prediction

package
v0.0.0-...-de2eba5 Latest Latest
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 Go to latest Published: May 19, 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/v1.6"
...
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 Analyze 

type Analyze struct {
	// DataDescription: Description of the data the model was trained on.
	DataDescription *AnalyzeDataDescription `json:"dataDescription,omitempty"`

	// Errors: List of errors with the data.
	Errors []map[string]string `json:"errors,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"`

	// ModelDescription: Description of the model.
	ModelDescription *AnalyzeModelDescription `json:"modelDescription,omitempty"`

	// SelfLink: A URL to re-request this resource.
	SelfLink string `json:"selfLink,omitempty"`
}

type AnalyzeDataDescription 

type AnalyzeDataDescription struct {
	// Features: Description of the input features in the data set.
	Features []*AnalyzeDataDescriptionFeatures `json:"features,omitempty"`

	// OutputFeature: Description of the output value or label.
	OutputFeature *AnalyzeDataDescriptionOutputFeature `json:"outputFeature,omitempty"`
}

type AnalyzeDataDescriptionFeatures 

type AnalyzeDataDescriptionFeatures struct {
	// Categorical: Description of the categorical values of this feature.
	Categorical *AnalyzeDataDescriptionFeaturesCategorical `json:"categorical,omitempty"`

	// Index: The feature index.
	Index int64 `json:"index,omitempty,string"`

	// Numeric: Description of the numeric values of this feature.
	Numeric *AnalyzeDataDescriptionFeaturesNumeric `json:"numeric,omitempty"`

	// Text: Description of multiple-word text values of this feature.
	Text *AnalyzeDataDescriptionFeaturesText `json:"text,omitempty"`
}

type AnalyzeDataDescriptionFeaturesCategorical 

type AnalyzeDataDescriptionFeaturesCategorical struct {
	// Count: Number of categorical values for this feature in the data.
	Count int64 `json:"count,omitempty,string"`

	// Values: List of all the categories for this feature in the data set.
	Values []*AnalyzeDataDescriptionFeaturesCategoricalValues `json:"values,omitempty"`
}

type AnalyzeDataDescriptionFeaturesCategoricalValues 

type AnalyzeDataDescriptionFeaturesCategoricalValues struct {
	// Count: Number of times this feature had this value.
	Count int64 `json:"count,omitempty,string"`

	// Value: The category name.
	Value string `json:"value,omitempty"`
}

type AnalyzeDataDescriptionFeaturesNumeric 

type AnalyzeDataDescriptionFeaturesNumeric struct {
	// Count: Number of numeric values for this feature in the data set.
	Count int64 `json:"count,omitempty,string"`

	// Mean: Mean of the numeric values of this feature in the data set.
	Mean string `json:"mean,omitempty"`

	// Variance: Variance of the numeric values of this feature in the data
	// set.
	Variance string `json:"variance,omitempty"`
}

type AnalyzeDataDescriptionFeaturesText 

type AnalyzeDataDescriptionFeaturesText struct {
	// Count: Number of multiple-word text values for this feature.
	Count int64 `json:"count,omitempty,string"`
}

type AnalyzeDataDescriptionOutputFeature 

type AnalyzeDataDescriptionOutputFeature struct {
	// Numeric: Description of the output values in the data set.
	Numeric *AnalyzeDataDescriptionOutputFeatureNumeric `json:"numeric,omitempty"`

	// Text: Description of the output labels in the data set.
	Text []*AnalyzeDataDescriptionOutputFeatureText `json:"text,omitempty"`
}

type AnalyzeDataDescriptionOutputFeatureNumeric 

type AnalyzeDataDescriptionOutputFeatureNumeric struct {
	// Count: Number of numeric output values in the data set.
	Count int64 `json:"count,omitempty,string"`

	// Mean: Mean of the output values in the data set.
	Mean string `json:"mean,omitempty"`

	// Variance: Variance of the output values in the data set.
	Variance string `json:"variance,omitempty"`
}

type AnalyzeDataDescriptionOutputFeatureText 

type AnalyzeDataDescriptionOutputFeatureText struct {
	// Count: Number of times the output label occurred in the data set.
	Count int64 `json:"count,omitempty,string"`

	// Value: The output label.
	Value string `json:"value,omitempty"`
}

type AnalyzeModelDescription 

type AnalyzeModelDescription struct {
	// 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 *AnalyzeModelDescriptionConfusionMatrix `json:"confusionMatrix,omitempty"`

	// ConfusionMatrixRowTotals: A list of the confusion matrix row totals.
	ConfusionMatrixRowTotals map[string]string `json:"confusionMatrixRowTotals,omitempty"`

	// Modelinfo: Basic information about the model.
	Modelinfo *Insert2 `json:"modelinfo,omitempty"`
}

type AnalyzeModelDescriptionConfusionMatrix 

type AnalyzeModelDescriptionConfusionMatrix struct {
}

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(project string, 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 Insert 

type Insert struct {
	// Id: The unique name for the predictive model.
	Id string `json:"id,omitempty"`

	// ModelType: Type of predictive model (classification or regression).
	ModelType string `json:"modelType,omitempty"`

	// SourceModel: The Id of the model to be copied over.
	SourceModel string `json:"sourceModel,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"`

	// TrainingInstances: Instances to train model on.
	TrainingInstances []*InsertTrainingInstances `json:"trainingInstances,omitempty"`

	// Utility: A class weighting function, which allows the importance
	// weights for class labels to be specified (Categorical models only).
	Utility []*InsertUtility `json:"utility,omitempty"`
}

type Insert2 

type Insert2 struct {
	// Created: Insert time of the model (as a RFC 3339 timestamp).
	Created string `json:"created,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 *Insert2ModelInfo `json:"modelInfo,omitempty"`

	// ModelType: Type of predictive model (CLASSIFICATION or REGRESSION).
	ModelType string `json:"modelType,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"`

	// TrainingComplete: Training completion time (as a RFC 3339 timestamp).
	TrainingComplete string `json:"trainingComplete,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"`
}

type Insert2ModelInfo 

type Insert2ModelInfo struct {
	// ClassWeightedAccuracy: Estimated accuracy of model taking utility
	// weights into account (Categorical models only).
	ClassWeightedAccuracy string `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 string `json:"classificationAccuracy,omitempty"`

	// MeanSquaredError: An estimated mean squared error. The can be used to
	// measure the quality of the predicted model (Regression models only).
	MeanSquaredError string `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 InsertTrainingInstances 

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

	// Output: The generic output value - could be regression or class
	// label.
	Output string `json:"output,omitempty"`
}

type InsertUtility 

type InsertUtility struct {
}

type List 

type List struct {
	// Items: List of models.
	Items []*Insert2 `json:"items,omitempty"`

	// Kind: What kind of resource this is.
	Kind string `json:"kind,omitempty"`

	// NextPageToken: Pagination token to fetch the next page, if one
	// exists.
	NextPageToken string `json:"nextPageToken,omitempty"`

	// SelfLink: A URL to re-request this resource.
	SelfLink string `json:"selfLink,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 string `json:"score,omitempty"`
}

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
}

func New 

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

type TrainedmodelsAnalyzeCall 

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

func (*TrainedmodelsAnalyzeCall) Do 

func (c *TrainedmodelsAnalyzeCall) Do() (*Analyze, error)

func (*TrainedmodelsAnalyzeCall) Fields 

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

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

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() (*Insert2, 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() (*Insert2, 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 TrainedmodelsListCall 

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

func (*TrainedmodelsListCall) Do 

func (c *TrainedmodelsListCall) Do() (*List, error)

func (*TrainedmodelsListCall) Fields 

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

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

func (*TrainedmodelsListCall) MaxResults 

func (c *TrainedmodelsListCall) MaxResults(maxResults int64) *TrainedmodelsListCall

MaxResults sets the optional parameter "maxResults": Maximum number of results to return.

func (*TrainedmodelsListCall) PageToken 

func (c *TrainedmodelsListCall) PageToken(pageToken string) *TrainedmodelsListCall

PageToken sets the optional parameter "pageToken": Pagination token.

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) Analyze 

func (r *TrainedmodelsService) Analyze(project string, id string) *TrainedmodelsAnalyzeCall

Analyze: Get analysis of the model and the data the model was trained on.

func (*TrainedmodelsService) Delete 

func (r *TrainedmodelsService) Delete(project string, id string) *TrainedmodelsDeleteCall

Delete: Delete a trained model.

func (*TrainedmodelsService) Get 

func (r *TrainedmodelsService) Get(project string, id string) *TrainedmodelsGetCall

Get: Check training status of your model.

func (*TrainedmodelsService) Insert 

func (r *TrainedmodelsService) Insert(project string, insert *Insert) *TrainedmodelsInsertCall

Insert: Train a Prediction API model.

func (*TrainedmodelsService) List 

func (r *TrainedmodelsService) List(project string) *TrainedmodelsListCall

List: List available models.

func (*TrainedmodelsService) Predict 

func (r *TrainedmodelsService) Predict(project string, id string, input *Input) *TrainedmodelsPredictCall

Predict: Submit model id and request a prediction.

func (*TrainedmodelsService) Update 

func (r *TrainedmodelsService) Update(project string, 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() (*Insert2, 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 Update 

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

	// Output: The generic output value - could be regression or class
	// label.
	Output string `json:"output,omitempty"`
}

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