def _predict_fn(self, arr: Union[np.ndarray, List]) -> np.ndarray:
print(arr)
if type(arr) == list:
arr = np.array(arr)
if self.protocol == Protocol.seldon_grpc:
return self._grpc(arr)
elif self.protocol == Protocol.seldon_http:
payload = seldon.create_request(arr, seldon.SeldonPayload.NDARRAY)
response_raw = requests.post(SELDON_PREDICTOR_URL_FORMAT.format(
self.predictor_host),
json=payload)
if response_raw.status_code == 200:
rh = seldon.SeldonRequestHandler(response_raw.json())
response_list = rh.extract_request()
return np.array(response_list)
else:
raise Exception(
"Failed to get response from model return_code:%d" %
response_raw.status_code)
elif self.protocol == Protocol.tensorflow_http:
instances = []
for req_data in arr:
if isinstance(req_data, np.ndarray):
instances.append(req_data.tolist())
else:
instances.append(req_data)
request = {"instances": instances}
response = requests.post(
TENSORFLOW_PREDICTOR_URL_FORMAT.format(self.predictor_host),
json.dumps(request))
if response.status_code != 200:
raise Exception(
"Failed to get response from model return_code:%d" %
response.status_code)
return np.array(response.json()["predictions"])
def explain(self, request: Dict, model_name=None) -> Any:
if self.method is ExplainerMethod.anchor_tabular or self.method is ExplainerMethod.anchor_images or \
self.method is ExplainerMethod.anchor_text or self.method is ExplainerMethod.kernel_shap or \
self.method is ExplainerMethod.integrated_gradients or self.method is ExplainerMethod.tree_shap:
if self.protocol == Protocol.tensorflow_http:
explanation: Explanation = self.wrapper.explain(
request["instances"])
elif self.protocol == Protocol.v2_http:
logging.info("model name %s", model_name)
rh = v2.KFServingV2RequestHandler()
self.v2_model_name = model_name
self.v2_name = rh.extract_name(request)
self.v2_type = rh.extract_type(request)
logging.info("v2 name from inputs %s:", self.v2_name)
response_list = rh.extract_request(request)
explanation = self.wrapper.explain(response_list)
else:
rh = seldon.SeldonRequestHandler(request)
response_list = rh.extract_request()
explanation = self.wrapper.explain(response_list)
explanationAsJsonStr = explanation.to_json()
logging.info("Explanation: %s", explanationAsJsonStr)
return json.loads(explanationAsJsonStr)
else:
raise NotImplementedError
def explain(self, request: Dict) -> Any:
if self.method is ExplainerMethod.anchor_tabular or self.method is ExplainerMethod.anchor_images or \
self.method is ExplainerMethod.anchor_text or self.method is ExplainerMethod.kernel_shap or \
self.method is ExplainerMethod.integrated_gradients:
if self.protocol == Protocol.tensorflow_http:
explanation: Explanation = self.wrapper.explain(request["instances"])
else:
rh = seldon.SeldonRequestHandler(request)
response_list = rh.extract_request()
explanation = self.wrapper.explain(response_list)
explanationAsJsonStr = explanation.to_json()
logging.info("Explanation: %s", explanationAsJsonStr)
return json.loads(explanationAsJsonStr)
else:
raise NotImplementedError