def Predict(): request = extract_message() sanity_check_request(request) features = get_data_from_json(request) names = request.get("data",{}).get("names") predictions = predict(user_model,features,names) # 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)
def TransformInput(): request = extract_message() sanity_check_request(request) features = get_data_from_json(request) names = request.get("data", {}).get("names") transformed = transform_input(user_model, features, names) # 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() if debug: print("SELDON DEBUGGING") print("Request received: ") print(request) sanity_check_request(request) 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 Predict(): request = extract_message() sanity_check_request(request) datadef = request.get("data") features = rest_datadef_to_array(datadef) predictions = np.array(predict(user_model,features,datadef.get("names"))) # TODO: check that predictions is 2 dimensional class_names = get_class_names(user_model, predictions.shape[1]) data = array_to_rest_datadef(predictions, class_names, datadef) return jsonify({"data":data})
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 TransformOutput(): request = extract_message() sanity_check_request(request) datadef = request.get("data") features = rest_datadef_to_array(datadef) transformed = np.array(transform_output(user_model,features,datadef.get("names"))) # TODO: check that predictions is 2 dimensional new_class_names = get_class_names(user_model, datadef.get("names")) data = array_to_rest_datadef(transformed, new_class_names, datadef) return jsonify({"data":data})
def Route(): request = extract_message() sanity_check_request(request) 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) return jsonify({"data":data})
def Predict(): request = extract_message() sanity_check_request(request) datadef = request.get("data") features = rest_datadef_to_array(datadef) predictions = np.array( predict(user_model, features, datadef.get("names"))) 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, datadef) return jsonify({"data": data})
def Predict(): request = extract_message() sanity_check_request(request) datadef = request.get("data") features = rest_datadef_to_array(datadef) predictions = np.array(predict(user_model,features,datadef.get("names"))) 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, datadef) response = {"data":data,"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 TransformOutput(): request = extract_message() sanity_check_request(request) features = get_data_from_json(request) names = request.get("data", {}).get("names") transformed = transform_output(user_model, features, names) 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 Predict(): request = extract_message() sanity_check_request(request) datadef = request.get("data") return jsonify(predict(user_model, request, datadef.get("names")))