diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/async_client.py b/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/async_client.py index 44da5bcd2e49..adc27c9982ac 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/async_client.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/async_client.py @@ -73,7 +73,7 @@ class AutoMlAsyncClient: 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. + snake_case or dash-case, either of those cases is accepted. """ _client: AutoMlClient @@ -1411,7 +1411,6 @@ async def deploy_model( r"""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.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number]) will reset the deployment state without pausing the model's availability. diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/client.py b/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/client.py index 127154a15f88..8fdeecddd4b5 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/client.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/client.py @@ -107,7 +107,7 @@ class AutoMlClient(metaclass=AutoMlClientMeta): 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. + snake_case or dash-case, either of those cases is accepted. """ @staticmethod @@ -1593,7 +1593,6 @@ def deploy_model( r"""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.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number]) will reset the deployment state without pausing the model's availability. diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/transports/grpc.py b/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/transports/grpc.py index 7ea7c5fc9921..6765f1db45fa 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/transports/grpc.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/transports/grpc.py @@ -53,7 +53,7 @@ class AutoMlGrpcTransport(AutoMlTransport): 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. + snake_case or dash-case, either of those cases is accepted. This class defines the same methods as the primary client, so the primary client can load the underlying transport implementation @@ -626,7 +626,6 @@ def deploy_model( 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.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number]) will reset the deployment state without pausing the model's availability. diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/transports/grpc_asyncio.py b/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/transports/grpc_asyncio.py index 26c4d73c47f1..f5644c10dd6c 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/transports/grpc_asyncio.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/transports/grpc_asyncio.py @@ -54,7 +54,7 @@ class AutoMlGrpcAsyncIOTransport(AutoMlTransport): 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. + snake_case or dash-case, either of those cases is accepted. This class defines the same methods as the primary client, so the primary client can load the underlying transport implementation @@ -638,7 +638,6 @@ def deploy_model( 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.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number]) will reset the deployment state without pausing the model's availability. diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/async_client.py b/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/async_client.py index 0ceb1544ef5f..e2eeb0f0c36d 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/async_client.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/async_client.py @@ -47,7 +47,7 @@ class PredictionServiceAsyncClient: """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. + snake_case or dash-case, either of those cases is accepted. """ _client: PredictionServiceClient @@ -270,7 +270,6 @@ async def predict( AutoML Tables ``feature_importance`` : (boolean) Whether - [feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance] is populated in the returned list of [TablesAnnotation][google.cloud.automl.v1.TablesAnnotation] diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/client.py b/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/client.py index 59cc17d58600..22de25cdff0b 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/client.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/client.py @@ -85,7 +85,7 @@ class PredictionServiceClient(metaclass=PredictionServiceClientMeta): """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. + snake_case or dash-case, either of those cases is accepted. """ @staticmethod @@ -464,7 +464,6 @@ def predict( AutoML Tables ``feature_importance`` : (boolean) Whether - [feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance] is populated in the returned list of [TablesAnnotation][google.cloud.automl.v1.TablesAnnotation] diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/transports/grpc.py b/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/transports/grpc.py index b3f09e018f03..ec174da7e84d 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/transports/grpc.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/transports/grpc.py @@ -36,7 +36,7 @@ class PredictionServiceGrpcTransport(PredictionServiceTransport): 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. + snake_case or dash-case, either of those cases is accepted. This class defines the same methods as the primary client, so the primary client can load the underlying transport implementation diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/transports/grpc_asyncio.py b/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/transports/grpc_asyncio.py index 2ec6ab926dfc..cca25f11f0f0 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/transports/grpc_asyncio.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/transports/grpc_asyncio.py @@ -37,7 +37,7 @@ class PredictionServiceGrpcAsyncIOTransport(PredictionServiceTransport): 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. + snake_case or dash-case, either of those cases is accepted. This class defines the same methods as the primary client, so the primary client can load the underlying transport implementation diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/types/annotation_spec.py b/packages/google-cloud-automl/google/cloud/automl_v1/types/annotation_spec.py index 5bcbcff3fa1f..05953b962fa5 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/types/annotation_spec.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/types/annotation_spec.py @@ -27,7 +27,6 @@ class AnnotationSpec(proto.Message): Attributes: name (str): Output only. Resource name of the annotation spec. Form: - 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}' display_name (str): Required. The name of the annotation spec to show in the diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/types/classification.py b/packages/google-cloud-automl/google/cloud/automl_v1/types/classification.py index 40202f16314f..2103966dda7a 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/types/classification.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/types/classification.py @@ -169,7 +169,6 @@ class ConfusionMatrix(proto.Message): annotation_spec_id (Sequence[str]): Output only. IDs of the annotation specs used in the confusion matrix. For Tables CLASSIFICATION - [prediction_type][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type] only list of [annotation_spec_display_name-s][] is populated. @@ -177,7 +176,6 @@ class ConfusionMatrix(proto.Message): 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][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type], distinct values of the target column at the moment of the model evaluation are populated here. diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/types/data_items.py b/packages/google-cloud-automl/google/cloud/automl_v1/types/data_items.py index f2895f5ce9ff..041552e47181 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/types/data_items.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/types/data_items.py @@ -139,7 +139,6 @@ class Layout(proto.Message): The position of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the page. Contains exactly 4 - [normalized_vertices][google.cloud.automl.v1p1beta.BoundingPoly.normalized_vertices] and they are connected by edges in the order provided, which will represent a rectangle parallel to the frame. The diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/types/image.py b/packages/google-cloud-automl/google/cloud/automl_v1/types/image.py index 129cb5d7dd78..e5d1e151d2b1 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/types/image.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/types/image.py @@ -61,13 +61,13 @@ class ImageClassificationModelMetadata(proto.Message): ``location`` as the new model to create, and have the same ``model_type``. train_budget_milli_node_hours (int): - 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 + Optional. 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``\ (default), the train budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192, 000 which represents one day in wall time. For model @@ -199,13 +199,13 @@ class ImageObjectDetectionModelMetadata(proto.Message): Output only. The reason that this create model operation stopped, e.g. ``BUDGET_REACHED``, ``MODEL_CONVERGED``. train_budget_milli_node_hours (int): - 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 + Optional. 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 @@ -242,7 +242,6 @@ class ImageClassificationModelDeploymentMetadata(proto.Message): 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][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps]. Must be between 1 and 100, inclusive on both ends. """ @@ -258,7 +257,6 @@ class ImageObjectDetectionModelDeploymentMetadata(proto.Message): 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.v1.ImageObjectDetectionModelMetadata.qps_per_node]. Must be between 1 and 100, inclusive on both ends. """ diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/types/io.py b/packages/google-cloud-automl/google/cloud/automl_v1/types/io.py index e071fa3e0c58..171d5484a60c 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/types/io.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/types/io.py @@ -613,7 +613,6 @@ class InputConfig(proto.Message): [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] or [bigquery_source][google.cloud.automl.v1.InputConfig.bigquery_source]. All input is concatenated into a single - [primary_table_spec_id][google.cloud.automl.v1.TablesDatasetMetadata.primary_table_spec_id] **For gcs_source:** @@ -632,9 +631,7 @@ class InputConfig(proto.Message): 
         "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"}]}
@@ -1012,7 +1009,6 @@ class BatchPredictInputConfig(proto.Message): contain values for the corresponding columns. The column names must contain the model's - [input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order doesn't matter). The columns corresponding to the model's @@ -1026,9 +1022,7 @@ class BatchPredictInputConfig(proto.Message): 
         "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"}]}
@@ -1038,7 +1032,6 @@ class BatchPredictInputConfig(proto.Message): table must be 100GB or smaller. The column names must contain the model's - [input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order doesn't matter). The columns corresponding to the model's @@ -1119,23 +1112,21 @@ class OutputConfig(proto.Message): - For Tables: Output depends on whether the dataset was imported from Google Cloud Storage or BigQuery. Google Cloud Storage case: - - [gcs_destination][google.cloud.automl.v1p1beta.OutputConfig.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][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] - pointing to a BigQuery project must be set. In the given project a - new dataset will be created with name - - ``export_data__`` - where will be made BigQuery-dataset-name compatible (e.g. most - special characters will become underscores), and timestamp will be - in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that - dataset a new table called ``primary_table`` will be created, and - filled with precisely the same data as this obtained on import. + [gcs_destination][google.cloud.automl.v1p1beta.OutputConfig.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][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] + pointing to a BigQuery project must be set. In the given + project a new dataset will be created with name + ``export_data__`` + where will be made BigQuery-dataset-name compatible (e.g. most + special characters will become underscores), and timestamp + will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" + format. In that dataset a new table called ``primary_table`` + will be created, and filled with precisely the same data as + this obtained on import. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields @@ -1162,7 +1153,6 @@ class BatchPredictOutputConfig(proto.Message): r"""Output configuration for BatchPredict Action. As destination the - [gcs_destination][google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination] must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory @@ -1187,10 +1177,8 @@ class BatchPredictOutputConfig(proto.Message): 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 ``message``\ fields. + ```google.rpc.Status`` `__ + containing only ``code`` and ``message``\ fields. - For Image Object Detection: In the created directory files ``image_object_detection_1.jsonl``, @@ -1209,10 +1197,8 @@ class BatchPredictOutputConfig(proto.Message): 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 ``message``\ fields. + ```google.rpc.Status`` `__ + containing only ``code`` and ``message``\ fields. - For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video @@ -1222,27 +1208,25 @@ class BatchPredictOutputConfig(proto.Message): :: 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][]). + 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 @@ -1254,24 +1238,22 @@ class BatchPredictOutputConfig(proto.Message): :: 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. + 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``, @@ -1294,10 +1276,8 @@ class BatchPredictOutputConfig(proto.Message): `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 file followed by exactly one - - [``google.rpc.Status``](https: - //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) - containing only ``code`` and ``message``. + [`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``, @@ -1320,10 +1300,8 @@ class BatchPredictOutputConfig(proto.Message): `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 file followed by exactly one - - [``google.rpc.Status``](https: - //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) - containing only ``code`` and ``message``. + [`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``, @@ -1354,98 +1332,78 @@ class BatchPredictOutputConfig(proto.Message): 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``. + ```google.rpc.Status`` `__ + containing only ``code`` and ``message``. - For Tables: Output depends on whether - - [gcs_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.gcs_destination] - or - - [bigquery_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.bigquery_destination] - is set (either is allowed). Google Cloud Storage 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][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: - Each .csv file will contain a header, listing all columns' - - [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] - given on input followed by M target column names in the format of - - "<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] - - [display_name][google.cloud.automl.v1p1beta.ColumnSpec.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][google.cloud.automl.v1p1beta.TablesAnnotation.score]. For - REGRESSION and FORECASTING - - [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: - Each .csv file will contain a header, listing all columns' - [display_name-s][google.cloud.automl.v1p1beta.display_name] given on - input followed by the predicted target column with name in the - format of - - "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] - - [display_name][google.cloud.automl.v1p1beta.ColumnSpec.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][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] - pointing to a BigQuery project must be set. In the given project a - new dataset will be created with name - ``prediction__`` - where will be made BigQuery-dataset-name compatible (e.g. most - special characters will become underscores), and timestamp will be - in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the - dataset two tables will be created, ``predictions``, and ``errors``. - The ``predictions`` table's column names will be the input columns' - - [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] - followed by the target column with name in the format of - - "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] - - [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>" - The input feature columns will contain the respective values of - successfully predicted rows, with the target column having an ARRAY - of - - [AnnotationPayloads][google.cloud.automl.v1p1beta.AnnotationPayload], - represented as STRUCT-s, containing - [TablesAnnotation][google.cloud.automl.v1p1beta.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][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] - - [display_name][google.cloud.automl.v1p1beta.ColumnSpec.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``. + [gcs_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.gcs_destination] + or + [bigquery_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.bigquery_destination] + is set (either is allowed). Google Cloud Storage 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][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: + Each .csv file will contain a header, listing all columns' + [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] + given on input followed by M target column names in the format of + "<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] + [display_name][google.cloud.automl.v1p1beta.ColumnSpec.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][google.cloud.automl.v1p1beta.TablesAnnotation.score]. + For REGRESSION and FORECASTING + [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: + Each .csv file will contain a header, listing all columns' + [display_name-s][google.cloud.automl.v1p1beta.display_name] given + on input followed by the predicted target column with name in the + format of + "predicted\ <[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] + [display_name][google.cloud.automl.v1p1beta.ColumnSpec.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`` `__\ *represented + as a JSON string, and containing only ``code`` and ``message``. + BigQuery case: + [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] + pointing to a BigQuery project must be set. In the given project + a new dataset will be created with name + ``prediction__`` + where will be made BigQuery-dataset-name compatible (e.g. most + special characters will become underscores), and timestamp will + be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the + dataset two tables will be created, ``predictions``, and + ``errors``. The ``predictions`` table's column names will be the + input columns' + [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] + followed by the target column with name in the format of + "predicted*\ <[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] + [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>" + The input feature columns will contain the respective values of + successfully predicted rows, with the target column having an + ARRAY of + [AnnotationPayloads][google.cloud.automl.v1p1beta.AnnotationPayload], + represented as STRUCT-s, containing + [TablesAnnotation][google.cloud.automl.v1p1beta.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][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] + [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>", + and as a value has + ```google.rpc.Status`` `__ + represented as a STRUCT, and containing only ``code`` and + ``message``. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields @@ -1518,10 +1476,8 @@ class ModelExportOutputConfig(proto.Message): - 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) + at `containers + quickstart `__ - core_ml - Used for iOS mobile devices. params (Sequence[google.cloud.automl_v1.types.ModelExportOutputConfig.ParamsEntry]): diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/types/model_evaluation.py b/packages/google-cloud-automl/google/cloud/automl_v1/types/model_evaluation.py index 07e7e1c4bbb8..2b483a46c18c 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/types/model_evaluation.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/types/model_evaluation.py @@ -66,7 +66,6 @@ class ModelEvaluation(proto.Message): This field is a member of `oneof`_ ``metrics``. name (str): 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 (str): Output only. The ID of the annotation spec that the model @@ -74,7 +73,6 @@ class ModelEvaluation(proto.Message): 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][google.cloud.automl.v1.TablesModelMetadata.prediction_type] the [display_name][google.cloud.automl.v1.ModelEvaluation.display_name] @@ -87,7 +85,6 @@ class ModelEvaluation(proto.Message): 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][google.cloud.automl.v1.TablesModelMetadata.prediction_type] distinct values of the target column at the moment of the model evaluation are populated here. The display_name is @@ -104,7 +101,6 @@ class ModelEvaluation(proto.Message): examples used for evaluation. Otherwise, this is the count of examples that according to the ground truth were annotated by the - [annotation_spec_id][google.cloud.automl.v1.ModelEvaluation.annotation_spec_id]. """ diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/types/operations.py b/packages/google-cloud-automl/google/cloud/automl_v1/types/operations.py index 7e68a1845d8d..14dde2667184 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/types/operations.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/types/operations.py @@ -237,7 +237,6 @@ class BatchPredictOperationMetadata(proto.Message): class BatchPredictOutputInfo(proto.Message): r"""Further describes this batch predict's output. Supplements - [BatchPredictOutputConfig][google.cloud.automl.v1.BatchPredictOutputConfig]. diff --git a/packages/google-cloud-automl/google/cloud/automl_v1/types/prediction_service.py b/packages/google-cloud-automl/google/cloud/automl_v1/types/prediction_service.py index 30660c699f47..1a8de0e2ca32 100644 --- a/packages/google-cloud-automl/google/cloud/automl_v1/types/prediction_service.py +++ b/packages/google-cloud-automl/google/cloud/automl_v1/types/prediction_service.py @@ -68,7 +68,6 @@ class PredictRequest(proto.Message): AutoML Tables ``feature_importance`` : (boolean) Whether - [feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance] is populated in the returned list of [TablesAnnotation][google.cloud.automl.v1.TablesAnnotation]