def test_make_post_request_to_ai_platform_404_error(
self, mock_request_header, mock_post_func):
mock_request_header.return_value = {}
mock_response = mock.Mock(spec=MockResponse)
type(mock_response()).status_code = mock.PropertyMock(return_value=404)
mock_post_func.return_value = mock_response()
with self.assertRaisesRegex(
ValueError,
('Target URI .* returns HTTP 404 error.\n'
'Please check if the project, model, and version names '
'are given correctly.')):
http_utils.make_post_request_to_ai_platform(
'uri/test_uri', {'data': 123})
def test_make_post_request_to_ai_platform_other_erors(
self, mock_request_header, mock_post_func):
mock_request_header.return_value = {}
mock_response = mock.Mock(spec=MockResponse)
type(mock_response()).status_code = mock.PropertyMock(return_value=403)
type(mock_response()).text = mock.PropertyMock(
return_value='test error')
mock_post_func.return_value = mock_response()
with self.assertRaisesRegex(ValueError,
('Target URI .* returns HTTP 403 error.\n'
'Please check the raw error message: \n'
'test error')):
http_utils.make_post_request_to_ai_platform(
'uri/test_uri', {'data': 123})
def test_make_post_request_to_ai_platform(self, mock_request_header,
mock_post_func):
mock_request_header.return_value = {}
mock_response = mock.Mock(spec=MockResponse)
type(mock_response()).status_code = mock.PropertyMock(return_value=200)
mock_response().json.return_value = 'results'
mock_post_func.return_value = mock_response()
res = http_utils.make_post_request_to_ai_platform(
'uri/test_uri', {'data': 123})
self.assertTrue(mock_post_func.called)
self.assertEqual(res, 'results')
def predict(self, instances, timeout_ms=constants.DEFAULT_TIMEOUT):
"""A method to call prediction services with given instances.
Args:
instances: A list of instances for getting predictions.
timeout_ms: Timeout for each service call to the api (in milliseconds).
Returns:
A list of the dictionaries.
"""
request_body = {'instances': instances}
response = http_utils.make_post_request_to_ai_platform(
self._endpoint + ':predict', request_body, self._credentials,
timeout_ms)
return response
def explain(self,
instances,
params=None,
timeout_ms=constants.DEFAULT_TIMEOUT):
"""A method to call explanation services with given instances.
Args:
instances: A list of instances for getting explanations.
params: Overridable parameters for the explain call. Parameters can not
be overriden in a remote model at the moment.
timeout_ms: Timeout for each service call to the api (in milliseconds).
Returns:
A list of Explanation objects.
Raises:
ValueError: When explanation service fails, raise ValueError with the
returned error message. This is likely due to details or formats of
the instances are not correct.
"""
if params:
logging.warn('Params can not be overriden in a remote model at the'
' moment.')
del params
request_body = {'instances': instances}
response = http_utils.make_post_request_to_ai_platform(
self._endpoint + ':explain', request_body, self._credentials,
timeout_ms)
if 'error' in response:
error_msg = response['error']
raise ValueError(('Explanation call failed. This is likely due to '
'incorrect instance formats. \nOriginal error '
'message: ' + json.dumps(error_msg)))
explanations = []
for idx, explanation_dict in enumerate(response['explanations']):
exp_obj = explanation.Explanation.from_ai_platform_response(
explanation_dict, instances[idx],
self._modality_input_list_map)
explanations.append(exp_obj)
return explanations
def explain(self,
instances: List[Any],
params: Optional[configs.AttributionParameters] = None,
timeout_ms: int = constants.DEFAULT_TIMEOUT
) -> List[explanation.Explanation]:
"""A method to call explanation services with given instances.
Args:
instances: A list of instances for getting explanations.
params: Overridable parameters for the explain call. Parameters can not
be overriden in a remote model at the moment.
timeout_ms: Timeout for each service call to the api (in milliseconds).
Returns:
A list of Explanation objects.
Raises:
ValueError: When explanation service fails, raise ValueError with the
returned error message. This is likely due to details or formats of
the instances are not correct.
KeyError: When the explanation dictionary doesn't contain 'attributions'
or 'attributions_by_label' in the response.
"""
if params:
logging.warn('Params can not be overriden in a remote model at the'
' moment.')
del params
request_body = {'instances': instances}
response = http_utils.make_post_request_to_ai_platform(
self._model_endpoint_uri + ':explain',
request_body,
self._credentials,
timeout_ms)
if 'error' in response:
error_msg = response['error']
raise ValueError(('Explanation call failed. This is likely due to '
'incorrect instance formats. \nOriginal error '
'message: ' + json.dumps(error_msg)))
explanations = []
for idx, explanation_dict in enumerate(response['explanations']):
if _CAIP_ATTRIBUTIONS_KEY in explanation_dict: # CAIP response.
attrs_key = _CAIP_ATTRIBUTIONS_KEY
feature_attrs_key = _CAIP_FEATURE_ATTRS_KEY
parse_fun = explanation.Explanation.from_ai_platform_response
elif _VERTEX_ATTRIBUTIONS_KEY in explanation_dict: # VERTEX response.
attrs_key = _VERTEX_ATTRIBUTIONS_KEY
feature_attrs_key = _VERTEX_FEATURE_ATTRS_KEY
parse_fun = explanation.Explanation.from_vertex_response
else:
raise KeyError(
'Attribution keys are not present in the AI Platform response.')
attrs = explanation_dict[attrs_key]
if not self._modality_to_inputs_map:
self._modality_to_inputs_map = _create_default_modality_map(
next(iter(attrs))[feature_attrs_key])
explanations.append(
parse_fun(attrs, instances[idx], self._modality_to_inputs_map))
return explanations