async def test_explain_field_headers_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(),
)
# Any value that is part of the HTTP/1.1 URI should be sent as
# a field header. Set these to a non-empty value.
request = prediction_service.ExplainRequest()
request.endpoint = "endpoint/value"
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client.transport.explain), "__call__") as call:
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
prediction_service.ExplainResponse()
)
await client.explain(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0] == request
# Establish that the field header was sent.
_, _, kw = call.mock_calls[0]
assert ("x-goog-request-params", "endpoint=endpoint/value",) in kw["metadata"]
async def test_explain_async(
transport: str = "grpc_asyncio", request_type=prediction_service.ExplainRequest
):
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), transport=transport,
)
# Everything is optional in proto3 as far as the runtime is concerned,
# and we are mocking out the actual API, so just send an empty request.
request = request_type()
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client.transport.explain), "__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
prediction_service.ExplainResponse(
deployed_model_id="deployed_model_id_value",
)
)
response = await client.explain(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0] == prediction_service.ExplainRequest()
# Establish that the response is the type that we expect.
assert isinstance(response, prediction_service.ExplainResponse)
assert response.deployed_model_id == "deployed_model_id_value"
def test_explain_flattened_error():
client = PredictionServiceClient(credentials=credentials.AnonymousCredentials(),)
# Attempting to call a method with both a request object and flattened
# fields is an error.
with pytest.raises(ValueError):
client.explain(
prediction_service.ExplainRequest(),
endpoint="endpoint_value",
instances=[struct.Value(null_value=struct.NullValue.NULL_VALUE)],
parameters=struct.Value(null_value=struct.NullValue.NULL_VALUE),
deployed_model_id="deployed_model_id_value",
)
async def explain(
self,
request: Union[prediction_service.ExplainRequest, dict] = None,
*,
endpoint: str = None,
instances: Sequence[struct_pb2.Value] = None,
parameters: struct_pb2.Value = None,
deployed_model_id: str = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> prediction_service.ExplainResponse:
r"""Perform an online explanation.
If
[deployed_model_id][google.cloud.aiplatform.v1beta1.ExplainRequest.deployed_model_id]
is specified, the corresponding DeployModel must have
[explanation_spec][google.cloud.aiplatform.v1beta1.DeployedModel.explanation_spec]
populated. If
[deployed_model_id][google.cloud.aiplatform.v1beta1.ExplainRequest.deployed_model_id]
is not specified, all DeployedModels must have
[explanation_spec][google.cloud.aiplatform.v1beta1.DeployedModel.explanation_spec]
populated. Only deployed AutoML tabular Models have
explanation_spec.
.. code-block:: python
from google.cloud import aiplatform_v1beta1
def sample_explain():
# Create a client
client = aiplatform_v1beta1.PredictionServiceClient()
# Initialize request argument(s)
instances = aiplatform_v1beta1.Value()
instances.null_value = "NULL_VALUE"
request = aiplatform_v1beta1.ExplainRequest(
endpoint="endpoint_value",
instances=instances,
)
# Make the request
response = client.explain(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.aiplatform_v1beta1.types.ExplainRequest, dict]):
The request object. Request message for
[PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain].
endpoint (:class:`str`):
Required. The name of the Endpoint requested to serve
the explanation. Format:
``projects/{project}/locations/{location}/endpoints/{endpoint}``
This corresponds to the ``endpoint`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
instances (:class:`Sequence[google.protobuf.struct_pb2.Value]`):
Required. The instances that are the input to the
explanation call. A DeployedModel may have an upper
limit on the number of instances it supports per
request, and when it is exceeded the explanation call
errors in case of AutoML Models, or, in case of customer
created Models, the behaviour is as documented by that
Model. The schema of any single instance may be
specified via Endpoint's DeployedModels'
[Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model]
[PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata]
[instance_schema_uri][google.cloud.aiplatform.v1beta1.PredictSchemata.instance_schema_uri].
This corresponds to the ``instances`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
parameters (:class:`google.protobuf.struct_pb2.Value`):
The parameters that govern the prediction. The schema of
the parameters may be specified via Endpoint's
DeployedModels' [Model's
][google.cloud.aiplatform.v1beta1.DeployedModel.model]
[PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata]
[parameters_schema_uri][google.cloud.aiplatform.v1beta1.PredictSchemata.parameters_schema_uri].
This corresponds to the ``parameters`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
deployed_model_id (:class:`str`):
If specified, this ExplainRequest will be served by the
chosen DeployedModel, overriding
[Endpoint.traffic_split][google.cloud.aiplatform.v1beta1.Endpoint.traffic_split].
This corresponds to the ``deployed_model_id`` 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.aiplatform_v1beta1.types.ExplainResponse:
Response message for
[PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain].
"""
# Create or coerce a protobuf request object.
# Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any(
[endpoint, instances, parameters, deployed_model_id])
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.")
request = prediction_service.ExplainRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if endpoint is not None:
request.endpoint = endpoint
if parameters is not None:
request.parameters = parameters
if deployed_model_id is not None:
request.deployed_model_id = deployed_model_id
if instances:
request.instances.extend(instances)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = gapic_v1.method_async.wrap_method(
self._client._transport.explain,
default_timeout=5.0,
client_info=DEFAULT_CLIENT_INFO,
)
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata(
(("endpoint", request.endpoint), )), )
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
def explain(
self,
request: prediction_service.ExplainRequest = None,
*,
endpoint: str = None,
instances: Sequence[struct_pb2.Value] = None,
parameters: struct_pb2.Value = None,
deployed_model_id: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> prediction_service.ExplainResponse:
r"""Perform an online explanation.
If
[deployed_model_id][google.cloud.aiplatform.v1beta1.ExplainRequest.deployed_model_id]
is specified, the corresponding DeployModel must have
[explanation_spec][google.cloud.aiplatform.v1beta1.DeployedModel.explanation_spec]
populated. If
[deployed_model_id][google.cloud.aiplatform.v1beta1.ExplainRequest.deployed_model_id]
is not specified, all DeployedModels must have
[explanation_spec][google.cloud.aiplatform.v1beta1.DeployedModel.explanation_spec]
populated. Only deployed AutoML tabular Models have
explanation_spec.
Args:
request (google.cloud.aiplatform_v1beta1.types.ExplainRequest):
The request object. Request message for
[PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain].
endpoint (str):
Required. The name of the Endpoint requested to serve
the explanation. Format:
``projects/{project}/locations/{location}/endpoints/{endpoint}``
This corresponds to the ``endpoint`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
instances (Sequence[google.protobuf.struct_pb2.Value]):
Required. The instances that are the input to the
explanation call. A DeployedModel may have an upper
limit on the number of instances it supports per
request, and when it is exceeded the explanation call
errors in case of AutoML Models, or, in case of customer
created Models, the behaviour is as documented by that
Model. The schema of any single instance may be
specified via Endpoint's DeployedModels'
[Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model]
[PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata]
[instance_schema_uri][google.cloud.aiplatform.v1beta1.PredictSchemata.instance_schema_uri].
This corresponds to the ``instances`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
parameters (google.protobuf.struct_pb2.Value):
The parameters that govern the prediction. The schema of
the parameters may be specified via Endpoint's
DeployedModels' [Model's
][google.cloud.aiplatform.v1beta1.DeployedModel.model]
[PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata]
[parameters_schema_uri][google.cloud.aiplatform.v1beta1.PredictSchemata.parameters_schema_uri].
This corresponds to the ``parameters`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
deployed_model_id (str):
If specified, this ExplainRequest will be served by the
chosen DeployedModel, overriding
[Endpoint.traffic_split][google.cloud.aiplatform.v1beta1.Endpoint.traffic_split].
This corresponds to the ``deployed_model_id`` 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.aiplatform_v1beta1.types.ExplainResponse:
Response message for
[PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain].
"""
# 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(
[endpoint, instances, parameters, deployed_model_id])
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 prediction_service.ExplainRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, prediction_service.ExplainRequest):
request = prediction_service.ExplainRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if endpoint is not None:
request.endpoint = endpoint
if instances is not None:
request.instances.extend(instances)
if parameters is not None:
request.parameters = parameters
if deployed_model_id is not None:
request.deployed_model_id = deployed_model_id
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.explain]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata(
(("endpoint", request.endpoint), )), )
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
