def TransformOutput():
request = extract_message()
logger.debug("Request: %s", request)
sanity_check_request(request)
if hasattr(user_model, "transform_output_rest"):
return jsonify(user_model.transform_output_rest(request))
else:
features = get_data_from_json(request)
names = request.get("data", {}).get("names")
meta = get_meta_from_json(request)
transformed = transform_output(user_model,
features,
names,
meta=meta)
logger.debug("Transformed: %s", transformed)
if isinstance(transformed, np.ndarray) or "data" in request:
new_class_names = get_class_names(user_model, names)
data = array_to_rest_datadef(transformed, new_class_names,
request.get("data", {}))
response = {"data": data, "meta": {}}
else:
response = {"binData": transformed, "meta": {}}
tags = get_custom_tags(user_model)
if tags:
response["meta"]["tags"] = tags
metrics = get_custom_metrics(user_model)
if metrics:
response["meta"]["metrics"] = metrics
return jsonify(response)
def TransformInput():
request = extract_message()
logger.debug("Request: %s", request)
sanity_check_request(request)
if hasattr(user_model, "transform_input_rest"):
return jsonify(user_model.transform_input_rest(request))
else:
features = get_data_from_json(request)
names = request.get("data", {}).get("names")
transformed = transform_input(user_model, features, names)
logger.debug("Transformed: %s", transformed)
# If predictions is an numpy array or we used the default data then return as numpy array
if isinstance(transformed, np.ndarray) or "data" in request:
new_feature_names = get_feature_names(user_model, names)
transformed = np.array(transformed)
data = array_to_rest_datadef(transformed, new_feature_names,
request.get("data", {}))
response = {"data": data, "meta": {}}
else:
response = {"binData": transformed, "meta": {}}
tags = get_custom_tags(user_model)
if tags:
response["meta"]["tags"] = tags
metrics = get_custom_metrics(user_model)
if metrics:
response["meta"]["metrics"] = metrics
return jsonify(response)
def Route():
request = extract_message()
logger.debug("Request: %s", request)
sanity_check_request(request)
if hasattr(user_router, "route_rest"):
return jsonify(user_router.route_rest(request))
else:
datadef = request.get("data")
features = rest_datadef_to_array(datadef)
routing = np.array(
[[route(user_router, features, datadef.get("names"))]])
# TODO: check that predictions is 2 dimensional
class_names = []
data = array_to_rest_datadef(routing, class_names, datadef)
response = {"data": data, "meta": {}}
tags = get_custom_tags(user_router)
if tags:
response["meta"]["tags"] = tags
metrics = get_custom_metrics(user_router)
if metrics:
response["meta"]["metrics"] = metrics
return jsonify(response)
def TransformInput():
request = extract_message()
sanity_check_request(request)
datadef = request.get("data")
features = rest_datadef_to_array(datadef)
outlier_scores = score(user_model, features, datadef.get("names"))
# TODO: check that predictions is 2 dimensional
request["meta"].setdefault("tags", {})
request["meta"]["tags"]["outlierScore"] = list(outlier_scores)
return jsonify(request)
def sanity_check_seldon_message_list(request):
if not "seldonMessages" in request:
raise SeldonMicroserviceException(
"Request must contain seldonMessages field")
msgs = request["seldonMessages"]
if not type(msgs) in [list, tuple]:
raise SeldonMicroserviceException("seldonMessages field is not a list")
if len(msgs) == 0:
raise SeldonMicroserviceException("seldonMessages field is empty")
for idx, msg in enumerate(msgs):
try:
sanity_check_request(msg)
except SeldonMicroserviceException as err:
raise SeldonMicroserviceException(
"Invalid SeldonMessage at index {0} : {1}".format(
idx, err.message))
def Predict():
request = extract_message()
logger.debug("Request: %s", request)
sanity_check_request(request)
if hasattr(user_model, "predict_rest"):
return jsonify(user_model.predict_rest(request))
else:
features = get_data_from_json(request)
names = request.get("data", {}).get("names")
meta = get_meta_from_json(request)
predictions = predict(user_model, features, names, meta=meta)
logger.debug("Predictions: %s", predictions)
# If predictions is an numpy array or we used the default data then return as numpy array
if isinstance(predictions, np.ndarray) or "data" in request:
predictions = np.array(predictions)
if len(predictions.shape) > 1:
class_names = get_class_names(user_model,
predictions.shape[1])
else:
class_names = []
data = array_to_rest_datadef(predictions, class_names,
request.get("data", {}))
response = {"data": data, "meta": {}}
else:
response = {"binData": predictions, "meta": {}}
tags = get_custom_tags(user_model)
if tags:
response["meta"]["tags"] = tags
metrics = get_custom_metrics(user_model)
if metrics:
response["meta"]["metrics"] = metrics
return jsonify(response)