Class TablesClient (2.7.3)

TablesClient(
    *,
    project=None,
    region="us-central1",
    credentials=None,
    client=None,
    prediction_client=None,
    gcs_client=None,
    **kwargs
)

AutoML Tables API helper.

This is intended to simplify usage of the auto-generated python client, in particular for the AutoML Tables product <https://cloud.google.com/automl-tables/>_.

Inheritance

builtins.object > TablesClient

Methods

TablesClient

TablesClient(
    *,
    project=None,
    region="us-central1",
    credentials=None,
    client=None,
    prediction_client=None,
    gcs_client=None,
    **kwargs
)

Constructor.

Example for US region:

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ...

Example for EU region:

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client_options = {'api_endpoint': 'eu-automl.googleapis.com:443'} client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='eu', client_options=client_options) ...

Parameters
NameDescription
project Optional[str]

The project ID of the GCP project all future calls will default to. Most methods take project as an optional parameter, and can override your choice of project supplied here.

region Optional[str]

The region all future calls will default to. Most methods take region as an optional parameter, and can override your choice of region supplied here. Note, only us-central1 is supported to-date.

credentials Optional[google.auth.credentials.Credentials]

The authorization credentials to attach to requests. These credentials identify this application to the service. If none are specified, the client will attempt to ascertain the credentials from the environment. This argument is mutually exclusive with providing a transport instance to transport; doing so will raise an exception.

client Optional[google.automl_v1beta1.AutoMlClient]

An AutoMl Client to use for requests.

prediction_client Optional[google.automl_v1beta1.PredictionClient]

A Prediction Client to use for requests.

gcs_client Optional[google.automl_v1beta1.GcsClient]

A Storage client to use for requests.

client_options Union[dict, google.api_core.client_options.ClientOptions]

Custom options for the client.

client_info google.api_core.gapic_v1.client_info.ClientInfo

The client info used to send a user-agent string along with API requests.

batch_predict

batch_predict(
    *,
    pandas_dataframe=None,
    bigquery_input_uri=None,
    bigquery_output_uri=None,
    gcs_input_uris=None,
    gcs_output_uri_prefix=None,
    model=None,
    model_name=None,
    model_display_name=None,
    project=None,
    region=None,
    credentials=None,
    inputs=None,
    params={},
    **kwargs
)

Makes a batch prediction on a model. This does not require the model to be deployed.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.batch_predict( ... gcs_input_uris='gs://inputs/input.csv', ... gcs_output_uri_prefix='gs://outputs/', ... model_display_name='my_model' ... ).result() ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the model. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

credentials Optional[google.auth.credentials.Credentials]

The authorization credentials to attach to requests. These credentials identify this application to the service. If none are specified, the client will attempt to ascertain the credentials from the environment.

pandas_dataframe Optional[pandas.DataFrame]

A Pandas Dataframe object containing the data you want to predict off of. The data will be converted to CSV, and this CSV will be staged to GCS in gs://{project}-automl-tables-staging/{uploaded_csv_name} This must be supplied if neither gcs_input_uris nor bigquery_input_uri is supplied.

gcs_input_uris Optional(Union[List[str], str])

Either a list of or a single GCS URI containing the data you want to predict off of. This must be supplied if neither pandas_dataframe nor bigquery_input_uri is supplied.

gcs_output_uri_prefix Optional[str]

The folder in GCS you want to write output to. This must be supplied if bigquery_output_uri is not.

bigquery_input_uri Optional[str]

The BigQuery table to input data from. This must be supplied if neither pandas_dataframe nor gcs_input_uris is supplied.

bigquery_output_uri Optional[str]

The BigQuery table to output data to. This must be supplied if gcs_output_uri_prefix is not.

model_display_name Optional[str]

The human-readable name given to the model you want to predict with. This must be supplied if model or model_name are not supplied.

model_name Optional[str]

The AutoML-assigned name given to the model you want to predict with. This must be supplied if model_display_name or model are not supplied.

model Optional[model]

The model instance you want to predict with . This must be supplied if model_display_name or model_name are not supplied.

params Optional[dict]

Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.
Returns
TypeDescription
google.api_core.operation.OperationAn operation future that can be used to check for completion synchronously or asynchronously.

clear_test_train_column

clear_test_train_column(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    project=None,
    region=None,
    **kwargs
)

Clears the test/train (ml_use) column which designates which data belongs to the test and train sets.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.clear_test_train_column(dataset_display_name='my_dataset')

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the table. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to update the test/train column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the test/train column of. This must be supplied if table_spec_name, dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to update the test/train column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the test/train column of. This must be supplied if table_spec_name, dataset or dataset_display_name are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to update the test/train column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the test/train column of. This must be supplied if table_spec_name, dataset_name or dataset_display_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

clear_time_column

clear_time_column(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    project=None,
    region=None,
    **kwargs
)

Clears the time column which designates which data will be of type timestamp and will be used for the timeseries data.

.. rubric:: Example

from google.cloud import automl_v1beta1

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.clear_time_column(dataset_display_name='my_dataset')

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the table. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to update the time column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the time column of. This must be supplied if table_spec_name, dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to update the time column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the time column of. This must be supplied if table_spec_name, dataset or dataset_display_name are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to update the time column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the time column of. This must be supplied if table_spec_name, dataset_name or dataset_display_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

clear_weight_column

clear_weight_column(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    project=None,
    region=None,
    **kwargs
)

Clears the weight column for a given dataset.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.clear_weight_column(dataset_display_name='my_dataset')

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the table. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to update the weight column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the weight column of. This must be supplied if table_spec_name, dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to update the weight column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the weight column of. This must be supplied if table_spec_name, dataset or dataset_display_name are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to update the weight column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the weight column of. This must be supplied if table_spec_name, dataset_name or dataset_display_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

create_dataset

create_dataset(
    dataset_display_name, *, metadata={}, project=None, region=None, **kwargs
)

Create a dataset. Keep in mind, importing data is a separate step.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... d = client.create_dataset(dataset_display_name='my_dataset')

Parameters
NameDescription
project Optional[str]

The ID of the project that will own the dataset. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

dataset_display_name str

A human-readable name to refer to this dataset by.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

create_model

create_model(
    model_display_name,
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    train_budget_milli_node_hours=None,
    optimization_objective=None,
    project=None,
    region=None,
    model_metadata=None,
    include_column_spec_names=None,
    exclude_column_spec_names=None,
    disable_early_stopping=False,
    **kwargs
)

Create a model. This will train your model on the given dataset.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... m = client.create_model( ... 'my_model', ... dataset_display_name='my_dataset', ... train_budget_milli_node_hours=1000 ... )

m.result() # blocks on result

Parameters
NameDescription
project Optional[str]

The ID of the project that will own the model. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

model_display_name str

A human-readable name to refer to this model by.

train_budget_milli_node_hours int

The amount of time (in thousandths of an hour) to spend training. This value must be between 1,000 and 72,000 inclusive (between 1 and 72 hours).

optimization_objective str

The metric AutoML tables should optimize for.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to train your model on. This must be supplied if dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to train your model on. This must be supplied if dataset_display_name or dataset are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to train your model on. This must be supplied if dataset_display_name or dataset_name are not supplied.

model_metadata Optional[Dict]

Optional model metadata to supply to the client.

include_column_spec_names Optional[str]

The list of the names of the columns you want to include to train your model on.

exclude_column_spec_names Optional[str]

The list of the names of the columns you want to exclude and not train your model on.

disable_early_stopping Optional[bool]

True if disable early stopping. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.
Returns
TypeDescription
google.api_core.operation.OperationAn operation future that can be used to check for completion synchronously or asynchronously.

delete_dataset

delete_dataset(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    project=None,
    region=None,
    **kwargs
)

Deletes a dataset. This does not delete any models trained on this dataset.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... op = client.delete_dataset(dataset_display_name='my_dataset')

op.result() # blocks on delete request

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the dataset. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to delete. This must be supplied if dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to delete. This must be supplied if dataset_display_name or dataset are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to delete. This must be supplied if dataset_display_name or dataset_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.
Returns
TypeDescription
google.api_core.operation.OperationAn operation future that can be used to check for completion synchronously or asynchronously.

delete_model

delete_model(
    *,
    model=None,
    model_display_name=None,
    model_name=None,
    project=None,
    region=None,
    **kwargs
)

Deletes a model. Note this will not delete any datasets associated with this model.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... op = client.delete_model(model_display_name='my_model')

op.result() # blocks on delete request

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the model. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

model_display_name Optional[str]

The human-readable name given to the model you want to delete. This must be supplied if model or model_name are not supplied.

model_name Optional[str]

The AutoML-assigned name given to the model you want to delete. This must be supplied if model_display_name or model are not supplied.

model Optional[model]

The model instance you want to delete. This must be supplied if model_display_name or model_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.
Returns
TypeDescription
google.api_core.operation.OperationAn operation future that can be used to check for completion synchronously or asynchronously.

deploy_model

deploy_model(
    *,
    model=None,
    model_name=None,
    model_display_name=None,
    project=None,
    region=None,
    **kwargs
)

Deploys a model. This allows you make online predictions using the model you've deployed.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... op = client.deploy_model(model_display_name='my_model')

op.result() # blocks on deploy request

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the model. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

model_display_name Optional[str]

The human-readable name given to the model you want to deploy. This must be supplied if model or model_name are not supplied.

model_name Optional[str]

The AutoML-assigned name given to the model you want to deploy. This must be supplied if model_display_name or model are not supplied.

model Optional[model]

The model instance you want to deploy. This must be supplied if model_display_name or model_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.
Returns
TypeDescription
google.api_core.operation.OperationAn operation future that can be used to check for completion synchronously or asynchronously.

export_data

export_data(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    gcs_output_uri_prefix=None,
    bigquery_output_uri=None,
    project=None,
    region=None,
    **kwargs
)

Exports data from a dataset.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... d = client.create_dataset(dataset_display_name='my_dataset')

response = client.export_data(dataset=d, ... gcs_output_uri_prefix='gs://cloud-ml-tables-data/bank-marketing.csv') ... def callback(operation_future): ... result = operation_future.result() ... response.add_done_callback(callback)

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the dataset. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to export data from. This must be supplied if dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to export data from. This must be supplied if dataset_display_name or dataset are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to export data from. This must be supplied if dataset_display_name or dataset_name are not supplied.

gcs_output_uri_prefix Optional[Union[str, Sequence[str]]]

A single gs://.. prefixed URI to export to. This must be supplied if bigquery_output_uri is not.

bigquery_output_uri Optional[str]

A URI pointing to the BigQuery table containing the data to export. This must be supplied if gcs_output_uri_prefix is not.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.
Returns
TypeDescription
google.api_core.operation.OperationAn operation future that can be used to check for completion synchronously or asynchronously.

get_column_spec

get_column_spec(column_spec_name, *, project=None, region=None, **kwargs)

Gets a single column spec in a particular project and region.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... d = client.get_column_spec('my_column_spec')

Parameters
NameDescription
column_spec_name str

This is the fully-qualified name generated by the AutoML API for this column spec.

project Optional[str]

The ID of the project that owns the column. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

get_dataset

get_dataset(
    *, project=None, region=None, dataset_name=None, dataset_display_name=None, **kwargs
)

Gets a single dataset in a particular project and region.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... d = client.get_dataset(dataset_display_name='my_dataset')

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the dataset. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

dataset_name Optional[str]

This is the fully-qualified name generated by the AutoML API for this dataset. This is not to be confused with the human-assigned dataset_display_name that is provided when creating a dataset. Either dataset_name or dataset_display_name must be provided.

dataset_display_name Optional[str]

This is the name you provided for the dataset when first creating it. Either dataset_name or dataset_display_name must be provided.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

get_model

get_model(
    *, project=None, region=None, model_name=None, model_display_name=None, **kwargs
)

Gets a single model in a particular project and region.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... d = client.get_model(model_display_name='my_model')

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the model. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

model_name Optional[str]

This is the fully-qualified name generated by the AutoML API for this model. This is not to be confused with the human-assigned model_display_name that is provided when creating a model. Either model_name or model_display_name must be provided.

model_display_name Optional[str]

This is the name you provided for the model when first creating it. Either model_name or model_display_name must be provided.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

get_model_evaluation

get_model_evaluation(model_evaluation_name, *, project=None, region=None, **kwargs)

Gets a single evaluation model in a particular project and region.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... d = client.get_model_evaluation('my_model_evaluation')

Parameters
NameDescription
model_evaluation_name str

This is the fully-qualified name generated by the AutoML API for this model evaluation.

project Optional[str]

The ID of the project that owns the model. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

get_table_spec

get_table_spec(table_spec_name, *, project=None, region=None, **kwargs)

Gets a single table spec in a particular project and region.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... d = client.get_table_spec('my_table_spec')

Parameters
NameDescription
table_spec_name str

This is the fully-qualified name generated by the AutoML API for this table spec.

project Optional[str]

The ID of the project that owns the table. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

import_data

import_data(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    pandas_dataframe=None,
    gcs_input_uris=None,
    bigquery_input_uri=None,
    project=None,
    region=None,
    credentials=None,
    **kwargs
)

Imports data into a dataset.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... d = client.create_dataset(dataset_display_name='my_dataset')

response = client.import_data(dataset=d, ... gcs_input_uris='gs://cloud-ml-tables-data/bank-marketing.csv') ... def callback(operation_future): ... result = operation_future.result() ... response.add_done_callback(callback)

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the dataset. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

credentials Optional[google.auth.credentials.Credentials]

The authorization credentials to attach to requests. These credentials identify this application to the service. If none are specified, the client will attempt to ascertain the credentials from the environment.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to import data into. This must be supplied if dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to import data into. This must be supplied if dataset_display_name or dataset are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to import data into. This must be supplied if dataset_display_name or dataset_name are not supplied.

pandas_dataframe Optional[pandas.DataFrame]

A Pandas Dataframe object containing the data to import. The data will be converted to CSV, and this CSV will be staged to GCS in gs://{project}-automl-tables-staging/{uploaded_csv_name} This parameter must be supplied if neither gcs_input_uris nor bigquery_input_uri is supplied.

gcs_input_uris Optional[Union[str, Sequence[str]]]

Either a single gs://.. prefixed URI, or a list of URIs referring to GCS-hosted CSV files containing the data to import. This must be supplied if neither bigquery_input_uri nor pandas_dataframe is supplied.

bigquery_input_uri Optional[str]

A URI pointing to the BigQuery table containing the data to import. This must be supplied if neither gcs_input_uris nor pandas_dataframe is supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.
Returns
TypeDescription
google.api_core.operation.OperationAn operation future that can be used to check for completion synchronously or asynchronously.

list_column_specs

list_column_specs(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    table_spec_name=None,
    table_spec_index=0,
    project=None,
    region=None,
    **kwargs
)

Lists column specs.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... for s in client.list_column_specs(dataset_display_name='my_dataset') ... # process the spec ... pass ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the columns. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

table_spec_name Optional[str]

The AutoML-assigned name for the table whose specs you want to read. If not supplied, the client can determine this name from a source Dataset object.

table_spec_index Optional[int]

If no table_spec_name was provided, we use this index to determine which table to read column specs from.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to read specs from. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to read specs from. This must be supplied if table_spec_name, dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to read specs from. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to read specs from. This must be supplied if table_spec_name, dataset or dataset_display_name are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to read specs from. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to read specs from. This must be supplied if table_spec_name, dataset_name or dataset_display_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

list_datasets

list_datasets(*, project=None, region=None, **kwargs)

List all datasets in a particular project and region.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... ds = client.list_datasets()

for d in ds: ... # do something ... pass ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the datasets. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

list_model_evaluations

list_model_evaluations(
    *,
    project=None,
    region=None,
    model=None,
    model_display_name=None,
    model_name=None,
    **kwargs
)

List all model evaluations for a given model.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... ms = client.list_model_evaluations(model_display_name='my_model')

for m in ms: ... # do something ... pass ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the model. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

model_display_name Optional[str]

The human-readable name given to the model you want to list evaluations for. This must be supplied if model or model_name are not supplied.

model_name Optional[str]

The AutoML-assigned name given to the model you want to list evaluations for. This must be supplied if model_display_name or model are not supplied.

model Optional[model]

The model instance you want to list evaluations for. This must be supplied if model_display_name or model_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

list_models

list_models(*, project=None, region=None, **kwargs)

List all models in a particular project and region.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... ms = client.list_models()

for m in ms: ... # do something ... pass ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the models. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

list_table_specs

list_table_specs(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    project=None,
    region=None,
    **kwargs
)

Lists table specs.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... for s in client.list_table_specs(dataset_display_name='my_dataset') ... # process the spec ... pass ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the dataset. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to read specs from. This must be supplied if dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to read specs from. This must be supplied if dataset_display_name or dataset are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to read specs from. This must be supplied if dataset_display_name or dataset_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

predict

predict(
    inputs,
    *,
    model=None,
    model_name=None,
    model_display_name=None,
    feature_importance=False,
    project=None,
    region=None,
    **kwargs
)

Makes a prediction on a deployed model. This will fail if the model was not deployed.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.predict(inputs={'Age': 30, 'Income': 12, 'Category': 'A'} ... model_display_name='my_model') ... client.predict([30, 12, 'A'], model_display_name='my_model')

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the model. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

inputs Union[List[str], Dict[str, str]]

Either the sorted list of column values to predict with, or a key-value map of column display name to value to predict with.

model_display_name Optional[str]

The human-readable name given to the model you want to predict with. This must be supplied if model or model_name are not supplied.

model_name Optional[str]

The AutoML-assigned name given to the model you want to predict with. This must be supplied if model_display_name or model are not supplied.

model Optional[model]

The model instance you want to predict with . This must be supplied if model_display_name or model_name are not supplied.

feature_importance bool

True if enable feature importance explainability. The default is False.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

set_target_column

set_target_column(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    table_spec_name=None,
    table_spec_index=0,
    column_spec_name=None,
    column_spec_display_name=None,
    project=None,
    region=None,
    **kwargs
)

Sets the target column for a given table.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.set_target_column(dataset_display_name='my_dataset', ... column_spec_display_name='Income') ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the table. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

column_spec_name Optional[str]

The name AutoML-assigned name for the column you want to set as the target column.

column_spec_display_name Optional[str]

The human-readable name of the column you want to set as the target column. If this is supplied in place of column_spec_name, you also need to provide either a way to lookup the source dataset (using one of the dataset* kwargs), or the table_spec_name of the table this column belongs to.

table_spec_name Optional[str]

The AutoML-assigned name for the table whose target column you want to set . If not supplied, the client can determine this name from a source Dataset object.

table_spec_index Optional[int]

If no table_spec_name or column_spec_name was provided, we use this index to determine which table to set the target column on.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to update the target column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the target column of. This must be supplied if table_spec_name, dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to update the target column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the target column of. This must be supplied if table_spec_name, dataset or dataset_display_name are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to update the target column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the target column of. This must be supplied if table_spec_name, dataset_name or dataset_display_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

set_test_train_column

set_test_train_column(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    table_spec_name=None,
    table_spec_index=0,
    column_spec_name=None,
    column_spec_display_name=None,
    project=None,
    region=None,
    **kwargs
)

Sets the test/train (ml_use) column which designates which data belongs to the test and train sets. This column must be categorical.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.set_test_train_column(dataset_display_name='my_dataset', ... column_spec_display_name='TestSplit') ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the table. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

column_spec_name Optional[str]

The name AutoML-assigned name for the column you want to set as the test/train column.

column_spec_display_name Optional[str]

The human-readable name of the column you want to set as the test/train column. If this is supplied in place of column_spec_name, you also need to provide either a way to lookup the source dataset (using one of the dataset* kwargs), or the table_spec_name of the table this column belongs to.

table_spec_name Optional[str]

The AutoML-assigned name for the table whose test/train column you want to set . If not supplied, the client can determine this name from a source Dataset object.

table_spec_index Optional[int]

If no table_spec_name or column_spec_name was provided, we use this index to determine which table to set the test/train column on.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to update the test/train column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the test/train column of. This must be supplied if table_spec_name, dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to update the test/train column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the test/train column of. This must be supplied if table_spec_name, dataset or dataset_display_name are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to update the test/train column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the test/train column of. This must be supplied if table_spec_name, dataset_name or dataset_display_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

set_time_column

set_time_column(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    table_spec_name=None,
    table_spec_index=0,
    column_spec_name=None,
    column_spec_display_name=None,
    project=None,
    region=None,
    **kwargs
)

Sets the time column which designates which data will be of type timestamp and will be used for the timeseries data. This column must be of type timestamp.

.. rubric:: Example

from google.cloud import automl_v1beta1

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.set_time_column(dataset_display_name='my_dataset', ... column_spec_display_name='Unix Time') ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the table. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

column_spec_name Optional[str]

The name AutoML-assigned name for the column you want to set as the time column.

column_spec_display_name Optional[str]

The human-readable name of the column you want to set as the time column. If this is supplied in place of column_spec_name, you also need to provide either a way to lookup the source dataset (using one of the dataset* kwargs), or the table_spec_name of the table this column belongs to.

table_spec_name Optional[str]

The AutoML-assigned name for the table whose time column you want to set . If not supplied, the client can determine this name from a source Dataset object.

table_spec_index Optional[int]

If no table_spec_name or column_spec_name was provided, we use this index to determine which table to set the time column on.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to update the time column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the time column of. This must be supplied if table_spec_name, dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to update the time column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the time column of. This must be supplied if table_spec_name, dataset or dataset_display_name are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to update the time column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the time column of. This must be supplied if table_spec_name, dataset_name or dataset_display_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

set_weight_column

set_weight_column(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    table_spec_name=None,
    table_spec_index=0,
    column_spec_name=None,
    column_spec_display_name=None,
    project=None,
    region=None,
    **kwargs
)

Sets the weight column for a given table.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.set_weight_column(dataset_display_name='my_dataset', ... column_spec_display_name='Income') ...

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the table. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

column_spec_name Optional[str]

The name AutoML-assigned name for the column you want to set as the weight column.

column_spec_display_name Optional[str]

The human-readable name of the column you want to set as the weight column. If this is supplied in place of column_spec_name, you also need to provide either a way to lookup the source dataset (using one of the dataset* kwargs), or the table_spec_name of the table this column belongs to.

table_spec_name Optional[str]

The AutoML-assigned name for the table whose weight column you want to set . If not supplied, the client can determine this name from a source Dataset object.

table_spec_index Optional[int]

If no table_spec_name or column_spec_name was provided, we use this index to determine which table to set the weight column on.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to update the weight column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the weight column of. This must be supplied if table_spec_name, dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to update the weight column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the weight column of. This must be supplied if table_spec_name, dataset or dataset_display_name are not supplied.

dataset Optional[Dataset]

The Dataset instance you want to update the weight column of. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update the weight column of. This must be supplied if table_spec_name, dataset_name or dataset_display_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.

undeploy_model

undeploy_model(
    *,
    model=None,
    model_name=None,
    model_display_name=None,
    project=None,
    region=None,
    **kwargs
)

Undeploys a model.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... op = client.undeploy_model(model_display_name='my_model')

op.result() # blocks on undeploy request

Parameters
NameDescription
project Optional[str]

The ID of the project that owns the model. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

model_display_name Optional[str]

The human-readable name given to the model you want to undeploy. This must be supplied if model or model_name are not supplied.

model_name Optional[str]

The AutoML-assigned name given to the model you want to undeploy. This must be supplied if model_display_name or model are not supplied.

model Optional[model]

The model instance you want to undeploy. This must be supplied if model_display_name or model_name are not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.
Returns
TypeDescription
google.api_core.operation.OperationAn operation future that can be used to check for completion synchronously or asynchronously.

update_column_spec

update_column_spec(
    *,
    dataset=None,
    dataset_display_name=None,
    dataset_name=None,
    table_spec_name=None,
    table_spec_index=0,
    column_spec_name=None,
    column_spec_display_name=None,
    type_code=None,
    nullable=None,
    project=None,
    region=None,
    **kwargs
)

Updates a column's specs.

.. rubric:: Example

from google.cloud import automl_v1beta1

from google.oauth2 import service_account

client = automl_v1beta1.TablesClient( ... credentials=service_account.Credentials.from_service_account_file('``/.gcp/account.json'), ... project='my-project', region='us-central1') ... client.update_column_spec(dataset_display_name='my_dataset', ... column_spec_display_name='Outcome', ... type_code=automl_v1beta1.TypeCode.CATEGORY) ...

Parameters
NameDescription
dataset Optional[Dataset]

The Dataset instance you want to update specs on. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update specs on. This must be supplied if table_spec_name, dataset_name or dataset_display_name are not supplied.

dataset_display_name Optional[str]

The human-readable name given to the dataset you want to update specs on. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update specs on. This must be supplied if table_spec_name, dataset or dataset_name are not supplied.

dataset_name Optional[str]

The AutoML-assigned name given to the dataset you want to update specs one. If no table_spec_name is supplied, this will be used together with table_spec_index to infer the name of table to update specs on. This must be supplied if table_spec_name, dataset or dataset_display_name are not supplied.

table_spec_name Optional[str]

The AutoML-assigned name for the table whose specs you want to update. If not supplied, the client can determine this name from a source Dataset object.

table_spec_index Optional[int]

If no table_spec_name was provided, we use this index to determine which table to update column specs on.

column_spec_name Optional[str]

The name AutoML-assigned name for the column you want to update.

column_spec_display_name Optional[str]

The human-readable name of the column you want to update. If this is supplied in place of column_spec_name, you also need to provide either a way to lookup the source dataset (using one of the dataset* kwargs), or the table_spec_name of the table this column belongs to.

type_code Optional[str]

The desired 'type_code' of the column. For more information on the available types, please see the documentation: https://cloud.google.com/automl-tables/docs/reference/rpc/google.cloud.automl.v1beta1#typecode

nullable Optional[bool]

Set to True or False to specify if this column's value must expected to be present in all rows or not.

project Optional[str]

The ID of the project that owns the columns. If you have initialized the client with a value for project it will be used if this parameter is not supplied. Keep in mind, the service account this client was initialized with must have access to this project.

region Optional[str]

If you have initialized the client with a value for region it will be used if this parameter is not supplied.

Exceptions
TypeDescription
google.api_core.exceptions.GoogleAPICallErrorIf the request failed for any reason.
google.api_core.exceptions.RetryErrorIf the request failed due to a retryable error and retry attempts failed.
ValueErrorIf required parameters are missing.