add ner models

utils/file_util.py

@@ -4,6 +4,9 @@
 import pickle
 import sys
 
+logger = logging.getLogger()
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+
 
 def pickle_dump_large_file(obj, filepath):
     max_bytes = 2 ** 31 - 1
@@ -44,3 +47,56 @@ def write_json(obj, path):
     with open(path, 'wb') as f:
         f.write(json.dumps(obj, indent=2, ensure_ascii=False).
                 encode('utf-8'))
+
+
+def _read_conll(path, encoding='utf-8', indexes=2, dropna=True):
+    """
+    Construct a generator to read conll items.
+
+    :param path: file path
+    :param encoding: file's encoding, default: utf-8
+    :param indexes: conll object's column indexes that needed, if None, all columns are needed. default: None
+    :param dropna: weather to ignore and drop invalid data,
+            :if False, raise ValueError when reading invalid data. default: True
+    :return: generator, every time yield (line number, conll item)
+    """
+
+    def parse_conll(sample):
+        sample = list(map(list, zip(*sample)))
+        sample = [sample[i] for i in range(indexes)]
+        for f in sample:
+            if len(f) <= 0:
+                raise ValueError('empty field')
+        return sample
+
+    with open(path, 'r', encoding=encoding) as f:
+        sample = []
+        start = next(f).strip()
+        if start != '':
+            sample.append(start.split())
+        for line_idx, line in enumerate(f, 1):
+            line = line[:-1]
+            if line == '':
+                if len(sample):
+                    try:
+                        res = parse_conll(sample)
+                        sample = []
+                        yield line_idx, res
+                    except Exception as e:
+                        if dropna:
+                            logger.warning('Invalid instance which ends at line: {} has been dropped.'.format(line_idx))
+                            continue
+                        raise ValueError('Invalid instance which ends at line: {}'.format(line_idx))
+            elif line.startswith('#'):
+                continue
+            else:
+                sample.append(line.split('\t'))
+        # if len(sample) > 0:
+        #     try:
+        #         res = parse_conll(sample)
+        #         yield line_idx, res
+        #     except Exception as e:
+        #         if dropna:
+        #             return
+        #         logger.error('invalid instance ends at line: {}'.format(line_idx))
+        #         raise e

utils/metrics.py

@@ -0,0 +1,151 @@
+from abc import abstractmethod
+from collections import defaultdict
+
+
+class MetricBase(object):
+
+    @abstractmethod
+    def evaluate(self, *args, **kwargs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_metric(self, reset=True):
+        raise NotImplemented
+
+    def __call__(self, p_ids, pred, eval_file):
+        return self.evaluate(p_ids, pred, eval_file)
+
+
+def _bmeso_tag_to_spans(tags, ignore_labels=None):
+    """
+    给定一个tags的lis，比如['O', 'B-singer', 'M-singer', 'E-singer', 'O', 'O']。
+    返回[('singer', (1, 4))] (左闭右开区间)
+
+    :param tags: List[str],
+    :param ignore_labels: List[str], 在该list中的label将被忽略
+    :return: List[Tuple[str, List[int, int]]]. [(label，[start, end])]
+    """
+    ignore_labels = set(ignore_labels) if ignore_labels else set()
+
+    spans = []
+    prev_bmes_tag = None
+    for idx, tag in enumerate(tags):
+        tag = tag.lower()
+        bmes_tag, label = tag[:1], tag[2:]
+        if bmes_tag in ('b', 's'):
+            spans.append((label, [idx, idx]))
+        elif bmes_tag in ('m', 'e') and prev_bmes_tag in ('b', 'm') and label == spans[-1][0]:
+            spans[-1][1][1] = idx
+        elif bmes_tag == 'o':
+            pass
+        else:
+            spans.append((label, [idx, idx]))
+        prev_bmes_tag = bmes_tag
+    return [(span[0], (span[1][0], span[1][1] + 1))
+            for span in spans
+            if span[0] not in ignore_labels
+            ]
+
+
+class SpanFPreRecMetric(MetricBase):
+    def __init__(self, tag_type, pred=None, target=None, encoding_type='bmeso',
+                 only_gross=True, f_type='micro', beta=1):
+        self.tag_type = tag_type
+        self.only_gross = only_gross
+        self.f_type = f_type
+        self.beta = beta
+        self.beta_square = self.beta ** 2
+        self.encoding_type = encoding_type
+        if self.encoding_type == 'bmeso':
+            self.tag_to_span_func = _bmeso_tag_to_spans
+
+        self._true_positives = defaultdict(int)
+        self._false_positives = defaultdict(int)
+        self._false_negatives = defaultdict(int)
+
+    def evaluate(self, p_ids, preds, eval_file):
+        answer_dict = {}
+        for p_id, pred in zip(p_ids.tolist(), preds.tolist()):
+            gold_ = eval_file[p_id].gold_answer
+            pred_ = [self.tag_type[tag] for tag in pred]
+            # gold_str_tags = [self.tag_type[tag] for tag in gold]
+            pred_spans = self.tag_to_span_func(pred_)
+            gold_spans = self.tag_to_span_func(gold_)
+            answer_dict[str(p_id)] = [pred_spans, gold_spans]
+            for span in pred_spans:
+                if span in gold_spans:
+                    self._true_positives[span[0]] += 1
+                    gold_spans.remove(span)
+                else:
+                    self._false_positives[span[0]] += 1
+            for span in gold_spans:
+                self._false_negatives[span[0]] += 1
+        return answer_dict
+
+    def get_metric(self, reset=True):
+        """get_metric函数将根据evaluate函数累计的评价指标统计量来计算最终的评价结果."""
+        evaluate_result = {}
+        if not self.only_gross or self.f_type == 'macro':
+            tags = set(self._false_negatives.keys())
+            tags.update(set(self._false_positives.keys()))
+            tags.update(set(self._true_positives.keys()))
+            f_sum = 0
+            pre_sum = 0
+            rec_sum = 0
+            for tag in tags:
+                tp = self._true_positives[tag]
+                fn = self._false_negatives[tag]
+                fp = self._false_positives[tag]
+                f, pre, rec = self._compute_f_pre_rec(tp, fn, fp)
+                f_sum += f
+                pre_sum += pre
+                rec_sum += rec
+                if not self.only_gross and tag != '':  # tag!=''防止无tag的情况
+                    f_key = 'f-{}'.format(tag)
+                    pre_key = 'pre-{}'.format(tag)
+                    rec_key = 'rec-{}'.format(tag)
+                    evaluate_result[f_key] = f
+                    evaluate_result[pre_key] = pre
+                    evaluate_result[rec_key] = rec
+
+            if self.f_type == 'macro':
+                evaluate_result['f'] = f_sum / len(tags)
+                evaluate_result['pre'] = pre_sum / len(tags)
+                evaluate_result['rec'] = rec_sum / len(tags)
+
+        if self.f_type == 'micro':
+            f, pre, rec,em,pre_num,gold_num = self._compute_f_pre_rec(sum(self._true_positives.values()),
+                                                  sum(self._false_negatives.values()),
+                                                  sum(self._false_positives.values()))
+            evaluate_result['f'] = f
+            evaluate_result['pre'] = pre
+            evaluate_result['rec'] = rec
+            evaluate_result['em'] = em
+            evaluate_result['pre'] = pre_num
+            evaluate_result['gold'] = gold_num
+
+
+        if reset:
+            self._true_positives = defaultdict(int)
+            self._false_positives = defaultdict(int)
+            self._false_negatives = defaultdict(int)
+
+        for key, value in evaluate_result.items():
+            evaluate_result[key] = round(value, 6)
+        print(evaluate_result)
+        return evaluate_result
+
+    def _compute_f_pre_rec(self, tp, fn, fp):
+        """
+
+        :param tp: int, true positive
+        :param fn: int, false negative
+        :param fp: int, false positive
+        :return: (f, pre, rec)
+        """
+        pre = tp / (fp + tp + 1e-13)
+        rec = tp / (fn + tp + 1e-13)
+        f = (1 + self.beta_square) * pre * rec / (self.beta_square * pre + rec + 1e-13)
+
+
+        return f, pre, rec,tp,fp + tp,fn + tp

utils/optimizer_util.py

@@ -4,7 +4,7 @@
 
 
 def set_optimizer(args, model, train_steps=None):
-    if args.use_bert:
+    if args.warm_up:
         print('using BertAdam')
         param_optimizer = list(model.named_parameters())
         param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]