def train(args):
if args.verbosity == 0:
logging.basicConfig(level=logging.WARNING)
elif args.verbosity == 1:
logging.basicConfig(level=logging.INFO)
else:
logging.basicConfig(level=logging.DEBUG)
assert args.role in ['leader', 'follower', 'local'], \
"role must be leader, follower, or local"
assert args.mode in ['train', 'test', 'eval'], \
"mode must be train, test, or eval"
if args.data_path.endswith('.csv'):
with open(args.data_path, 'rb') as fin:
data = np.loadtxt(fin, delimiter=',')
if args.mode == 'train' or args.mode == 'test':
if args.role == 'leader' or args.role == 'local':
X = data[:, :-1]
y = data[:, -1]
else:
X = data
y = None
else: # eval
X = data
y = None
else:
raise ValueError("Unsupported data type %s" % args.data_path)
if args.role != 'local':
bridge = Bridge(args.role, int(args.local_addr.split(':')[1]),
args.peer_addr, args.application_id, 0)
else:
bridge = None
booster = BoostingTreeEnsamble(bridge,
learning_rate=args.learning_rate,
max_iters=args.max_iters,
max_depth=args.max_depth,
l2_regularization=args.l2_regularization,
max_bins=args.max_bins,
num_parallel=args.num_parallel)
if args.load_model_path:
booster.load_saved_model(args.load_model_path)
if args.mode == 'train':
booster.fit(X, y, args.checkpoint_path)
elif args.mode == 'test':
pred = booster.batch_predict(X)
acc = sum((pred > 0.5) == y) / len(y)
logging.info("Test accuracy: %f", acc)
else:
pred = booster.batch_predict(X)
for i in pred:
print(i)
if args.export_path:
booster.save_model(args.export_path)
def run(args):
if args.verbosity == 0:
logging.basicConfig(level=logging.WARNING)
elif args.verbosity == 1:
logging.basicConfig(level=logging.INFO)
else:
logging.basicConfig(level=logging.DEBUG)
assert args.role in ['leader', 'follower', 'local'], \
"role must be leader, follower, or local"
assert args.mode in ['train', 'test', 'eval'], \
"mode must be train, test, or eval"
#follower或leader
if args.role != 'local':
bridge = Bridge(args.role, int(args.local_addr.split(':')[1]),
args.peer_addr, args.application_id, 0,
streaming_mode=args.use_streaming)
else:
bridge = None
try:
#boost
booster = BoostingTreeEnsamble(
bridge,
learning_rate=args.learning_rate,
max_iters=args.max_iters,
max_depth=args.max_depth,
l2_regularization=args.l2_regularization,
max_bins=args.max_bins,
num_parallel=args.num_parallel,
loss_type=args.loss_type,
send_scores_to_follower=args.send_scores_to_follower,
send_metrics_to_follower=args.send_metrics_to_follower)
#加载已存储的模型
if args.load_model_path:
booster.load_saved_model(args.load_model_path)
#训练不需要bridge，为什么呢
if args.mode == 'train':
train(args, booster)
#测试，评估模型需要bridge
else: # args.mode == 'test, eval'
test(args, bridge, booster)
#把模型存起来
if args.export_path:
booster.save_model(args.export_path)
except Exception as e:
logging.fatal(
'Exception raised during training: %s',
traceback.format_exc())
raise e
finally:
#结束bridge
if bridge:
bridge.terminate()