def main():
opt = parser.parse_args()
opt = change_args(opt)
np.save("params.npy", opt, allow_pickle=True)
logging.basicConfig(filename=os.path.join(opt.save_dir, 'output.log')
if opt.logfolder else None,
level=logging.INFO)
unk_model = train_char_model(opt) if (opt.input in ['hybrid', 'spelling'
]) else None
if (opt.input == 'spelling'): exit()
train_data, valid_data, test_data, vocab, mappings = lib.data.create_datasets(
opt)
model, optim = lib.model.create_model((vocab['src'], vocab['tgt']), opt)
evaluator = lib.train.Evaluator(model, opt, unk_model)
test_evaluator = lib.train.Evaluator(model, opt, unk_model)
logger.info(model.opt)
logger.info('Loading test data from "%s"' % opt.testdata)
logger.info('Loading training data from "%s"' % opt.traindata)
logger.info(' * Vocabulary size. source = %d; target = %d' %
(len(vocab['src']), len(vocab['tgt'])))
logger.info(' * Maximum batch size. %d' % opt.batch_size)
logger.info(model)
if opt.interactive:
while True:
# var = input("Please enter the text to be normalized (q to quit): ")
var = opt.noisy_text
if var.lower() == 'q': break
tweets = [
Tweet(var.split(), var.split(), '1', '1') for i in range(2)
] # suboptimal but works with minimal changes
test_data, test_vocab, mappings = create_data(tweets,
opt=opt,
vocab=vocab,
mappings=mappings)
prediction = test_evaluator.eval(test_data)
print('Prediction is: {}'.format(' '.join(prediction)))
elif opt.eval: # Evaluation only
logger.info("=======Eval on test set=============")
pred_file = os.path.join(opt.save_dir, 'test.pred')
test_evaluator.eval(test_data, pred_file=pred_file)
logger.info("=======Eval on validation set=============")
pred_file = os.path.join(opt.save_dir, 'valid.pred')
evaluator.eval(valid_data, pred_file=pred_file)
else: # Training
trainer = lib.train.Trainer(model, evaluator, train_data, valid_data,
optim, opt)
trainer.train(opt.start_epoch, opt.end_epoch)
logger.info("=======Eval on test set=============")
pred_file = os.path.join(opt.save_dir, 'test.pred')
test_evaluator.eval(test_data, pred_file=pred_file)
logger.info("=======Eval on validation set=============")
pred_file = os.path.join(opt.save_dir, 'valid.pred')
evaluator.eval(valid_data, pred_file=pred_file)
import classifier
from classifier import loop_dataset
import subgraphs
import argparse
import torch
import torch.optim as optim
import networkx as nx
import until
import random
from tqdm import tqdm
#导入配置参数
from parameters import parser, cmd_embed, cmd_opt
#参数转换
args = parser.parse_args()
args.cuda = not args.noCuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.hop != 'auto':
args.hop = int(args.hop)
if args.maxNodesPerHop is not None:
args.maxNodesPerHop = int(args.maxNodesPerHop)
#读取数据
g = graph.Graph()
g.read_edgelist(filename=args.dataName,
weighted=args.weighted,
directed=args.directed)
g.read_node_status(filename=args.labelName)