def SendFeedback(self,feedback,context):
datadef_request = feedback.request.data
features = grpc_datadef_to_array(datadef_request)
truth = grpc_datadef_to_array(feedback.truth)
reward = feedback.reward
send_feedback(self.user_model,features,datadef_request.names,truth,reward)
return prediction_pb2.SeldonMessage()
def SendFeedback(self,feedback,context):
datadef_request = feedback.request.data
features = grpc_datadef_to_array(datadef_request)
truth = grpc_datadef_to_array(feedback.truth)
reward = feedback.reward
routing = feedback.response.meta.routing.get(PRED_UNIT_ID)
send_feedback(self.user_model,features,datadef_request.names,routing,reward,truth)
return prediction_pb2.SeldonMessage()
def Predict(self, request, context):
datadef = request.data
features = grpc_datadef_to_array(datadef)
predictions = np.array(
predict(self.user_model, features, datadef.names))
if len(predictions.shape) > 1:
class_names = get_class_names(self.user_model,
predictions.shape[1])
else:
class_names = []
data = array_to_grpc_datadef(predictions, class_names,
request.data.WhichOneof("data_oneof"))
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson, meta)
return prediction_pb2.SeldonMessage(data=data, meta=meta)
def test_proto_tftensor_to_array():
names = ["a", "b"]
array = np.array([[1, 2], [3, 4]])
datadef = prediction_pb2.DefaultData(names=names,
tftensor=tf.make_tensor_proto(array))
array2 = grpc_datadef_to_array(datadef)
assert array.shape == array2.shape
assert np.array_equal(array, array2)
def TransformInput(self,request,context):
datadef = request.data
features = grpc_datadef_to_array(datadef)
outlier_scores = score(self.user_model,features,datadef.names)
request.meta.tags["outlierScore"] = list(outlier_scores)
return request
def Predict(self,request,context):
datadef = request.data
features = grpc_datadef_to_array(datadef)
predictions = np.array(predict(self.user_model,features,datadef.names))
#TODO: check that predictions is 2 dimensional
class_names = get_class_names(self.user_model, predictions.shape[1])
data = array_to_grpc_datadef(predictions, class_names, request.data.WhichOneof("data_oneof"))
return prediction_pb2.SeldonMessage(data=data)
def Route(self,request,context):
datadef = request.data
features = grpc_datadef_to_array(datadef)
routing = np.array([[route(self.user_model,features,datadef.names)]])
#TODO: check that predictions is 2 dimensional
class_names = []
data = array_to_grpc_datadef(routing, class_names, request.data.WhichOneof("data_oneof"))
return prediction_pb2.SeldonMessage(data=data)
def TransformOutput(self,request,context):
datadef = request.data
features = grpc_datadef_to_array(datadef)
transformed = np.array(transform_output(self.user_model,features,datadef.names))
#TODO: check that predictions is 2 dimensional
class_names = get_class_names(self.user_model, datadef.names)
data = array_to_grpc_datadef(transformed, class_names, request.data.WhichOneof("data_oneof"))
return prediction_pb2.SeldonMessage(data=data)
def Predict(self, request, context):
datadef = request.data
features = grpc_datadef_to_array(datadef)
predictions = np.array(
predict(self.user_model, features, datadef.names))
if len(predictions.shape) > 1:
class_names = get_class_names(self.user_model,
predictions.shape[1])
else:
class_names = []
data = array_to_grpc_datadef(predictions, class_names,
request.data.WhichOneof("data_oneof"))
return prediction_pb2.SeldonMessage(data=data)
def Route(self,request,context):
datadef = request.data
features = grpc_datadef_to_array(datadef)
routing = np.array([[route(self.user_model,features,datadef.names)]])
#TODO: check that predictions is 2 dimensional
class_names = []
data = array_to_grpc_datadef(routing, class_names, request.data.WhichOneof("data_oneof"))
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson,meta)
return prediction_pb2.SeldonMessage(data=data,meta=meta)
