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fl_client_shed.py
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fl_client_shed.py
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import sys
import logging
import gc
arg_names = [
'client_id',
'path_weights',
'path_nodes',
'path_edges',
'graph_id',
'partition_ids',
'epochs',
'IP',
'PORT'
]
args = dict(zip(arg_names, sys.argv[1:]))
client_id = args['client_id']
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s : [%(levelname)s] %(message)s',
handlers=[
logging.FileHandler(f'client_shed_{client_id}.log'),
logging.StreamHandler(sys.stdout)
]
)
import socket
import pickle
import select
import numpy as np
import pandas as pd
from timeit import default_timer as timer
import time
from models.supervised import Model
class Client:
def __init__(self, client_id, weights_path, graph_id, partition_ids, epochs = 10, IP = socket.gethostname(), PORT = 5000, HEADER_LENGTH = 10):
self.HEADER_LENGTH = HEADER_LENGTH
self.IP = IP
self.PORT = PORT
self.client_id = client_id
self.weights_path = weights_path
self.graph_id = graph_id
self.partition_ids = partition_ids
self.epochs = epochs
self.client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
connected = False
while not connected:
try:
self.client_socket.connect((IP, PORT))
except ConnectionRefusedError:
time.sleep(5)
else:
logging.info('Connected to the server')
connected = True
self.GLOBAL_MODEL = None
self.MODEL = None
self.LOCAL_MODELS = []
self.partition_sizes = []
self.STOP_FLAG = False
self.rounds = 0
def send_models(self):
data = {"CLIENT_ID":self.client_id,"PARTITIONS":self.partition_ids,"PARTITION_SIEZES":self.partition_sizes,"WEIGHTS":self.LOCAL_MODELS}
data = pickle.dumps(data)
data = bytes(f"{len(data):<{self.HEADER_LENGTH}}", 'utf-8') + data
self.client_socket.sendall(data)
self.LOCAL_MODELS = []
self.partition_sizes = []
def fetch_model(self):
message_header = self.client_socket.recv(self.HEADER_LENGTH)
if not len(message_header):
return False
message_length = int(message_header.decode('utf-8').strip())
full_msg = b''
while True:
msg = self.client_socket.recv(message_length)
full_msg += msg
if len(full_msg) == message_length:
break
data = pickle.loads(full_msg)
self.STOP_FLAG = data["STOP_FLAG"]
self.GLOBAL_MODEL = data["WEIGHTS"]
return True
def run(self):
while not self.STOP_FLAG:
read_sockets, _, exception_sockets = select.select([self.client_socket], [], [self.client_socket])
success = False
for soc in read_sockets:
success = self.fetch_model()
if success:
self.rounds += 1
logging.info('Global model v%s fetched',self.rounds - 1)
else:
logging.error('Global model fetching failed')
if not success:
logging.error('Stop training')
break
if self.STOP_FLAG:
self.MODEL.set_weights(self.GLOBAL_MODEL)
logging.info('_____________________________________________________ Final model evalution ____________________________________________________________')
for partition in self.partition_ids:
logging.info('********************************************************* Partition - %s ******************************************************',partition)
eval = self.MODEL.evaluate()
f1_train = (2 * eval[0][2] * eval[0][4]) / (eval[0][2] + eval[0][4])
f1_test = (2 * eval[1][2] * eval[1][4]) / (eval[1][2] + eval[1][4])
logging.info('Final model (v%s) fetched',self.rounds)
logging.info('Training set : accuracy - %s, recall - %s, AUC - %s, F1 - %s, precision - %s',eval[0][1],eval[0][2],eval[0][3],f1_train,eval[0][4])
logging.info('Testing set : accuracy - %s, recall - %s, AUC - %s, F1 - %s, precision - %s',eval[1][1],eval[1][2],eval[1][3],f1_test,eval[1][4])
else:
logging.info('_____________________________________________________ Training Round %s ____________________________________________________________',self.rounds)
for partition in self.partition_ids:
logging.info('********************************************************* Partition - %s ******************************************************',partition)
path_nodes = args['path_nodes'] + args['graph_id'] + '_nodes_' + partition + ".csv"
nodes = pd.read_csv(path_nodes,index_col=0)
nodes = nodes.astype("uint8")
path_edges = args['path_edges'] + args['graph_id'] + '_edges_' + partition + ".csv"
edges = pd.read_csv(path_edges)
edges = edges.astype({"source":"uint32","target":"uint32"})
logging.info('Model initialized')
self.MODEL = Model(nodes,edges)
num_train_ex,num_test_ex = self.MODEL.initialize()
self.partition_sizes.append(num_train_ex)
self.MODEL.set_weights(self.GLOBAL_MODEL)
logging.info('Number of training examples - %s, Number of testing examples %s',num_train_ex,num_test_ex)
eval = self.MODEL.evaluate()
f1_train = (2 * eval[0][2] * eval[0][4]) / (eval[0][2] + eval[0][4])
f1_test = (2 * eval[1][2] * eval[1][4]) / (eval[1][2] + eval[1][4])
logging.info('Global model v%s - Training set evaluation : accuracy - %s, recall - %s, AUC - %s, F1 - %s, precision - %s',self.rounds - 1,eval[0][1],eval[0][2],eval[0][3],f1_train,eval[0][4])
logging.info('Global model v%s - Testing set evaluation : accuracy - %s, recall - %s, AUC - %s, F1 - %s, precision - %s',self.rounds - 1,eval[1][1],eval[1][2],eval[1][3],f1_test,eval[1][4])
logging.info('Training started')
self.MODEL.fit(epochs = self.epochs)
self.LOCAL_MODELS.append(np.array(self.MODEL.get_weights()))
logging.info('Training done')
del self.MODEL
del nodes
del edges
gc.collect()
# eval = self.MODEL.evaluate()
# f1_train = (2 * eval[0][2] * eval[0][4]) / (eval[0][2] + eval[0][4])
# f1_test = (2 * eval[1][2] * eval[1][4]) / (eval[1][2] + eval[1][4])
# logging.info('After Round %s - Local model - Training set evaluation : accuracy - %s, recall - %s, AUC - %s, F1 - %s, precision - %s',self.rounds,eval[0][1],eval[0][2],eval[0][3],f1_train,eval[0][4])
# logging.info('After Round %s - Local model - Testing set evaluation : accuracy - %s, recall - %s, AUC - %s, F1 - %s, precision - %s',self.rounds,eval[1][1],eval[1][2],eval[1][3],f1_test,eval[1][4])
logging.info('********************************************* All partitions trained **********************************************')
logging.info('Sent local models to the aggregator')
self.send_models()
if __name__ == "__main__":
if 'IP' not in args.keys() or args['IP'] == 'localhost':
args['IP'] = socket.gethostname()
if 'PORT' not in args.keys():
args['PORT'] = 5000
if 'epochs' not in args.keys():
args['epoch'] = 10
logging.warning('####################################### New Training Session #######################################')
logging.info('Client started, graph ID %s, partition IDs %s , epochs %s',args['graph_id'],args['partition_ids'],args['epochs'])
client = Client(args['client_id'],weights_path=args['path_weights'],graph_id=args['graph_id'],partition_ids=args['partition_ids'].split(","),epochs = int(args['epochs']) ,IP=args['IP'],PORT=int(args['PORT']))
logging.info('Federated training started!')
start = timer()
client.run()
end = timer()
elapsed_time = end -start
logging.info('Federated training done!')
logging.info('Training report : Elapsed time %s seconds, graph ID %s, partition IDs %s, epochs %s',elapsed_time,args['graph_id'],args['partition_ids'],args['epochs'])