class StompListener(object):
def __init__(self, config):
self.config_ = config
self.logger_ = logging.getLogger("main")
self.extractor = EsaFeatureExtractor(prefix = config['prefix'])
def __extract_features(self, message):
'''
Extracts features from clean content and sends it on
'''
self.logger_.debug("Got article '%s'" % message['headline'])
features = self.extractor.get_features(message['clean_content'])
version = self.extractor.get_version()
#add features to json representation of article
message['features'] = {'version': version,
'data': features}
#send message on to Article Ranker
try:
self.conn_.send(json.dumps(message), destination="queue/features")
except Exception as inst:
self.logger_.error("Could not send message to feature queue. "
"Unknown Error %s: %s" % (type(inst), inst))
def on_error(self, hears, message):
self.logger_ .error('received an error %s' % message)
def on_message(self, headers, message):
received_message = json.loads(message)
self.__extract_features(received_message)
def set_stomp_connection(self, connection):
self.conn_ = connection
def __init__(self, config):
self.config_ = config
self.logger_ = logging.getLogger("main")
self.extractor = EsaFeatureExtractor(prefix = config['prefix'])
logger.info("Load config from %s" % options.config)
config = load_config(options.config, logger, exit_with_error = True)
#Connect to mongo database
try:
connect(config['database']['db-name'],
username= config['database']['user'],
password= config['database']['passwd'],
port = config['database']['port'])
except connection.ConnectionError as e:
logger.error("Could not connect to mongodb: %s" % e)
sys.exit(1)
feature_extractor = EsaFeatureExtractor(prefix = config['prefix'] )
#go through each article and convert features
count = 0
for article in Article.objects(features__version__ne = feature_extractor.get_version()):
if count % 10 == 0:
logger.info("PROGRESS: processing article #%d" % count)
count += 1
if article.features.version == EsaFeatureExtractor.get_version():
continue
clean_content = article.clean_content
#get new features
new_features = feature_extractor.get_features(clean_content)
