| // 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/v1.5" |
| // ... |
| // predictionService, err := prediction.New(oauthHttpClient) |
| package prediction |
| |
| import ( |
| "bytes" |
| "encoding/json" |
| "errors" |
| "fmt" |
| "golang.org/x/net/context" |
| "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 |
| var _ = context.Background |
| |
| const apiId = "prediction:v1.5" |
| const apiName = "prediction" |
| const apiVersion = "v1.5" |
| const basePath = "https://www.googleapis.com/prediction/v1.5/" |
| |
| // 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 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 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 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 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 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 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 float64 `json:"mean,omitempty"` |
| |
| // Variance: Variance of the numeric values of this feature in the data |
| // set. |
| Variance float64 `json:"variance,omitempty"` |
| } |
| |
| type AnalyzeDataDescriptionFeaturesText struct { |
| // Count: Number of multiple-word text values for this feature. |
| Count int64 `json:"count,omitempty,string"` |
| } |
| |
| 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 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 float64 `json:"mean,omitempty"` |
| |
| // Variance: Variance of the output values in the data set. |
| Variance float64 `json:"variance,omitempty"` |
| } |
| |
| 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 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 *AnalyzeModelDescriptionConfusionMatrixRowTotals `json:"confusionMatrixRowTotals,omitempty"` |
| |
| // Modelinfo: Basic information about the model. |
| Modelinfo *Training `json:"modelinfo,omitempty"` |
| } |
| |
| type AnalyzeModelDescriptionConfusionMatrix struct { |
| } |
| |
| type AnalyzeModelDescriptionConfusionMatrixRowTotals struct { |
| } |
| |
| 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 List struct { |
| // Items: List of models. |
| Items []*Training `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 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 { |
| // 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 *TrainingModelInfo `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"` |
| |
| // TrainingInstances: Instances to train model on. |
| TrainingInstances []*TrainingTrainingInstances `json:"trainingInstances,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 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"` |
| |
| // 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 TrainingTrainingInstances 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 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.analyze": |
| |
| type TrainedmodelsAnalyzeCall struct { |
| s *Service |
| id string |
| opt_ map[string]interface{} |
| } |
| |
| // Analyze: Get analysis of the model and the data the model was trained |
| // on. |
| func (r *TrainedmodelsService) Analyze(id string) *TrainedmodelsAnalyzeCall { |
| c := &TrainedmodelsAnalyzeCall{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 *TrainedmodelsAnalyzeCall) Fields(s ...googleapi.Field) *TrainedmodelsAnalyzeCall { |
| c.opt_["fields"] = googleapi.CombineFields(s) |
| return c |
| } |
| |
| func (c *TrainedmodelsAnalyzeCall) Do() (*Analyze, 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}/analyze") |
| 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 *Analyze |
| if err := json.NewDecoder(res.Body).Decode(&ret); err != nil { |
| return nil, err |
| } |
| return ret, nil |
| // { |
| // "description": "Get analysis of the model and the data the model was trained on.", |
| // "httpMethod": "GET", |
| // "id": "prediction.trainedmodels.analyze", |
| // "parameterOrder": [ |
| // "id" |
| // ], |
| // "parameters": { |
| // "id": { |
| // "description": "The unique name for the predictive model.", |
| // "location": "path", |
| // "required": true, |
| // "type": "string" |
| // } |
| // }, |
| // "path": "trainedmodels/{id}/analyze", |
| // "response": { |
| // "$ref": "Analyze" |
| // }, |
| // "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.list": |
| |
| type TrainedmodelsListCall struct { |
| s *Service |
| opt_ map[string]interface{} |
| } |
| |
| // List: List available models. |
| func (r *TrainedmodelsService) List() *TrainedmodelsListCall { |
| c := &TrainedmodelsListCall{s: r.s, opt_: make(map[string]interface{})} |
| return c |
| } |
| |
| // MaxResults sets the optional parameter "maxResults": Maximum number |
| // of results to return |
| func (c *TrainedmodelsListCall) MaxResults(maxResults int64) *TrainedmodelsListCall { |
| c.opt_["maxResults"] = maxResults |
| return c |
| } |
| |
| // PageToken sets the optional parameter "pageToken": Pagination token |
| func (c *TrainedmodelsListCall) PageToken(pageToken string) *TrainedmodelsListCall { |
| c.opt_["pageToken"] = pageToken |
| 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 *TrainedmodelsListCall) Fields(s ...googleapi.Field) *TrainedmodelsListCall { |
| c.opt_["fields"] = googleapi.CombineFields(s) |
| return c |
| } |
| |
| func (c *TrainedmodelsListCall) Do() (*List, error) { |
| var body io.Reader = nil |
| params := make(url.Values) |
| params.Set("alt", "json") |
| if v, ok := c.opt_["maxResults"]; ok { |
| params.Set("maxResults", fmt.Sprintf("%v", v)) |
| } |
| if v, ok := c.opt_["pageToken"]; ok { |
| params.Set("pageToken", fmt.Sprintf("%v", v)) |
| } |
| if v, ok := c.opt_["fields"]; ok { |
| params.Set("fields", fmt.Sprintf("%v", v)) |
| } |
| urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels/list") |
| urls += "?" + params.Encode() |
| req, _ := http.NewRequest("GET", urls, body) |
| googleapi.SetOpaque(req.URL) |
| 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 *List |
| if err := json.NewDecoder(res.Body).Decode(&ret); err != nil { |
| return nil, err |
| } |
| return ret, nil |
| // { |
| // "description": "List available models.", |
| // "httpMethod": "GET", |
| // "id": "prediction.trainedmodels.list", |
| // "parameters": { |
| // "maxResults": { |
| // "description": "Maximum number of results to return", |
| // "format": "uint32", |
| // "location": "query", |
| // "minimum": "0", |
| // "type": "integer" |
| // }, |
| // "pageToken": { |
| // "description": "Pagination token", |
| // "location": "query", |
| // "type": "string" |
| // } |
| // }, |
| // "path": "trainedmodels/list", |
| // "response": { |
| // "$ref": "List" |
| // }, |
| // "scopes": [ |
| // "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" |
| // ] |
| // } |
| |
| } |