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@datafire/google_automl

Client library for Cloud AutoML API

Installation and Usage

npm install --save @datafire/google_automl

let google_automl = require('@datafire/google_automl').create({
  access_token: "",
  refresh_token: "",
  client_id: "",
  client_secret: "",
  redirect_uri: ""
});

.then(data => {
  console.log(data);
});

Description

Train high-quality custom machine learning models with minimum effort and machine learning expertise.

Actions

oauthCallback

Exchange the code passed to your redirect URI for an access_token

google_automl.oauthCallback({
  "code": ""
}, context)

Input

input object
- code required string

Output

output object
- access_token string
- refresh_token string
- token_type string
- scope string
- expiration string

oauthRefresh

Exchange a refresh_token for an access_token

google_automl.oauthRefresh(null, context)

Input

This action has no parameters

Output

output object
- access_token string
- refresh_token string
- token_type string
- scope string
- expiration string

automl.projects.locations.operations.delete

Deletes a long-running operation. This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

google_automl.automl.projects.locations.operations.delete({
  "name": ""
}, context)

Input

input object
- name required string: The name of the operation resource to be deleted.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Empty

automl.projects.locations.operations.get

Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.

google_automl.automl.projects.locations.operations.get({
  "name": ""
}, context)

Input

input object
- name required string: The name of the operation resource.
- fieldMask string: Mask specifying which fields to read.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.datasets.tableSpecs.columnSpecs.patch

Updates a column spec.

google_automl.automl.projects.locations.datasets.tableSpecs.columnSpecs.patch({
  "name": ""
}, context)

Input

input object
- name required string: Output only. The resource name of the column specs. Form: projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}/columnSpecs/{column_spec_id}
- updateMask string: The update mask applies to the resource.
- body ColumnSpec
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output ColumnSpec

automl.projects.locations.list

Lists information about the supported locations for this service.

google_automl.automl.projects.locations.list({
  "name": ""
}, context)

Input

input object
- name required string: The resource that owns the locations collection, if applicable.
- filter string: The standard list filter.
- pageSize integer: The standard list page size.
- pageToken string: The standard list page token.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output ListLocationsResponse

automl.projects.locations.operations.list

Lists operations that match the specified filter in the request. If the server doesn't support this method, it returns UNIMPLEMENTED. NOTE: the name binding allows API services to override the binding to use different resource name schemes, such as users/*/operations. To override the binding, API services can add a binding such as "/v1/{name=users/*}/operations" to their service configuration. For backwards compatibility, the default name includes the operations collection id, however overriding users must ensure the name binding is the parent resource, without the operations collection id.

google_automl.automl.projects.locations.operations.list({
  "name": ""
}, context)

Input

input object
- name required string: The name of the operation's parent resource.
- filter string: An expression for filtering the results of the request. * operation_id - for = or !=. * done - for = or !=. * works_on - for = or !=. Some examples of using the filter are: * done=true --> The operation has finished running. * works_on = projects/my-project/locations/us-central1/datasets/5 --> The operation works on a dataset with ID 5. * works_on = projects/my-project/locations/us-central1/models/15 --> The operation works on a model with ID 15.
- pageSize integer: The standard list page size.
- pageToken string: The standard list page token.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output ListOperationsResponse

automl.projects.locations.models.batchPredict

Perform a batch prediction. Unlike the online Predict, batch prediction result won't be immediately available in the response. Instead, a long running operation object is returned. User can poll the operation result via GetOperation method. Once the operation is done, BatchPredictResult is returned in the response field. Available for following ML problems: * Image Classification * Image Object Detection * Video Classification * Video Object Tracking * Text Extraction * Tables

google_automl.automl.projects.locations.models.batchPredict({
  "name": ""
}, context)

Input

input object
- name required string: Required. Name of the model requested to serve the batch prediction.
- body BatchPredictRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.operations.cancel

Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED.

google_automl.automl.projects.locations.operations.cancel({
  "name": ""
}, context)

Input

input object
- name required string: The name of the operation resource to be cancelled.
- body CancelOperationRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Empty

automl.projects.locations.models.deploy

Deploys a model. If a model is already deployed, deploying it with the same parameters has no effect. Deploying with different parametrs (as e.g. changing node_number) will reset the deployment state without pausing the model's availability. Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage deployment automatically. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.models.deploy({
  "name": ""
}, context)

Input

input object
- name required string: Required. Resource name of the model to deploy.
- body DeployModelRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.models.export

Exports a trained, "export-able", model to a user specified Google Cloud Storage location. A model is considered export-able if and only if it has an export format defined for it in ModelExportOutputConfig. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.models.export({
  "name": ""
}, context)

Input

input object
- name required string: Required. The resource name of the model to export.
- body ExportModelRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.datasets.exportData

Exports dataset's data to the provided output location. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.datasets.exportData({
  "name": ""
}, context)

Input

input object
- name required string: Required. The resource name of the dataset.
- body ExportDataRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.models.exportEvaluatedExamples

Exports examples on which the model was evaluated (i.e. which were in the TEST set of the dataset the model was created from), together with their ground truth annotations and the annotations created (predicted) by the model. The examples, ground truth and predictions are exported in the state they were at the moment the model was evaluated. This export is available only for 30 days since the model evaluation is created. Currently only available for Tables. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.models.exportEvaluatedExamples({
  "name": ""
}, context)

Input

input object
- name required string: Required. The resource name of the model whose evaluated examples are to be exported.
- body ExportEvaluatedExamplesRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.datasets.importData

Imports data into a dataset. For Tables this method can only be called on an empty Dataset. For Tables: * A schema_inference_version parameter must be explicitly set. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.datasets.importData({
  "name": ""
}, context)

Input

input object
- name required string: Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added.
- body ImportDataRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.models.predict

Perform an online prediction. The prediction result will be directly returned in the response. Available for following ML problems, and their expected request payloads: * Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. * Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. * Text Classification - TextSnippet, content up to 60,000 characters, UTF-8 encoded. * Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8 NFC encoded. * Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded. * Tables - Row, with column values matching the columns of the model, up to 5MB. Not available for FORECASTING prediction_type. * Text Sentiment - TextSnippet, content up 500 characters, UTF-8 encoded.

google_automl.automl.projects.locations.models.predict({
  "name": ""
}, context)

Input

input object
- name required string: Required. Name of the model requested to serve the prediction.
- body PredictRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output PredictResponse

automl.projects.locations.models.undeploy

Undeploys a model. If the model is not deployed this method has no effect. Only applicable for Text Classification, Image Object Detection and Tables; all other domains manage deployment automatically. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.models.undeploy({
  "name": ""
}, context)

Input

input object
- name required string: Required. Resource name of the model to undeploy.
- body UndeployModelRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.operations.wait

Waits for the specified long-running operation until it is done or reaches at most a specified timeout, returning the latest state. If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED. Note that this method is on a best-effort basis. It may return the latest state before the specified timeout (including immediately), meaning even an immediate response is no guarantee that the operation is done.

google_automl.automl.projects.locations.operations.wait({
  "name": ""
}, context)

Input

input object
- name required string: The name of the operation resource to wait on.
- body WaitOperationRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.datasets.tableSpecs.columnSpecs.list

Lists column specs in a table spec.

google_automl.automl.projects.locations.datasets.tableSpecs.columnSpecs.list({
  "parent": ""
}, context)

Input

input object
- parent required string: Required. The resource name of the table spec to list column specs from.
- fieldMask string: Mask specifying which fields to read.
- filter string: Filter expression, see go/filtering.
- pageSize integer: Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
- pageToken string: A token identifying a page of results for the server to return. Typically obtained from the ListColumnSpecsResponse.next_page_token field of the previous AutoMl.ListColumnSpecs call.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output ListColumnSpecsResponse

automl.projects.locations.datasets.list

Lists datasets in a project.

google_automl.automl.projects.locations.datasets.list({
  "parent": ""
}, context)

Input

input object
- parent required string: Required. The resource name of the project from which to list datasets.
- filter string: An expression for filtering the results of the request. * dataset_metadata - for existence of the case (e.g. image_classification_dataset_metadata:*). Some examples of using the filter are: * translation_dataset_metadata:* --> The dataset has translation_dataset_metadata.
- pageSize integer: Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.
- pageToken string: A token identifying a page of results for the server to return Typically obtained via ListDatasetsResponse.next_page_token of the previous AutoMl.ListDatasets call.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output ListDatasetsResponse

automl.projects.locations.datasets.create

Creates a dataset.

google_automl.automl.projects.locations.datasets.create({
  "parent": ""
}, context)

Input

input object
- parent required string: Required. The resource name of the project to create the dataset for.
- body Dataset
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Dataset

automl.projects.locations.models.modelEvaluations.list

Lists model evaluations.

google_automl.automl.projects.locations.models.modelEvaluations.list({
  "parent": ""
}, context)

Input

input object
- parent required string: Required. Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location.
- filter string: An expression for filtering the results of the request. * annotation_spec_id - for =, != or existence. See example below for the last. Some examples of using the filter are: * annotation_spec_id!=4 --> The model evaluation was done for annotation spec with ID different than 4. * NOT annotation_spec_id:* --> The model evaluation was done for aggregate of all annotation specs.
- pageSize integer: Requested page size.
- pageToken string: A token identifying a page of results for the server to return. Typically obtained via ListModelEvaluationsResponse.next_page_token of the previous AutoMl.ListModelEvaluations call.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output ListModelEvaluationsResponse

automl.projects.locations.models.list

Lists models.

google_automl.automl.projects.locations.models.list({
  "parent": ""
}, context)

Input

input object
- parent required string: Required. Resource name of the project, from which to list the models.
- filter string: An expression for filtering the results of the request. * model_metadata - for existence of the case (e.g. video_classification_model_metadata:*). * dataset_id - for = or !=. Some examples of using the filter are: * image_classification_model_metadata:* --> The model has image_classification_model_metadata. * dataset_id=5 --> The model was created from a dataset with ID 5.
- pageSize integer: Requested page size.
- pageToken string: A token identifying a page of results for the server to return Typically obtained via ListModelsResponse.next_page_token of the previous AutoMl.ListModels call.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output ListModelsResponse

automl.projects.locations.models.create

Creates a model. Returns a Model in the response field when it completes. When you create a model, several model evaluations are created for it: a global evaluation, and one evaluation for each annotation spec.

google_automl.automl.projects.locations.models.create({
  "parent": ""
}, context)

Input

input object
- parent required string: Required. Resource name of the parent project where the model is being created.
- body Model
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Operation

automl.projects.locations.datasets.tableSpecs.list

Lists table specs in a dataset.

google_automl.automl.projects.locations.datasets.tableSpecs.list({
  "parent": ""
}, context)

Input

input object
- parent required string: Required. The resource name of the dataset to list table specs from.
- fieldMask string: Mask specifying which fields to read.
- filter string: Filter expression, see go/filtering.
- pageSize integer: Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
- pageToken string: A token identifying a page of results for the server to return. Typically obtained from the ListTableSpecsResponse.next_page_token field of the previous AutoMl.ListTableSpecs call.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output ListTableSpecsResponse

automl.projects.locations.models.getIamPolicy

Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.

google_automl.automl.projects.locations.models.getIamPolicy({
  "resource": ""
}, context)

Input

input object
- resource required string: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field.
- options.requestedPolicyVersion integer: Optional. The policy format version to be returned. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional bindings must specify version 3. Policies without any conditional bindings may specify any valid value or leave the field unset. To learn which resources support conditions in their IAM policies, see the IAM documentation.
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Policy

automl.projects.locations.models.setIamPolicy

Sets the access control policy on the specified resource. Replaces any existing policy. Can return NOT_FOUND, INVALID_ARGUMENT, and PERMISSION_DENIED errors.

google_automl.automl.projects.locations.models.setIamPolicy({
  "resource": ""
}, context)

Input

input object
- resource required string: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field.
- body SetIamPolicyRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output Policy

automl.projects.locations.testIamPermissions

Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a NOT_FOUND error. Note: This operation is designed to be used for building permission-aware UIs and command-line tools, not for authorization checking. This operation may "fail open" without warning.

google_automl.automl.projects.locations.testIamPermissions({
  "resource": ""
}, context)

Input

input object
- resource required string: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field.
- body TestIamPermissionsRequest
- $.xgafv string (values: 1, 2): V1 error format.
- access_token string: OAuth access token.
- alt string (values: json, media, proto): Data format for response.
- callback string: JSONP
- fields string: Selector specifying which fields to include in a partial response.
- key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
- oauth_token string: OAuth 2.0 token for the current user.
- prettyPrint boolean: Returns response with indentations and line breaks.
- quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
- upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
- uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

output TestIamPermissionsResponse

Definitions

AnnotationPayload

AnnotationPayload object: Contains annotation information that is relevant to AutoML.
- annotationSpecId string: Output only . The resource ID of the annotation spec that this annotation pertains to. The annotation spec comes from either an ancestor dataset, or the dataset that was used to train the model in use.
- classification ClassificationAnnotation
- displayName string: Output only. The value of display_name when the model was trained. Because this field returns a value at model training time, for different models trained using the same dataset, the returned value could be different as model owner could update the display_name between any two model training.
- imageObjectDetection ImageObjectDetectionAnnotation
- tables TablesAnnotation
- textExtraction TextExtractionAnnotation
- textSentiment TextSentimentAnnotation
- translation TranslationAnnotation
- videoClassification VideoClassificationAnnotation
- videoObjectTracking VideoObjectTrackingAnnotation

AnnotationSpec

AnnotationSpec object: A definition of an annotation spec.
- displayName string: Required. The name of the annotation spec to show in the interface. The name can be up to 32 characters long and must match the regexp [a-zA-Z0-9_]+.
- exampleCount integer: Output only. The number of examples in the parent dataset labeled by the annotation spec.
- name string: Output only. Resource name of the annotation spec. Form: 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'

ArrayStats

ArrayStats object: The data statistics of a series of ARRAY values.
- memberStats DataStats

BatchPredictInputConfig

BatchPredictInputConfig object: Input configuration for BatchPredict Action. The format of input depends on the ML problem of the model used for prediction. As input source the gcs_source is expected, unless specified otherwise. The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are: * For Image Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png * For Image Object Detection: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png * For Video Classification: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf * For Video Object Tracking: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,240 gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf * For Text Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 60,000 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf * For Text Sentiment: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 500 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf * For Text Extraction .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or as documents (for a single BatchPredict call only one of the these formats may be used). The in-line .JSONL file(s) contain per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called "id", a TextSnippet proto (in json representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique. The document .JSONL file(s) contain, per line, a proto that wraps a Document proto with input_config set. Only PDF documents are supported now, and each document must be up to 2MB large. Any given .JSONL file must be 100MB or smaller, and no more than 20 files may be given. Sample in-line JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n): { "id": "my_first_id", "text_snippet": { "content": "dog car cat"}, "text_features": [ { "text_segment": {"start_offset": 4, "end_offset": 6}, "structural_type": PARAGRAPH, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ] }, } ], }\n { "id": "2", "text_snippet": { "content": "An elaborate content", "mime_type": "text/plain" } } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ] } } } } * For Tables: Either gcs_source or bigquery_source. GCS case: CSV file(s), each by itself 10GB or smaller and total size must be 100GB or smaller, where first file must have a header containing column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. The column names must contain the model's input_feature_column_specs' display_name-s (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows, i.e. the CSV lines, will be attempted. For FORECASTING prediction_type: all columns having TIME_SERIES_AVAILABLE_PAST_ONLY type will be ignored. First three sample rows of a CSV file: "First Name","Last Name","Dob","Addresses" "John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]" "Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} BigQuery case: An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. The column names must contain the model's input_feature_column_specs' display_name-s (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows of the table will be attempted. For FORECASTING prediction_type: all columns having TIME_SERIES_AVAILABLE_PAST_ONLY type will be ignored. Definitions: GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi". TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within double quotes ("") TIME_SEGMENT_START = TIME_OFFSET Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_SEGMENT_END = TIME_OFFSET Expresses an end, exclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_OFFSET = A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is allowed and it means the end of the example. Errors: If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and prediction does not happen. Regardless of overall success or failure the per-row failures, up to a certain count cap, will be listed in Operation.metadata.partial_failures.
- bigquerySource BigQuerySource
- gcsSource GcsSource

BatchPredictOperationMetadata

BatchPredictOperationMetadata object: Details of BatchPredict operation.
- inputConfig BatchPredictInputConfig
- outputInfo BatchPredictOutputInfo

BatchPredictOutputConfig

BatchPredictOutputConfig object: Output configuration for BatchPredict Action. As destination the gcs_destination must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be "prediction--", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for. * For Image Classification: In the created directory files image_classification_1.jsonl, image_classification_2.jsonl,...,image_classification_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "" followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "" but here followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and messagefields. * For Image Object Detection: In the created directory files image_object_detection_1.jsonl, image_object_detection_2.jsonl,...,image_object_detection_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "" followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "" but here followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and messagefields. * For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video classification requested in the input (i.e. each line in given CSV(s)), will be created. The format of video_classification.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_classification.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for the video time segment the file is assigned to in the video_classification.csv. All AnnotationPayload protos will have video_classification field set, and will be sorted by video_classification.type field (note that the returned types are governed by classifaction_types parameter in PredictService.BatchPredictRequest.params). * For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,..., video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)). The format of video_object_tracking.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for each frame of the video time segment the file is assigned to in video_object_tracking.csv. All AnnotationPayload protos will have video_object_tracking field set. * For Text Classification: In the created directory files text_classification_1.jsonl, text_classification_2.jsonl,...,text_classification_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text snippet or input text file and a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. A single text snippet or file will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet or file failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input text snippet or input text file followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message. * For Text Sentiment: In the created directory files text_sentiment_1.jsonl, text_sentiment_2.jsonl,...,text_sentiment_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text snippet or input text file and a list of zero or more AnnotationPayload protos (called annotations), which have text_sentiment detail populated. A single text snippet or file will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet or file failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input text snippet or input text file followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message. * For Text Extraction: In the created directory files text_extraction_1.jsonl, text_extraction_2.jsonl,...,text_extraction_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet's "id" (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the "id" : "" (in case of inline) or the document proto (in case of document) but here followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message. * For Tables: Output depends on whether gcs_destination or bigquery_destination is set (either is allowed). GCS case: In the created directory files tables_1.csv, tables_2.csv,..., tables_N.csv will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by M target column names in the format of "score" where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction scores. For REGRESSION and FORECASTING prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by the predicted target column with name in the format of "predicted" Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additional errors_1.csv, errors_2.csv,..., errors_N.csv will be created (N depends on total number of failed rows). These files will have analogous format as tables_*.csv, but always with a single target column having [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a JSON string, and containing only code and message. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with name prediction__ where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. The predictions table's column names will be the input columns' display_name-s followed by the target column with name in the format of "predicted" The input feature columns will contain the respective values of successfully predicted rows, with the target column having an ARRAY of AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation. The errors table contains rows for which the prediction has failed, it has analogous input columns while the target column name is in the format of "errors_", and as a value has [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a STRUCT, and containing only code and message.
- bigqueryDestination BigQueryDestination
- gcsDestination GcsDestination

BatchPredictOutputInfo

BatchPredictOutputInfo object: Further describes this batch predict's output. Supplements BatchPredictOutputConfig.
- bigqueryOutputDataset string: The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
- gcsOutputDirectory string: The full path of the Google Cloud Storage directory created, into which the prediction output is written.

BatchPredictRequest

BatchPredictRequest object: Request message for PredictionService.BatchPredict.
- inputConfig BatchPredictInputConfig
- outputConfig BatchPredictOutputConfig
- params object: Required. Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. * For Text Classification: score_threshold - (float) A value from 0.0 to 1.0. When the model makes predictions for a text snippet, it will only produce results that have at least this confidence score. The default is 0.5. * For Image Classification: score_threshold - (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. * For Image Object Detection: score_threshold - (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. max_bounding_box_count - (int64) No more than this number of bounding boxes will be produced per image. Default is 100, the requested value may be limited by server. * For Video Classification : score_threshold - (float) A value from 0.0 to 1.0. When the model makes predictions for a video, it will only produce results that have at least this confidence score. The default is 0.5. segment_classification - (boolean) Set to true to request segment-level classification. AutoML Video Intelligence returns labels and their confidence scores for the entire segment of the video that user specified in the request configuration. The default is "true". shot_classification - (boolean) Set to true to request shot-level classification. AutoML Video Intelligence determines the boundaries for each camera shot in the entire segment of the video that user specified in the request configuration. AutoML Video Intelligence then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. The default is "false". 1s_interval_classification - (boolean) Set to true to request classification for a video at one-second intervals. AutoML Video Intelligence returns labels and their confidence scores for each second of the entire segment of the video that user specified in the request configuration. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. The default is "false". * For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotations. The default is false. * For Video Object Tracking: score_threshold - (float) When Model detects objects on video frames, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. max_bounding_box_count - (int64) No more than this number of bounding boxes will be returned per frame. Default is 100, the requested value may be limited by server. min_bounding_box_size - (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size will be returned. Value in 0 to 1 range. Default is 0.

BatchPredictResult

BatchPredictResult object: Result of the Batch Predict. This message is returned in response of the operation returned by the PredictionService.BatchPredict.
- metadata object: Additional domain-specific prediction response metadata. * For Image Object Detection: max_bounding_box_count - (int64) At most that many bounding boxes per image could have been returned. * For Video Object Tracking: max_bounding_box_count - (int64) At most that many bounding boxes per frame could have been returned.

BigQueryDestination

BigQueryDestination object: The BigQuery location for the output content.
- outputUri string: Required. BigQuery URI to a project, up to 2000 characters long. Accepted forms: * BigQuery path e.g. bq://projectId

BigQuerySource

BigQuerySource object: The BigQuery location for the input content.
- inputUri string: Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path e.g. bq://projectId.bqDatasetId.bqTableId

Binding

Binding object: Associates members with a role.
- condition Expr
- members array: Specifies the identities requesting access for a Cloud Platform resource. members can have the following values: * allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. * allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. * user:{emailid}: An email address that represents a specific Google account. For example, [email protected] . * serviceAccount:{emailid}: An email address that represents a service account. For example, [email protected]. * group:{emailid}: An email address that represents a Google group. For example, [email protected]. * deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, [email protected]?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. * deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, [email protected]?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. * deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, [email protected]?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. * domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com.
  - items string
- role string: Role that is assigned to members. For example, roles/viewer, roles/editor, or roles/owner.

BoundingBoxMetricsEntry

BoundingBoxMetricsEntry object: Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.
- confidenceMetricsEntries array: Output only. Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them.
  - items BoundingBoxMetricsEntryConfidenceMetricsEntry
- iouThreshold number: Output only. The intersection-over-union threshold value used to compute this metrics entry.
- meanAveragePrecision number: Output only. The mean average precision, most often close to au_prc.

BoundingBoxMetricsEntryConfidenceMetricsEntry

BoundingBoxMetricsEntryConfidenceMetricsEntry object: Metrics for a single confidence threshold.
- confidenceThreshold number: Output only. The confidence threshold value used to compute the metrics.
- f1Score number: Output only. The harmonic mean of recall and precision.
- precision number: Output only. Precision under the given confidence threshold.
- recall number: Output only. Recall under the given confidence threshold.

BoundingPoly

BoundingPoly object: A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.
- normalizedVertices array: Output only . The bounding polygon normalized vertices.
  - items NormalizedVertex

CancelOperationRequest

CancelOperationRequest object: The request message for Operations.CancelOperation.

CategoryStats

CategoryStats object: The data statistics of a series of CATEGORY values.
- topCategoryStats array: The statistics of the top 20 CATEGORY values, ordered by count.
  - items SingleCategoryStats

ClassificationAnnotation

ClassificationAnnotation object: Contains annotation details specific to classification.
- score number: Output only. A confidence estimate between 0.0 and 1.0. A higher value means greater confidence that the annotation is positive. If a user approves an annotation as negative or positive, the score value remains unchanged. If a user creates an annotation, the score is 0 for negative or 1 for positive.

ClassificationEvaluationMetrics

ClassificationEvaluationMetrics object: Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.
- annotationSpecId array: Output only. The annotation spec ids used for this evaluation.
  - items string
- auPrc number: Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.
- auRoc number: Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.
- baseAuPrc number: Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.
- confidenceMetricsEntry array: Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.
  - items ClassificationEvaluationMetricsConfidenceMetricsEntry
- confusionMatrix ConfusionMatrix
- logLoss number: Output only. The Log Loss metric.

ClassificationEvaluationMetricsConfidenceMetricsEntry

ClassificationEvaluationMetricsConfidenceMetricsEntry object: Metrics for a single confidence threshold.
- confidenceThreshold number: Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.
- f1Score number: Output only. The harmonic mean of recall and precision.
- f1ScoreAt1 number: Output only. The harmonic mean of recall_at1 and precision_at1.
- falseNegativeCount string: Output only. The number of ground truth labels that are not matched by a model created label.
- falsePositiveCount string: Output only. The number of model created labels that do not match a ground truth label.
- falsePositiveRate number: Output only. False Positive Rate for the given confidence threshold.
- falsePositiveRateAt1 number: Output only. The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
- positionThreshold integer: Output only. Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.
- precision number: Output only. Precision for the given confidence threshold.
- precisionAt1 number: Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
- recall number: Output only. Recall (True Positive Rate) for the given confidence threshold.
- recallAt1 number: Output only. The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
- trueNegativeCount string: Output only. The number of labels that were not created by the model, but if they would, they would not match a ground truth label.
- truePositiveCount string: Output only. The number of model created labels that match a ground truth label.

ClassificationEvaluationMetricsConfusionMatrixRow

ClassificationEvaluationMetricsConfusionMatrixRow object: Output only. A row in the confusion matrix.
- exampleCount array: Output only. Value of the specific cell in the confusion matrix. The number of values each row has (i.e. the length of the row) is equal to the length of the annotation_spec_id field or, if that one is not populated, length of the display_name field.
  - items integer

ColumnSpec

ColumnSpec object: A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by: * Tables
- dataStats DataStats
- dataType DataType
- displayName string: Output only. The name of the column to show in the interface. The name can be up to 100 characters long and can consist only of ASCII Latin letters A-Z and a-z, ASCII digits 0-9, underscores(_), and forward slashes(/), and must start with a letter or a digit.
- etag string: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
- name string: Output only. The resource name of the column specs. Form: projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}/columnSpecs/{column_spec_id}
- topCorrelatedColumns array: Deprecated.
  - items CorrelatedColumn

ConfusionMatrix

ConfusionMatrix object: Confusion matrix of the model running the classification.
- annotationSpecId array: Output only. IDs of the annotation specs used in the confusion matrix. For Tables CLASSIFICATION prediction_type only list of annotation_spec_display_name-s is populated.
  - items string
- displayName array: Output only. Display name of the annotation specs used in the confusion matrix, as they were at the moment of the evaluation. For Tables CLASSIFICATION prediction_type-s, distinct values of the target column at the moment of the model evaluation are populated here.
  - items string
- row array: Output only. Rows in the confusion matrix. The number of rows is equal to the size of annotation_spec_id. row[i].example_count[j] is the number of examples that have ground truth of the annotation_spec_id[i] and are predicted as annotation_spec_id[j] by the model being evaluated.
  - items ClassificationEvaluationMetricsConfusionMatrixRow

CorrelatedColumn

CorrelatedColumn object: Identifies the table's column, and its correlation with the column this ColumnSpec describes.
- columnSpecId string: The column_spec_id of the correlated column, which belongs to the same table as the in-context column.
- correlationStats CorrelationStats

CorrelationStats

CorrelationStats object: A correlation statistics between two series of DataType values. The series may have differing DataType-s, but within a single series the DataType must be the same.
- cramersV number: The correlation value using the Cramer's V measure.

CreateModelOperationMetadata

CreateModelOperationMetadata object: Details of CreateModel operation.

DataStats

DataStats object: The data statistics of a series of values that share the same DataType.
- arrayStats ArrayStats
- categoryStats CategoryStats
- distinctValueCount string: The number of distinct values.
- float64Stats Float64Stats
- nullValueCount string: The number of values that are null.
- stringStats StringStats
- structStats StructStats
- timestampStats TimestampStats
- validValueCount string: The number of values that are valid.

DataType

DataType object: Indicated the type of data that can be stored in a structured data entity (e.g. a table).
- listElementType DataType
- nullable boolean: If true, this DataType can also be NULL. In .CSV files NULL value is expressed as an empty string.
- structType StructType
- timeFormat string: If type_code == TIMESTAMP then time_format provides the format in which that time field is expressed. The time_format must either be one of: * UNIX_SECONDS * UNIX_MILLISECONDS * UNIX_MICROSECONDS * UNIX_NANOSECONDS (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in strftime syntax. If time_format is not set, then the default format as described on the type_code is used.
- typeCode string (values: TYPE_CODE_UNSPECIFIED, FLOAT64, TIMESTAMP, STRING, ARRAY, STRUCT, CATEGORY): Required. The TypeCode for this type.

Dataset

Dataset object: A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.
- createTime string: Output only. Timestamp when this dataset was created.
- description string: User-provided description of the dataset. The description can be up to 25000 characters long.
- displayName string: Required. The name of the dataset to show in the interface. The name can be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores (_), and ASCII digits 0-9.
- etag string: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
- exampleCount integer: Output only. The number of examples in the dataset.
- imageClassificationDatasetMetadata ImageClassificationDatasetMetadata
- imageObjectDetectionDatasetMetadata ImageObjectDetectionDatasetMetadata
- name string: Output only. The resource name of the dataset. Form: projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
- tablesDatasetMetadata TablesDatasetMetadata
- textClassificationDatasetMetadata TextClassificationDatasetMetadata
- textExtractionDatasetMetadata TextExtractionDatasetMetadata
- textSentimentDatasetMetadata TextSentimentDatasetMetadata
- translationDatasetMetadata TranslationDatasetMetadata
- videoClassificationDatasetMetadata VideoClassificationDatasetMetadata
- videoObjectTrackingDatasetMetadata VideoObjectTrackingDatasetMetadata

DeleteOperationMetadata

DeleteOperationMetadata object: Details of operations that perform deletes of any entities.

DeployModelOperationMetadata

DeployModelOperationMetadata object: Details of DeployModel operation.

DeployModelRequest

DeployModelRequest object: Request message for AutoMl.DeployModel.
- imageClassificationModelDeploymentMetadata ImageClassificationModelDeploymentMetadata
- imageObjectDetectionModelDeploymentMetadata ImageObjectDetectionModelDeploymentMetadata

Document

Document object: A structured text document e.g. a PDF.
- documentDimensions DocumentDimensions
- documentText TextSnippet
- inputConfig DocumentInputConfig
- layout array: Describes the layout of the document. Sorted by page_number.
  - items Layout
- pageCount integer: Number of pages in the document.

DocumentDimensions

DocumentDimensions object: Message that describes dimension of a document.
- height number: Height value of the document, works together with the unit.
- unit string (values: DOCUMENT_DIMENSION_UNIT_UNSPECIFIED, INCH, CENTIMETER, POINT): Unit of the dimension.
- width number: Width value of the document, works together with the unit.

DocumentInputConfig

DocumentInputConfig object: Input configuration of a Document.
- gcsSource GcsSource

DoubleRange

DoubleRange object: A range between two double numbers.
- end number: End of the range, exclusive.
- start number: Start of the range, inclusive.

Empty

Empty object: A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

ExamplePayload

ExamplePayload object: Example data used for training or prediction.
- document Document
- image Image
- row Row
- textSnippet TextSnippet

ExportDataOperationMetadata

ExportDataOperationMetadata object: Details of ExportData operation.
- outputInfo ExportDataOutputInfo

ExportDataOutputInfo

ExportDataOutputInfo object: Further describes this export data's output. Supplements OutputConfig.
- bigqueryOutputDataset string: The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the exported data is written.
- gcsOutputDirectory string: The full path of the Google Cloud Storage directory created, into which the exported data is written.

ExportDataRequest

ExportDataRequest object: Request message for AutoMl.ExportData.
- outputConfig OutputConfig

ExportEvaluatedExamplesOperationMetadata

ExportEvaluatedExamplesOperationMetadata object: Details of EvaluatedExamples operation.
- outputInfo ExportEvaluatedExamplesOutputInfo

ExportEvaluatedExamplesOutputConfig

ExportEvaluatedExamplesOutputConfig object: Output configuration for ExportEvaluatedExamples Action. Note that this call is available only for 30 days since the moment the model was evaluated. The output depends on the domain, as follows (note that only examples from the TEST set are exported): * For Tables: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with name export_evaluated_examples__ where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset an evaluated_examples table will be created. It will have all the same columns as the primary_table of the dataset from which the model was created, as they were at the moment of model's evaluation (this includes the target column with its ground truth), followed by a column called "predicted_". That last column will contain the model's prediction result for each respective row, given as ARRAY of AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation.
- bigqueryDestination BigQueryDestination

ExportEvaluatedExamplesOutputInfo

ExportEvaluatedExamplesOutputInfo object: Further describes the output of the evaluated examples export. Supplements ExportEvaluatedExamplesOutputConfig.
- bigqueryOutputDataset string: The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the output of export evaluated examples is written.

ExportEvaluatedExamplesRequest

ExportEvaluatedExamplesRequest object: Request message for AutoMl.ExportEvaluatedExamples.
- outputConfig ExportEvaluatedExamplesOutputConfig

ExportModelOperationMetadata

ExportModelOperationMetadata object: Details of ExportModel operation.
- outputInfo ExportModelOutputInfo

ExportModelOutputInfo

ExportModelOutputInfo object: Further describes the output of model export. Supplements ModelExportOutputConfig.
- gcsOutputDirectory string: The full path of the Google Cloud Storage directory created, into which the model will be exported.

ExportModelRequest

ExportModelRequest object: Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned.
- outputConfig ModelExportOutputConfig

Expr

Expr object: Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec. Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information.
- description string: Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
- expression string: Textual representation of an expression in Common Expression Language syntax.
- location string: Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
- title string: Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.

Float64Stats

Float64Stats object: The data statistics of a series of FLOAT64 values.
- histogramBuckets array: Histogram buckets of the data series. Sorted by the min value of the bucket, ascendingly, and the number of the buckets is dynamically generated. The buckets are non-overlapping and completely cover whole FLOAT64 range with min of first bucket being "-Infinity", and max of the last one being "Infinity".
  - items HistogramBucket
- mean number: The mean of the series.
- quantiles array: Ordered from 0 to k k-quantile values of the data series of n values. The value at index i is, approximately, the i*n/k-th smallest value in the series; for i = 0 and i = k these are, respectively, the min and max values.
  - items number
- standardDeviation number: The standard deviation of the series.

GcrDestination

GcrDestination object: The GCR location where the image must be pushed to.
- outputUri string: Required. Google Contained Registry URI of the new image, up to 2000 characters long. See https: //cloud.google.com/container-registry/do // cs/pushing-and-pulling#pushing_an_image_to_a_registry Accepted forms: * [HOSTNAME]/[PROJECT-ID]/[IMAGE] * [HOSTNAME]/[PROJECT-ID]/[IMAGE]:[TAG] The requesting user must have permission to push images the project.

GcsDestination

GcsDestination object: The Google Cloud Storage location where the output is to be written to.
- outputUriPrefix string: Required. Google Cloud Storage URI to output directory, up to 2000 characters long. Accepted forms: * Prefix path: gs://bucket/directory The requesting user must have write permission to the bucket. The directory is created if it doesn't exist.

GcsSource

GcsSource object: The Google Cloud Storage location for the input content.
- inputUris array: Required. Google Cloud Storage URIs to input files, up to 2000 characters long. Accepted forms: * Full object path, e.g. gs://bucket/directory/object.csv
  - items string

GranularStats

GranularStats object: Stats split by a defined in context granularity.
- buckets object: A map from granularity key to example count for that key. E.g. for hour_of_day 13 means 1pm, or for month_of_year 5 means May).

HistogramBucket

HistogramBucket object: A bucket of a histogram.
- count string: The number of data values that are in the bucket, i.e. are between min and max values.
- max number: The maximum value of the bucket, exclusive unless max = "Infinity", in which case it's inclusive.
- min number: The minimum value of the bucket, inclusive.

Image

Image object: A representation of an image. Only images up to 30MB in size are supported.
- imageBytes string: Image content represented as a stream of bytes. Note: As with all bytes fields, protobuffers use a pure binary representation, whereas JSON representations use base64.
- inputConfig InputConfig
- thumbnailUri string: Output only. HTTP URI to the thumbnail image.

ImageClassificationDatasetMetadata

ImageClassificationDatasetMetadata object: Dataset metadata that is specific to image classification.
- classificationType string (values: CLASSIFICATION_TYPE_UNSPECIFIED, MULTICLASS, MULTILABEL): Required. Type of the classification problem.

ImageClassificationModelDeploymentMetadata

ImageClassificationModelDeploymentMetadata object: Model deployment metadata specific to Image Classification.
- nodeCount string: Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model's node_qps. Must be between 1 and 100, inclusive on both ends.

ImageClassificationModelMetadata

ImageClassificationModelMetadata object: Model metadata for image classification.
- baseModelId string: Optional. The ID of the base model. If it is specified, the new model will be created based on the base model. Otherwise, the new model will be created from scratch. The base model must be in the same project and location as the new model to create, and have the same model_type.
- modelType string: Optional. Type of the model. The available values are: * cloud - Model to be used via prediction calls to AutoML API. This is the default value. * mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models. * mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. * mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models. * mobile-core-ml-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models. * mobile-core-ml-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. * mobile-core-ml-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
- nodeCount string: Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field.
- nodeQps number: Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
- stopReason string: Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.
- trainBudget string: Required. The train budget of creating this model, expressed in hours. The actual train_cost will be equal or less than this value.
- trainCost string: Output only. The actual train cost of creating this model, expressed in hours. If this model is created from a base model, the train cost used to create the base model are not included.

ImageObjectDetectionAnnotation

ImageObjectDetectionAnnotation object: Annotation details for image object detection.
- boundingBox BoundingPoly
- score number: Output only. The confidence that this annotation is positive for the parent example, value in [0, 1], higher means higher positivity confidence.

ImageObjectDetectionDatasetMetadata

ImageObjectDetectionDatasetMetadata object: Dataset metadata specific to image object detection.

ImageObjectDetectionEvaluationMetrics

ImageObjectDetectionEvaluationMetrics object: Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.
- boundingBoxMeanAveragePrecision number: Output only. The single metric for bounding boxes evaluation: the mean_average_precision averaged over all bounding_box_metrics_entries.
- boundingBoxMetricsEntries array: Output only. The bounding boxes match metrics for each Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair.
  - items BoundingBoxMetricsEntry
- evaluatedBoundingBoxCount integer: Output only. The total number of bounding boxes (i.e. summed over all images) the ground truth used to create this evaluation had.

ImageObjectDetectionModelDeploymentMetadata

ImageObjectDetectionModelDeploymentMetadata object: Model deployment metadata specific to Image Object Detection.
- nodeCount string: Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model's qps_per_node. Must be between 1 and 100, inclusive on both ends.

ImageObjectDetectionModelMetadata

ImageObjectDetectionModelMetadata object: Model metadata specific to image object detection.
- modelType string: Optional. Type of the model. The available values are: * cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models. * cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models. * mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models. * mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. * mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
- nodeCount string: Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field.
- nodeQps number: Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
- stopReason string: Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.
- trainBudgetMilliNodeHours string: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual train_cost will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be MODEL_CONVERGED. Note, node_hour = actual_hour * number_of_nodes_invovled. For model type cloud-high-accuracy-1(default) and cloud-low-latency-1, the train budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216, 000 which represents one day in wall time. For model type mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1, mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1, the train budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24, 000 which represents one day in wall time.
- trainCostMilliNodeHours string: Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

ImportDataOperationMetadata

ImportDataOperationMetadata object: Details of ImportData operation.

ImportDataRequest

ImportDataRequest object: Request message for AutoMl.ImportData.
- inputConfig InputConfig

InputConfig

InputConfig object: Input configuration for ImportData Action. The format of input depends on dataset_metadata the Dataset into which the import is happening has. As input source the gcs_source is expected, unless specified otherwise. Additionally any input .CSV file by itself must be 100MB or smaller, unless specified otherwise. If an "example" file (that is, image, video etc.) with identical content (even if it had different GCS_FILE_PATH) is mentioned multiple times, then its label, bounding boxes etc. are appended. The same file should be always provided with the same ML_USE and GCS_FILE_PATH, if it is not, then these values are nondeterministically selected from the given ones. The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are: * For Image Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO For MULTICLASS classification type, at most one LABEL is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with no LABEL. Some sample rows: TRAIN,gs://folder/image1.jpg,daisy TEST,gs://folder/image2.jpg,dandelion,tulip,rose UNASSIGNED,gs://folder/image3.jpg,daisy UNASSIGNED,gs://folder/image4.jpg * For Image Object Detection: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,) GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined per line). If an image has not yet been labeled, then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. For images which are known to not contain any bounding boxes, they should be labelled explictly as "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the BOUNDING_BOX. Sample rows: TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,, TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,, UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3 TEST,gs://folder/im3.png,,,,,,,,, TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,, * For Video Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have given ML_USE, using the following row format: GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,) Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it shuold be mentioned just once with ",," in place of LABEL, TIME_SEGMENT_START,TIME_SEGMENT_END. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,120,180.000021 gs://folder/video1.avi,bike,150,180.000021 gs://folder/vid2.avi,car,0,60.5 gs://folder/vid3.avi,,, * For Video Object Tracking: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have given ML_USE, using one of the following row format: GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or GCS_FILE_PATH,,,,,,,,,, Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. Providing INSTANCE_IDs can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the same INSTANCE_ID is given to it. TIMESTAMP must be within the length of the video, the BOUNDING_BOX is assumed to be drawn on the closest video's frame to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with ",,,,,,,,,," in place of LABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9 gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9 gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3 gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,, gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,, gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,, gs://folder/video2.avi,,,,,,,,,,, * For Text Extraction: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which either imports text in-line or as documents. Any given .JSONL file must be 100MB or smaller. The in-line .JSONL file contains, per line, a proto that wraps a TextSnippet proto (in json representation) followed by one or more AnnotationPayload protos (called annotations), which have display_name and text_extraction detail populated. The given text is expected to be annotated exhaustively, for example, if you look for animals and text contains "dolphin" that is not labeled, then "dolphin" is assumed to not be an animal. Any given text snippet content must be 10KB or smaller, and also be UTF-8 NFC encoded (ASCII already is). The document .JSONL file contains, per line, a proto that wraps a Document proto. The Document proto must have either document_text or input_config set. In document_text case, the Document proto may also contain the spatial information of the document, including layout, document dimension and page number. In input_config case, only PDF documents are supported now, and each document may be up to 2MB large. Currently, annotations on documents cannot be specified at import. Three sample CSV rows: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl Sample in-line JSON Lines file for entity extraction (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "document_text": {"content": "dog cat"} "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 3, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ], }, "text_segment_type": TOKEN, }, { "text_segment": { "start_offset": 4, "end_offset": 7, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.4, "y": 0.1}, {"x": 0.4, "y": 0.3}, {"x": 0.8, "y": 0.3}, {"x": 0.8, "y": 0.1}, ], }, "text_segment_type": TOKEN, } ], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 1, }, "annotations": [ { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 0, "end_offset": 3}} }, { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 4, "end_offset": 7}} } ], }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 8} } } ] } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ] } } } } * For Text Classification: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, i.e. prefixed by "gs://", it will be treated as a GCS_FILE_PATH, else if the content is enclosed within double quotes (""), it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content excluding quotes is treated as to be imported text snippet. In both cases, the text snippet/file size must be within 128kB. Maximum 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood TRAIN,gs://folder/content.txt,SlowService TEST,"Typically always bad service there.",RudeService VALIDATE,"Stomach ache to go.",BadFood * For Text Sentiment: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content itself is treated as to be imported text snippet. In both cases, the text snippet must be up to 500 characters long. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 TRAIN,"I need this product so bad",3 TEST,"Thank you for this product.",4 VALIDATE,gs://folder/content.txt,2 * For Tables: Either gcs_source or bigquery_source can be used. All inputs is concatenated into a single primary_table For gcs_source: CSV file(s), where the first row of the first file is the header, containing unique column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller. First three sample rows of a CSV file: "Id","First Name","Last Name","Dob","Addresses" "1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]" "2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} For bigquery_source: An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel. Definitions: ML_USE = "TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED" Describes how the given example (file) should be used for model training. "UNASSIGNED" can be used when user has no preference. GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/image1.png". LABEL = A display name of an object on an image, video etc., e.g. "dog". Must be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9. For each label an AnnotationSpec is created which display_name becomes the label; AnnotationSpecs are given back in predictions. INSTANCE_ID = A positive integer that identifies a specific instance of a labeled entity on an example. Used e.g. to track two cars on a video while being able to tell apart which one is which. BOUNDING_BOX = VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,, A rectangle parallel to the frame of the example (image, video). If 4 vertices are given they are connected by edges in the order provided, if 2 are given they are recognized as diagonally opposite vertices of the rectangle. VERTEX = COORDINATE,COORDINATE First coordinate is horizontal (x), the second is vertical (y). COORDINATE = A float in 0 to 1 range, relative to total length of image or video in given dimension. For fractions the leading non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is in top left. TIME_SEGMENT_START = TIME_OFFSET Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_SEGMENT_END = TIME_OFFSET Expresses an end, exclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_OFFSET = A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is allowed, and it means the end of the example. TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within double quotes (""). SENTIMENT = An integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive). Describes the ordinal of the sentiment - higher value means a more positive sentiment. All the values are completely relative, i.e. neither 0 needs to mean a negative or neutral sentiment nor sentiment_max needs to mean a positive one - it is just required that 0 is the least positive sentiment in the data, and sentiment_max is the most positive one. The SENTIMENT shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API. All SENTIMENT values between 0 and sentiment_max must be represented in the imported data. On prediction the same 0 to sentiment_max range will be used. The difference between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 may be similar whereas the difference between 2 and 3 may be huge. Errors: If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and nothing is imported. Regardless of overall success or failure the per-row failures, up to a certain count cap, is listed in Operation.metadata.partial_failures.
- bigquerySource BigQuerySource
- gcsSource GcsSource
- params object: Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. * For Tables: schema_inference_version - (integer) Required. The version of the algorithm that should be used for the initial inference of the schema (columns' DataTypes) of the table the data is being imported into. Allowed values: "1".

Layout

Layout object: Describes the layout information of a text_segment in the document.
- boundingPoly BoundingPoly
- pageNumber integer: Page number of the text_segment in the original document, starts from 1.
- textSegment TextSegment
- textSegmentType string (values: TEXT_SEGMENT_TYPE_UNSPECIFIED, TOKEN, PARAGRAPH, FORM_FIELD, FORM_FIELD_NAME, FORM_FIELD_CONTENTS, TABLE, TABLE_HEADER, TABLE_ROW, TABLE_CELL): The type of the text_segment in document.

ListColumnSpecsResponse

ListColumnSpecsResponse object: Response message for AutoMl.ListColumnSpecs.
- columnSpecs array: The column specs read.
  - items ColumnSpec
- nextPageToken string: A token to retrieve next page of results. Pass to ListColumnSpecsRequest.page_token to obtain that page.

ListDatasetsResponse

ListDatasetsResponse object: Response message for AutoMl.ListDatasets.
- datasets array: The datasets read.
  - items Dataset
- nextPageToken string: A token to retrieve next page of results. Pass to ListDatasetsRequest.page_token to obtain that page.

ListLocationsResponse

ListLocationsResponse object: The response message for Locations.ListLocations.
- locations array: A list of locations that matches the specified filter in the request.
  - items Location
- nextPageToken string: The standard List next-page token.

ListModelEvaluationsResponse

ListModelEvaluationsResponse object: Response message for AutoMl.ListModelEvaluations.
- modelEvaluation array: List of model evaluations in the requested page.
  - items ModelEvaluation
- nextPageToken string: A token to retrieve next page of results. Pass to the ListModelEvaluationsRequest.page_token field of a new AutoMl.ListModelEvaluations request to obtain that page.

ListModelsResponse

ListModelsResponse object: Response message for AutoMl.ListModels.
- model array: List of models in the requested page.
  - items Model
- nextPageToken string: A token to retrieve next page of results. Pass to ListModelsRequest.page_token to obtain that page.

ListOperationsResponse

ListOperationsResponse object: The response message for Operations.ListOperations.
- nextPageToken string: The standard List next-page token.
- operations array: A list of operations that matches the specified filter in the request.
  - items Operation

ListTableSpecsResponse

ListTableSpecsResponse object: Response message for AutoMl.ListTableSpecs.
- nextPageToken string: A token to retrieve next page of results. Pass to ListTableSpecsRequest.page_token to obtain that page.
- tableSpecs array: The table specs read.
  - items TableSpec

Location

Location object: A resource that represents Google Cloud Platform location.
- displayName string: The friendly name for this location, typically a nearby city name. For example, "Tokyo".
- labels object: Cross-service attributes for the location. For example {"cloud.googleapis.com/region": "us-east1"}
- locationId string: The canonical id for this location. For example: "us-east1".
- metadata object: Service-specific metadata. For example the available capacity at the given location.
- name string: Resource name for the location, which may vary between implementations. For example: "projects/example-project/locations/us-east1"

Model

Model object: API proto representing a trained machine learning model.
- createTime string: Output only. Timestamp when the model training finished and can be used for prediction.
- datasetId string: Required. The resource ID of the dataset used to create the model. The dataset must come from the same ancestor project and location.
- deploymentState string (values: DEPLOYMENT_STATE_UNSPECIFIED, DEPLOYED, UNDEPLOYED): Output only. Deployment state of the model. A model can only serve prediction requests after it gets deployed.
- displayName string: Required. The name of the model to show in the interface. The name can be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores (_), and ASCII digits 0-9. It must start with a letter.
- imageClassificationModelMetadata ImageClassificationModelMetadata
- imageObjectDetectionModelMetadata ImageObjectDetectionModelMetadata
- name string: Output only. Resource name of the model. Format: projects/{project_id}/locations/{location_id}/models/{model_id}
- tablesModelMetadata TablesModelMetadata
- textClassificationModelMetadata TextClassificationModelMetadata
- textExtractionModelMetadata TextExtractionModelMetadata
- textSentimentModelMetadata TextSentimentModelMetadata
- translationModelMetadata TranslationModelMetadata
- updateTime string: Output only. Timestamp when this model was last updated.
- videoClassificationModelMetadata VideoClassificationModelMetadata
- videoObjectTrackingModelMetadata VideoObjectTrackingModelMetadata

ModelEvaluation

ModelEvaluation object: Evaluation results of a model.
- annotationSpecId string: Output only. The ID of the annotation spec that the model evaluation applies to. The The ID is empty for the overall model evaluation. For Tables annotation specs in the dataset do not exist and this ID is always not set, but for CLASSIFICATION prediction_type-s the display_name field is used.
- classificationEvaluationMetrics ClassificationEvaluationMetrics
- createTime string: Output only. Timestamp when this model evaluation was created.
- displayName string: Output only. The value of display_name at the moment when the model was trained. Because this field returns a value at model training time, for different models trained from the same dataset, the values may differ, since display names could had been changed between the two model's trainings. For Tables CLASSIFICATION prediction_type-s distinct values of the target column at the moment of the model evaluation are populated here. The display_name is empty for the overall model evaluation.
- evaluatedExampleCount integer: Output only. The number of examples used for model evaluation, i.e. for which ground truth from time of model creation is compared against the predicted annotations created by the model. For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is the total number of all examples used for evaluation. Otherwise, this is the count of examples that according to the ground truth were annotated by the annotation_spec_id.
- imageObjectDetectionEvaluationMetrics ImageObjectDetectionEvaluationMetrics
- name string: Output only. Resource name of the model evaluation. Format: projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}
- regressionEvaluationMetrics RegressionEvaluationMetrics
- textExtractionEvaluationMetrics TextExtractionEvaluationMetrics
- textSentimentEvaluationMetrics TextSentimentEvaluationMetrics
- translationEvaluationMetrics TranslationEvaluationMetrics
- videoObjectTrackingEvaluationMetrics VideoObjectTrackingEvaluationMetrics

ModelExportOutputConfig

ModelExportOutputConfig object: Output configuration for ModelExport Action.
- gcrDestination GcrDestination
- gcsDestination GcsDestination
- modelFormat string: The format in which the model must be exported. The available, and default, formats depend on the problem and model type (if given problem and type combination doesn't have a format listed, it means its models are not exportable): * For Image Classification mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1: "tflite" (default), "edgetpu_tflite", "tf_saved_model", "tf_js", "docker". * For Image Classification mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1: "core_ml" (default). * For Image Object Detection mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1: "tflite", "tf_saved_model", "tf_js". * For Video Classification cloud, "tf_saved_model". * For Video Object Tracking cloud, "tf_saved_model". * For Video Object Tracking mobile-versatile-1: "tflite", "edgetpu_tflite", "tf_saved_model", "docker". * For Video Object Tracking mobile-coral-versatile-1: "tflite", "edgetpu_tflite", "docker". * For Video Object Tracking mobile-coral-low-latency-1: "tflite", "edgetpu_tflite", "docker". * For Video Object Tracking mobile-jetson-versatile-1: "tf_saved_model", "docker". * For Tables: "docker". Formats description: * tflite - Used for Android mobile devices. * edgetpu_tflite - Used for Edge TPU devices. * tf_saved_model - A tensorflow model in SavedModel format. * tf_js - A TensorFlow.js model that can be used in the browser and in Node.js using JavaScript. * docker - Used for Docker containers. Use the params field to customize the container. The container is verified to work correctly on ubuntu 16.04 operating system. See more at [containers quickstart](https: //cloud.google.com/vision/automl/docs/containers-gcs-quickstart) * core_ml - Used for iOS mobile devices.
- params object: Additional model-type and format specific parameters describing the requirements for the to be exported model files, any string must be up to 25000 characters long. * For docker format: cpu_architecture - (string) "x86_64" (default). gpu_architecture - (string) "none" (default), "nvidia".

NormalizedVertex

NormalizedVertex object: A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.
- x number: Required. Horizontal coordinate.
- y number: Required. Vertical coordinate.

Operation

Operation object: This resource represents a long-running operation that is the result of a network API call.
- done boolean: If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
- error Status
- metadata object: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
- name string: The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
- response object: The normal response of the operation in case of success. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.

OperationMetadata

OperationMetadata object: Metadata used across all long running operations returned by AutoML API.
- batchPredictDetails BatchPredictOperationMetadata
- createModelDetails CreateModelOperationMetadata
- createTime string: Output only. Time when the operation was created.
- deleteDetails DeleteOperationMetadata
- deployModelDetails DeployModelOperationMetadata
- exportDataDetails ExportDataOperationMetadata
- exportEvaluatedExamplesDetails ExportEvaluatedExamplesOperationMetadata
- exportModelDetails ExportModelOperationMetadata
- importDataDetails ImportDataOperationMetadata
- partialFailures array: Output only. Partial failures encountered. E.g. single files that couldn't be read. This field should never exceed 20 entries. Status details field will contain standard GCP error details.
  - items Status
- progressPercent integer: Output only. Progress of operation. Range: [0, 100]. Not used currently.
- undeployModelDetails UndeployModelOperationMetadata
- updateTime string: Output only. Time when the operation was updated for the last time.

OutputConfig

OutputConfig object: * For Translation: CSV file translation.csv, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes examples that have given ML_USE, using the following row format per line: TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target language) * For Tables: Output depends on whether the dataset was imported from GCS or BigQuery. GCS case: gcs_destination must be set. Exported are CSV file(s) tables_1.csv, tables_2.csv,...,tables_N.csv with each having as header line the table's column names, and all other lines contain values for the header columns. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with name export_data__ where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that dataset a new table called primary_table will be created, and filled with precisely the same data as this obtained on import.
- bigqueryDestination BigQueryDestination
- gcsDestination GcsDestination

Policy

Policy object: An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role. For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation. JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:[email protected]", "group:[email protected]", "domain:google.com", "serviceAccount:[email protected]" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:[email protected]" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:[email protected] - group:[email protected] - domain:google.com - serviceAccount:[email protected] role: roles/resourcemanager.organizationAdmin - members: - user:[email protected] role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') - etag: BwWWja0YfJA= - version: 3 For a description of IAM and its features, see the IAM documentation.
- bindings array: Associates a list of members to a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one member.
  - items Binding
- etag string: etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy. Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
- version integer: Specifies the format of the policy. Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected. Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: * Getting a policy that includes a conditional role binding * Adding a conditional role binding to a policy * Changing a conditional role binding in a policy * Removing any role binding, with or without a condition, from a policy that includes conditions Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost. If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset. To learn which resources support conditions in their IAM policies, see the IAM documentation.

PredictRequest

PredictRequest object: Request message for PredictionService.Predict.
- params object: Additional domain-specific parameters, any string must be up to 25000 characters long. * For Image Classification: score_threshold - (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. * For Image Object Detection: score_threshold - (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. max_bounding_box_count - (int64) No more than this number of bounding boxes will be returned in the response. Default is 100, the requested value may be limited by server. * For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotation. The default is false.
- payload ExamplePayload

PredictResponse

PredictResponse object: Response message for PredictionService.Predict.
- metadata object: Additional domain-specific prediction response metadata. * For Image Object Detection: max_bounding_box_count - (int64) At most that many bounding boxes per image could have been returned. * For Text Sentiment: sentiment_score - (float, deprecated) A value between -1 and 1, -1 maps to least positive sentiment, while 1 maps to the most positive one and the higher the score, the more positive the sentiment in the document is. Yet these values are relative to the training data, so e.g. if all data was positive then -1 will be also positive (though the least). The sentiment_score shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API.
- payload array: Prediction result. Translation and Text Sentiment will return precisely one payload.
  - items AnnotationPayload
- preprocessedInput ExamplePayload

RegressionEvaluationMetrics

RegressionEvaluationMetrics object: Metrics for regression problems.
- meanAbsoluteError number: Output only. Mean Absolute Error (MAE).
- meanAbsolutePercentageError number: Output only. Mean absolute percentage error. Only set if all ground truth values are are positive.
- rSquared number: Output only. R squared.
- rootMeanSquaredError number: Output only. Root Mean Squared Error (RMSE).
- rootMeanSquaredLogError number: Output only. Root mean squared log error.

Row

Row object: A representation of a row in a relational table.
- columnSpecIds array: The resource IDs of the column specs describing the columns of the row. If set must contain, but possibly in a different order, all input feature column_spec_ids of the Model this row is being passed to. Note: The below values field must match order of this field, if this field is set.
  - items string
- values array: Required. The values of the row cells, given in the same order as the column_spec_ids, or, if not set, then in the same order as input feature column_specs of the Model this row is being passed to.

SetIamPolicyRequest

SetIamPolicyRequest object: Request message for SetIamPolicy method.
- policy Policy

SingleCategoryStats

SingleCategoryStats object: The statistics of a single CATEGORY value.
- count string: The number of occurrences of this value in the series.
- value string: The CATEGORY value.

Status

Status object: The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the API Design Guide.
- code integer: The status code, which should be an enum value of google.rpc.Code.
- details array: A list of messages that carry the error details. There is a common set of message types for APIs to use.
  - items object
- message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

StringStats

StringStats object: The data statistics of a series of STRING values.
- topUnigramStats array: The statistics of the top 20 unigrams, ordered by count.
  - items UnigramStats

StructStats

StructStats object: The data statistics of a series of STRUCT values.
- fieldStats object: Map from a field name of the struct to data stats aggregated over series of all data in that field across all the structs.

StructType

StructType object: StructType defines the DataType-s of a STRUCT type.
- fields object: Unordered map of struct field names to their data types. Fields cannot be added or removed via Update. Their names and data types are still mutable.

TableSpec

TableSpec object: A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by: * Tables
- columnCount string: Output only. The number of columns of the table. That is, the number of child ColumnSpec-s.
- etag string: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
- inputConfigs array: Output only. Input configs via which data currently residing in the table had been imported.
  - items InputConfig
- name string: Output only. The resource name of the table spec. Form: projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}
- rowCount string: Output only. The number of rows (i.e. examples) in the table.
- timeColumnSpecId string: column_spec_id of the time column. Only used if the parent dataset's ml_use_column_spec_id is not set. Used to split rows into TRAIN, VALIDATE and TEST sets such that oldest rows go to TRAIN set, newest to TEST, and those in between to VALIDATE. Required type: TIMESTAMP. If both this column and ml_use_column are not set, then ML use of all rows will be assigned by AutoML. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
- validRowCount string: Output only. The number of valid rows (i.e. without values that don't match DataType-s of their columns).

TablesAnnotation

TablesAnnotation object: Contains annotation details specific to Tables.
- baselineScore number: Output only. Stores the prediction score for the baseline example, which is defined as the example with all values set to their baseline values. This is used as part of the Sampled Shapley explanation of the model's prediction. This field is populated only when feature importance is requested. For regression models, this holds the baseline prediction for the baseline example. For classification models, this holds the baseline prediction for the baseline example for the argmax class.
- predictionInterval DoubleRange
- score number: Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher value means greater confidence in the returned value. For target_column_spec of FLOAT64 data type the score is not populated.
- tablesModelColumnInfo array: Output only. Auxiliary information for each of the model's input_feature_column_specs with respect to this particular prediction. If no other fields than column_spec_name and column_display_name would be populated, then this whole field is not.
  - items TablesModelColumnInfo

TablesDatasetMetadata

TablesDatasetMetadata object: Metadata for a dataset used for AutoML Tables.
- mlUseColumnSpecId string: column_spec_id of the primary table column which specifies a possible ML use of the row, i.e. the column will be used to split the rows into TRAIN, VALIDATE and TEST sets. Required type: STRING. This column, if set, must either have all of TRAIN, VALIDATE, TEST among its values, or only have TEST, UNASSIGNED values. In the latter case the rows with UNASSIGNED value will be assigned by AutoML. Note that if a given ml use distribution makes it impossible to create a "good" model, that call will error describing the issue. If both this column_spec_id and primary table's time_column_spec_id are not set, then all rows are treated as UNASSIGNED. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
- primaryTableSpecId string: Output only. The table_spec_id of the primary table of this dataset.
- statsUpdateTime string: Output only. The most recent timestamp when target_column_correlations field and all descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns fields were last (re-)generated. Any changes that happened to the dataset afterwards are not reflected in these fields values. The regeneration happens in the background on a best effort basis.
- targetColumnCorrelations object: Output only. Correlations between TablesDatasetMetadata.target_column_spec_id, and other columns of the TablesDatasetMetadataprimary_table. Only set if the target column is set. Mapping from other column spec id to its CorrelationStats with the target column. This field may be stale, see the stats_update_time field for for the timestamp at which these stats were last updated.
- targetColumnSpecId string: column_spec_id of the primary table's column that should be used as the training & prediction target. This column must be non-nullable and have one of following data types (otherwise model creation will error): * CATEGORY * FLOAT64 If the type is CATEGORY , only up to 100 unique values may exist in that column across all rows. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
- weightColumnSpecId string: column_spec_id of the primary table's column that should be used as the weight column, i.e. the higher the value the more important the row will be during model training. Required type: FLOAT64. Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is ignored for training. If not set all rows are assumed to have equal weight of 1. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.

TablesModelColumnInfo

TablesModelColumnInfo object: An information specific to given column and Tables Model, in context of the Model and the predictions created by it.
- columnDisplayName string: Output only. The display name of the column (same as the display_name of its ColumnSpec).
- columnSpecName string: Output only. The name of the ColumnSpec describing the column. Not populated when this proto is outputted to BigQuery.
- featureImportance number: Output only. When given as part of a Model (always populated): Measurement of how much model predictions correctness on the TEST data depend on values in this column. A value between 0 and 1, higher means higher influence. These values are normalized - for all input feature columns of a given model they add to 1. When given back by Predict (populated iff feature_importance param is set) or Batch Predict (populated iff feature_importance param is set): Measurement of how impactful for the prediction returned for the given row the value in this column was. Specifically, the feature importance specifies the marginal contribution that the feature made to the prediction score compared to the baseline score. These values are computed using the Sampled Shapley method.

TablesModelMetadata

TablesModelMetadata object: Model metadata specific to AutoML Tables.
- disableEarlyStopping boolean: Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
- inputFeatureColumnSpecs array: Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used: * name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input. * display_name - Output only. * data_type - Output only.
  - items ColumnSpec
- optimizationObjective string: Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. CLASSIFICATION_BINARY: "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "MINIMIZE_LOG_LOSS" - Minimize log loss. "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve. "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified recall value. "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified precision value. CLASSIFICATION_MULTI_CLASS : "MINIMIZE_LOG_LOSS" (default) - Minimize log loss. REGRESSION: "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE). "MINIMIZE_MAE" - Minimize mean-absolute error (MAE). "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
- optimizationObjectivePrecisionValue number: Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". Must be between 0 and 1, inclusive.
- optimizationObjectiveRecallValue number: Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". Must be between 0 and 1, inclusive.
- tablesModelColumnInfo array: Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.
  - items TablesModelColumnInfo
- targetColumnSpec ColumnSpec
- trainBudgetMilliNodeHours string: Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
- trainCostMilliNodeHours string: Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

TestIamPermissionsRequest

TestIamPermissionsRequest object: Request message for TestIamPermissions method.
- permissions array: The set of permissions to check for the resource. Permissions with wildcards (such as '' or 'storage.') are not allowed. For more information see IAM Overview.
  - items string

TestIamPermissionsResponse

TestIamPermissionsResponse object: Response message for TestIamPermissions method.
- permissions array: A subset of TestPermissionsRequest.permissions that the caller is allowed.
  - items string

TextClassificationDatasetMetadata

TextClassificationDatasetMetadata object: Dataset metadata for classification.
- classificationType string (values: CLASSIFICATION_TYPE_UNSPECIFIED, MULTICLASS, MULTILABEL): Required. Type of the classification problem.

TextClassificationModelMetadata

TextClassificationModelMetadata object: Model metadata that is specific to text classification.
- classificationType string (values: CLASSIFICATION_TYPE_UNSPECIFIED, MULTICLASS, MULTILABEL): Output only. Classification type of the dataset used to train this model.

TextExtractionAnnotation

TextExtractionAnnotation object: Annotation for identifying spans of text.
- score number: Output only. A confidence estimate between 0.0 and 1.0. A higher value means greater confidence in correctness of the annotation.
- textSegment TextSegment

TextExtractionDatasetMetadata

TextExtractionDatasetMetadata object: Dataset metadata that is specific to text extraction

TextExtractionEvaluationMetrics

TextExtractionEvaluationMetrics object: Model evaluation metrics for text extraction problems.
- auPrc number: Output only. The Area under precision recall curve metric.
- confidenceMetricsEntries array: Output only. Metrics that have confidence thresholds. Precision-recall curve can be derived from it.
  - items TextExtractionEvaluationMetricsConfidenceMetricsEntry

TextExtractionEvaluationMetricsConfidenceMetricsEntry

TextExtractionEvaluationMetricsConfidenceMetricsEntry object: Metrics for a single confidence threshold.
- confidenceThreshold number: Output only. The confidence threshold value used to compute the metrics. Only annotations with score of at least this threshold are considered to be ones the model would return.
- f1Score number: Output only. The harmonic mean of recall and precision.
- precision number: Output only. Precision under the given confidence threshold.
- recall number: Output only. Recall under the given confidence threshold.

TextExtractionModelMetadata

TextExtractionModelMetadata object: Model metadata that is specific to text extraction.
- modelHint string: Indicates the scope of model use case. * default: Use to train a general text extraction model. Default value. * health_care: Use to train a text extraction model that is tuned for healthcare applications.

TextSegment

TextSegment object: A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
- content string: Output only. The content of the TextSegment.
- endOffset string: Required. Zero-based character index of the first character past the end of the text segment (counting character from the beginning of the text). The character at the end_offset is NOT included in the text segment.
- startOffset string: Required. Zero-based character index of the first character of the text segment (counting characters from the beginning of the text).

TextSentimentAnnotation

TextSentimentAnnotation object: Contains annotation details specific to text sentiment.
- sentiment integer: Output only. The sentiment with the semantic, as given to the AutoMl.ImportData when populating the dataset from which the model used for the prediction had been trained. The sentiment values are between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive), with higher value meaning more positive sentiment. They are completely relative, i.e. 0 means least positive sentiment and sentiment_max means the most positive from the sentiments present in the train data. Therefore e.g. if train data had only negative sentiment, then sentiment_max, would be still negative (although least negative). The sentiment shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API.

TextSentimentDatasetMetadata

TextSentimentDatasetMetadata object: Dataset metadata for text sentiment.
- sentimentMax integer: Required. A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentiment_max (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. sentiment_max value must be between 1 and 10 (inclusive).

TextSentimentEvaluationMetrics

TextSentimentEvaluationMetrics object: Model evaluation metrics for text sentiment problems.
- annotationSpecId array: Output only. The annotation spec ids used for this evaluation. Deprecated .
  - items string
- confusionMatrix ConfusionMatrix
- f1Score number: Output only. The harmonic mean of recall and precision.
- linearKappa number: Output only. Linear weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
- meanAbsoluteError number: Output only. Mean absolute error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
- meanSquaredError number: Output only. Mean squared error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
- precision number: Output only. Precision.
- quadraticKappa number: Output only. Quadratic weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
- recall number: Output only. Recall.

TextSentimentModelMetadata

TextSentimentModelMetadata object: Model metadata that is specific to text sentiment.

TextSnippet

TextSnippet object: A representation of a text snippet.
- content string: Required. The content of the text snippet as a string. Up to 250000 characters long.
- contentUri string: Output only. HTTP URI where you can download the content.
- mimeType string: Optional. The format of content. Currently the only two allowed values are "text/html" and "text/plain". If left blank, the format is automatically determined from the type of the uploaded content.

TimeSegment

TimeSegment object: A time period inside of an example that has a time dimension (e.g. video).
- endTimeOffset string: End of the time segment (exclusive), represented as the duration since the example start.
- startTimeOffset string: Start of the time segment (inclusive), represented as the duration since the example start.

TimestampStats

TimestampStats object: The data statistics of a series of TIMESTAMP values.
- granularStats object: The string key is the pre-defined granularity. Currently supported: hour_of_day, day_of_week, month_of_year. Granularities finer that the granularity of timestamp data are not populated (e.g. if timestamps are at day granularity, then hour_of_day is not populated).

TranslationAnnotation

TranslationAnnotation object: Annotation details specific to translation.
- translatedContent TextSnippet

TranslationDatasetMetadata

TranslationDatasetMetadata object: Dataset metadata that is specific to translation.
- sourceLanguageCode string: Required. The BCP-47 language code of the source language.
- targetLanguageCode string: Required. The BCP-47 language code of the target language.

TranslationEvaluationMetrics

TranslationEvaluationMetrics object: Evaluation metrics for the dataset.
- baseBleuScore number: Output only. BLEU score for base model.
- bleuScore number: Output only. BLEU score.

TranslationModelMetadata

TranslationModelMetadata object: Model metadata that is specific to translation.
- baseModel string: The resource name of the model to use as a baseline to train the custom model. If unset, we use the default base model provided by Google Translate. Format: projects/{project_id}/locations/{location_id}/models/{model_id}
- sourceLanguageCode string: Output only. Inferred from the dataset. The source languge (The BCP-47 language code) that is used for training.
- targetLanguageCode string: Output only. The target languge (The BCP-47 language code) that is used for training.

UndeployModelOperationMetadata

UndeployModelOperationMetadata object: Details of UndeployModel operation.

UndeployModelRequest

UndeployModelRequest object: Request message for AutoMl.UndeployModel.

UnigramStats

UnigramStats object: The statistics of a unigram.
- count string: The number of occurrences of this unigram in the series.
- value string: The unigram.

VideoClassificationAnnotation

VideoClassificationAnnotation object: Contains annotation details specific to video classification.
- classificationAnnotation ClassificationAnnotation
- timeSegment TimeSegment
- type string: Output only. Expresses the type of video classification. Possible values: * segment - Classification done on a specified by user time segment of a video. AnnotationSpec is answered to be present in that time segment, if it is present in any part of it. The video ML model evaluations are done only for this type of classification. * shot- Shot-level classification. AutoML Video Intelligence determines the boundaries for each camera shot in the entire segment of the video that user specified in the request configuration. AutoML Video Intelligence then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. * 1s_interval - AutoML Video Intelligence returns labels and their confidence scores for each second of the entire segment of the video that user specified in the request configuration. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality.

VideoClassificationDatasetMetadata

VideoClassificationDatasetMetadata object: Dataset metadata specific to video classification. All Video Classification datasets are treated as multi label.

VideoClassificationModelMetadata

VideoClassificationModelMetadata object: Model metadata specific to video classification.

VideoObjectTrackingAnnotation

VideoObjectTrackingAnnotation object: Annotation details for video object tracking.
- boundingBox BoundingPoly
- instanceId string: Optional. The instance of the object, expressed as a positive integer. Used to tell apart objects of the same type (i.e. AnnotationSpec) when multiple are present on a single example. NOTE: Instance ID prediction quality is not a part of model evaluation and is done as best effort. Especially in cases when an entity goes off-screen for a longer time (minutes), when it comes back it may be given a new instance ID.
- score number: Output only. The confidence that this annotation is positive for the video at the time_offset, value in [0, 1], higher means higher positivity confidence. For annotations created by the user the score is 1. When user approves an annotation, the original float score is kept (and not changed to 1).
- timeOffset string: Required. A time (frame) of a video to which this annotation pertains. Represented as the duration since the video's start.

VideoObjectTrackingDatasetMetadata

VideoObjectTrackingDatasetMetadata object: Dataset metadata specific to video object tracking.

VideoObjectTrackingEvaluationMetrics

VideoObjectTrackingEvaluationMetrics object: Model evaluation metrics for video object tracking problems. Evaluates prediction quality of both labeled bounding boxes and labeled tracks (i.e. series of bounding boxes sharing same label and instance ID).
- boundingBoxMeanAveragePrecision number: Output only. The single metric for bounding boxes evaluation: the mean_average_precision averaged over all bounding_box_metrics_entries.
- boundingBoxMetricsEntries array: Output only. The bounding boxes match metrics for each Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair.
  - items BoundingBoxMetricsEntry
- evaluatedBoundingBoxCount integer: Output only. The total number of bounding boxes (i.e. summed over all frames) the ground truth used to create this evaluation had.
- evaluatedFrameCount integer: Output only. The number of video frames used to create this evaluation.

VideoObjectTrackingModelMetadata

VideoObjectTrackingModelMetadata object: Model metadata specific to video object tracking.

WaitOperationRequest

WaitOperationRequest object: The request message for Operations.WaitOperation.
- timeout string: The maximum duration to wait before timing out. If left blank, the wait will be at most the time permitted by the underlying HTTP/RPC protocol. If RPC context deadline is also specified, the shorter one will be used.

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@datafire/google_automl

Installation and Usage

Description

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oauthCallback

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oauthRefresh

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automl.projects.locations.operations.delete

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automl.projects.locations.operations.get

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automl.projects.locations.datasets.tableSpecs.columnSpecs.patch

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automl.projects.locations.list

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automl.projects.locations.operations.list

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automl.projects.locations.models.batchPredict

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automl.projects.locations.operations.cancel

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automl.projects.locations.models.deploy

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automl.projects.locations.models.export

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automl.projects.locations.datasets.exportData

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automl.projects.locations.models.exportEvaluatedExamples

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automl.projects.locations.datasets.importData

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automl.projects.locations.models.predict

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automl.projects.locations.models.undeploy

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automl.projects.locations.operations.wait

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automl.projects.locations.datasets.tableSpecs.columnSpecs.list

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automl.projects.locations.datasets.list

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automl.projects.locations.datasets.create

Input

Output

automl.projects.locations.models.modelEvaluations.list

Input

Output

automl.projects.locations.models.list