def __init__(self, bot):
threading.Thread.__init__(self)
self.db = MongoConnector()
self.db.connect(cp['DATABASE']['Address'], cp['DATABASE']['Name'])
self.collection = cp['DATABASE']['MonitoringCollection']
self.telegram_bot = bot
self.running = True
print('Reward crawler started')
def main():
tornado.options.options['logging'] = "INFO"
tornado.options.parse_command_line()
io_loop = tornado.ioloop.IOLoop.instance()
app = make_app()
# MongoConnector is our mongodb connector
mongo_connector = MongoConnector('test_database',
mongo_address=settings.MONGO_ADDRESS)
app.mongo_connector = mongo_connector
# PikaConnector is our rabbitmq consumer
app.pika_connector = PikaConnector(
io_loop, mongo_connector, rabbitmq_address=settings.RABBITMQ_ADDRESS)
app.pika_connector.run()
try:
app.listen(8888)
io_loop.start()
except KeyboardInterrupt:
app.pika_connector.stop()
import time
import logging
import datetime
import re
# Enable logging
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
level=logging.INFO)
logger = logging.getLogger(__name__)
cp = ConfigParser()
cp.optionxform = str
cp.read('../config.ini')
db = MongoConnector()
db.connect(cp['DATABASE']['Address'], cp['DATABASE']['Name'])
blockchain = BlockchainConnector()
blockchain.connect(cp['POSTGRES'])
monitoring_collection = cp['DATABASE']['MonitoringCollection']
CRAWLER_SLEEP_TIME = 120
NEW_TRANSACTION_MESSAGE_TEMPLATE = 'New transaction for "{}" ({}): {} XSN'
telegram_bot_token = cp['TELEGRAM']['SecretKey']
DATE_FORMAT = '%d/%m/%Y %H:%M:%S'
ADD_ADDRESS_MESSAGE = 'Enter address for '
ADD_NAME_MESSAGE = 'Enter monitor name'
classifications = clf.predict_proba(transformed)
#print("classifications", classifications)
sorted_prob_themes = sorted(
[(prob, theme_nr)
for prob, theme_nr in zip(classifications[0], classes)],
reverse=True)
sorted_themes = [
int(theme_nr) for (prob, theme_nr) in sorted_prob_themes
]
print("Used logistic regression classification")
sorted_themes_using_topic_connection = self.rank_according_to_topic_connection(
document_id, sorted_themes, potential_theme_dict)
for theme in all_theme_nrs: # themes that have no associate documents or description, and therefore aren't classifier ranked, are ranked as last
if theme not in sorted_themes_using_topic_connection:
sorted_themes_using_topic_connection.append(theme)
themes_str = [
str(theme) for theme in sorted_themes_using_topic_connection
]
return themes_str
if __name__ == '__main__':
mc = MongoConnector()
ts = ThemeSorter(mc)
ts.retrain_model("61ea6c0301c7c1346b1ff9f4")
print(ts.rank_themes_for_document("61ea6c0301c7c1346b1ff9f4", "14"))
def get(self):
global mongoConnector
word_document = mongoConnector.getDocumentByWord(
request.args.get("word")[1:-1])
return word_document.guessNextWord()
class Ping(Resource):
def get(self):
return "Alive"
if __name__ == "__main__":
if len(sys.argv) != 5:
print("Usage: problem3.py <host> <port> <database> <collection>")
sys.exit(-1)
mongoConnector = MongoConnector(sys.argv[1], int(sys.argv[2]), sys.argv[3],
sys.argv[4])
app = Flask(__name__)
api = Api(app)
api.add_resource(Ping, '/')
api.add_resource(Next, '/gutenberg/predict/next/')
api.add_resource(Guess, '/gutenberg/predict/random/')
app.run(debug=True)
import topics2themes.make_topic_models as make_topic_models
from topics2themes.mongo_connector import MongoConnector
from topics2themes.theme_sorter import ThemeSorter
from topics2themes.environment_configuration import *
from topics2themes.topic_model_constants import *
app = Flask(__name__, template_folder="user_interface")
if RUN_LOCALLY:
CORS(app)
else:
app.config['MONGO_CONNECT'] = False
try:
mongo_con = MongoConnector()
except:
e = sys.exc_info()
print("The following error occurred: ")
print(e)
print("The pymongo database might not be running")
mongo_con = MongoConnector()
exit(1)
theme_sort = ThemeSorter(mongo_con)
# To not have a lot of space in the output
app.config['JSONIFY_PRETTYPRINT_REGULAR'] = False
def get_more_exception_info():
trace_back = traceback.format_exc()