ctr

package
v0.5.4 Latest Latest
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 Go to latest Published: Feb 2, 2026 License: Apache-2.0 Imports: 30 Imported by: 0

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Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func AUC 

func AUC(posPrediction, negPrediction []float32) float32

func Accuracy 

func Accuracy(posPrediction, negPrediction []float32) float32

func LoadLibFMFile 

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

LoadLibFMFile loads libFM format file.

func MarshalModel 

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

func Precision 

func Precision(posPrediction, negPrediction []float32) float32

func Recall 

func Recall(posPrediction, _ []float32) float32

Types

type AFM added in v0.5.2 

type AFM struct {
	BaseFactorizationMachines

	// parameters
	B *nn.Tensor
	W nn.Layer
	V nn.Layer
	A []nn.Layer
	E []nn.Layer
	// contains filtered or unexported fields
}

func NewAFM added in v0.5.2 

func NewAFM(params model.Params) *AFM

func (*AFM) BatchInternalPredict added in v0.5.2 

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

func (*AFM) BatchPredict added in v0.5.2 

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

func (*AFM) Clear added in v0.5.2 

func (fm *AFM) Clear()

func (*AFM) Fit added in v0.5.2 

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

func (*AFM) Forward added in v0.5.2 

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

func (*AFM) Init added in v0.5.2 

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

func (*AFM) InternalPredict added in v0.5.2 

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

func (*AFM) Invalid added in v0.5.2 

func (fm *AFM) Invalid() bool

func (*AFM) Marshal added in v0.5.2 

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

func (*AFM) Parameters added in v0.5.2 

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

func (*AFM) Predict added in v0.5.2 

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

func (*AFM) SetParams added in v0.5.2 

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

func (*AFM) SuggestParams added in v0.5.2 

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

func (*AFM) Unmarshal added in v0.5.2 

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

type BaseFactorizationMachines 

type BaseFactorizationMachines struct {
	model.BaseModel
	Index dataset.UnifiedIndex
}

func (*BaseFactorizationMachines) Init 

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

type BatchInference 

type BatchInference interface {
	BatchPredict(inputs []lo.Tuple4[string, string, []Label, []Label], e [][]Embedding, jobs int) []float32
	BatchInternalPredict(x []lo.Tuple2[[]int32, []float32], e [][][]float32, jobs int) []float32
}

type Dataset 

type Dataset struct {
	Index                  dataset.UnifiedIndex
	UserLabels             [][]lo.Tuple2[int32, float32]
	ItemLabels             [][]lo.Tuple2[int32, float32]
	ContextLabels          [][]lo.Tuple2[int32, float32]
	Users                  []int32
	Items                  []int32
	Target                 []float32
	ItemEmbeddings         [][][]float32 // Index by row id, embedding id, embedding dimension
	ItemEmbeddingDimension []int
	ItemEmbeddingIndex     *dataset.Index
	PositiveCount          int
	NegativeCount          int
}

Dataset for click-through-rate models.

func LoadDataFromBuiltIn 

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

LoadDataFromBuiltIn loads built-in dataset.

func (*Dataset) Count 

func (dataset *Dataset) Count() int

Count returns the number of samples.

func (*Dataset) CountContextLabels 

func (dataset *Dataset) CountContextLabels() int

func (*Dataset) CountItemLabels 

func (dataset *Dataset) CountItemLabels() int

func (*Dataset) CountItems 

func (dataset *Dataset) CountItems() int

CountItems returns the number of items.

func (*Dataset) CountNegative 

func (dataset *Dataset) CountNegative() int

func (*Dataset) CountPositive 

func (dataset *Dataset) CountPositive() int

func (*Dataset) CountUserLabels 

func (dataset *Dataset) CountUserLabels() int

func (*Dataset) CountUsers 

func (dataset *Dataset) CountUsers() int

CountUsers returns the number of users.

func (*Dataset) Get 

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

Get returns the i-th sample.

func (*Dataset) GetIndex 

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

func (*Dataset) GetItemEmbeddingDim added in v0.5.2 

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

func (*Dataset) GetItemEmbeddingIndex added in v0.5.2 

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

func (*Dataset) GetTarget 

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

func (*Dataset) Split 

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

Split a dataset to training set and test set.

type Embedding 

type Embedding struct {
	Name  string
	Value []float32
}

func ConvertEmbeddings 

func ConvertEmbeddings(o any) []Embedding

type FactorizationMachines 

type FactorizationMachines interface {
	model.Model
	Predict(userId, itemId string, userFeatures, itemFeatures []Label) float32
	InternalPredict(x []int32, values []float32) float32
	Fit(ctx context.Context, trainSet, testSet dataset.CTRSplit, config *FitConfig) Score
	Marshal(w io.Writer) error
}

func UnmarshalModel 

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

type FitConfig 

type FitConfig struct {
	Jobs     int
	Verbose  int
	Patience int
}

func NewFitConfig 

func NewFitConfig() *FitConfig

func (*FitConfig) LoadDefaultIfNil 

func (config *FitConfig) LoadDefaultIfNil() *FitConfig

func (*FitConfig) SetJobs 

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

func (*FitConfig) SetPatience 

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

func (*FitConfig) SetVerbose 

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

type Label 

type Label struct {
	Name  string
	Value float32
}

func ConvertLabels 

func ConvertLabels(o any) []Label

type ModelCreator 

type ModelCreator func() FactorizationMachines

type ModelSearch 

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

func NewModelSearch 

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

func (*ModelSearch) Objective 

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

func (*ModelSearch) Result 

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

func (*ModelSearch) WithContext 

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

func (*ModelSearch) WithSpan 

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

type Score 

type Score struct {
	RMSE      float32
	Precision float32
	Recall    float32
	Accuracy  float32
	AUC       float32
}

func EvaluateClassification 

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

EvaluateClassification evaluates factorization machines in classification task.

func EvaluateRegression 

func EvaluateRegression(estimator FactorizationMachines, testSet *Dataset) Score

EvaluateRegression evaluates factorization machines in regression task.

func (Score) BetterThan 

func (score Score) BetterThan(s Score) bool

func (Score) GetValue 

func (score Score) GetValue() float32

func (Score) ZapFields 

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

Source Files

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