prediction

package
v0.0.0-...-0d8a8f5 Latest Latest
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 Go to latest Published: Nov 17, 2014 License: BSD-3-Clause Imports: 10 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 "code.google.com/p/google-api-go-client/prediction/v1.4"
...
predictionService, err := prediction.New(oauthHttpClient)

Index

Constants

View Source 
const (
	// Manage your data and permissions in Google Cloud Storage
	DevstorageFull_controlScope = "https://www.googleapis.com/auth/devstorage.full_control"

	// View your data in Google Cloud Storage
	DevstorageRead_onlyScope = "https://www.googleapis.com/auth/devstorage.read_only"

	// Manage your data in Google Cloud Storage
	DevstorageRead_writeScope = "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"`
}

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 OutputOutputMulti struct {
	// Label: The class label.
	Label string `json:"label,omitempty"`

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

type Service 

type Service struct {
	BasePath string // API endpoint base URL

	Hostedmodels *HostedmodelsService

	Trainedmodels *TrainedmodelsService
	// contains filtered or unexported fields
}

func New 

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

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"`
}

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"`
}

type TrainingModelInfoConfusionMatrix 

type TrainingModelInfoConfusionMatrix struct {
}

type TrainingModelInfoConfusionMatrixRowTotals 

type TrainingModelInfoConfusionMatrixRowTotals struct {
}

type TrainingUtility 

type TrainingUtility struct {
}

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"`
}

Source Files

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