Merge branch 'dev0.5.0' of https://github.com/fastnlp/fastNLP into dev0.5.0

fastNLP/core/metrics.py

@@ -23,6 +23,7 @@ from .utils import _get_func_signature
 from .utils import seq_len_to_mask
 from .vocabulary import Vocabulary
 from abc import abstractmethod
+import warnings
 
 
 class MetricBase(object):
@@ -492,6 +493,30 @@ def _bio_tag_to_spans(tags, ignore_labels=None):
     return [(span[0], (span[1][0], span[1][1] + 1)) for span in spans if span[0] not in ignore_labels]
 
 
+def _check_tag_vocab_and_encoding_type(vocab:Vocabulary, encoding_type:str):
+    """
+    检查vocab中的tag是否与encoding_type是匹配的
+
+    :param vocab: target的Vocabulary
+    :param encoding_type: bio, bmes, bioes, bmeso
+    :return:
+    """
+    tag_set = set()
+    for tag, idx in vocab:
+        if idx in (vocab.unknown_idx, vocab.padding_idx):
+            continue
+        tag = tag[:1].lower()
+        tag_set.add(tag)
+    tags = encoding_type
+    for tag in tag_set:
+        assert tag in tags, f"{tag} is not a valid tag in encoding type:{encoding_type}. Please check your " \
+            f"encoding_type."
+        tags = tags.replace(tag, '')  # 删除该值
+    if tags:  # 如果不为空，说明出现了未使用的tag
+        warnings.warn(f"Tag:{tags} in encoding type:{encoding_type} is not presented in your Vocabulary. Check your "
+                      "encoding_type.")
+
+
 class SpanFPreRecMetric(MetricBase):
     r"""
     别名：:class:`fastNLP.SpanFPreRecMetric` :class:`fastNLP.core.metrics.SpanFPreRecMetric`
@@ -546,6 +571,7 @@ class SpanFPreRecMetric(MetricBase):
             raise ValueError("f_type only supports `micro` or `macro`', got {}.".format(f_type))
         
         self.encoding_type = encoding_type
+        _check_tag_vocab_and_encoding_type(tag_vocab, encoding_type)
         if self.encoding_type == 'bmes':
             self.tag_to_span_func = _bmes_tag_to_spans
         elif self.encoding_type == 'bio':

test/core/test_metrics.py

@@ -338,6 +338,41 @@ class SpanF1PreRecMetric(unittest.TestCase):
         for key, value in expected_metric.items():
             self.assertAlmostEqual(value, metric_value[key], places=5)
 
+    def test_encoding_type(self):
+        # 检查传入的tag_vocab与encoding_type不符合时，是否会报错
+        vocabs = {}
+        import random
+        from itertools import product
+        for encoding_type in ['bio', 'bioes', 'bmeso']:
+            vocab = Vocabulary(unknown=None, padding=None)
+            for i in range(random.randint(10, 100)):
+                label = str(random.randint(1, 10))
+                for tag in encoding_type:
+                    if tag!='o':
+                        vocab.add_word(f'{tag}-{label}')
+                    else:
+                        vocab.add_word('o')
+            vocabs[encoding_type] = vocab
+        for e1, e2 in product(['bio', 'bioes', 'bmeso'], ['bio', 'bioes', 'bmeso']):
+            with self.subTest(e1=e1, e2=e2):
+                if e1==e2:
+                    metric = SpanFPreRecMetric(vocabs[e1], encoding_type=e2)
+                else:
+                    s2 = set(e2)
+                    s2.update(set(e1))
+                    if s2==set(e2):
+                        continue
+                    with self.assertRaises(AssertionError):
+                        metric = SpanFPreRecMetric(vocabs[e1], encoding_type=e2)
+        for encoding_type in ['bio', 'bioes', 'bmeso']:
+            with self.assertRaises(AssertionError):
+                metric = SpanFPreRecMetric(vocabs[encoding_type], encoding_type='bmes')
+
+        with self.assertWarns(Warning):
+            vocab = Vocabulary(unknown=None, padding=None).add_word_lst(list('bmes'))
+            metric = SpanFPreRecMetric(vocab, encoding_type='bmeso')
+            vocab = Vocabulary().add_word_lst(list('bmes'))
+            metric = SpanFPreRecMetric(vocab, encoding_type='bmeso')
 
 class TestUsefulFunctions(unittest.TestCase):
     # 测试metrics.py中一些看上去挺有用的函数