prediction

package
v0.0.0-...-7ca0ba9 Latest Latest
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 Go to latest Published: Aug 28, 2015 License: BSD-3-Clause Imports: 11 Imported by: 0

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/v1.3"
...
predictionService, err := prediction.New(oauthHttpClient)

Index

Constants

View Source 
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 [Categorical models only].
	OutputLabel string `json:"outputLabel,omitempty"`

	// OutputMulti: A list of classes 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 OutputOutputMulti struct {
	// Label: The class label.
	Label string `json:"label,omitempty"`

	// Score: The probability of the class.
	Score float64 `json:"score,omitempty"`
}

type Service 

type Service struct {
	BasePath  string // API endpoint base URL
	UserAgent string // optional additional User-Agent fragment

	Hostedmodels *HostedmodelsService

	Training *TrainingService
	// contains filtered or unexported fields
}

func New 

func New(client *http.Client) (*Service, error)

type Training 

type Training 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"`

	// ModelInfo: Model metadata.
	ModelInfo *TrainingModelInfo `json:"modelInfo,omitempty"`

	// SelfLink: A URL to re-request this resource.
	SelfLink string `json:"selfLink,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 classes to be specified [Categorical models only].
	Utility []*TrainingUtility `json:"utility,omitempty"`
}

type TrainingDeleteCall 

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

func (*TrainingDeleteCall) Do 

func (c *TrainingDeleteCall) Do() error

func (*TrainingDeleteCall) Fields 

func (c *TrainingDeleteCall) Fields(s ...googleapi.Field) *TrainingDeleteCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type TrainingGetCall 

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

func (*TrainingGetCall) Do 

func (c *TrainingGetCall) Do() (*Training, error)

func (*TrainingGetCall) Fields 

func (c *TrainingGetCall) Fields(s ...googleapi.Field) *TrainingGetCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type TrainingInsertCall 

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

func (*TrainingInsertCall) Do 

func (c *TrainingInsertCall) Do() (*Training, error)

func (*TrainingInsertCall) Fields 

func (c *TrainingInsertCall) Fields(s ...googleapi.Field) *TrainingInsertCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

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"`

	// NumberClasses: Number of classes in the trained model [Categorical
	// models only].
	NumberClasses int64 `json:"numberClasses,omitempty,string"`

	// NumberInstances: Number of valid data instances used in the trained
	// model.
	NumberInstances int64 `json:"numberInstances,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 TrainingPredictCall 

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

func (*TrainingPredictCall) Do 

func (c *TrainingPredictCall) Do() (*Output, error)

func (*TrainingPredictCall) Fields 

func (c *TrainingPredictCall) Fields(s ...googleapi.Field) *TrainingPredictCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type TrainingService 

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

func NewTrainingService 

func NewTrainingService(s *Service) *TrainingService

func (*TrainingService) Delete 

func (r *TrainingService) Delete(data string) *TrainingDeleteCall

Delete: Delete a trained model

func (*TrainingService) Get 

func (r *TrainingService) Get(data string) *TrainingGetCall

Get: Check training status of your model

func (*TrainingService) Insert 

func (r *TrainingService) Insert(training *Training) *TrainingInsertCall

Insert: Begin training your model

func (*TrainingService) Predict 

func (r *TrainingService) Predict(data string, input *Input) *TrainingPredictCall

Predict: Submit data and request a prediction

func (*TrainingService) Update 

func (r *TrainingService) Update(data string, update *Update) *TrainingUpdateCall

Update: Add new data to a trained model

type TrainingUpdateCall 

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

func (*TrainingUpdateCall) Do 

func (c *TrainingUpdateCall) Do() (*Training, error)

func (*TrainingUpdateCall) Fields 

func (c *TrainingUpdateCall) Fields(s ...googleapi.Field) *TrainingUpdateCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type TrainingUtility 

type TrainingUtility struct {
}

TrainingUtility: Class label (string).

type Update 

type Update struct {
	// ClassLabel: The true class label of this instance
	ClassLabel string `json:"classLabel,omitempty"`

	// CsvInstance: The input features for this instance
	CsvInstance []interface{} `json:"csvInstance,omitempty"`
}

Source Files

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