model[Score]

+ func (ms *ModelSearch) WithContext(ctx context.Context) *ModelSearch

+ func (ms *ModelSearch) WithSpan(span *monitor.Span) *ModelSearch

+ type Score struct

+ AUC float32

+ Accuracy float32

+ Precision float32

+ RMSE float32

+ Recall float32

+ func EvaluateClassification(estimator FactorizationMachines, testSet dataset.CTRSplit, jobs int) Score

+ func EvaluateRegression(estimator FactorizationMachines, testSet *Dataset) Score

+ func (score Score) BetterThan(s Score) bool

+ func (score Score) GetValue() float32

+ func (score Score) ZapFields() []zap.Field

v0.5.0-rc

Nov 22, 2025

v0.5.0-alpha.8

Nov 15, 2025

v0.5.0-alpha.7

Nov 2, 2025

v0.5.0-alpha.6

Sep 21, 2025

v0.5.0-alpha.5

Aug 25, 2025

v0.5.0-alpha.4

Aug 15, 2025