Documentation ¶
Overview ¶
Package prediction provides access to the Prediction API.
See https://developers.google.com/prediction/docs/developer-guide
Usage example:
import "google.golang.org/api/prediction/v1.4" ... predictionService, err := prediction.New(oauthHttpClient)
Index ¶
- Constants
- type HostedmodelsPredictCall
- type HostedmodelsService
- type Input
- type InputInput
- type Output
- type OutputOutputMulti
- type Service
- type TrainedmodelsDeleteCall
- type TrainedmodelsGetCall
- type TrainedmodelsInsertCall
- type TrainedmodelsPredictCall
- type TrainedmodelsService
- func (r *TrainedmodelsService) Delete(id string) *TrainedmodelsDeleteCall
- func (r *TrainedmodelsService) Get(id string) *TrainedmodelsGetCall
- func (r *TrainedmodelsService) Insert(training *Training) *TrainedmodelsInsertCall
- func (r *TrainedmodelsService) Predict(id string, input *Input) *TrainedmodelsPredictCall
- func (r *TrainedmodelsService) Update(id string, update *Update) *TrainedmodelsUpdateCall
- type TrainedmodelsUpdateCall
- type Training
- type TrainingDataAnalysis
- type TrainingModelInfo
- type TrainingModelInfoConfusionMatrix
- type TrainingModelInfoConfusionMatrixRowTotals
- type TrainingUtility
- type Update
Constants ¶
const ( // Manage your data and permissions in Google Cloud Storage DevstorageFullControlScope = "https://www.googleapis.com/auth/devstorage.full_control" // View your data in Google Cloud Storage DevstorageReadOnlyScope = "https://www.googleapis.com/auth/devstorage.read_only" // Manage your data in Google Cloud Storage DevstorageReadWriteScope = "https://www.googleapis.com/auth/devstorage.read_write" // Manage your data in the Google Prediction API PredictionScope = "https://www.googleapis.com/auth/prediction" )
OAuth2 scopes used by this API.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type HostedmodelsPredictCall ¶
type HostedmodelsPredictCall struct {
// contains filtered or unexported fields
}
func (*HostedmodelsPredictCall) Do ¶
func (c *HostedmodelsPredictCall) Do() (*Output, error)
func (*HostedmodelsPredictCall) Fields ¶
func (c *HostedmodelsPredictCall) Fields(s ...googleapi.Field) *HostedmodelsPredictCall
Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.
type HostedmodelsService ¶
type HostedmodelsService struct {
// contains filtered or unexported fields
}
func NewHostedmodelsService ¶
func NewHostedmodelsService(s *Service) *HostedmodelsService
func (*HostedmodelsService) Predict ¶
func (r *HostedmodelsService) Predict(hostedModelName string, input *Input) *HostedmodelsPredictCall
Predict: Submit input and request an output against a hosted model.
type Input ¶
type Input struct { // Input: Input to the model for a prediction Input *InputInput `json:"input,omitempty"` }
type InputInput ¶
type InputInput struct { // CsvInstance: A list of input features, these can be strings or // doubles. CsvInstance []interface{} `json:"csvInstance,omitempty"` }
InputInput: Input to the model for a prediction
type Output ¶
type Output struct { // Id: The unique name for the predictive model. Id string `json:"id,omitempty"` // Kind: What kind of resource this is. Kind string `json:"kind,omitempty"` // OutputLabel: The most likely class label [Categorical models only]. OutputLabel string `json:"outputLabel,omitempty"` // OutputMulti: A list of class labels with their estimated // probabilities [Categorical models only]. OutputMulti []*OutputOutputMulti `json:"outputMulti,omitempty"` // OutputValue: The estimated regression value [Regression models only]. OutputValue float64 `json:"outputValue,omitempty"` // SelfLink: A URL to re-request this resource. SelfLink string `json:"selfLink,omitempty"` }
type OutputOutputMulti ¶
type Service ¶
type Service struct { BasePath string // API endpoint base URL UserAgent string // optional additional User-Agent fragment Hostedmodels *HostedmodelsService Trainedmodels *TrainedmodelsService // contains filtered or unexported fields }
type TrainedmodelsDeleteCall ¶
type TrainedmodelsDeleteCall struct {
// contains filtered or unexported fields
}
func (*TrainedmodelsDeleteCall) Do ¶
func (c *TrainedmodelsDeleteCall) Do() error
func (*TrainedmodelsDeleteCall) Fields ¶
func (c *TrainedmodelsDeleteCall) Fields(s ...googleapi.Field) *TrainedmodelsDeleteCall
Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.
type TrainedmodelsGetCall ¶
type TrainedmodelsGetCall struct {
// contains filtered or unexported fields
}
func (*TrainedmodelsGetCall) Do ¶
func (c *TrainedmodelsGetCall) Do() (*Training, error)
func (*TrainedmodelsGetCall) Fields ¶
func (c *TrainedmodelsGetCall) Fields(s ...googleapi.Field) *TrainedmodelsGetCall
Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.
type TrainedmodelsInsertCall ¶
type TrainedmodelsInsertCall struct {
// contains filtered or unexported fields
}
func (*TrainedmodelsInsertCall) Do ¶
func (c *TrainedmodelsInsertCall) Do() (*Training, error)
func (*TrainedmodelsInsertCall) Fields ¶
func (c *TrainedmodelsInsertCall) Fields(s ...googleapi.Field) *TrainedmodelsInsertCall
Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.
type TrainedmodelsPredictCall ¶
type TrainedmodelsPredictCall struct {
// contains filtered or unexported fields
}
func (*TrainedmodelsPredictCall) Do ¶
func (c *TrainedmodelsPredictCall) Do() (*Output, error)
func (*TrainedmodelsPredictCall) Fields ¶
func (c *TrainedmodelsPredictCall) Fields(s ...googleapi.Field) *TrainedmodelsPredictCall
Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.
type TrainedmodelsService ¶
type TrainedmodelsService struct {
// contains filtered or unexported fields
}
func NewTrainedmodelsService ¶
func NewTrainedmodelsService(s *Service) *TrainedmodelsService
func (*TrainedmodelsService) Delete ¶
func (r *TrainedmodelsService) Delete(id string) *TrainedmodelsDeleteCall
Delete: Delete a trained model.
func (*TrainedmodelsService) Get ¶
func (r *TrainedmodelsService) Get(id string) *TrainedmodelsGetCall
Get: Check training status of your model.
func (*TrainedmodelsService) Insert ¶
func (r *TrainedmodelsService) Insert(training *Training) *TrainedmodelsInsertCall
Insert: Begin training your model.
func (*TrainedmodelsService) Predict ¶
func (r *TrainedmodelsService) Predict(id string, input *Input) *TrainedmodelsPredictCall
Predict: Submit model id and request a prediction
func (*TrainedmodelsService) Update ¶
func (r *TrainedmodelsService) Update(id string, update *Update) *TrainedmodelsUpdateCall
Update: Add new data to a trained model.
type TrainedmodelsUpdateCall ¶
type TrainedmodelsUpdateCall struct {
// contains filtered or unexported fields
}
func (*TrainedmodelsUpdateCall) Do ¶
func (c *TrainedmodelsUpdateCall) Do() (*Training, error)
func (*TrainedmodelsUpdateCall) Fields ¶
func (c *TrainedmodelsUpdateCall) Fields(s ...googleapi.Field) *TrainedmodelsUpdateCall
Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.
type Training ¶
type Training struct { // DataAnalysis: Data Analysis. DataAnalysis *TrainingDataAnalysis `json:"dataAnalysis,omitempty"` // Id: The unique name for the predictive model. Id string `json:"id,omitempty"` // Kind: What kind of resource this is. Kind string `json:"kind,omitempty"` // ModelInfo: Model metadata. ModelInfo *TrainingModelInfo `json:"modelInfo,omitempty"` // SelfLink: A URL to re-request this resource. SelfLink string `json:"selfLink,omitempty"` // StorageDataLocation: Google storage location of the training data // file. StorageDataLocation string `json:"storageDataLocation,omitempty"` // StoragePMMLLocation: Google storage location of the preprocessing // pmml file. StoragePMMLLocation string `json:"storagePMMLLocation,omitempty"` // StoragePMMLModelLocation: Google storage location of the pmml model // file. StoragePMMLModelLocation string `json:"storagePMMLModelLocation,omitempty"` // TrainingStatus: The current status of the training job. This can be // one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND TrainingStatus string `json:"trainingStatus,omitempty"` // Utility: A class weighting function, which allows the importance // weights for class labels to be specified [Categorical models only]. Utility []*TrainingUtility `json:"utility,omitempty"` }
type TrainingDataAnalysis ¶
type TrainingDataAnalysis struct {
Warnings []string `json:"warnings,omitempty"`
}
TrainingDataAnalysis: Data Analysis.
type TrainingModelInfo ¶
type TrainingModelInfo struct { // ClassWeightedAccuracy: Estimated accuracy of model taking utility // weights into account [Categorical models only]. ClassWeightedAccuracy float64 `json:"classWeightedAccuracy,omitempty"` // ClassificationAccuracy: A number between 0.0 and 1.0, where 1.0 is // 100% accurate. This is an estimate, based on the amount and quality // of the training data, of the estimated prediction accuracy. You can // use this is a guide to decide whether the results are accurate enough // for your needs. This estimate will be more reliable if your real // input data is similar to your training data [Categorical models // only]. ClassificationAccuracy float64 `json:"classificationAccuracy,omitempty"` // ConfusionMatrix: An output confusion matrix. This shows an estimate // for how this model will do in predictions. This is first indexed by // the true class label. For each true class label, this provides a pair // {predicted_label, count}, where count is the estimated number of // times the model will predict the predicted label given the true // label. Will not output if more then 100 classes [Categorical models // only]. ConfusionMatrix *TrainingModelInfoConfusionMatrix `json:"confusionMatrix,omitempty"` // ConfusionMatrixRowTotals: A list of the confusion matrix row totals ConfusionMatrixRowTotals *TrainingModelInfoConfusionMatrixRowTotals `json:"confusionMatrixRowTotals,omitempty"` // MeanSquaredError: An estimated mean squared error. The can be used to // measure the quality of the predicted model [Regression models only]. MeanSquaredError float64 `json:"meanSquaredError,omitempty"` // ModelType: Type of predictive model (CLASSIFICATION or REGRESSION) ModelType string `json:"modelType,omitempty"` // NumberInstances: Number of valid data instances used in the trained // model. NumberInstances int64 `json:"numberInstances,omitempty,string"` // NumberLabels: Number of class labels in the trained model // [Categorical models only]. NumberLabels int64 `json:"numberLabels,omitempty,string"` }
TrainingModelInfo: Model metadata.
type TrainingModelInfoConfusionMatrix ¶
type TrainingModelInfoConfusionMatrix struct { }
TrainingModelInfoConfusionMatrix: An output confusion matrix. This shows an estimate for how this model will do in predictions. This is first indexed by the true class label. For each true class label, this provides a pair {predicted_label, count}, where count is the estimated number of times the model will predict the predicted label given the true label. Will not output if more then 100 classes [Categorical models only].
type TrainingModelInfoConfusionMatrixRowTotals ¶
type TrainingModelInfoConfusionMatrixRowTotals struct { }
TrainingModelInfoConfusionMatrixRowTotals: A list of the confusion matrix row totals
type Update ¶
type Update struct { // CsvInstance: The input features for this instance CsvInstance []interface{} `json:"csvInstance,omitempty"` // Label: The class label of this instance Label string `json:"label,omitempty"` // Output: The generic output value - could be regression value or class // label Output string `json:"output,omitempty"` }