Package google.cloud.automl.v1beta1

Index

• AutoMl (interface)
• PredictionService (interface)
• StreamingPredictionService (interface)
• AnnotationPayload (message)
• AnnotationSpec (message)
• ArrayStats (message)
• BatchPredictInputConfig (message)
• BatchPredictOperationMetadata (message)
• BatchPredictOperationMetadata.BatchPredictOutputInfo (message)
• BatchPredictOutputConfig (message)
• BatchPredictRequest (message)
• BatchPredictResult (message)
• BigQueryDestination (message)
• BigQuerySource (message)
• BoundingBoxMetricsEntry (message)
• BoundingBoxMetricsEntry.ConfidenceMetricsEntry (message)
• BoundingPoly (message)
• CategoryStats (message)
• CategoryStats.SingleCategoryStats (message)
• ClassificationAnnotation (message)
• ClassificationEvaluationMetrics (message)
• ClassificationEvaluationMetrics.ConfidenceMetricsEntry (message)
• ClassificationEvaluationMetrics.ConfusionMatrix (message)
• ClassificationEvaluationMetrics.ConfusionMatrix.Row (message)
• ClassificationType (enum)
• ColumnSpec (message)
• ColumnSpec.CorrelatedColumn (message)
• CorrelationStats (message)
• CreateDatasetRequest (message)
• CreateModelOperationMetadata (message)
• CreateModelRequest (message)
• DataStats (message)
• DataType (message)
• Dataset (message)
• DeleteDatasetRequest (message)
• DeleteModelRequest (message)
• DeleteOperationMetadata (message)
• DeployModelOperationMetadata (message)
• DeployModelRequest (message)
• Document (message)
• Document.Layout (message)
• Document.Layout.TextSegmentType (enum)
• DocumentDimensions (message)
• DocumentDimensions.DocumentDimensionUnit (enum)
• DocumentInputConfig (message)
• DoubleRange (message)
• ExamplePayload (message)
• ExportDataOperationMetadata (message)
• ExportDataOperationMetadata.ExportDataOutputInfo (message)
• ExportDataRequest (message)
• ExportEvaluatedExamplesOperationMetadata (message)
• ExportEvaluatedExamplesOperationMetadata.ExportEvaluatedExamplesOutputInfo (message)
• ExportEvaluatedExamplesOutputConfig (message)
• ExportEvaluatedExamplesRequest (message)
• ExportModelOperationMetadata (message)
• ExportModelOperationMetadata.ExportModelOutputInfo (message)
• ExportModelRequest (message)
• Float64Stats (message)
• Float64Stats.HistogramBucket (message)
• GcrDestination (message)
• GcsDestination (message)
• GcsSource (message)
• GetAnnotationSpecRequest (message)
• GetColumnSpecRequest (message)
• GetDatasetRequest (message)
• GetModelEvaluationRequest (message)
• GetModelRequest (message)
• GetTableSpecRequest (message)
• Image (message)
• ImageClassificationDatasetMetadata (message)
• ImageClassificationModelDeploymentMetadata (message)
• ImageClassificationModelMetadata (message)
• ImageObjectDetectionAnnotation (message)
• ImageObjectDetectionDatasetMetadata (message)
• ImageObjectDetectionEvaluationMetrics (message)
• ImageObjectDetectionModelDeploymentMetadata (message)
• ImageObjectDetectionModelMetadata (message)
• ImportDataOperationMetadata (message)
• ImportDataRequest (message)
• InputConfig (message)
• ListColumnSpecsRequest (message)
• ListColumnSpecsResponse (message)
• ListDatasetsRequest (message)
• ListDatasetsResponse (message)
• ListModelEvaluationsRequest (message)
• ListModelEvaluationsResponse (message)
• ListModelsRequest (message)
• ListModelsResponse (message)
• ListTableSpecsRequest (message)
• ListTableSpecsResponse (message)
• Model (message)
• Model.DeploymentState (enum)
• ModelEvaluation (message)
• ModelExportOutputConfig (message)
• NormalizedVertex (message)
• OperationMetadata (message)
• OutputConfig (message)
• PredictRequest (message)
• PredictResponse (message)
• RegressionEvaluationMetrics (message)
• Row (message)
• StringStats (message)
• StringStats.UnigramStats (message)
• StructStats (message)
• StructType (message)
• TableSpec (message)
• TablesAnnotation (message)
• TablesDatasetMetadata (message)
• TablesModelColumnInfo (message)
• TablesModelMetadata (message)
• TextClassificationDatasetMetadata (message)
• TextClassificationModelMetadata (message)
• TextExtractionAnnotation (message)
• TextExtractionDatasetMetadata (message)
• TextExtractionEvaluationMetrics (message)
• TextExtractionEvaluationMetrics.ConfidenceMetricsEntry (message)
• TextExtractionModelMetadata (message)
• TextSegment (message)
• TextSentimentAnnotation (message)
• TextSentimentDatasetMetadata (message)
• TextSentimentEvaluationMetrics (message)
• TextSentimentModelMetadata (message)
• TextSnippet (message)
• TimeSegment (message)
• TimestampStats (message)
• TimestampStats.GranularStats (message)
• TranslationAnnotation (message)
• TranslationDatasetMetadata (message)
• TranslationEvaluationMetrics (message)
• TranslationModelMetadata (message)
• TypeCode (enum)
• UndeployModelOperationMetadata (message)
• UndeployModelRequest (message)
• UpdateColumnSpecRequest (message)
• UpdateDatasetRequest (message)
• UpdateTableSpecRequest (message)
• VideoClassificationAnnotation (message)
• VideoClassificationDatasetMetadata (message)
• VideoClassificationModelMetadata (message)
• VideoObjectTrackingAnnotation (message)
• VideoObjectTrackingDatasetMetadata (message)
• VideoObjectTrackingEvaluationMetrics (message)
• VideoObjectTrackingModelMetadata (message)

AutoMl

AutoML Server API.

The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted.

An ID of a resource is the last element of the item's resource name. For projects/{project_id}/locations/{location_id}/datasets/{dataset_id}, then the id for the item is {dataset_id}.

Currently the only supported location_id is "us-central1".

On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.

CreateDataset
rpc CreateDataset(CreateDatasetRequest) returns (Dataset) Creates a dataset. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

CreateModel

rpc CreateModel(CreateModelRequest) returns (Operation) 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. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DeleteDataset
rpc DeleteDataset(DeleteDatasetRequest) returns (Operation) Deletes a dataset and all of its contents. Returns empty response in the response field when it completes, and delete_details in the metadata field. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DeleteModel
rpc DeleteModel(DeleteModelRequest) returns (Operation) Deletes a model. Returns google.protobuf.Empty in the response field when it completes, and delete_details in the metadata field. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DeployModel

rpc DeployModel(DeployModelRequest) returns (Operation) 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][google.cloud.automl.v1beta1.ImageObjectDetectionModelDeploymentMetadata.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. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ExportData
rpc ExportData(ExportDataRequest) returns (Operation) Exports dataset's data to the provided output location. Returns an empty response in the response field when it completes. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ExportEvaluatedExamples

rpc ExportEvaluatedExamples(ExportEvaluatedExamplesRequest) returns (Operation) 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. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ExportModel

rpc ExportModel(ExportModelRequest) returns (Operation) 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. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetAnnotationSpec
rpc GetAnnotationSpec(GetAnnotationSpecRequest) returns (AnnotationSpec) Gets an annotation spec. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetColumnSpec
rpc GetColumnSpec(GetColumnSpecRequest) returns (ColumnSpec) Gets a column spec. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetDataset
rpc GetDataset(GetDatasetRequest) returns (Dataset) Gets a dataset. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetModel
rpc GetModel(GetModelRequest) returns (Model) Gets a model. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetModelEvaluation
rpc GetModelEvaluation(GetModelEvaluationRequest) returns (ModelEvaluation) Gets a model evaluation. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetTableSpec
rpc GetTableSpec(GetTableSpecRequest) returns (TableSpec) Gets a table spec. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ImportData

rpc ImportData(ImportDataRequest) returns (Operation) 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. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListColumnSpecs
rpc ListColumnSpecs(ListColumnSpecsRequest) returns (ListColumnSpecsResponse) Lists column specs in a table spec. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListDatasets
rpc ListDatasets(ListDatasetsRequest) returns (ListDatasetsResponse) Lists datasets in a project. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListModelEvaluations
rpc ListModelEvaluations(ListModelEvaluationsRequest) returns (ListModelEvaluationsResponse) Lists model evaluations. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListModels
rpc ListModels(ListModelsRequest) returns (ListModelsResponse) Lists models. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListTableSpecs
rpc ListTableSpecs(ListTableSpecsRequest) returns (ListTableSpecsResponse) Lists table specs in a dataset. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UndeployModel

rpc UndeployModel(UndeployModelRequest) returns (Operation) 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. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdateColumnSpec
rpc UpdateColumnSpec(UpdateColumnSpecRequest) returns (ColumnSpec) Updates a column spec. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdateDataset
rpc UpdateDataset(UpdateDatasetRequest) returns (Dataset) Updates a dataset. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdateTableSpec
rpc UpdateTableSpec(UpdateTableSpecRequest) returns (TableSpec) Updates a table spec. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

PredictionService

AutoML Prediction API.

On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.

BatchPredict

rpc BatchPredict(BatchPredictRequest) returns (Operation) 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 Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

Predict

rpc Predict(PredictRequest) returns (PredictResponse) 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. Authorization Scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

StreamingPredictionService

AutoML Streaming Prediction API.

On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.

AnnotationPayload

Contains annotation information that is relevant to AutoML.

Fields
annotation_spec_id	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.
display_name	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.
Union field detail. Output only . Additional information about the annotation specific to the AutoML domain. detail can be only one of the following:
translation	TranslationAnnotation Annotation details for translation.
classification	ClassificationAnnotation Annotation details for content or image classification.
image_object_detection	ImageObjectDetectionAnnotation Annotation details for image object detection.
video_classification	VideoClassificationAnnotation Annotation details for video classification. Returned for Video Classification predictions.
video_object_tracking	VideoObjectTrackingAnnotation Annotation details for video object tracking.
text_extraction	TextExtractionAnnotation Annotation details for text extraction.
text_sentiment	TextSentimentAnnotation Annotation details for text sentiment.
tables	TablesAnnotation Annotation details for Tables.

AnnotationSpec

A definition of an annotation spec.

Fields
name	string Output only. Resource name of the annotation spec. Form: 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'
display_name	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_]+.
example_count	int32 Output only. The number of examples in the parent dataset labeled by the annotation spec.

ArrayStats

The data statistics of a series of ARRAY values.

Fields
member_stats	DataStats Stats of all the values of all arrays, as if they were a single long series of data. The type depends on the element type of the array.

BatchPredictInputConfig

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.

Fields
Union field source. Required. The source of the input. source can be only one of the following:
gcs_source	GcsSource The Google Cloud Storage location for the input content.
bigquery_source	BigQuerySource The BigQuery location for the input content.

BatchPredictOperationMetadata

Details of BatchPredict operation.

Fields
input_config	BatchPredictInputConfig Output only. The input config that was given upon starting this batch predict operation.
output_info	BatchPredictOutputInfo Output only. Information further describing this batch predict's output.

BatchPredictOutputInfo

Further describes this batch predict's output. Supplements

BatchPredictOutputConfig.

Fields
Union field output_location. The output location into which prediction output is written. output_location can be only one of the following:
gcs_output_directory	string The full path of the Google Cloud Storage directory created, into which the prediction output is written.
bigquery_output_dataset	string The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.

BatchPredictOutputConfig

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

"<target_column_specs

display_name>__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][google.cloud.automl.v1beta1.display_name] given on input followed by the predicted target column with name in the format of

"predicted_<target_column_specs

display_name>" 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_<model-display-name>_<timestamp-of-prediction-call> 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_<target_column_specs

display_name>" 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_<target_column_specs

display_name>", 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.

Fields
Union field destination. Required. The destination of the output. destination can be only one of the following:
gcs_destination	GcsDestination The Google Cloud Storage location of the directory where the output is to be written to.
bigquery_destination	BigQueryDestination The BigQuery location where the output is to be written to.

BatchPredictRequest

Request message for PredictionService.BatchPredict.

Fields
name	string Name of the model requested to serve the batch prediction. Authorization requires the following Google IAM permission on the specified resource name: automl.models.predict
input_config	BatchPredictInputConfig Required. The input configuration for batch prediction.
output_config	BatchPredictOutputConfig Required. The Configuration specifying where output predictions should be written.
params	map<string, string> 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 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

Result of the Batch Predict. This message is returned in response of the operation returned by the PredictionService.BatchPredict.

Fields
metadata	map<string, string> 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

The BigQuery location for the output content.

Fields
output_uri	string Required. BigQuery URI to a project, up to 2000 characters long. Accepted forms: * BigQuery path e.g. bq://projectId

BigQuerySource

The BigQuery location for the input content.

Fields
input_uri	string Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path e.g. bq://projectId.bqDatasetId.bqTableId

BoundingBoxMetricsEntry

Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.

Fields
iou_threshold	float Output only. The intersection-over-union threshold value used to compute this metrics entry.
mean_average_precision	float Output only. The mean average precision, most often close to au_prc.
confidence_metrics_entries[]	ConfidenceMetricsEntry 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.

ConfidenceMetricsEntry

Metrics for a single confidence threshold.

Fields
confidence_threshold	float Output only. The confidence threshold value used to compute the metrics.
recall	float Output only. Recall under the given confidence threshold.
precision	float Output only. Precision under the given confidence threshold.
f1_score	float Output only. The harmonic mean of recall and precision.

BoundingPoly

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.

Fields
normalized_vertices[]	NormalizedVertex Output only . The bounding polygon normalized vertices.

CategoryStats

The data statistics of a series of CATEGORY values.

Fields
top_category_stats[]	SingleCategoryStats The statistics of the top 20 CATEGORY values, ordered by count.

SingleCategoryStats

The statistics of a single CATEGORY value.

Fields
value	string The CATEGORY value.
count	int64 The number of occurrences of this value in the series.

ClassificationAnnotation

Contains annotation details specific to classification.

Fields
score	float 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

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.

Fields
au_prc	float Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.
base_au_prc (deprecated)	float This item is deprecated! Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.
au_roc	float Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.
log_loss	float Output only. The Log Loss metric.
confidence_metrics_entry[]	ConfidenceMetricsEntry 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.
confusion_matrix	ConfusionMatrix Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.
annotation_spec_id[]	string Output only. The annotation spec ids used for this evaluation.

ConfidenceMetricsEntry

Metrics for a single confidence threshold.

Fields
confidence_threshold	float Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.
position_threshold	int32 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.
recall	float Output only. Recall (True Positive Rate) for the given confidence threshold.
precision	float Output only. Precision for the given confidence threshold.
false_positive_rate	float Output only. False Positive Rate for the given confidence threshold.
f1_score	float Output only. The harmonic mean of recall and precision.
recall_at1	float 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.
precision_at1	float Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
false_positive_rate_at1	float 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.
f1_score_at1	float Output only. The harmonic mean of recall_at1 and precision_at1.
true_positive_count	int64 Output only. The number of model created labels that match a ground truth label.
false_positive_count	int64 Output only. The number of model created labels that do not match a ground truth label.
false_negative_count	int64 Output only. The number of ground truth labels that are not matched by a model created label.
true_negative_count	int64 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.

ConfusionMatrix

Confusion matrix of the model running the classification.

Fields
annotation_spec_id[]	string 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.
display_name[]	string 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.
row[]	Row 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.

Row

Output only. A row in the confusion matrix.

Fields
example_count[]	int32 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.

ClassificationType

Type of the classification problem.

Enums
CLASSIFICATION_TYPE_UNSPECIFIED	An un-set value of this enum.
MULTICLASS	At most one label is allowed per example.
MULTILABEL	Multiple labels are allowed for one example.

ColumnSpec

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

Fields
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}
data_type	DataType The data type of elements stored in the column.
display_name	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.
data_stats	DataStats Output only. Stats of the series of values in the column. This field may be stale, see the ancestor's Dataset.tables_dataset_metadata.stats_update_time field for the timestamp at which these stats were last updated.
top_correlated_columns[]	CorrelatedColumn Deprecated.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

CorrelatedColumn

Identifies the table's column, and its correlation with the column this ColumnSpec describes.

Fields
column_spec_id	string The column_spec_id of the correlated column, which belongs to the same table as the in-context column.
correlation_stats	CorrelationStats Correlation between this and the in-context column.

CorrelationStats

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.

Fields
cramers_v	double The correlation value using the Cramer's V measure.

CreateDatasetRequest

Request message for AutoMl.CreateDataset.

Fields
parent	string The resource name of the project to create the dataset for. Authorization requires the following Google IAM permission on the specified resource parent: automl.datasets.create
dataset	Dataset The dataset to create.

CreateModelOperationMetadata

Details of CreateModel operation.

CreateModelRequest

Request message for AutoMl.CreateModel.

Fields
parent	string Resource name of the parent project where the model is being created. Authorization requires the following Google IAM permission on the specified resource parent: automl.models.create
model	Model The model to create.

DataStats

The data statistics of a series of values that share the same DataType.

Fields
distinct_value_count	int64 The number of distinct values.
null_value_count	int64 The number of values that are null.
valid_value_count	int64 The number of values that are valid.
Union field stats. The data statistics specific to a DataType. stats can be only one of the following:
float64_stats	Float64Stats The statistics for FLOAT64 DataType.
string_stats	StringStats The statistics for STRING DataType.
timestamp_stats	TimestampStats The statistics for TIMESTAMP DataType.
array_stats	ArrayStats The statistics for ARRAY DataType.
struct_stats	StructStats The statistics for STRUCT DataType.
category_stats	CategoryStats The statistics for CATEGORY DataType.

DataType

Indicated the type of data that can be stored in a structured data entity (e.g. a table).

Fields
type_code	TypeCode Required. The TypeCode for this type.
nullable	bool If true, this DataType can also be NULL. In .CSV files NULL value is expressed as an empty string.
Union field details. Details of DataType-s that need additional specification. details can be only one of the following:
list_element_type	DataType If type_code == ARRAY, then list_element_type is the type of the elements.
struct_type	StructType If type_code == STRUCT, then struct_type provides type information for the struct's fields.
time_format	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.

Dataset

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

Fields
name	string Output only. The resource name of the dataset. Form: projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
display_name	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.
description	string User-provided description of the dataset. The description can be up to 25000 characters long.
example_count	int32 Output only. The number of examples in the dataset.
create_time	Timestamp Output only. Timestamp when this dataset was created.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
Union field dataset_metadata. Required. The dataset metadata that is specific to the problem type. dataset_metadata can be only one of the following:
translation_dataset_metadata	TranslationDatasetMetadata Metadata for a dataset used for translation.
image_classification_dataset_metadata	ImageClassificationDatasetMetadata Metadata for a dataset used for image classification.
text_classification_dataset_metadata	TextClassificationDatasetMetadata Metadata for a dataset used for text classification.
image_object_detection_dataset_metadata	ImageObjectDetectionDatasetMetadata Metadata for a dataset used for image object detection.
video_classification_dataset_metadata	VideoClassificationDatasetMetadata Metadata for a dataset used for video classification.
video_object_tracking_dataset_metadata	VideoObjectTrackingDatasetMetadata Metadata for a dataset used for video object tracking.
text_extraction_dataset_metadata	TextExtractionDatasetMetadata Metadata for a dataset used for text extraction.
text_sentiment_dataset_metadata	TextSentimentDatasetMetadata Metadata for a dataset used for text sentiment.
tables_dataset_metadata	TablesDatasetMetadata Metadata for a dataset used for Tables.

DeleteDatasetRequest

Request message for AutoMl.DeleteDataset.

Fields
name	string The resource name of the dataset to delete. Authorization requires the following Google IAM permission on the specified resource name: automl.datasets.delete

DeleteModelRequest

Request message for AutoMl.DeleteModel.

Fields
name	string Resource name of the model being deleted. Authorization requires the following Google IAM permission on the specified resource name: automl.models.delete

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

DeployModelOperationMetadata

Details of DeployModel operation.

DeployModelRequest

Request message for AutoMl.DeployModel.

Fields
name	string Resource name of the model to deploy. Authorization requires the following Google IAM permission on the specified resource name: automl.models.deploy
Union field model_deployment_metadata. The per-domain specific deployment parameters. model_deployment_metadata can be only one of the following:
image_object_detection_model_deployment_metadata	ImageObjectDetectionModelDeploymentMetadata Model deployment metadata specific to Image Object Detection.
image_classification_model_deployment_metadata	ImageClassificationModelDeploymentMetadata Model deployment metadata specific to Image Classification.

Document

A structured text document e.g. a PDF.

Fields
input_config	DocumentInputConfig An input config specifying the content of the document.
document_text	TextSnippet The plain text version of this document.
layout[]	Layout Describes the layout of the document. Sorted by [page_number][].
document_dimensions	DocumentDimensions The dimensions of the page in the document.
page_count	int32 Number of pages in the document.

Layout

Describes the layout information of a text_segment in the document.

Fields
text_segment	TextSegment Text Segment that represents a segment in document_text.
page_number	int32 Page number of the text_segment in the original document, starts from 1.
bounding_poly	BoundingPoly The position of the text_segment in the page. Contains exactly 4 normalized_vertices and they are connected by edges in the order provided, which will represent a rectangle parallel to the frame. The NormalizedVertex-s are relative to the page. Coordinates are based on top-left as point (0,0).
text_segment_type	TextSegmentType The type of the text_segment in document.

TextSegmentType

The type of TextSegment in the context of the original document.

Enums
TEXT_SEGMENT_TYPE_UNSPECIFIED	Should not be used.
TOKEN	The text segment is a token. e.g. word.
PARAGRAPH	The text segment is a paragraph.
FORM_FIELD	The text segment is a form field.
FORM_FIELD_NAME	The text segment is the name part of a form field. It will be treated as child of another FORM_FIELD TextSegment if its span is subspan of another TextSegment with type FORM_FIELD.
FORM_FIELD_CONTENTS	The text segment is the text content part of a form field. It will be treated as child of another FORM_FIELD TextSegment if its span is subspan of another TextSegment with type FORM_FIELD.
TABLE	The text segment is a whole table, including headers, and all rows.
TABLE_HEADER	The text segment is a table's headers. It will be treated as child of another TABLE TextSegment if its span is subspan of another TextSegment with type TABLE.
TABLE_ROW	The text segment is a row in table. It will be treated as child of another TABLE TextSegment if its span is subspan of another TextSegment with type TABLE.
TABLE_CELL	The text segment is a cell in table. It will be treated as child of another TABLE_ROW TextSegment if its span is subspan of another TextSegment with type TABLE_ROW.

DocumentDimensions

Message that describes dimension of a document.

Fields
unit	DocumentDimensionUnit Unit of the dimension.
width	float Width value of the document, works together with the unit.
height	float Height value of the document, works together with the unit.

DocumentDimensionUnit

Unit of the document dimension.

Enums
DOCUMENT_DIMENSION_UNIT_UNSPECIFIED	Should not be used.
INCH	Document dimension is measured in inches.
CENTIMETER	Document dimension is measured in centimeters.
POINT	Document dimension is measured in points. 72 points = 1 inch.

DocumentInputConfig

Input configuration of a Document.

Fields
gcs_source	GcsSource The Google Cloud Storage location of the document file. Only a single path should be given. Max supported size: 512MB. Supported extensions: .PDF.

DoubleRange

A range between two double numbers.

Fields
start	double Start of the range, inclusive.
end	double End of the range, exclusive.

ExamplePayload

Example data used for training or prediction.

Fields
Union field payload. Required. Input only. The example data. payload can be only one of the following:
image	Image Example image.
text_snippet	TextSnippet Example text.
document	Document Example document.
row	Row Example relational table row.

ExportDataOperationMetadata

Details of ExportData operation.

Fields
output_info	ExportDataOutputInfo Output only. Information further describing this export data's output.

ExportDataOutputInfo

Further describes this export data's output. Supplements OutputConfig.

Fields
Union field output_location. The output location to which the exported data is written. output_location can be only one of the following:
gcs_output_directory	string The full path of the Google Cloud Storage directory created, into which the exported data is written.
bigquery_output_dataset	string The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the exported data is written.

ExportDataRequest

Request message for AutoMl.ExportData.

Fields
name	string Required. The resource name of the dataset. Authorization requires the following Google IAM permission on the specified resource name: automl.datasets.export
output_config	OutputConfig Required. The desired output location.

ExportEvaluatedExamplesOperationMetadata

Details of EvaluatedExamples operation.

Fields
output_info	ExportEvaluatedExamplesOutputInfo Output only. Information further describing the output of this evaluated examples export.

ExportEvaluatedExamplesOutputInfo

Further describes the output of the evaluated examples export. Supplements

ExportEvaluatedExamplesOutputConfig.

Fields
bigquery_output_dataset	string The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the output of export evaluated examples is written.

ExportEvaluatedExamplesOutputConfig

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_<model-display-name>_<timestamp-of-export-call> 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.

Fields
bigquery_destination	BigQueryDestination The BigQuery location where the output is to be written to.

ExportEvaluatedExamplesRequest

Request message for AutoMl.ExportEvaluatedExamples.

Fields
name	string Required. The resource name of the model whose evaluated examples are to be exported. Authorization requires the following Google IAM permission on the specified resource name: automl.modelEvaluations.get
output_config	ExportEvaluatedExamplesOutputConfig Required. The desired output location and configuration.

ExportModelOperationMetadata

Details of ExportModel operation.

Fields
output_info	ExportModelOutputInfo Output only. Information further describing the output of this model export.

ExportModelOutputInfo

Further describes the output of model export. Supplements

ModelExportOutputConfig.

Fields
gcs_output_directory	string The full path of the Google Cloud Storage directory created, into which the model will be exported.

ExportModelRequest

Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned.

Fields
name	string Required. The resource name of the model to export. Authorization requires the following Google IAM permission on the specified resource name: automl.models.export
output_config	ModelExportOutputConfig Required. The desired output location and configuration.

Float64Stats

The data statistics of a series of FLOAT64 values.

Fields
mean	double The mean of the series.
standard_deviation	double The standard deviation of the series.
quantiles[]	double 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.
histogram_buckets[]	HistogramBucket 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".

HistogramBucket

A bucket of a histogram.

Fields
min	double The minimum value of the bucket, inclusive.
max	double The maximum value of the bucket, exclusive unless max = "Infinity", in which case it's inclusive.
count	int64 The number of data values that are in the bucket, i.e. are between min and max values.

GcrDestination

The GCR location where the image must be pushed to.

Fields
output_uri	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

The Google Cloud Storage location where the output is to be written to.

Fields
output_uri_prefix	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

The Google Cloud Storage location for the input content.

Fields
input_uris[]	string 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

GetAnnotationSpecRequest

Request message for AutoMl.GetAnnotationSpec.

Fields
name	string The resource name of the annotation spec to retrieve. Authorization requires the following Google IAM permission on the specified resource name: automl.annotationSpecs.get

GetColumnSpecRequest

Request message for AutoMl.GetColumnSpec.

Fields
name	string The resource name of the column spec to retrieve. Authorization requires the following Google IAM permission on the specified resource name: automl.columnSpecs.get
field_mask	FieldMask Mask specifying which fields to read.

GetDatasetRequest

Request message for AutoMl.GetDataset.

Fields
name	string The resource name of the dataset to retrieve. Authorization requires the following Google IAM permission on the specified resource name: automl.datasets.get

GetModelEvaluationRequest

Request message for AutoMl.GetModelEvaluation.

Fields
name	string Resource name for the model evaluation. Authorization requires the following Google IAM permission on the specified resource name: automl.modelEvaluations.get

GetModelRequest

Request message for AutoMl.GetModel.

Fields
name	string Resource name of the model. Authorization requires the following Google IAM permission on the specified resource name: automl.models.get

GetTableSpecRequest

Request message for AutoMl.GetTableSpec.

Fields
name	string The resource name of the table spec to retrieve. Authorization requires the following Google IAM permission on the specified resource name: automl.tableSpecs.get
field_mask	FieldMask Mask specifying which fields to read.

Image

A representation of an image. Only images up to 30MB in size are supported.

Fields
thumbnail_uri	string Output only. HTTP URI to the thumbnail image.
Union field data. Input only. The data representing the image. For Predict calls image_bytes must be set, as other options are not currently supported by prediction API. You can read the contents of an uploaded image by using the content_uri field. data can be only one of the following:
image_bytes	bytes 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.
input_config	InputConfig An input config specifying the content of the image.

ImageClassificationDatasetMetadata

Dataset metadata that is specific to image classification.

Fields
classification_type	ClassificationType Required. Type of the classification problem.

ImageClassificationModelDeploymentMetadata

Model deployment metadata specific to Image Classification.

Fields
node_count	int64 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

Model metadata for image classification.

Fields
base_model_id	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.
train_budget	int64 Required. The train budget of creating this model, expressed in hours. The actual train_cost will be equal or less than this value.
train_cost	int64 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.
stop_reason	string Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.
model_type	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.
node_qps	double Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
node_count	int64 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.

ImageObjectDetectionAnnotation

Annotation details for image object detection.

Fields
bounding_box	BoundingPoly Output only. The rectangle representing the object location.
score	float Output only. The confidence that this annotation is positive for the parent example, value in [0, 1], higher means higher positivity confidence.

ImageObjectDetectionDatasetMetadata

Dataset metadata specific to image object detection.

ImageObjectDetectionEvaluationMetrics

Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.

Fields
evaluated_bounding_box_count	int32 Output only. The total number of bounding boxes (i.e. summed over all images) the ground truth used to create this evaluation had.
bounding_box_metrics_entries[]	BoundingBoxMetricsEntry 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.
bounding_box_mean_average_precision	float Output only. The single metric for bounding boxes evaluation: the mean_average_precision averaged over all bounding_box_metrics_entries.

ImageObjectDetectionModelDeploymentMetadata

Model deployment metadata specific to Image Object Detection.

Fields
node_count	int64 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][google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata.qps_per_node]. Must be between 1 and 100, inclusive on both ends.

ImageObjectDetectionModelMetadata

Model metadata specific to image object detection.

Fields
model_type	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.
node_count	int64 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.
node_qps	double Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
stop_reason	string Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.
train_budget_milli_node_hours	int64 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.
train_cost_milli_node_hours	int64 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

Details of ImportData operation.

ImportDataRequest

Request message for AutoMl.ImportData.

Fields
name	string Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added. Authorization requires the following Google IAM permission on the specified resource name: automl.datasets.import
input_config	InputConfig Required. The desired input location and its domain specific semantics, if any.

InputConfig

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][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name] 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.

Fields
params	map<string, string> 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".
Union field source. The source of the input. source can be only one of the following:
gcs_source	GcsSource The Google Cloud Storage location for the input content. In ImportData, the gcs_source points to a csv with structure described in the comment.
bigquery_source	BigQuerySource The BigQuery location for the input content.

ListColumnSpecsRequest

Request message for AutoMl.ListColumnSpecs.

Fields
parent	string The resource name of the table spec to list column specs from. Authorization requires the following Google IAM permission on the specified resource parent: automl.columnSpecs.list
field_mask	FieldMask Mask specifying which fields to read.
filter	string Filter expression, see go/filtering.
page_size	int32 Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
page_token	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.

ListColumnSpecsResponse

Response message for AutoMl.ListColumnSpecs.

Fields
column_specs[]	ColumnSpec The column specs read.
next_page_token	string A token to retrieve next page of results. Pass to ListColumnSpecsRequest.page_token to obtain that page.

ListDatasetsRequest

Request message for AutoMl.ListDatasets.

Fields
parent	string The resource name of the project from which to list datasets. Authorization requires the following Google IAM permission on the specified resource parent: automl.datasets.list
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.
page_size	int32 Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.
page_token	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.

ListDatasetsResponse

Response message for AutoMl.ListDatasets.

Fields
datasets[]	Dataset The datasets read.
next_page_token	string A token to retrieve next page of results. Pass to ListDatasetsRequest.page_token to obtain that page.

ListModelEvaluationsRequest

Request message for AutoMl.ListModelEvaluations.

Fields
parent	string 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. Authorization requires the following Google IAM permission on the specified resource parent: automl.modelEvaluations.list
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.
page_size	int32 Requested page size.
page_token	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.

ListModelEvaluationsResponse

Response message for AutoMl.ListModelEvaluations.

Fields
model_evaluation[]	ModelEvaluation List of model evaluations in the requested page.
next_page_token	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.

ListModelsRequest

Request message for AutoMl.ListModels.

Fields
parent	string Resource name of the project, from which to list the models. Authorization requires the following Google IAM permission on the specified resource parent: automl.models.list
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.
page_size	int32 Requested page size.
page_token	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.

ListModelsResponse

Response message for AutoMl.ListModels.

Fields
model[]	Model List of models in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to ListModelsRequest.page_token to obtain that page.

ListTableSpecsRequest

Request message for AutoMl.ListTableSpecs.

Fields
parent	string The resource name of the dataset to list table specs from. Authorization requires the following Google IAM permission on the specified resource parent: automl.tableSpecs.list
field_mask	FieldMask Mask specifying which fields to read.
filter	string Filter expression, see go/filtering.
page_size	int32 Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
page_token	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.

ListTableSpecsResponse

Response message for AutoMl.ListTableSpecs.

Fields
table_specs[]	TableSpec The table specs read.
next_page_token	string A token to retrieve next page of results. Pass to ListTableSpecsRequest.page_token to obtain that page.

Model

API proto representing a trained machine learning model.

Fields
name	string Output only. Resource name of the model. Format: projects/{project_id}/locations/{location_id}/models/{model_id}
display_name	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.
dataset_id	string Required. The resource ID of the dataset used to create the model. The dataset must come from the same ancestor project and location.
create_time	Timestamp Output only. Timestamp when the model training finished and can be used for prediction.
update_time	Timestamp Output only. Timestamp when this model was last updated.
deployment_state	DeploymentState Output only. Deployment state of the model. A model can only serve prediction requests after it gets deployed.
Union field model_metadata. Required. The model metadata that is specific to the problem type. Must match the metadata type of the dataset used to train the model. model_metadata can be only one of the following:
translation_model_metadata	TranslationModelMetadata Metadata for translation models.
image_classification_model_metadata	ImageClassificationModelMetadata Metadata for image classification models.
text_classification_model_metadata	TextClassificationModelMetadata Metadata for text classification models.
image_object_detection_model_metadata	ImageObjectDetectionModelMetadata Metadata for image object detection models.
video_classification_model_metadata	VideoClassificationModelMetadata Metadata for video classification models.
video_object_tracking_model_metadata	VideoObjectTrackingModelMetadata Metadata for video object tracking models.
text_extraction_model_metadata	TextExtractionModelMetadata Metadata for text extraction models.
tables_model_metadata	TablesModelMetadata Metadata for Tables models.
text_sentiment_model_metadata	TextSentimentModelMetadata Metadata for text sentiment models.

DeploymentState

Deployment state of the model.

Enums
DEPLOYMENT_STATE_UNSPECIFIED	Should not be used, an un-set enum has this value by default.
DEPLOYED	Model is deployed.
UNDEPLOYED	Model is not deployed.

ModelEvaluation

Evaluation results of a model.

Fields
name	string Output only. Resource name of the model evaluation. Format: projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}
annotation_spec_id	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.
display_name	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.
create_time	Timestamp Output only. Timestamp when this model evaluation was created.
evaluated_example_count	int32 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.
Union field metrics. Output only. Problem type specific evaluation metrics. metrics can be only one of the following:
classification_evaluation_metrics	ClassificationEvaluationMetrics Model evaluation metrics for image, text, video and tables classification. Tables problem is considered a classification when the target column is CATEGORY DataType.
regression_evaluation_metrics	RegressionEvaluationMetrics Model evaluation metrics for Tables regression. Tables problem is considered a regression when the target column has FLOAT64 DataType.
translation_evaluation_metrics	TranslationEvaluationMetrics Model evaluation metrics for translation.
image_object_detection_evaluation_metrics	ImageObjectDetectionEvaluationMetrics Model evaluation metrics for image object detection.
video_object_tracking_evaluation_metrics	VideoObjectTrackingEvaluationMetrics Model evaluation metrics for video object tracking.
text_sentiment_evaluation_metrics	TextSentimentEvaluationMetrics Evaluation metrics for text sentiment models.
text_extraction_evaluation_metrics	TextExtractionEvaluationMetrics Evaluation metrics for text extraction models.

ModelExportOutputConfig

Output configuration for ModelExport Action.

Fields
model_format	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). 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	map<string, string> 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".
Union field destination. Required. The destination of the output. destination can be only one of the following:
gcs_destination	GcsDestination The Google Cloud Storage location where the model is to be written to. This location may only be set for the following model formats: "tflite", "edgetpu_tflite", "tf_saved_model", "tf_js", "core_ml". Under the directory given as the destination a new one with name "model-export--", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format, will be created. Inside the model and any of its supporting files will be written.
gcr_destination	GcrDestination The GCR location where model image is to be pushed to. This location may only be set for the following model formats: "docker". The model image will be created under the given URI.

NormalizedVertex

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.

Fields
x	float Required. Horizontal coordinate.
y	float Required. Vertical coordinate.

OperationMetadata

Metadata used across all long running operations returned by AutoML API.

Fields
progress_percent	int32 Output only. Progress of operation. Range: [0, 100]. Not used currently.
partial_failures[]	Status 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.
create_time	Timestamp Output only. Time when the operation was created.
update_time	Timestamp Output only. Time when the operation was updated for the last time.
Union field details. Ouptut only. Details of specific operation. Even if this field is empty, the presence allows to distinguish different types of operations. details can be only one of the following:
delete_details	DeleteOperationMetadata Details of a Delete operation.
deploy_model_details	DeployModelOperationMetadata Details of a DeployModel operation.
undeploy_model_details	UndeployModelOperationMetadata Details of an UndeployModel operation.
create_model_details	CreateModelOperationMetadata Details of CreateModel operation.
import_data_details	ImportDataOperationMetadata Details of ImportData operation.
batch_predict_details	BatchPredictOperationMetadata Details of BatchPredict operation.
export_data_details	ExportDataOperationMetadata Details of ExportData operation.
export_model_details	ExportModelOperationMetadata Details of ExportModel operation.
export_evaluated_examples_details	ExportEvaluatedExamplesOperationMetadata Details of ExportEvaluatedExamples operation.

OutputConfig

• 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_<automl-dataset-display-name>_<timestamp-of-export-call> 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.

Fields
Union field destination. Required. The destination of the output. destination can be only one of the following:
gcs_destination	GcsDestination The Google Cloud Storage location where the output is to be written to. For Image Object Detection, Text Extraction, Video Classification and Tables, in the given directory a new directory will be created with name: export_data-- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory.
bigquery_destination	BigQueryDestination The BigQuery location where the output is to be written to.

PredictRequest

Request message for PredictionService.Predict.

Fields
name	string Name of the model requested to serve the prediction. Authorization requires the following Google IAM permission on the specified resource name: automl.models.predict
payload	ExamplePayload Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.
params	map<string, string> 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][google.cloud.automl.v1beta1.TablesModelColumnInfo.feature_importance should be populated in the returned [TablesAnnotation(-s)][google.cloud.automl.v1beta1.TablesAnnotation. The default is false.

PredictResponse

Response message for PredictionService.Predict.

Fields
payload[]	AnnotationPayload Prediction result. Translation and Text Sentiment will return precisely one payload.
preprocessed_input	ExamplePayload The preprocessed example that AutoML actually makes prediction on. Empty if AutoML does not preprocess the input example. * For Text Extraction: If the input is a .pdf file, the OCR'ed text will be provided in document_text.
metadata	map<string, string> 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.

RegressionEvaluationMetrics

Metrics for regression problems.

Fields
root_mean_squared_error	float Output only. Root Mean Squared Error (RMSE).
mean_absolute_error	float Output only. Mean Absolute Error (MAE).
mean_absolute_percentage_error	float Output only. Mean absolute percentage error. Only set if all ground truth values are are positive.
r_squared	float Output only. R squared.
root_mean_squared_log_error	float Output only. Root mean squared log error.

Row

A representation of a row in a relational table.

Fields
column_spec_ids[]	string 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.
values[]	Value 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.

StringStats

The data statistics of a series of STRING values.

Fields
top_unigram_stats[]	UnigramStats The statistics of the top 20 unigrams, ordered by count.

UnigramStats

The statistics of a unigram.

Fields
value	string The unigram.
count	int64 The number of occurrences of this unigram in the series.

StructStats

The data statistics of a series of STRUCT values.

Fields
field_stats	map<string, DataStats> 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 defines the DataType-s of a STRUCT type.

Fields
fields	map<string, DataType> 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

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

Fields
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}
time_column_spec_id	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.
row_count	int64 Output only. The number of rows (i.e. examples) in the table.
valid_row_count	int64 Output only. The number of valid rows (i.e. without values that don't match DataType-s of their columns).
column_count	int64 Output only. The number of columns of the table. That is, the number of child ColumnSpec-s.
input_configs[]	InputConfig Output only. Input configs via which data currently residing in the table had been imported.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

TablesAnnotation

Contains annotation details specific to Tables.

Fields
score	float 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.
prediction_interval	DoubleRange Output only. Only populated when target_column_spec has FLOAT64 data type. An interval in which the exactly correct target value has 95% chance to be in.
value	Value The predicted value of the row's target_column. The value depends on the column's DataType: CATEGORY - the predicted (with the above confidence score) CATEGORY value. FLOAT64 - the predicted (with above prediction_interval) FLOAT64 value.
tables_model_column_info[]	TablesModelColumnInfo 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.

TablesDatasetMetadata

Metadata for a dataset used for AutoML Tables.

Fields
primary_table_spec_id	string Output only. The table_spec_id of the primary table of this dataset.
target_column_spec_id	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.
weight_column_spec_id	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.
ml_use_column_spec_id	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.
target_column_correlations	map<string, CorrelationStats> 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.
stats_update_time	Timestamp 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.

TablesModelColumnInfo

An information specific to given column and Tables Model, in context of the Model and the predictions created by it.

Fields
column_spec_name	string Output only. The name of the ColumnSpec describing the column. Not populated when this proto is outputted to BigQuery.
column_display_name	string Output only. The display name of the column (same as the display_name of its ColumnSpec).
feature_importance	float 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

Model metadata specific to AutoML Tables.

Fields
target_column_spec	ColumnSpec Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.
input_feature_column_specs[]	ColumnSpec 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.
optimization_objective	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).
tables_model_column_info[]	TablesModelColumnInfo Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.
train_budget_milli_node_hours	int64 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.
train_cost_milli_node_hours	int64 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.
disable_early_stopping	bool 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.
Union field additional_optimization_objective_config. Additional optimization objective configuration. Required for MAXIMIZE_PRECISION_AT_RECALL and MAXIMIZE_RECALL_AT_PRECISION, otherwise unused. additional_optimization_objective_config can be only one of the following:
optimization_objective_recall_value	float Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". Must be between 0 and 1, inclusive.
optimization_objective_precision_value	float Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". Must be between 0 and 1, inclusive.

TextClassificationDatasetMetadata

Dataset metadata for classification.

Fields
classification_type	ClassificationType Required. Type of the classification problem.

TextClassificationModelMetadata

Model metadata that is specific to text classification.

Fields
classification_type	ClassificationType Output only. Classification type of the dataset used to train this model.

TextExtractionAnnotation

Annotation for identifying spans of text.

Fields
score	float Output only. A confidence estimate between 0.0 and 1.0. A higher value means greater confidence in correctness of the annotation.
text_segment	TextSegment An entity annotation will set this, which is the part of the original text to which the annotation pertains.

TextExtractionDatasetMetadata

Dataset metadata that is specific to text extraction

TextExtractionEvaluationMetrics

Model evaluation metrics for text extraction problems.

Fields
au_prc	float Output only. The Area under precision recall curve metric.
confidence_metrics_entries[]	ConfidenceMetricsEntry Output only. Metrics that have confidence thresholds. Precision-recall curve can be derived from it.

ConfidenceMetricsEntry

Metrics for a single confidence threshold.

Fields
confidence_threshold	float 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.
recall	float Output only. Recall under the given confidence threshold.
precision	float Output only. Precision under the given confidence threshold.
f1_score	float Output only. The harmonic mean of recall and precision.

TextExtractionModelMetadata

Model metadata that is specific to text extraction.

TextSegment

A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.

Fields
content	string Output only. The content of the TextSegment.
start_offset	int64 Required. Zero-based character index of the first character of the text segment (counting characters from the beginning of the text).
end_offset	int64 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.

TextSentimentAnnotation

Contains annotation details specific to text sentiment.

Fields

sentiment

int32 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

Dataset metadata for text sentiment.

Fields
sentiment_max	int32 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

Model evaluation metrics for text sentiment problems.

Fields
precision	float Output only. Precision.
recall	float Output only. Recall.
f1_score	float Output only. The harmonic mean of recall and precision.
mean_absolute_error	float Output only. Mean absolute error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
mean_squared_error	float Output only. Mean squared error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
linear_kappa	float Output only. Linear weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
quadratic_kappa	float Output only. Quadratic weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
confusion_matrix	ConfusionMatrix Output only. Confusion matrix of the evaluation. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
annotation_spec_id[] (deprecated)	string This item is deprecated! Output only. The annotation spec ids used for this evaluation. Deprecated .

TextSentimentModelMetadata

Model metadata that is specific to text sentiment.

TextSnippet

A representation of a text snippet.

Fields
content	string Required. The content of the text snippet as a string. Up to 250000 characters long.
mime_type	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.
content_uri	string Output only. HTTP URI where you can download the content.

TimeSegment

A time period inside of an example that has a time dimension (e.g. video).

Fields
start_time_offset	Duration Start of the time segment (inclusive), represented as the duration since the example start.
end_time_offset	Duration End of the time segment (exclusive), represented as the duration since the example start.

TimestampStats

The data statistics of a series of TIMESTAMP values.

Fields
granular_stats	map<string, GranularStats> 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).

GranularStats

Stats split by a defined in context granularity.

Fields
buckets	map<int32, int64> 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).

TranslationAnnotation

Annotation details specific to translation.

Fields
translated_content	TextSnippet Output only . The translated content.

TranslationDatasetMetadata

Dataset metadata that is specific to translation.

Fields
source_language_code	string Required. The BCP-47 language code of the source language.
target_language_code	string Required. The BCP-47 language code of the target language.

TranslationEvaluationMetrics

Evaluation metrics for the dataset.

Fields
bleu_score	double Output only. BLEU score.
base_bleu_score	double Output only. BLEU score for base model.

TranslationModelMetadata

Model metadata that is specific to translation.

Fields
base_model	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}
source_language_code	string Output only. Inferred from the dataset. The source languge (The BCP-47 language code) that is used for training.
target_language_code	string Output only. The target languge (The BCP-47 language code) that is used for training.

TypeCode

TypeCode is used as a part of DataType.

Enums
TYPE_CODE_UNSPECIFIED	Not specified. Should not be used.
FLOAT64	Encoded as number, or the strings "NaN", "Infinity", or "-Infinity".
TIMESTAMP	Must be between 0AD and 9999AD. Encoded as string according to time_format, or, if that format is not set, then in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z).
STRING	Encoded as string.
ARRAY	Encoded as list, where the list elements are represented according to list_element_type.
STRUCT	Encoded as struct, where field values are represented according to struct_type.
CATEGORY	Values of this type are not further understood by AutoML, e.g. AutoML is unable to tell the order of values (as it could with FLOAT64), or is unable to say if one value contains another (as it could with STRING). Encoded as string (bytes should be base64-encoded, as described in RFC 4648, section 4).

UndeployModelOperationMetadata

Details of UndeployModel operation.

UndeployModelRequest

Request message for AutoMl.UndeployModel.

Fields
name	string Resource name of the model to undeploy. Authorization requires the following Google IAM permission on the specified resource name: automl.models.undeploy

UpdateColumnSpecRequest

Request message for AutoMl.UpdateColumnSpec

Fields
column_spec	ColumnSpec The column spec which replaces the resource on the server. Authorization requires the following Google IAM permission on the specified resource columnSpec: automl.columnSpecs.update
update_mask	FieldMask The update mask applies to the resource.

UpdateDatasetRequest

Request message for AutoMl.UpdateDataset

Fields
dataset	Dataset The dataset which replaces the resource on the server. Authorization requires the following Google IAM permission on the specified resource dataset: automl.datasets.update
update_mask	FieldMask The update mask applies to the resource.

UpdateTableSpecRequest

Request message for AutoMl.UpdateTableSpec

Fields
table_spec	TableSpec The table spec which replaces the resource on the server. Authorization requires the following Google IAM permission on the specified resource tableSpec: automl.tableSpecs.update
update_mask	FieldMask The update mask applies to the resource.

VideoClassificationAnnotation

Contains annotation details specific to video classification.

Fields
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.
classification_annotation	ClassificationAnnotation Output only . The classification details of this annotation.
time_segment	TimeSegment Output only . The time segment of the video to which the annotation applies.

VideoClassificationDatasetMetadata

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

VideoClassificationModelMetadata

Model metadata specific to video classification.

VideoObjectTrackingAnnotation

Annotation details for video object tracking.

Fields
instance_id	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.
time_offset	Duration Required. A time (frame) of a video to which this annotation pertains. Represented as the duration since the video's start.
bounding_box	BoundingPoly Required. The rectangle representing the object location on the frame (i.e. at the time_offset of the video).
score	float 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).

VideoObjectTrackingDatasetMetadata

Dataset metadata specific to video object tracking.

VideoObjectTrackingEvaluationMetrics

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).

Fields
evaluated_frame_count	int32 Output only. The number of video frames used to create this evaluation.
evaluated_bounding_box_count	int32 Output only. The total number of bounding boxes (i.e. summed over all frames) the ground truth used to create this evaluation had.
bounding_box_metrics_entries[]	BoundingBoxMetricsEntry 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.
bounding_box_mean_average_precision	float Output only. The single metric for bounding boxes evaluation: the mean_average_precision averaged over all bounding_box_metrics_entries.

VideoObjectTrackingModelMetadata

Model metadata specific to video object tracking.