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# Copyright 2022-2023 ETSI TeraFlowSDN - TFS OSG (https://tfs.etsi.org/)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import grpc
from sklearn.cluster import DBSCAN
from common.method_wrappers.Decorator import (MetricsPool,
safe_and_metered_rpc_method)
from common.proto.dbscanserving_pb2 import DetectionRequest, DetectionResponse
from common.proto.dbscanserving_pb2_grpc import DetectorServicer
LOGGER = logging.getLogger(__name__)
METRICS_POOL = MetricsPool("DBSCANServing", "RPC")
class DbscanServiceServicerImpl(DetectorServicer):
def __init__(self):
LOGGER.debug("Creating Servicer...")
LOGGER.debug("Servicer Created")
def Detect(
self, request: DetectionRequest, context: grpc.ServicerContext
) -> DetectionResponse:
if request.num_samples != len(request.samples):
context.set_details(
f"The sample dimension declared ({request.num_samples}) does not match with the number of samples received ({len(request.samples)})."
)
LOGGER.debug(
f"The sample dimension declared does not match with the number of samples received. Declared: {request.num_samples} - Received: {len(request.samples)}"
)
context.set_code(grpc.StatusCode.INVALID_ARGUMENT)
return DetectionResponse()
# TODO: implement the validation of the features dimension
clusters = DBSCAN(eps=request.eps, min_samples=request.min_samples).fit_predict(
[[x for x in sample.features] for sample in request.samples]
)
response = DetectionResponse()
for cluster in clusters:
response.cluster_indices.append(cluster)
return response