import unittest import numpy as np import torch from fastNLP.core.metrics import AccuracyMetric from fastNLP.core.metrics import BMESF1PreRecMetric from fastNLP.core.metrics import pred_topk, accuracy_topk class TestAccuracyMetric(unittest.TestCase): def test_AccuracyMetric1(self): # (1) only input, targets passed pred_dict = {"pred": torch.zeros(4, 3)} target_dict = {'target': torch.zeros(4)} metric = AccuracyMetric() metric(pred_dict=pred_dict, target_dict=target_dict, ) print(metric.get_metric()) def test_AccuracyMetric2(self): # (2) with corrupted size try: pred_dict = {"pred": torch.zeros(4, 3, 2)} target_dict = {'target': torch.zeros(4)} metric = AccuracyMetric() metric(pred_dict=pred_dict, target_dict=target_dict, ) print(metric.get_metric()) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_AccuracyMetric3(self): # (3) the second batch is corrupted size try: metric = AccuracyMetric() pred_dict = {"pred": torch.zeros(4, 3, 2)} target_dict = {'target': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) pred_dict = {"pred": torch.zeros(4, 3, 2)} target_dict = {'target': torch.zeros(4)} metric(pred_dict=pred_dict, target_dict=target_dict) print(metric.get_metric()) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_AccuaryMetric4(self): # (5) check reset metric = AccuracyMetric() pred_dict = {"pred": torch.randn(4, 3, 2)} target_dict = {'target': torch.ones(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) ans = torch.argmax(pred_dict["pred"], dim=2).to(target_dict["target"]) == target_dict["target"] res = metric.get_metric() self.assertTrue(isinstance(res, dict)) self.assertTrue("acc" in res) self.assertAlmostEqual(res["acc"], float(ans.float().mean()), places=3) def test_AccuaryMetric5(self): # (5) check reset metric = AccuracyMetric() pred_dict = {"pred": torch.randn(4, 3, 2)} target_dict = {'target': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) res = metric.get_metric(reset=False) ans = (torch.argmax(pred_dict["pred"], dim=2).float() == target_dict["target"]).float().mean() self.assertAlmostEqual(res["acc"], float(ans), places=4) def test_AccuaryMetric6(self): # (6) check numpy array is not acceptable try: metric = AccuracyMetric() pred_dict = {"pred": np.zeros((4, 3, 2))} target_dict = {'target': np.zeros((4, 3))} metric(pred_dict=pred_dict, target_dict=target_dict) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_AccuaryMetric7(self): # (7) check map, match metric = AccuracyMetric(pred='predictions', target='targets') pred_dict = {"predictions": torch.randn(4, 3, 2)} target_dict = {'targets': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) res = metric.get_metric() ans = (torch.argmax(pred_dict["predictions"], dim=2).float() == target_dict["targets"]).float().mean() self.assertAlmostEqual(res["acc"], float(ans), places=4) def test_AccuaryMetric8(self): # (8) check map, does not match. use stop_fast_param to stop fast param map try: metric = AccuracyMetric(pred='predictions', target='targets') pred_dict = {"prediction": torch.zeros(4, 3, 2), "stop_fast_param": 1} target_dict = {'targets': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict, ) self.assertDictEqual(metric.get_metric(), {'acc': 1}) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_AccuaryMetric9(self): # (9) check map, include unused try: metric = AccuracyMetric(pred='prediction', target='targets') pred_dict = {"prediction": torch.zeros(4, 3, 2), 'unused': 1} target_dict = {'targets': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) self.assertDictEqual(metric.get_metric(), {'acc': 1}) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_AccuaryMetric10(self): # (10) check _fast_metric try: metric = AccuracyMetric() pred_dict = {"predictions": torch.zeros(4, 3, 2), "masks": torch.zeros(4, 3)} target_dict = {'targets': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) self.assertDictEqual(metric.get_metric(), {'acc': 1}) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." class SpanF1PreRecMetric(unittest.TestCase): def test_case1(self): from fastNLP.core.metrics import bmes_tag_to_spans from fastNLP.core.metrics import bio_tag_to_spans bmes_lst = ['M-8', 'S-2', 'S-0', 'B-9', 'B-6', 'E-5', 'B-7', 'S-2', 'E-7', 'S-8'] bio_lst = ['O-8', 'O-2', 'B-0', 'O-9', 'I-6', 'I-5', 'I-7', 'I-2', 'I-7', 'O-8'] expect_bmes_res = set() expect_bmes_res.update([('8', (0, 0)), ('2', (1, 1)), ('0', (2, 2)), ('9', (3, 3)), ('6', (4, 4)), ('5', (5, 5)), ('7', (6, 6)), ('2', (7, 7)), ('7', (8, 8)), ('8', (9, 9))]) expect_bio_res = set() expect_bio_res.update([('7', (8, 8)), ('0', (2, 2)), ('2', (7, 7)), ('5', (5, 5)), ('6', (4, 4)), ('7', (6, 6))]) self.assertSetEqual(expect_bmes_res,set(bmes_tag_to_spans(bmes_lst))) self.assertSetEqual(expect_bio_res, set(bio_tag_to_spans(bio_lst))) # 已与allennlp对应函数做过验证，但由于测试不能依赖allennlp，所以这里只是截取上面的例子做固定测试 # from allennlp.data.dataset_readers.dataset_utils import bio_tags_to_spans as allen_bio_tags_to_spans # from allennlp.data.dataset_readers.dataset_utils import bmes_tags_to_spans as allen_bmes_tags_to_spans # for i in range(1000): # strs = list(map(str, np.random.randint(100, size=1000))) # bmes = list('bmes'.upper()) # bmes_strs = [str_ + '-' + tag for tag, str_ in zip(strs, np.random.choice(bmes, size=len(strs)))] # bio = list('bio'.upper()) # bio_strs = [str_ + '-' + tag for tag, str_ in zip(strs, np.random.choice(bio, size=len(strs)))] # self.assertSetEqual(set(allen_bmes_tags_to_spans(bmes_strs)),set(bmes_tag_to_spans(bmes_strs))) # self.assertSetEqual(set(allen_bio_tags_to_spans(bio_strs)), set(bio_tag_to_spans(bio_strs))) def test_case2(self): # 测试不带label的 from fastNLP.core.metrics import bmes_tag_to_spans from fastNLP.core.metrics import bio_tag_to_spans bmes_lst = ['B', 'E', 'B', 'S', 'B', 'M', 'E', 'M', 'B', 'E'] bio_lst = ['I', 'B', 'O', 'O', 'I', 'O', 'I', 'B', 'O', 'O'] expect_bmes_res = set() expect_bmes_res.update([('', (0, 1)), ('', (2, 2)), ('', (3, 3)), ('', (4, 6)), ('', (7, 7)), ('', (8, 9))]) expect_bio_res = set() expect_bio_res.update([('', (7, 7)), ('', (6, 6)), ('', (4, 4)), ('', (0, 0)), ('', (1, 1))]) self.assertSetEqual(expect_bmes_res,set(bmes_tag_to_spans(bmes_lst))) self.assertSetEqual(expect_bio_res, set(bio_tag_to_spans(bio_lst))) # 已与allennlp对应函数做过验证，但由于测试不能依赖allennlp，所以这里只是截取上面的例子做固定测试 # from allennlp.data.dataset_readers.dataset_utils import bio_tags_to_spans as allen_bio_tags_to_spans # from allennlp.data.dataset_readers.dataset_utils import bmes_tags_to_spans as allen_bmes_tags_to_spans # for i in range(1000): # bmes = list('bmes'.upper()) # bmes_strs = np.random.choice(bmes, size=1000) # bio = list('bio'.upper()) # bio_strs = np.random.choice(bio, size=100) # self.assertSetEqual(set(allen_bmes_tags_to_spans(bmes_strs)),set(bmes_tag_to_spans(bmes_strs))) # self.assertSetEqual(set(allen_bio_tags_to_spans(bio_strs)), set(bio_tag_to_spans(bio_strs))) def tese_case3(self): from fastNLP.core.vocabulary import Vocabulary from collections import Counter from fastNLP.core.metrics import SpanFPreRecMetric # 与allennlp测试能否正确计算f metric # def generate_allen_tags(encoding_type, number_labels=4): vocab = {} for i in range(number_labels): label = str(i) for tag in encoding_type: if tag == 'O': if tag not in vocab: vocab['O'] = len(vocab) + 1 continue vocab['{}-{}'.format(tag, label)] = len(vocab) + 1 # 其实表达的是这个的count return vocab number_labels = 4 # bio tag fastnlp_bio_vocab = Vocabulary(unknown=None, padding=None) fastnlp_bio_vocab.word_count = Counter(generate_allen_tags('BIO', number_labels)) fastnlp_bio_metric = SpanFPreRecMetric(tag_vocab=fastnlp_bio_vocab, only_gross=False) bio_sequence = torch.FloatTensor( [[[-0.9543, -1.4357, -0.2365, 0.2438, 1.0312, -1.4302, 0.3011, 0.0470, 0.0971], [-0.6638, -0.7116, -1.9804, 0.2787, -0.2732, -0.9501, -1.4523, 0.7987, -0.3970], [0.2939, 0.8132, -0.0903, -2.8296, 0.2080, -0.9823, -0.1898, 0.6880, 1.4348], [-0.1886, 0.0067, -0.6862, -0.4635, 2.2776, 0.0710, -1.6793, -1.6876, -0.8917], [-0.7663, 0.6377, 0.8669, 0.1237, 1.7628, 0.0313, -1.0824, 1.4217, 0.2622]], [[0.1529, 0.7474, -0.9037, 1.5287, 0.2771, 0.2223, 0.8136, 1.3592, -0.8973], [0.4515, -0.5235, 0.3265, -1.1947, 0.8308, 1.8754, -0.4887, -0.4025, -0.3417], [-0.7855, 0.1615, -0.1272, -1.9289, -0.5181, 1.9742, -0.9698, 0.2861, -0.3966], [-0.8291, -0.8823, -1.1496, 0.2164, 1.3390, -0.3964, -0.5275, 0.0213, 1.4777], [-1.1299, 0.0627, -0.1358, -1.5951, 0.4484, -0.6081, -1.9566, 1.3024, 0.2001]]] ) bio_target = torch.LongTensor([[5., 0., 3., 3., 3.], [5., 6., 8., 6., 0.]]) fastnlp_bio_metric({'pred': bio_sequence, 'seq_lens': torch.LongTensor([5, 5])}, {'target': bio_target}) expect_bio_res = {'pre-1': 0.24999999999999373, 'rec-1': 0.499999999999975, 'f-1': 0.33333333333327775, 'pre-2': 0.0, 'rec-2': 0.0, 'f-2': 0.0, 'pre-3': 0.0, 'rec-3': 0.0, 'f-3': 0.0, 'pre-0': 0.0, 'rec-0': 0.0, 'f-0': 0.0, 'pre': 0.12499999999999845, 'rec': 0.12499999999999845, 'f': 0.12499999999994846} self.assertDictEqual(expect_bio_res, fastnlp_bio_metric.get_metric()) #bmes tag bmes_sequence = torch.FloatTensor( [[[0.6536, -0.7179, 0.6579, 1.2503, 0.4176, 0.6696, 0.2352, -0.4085, 0.4084, -0.4185, 1.4172, -0.9162, -0.2679, 0.3332, -0.3505, -0.6002], [0.3238, -1.2378, -1.3304, -0.4903, 1.4518, -0.1868, -0.7641, 1.6199, -0.8877, 0.1449, 0.8995, -0.5810, 0.1041, 0.1002, 0.4439, 0.2514], [-0.8362, 2.9526, 0.8008, 0.1193, 1.0488, 0.6670, 1.1696, -1.1006, -0.8540, -0.1600, -0.9519, -0.2749, -0.4948, -1.4753, 0.5802, -0.0516], [-0.8383, -1.7292, -1.4079, -1.5023, 0.5383, 0.6653, 0.3121, 4.1249, -0.4173, -0.2043, 1.7755, 1.1110, -1.7069, -0.0390, -0.9242, -0.0333], [0.9088, -0.4955, -0.5076, 0.3732, 0.0283, -0.0263, -1.0393, 0.7734, 1.0968, 0.4132, -1.3647, -0.5762, 0.6678, 0.8809, -0.3779, -0.3195]], [[-0.4638, -0.5939, -0.1052, -0.5573, 0.4600, -1.3484, 0.1753, 0.0685, 0.3663, -0.6789, 0.0097, 1.0327, -0.0212, -0.9957, -0.1103, 0.4417], [-0.2903, 0.9205, -1.5758, -1.0421, 0.2921, -0.2142, -0.3049, -0.0879, -0.4412, -1.3195, -0.0657, -0.2986, 0.7214, 0.0631, -0.6386, 0.2797], [0.6440, -0.3748, 1.2912, -0.0170, 0.7447, 1.4075, -0.4947, 0.4123, -0.8447, -0.5502, 0.3520, -0.2832, 0.5019, -0.1522, 1.1237, -1.5385], [0.2839, -0.7649, 0.9067, -0.1163, -1.3789, 0.2571, -1.3977, -0.3680, -0.8902, -0.6983, -1.1583, 1.2779, 0.2197, 0.1376, -0.0591, -0.2461], [-0.2977, -1.8564, -0.5347, 1.0011, -1.1260, 0.4252, -2.0097, 2.6973, -0.8308, -1.4939, 0.9865, -0.3935, 0.2743, 0.1142, -0.7344, -1.2046]]] ) bmes_target = torch.LongTensor([[ 9., 6., 1., 9., 15.], [ 6., 15., 6., 15., 5.]]) fastnlp_bmes_vocab = Vocabulary(unknown=None, padding=None) fastnlp_bmes_vocab.word_count = Counter(generate_allen_tags('BMES', number_labels)) fastnlp_bmes_metric = SpanFPreRecMetric(tag_vocab=fastnlp_bmes_vocab, only_gross=False, encoding_type='bmes') fastnlp_bmes_metric({'pred': bmes_sequence, 'seq_lens': torch.LongTensor([20, 20])}, {'target': bmes_target}) expect_bmes_res = {'f-3': 0.6666666666665778, 'pre-3': 0.499999999999975, 'rec-3': 0.9999999999999001, 'f-0': 0.0, 'pre-0': 0.0, 'rec-0': 0.0, 'f-1': 0.33333333333327775, 'pre-1': 0.24999999999999373, 'rec-1': 0.499999999999975, 'f-2': 0.7499999999999314, 'pre-2': 0.7499999999999812, 'rec-2': 0.7499999999999812, 'f': 0.49999999999994504, 'pre': 0.499999999999995, 'rec': 0.499999999999995} self.assertDictEqual(fastnlp_bmes_metric.get_metric(), expect_bmes_res) # 已经和allennlp做过验证，但由于不能依赖allennlp，所以注释了以下代码 # from allennlp.data.vocabulary import Vocabulary as allen_Vocabulary # from allennlp.training.metrics import SpanBasedF1Measure # allen_bio_vocab = allen_Vocabulary({"tags": generate_allen_tags('BIO', number_labels)}, # non_padded_namespaces=['tags']) # allen_bio_metric = SpanBasedF1Measure(allen_bio_vocab, 'tags') # bio_sequence = torch.randn(size=(2, 20, 2 * number_labels + 1)) # bio_target = torch.randint(2 * number_labels + 1, size=(2, 20)) # allen_bio_metric(bio_sequence, bio_target, torch.ones(2, 20)) # fastnlp_bio_vocab = Vocabulary(unknown=None, padding=None) # fastnlp_bio_vocab.word_count = Counter(generate_allen_tags('BIO', number_labels)) # fastnlp_bio_metric = SpanFPreRecMetric(tag_vocab=fastnlp_bio_vocab, only_gross=False) # # def convert_allen_res_to_fastnlp_res(metric_result): # allen_result = {} # key_map = {'f1-measure-overall': "f", "recall-overall": "rec", "precision-overall": "pre"} # for key, value in metric_result.items(): # if key in key_map: # key = key_map[key] # else: # label = key.split('-')[-1] # if key.startswith('f1'): # key = 'f-{}'.format(label) # else: # key = '{}-{}'.format(key[:3], label) # allen_result[key] = value # return allen_result # # # print(convert_allen_res_to_fastnlp_res(allen_bio_metric.get_metric())) # # print(fastnlp_bio_metric.get_metric()) # self.assertDictEqual(convert_allen_res_to_fastnlp_res(allen_bio_metric.get_metric()), # fastnlp_bio_metric.get_metric()) # # allen_bmes_vocab = allen_Vocabulary({"tags": generate_allen_tags('BMES', number_labels)}) # allen_bmes_metric = SpanBasedF1Measure(allen_bmes_vocab, 'tags', label_encoding='BMES') # fastnlp_bmes_vocab = Vocabulary(unknown=None, padding=None) # fastnlp_bmes_vocab.word_count = Counter(generate_allen_tags('BMES', number_labels)) # fastnlp_bmes_metric = SpanFPreRecMetric(tag_vocab=fastnlp_bmes_vocab, only_gross=False, encoding_type='bmes') # bmes_sequence = torch.randn(size=(2, 20, 4 * number_labels)) # bmes_target = torch.randint(4 * number_labels, size=(2, 20)) # allen_bmes_metric(bmes_sequence, bmes_target, torch.ones(2, 20)) # fastnlp_bmes_metric({'pred': bmes_sequence, 'seq_lens': torch.LongTensor([20, 20])}, {'target': bmes_target}) # # # print(convert_allen_res_to_fastnlp_res(allen_bmes_metric.get_metric())) # # print(fastnlp_bmes_metric.get_metric()) # self.assertDictEqual(convert_allen_res_to_fastnlp_res(allen_bmes_metric.get_metric()), # fastnlp_bmes_metric.get_metric()) class TestBMESF1PreRecMetric(unittest.TestCase): def test_case1(self): seq_lens = torch.LongTensor([4, 2]) pred = torch.randn(2, 4, 4) target = torch.LongTensor([[0, 1, 2, 3], [3, 3, 0, 0]]) pred_dict = {'pred': pred} target_dict = {'target': target, 'seq_lens': seq_lens} metric = BMESF1PreRecMetric() metric(pred_dict, target_dict) metric.get_metric() def test_case2(self): # 测试相同两个seqence，应该给出{f1: 1, precision:1, recall:1} seq_lens = torch.LongTensor([4, 2]) target = torch.LongTensor([[0, 1, 2, 3], [3, 3, 0, 0]]) pred_dict = {'pred': target} target_dict = {'target': target, 'seq_lens': seq_lens} metric = BMESF1PreRecMetric() metric(pred_dict, target_dict) self.assertDictEqual(metric.get_metric(), {'f': 1.0, 'pre': 1.0, 'rec': 1.0}) class TestUsefulFunctions(unittest.TestCase): # 测试metrics.py中一些看上去挺有用的函数 def test_case_1(self): # multi-class _ = accuracy_topk(np.random.randint(0, 3, size=(10, 1)), np.random.randint(0, 3, size=(10, 1)), k=3) _ = pred_topk(np.random.randint(0, 3, size=(10, 1))) # 跑通即可