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from collections import OrderedDict,defaultdict,Counter
from collections.abc import Sequence
import math
import numpy as np
import os
import pdb
import random
import torch
import ujson as json
from ...utils import xtqdm as tqdm
from ...utils import get_logger
from ..models import NERModel
from ...data import ExampleInstance, ExampleSet, DynamicDataset
from ...data.example import *
from ...data.example import _truncate_segments
from .task import EvalData, Task
from .task_registry import register_task
from seqeval import metrics as seq_metrics
__all__ = ['NERTask']
logger = get_logger()
@register_task(name="NER", desc="Named-entity recognition task")
class NERTask(Task):
def __init__(self, data_dir, tokenizer, args, **kwargs):
super().__init__(tokenizer, args, **kwargs)
self.data_dir = data_dir
def train_data(self, max_seq_len=512, dataset_size=None, epochs=1, mask_gen=None, **kwargs):
train = self.load_data(os.path.join(self.data_dir, 'train.txt'), max_seq_len=max_seq_len)
examples = ExampleSet(train)
if dataset_size is None:
dataset_size = len(examples)*epochs
return DynamicDataset(examples, feature_fn = self.get_feature_fn(max_seq_len=max_seq_len, mask_gen=mask_gen), \
dataset_size = dataset_size, shuffle=True, **kwargs)
def eval_data(self, max_seq_len=512, dataset_size=None, **kwargs):
ds = [
self._data('dev', 'valid.txt', 'dev', max_seq_len=max_seq_len),
self._data('test', 'test.txt', 'test', max_seq_len=max_seq_len)
]
for d in ds:
if dataset_size is None:
_size = len(d.data)
d.data = DynamicDataset(d.data, feature_fn = self.get_feature_fn(max_seq_len=max_seq_len), dataset_size = _size, **kwargs)
return ds
def test_data(self,max_seq_len=512, dataset_size = None, **kwargs):
"""See base class."""
ds = [
self._data('test', 'test.txt', 'test', max_seq_len=max_seq_len)
]
for d in ds:
if dataset_size is None:
_size = len(d.data)
d.data = DynamicDataset(d.data, feature_fn = self.get_feature_fn(max_seq_len=max_seq_len), dataset_size = _size, **kwargs)
return ds
def _data(self, name, path, type_name = 'dev', ignore_metric=False, max_examples=None, shuffle=False, max_seq_len=512):
input_src = os.path.join(self.data_dir, path)
assert os.path.exists(input_src), f"{input_src} doesn't exists"
data = self.load_data(input_src, max_seq_len=max_seq_len, max_examples=max_examples, shuffle=shuffle)
examples = ExampleSet(data)
predict_fn = self.get_predict_fn(examples)
metrics_fn = self.get_metrics_fn()
return EvalData(name, examples,
metrics_fn = metrics_fn, predict_fn = predict_fn, ignore_metric=ignore_metric, critial_metrics=['f1'])
def get_metrics_fn(self):
"""Calcuate metrics based on prediction results"""
def metrics_fn(logits, labels):
preds = np.argmax(logits, axis=-1)
label_names = self.get_labels()
y_true = []
y_pred = []
for pred,label in zip(preds, labels):
y_true.append([label_names[l] for l in label if l>=0])
y_pred.append([label_names[p] for p,l in zip(pred, label) if l>=0])
return OrderedDict(
accuracy = seq_metrics.accuracy_score(y_true, y_pred),
f1 = seq_metrics.f1_score(y_true, y_pred),
precision = seq_metrics.precision_score(y_true, y_pred),
recall = seq_metrics.recall_score(y_true, y_pred)
)
return metrics_fn
def get_predict_fn(self, examples):
"""Calcuate metrics based on prediction results"""
def predict_fn(logits, output_dir, name, prefix):
output=os.path.join(output_dir, 'submit-{}-{}.tsv'.format(name, prefix))
preds = np.argmax(logits, axis=-1)
labels = self.get_labels()
with open(output, 'w', encoding='utf-8') as fs:
fs.write('index\tpredictions\n')
for i,(e,p) in enumerate(zip(examples,preds)):
words = ''.join(e.sentence).split(' ')
tokens = e.segments[0]
bw = 0
for w,t in zip(words,tokens):
fs.write(f'{w} {labels[p[bw]]}\n')
bw += len(t)
fs.write('\n')
return predict_fn
def get_feature_fn(self, max_seq_len = 512, mask_gen = None):
def _example_to_feature(example, rng=None, ext_params=None, **kwargs):
return self.example_to_feature(self.tokenizer, example, max_seq_len = max_seq_len, \
rng = rng, mask_generator = mask_gen, ext_params = ext_params, **kwargs)
return _example_to_feature
def get_model_class_fn(self):
def partial_class(*wargs, **kwargs):
return NERModel.load_model(*wargs, **kwargs)
return partial_class
def get_labels(self):
"""See base class."""
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
def load_data(self, path, max_seq_len=512, max_examples=None, shuffle=False):
docs = self.extract_docs(path)
examples=[]
for doc in docs:
merged_words = []
merged_tokens = []
merged_labels = []
size = 0
for sent in doc:
words = [t[0] if i==0 else (' ' + t[0]) for i,t in enumerate(sent)]
labels = [t[1] for t in sent]
tokens = [self.tokenizer.tokenize(w) for w in words]
l = sum(len(t) for t in tokens)
if size+l > max_seq_len-2:
examples.append(ExampleInstance(segments=[merged_tokens], label=merged_labels, sentence=merged_words))
size = 0
merged_words = []
merged_tokens = []
merged_labels = []
size += l
merged_words.extend(words)
merged_tokens.extend(tokens)
merged_labels.extend(labels)
if size>0:
examples.append(ExampleInstance(segments=[merged_tokens], label=merged_labels, sentence=merged_words))
def get_stats(l):
return f'Max={max(l)}, min={min(l)}, avg={np.mean(l)}'
ctx_token_size = [sum(len(w) for w in e.segments[0]) for e in examples]
logger.info(f'Statistics: {get_stats(ctx_token_size)}, long={len([t for t in ctx_token_size if t > 500])}/{len(ctx_token_size)}')
return examples
def example_to_feature(self, tokenizer, example, max_seq_len=512, rng=None, mask_generator = None, ext_params=None, label_type='int', **kwargs):
if not rng:
rng = random
max_num_tokens = max_seq_len - 2
features = OrderedDict()
tokens = ['[CLS]']
target_labels = [-1]
type_ids = [0]
for i,w in enumerate(example.segments[0]):
tokens.extend(w)
type_ids.extend([0]*len(w))
if example.label is not None:
target_labels.append(self.label2id(example.label[i]))
target_labels.extend([-1]*(len(w)-1))
tokens.append('[SEP]')
if example.label is not None:
target_labels.extend([-1]*(max_seq_len-len(target_labels)))
type_ids.append(0)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
pos_ids = list(range(len(token_ids)))
input_mask = [1]*len(token_ids)
features['input_ids'] = token_ids
features['type_ids'] = type_ids
features['position_ids'] = pos_ids
features['input_mask'] = input_mask
padding_size = max(0, max_seq_len - len(token_ids))
for f in features:
features[f].extend([0]*padding_size)
for f in features:
features[f] = torch.tensor(features[f], dtype=torch.int)
if example.label is not None: # and example.label[0]>=0 and example.label[1]>=0:
features['labels'] = torch.tensor(target_labels, dtype=torch.int)
return features
def extract_docs(self, path):
docs = []
with open(path, 'r', encoding='utf-8') as fs:
doc = []
sent = []
for line in fs:
if line.startswith('-DOCSTART- '):
if len(sent) > 0:
doc.append(sent)
sent = []
if len(doc) > 0:
docs.append(doc)
doc = []
elif line.strip() == '':
if len(sent) > 0:
doc.append(sent)
sent = []
else:
tabs = line.split(' ')
sent.append([tabs[0], tabs[-1].strip()])
if len(sent) > 0:
doc.append(sent)
sent = []
if len(doc) > 0:
docs.append(doc)
doc = []
logger.info(f'Loaded {len(docs)} docs, {sum([len(d) for d in docs])} sentences.')
return docs
def test_ner_load_data():
tokenizer = GPT2Tokenizer()
data='/mount/biglm/bert/NER/data/train.txt'
task = NERTask(os.path.dirname(data), tokenizer)
#docs = task.extract_docs(data)
examples = task.load_data(data)
feature = task.example_to_feature(tokenizer, examples[0], max_seq_len=512)
pdb.set_trace()
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