dex/vendor/google.golang.org/api/prediction/v1.4/prediction-gen.go

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2016-04-09 00:26:29 +05:30
// 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)
package prediction
import (
"bytes"
"encoding/json"
"errors"
"fmt"
"google.golang.org/api/googleapi"
"io"
"net/http"
"net/url"
"strconv"
"strings"
)
// Always reference these packages, just in case the auto-generated code
// below doesn't.
var _ = bytes.NewBuffer
var _ = strconv.Itoa
var _ = fmt.Sprintf
var _ = json.NewDecoder
var _ = io.Copy
var _ = url.Parse
var _ = googleapi.Version
var _ = errors.New
var _ = strings.Replace
const apiId = "prediction:v1.4"
const apiName = "prediction"
const apiVersion = "v1.4"
const basePath = "https://www.googleapis.com/prediction/v1.4/"
// OAuth2 scopes used by this API.
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"
)
func New(client *http.Client) (*Service, error) {
if client == nil {
return nil, errors.New("client is nil")
}
s := &Service{client: client, BasePath: basePath}
s.Hostedmodels = NewHostedmodelsService(s)
s.Trainedmodels = NewTrainedmodelsService(s)
return s, nil
}
type Service struct {
client *http.Client
BasePath string // API endpoint base URL
Hostedmodels *HostedmodelsService
Trainedmodels *TrainedmodelsService
}
func NewHostedmodelsService(s *Service) *HostedmodelsService {
rs := &HostedmodelsService{s: s}
return rs
}
type HostedmodelsService struct {
s *Service
}
func NewTrainedmodelsService(s *Service) *TrainedmodelsService {
rs := &TrainedmodelsService{s: s}
return rs
}
type TrainedmodelsService struct {
s *Service
}
type Input struct {
// Input: Input to the model for a prediction
Input *InputInput `json:"input,omitempty"`
}
type InputInput struct {
// CsvInstance: A list of input features, these can be strings or
// doubles.
CsvInstance []interface{} `json:"csvInstance,omitempty"`
}
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 struct {
// Label: The class label.
Label string `json:"label,omitempty"`
// Score: The probability of the class label.
Score float64 `json:"score,omitempty"`
}
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 struct {
Warnings []string `json:"warnings,omitempty"`
}
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 struct {
}
type TrainingModelInfoConfusionMatrixRowTotals struct {
}
type TrainingUtility struct {
}
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"`
}
// method id "prediction.hostedmodels.predict":
type HostedmodelsPredictCall struct {
s *Service
hostedModelName string
input *Input
opt_ map[string]interface{}
}
// Predict: Submit input and request an output against a hosted model.
func (r *HostedmodelsService) Predict(hostedModelName string, input *Input) *HostedmodelsPredictCall {
c := &HostedmodelsPredictCall{s: r.s, opt_: make(map[string]interface{})}
c.hostedModelName = hostedModelName
c.input = input
return c
}
// Fields allows partial responses to be retrieved.
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
// for more information.
func (c *HostedmodelsPredictCall) Fields(s ...googleapi.Field) *HostedmodelsPredictCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *HostedmodelsPredictCall) Do() (*Output, error) {
var body io.Reader = nil
body, err := googleapi.WithoutDataWrapper.JSONReader(c.input)
if err != nil {
return nil, err
}
ctype := "application/json"
params := make(url.Values)
params.Set("alt", "json")
if v, ok := c.opt_["fields"]; ok {
params.Set("fields", fmt.Sprintf("%v", v))
}
urls := googleapi.ResolveRelative(c.s.BasePath, "hostedmodels/{hostedModelName}/predict")
urls += "?" + params.Encode()
req, _ := http.NewRequest("POST", urls, body)
googleapi.Expand(req.URL, map[string]string{
"hostedModelName": c.hostedModelName,
})
req.Header.Set("Content-Type", ctype)
req.Header.Set("User-Agent", "google-api-go-client/0.5")
res, err := c.s.client.Do(req)
if err != nil {
return nil, err
}
defer googleapi.CloseBody(res)
if err := googleapi.CheckResponse(res); err != nil {
return nil, err
}
var ret *Output
if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
return nil, err
}
return ret, nil
// {
// "description": "Submit input and request an output against a hosted model.",
// "httpMethod": "POST",
// "id": "prediction.hostedmodels.predict",
// "parameterOrder": [
// "hostedModelName"
// ],
// "parameters": {
// "hostedModelName": {
// "description": "The name of a hosted model.",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "hostedmodels/{hostedModelName}/predict",
// "request": {
// "$ref": "Input"
// },
// "response": {
// "$ref": "Output"
// },
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}
// method id "prediction.trainedmodels.delete":
type TrainedmodelsDeleteCall struct {
s *Service
id string
opt_ map[string]interface{}
}
// Delete: Delete a trained model.
func (r *TrainedmodelsService) Delete(id string) *TrainedmodelsDeleteCall {
c := &TrainedmodelsDeleteCall{s: r.s, opt_: make(map[string]interface{})}
c.id = id
return c
}
// Fields allows partial responses to be retrieved.
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
// for more information.
func (c *TrainedmodelsDeleteCall) Fields(s ...googleapi.Field) *TrainedmodelsDeleteCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *TrainedmodelsDeleteCall) Do() error {
var body io.Reader = nil
params := make(url.Values)
params.Set("alt", "json")
if v, ok := c.opt_["fields"]; ok {
params.Set("fields", fmt.Sprintf("%v", v))
}
urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels/{id}")
urls += "?" + params.Encode()
req, _ := http.NewRequest("DELETE", urls, body)
googleapi.Expand(req.URL, map[string]string{
"id": c.id,
})
req.Header.Set("User-Agent", "google-api-go-client/0.5")
res, err := c.s.client.Do(req)
if err != nil {
return err
}
defer googleapi.CloseBody(res)
if err := googleapi.CheckResponse(res); err != nil {
return err
}
return nil
// {
// "description": "Delete a trained model.",
// "httpMethod": "DELETE",
// "id": "prediction.trainedmodels.delete",
// "parameterOrder": [
// "id"
// ],
// "parameters": {
// "id": {
// "description": "The unique name for the predictive model.",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "trainedmodels/{id}",
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}
// method id "prediction.trainedmodels.get":
type TrainedmodelsGetCall struct {
s *Service
id string
opt_ map[string]interface{}
}
// Get: Check training status of your model.
func (r *TrainedmodelsService) Get(id string) *TrainedmodelsGetCall {
c := &TrainedmodelsGetCall{s: r.s, opt_: make(map[string]interface{})}
c.id = id
return c
}
// Fields allows partial responses to be retrieved.
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
// for more information.
func (c *TrainedmodelsGetCall) Fields(s ...googleapi.Field) *TrainedmodelsGetCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *TrainedmodelsGetCall) Do() (*Training, error) {
var body io.Reader = nil
params := make(url.Values)
params.Set("alt", "json")
if v, ok := c.opt_["fields"]; ok {
params.Set("fields", fmt.Sprintf("%v", v))
}
urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels/{id}")
urls += "?" + params.Encode()
req, _ := http.NewRequest("GET", urls, body)
googleapi.Expand(req.URL, map[string]string{
"id": c.id,
})
req.Header.Set("User-Agent", "google-api-go-client/0.5")
res, err := c.s.client.Do(req)
if err != nil {
return nil, err
}
defer googleapi.CloseBody(res)
if err := googleapi.CheckResponse(res); err != nil {
return nil, err
}
var ret *Training
if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
return nil, err
}
return ret, nil
// {
// "description": "Check training status of your model.",
// "httpMethod": "GET",
// "id": "prediction.trainedmodels.get",
// "parameterOrder": [
// "id"
// ],
// "parameters": {
// "id": {
// "description": "The unique name for the predictive model.",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "trainedmodels/{id}",
// "response": {
// "$ref": "Training"
// },
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}
// method id "prediction.trainedmodels.insert":
type TrainedmodelsInsertCall struct {
s *Service
training *Training
opt_ map[string]interface{}
}
// Insert: Begin training your model.
func (r *TrainedmodelsService) Insert(training *Training) *TrainedmodelsInsertCall {
c := &TrainedmodelsInsertCall{s: r.s, opt_: make(map[string]interface{})}
c.training = training
return c
}
// Fields allows partial responses to be retrieved.
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
// for more information.
func (c *TrainedmodelsInsertCall) Fields(s ...googleapi.Field) *TrainedmodelsInsertCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *TrainedmodelsInsertCall) Do() (*Training, error) {
var body io.Reader = nil
body, err := googleapi.WithoutDataWrapper.JSONReader(c.training)
if err != nil {
return nil, err
}
ctype := "application/json"
params := make(url.Values)
params.Set("alt", "json")
if v, ok := c.opt_["fields"]; ok {
params.Set("fields", fmt.Sprintf("%v", v))
}
urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels")
urls += "?" + params.Encode()
req, _ := http.NewRequest("POST", urls, body)
googleapi.SetOpaque(req.URL)
req.Header.Set("Content-Type", ctype)
req.Header.Set("User-Agent", "google-api-go-client/0.5")
res, err := c.s.client.Do(req)
if err != nil {
return nil, err
}
defer googleapi.CloseBody(res)
if err := googleapi.CheckResponse(res); err != nil {
return nil, err
}
var ret *Training
if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
return nil, err
}
return ret, nil
// {
// "description": "Begin training your model.",
// "httpMethod": "POST",
// "id": "prediction.trainedmodels.insert",
// "path": "trainedmodels",
// "request": {
// "$ref": "Training"
// },
// "response": {
// "$ref": "Training"
// },
// "scopes": [
// "https://www.googleapis.com/auth/devstorage.full_control",
// "https://www.googleapis.com/auth/devstorage.read_only",
// "https://www.googleapis.com/auth/devstorage.read_write",
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}
// method id "prediction.trainedmodels.predict":
type TrainedmodelsPredictCall struct {
s *Service
id string
input *Input
opt_ map[string]interface{}
}
// Predict: Submit model id and request a prediction
func (r *TrainedmodelsService) Predict(id string, input *Input) *TrainedmodelsPredictCall {
c := &TrainedmodelsPredictCall{s: r.s, opt_: make(map[string]interface{})}
c.id = id
c.input = input
return c
}
// Fields allows partial responses to be retrieved.
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
// for more information.
func (c *TrainedmodelsPredictCall) Fields(s ...googleapi.Field) *TrainedmodelsPredictCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *TrainedmodelsPredictCall) Do() (*Output, error) {
var body io.Reader = nil
body, err := googleapi.WithoutDataWrapper.JSONReader(c.input)
if err != nil {
return nil, err
}
ctype := "application/json"
params := make(url.Values)
params.Set("alt", "json")
if v, ok := c.opt_["fields"]; ok {
params.Set("fields", fmt.Sprintf("%v", v))
}
urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels/{id}/predict")
urls += "?" + params.Encode()
req, _ := http.NewRequest("POST", urls, body)
googleapi.Expand(req.URL, map[string]string{
"id": c.id,
})
req.Header.Set("Content-Type", ctype)
req.Header.Set("User-Agent", "google-api-go-client/0.5")
res, err := c.s.client.Do(req)
if err != nil {
return nil, err
}
defer googleapi.CloseBody(res)
if err := googleapi.CheckResponse(res); err != nil {
return nil, err
}
var ret *Output
if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
return nil, err
}
return ret, nil
// {
// "description": "Submit model id and request a prediction",
// "httpMethod": "POST",
// "id": "prediction.trainedmodels.predict",
// "parameterOrder": [
// "id"
// ],
// "parameters": {
// "id": {
// "description": "The unique name for the predictive model.",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "trainedmodels/{id}/predict",
// "request": {
// "$ref": "Input"
// },
// "response": {
// "$ref": "Output"
// },
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}
// method id "prediction.trainedmodels.update":
type TrainedmodelsUpdateCall struct {
s *Service
id string
update *Update
opt_ map[string]interface{}
}
// Update: Add new data to a trained model.
func (r *TrainedmodelsService) Update(id string, update *Update) *TrainedmodelsUpdateCall {
c := &TrainedmodelsUpdateCall{s: r.s, opt_: make(map[string]interface{})}
c.id = id
c.update = update
return c
}
// Fields allows partial responses to be retrieved.
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
// for more information.
func (c *TrainedmodelsUpdateCall) Fields(s ...googleapi.Field) *TrainedmodelsUpdateCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *TrainedmodelsUpdateCall) Do() (*Training, error) {
var body io.Reader = nil
body, err := googleapi.WithoutDataWrapper.JSONReader(c.update)
if err != nil {
return nil, err
}
ctype := "application/json"
params := make(url.Values)
params.Set("alt", "json")
if v, ok := c.opt_["fields"]; ok {
params.Set("fields", fmt.Sprintf("%v", v))
}
urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels/{id}")
urls += "?" + params.Encode()
req, _ := http.NewRequest("PUT", urls, body)
googleapi.Expand(req.URL, map[string]string{
"id": c.id,
})
req.Header.Set("Content-Type", ctype)
req.Header.Set("User-Agent", "google-api-go-client/0.5")
res, err := c.s.client.Do(req)
if err != nil {
return nil, err
}
defer googleapi.CloseBody(res)
if err := googleapi.CheckResponse(res); err != nil {
return nil, err
}
var ret *Training
if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
return nil, err
}
return ret, nil
// {
// "description": "Add new data to a trained model.",
// "httpMethod": "PUT",
// "id": "prediction.trainedmodels.update",
// "parameterOrder": [
// "id"
// ],
// "parameters": {
// "id": {
// "description": "The unique name for the predictive model.",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "trainedmodels/{id}",
// "request": {
// "$ref": "Update"
// },
// "response": {
// "$ref": "Training"
// },
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}