func Version()string

type BagOfWordsCosineSimilarityFeatureContribution struct {// This is the name of the source column a for the feature group.ColumnNameAstring// This is the name of the source column b for the feature group.ColumnNameBstring// This is the value of the feature.FeatureValuefloat32// This is the amount that the feature contributed to the output.FeatureContributionValuefloat32}

type BagOfWordsFeatureContribution struct {// This is the name of the source column for the feature group.ColumnNamestring// This is the ngram for the feature.NGramNGram// This is the value of the feature.FeatureValuefloat32// This is the amount that the feature contributed to the output.FeatureContributionValuefloat32}

type Bigram struct {// This is the first token in the bigram.TokenAstring// This is the second token in the bigram.TokenBstring}

type BinaryClassificationPredictOutput struct {// This is the name of the predicted class.ClassNamestring `json:"className"`// This is the probability the model assigned to the predicted class.Probabilityfloat32 `json:"probability"`// If computing feature contributions was enabled in the predict options, this value will explain the model's output, showing how much each feature contributed to the output.FeatureContributionsFeatureContributions `json:"-"`}

type FeatureContributionEntry interface {// contains filtered or unexported methods}

type FeatureContributionTypeint

type FeatureContributions struct {// This is the value the model would output if all features had baseline values.BaselineValuefloat32// This is the value the model output. Any difference from the `baseline_value` is because of the deviation of the features from their baseline values.OutputValuefloat32// This list will contain one entry for each of the model's features. Note that features are computed from columns, so there will likely be more features than columns.Entries []FeatureContributionEntry}

type IdentityFeatureContribution struct {// This is the name of the source column for the feature group.ColumnNamestring// This is the value of the feature.FeatureValuefloat32// This is the amount that the feature contributed to the output.FeatureContributionValuefloat32}

type LoadModelOptions struct {// If you are running the app locally or on your own server, use this field to provide the url to it. If not specified, the default value ishttps://app.modelfox.dev.ModelFoxURLstring}

type LogPredictionArgs struct {// This is a unique identifier for the prediction, which will associate it with a true value event and allow you to look it up in the app.Identifierstring// This is the same `PredictInput` value that you passed to `model.Predict`.InputPredictInput// This is the same `PredictOptions` value that you passed to `model.Predict`.OptionsPredictOptions// This is the output returned by `model.Predict`.OutputPredictOutput}

type LogTrueValueArgs struct {// This is a unique identifier for the prediction, which will associate it with a true value event and allow you to look it up in the app.Identifierstring// This is the true value for the prediction.TrueValue interface{}}

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

funcLoadModelFromBytes¶

func LoadModelFromBytes(data []byte, options *LoadModelOptions) (*Model,error)

Load a model from bytes instead of a file. You should use this only if you already have a `.modelfox` loaded into memory. Otherwise, use `model.LoadModelFromPath`, which is faster because it memory maps the file.

funcLoadModelFromPath¶

func LoadModelFromPath(pathstring, options *LoadModelOptions) (*Model,error)

Load a model from a `.modelfox` file at `path`.

func (Model)Destroy¶

func (mModel) Destroy()

Destroy frees up the memory used by the model. You should call this with defer after loading your model.

func (Model)EnqueueLogPrediction¶

func (mModel) EnqueueLogPrediction(argsLogPredictionArgs)

Add a prediction event to the queue. Remember to call `model.FlushLogQueue` at a later point to send the event to the app.

func (Model)EnqueueLogTrueValue¶

func (mModel) EnqueueLogTrueValue(argsLogTrueValueArgs)

Add a true value event to the queue. Remember to call `model.FlushLogQueue` at a later point to send the event to the app.

func (Model)FlushLogQueue¶

func (mModel) FlushLogQueue()error

Send all events in the queue to the app.

func (Model)ID¶

func (mModel) ID()string

Retrieve the model's id.

func (Model)LogPrediction¶

func (mModel) LogPrediction(argsLogPredictionArgs)error

Send a prediction event to the app. If you want to batch events, you can use `model.EnqueueLogPrediction` instead.

func (Model)LogTrueValue¶

func (mModel) LogTrueValue(argsLogTrueValueArgs)error

Send a true value event to the app. If you want to batch events, you can use `model.EnqueueLogTrueValue` instead.

func (Model)Predict¶

func (mModel) Predict(input []PredictInput, options *PredictOptions) []PredictOutput

Make a prediction with multiple inputs.

func (Model)PredictOne¶

func (mModel) PredictOne(inputPredictInput, options *PredictOptions)PredictOutput

Make a prediction with a single input.

type MulticlassClassificationPredictOutput struct {// This is the name of the predicted class.ClassNamestring `json:"className`// This is the probability the model assigned to the predicted class.Probabilityfloat32 `json:"probability"`// This value maps from class names to the probability the model assigned to each class.Probabilities map[string]float32 `json:"probabilities"`// If computing feature contributions was enabled in the predict options, this value will explain the model's output, showing how much each feature contributed to the output. This value maps from class names to `FeatureContributions` values for each class. The class with the `FeatureContributions` value with the highest `output_value` is the predicted class.FeatureContributions map[string]FeatureContributions `json:"-"`}

type NGram interface {// contains filtered or unexported methods}

type NormalizedFeatureContribution struct {// This is the name of the source column for the feature group.ColumnNamestring// This is the value of the feature.FeatureValuefloat32// This is the amount that the feature contributed to the output.FeatureContributionValuefloat32}

type OneHotEncodedFeatureContribution struct {// This is the name of the source column for the feature group.ColumnNamestring// This is the enum variant the feature indicates the presence of.Variantstring// This is the value of the feature.FeatureValuebool// This is the amount that the feature contributed to the output.FeatureContributionValuefloat32}

type PredictInput map[string]interface{}

type PredictOptions struct {// If your model is a binary classifier, use this field to make predictions using a threshold chosen on the tuning page of the app. The default value is `0.5`.Thresholdfloat32 `json:"threshold"`// Computing feature contributions is disabled by default. If you set this field to `true`, you will be able to access the feature contributions with the `feature_contributions` field of the predict output.ComputeFeatureContributionsbool `json:"computeFeatureContributions"`}

type PredictOutput interface {// contains filtered or unexported methods}

type RegressionPredictOutput struct {// This is the predicted value.Valuefloat32 `json:"value"`// If computing feature contributions was enabled in the predict options, this value will explain the model's output, showing how much each feature contributed to the output.FeatureContributionsFeatureContributions `json:"-"`}

type TaskTypeint

type Unigram struct {// This is the token.Tokenstring}

type WordEmbeddingFeatureContribution struct {// This is the name of the source column for the feature group.ColumnNamestring// This is the index of the feature in the word embedding.ValueIndexint// This is the amount that the feature contributed to the output.FeatureContributionValuefloat32}