def test_analyze_entity_sentiment(self):
# Setup Expected Response
language = "language-1613589672"
expected_response = {"language": language}
expected_response = language_service_pb2.AnalyzeEntitySentimentResponse(
**expected_response)
# Mock the API response
channel = ChannelStub(responses=[expected_response])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = language_v1.LanguageServiceClient()
# Setup Request
document = {}
response = client.analyze_entity_sentiment(document)
assert expected_response == response
assert len(channel.requests) == 1
expected_request = language_service_pb2.AnalyzeEntitySentimentRequest(
document=document)
actual_request = channel.requests[0][1]
assert expected_request == actual_request
def analyze_entity_sentiment(self,
document,
encoding_type=None,
options=None):
"""
Finds entities, similar to ``AnalyzeEntities`` in the text and analyzes
sentiment associated with each entity and its mentions.
Example:
>>> from google.cloud import language_v1
>>>
>>> client = language_v1.LanguageServiceClient()
>>>
>>> document = {}
>>>
>>> response = client.analyze_entity_sentiment(document)
Args:
document (Union[dict, ~google.cloud.language_v1.types.Document]): Input document.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.language_v1.types.Document`
encoding_type (~google.cloud.language_v1.types.EncodingType): The encoding type used by the API to calculate offsets.
options (~google.gax.CallOptions): Overrides the default
settings for this call, e.g, timeout, retries etc.
Returns:
A :class:`~google.cloud.language_v1.types.AnalyzeEntitySentimentResponse` instance.
Raises:
:exc:`google.gax.errors.GaxError` if the RPC is aborted.
:exc:`ValueError` if the parameters are invalid.
"""
request = language_service_pb2.AnalyzeEntitySentimentRequest(
document=document, encoding_type=encoding_type)
return self._analyze_entity_sentiment(request, options)
def test_analyze_entity_sentiment(self, mock_create_stub):
# Mock gRPC layer
grpc_stub = mock.Mock()
mock_create_stub.return_value = grpc_stub
client = language_v1.LanguageServiceClient()
# Mock request
document = {}
# Mock response
language = 'language-1613589672'
expected_response = {'language': language}
expected_response = language_service_pb2.AnalyzeEntitySentimentResponse(
**expected_response)
grpc_stub.AnalyzeEntitySentiment.return_value = expected_response
response = client.analyze_entity_sentiment(document)
self.assertEqual(expected_response, response)
grpc_stub.AnalyzeEntitySentiment.assert_called_once()
args, kwargs = grpc_stub.AnalyzeEntitySentiment.call_args
self.assertEqual(len(args), 2)
self.assertEqual(len(kwargs), 1)
self.assertIn('metadata', kwargs)
actual_request = args[0]
expected_request = language_service_pb2.AnalyzeEntitySentimentRequest(
document=document)
self.assertEqual(expected_request, actual_request)
def analyze_entity_sentiment(
self,
document,
encoding_type=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT):
"""
Finds entities, similar to ``AnalyzeEntities`` in the text and analyzes
sentiment associated with each entity and its mentions.
Example:
>>> from google.cloud import language_v1
>>>
>>> client = language_v1.LanguageServiceClient()
>>>
>>> document = {}
>>>
>>> response = client.analyze_entity_sentiment(document)
Args:
document (Union[dict, ~google.cloud.language_v1.types.Document]): Input document.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.language_v1.types.Document`
encoding_type (~google.cloud.language_v1.types.EncodingType): The encoding type used by the API to calculate offsets.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
Returns:
A :class:`~google.cloud.language_v1.types.AnalyzeEntitySentimentResponse` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
request = language_service_pb2.AnalyzeEntitySentimentRequest(
document=document, encoding_type=encoding_type)
return self._analyze_entity_sentiment(
request, retry=retry, timeout=timeout)
def test_analyze_entity_sentiment(self):
# Setup Expected Response
language = 'language-1613589672'
expected_response = {'language': language}
expected_response = language_service_pb2.AnalyzeEntitySentimentResponse(
**expected_response)
# Mock the API response
channel = ChannelStub(responses=[expected_response])
client = language_v1.LanguageServiceClient(channel=channel)
# Setup Request
document = {}
response = client.analyze_entity_sentiment(document)
assert expected_response == response
assert len(channel.requests) == 1
expected_request = language_service_pb2.AnalyzeEntitySentimentRequest(
document=document)
actual_request = channel.requests[0][1]
assert expected_request == actual_request
def analyze_entity_sentiment(
self,
document,
encoding_type=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Finds entities, similar to ``AnalyzeEntities`` in the text and analyzes
sentiment associated with each entity and its mentions.
Example:
>>> from google.cloud import language_v1
>>>
>>> client = language_v1.LanguageServiceClient()
>>>
>>> # TODO: Initialize `document`:
>>> document = {}
>>>
>>> response = client.analyze_entity_sentiment(document)
Args:
document (Union[dict, ~google.cloud.language_v1.types.Document]): Input document.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.language_v1.types.Document`
encoding_type (~google.cloud.language_v1.enums.EncodingType): The encoding type used by the API to calculate offsets.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.language_v1.types.AnalyzeEntitySentimentResponse` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if "analyze_entity_sentiment" not in self._inner_api_calls:
self._inner_api_calls[
"analyze_entity_sentiment"] = google.api_core.gapic_v1.method.wrap_method(
self.transport.analyze_entity_sentiment,
default_retry=self.
_method_configs["AnalyzeEntitySentiment"].retry,
default_timeout=self.
_method_configs["AnalyzeEntitySentiment"].timeout,
client_info=self._client_info,
)
request = language_service_pb2.AnalyzeEntitySentimentRequest(
document=document, encoding_type=encoding_type)
return self._inner_api_calls["analyze_entity_sentiment"](
request, retry=retry, timeout=timeout, metadata=metadata)