修改ClassifyF1PreRecMetric及对应的测试用例

fastNLP/core/metrics/classify_f1_pre_rec_metric.py

@@ -29,14 +29,16 @@ def _compute_f_pre_rec(beta_square, tp, fn, fp):
 
 
 class ClassifyFPreRecMetric(Metric):
-    def __init__(self, backend: Union[str, Backend, None] = 'auto', aggregate_when_get_metric: bool = False,
-                 tag_vocab: Vocabulary = None, encoding_type: str = None, ignore_labels: List[str] = None,
-                 only_gross: bool = True, f_type='micro', beta=1) -> None:
+    def __init__(self, tag_vocab: Vocabulary = None, ignore_labels: List[str] = None, num_class: int = 0,
+                 only_gross: bool = True, f_type='micro', beta=1, backend: Union[str, Backend, None] = 'auto',
+                 aggregate_when_get_metric: bool = False) -> None:
         super(ClassifyFPreRecMetric, self).__init__(backend=backend,
                                                     aggregate_when_get_metric=aggregate_when_get_metric)
         if f_type not in ('micro', 'macro'):
             raise ValueError("f_type only supports `micro` or `macro`', got {}.".format(f_type))
-
+        if tag_vocab:
+            if not isinstance(tag_vocab, Vocabulary):
+                raise TypeError("tag_vocab can only be fastNLP.Vocabulary, not {}.".format(type(tag_vocab)))
         self.ignore_labels = ignore_labels
         self.f_type = f_type
         self.beta = beta
@@ -45,9 +47,32 @@ class ClassifyFPreRecMetric(Metric):
 
         self.tag_vocab = tag_vocab
 
-        self._tp, self._fp, self._fn = defaultdict(partial(self.register_element, aggregate_method='sum')),\
-                                       defaultdict(partial(self.register_element, aggregate_method='sum')),\
-                                       defaultdict(partial(self.register_element, aggregate_method='sum'))
+        self._tp = {}
+        self._fp = {}
+        self._fn = {}
+        if tag_vocab:
+            for word, _ in tag_vocab:
+                word = word.lower()
+                if word != 'o':
+                    word = word[2:]
+                if word in self._true_positives:
+                    continue
+                self._tp[word] = self.register_element(name=f'tp_{word}', aggregate_method='sum',
+                                                       backend=backend)
+                self._fn[word] = self.register_element(name=f'fn_{word}', aggregate_method='sum',
+                                                       backend=backend)
+                self._fp[word] = self.register_element(name=f'fp_{word}', aggregate_method='sum',
+                                                       backend=backend)
+        elif num_class > 0:
+            for word in range(num_class):
+                self._tp[word] = self.register_element(name=f'tp_{word}', aggregate_method='sum',
+                                                       backend=backend)
+                self._fn[word] = self.register_element(name=f'fn_{word}', aggregate_method='sum',
+                                                       backend=backend)
+                self._fp[word] = self.register_element(name=f'fp_{word}', aggregate_method='sum',
+                                                       backend=backend)
+        else:
+            raise ValueError()
 
     def get_metric(self) -> dict:
         r"""
@@ -68,9 +93,11 @@ class ClassifyFPreRecMetric(Metric):
                     tag_name = self.tag_vocab.to_word(tag)
                 else:
                     tag_name = int(tag)
-                tp = self._tp[tag]
-                fn = self._fn[tag]
-                fp = self._fp[tag]
+                tp = self._tp[tag].get_scalar()
+                fn = self._fn[tag].get_scalar()
+                fp = self._fp[tag].get_scalar()
+                if tp == fn == fp == 0:
+                    continue
                 f, pre, rec = _compute_f_pre_rec(self.beta_square, tp, fn, fp)
                 f_sum += f
                 pre_sum += pre
@@ -90,20 +117,29 @@ class ClassifyFPreRecMetric(Metric):
 
         if self.f_type == 'micro':
             f, pre, rec = _compute_f_pre_rec(self.beta_square,
-                                             sum(self._tp.values()),
-                                             sum(self._fn.values()),
-                                             sum(self._fp.values()))
+                                             sum(val.get_scalar() for val in self._tp.values()),
+                                             sum(val.get_scalar() for val in self._fn.values()),
+                                             sum(val.get_scalar() for val in self._fp.values()))
             evaluate_result['f'] = f
             evaluate_result['pre'] = pre
             evaluate_result['rec'] = rec
 
-
         for key, value in evaluate_result.items():
             evaluate_result[key] = round(value, 6)
 
         return evaluate_result
 
     def update(self, pred, target, seq_len=None):
+        r"""
+        evaluate函数将针对一个批次的预测结果做评价指标的累计
+
+        :param torch.Tensor pred: 预测的tensor, tensor的形状可以是torch.Size([B,]), torch.Size([B, n_classes]),
+                torch.Size([B, max_len]), 或者torch.Size([B, max_len, n_classes])
+        :param torch.Tensor target: 真实值的tensor, tensor的形状可以是Element's can be: torch.Size([B,]),
+                torch.Size([B,]), torch.Size([B, max_len]), 或者torch.Size([B, max_len])
+        :param torch.Tensor seq_len: 序列长度标记, 标记的形状可以是None, None, torch.Size([B]), 或者torch.Size([B]).
+                如果mask也被传进来的话seq_len会被忽略.
+        """
         pred = self.tensor2numpy(pred)
         target = self.tensor2numpy(target)
         if seq_len is not None:
@@ -122,14 +158,14 @@ class ClassifyFPreRecMetric(Metric):
                                    f"pred have element numbers: {len(target.flatten())}")
 
             pass
-        elif len(pred.ndim) == len(target.ndim) + 1:
+        elif pred.ndim == target.ndim + 1:
             pred = pred.argmax(axis=-1)
-            if seq_len is None and len(target.ndim) > 1:
+            if seq_len is None and target.ndim > 1:
                 warnings.warn("You are not passing `seq_len` to exclude pad when calculate accuracy.")
         else:
             raise RuntimeError(f"when pred have "
-                               f"size:{pred.ndim}, target should have size: {pred.ndim} or "
-                               f"{pred.ndim[:-1]}, got {target.ndim}.")
+                               f"size:{pred.shape}, target should have size: {pred.shape} or "
+                               f"{pred.shape[:-1]}, got {target.shape}.")
         if masks is not None:
             target = target * masks
             pred = pred * masks
@@ -138,5 +174,3 @@ class ClassifyFPreRecMetric(Metric):
             self._tp[target_idx] += ((pred == target_idx) * (target != target_idx)).sum().item()
             self._fp[target_idx] += ((pred == target_idx) * (target == target_idx)).sum().item()
             self._fn[target_idx] += ((pred != target_idx) * (target != target_idx)).sum().item()
-
-

tests/core/metrics/test_classify_f1_pre_rec_metric_torch.py

@@ -0,0 +1,88 @@
+import pytest
+import torch
+import numpy as np
+
+from fastNLP.core.metrics import ClassifyFPreRecMetric
+
+
+class TestClassfiyFPreRecMetric:
+    def test_case_1(self):
+        pred = torch.tensor([[-0.4375, -0.1779, -1.0985, -1.1592, 0.4910],
+                             [1.3410, 0.2889, -0.8667, -1.8580, 0.3029],
+                             [0.7459, -1.1957, 0.3231, 0.0308, -0.1847],
+                             [1.1439, -0.0057, 0.8203, 0.0312, -1.0051],
+                             [-0.4870, 0.3215, -0.8290, 0.9221, 0.4683],
+                             [0.9078, 1.0674, -0.5629, 0.3895, 0.8917],
+                             [-0.7743, -0.4041, -0.9026, 0.2112, 1.0892],
+                             [1.8232, -1.4188, -2.5615, -2.4187, 0.5907],
+                             [-1.0592, 0.4164, -0.1192, 1.4238, -0.9258],
+                             [-1.1137, 0.5773, 2.5778, 0.5398, -0.3323],
+                             [-0.3868, -0.5165, 0.2286, -1.3876, 0.5561],
+                             [-0.3304, 1.3619, -1.5744, 0.4902, -0.7661],
+                             [1.8387, 0.5234, 0.4269, 1.3748, -1.2793],
+                             [0.6692, 0.2571, 1.2425, -0.5894, -0.0184],
+                             [0.4165, 0.4084, -0.1280, 1.4489, -2.3058],
+                             [-0.5826, -0.5469, 1.5898, -0.2786, -0.9882],
+                             [-1.5548, -2.2891, 0.2983, -1.2145, -0.1947],
+                             [-0.7222, 2.3543, -0.5801, -0.0640, -1.5614],
+                             [-1.4978, 1.9297, -1.3652, -0.2358, 2.5566],
+                             [0.1561, -0.0316, 0.9331, 1.0363, 2.3949],
+                             [0.2650, -0.8459, 1.3221, 0.1321, -1.1900],
+                             [0.0664, -1.2353, -0.5242, -1.4491, 1.3300],
+                             [-0.2744, 0.0941, 0.7157, 0.1404, 1.2046],
+                             [0.9341, -0.6652, 1.4512, 0.9608, -0.3623],
+                             [-1.1641, 0.0873, 0.1163, -0.2068, -0.7002],
+                             [1.4775, -2.0025, -0.5634, -0.1589, 0.0247],
+                             [1.0151, 1.0304, -0.1042, -0.6955, -0.0629],
+                             [-0.3119, -0.4558, 0.7757, 0.0758, -1.6297],
+                             [1.0654, 0.0313, -0.7716, 0.1194, 0.6913],
+                             [-0.8088, -0.6648, -0.5018, -0.0230, -0.8207],
+                             [-0.7753, -0.3508, 1.6163, 0.7158, 1.5207],
+                             [0.8692, 0.7718, -0.6734, 0.6515, 0.0641]])
+        arg_max_pred = torch.argmax(pred, dim=-1)
+        target = torch.tensor([0, 2, 4, 1, 4, 0, 1, 3, 3, 3, 1, 3, 4, 4, 3, 4, 0, 2, 4, 4, 3, 4, 4, 3,
+                               0, 3, 0, 0, 0, 1, 3, 1])
+
+        metric = ClassifyFPreRecMetric(f_type='macro', num_class=5)
+        metric.update(pred, target)
+        result_dict = metric.get_metric()
+        f1_score = 0.1882051282051282
+        recall = 0.1619047619047619
+        pre = 0.23928571428571427
+
+        ground_truth = {'f': f1_score, 'pre': pre, 'rec': recall}
+        for keys in ['f', 'pre', 'rec']:
+            np.allclose(result_dict[keys], ground_truth[keys], atol=0.000001)
+
+        metric = ClassifyFPreRecMetric(f_type='micro', num_class=5)
+        metric.update(pred, target)
+        result_dict = metric.get_metric()
+        f1_score = 0.21875
+        recall = 0.21875
+        pre = 0.21875
+
+        ground_truth = {'f': f1_score, 'pre': pre, 'rec': recall}
+        for keys in ['f', 'pre', 'rec']:
+            np.allclose(result_dict[keys], ground_truth[keys], atol=0.000001)
+
+        metric = ClassifyFPreRecMetric(only_gross=False, f_type='macro', num_class=5)
+        metric.update(pred, target)
+        result_dict = metric.get_metric()
+        ground_truth = {
+            '0': {'f1-score': 0.13333333333333333, 'precision': 0.125, 'recall': 0.14285714285714285, 'support': 7},
+            '1': {'f1-score': 0.0, 'precision': 0.0, 'recall': 0.0, 'support': 5},
+            '2': {'f1-score': 0.0, 'precision': 0.0, 'recall': 0.0, 'support': 2},
+            '3': {'f1-score': 0.30769230769230765, 'precision': 0.5, 'recall': 0.2222222222222222, 'support': 9},
+            '4': {'f1-score': 0.5, 'precision': 0.5714285714285714, 'recall': 0.4444444444444444, 'support': 9},
+            'macro avg': {'f1-score': 0.1882051282051282, 'precision': 0.23928571428571427,
+                          'recall': 0.1619047619047619, 'support': 32},
+            'micro avg': {'f1-score': 0.21875, 'precision': 0.21875, 'recall': 0.21875, 'support': 32},
+            'weighted avg': {'f1-score': 0.2563301282051282, 'precision': 0.3286830357142857, 'recall': 0.21875,
+                             'support': 32}}
+        for keys in result_dict.keys():
+            if keys == "f" or "pre" or "rec":
+                continue
+            gl = str(keys[-1])
+            tmp_d = {"p": "precision", "r": "recall", "f": "f1-score"}
+            gk = tmp_d[keys[0]]
+            np.allclose(result_dict[keys], ground_truth[gl][gk], atol=0.000001)