As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Source code for google.cloud.automl_v1beta1.services.auto_ml.client

# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from collections import OrderedDict
from distutils import util
import os
import re
from typing import Callable, Dict, Optional, Sequence, Tuple, Type, Union
import pkg_resources

from google.api_core import client_options as client_options_lib  # type: ignore
from google.api_core import exceptions as core_exceptions  # type: ignore
from google.api_core import gapic_v1  # type: ignore
from google.api_core import retry as retries  # type: ignore
from google.auth import credentials as ga_credentials  # type: ignore
from google.auth.transport import mtls  # type: ignore
from google.auth.transport.grpc import SslCredentials  # type: ignore
from google.auth.exceptions import MutualTLSChannelError  # type: ignore
from google.oauth2 import service_account  # type: ignore

from google.api_core import operation  # type: ignore
from google.api_core import operation_async  # type: ignore
from google.cloud.automl_v1beta1.services.auto_ml import pagers
from google.cloud.automl_v1beta1.types import annotation_spec
from google.cloud.automl_v1beta1.types import classification
from google.cloud.automl_v1beta1.types import column_spec
from google.cloud.automl_v1beta1.types import column_spec as gca_column_spec
from google.cloud.automl_v1beta1.types import data_stats
from google.cloud.automl_v1beta1.types import data_types
from google.cloud.automl_v1beta1.types import dataset
from google.cloud.automl_v1beta1.types import dataset as gca_dataset
from google.cloud.automl_v1beta1.types import detection
from google.cloud.automl_v1beta1.types import image
from google.cloud.automl_v1beta1.types import io
from google.cloud.automl_v1beta1.types import model
from google.cloud.automl_v1beta1.types import model as gca_model
from google.cloud.automl_v1beta1.types import model_evaluation
from google.cloud.automl_v1beta1.types import operations
from google.cloud.automl_v1beta1.types import regression
from google.cloud.automl_v1beta1.types import service
from google.cloud.automl_v1beta1.types import table_spec
from google.cloud.automl_v1beta1.types import table_spec as gca_table_spec
from google.cloud.automl_v1beta1.types import tables
from google.cloud.automl_v1beta1.types import text
from google.cloud.automl_v1beta1.types import text_extraction
from google.cloud.automl_v1beta1.types import text_sentiment
from google.cloud.automl_v1beta1.types import translation
from google.cloud.automl_v1beta1.types import video
from google.protobuf import empty_pb2  # type: ignore
from google.protobuf import timestamp_pb2  # type: ignore
from .transports.base import AutoMlTransport, DEFAULT_CLIENT_INFO
from .transports.grpc import AutoMlGrpcTransport
from .transports.grpc_asyncio import AutoMlGrpcAsyncIOTransport


class AutoMlClientMeta(type):
    """Metaclass for the AutoMl client.

    This provides class-level methods for building and retrieving
    support objects (e.g. transport) without polluting the client instance
    objects.
    """

    _transport_registry = OrderedDict()  # type: Dict[str, Type[AutoMlTransport]]
    _transport_registry["grpc"] = AutoMlGrpcTransport
    _transport_registry["grpc_asyncio"] = AutoMlGrpcAsyncIOTransport

    def get_transport_class(cls, label: str = None,) -> Type[AutoMlTransport]:
        """Returns an appropriate transport class.

        Args:
            label: The name of the desired transport. If none is
                provided, then the first transport in the registry is used.

        Returns:
            The transport class to use.
        """
        # If a specific transport is requested, return that one.
        if label:
            return cls._transport_registry[label]

        # No transport is requested; return the default (that is, the first one
        # in the dictionary).
        return next(iter(cls._transport_registry.values()))


[docs]class AutoMlClient(metaclass=AutoMlClientMeta): """AutoML Server API. The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted. An ID of a resource is the last element of the item's resource name. For ``projects/{project_id}/locations/{location_id}/datasets/{dataset_id}``, then the id for the item is ``{dataset_id}``. Currently the only supported ``location_id`` is "us-central1". On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted. """ @staticmethod def _get_default_mtls_endpoint(api_endpoint): """Converts api endpoint to mTLS endpoint. Convert "*.sandbox.googleapis.com" and "*.googleapis.com" to "*.mtls.sandbox.googleapis.com" and "*.mtls.googleapis.com" respectively. Args: api_endpoint (Optional[str]): the api endpoint to convert. Returns: str: converted mTLS api endpoint. """ if not api_endpoint: return api_endpoint mtls_endpoint_re = re.compile( r"(?P<name>[^.]+)(?P<mtls>\.mtls)?(?P<sandbox>\.sandbox)?(?P<googledomain>\.googleapis\.com)?" ) m = mtls_endpoint_re.match(api_endpoint) name, mtls, sandbox, googledomain = m.groups() if mtls or not googledomain: return api_endpoint if sandbox: return api_endpoint.replace( "sandbox.googleapis.com", "mtls.sandbox.googleapis.com" ) return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com") DEFAULT_ENDPOINT = "automl.googleapis.com" DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore DEFAULT_ENDPOINT )
[docs] @classmethod def from_service_account_info(cls, info: dict, *args, **kwargs): """Creates an instance of this client using the provided credentials info. Args: info (dict): The service account private key info. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: AutoMlClient: The constructed client. """ credentials = service_account.Credentials.from_service_account_info(info) kwargs["credentials"] = credentials return cls(*args, **kwargs)
[docs] @classmethod def from_service_account_file(cls, filename: str, *args, **kwargs): """Creates an instance of this client using the provided credentials file. Args: filename (str): The path to the service account private key json file. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: AutoMlClient: The constructed client. """ credentials = service_account.Credentials.from_service_account_file(filename) kwargs["credentials"] = credentials return cls(*args, **kwargs)
from_service_account_json = from_service_account_file @property def transport(self) -> AutoMlTransport: """Returns the transport used by the client instance. Returns: AutoMlTransport: The transport used by the client instance. """ return self._transport
[docs] @staticmethod def annotation_spec_path( project: str, location: str, dataset: str, annotation_spec: str, ) -> str: """Returns a fully-qualified annotation_spec string.""" return "projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}".format( project=project, location=location, dataset=dataset, annotation_spec=annotation_spec, )
[docs] @staticmethod def parse_annotation_spec_path(path: str) -> Dict[str, str]: """Parses a annotation_spec path into its component segments.""" m = re.match( r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)/annotationSpecs/(?P<annotation_spec>.+?)$", path, ) return m.groupdict() if m else {}
[docs] @staticmethod def column_spec_path( project: str, location: str, dataset: str, table_spec: str, column_spec: str, ) -> str: """Returns a fully-qualified column_spec string.""" return "projects/{project}/locations/{location}/datasets/{dataset}/tableSpecs/{table_spec}/columnSpecs/{column_spec}".format( project=project, location=location, dataset=dataset, table_spec=table_spec, column_spec=column_spec, )
[docs] @staticmethod def parse_column_spec_path(path: str) -> Dict[str, str]: """Parses a column_spec path into its component segments.""" m = re.match( r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)/tableSpecs/(?P<table_spec>.+?)/columnSpecs/(?P<column_spec>.+?)$", path, ) return m.groupdict() if m else {}
[docs] @staticmethod def dataset_path(project: str, location: str, dataset: str,) -> str: """Returns a fully-qualified dataset string.""" return "projects/{project}/locations/{location}/datasets/{dataset}".format( project=project, location=location, dataset=dataset, )
[docs] @staticmethod def parse_dataset_path(path: str) -> Dict[str, str]: """Parses a dataset path into its component segments.""" m = re.match( r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$", path, ) return m.groupdict() if m else {}
[docs] @staticmethod def model_path(project: str, location: str, model: str,) -> str: """Returns a fully-qualified model string.""" return "projects/{project}/locations/{location}/models/{model}".format( project=project, location=location, model=model, )
[docs] @staticmethod def parse_model_path(path: str) -> Dict[str, str]: """Parses a model path into its component segments.""" m = re.match( r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/models/(?P<model>.+?)$", path, ) return m.groupdict() if m else {}
[docs] @staticmethod def model_evaluation_path( project: str, location: str, model: str, model_evaluation: str, ) -> str: """Returns a fully-qualified model_evaluation string.""" return "projects/{project}/locations/{location}/models/{model}/modelEvaluations/{model_evaluation}".format( project=project, location=location, model=model, model_evaluation=model_evaluation, )
[docs] @staticmethod def parse_model_evaluation_path(path: str) -> Dict[str, str]: """Parses a model_evaluation path into its component segments.""" m = re.match( r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/models/(?P<model>.+?)/modelEvaluations/(?P<model_evaluation>.+?)$", path, ) return m.groupdict() if m else {}
[docs] @staticmethod def table_spec_path( project: str, location: str, dataset: str, table_spec: str, ) -> str: """Returns a fully-qualified table_spec string.""" return "projects/{project}/locations/{location}/datasets/{dataset}/tableSpecs/{table_spec}".format( project=project, location=location, dataset=dataset, table_spec=table_spec, )
[docs] @staticmethod def parse_table_spec_path(path: str) -> Dict[str, str]: """Parses a table_spec path into its component segments.""" m = re.match( r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)/tableSpecs/(?P<table_spec>.+?)$", path, ) return m.groupdict() if m else {}
[docs] @staticmethod def common_billing_account_path(billing_account: str,) -> str: """Returns a fully-qualified billing_account string.""" return "billingAccounts/{billing_account}".format( billing_account=billing_account, )
[docs] @staticmethod def parse_common_billing_account_path(path: str) -> Dict[str, str]: """Parse a billing_account path into its component segments.""" m = re.match(r"^billingAccounts/(?P<billing_account>.+?)$", path) return m.groupdict() if m else {}
[docs] @staticmethod def common_folder_path(folder: str,) -> str: """Returns a fully-qualified folder string.""" return "folders/{folder}".format(folder=folder,)
[docs] @staticmethod def parse_common_folder_path(path: str) -> Dict[str, str]: """Parse a folder path into its component segments.""" m = re.match(r"^folders/(?P<folder>.+?)$", path) return m.groupdict() if m else {}
[docs] @staticmethod def common_organization_path(organization: str,) -> str: """Returns a fully-qualified organization string.""" return "organizations/{organization}".format(organization=organization,)
[docs] @staticmethod def parse_common_organization_path(path: str) -> Dict[str, str]: """Parse a organization path into its component segments.""" m = re.match(r"^organizations/(?P<organization>.+?)$", path) return m.groupdict() if m else {}
[docs] @staticmethod def common_project_path(project: str,) -> str: """Returns a fully-qualified project string.""" return "projects/{project}".format(project=project,)
[docs] @staticmethod def parse_common_project_path(path: str) -> Dict[str, str]: """Parse a project path into its component segments.""" m = re.match(r"^projects/(?P<project>.+?)$", path) return m.groupdict() if m else {}
[docs] @staticmethod def common_location_path(project: str, location: str,) -> str: """Returns a fully-qualified location string.""" return "projects/{project}/locations/{location}".format( project=project, location=location, )
[docs] @staticmethod def parse_common_location_path(path: str) -> Dict[str, str]: """Parse a location path into its component segments.""" m = re.match(r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)$", path) return m.groupdict() if m else {}
def __init__( self, *, credentials: Optional[ga_credentials.Credentials] = None, transport: Union[str, AutoMlTransport, None] = None, client_options: Optional[client_options_lib.ClientOptions] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, ) -> None: """Instantiates the auto ml client. Args: credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. transport (Union[str, AutoMlTransport]): The transport to use. If set to None, a transport is chosen automatically. client_options (google.api_core.client_options.ClientOptions): Custom options for the client. It won't take effect if a ``transport`` instance is provided. (1) The ``api_endpoint`` property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the ``api_endpoint`` property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the ``client_cert_source`` property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. Raises: google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport creation failed for any reason. """ if isinstance(client_options, dict): client_options = client_options_lib.from_dict(client_options) if client_options is None: client_options = client_options_lib.ClientOptions() # Create SSL credentials for mutual TLS if needed. use_client_cert = bool( util.strtobool(os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false")) ) client_cert_source_func = None is_mtls = False if use_client_cert: if client_options.client_cert_source: is_mtls = True client_cert_source_func = client_options.client_cert_source else: is_mtls = mtls.has_default_client_cert_source() if is_mtls: client_cert_source_func = mtls.default_client_cert_source() else: client_cert_source_func = None # Figure out which api endpoint to use. if client_options.api_endpoint is not None: api_endpoint = client_options.api_endpoint else: use_mtls_env = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto") if use_mtls_env == "never": api_endpoint = self.DEFAULT_ENDPOINT elif use_mtls_env == "always": api_endpoint = self.DEFAULT_MTLS_ENDPOINT elif use_mtls_env == "auto": if is_mtls: api_endpoint = self.DEFAULT_MTLS_ENDPOINT else: api_endpoint = self.DEFAULT_ENDPOINT else: raise MutualTLSChannelError( "Unsupported GOOGLE_API_USE_MTLS_ENDPOINT value. Accepted " "values: never, auto, always" ) # Save or instantiate the transport. # Ordinarily, we provide the transport, but allowing a custom transport # instance provides an extensibility point for unusual situations. if isinstance(transport, AutoMlTransport): # transport is a AutoMlTransport instance. if credentials or client_options.credentials_file: raise ValueError( "When providing a transport instance, " "provide its credentials directly." ) if client_options.scopes: raise ValueError( "When providing a transport instance, provide its scopes " "directly." ) self._transport = transport else: Transport = type(self).get_transport_class(transport) self._transport = Transport( credentials=credentials, credentials_file=client_options.credentials_file, host=api_endpoint, scopes=client_options.scopes, client_cert_source_for_mtls=client_cert_source_func, quota_project_id=client_options.quota_project_id, client_info=client_info, always_use_jwt_access=( Transport == type(self).get_transport_class("grpc") or Transport == type(self).get_transport_class("grpc_asyncio") ), )
[docs] def create_dataset( self, request: service.CreateDatasetRequest = None, *, parent: str = None, dataset: gca_dataset.Dataset = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> gca_dataset.Dataset: r"""Creates a dataset. Args: request (google.cloud.automl_v1beta1.types.CreateDatasetRequest): The request object. Request message for [AutoMl.CreateDataset][google.cloud.automl.v1beta1.AutoMl.CreateDataset]. parent (str): Required. The resource name of the project to create the dataset for. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. dataset (google.cloud.automl_v1beta1.types.Dataset): Required. The dataset to create. This corresponds to the ``dataset`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.Dataset: A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, dataset]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.CreateDatasetRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.CreateDatasetRequest): request = service.CreateDatasetRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if dataset is not None: request.dataset = dataset # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.create_dataset] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def get_dataset( self, request: service.GetDatasetRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> dataset.Dataset: r"""Gets a dataset. Args: request (google.cloud.automl_v1beta1.types.GetDatasetRequest): The request object. Request message for [AutoMl.GetDataset][google.cloud.automl.v1beta1.AutoMl.GetDataset]. name (str): Required. The resource name of the dataset to retrieve. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.Dataset: A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.GetDatasetRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.GetDatasetRequest): request = service.GetDatasetRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_dataset] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def list_datasets( self, request: service.ListDatasetsRequest = None, *, parent: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListDatasetsPager: r"""Lists datasets in a project. Args: request (google.cloud.automl_v1beta1.types.ListDatasetsRequest): The request object. Request message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets]. parent (str): Required. The resource name of the project from which to list datasets. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.services.auto_ml.pagers.ListDatasetsPager: Response message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.ListDatasetsRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.ListDatasetsRequest): request = service.ListDatasetsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_datasets] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListDatasetsPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response
[docs] def update_dataset( self, request: service.UpdateDatasetRequest = None, *, dataset: gca_dataset.Dataset = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> gca_dataset.Dataset: r"""Updates a dataset. Args: request (google.cloud.automl_v1beta1.types.UpdateDatasetRequest): The request object. Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1beta1.AutoMl.UpdateDataset] dataset (google.cloud.automl_v1beta1.types.Dataset): Required. The dataset which replaces the resource on the server. This corresponds to the ``dataset`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.Dataset: A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([dataset]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.UpdateDatasetRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.UpdateDatasetRequest): request = service.UpdateDatasetRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if dataset is not None: request.dataset = dataset # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.update_dataset] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("dataset.name", request.dataset.name),) ), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def delete_dataset( self, request: service.DeleteDatasetRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""Deletes a dataset and all of its contents. Returns empty response in the [response][google.longrunning.Operation.response] field when it completes, and ``delete_details`` in the [metadata][google.longrunning.Operation.metadata] field. Args: request (google.cloud.automl_v1beta1.types.DeleteDatasetRequest): The request object. Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1beta1.AutoMl.DeleteDataset]. name (str): Required. The resource name of the dataset to delete. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.DeleteDatasetRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.DeleteDatasetRequest): request = service.DeleteDatasetRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.delete_dataset] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
[docs] def import_data( self, request: service.ImportDataRequest = None, *, name: str = None, input_config: io.InputConfig = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""Imports data into a dataset. For Tables this method can only be called on an empty Dataset. For Tables: - A [schema_inference_version][google.cloud.automl.v1beta1.InputConfig.params] parameter must be explicitly set. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes. Args: request (google.cloud.automl_v1beta1.types.ImportDataRequest): The request object. Request message for [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData]. name (str): Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. input_config (google.cloud.automl_v1beta1.types.InputConfig): Required. The desired input location and its domain specific semantics, if any. This corresponds to the ``input_config`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name, input_config]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.ImportDataRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.ImportDataRequest): request = service.ImportDataRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name if input_config is not None: request.input_config = input_config # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.import_data] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
[docs] def export_data( self, request: service.ExportDataRequest = None, *, name: str = None, output_config: io.OutputConfig = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""Exports dataset's data to the provided output location. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes. Args: request (google.cloud.automl_v1beta1.types.ExportDataRequest): The request object. Request message for [AutoMl.ExportData][google.cloud.automl.v1beta1.AutoMl.ExportData]. name (str): Required. The resource name of the dataset. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. output_config (google.cloud.automl_v1beta1.types.OutputConfig): Required. The desired output location. This corresponds to the ``output_config`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name, output_config]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.ExportDataRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.ExportDataRequest): request = service.ExportDataRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name if output_config is not None: request.output_config = output_config # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.export_data] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
[docs] def get_annotation_spec( self, request: service.GetAnnotationSpecRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> annotation_spec.AnnotationSpec: r"""Gets an annotation spec. Args: request (google.cloud.automl_v1beta1.types.GetAnnotationSpecRequest): The request object. Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1beta1.AutoMl.GetAnnotationSpec]. name (str): Required. The resource name of the annotation spec to retrieve. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.AnnotationSpec: A definition of an annotation spec. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.GetAnnotationSpecRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.GetAnnotationSpecRequest): request = service.GetAnnotationSpecRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_annotation_spec] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def get_table_spec( self, request: service.GetTableSpecRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> table_spec.TableSpec: r"""Gets a table spec. Args: request (google.cloud.automl_v1beta1.types.GetTableSpecRequest): The request object. Request message for [AutoMl.GetTableSpec][google.cloud.automl.v1beta1.AutoMl.GetTableSpec]. name (str): Required. The resource name of the table spec to retrieve. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.TableSpec: A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by: \* Tables """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.GetTableSpecRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.GetTableSpecRequest): request = service.GetTableSpecRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_table_spec] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def list_table_specs( self, request: service.ListTableSpecsRequest = None, *, parent: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListTableSpecsPager: r"""Lists table specs in a dataset. Args: request (google.cloud.automl_v1beta1.types.ListTableSpecsRequest): The request object. Request message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs]. parent (str): Required. The resource name of the dataset to list table specs from. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.services.auto_ml.pagers.ListTableSpecsPager: Response message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.ListTableSpecsRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.ListTableSpecsRequest): request = service.ListTableSpecsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_table_specs] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListTableSpecsPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response
[docs] def update_table_spec( self, request: service.UpdateTableSpecRequest = None, *, table_spec: gca_table_spec.TableSpec = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> gca_table_spec.TableSpec: r"""Updates a table spec. Args: request (google.cloud.automl_v1beta1.types.UpdateTableSpecRequest): The request object. Request message for [AutoMl.UpdateTableSpec][google.cloud.automl.v1beta1.AutoMl.UpdateTableSpec] table_spec (google.cloud.automl_v1beta1.types.TableSpec): Required. The table spec which replaces the resource on the server. This corresponds to the ``table_spec`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.TableSpec: A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by: \* Tables """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([table_spec]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.UpdateTableSpecRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.UpdateTableSpecRequest): request = service.UpdateTableSpecRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if table_spec is not None: request.table_spec = table_spec # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.update_table_spec] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("table_spec.name", request.table_spec.name),) ), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def get_column_spec( self, request: service.GetColumnSpecRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> column_spec.ColumnSpec: r"""Gets a column spec. Args: request (google.cloud.automl_v1beta1.types.GetColumnSpecRequest): The request object. Request message for [AutoMl.GetColumnSpec][google.cloud.automl.v1beta1.AutoMl.GetColumnSpec]. name (str): Required. The resource name of the column spec to retrieve. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.ColumnSpec: A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by: \* Tables """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.GetColumnSpecRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.GetColumnSpecRequest): request = service.GetColumnSpecRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_column_spec] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def list_column_specs( self, request: service.ListColumnSpecsRequest = None, *, parent: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListColumnSpecsPager: r"""Lists column specs in a table spec. Args: request (google.cloud.automl_v1beta1.types.ListColumnSpecsRequest): The request object. Request message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs]. parent (str): Required. The resource name of the table spec to list column specs from. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.services.auto_ml.pagers.ListColumnSpecsPager: Response message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.ListColumnSpecsRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.ListColumnSpecsRequest): request = service.ListColumnSpecsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_column_specs] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListColumnSpecsPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response
[docs] def update_column_spec( self, request: service.UpdateColumnSpecRequest = None, *, column_spec: gca_column_spec.ColumnSpec = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> gca_column_spec.ColumnSpec: r"""Updates a column spec. Args: request (google.cloud.automl_v1beta1.types.UpdateColumnSpecRequest): The request object. Request message for [AutoMl.UpdateColumnSpec][google.cloud.automl.v1beta1.AutoMl.UpdateColumnSpec] column_spec (google.cloud.automl_v1beta1.types.ColumnSpec): Required. The column spec which replaces the resource on the server. This corresponds to the ``column_spec`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.ColumnSpec: A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by: \* Tables """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([column_spec]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.UpdateColumnSpecRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.UpdateColumnSpecRequest): request = service.UpdateColumnSpecRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if column_spec is not None: request.column_spec = column_spec # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.update_column_spec] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("column_spec.name", request.column_spec.name),) ), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def create_model( self, request: service.CreateModelRequest = None, *, parent: str = None, model: gca_model.Model = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""Creates a model. Returns a Model in the [response][google.longrunning.Operation.response] field when it completes. When you create a model, several model evaluations are created for it: a global evaluation, and one evaluation for each annotation spec. Args: request (google.cloud.automl_v1beta1.types.CreateModelRequest): The request object. Request message for [AutoMl.CreateModel][google.cloud.automl.v1beta1.AutoMl.CreateModel]. parent (str): Required. Resource name of the parent project where the model is being created. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. model (google.cloud.automl_v1beta1.types.Model): Required. The model to create. This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.automl_v1beta1.types.Model` API proto representing a trained machine learning model. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, model]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.CreateModelRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.CreateModelRequest): request = service.CreateModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if model is not None: request.model = model # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.create_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, gca_model.Model, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
[docs] def get_model( self, request: service.GetModelRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> model.Model: r"""Gets a model. Args: request (google.cloud.automl_v1beta1.types.GetModelRequest): The request object. Request message for [AutoMl.GetModel][google.cloud.automl.v1beta1.AutoMl.GetModel]. name (str): Required. Resource name of the model. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.Model: API proto representing a trained machine learning model. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.GetModelRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.GetModelRequest): request = service.GetModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def list_models( self, request: service.ListModelsRequest = None, *, parent: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListModelsPager: r"""Lists models. Args: request (google.cloud.automl_v1beta1.types.ListModelsRequest): The request object. Request message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels]. parent (str): Required. Resource name of the project, from which to list the models. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.services.auto_ml.pagers.ListModelsPager: Response message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.ListModelsRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.ListModelsRequest): request = service.ListModelsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_models] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListModelsPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response
[docs] def delete_model( self, request: service.DeleteModelRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""Deletes a model. Returns ``google.protobuf.Empty`` in the [response][google.longrunning.Operation.response] field when it completes, and ``delete_details`` in the [metadata][google.longrunning.Operation.metadata] field. Args: request (google.cloud.automl_v1beta1.types.DeleteModelRequest): The request object. Request message for [AutoMl.DeleteModel][google.cloud.automl.v1beta1.AutoMl.DeleteModel]. name (str): Required. Resource name of the model being deleted. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.DeleteModelRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.DeleteModelRequest): request = service.DeleteModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.delete_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
[docs] def deploy_model( self, request: service.DeployModelRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: 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.v1beta1.ImageObjectDetectionModelDeploymentMetadata.node_number]) will reset the deployment state without pausing the model's availability. Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage deployment automatically. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes. Args: request (google.cloud.automl_v1beta1.types.DeployModelRequest): The request object. Request message for [AutoMl.DeployModel][google.cloud.automl.v1beta1.AutoMl.DeployModel]. name (str): Required. Resource name of the model to deploy. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.DeployModelRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.DeployModelRequest): request = service.DeployModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.deploy_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
[docs] def undeploy_model( self, request: service.UndeployModelRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""Undeploys a model. If the model is not deployed this method has no effect. Only applicable for Text Classification, Image Object Detection and Tables; all other domains manage deployment automatically. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes. Args: request (google.cloud.automl_v1beta1.types.UndeployModelRequest): The request object. Request message for [AutoMl.UndeployModel][google.cloud.automl.v1beta1.AutoMl.UndeployModel]. name (str): Required. Resource name of the model to undeploy. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.UndeployModelRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.UndeployModelRequest): request = service.UndeployModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.undeploy_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
[docs] def export_model( self, request: service.ExportModelRequest = None, *, name: str = None, output_config: io.ModelExportOutputConfig = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""Exports a trained, "export-able", model to a user specified Google Cloud Storage location. A model is considered export-able if and only if it has an export format defined for it in [ModelExportOutputConfig][google.cloud.automl.v1beta1.ModelExportOutputConfig]. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes. Args: request (google.cloud.automl_v1beta1.types.ExportModelRequest): The request object. Request message for [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]. Models need to be enabled for exporting, otherwise an error code will be returned. name (str): Required. The resource name of the model to export. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. output_config (google.cloud.automl_v1beta1.types.ModelExportOutputConfig): Required. The desired output location and configuration. This corresponds to the ``output_config`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name, output_config]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.ExportModelRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.ExportModelRequest): request = service.ExportModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name if output_config is not None: request.output_config = output_config # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.export_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
[docs] def export_evaluated_examples( self, request: service.ExportEvaluatedExamplesRequest = None, *, name: str = None, output_config: io.ExportEvaluatedExamplesOutputConfig = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""Exports examples on which the model was evaluated (i.e. which were in the TEST set of the dataset the model was created from), together with their ground truth annotations and the annotations created (predicted) by the model. The examples, ground truth and predictions are exported in the state they were at the moment the model was evaluated. This export is available only for 30 days since the model evaluation is created. Currently only available for Tables. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes. Args: request (google.cloud.automl_v1beta1.types.ExportEvaluatedExamplesRequest): The request object. Request message for [AutoMl.ExportEvaluatedExamples][google.cloud.automl.v1beta1.AutoMl.ExportEvaluatedExamples]. name (str): Required. The resource name of the model whose evaluated examples are to be exported. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. output_config (google.cloud.automl_v1beta1.types.ExportEvaluatedExamplesOutputConfig): Required. The desired output location and configuration. This corresponds to the ``output_config`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name, output_config]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.ExportEvaluatedExamplesRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.ExportEvaluatedExamplesRequest): request = service.ExportEvaluatedExamplesRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name if output_config is not None: request.output_config = output_config # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[ self._transport.export_evaluated_examples ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
[docs] def get_model_evaluation( self, request: service.GetModelEvaluationRequest = None, *, name: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> model_evaluation.ModelEvaluation: r"""Gets a model evaluation. Args: request (google.cloud.automl_v1beta1.types.GetModelEvaluationRequest): The request object. Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1beta1.AutoMl.GetModelEvaluation]. name (str): Required. Resource name for the model evaluation. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.types.ModelEvaluation: Evaluation results of a model. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.GetModelEvaluationRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.GetModelEvaluationRequest): request = service.GetModelEvaluationRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_model_evaluation] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def list_model_evaluations( self, request: service.ListModelEvaluationsRequest = None, *, parent: str = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListModelEvaluationsPager: r"""Lists model evaluations. Args: request (google.cloud.automl_v1beta1.types.ListModelEvaluationsRequest): The request object. Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations]. parent (str): Required. Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.automl_v1beta1.services.auto_ml.pagers.ListModelEvaluationsPager: Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a service.ListModelEvaluationsRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, service.ListModelEvaluationsRequest): request = service.ListModelEvaluationsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_model_evaluations] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListModelEvaluationsPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response
try: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=pkg_resources.get_distribution("google-cloud-automl",).version, ) except pkg_resources.DistributionNotFound: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo() __all__ = ("AutoMlClient",)