def run_test(args):
from pybert.io.task_data import TaskData
from pybert.test.predictor import Predictor
data = TaskData()
targets, sentences = data.read_data(raw_data_path=config['test_path'],
preprocessor=EnglishPreProcessor(),
is_train=False)
lines = list(zip(sentences, targets))
processor = BertProcessor(vocab_path=config['bert_vocab_path'], do_lower_case=args.do_lower_case)
label_list = processor.get_labels()
id2label = {i: label for i, label in enumerate(label_list)}
test_data = processor.get_test(lines=lines)
test_examples = processor.create_examples(lines=test_data,
example_type='test',
cached_examples_file=config[
'data_dir'] / f"cached_test_examples_{args.arch}")
test_features = processor.create_features(examples=test_examples,
max_seq_len=args.eval_max_seq_len,
cached_features_file=config[
'data_dir'] / "cached_test_features_{}_{}".format(
args.eval_max_seq_len, args.arch
))
test_dataset = processor.create_dataset(test_features)
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=args.train_batch_size)
model = BertForMultiLable.from_pretrained(config['checkpoint_dir'], num_labels=len(label_list))
# ----------- predicting
logger.info('model predicting....')
predictor = Predictor(model=model,
logger=logger,
n_gpu=args.n_gpu)
result = predictor.predict(data=test_dataloader)
print(result)
def run_test(args):
from pybert.io.task_data import TaskData
from pybert.test.predictor import Predictor
data = TaskData()
ids, targets, sentences = data.read_data(
raw_data_path=config['test_path'],
preprocessor=ChinesePreProcessor(),
is_train=False)
lines = list(zip(sentences, targets))
#print(ids,sentences)
processor = BertProcessor(vocab_path=config['bert_vocab_path'],
do_lower_case=args.do_lower_case)
label_list = processor.get_labels()
id2label = {i: label for i, label in enumerate(label_list)}
test_data = processor.get_test(lines=lines)
test_examples = processor.create_examples(
lines=test_data,
example_type='test',
cached_examples_file=config['data_dir'] /
f"cached_test_examples_{args.arch}")
test_features = processor.create_features(
examples=test_examples,
max_seq_len=args.eval_max_seq_len,
cached_features_file=config['data_dir'] /
"cached_test_features_{}_{}".format(args.eval_max_seq_len, args.arch))
test_dataset = processor.create_dataset(test_features)
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset,
sampler=test_sampler,
batch_size=args.train_batch_size,
collate_fn=collate_fn)
model = BertForMultiLable.from_pretrained(config['checkpoint_dir'],
num_labels=len(label_list))
# ----------- predicting
logger.info('model predicting....')
predictor = Predictor(model=model, logger=logger, n_gpu=args.n_gpu)
result = predictor.predict(data=test_dataloader)
ids = np.array(ids)
df1 = pd.DataFrame(ids, index=None)
df2 = pd.DataFrame(result, index=None)
all_df = pd.concat([df1, df2], axis=1)
all_df.columns = ['id', 'sg', 'pj']
all_df['sg'] = all_df['sg'].apply(lambda x: 1 if x > 0.5 else 0)
all_df['pj'] = all_df['pj'].apply(lambda x: 1 if x > 0.5 else 0)
#all_df['qs'] = all_df['qs'].apply(lambda x: 1 if x>0.5 else 0)
#all_df['tz'] = all_df['tz'].apply(lambda x: 1 if x>0.5 else 0)
#all_df['ggjc'] = all_df['ggjc'].apply(lambda x: 1 if x>0.5 else 0)
#all_df.columns = ['id','zy','gfgqzr','qs','tz','ggjc']
#all_df['zy'] = all_df['zy'].apply(lambda x: 1 if x>0.5 else 0)
#all_df['gfgqzr'] = all_df['gfgqzr'].apply(lambda x: 1 if x>0.5 else 0)
#all_df['qs'] = all_df['qs'].apply(lambda x: 1 if x>0.5 else 0)
#all_df['tz'] = all_df['tz'].apply(lambda x: 1 if x>0.5 else 0)
#all_df['ggjc'] = all_df['ggjc'].apply(lambda x: 1 if x>0.5 else 0)
all_df.to_csv(
"/home/LAB/liqian/test/game/Fin/CCKS-Cls/test_output/cls_out.csv",
index=False)
def run_test(args):
from pybert.io.task_data import TaskData
from pybert.test.predictor import Predictor
data = TaskData()
# targets, sentences = data.read_data(raw_data_path=config['test_path'],
# preprocessor=EnglishPreProcessor(),
# is_train=False)
_, _, targets, sentences = data.read_data(config, raw_data_path=config['test_path'],
is_train=False)
lines = list(zip(sentences, targets))
# processor = BertProcessor(vocab_path=config['bert_vocab_path'], do_lower_case=args.do_lower_case)
processor = BertProcessor()
label_list = processor.get_labels()
id2label = {i: label for i, label in enumerate(label_list)}
test_data = processor.get_test(lines=lines)
test_examples = processor.create_examples(lines=test_data,
example_type='test',
cached_examples_file=config[
'data_dir'] / f"cached_test_examples_{args.arch}")
test_features = processor.create_features(examples=test_examples,
max_seq_len=args.eval_max_seq_len,
cached_features_file=config[
'data_dir'] / "cached_test_features_{}_{}".format(
args.eval_max_seq_len, args.arch
))
test_dataset = processor.create_dataset(test_features)
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=args.train_batch_size,
collate_fn=collate_fn)
model = BertForMultiLable.from_pretrained(config['checkpoint_dir'], num_labels=len(label_list))
# ----------- predicting
logger.info('model predicting....')
predictor = Predictor(model=model,
logger=logger,
n_gpu=args.n_gpu)
result = predictor.predict(data=test_dataloader)
result[result<0.5] = 0
result[result>=0.5] = 1
labels = []
for i in range(result.shape[0]):
ids = np.where(result[i]==1)[0]
each_patent_label = [id2label[id] for id in ids]
labels.append(each_patent_label)
if os.path.exists(config['predictions']):
os.remove(config['predictions'])
with open(config['test_path'], 'r') as f:
reader = csv.reader(f)
for j, line in enumerate(reader):
id = line[0]
with open(config['predictions'], 'a+') as g:
g.write("{}\t".format(id))
for label in labels[j]:
g.write("{}\t".format(label))
g.write("\n")
def run_test(args):
from pybert.test.predictor import Predictor
processor = BertProcessor(vocab_path=config['bert_vocab_path'],
do_lower_case=args.do_lower_case)
test_data = processor.get_test(config['test_path'])
test_examples = processor.create_examples(
lines=test_data,
example_type='test',
cached_examples_file=config['data_dir'] /
f"cached_test_examples_{args.arch}")
test_features = processor.create_features(
examples=test_examples,
max_seq_len=args.eval_max_seq_len,
cached_features_file=config['data_dir'] /
"cached_test_features_{}_{}".format(args.eval_max_seq_len, args.arch))
test_dataset = processor.create_dataset(test_features)
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset,
sampler=test_sampler,
batch_size=args.eval_batch_size)
idx2word = {}
for (w, i) in processor.tokenizer.vocab.items():
idx2word[i] = w
label_list = processor.get_labels(label_path=config['data_label_path'])
idx2label = {i: label for i, label in enumerate(label_list)}
if args.test_path:
args.test_path = Path(args.test_path)
model = BertForMultiLable.from_pretrained(args.test_path,
num_labels=len(label_list))
else:
model = BertForMultiLable.from_pretrained(config['bert_model_dir'],
num_labels=len(label_list))
for p in model.bert.parameters():
p.require_grad = False
# ----------- predicting -----------
writer = SummaryWriter()
logger.info('model predicting....')
predictor = Predictor(model=model,
logger=logger,
n_gpu=args.n_gpu,
i2w=idx2word,
i2l=idx2label)
result = predictor.predict(data=test_dataloader)
if args.predict_labels:
predictor.labels(result, args.predict_idx)
def run_test(args):
from pybert.io.task_data import TaskData
from pybert.test.predictor import Predictor
data = TaskData()
ids,targets, sentences = data.read_data(raw_data_path=config['test_path'],
preprocessor=None,
is_train=False)
lines = list(zip(sentences, targets))
processor = BertProcessor(vocab_path=config['bert_vocab_path'], do_lower_case=args.do_lower_case)
label_list = processor.get_labels()
id2label = {i: label for i, label in enumerate(label_list)}
test_data = processor.get_test(lines=lines)
test_examples = processor.create_examples(lines=test_data,
example_type='test',
cached_examples_file=config[
'data_dir'] / f"cached_test_examples_{args.arch}")
test_features = processor.create_features(examples=test_examples,
max_seq_len=args.eval_max_seq_len,
cached_features_file=config[
'data_dir'] / "cached_test_features_{}_{}".format(
args.eval_max_seq_len, args.arch
))
test_dataset = processor.create_dataset(test_features)
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=args.train_batch_size)
model = BertForMultiClass.from_pretrained(config['checkpoint_dir'], num_labels=len(label_list))
# ----------- predicting
logger.info('model predicting....')
predictor = Predictor(model=model, logger=logger, n_gpu=args.n_gpu)
result = predictor.predict(data=test_dataloader)
import numpy as np
result=np.argmax(result,axis=1)
with open('submit1.csv','w',encoding='utf-8') as f:
for id,pre in zip(ids,result):
f.write(id+','+str(pre)+'\n')
print(result)
def run_test(args, test=False, k=7, med_map='pybert/dataset/med_map.csv'):
from pybert.io.task_data import TaskData
from pybert.test.predictor import Predictor
data = TaskData()
targets, sentences = data.read_data(raw_data_path=config['test_path'],
preprocessor=EnglishPreProcessor(),
is_train=test)
print(
f'-----------------------------------------\ntargets {targets}\n---------------------------------------------------'
)
lines = list(zip(sentences, targets))
processor = BertProcessor(vocab_path=config['bert_vocab_path'],
do_lower_case=args.do_lower_case)
label_list = processor.get_labels()
id2label = {i: label for i, label in enumerate(label_list)}
test_data = processor.get_test(lines=lines)
test_examples = processor.create_examples(
lines=test_data,
example_type='test',
cached_examples_file=config['data_dir'] /
f"cached_test_examples_{args.arch}")
test_features = processor.create_features(
examples=test_examples,
max_seq_len=args.eval_max_seq_len,
cached_features_file=config['data_dir'] /
"cached_test_features_{}_{}".format(args.eval_max_seq_len, args.arch))
test_dataset = processor.create_dataset(test_features)
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset,
sampler=test_sampler,
batch_size=args.train_batch_size)
model = BertForMultiLable.from_pretrained(config['checkpoint_dir'])
# ----------- predicting
logger.info('model predicting....')
predictor = Predictor(model=model,
logger=logger,
n_gpu=args.n_gpu,
test=test)
if test:
results, targets = predictor.predict(data=test_dataloader)
#print(f'results {results.shape}')
#print(f'targets {targets.shape}')
result = dict()
metrics = [Recall(), Acc()]
for metric in metrics:
metric.reset()
metric(logits=results, target=targets)
value = metric.value()
if value is not None:
result[f'valid_{metric.name()}'] = value
return result
else:
results = predictor.predict(data=test_dataloader)
pred = np.argsort(results)[:, -k:][:, ::-1]
with open('pybert/dataset/med_map.csv', mode='r') as infile:
reader = csv.reader(infile)
med_dict = {int(rows[0]): rows[1] for rows in reader}
pred = np.vectorize(med_dict.get)(pred)
return pred
def run_test(args):
# TODO: 对训练集使用micro F1-score进行结果评测
from pybert.io.task_data import TaskData
from pybert.test.predictor import Predictor
data = TaskData()
ids, targets, sentences = data.read_data(
raw_data_path=config['test_path'],
preprocessor=ChinesePreProcessor(),
is_train=True) # 设置为True
lines = list(zip(sentences, targets))
#print(ids,sentences)
processor = BertProcessor(vocab_path=config['bert_vocab_path'],
do_lower_case=args.do_lower_case)
label_list = processor.get_labels(args.task_type)
id2label = {i: label for i, label in enumerate(label_list)}
test_data = processor.get_test(lines=lines)
test_examples = processor.create_examples(
lines=test_data,
example_type=f'test_{args.task_type}',
cached_examples_file=config['data_dir'] /
f"cached_test_{args.task_type}_examples_{args.arch}")
test_features = processor.create_features(
examples=test_examples,
max_seq_len=args.eval_max_seq_len,
cached_features_file=config['data_dir'] /
"cached_test_{}_features_{}_{}".format(
args.task_type, args.eval_max_seq_len, args.arch))
test_dataset = processor.create_dataset(test_features)
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset,
sampler=test_sampler,
batch_size=args.train_batch_size,
collate_fn=collate_fn)
model = None
if args.task_type == 'base':
model = BertForMultiLable.from_pretrained(config['checkpoint_dir'],
num_labels=len(label_list))
else:
# model = BertForMultiLable.from_pretrained(config['checkpoint_dir'], num_labels=len(label_list))
model = BertForMultiLable_Fewshot.from_pretrained(
config['checkpoint_dir'], num_labels=len(label_list))
# ----------- predicting
logger.info('model predicting....')
predictor = Predictor(model=model, logger=logger, n_gpu=args.n_gpu)
result = predictor.predict(data=test_dataloader) # 感觉这个变量名叫all_logits可能更好
# TODO: 计算F1-score，这个功能模块需要用代码测试一下~
f1_metric = F1Score(task_type='binary',
average='micro',
search_thresh=True)
all_logits = torch.tensor(result, dtype=torch.float) # 转换成tensor
all_labels = torch.tensor(targets, dtype=torch.long) # 转换成tensor
f1_metric(all_logits, all_labels) # 会自动打印结果
print(f1_metric.value())
# 将结果写入一个文件之中
with open('test_output/test.log', 'a+') as f:
f.write(str(f1_metric.value()) + "\n")
thresh = f1_metric.thresh
ids = np.array(ids)
df1 = pd.DataFrame(ids, index=None)
df2 = pd.DataFrame(result, index=None)
all_df = pd.concat([df1, df2], axis=1)
if args.task_type == 'base':
all_df.columns = ['id', 'zy', 'gfgqzr', 'qs', 'tz', 'ggjc']
else:
all_df.columns = ['id', 'sg', 'pj', 'zb', 'qsht', 'db']
for column in all_df.columns[1:]:
all_df[column] = all_df[column].apply(lambda x: 1 if x > thresh else 0)
# all_df['zy'] = all_df['zy'].apply(lambda x: 1 if x>thresh else 0)
# all_df['gfgqzr'] = all_df['gfgqzr'].apply(lambda x: 1 if x>thresh else 0)
# all_df['qs'] = all_df['qs'].apply(lambda x: 1 if x>thresh else 0)
# all_df['tz'] = all_df['tz'].apply(lambda x: 1 if x>thresh else 0)
# all_df['ggjc'] = all_df['ggjc'].apply(lambda x: 1 if x>thresh else 0)
all_df.to_csv(f"test_output/{args.task_type}/cls_out.csv", index=False)
