def test_seq_correct_score(self):
metric = explainaboard.metrics.accuracy.SeqCorrectCountConfig(
name='SeqCorrectCount'
).to_metric()
true = [
{
"start_idx": [8, 17, 39, 46, 58, 65, 65, 80],
"end_idx": [8, 18, 40, 47, 59, 65, 66, 81],
"corrections": [
["the"],
["found"],
["other"],
["there"],
["chickens."],
["in"],
["which"],
["selling"],
],
}
]
pred = [
{
"start_idx": [8, 17, 39, 46, 58],
"end_idx": [8, 18, 40, 47, 59],
"corrections": [
["the"],
["found"],
["other"],
["there"],
["chickens."],
],
}
]
result = metric.evaluate(true, pred)
self.assertAlmostEqual(result.value, 5)
def test_correct_score(self):
metric = explainaboard.metrics.accuracy.CorrectCountConfig(
name='CorrectCount'
).to_metric()
true = ['a', 'b', 'a', 'b', 'a', 'b']
pred = ['a', 'b', 'a', 'b', 'b', 'a']
result = metric.evaluate(true, pred, conf_value=0.05)
self.assertAlmostEqual(result.value, 4)
def test_accuracy(self):
metric = explainaboard.metrics.accuracy.AccuracyConfig(
name='Accuracy'
).to_metric()
true = ['a', 'b', 'a', 'b', 'a', 'b']
pred = ['a', 'b', 'a', 'b', 'b', 'a']
result = metric.evaluate(true, pred, conf_value=0.05)
self.assertAlmostEqual(result.value, 2.0 / 3.0)
def test_mrr(self):
metric = explainaboard.metrics.ranking.MeanReciprocalRankConfig(
name='MRR'
).to_metric()
true = ['a', 'b', 'a', 'b', 'a', 'b']
pred = [['a', 'b'], ['c', 'd'], ['c', 'a'], ['a', 'c'], ['b', 'a'], ['a', 'b']]
result = metric.evaluate(true, pred, conf_value=0.05)
self.assertAlmostEqual(result.value, 2.5 / 6.0)
def test_f1_macro(self):
metric = explainaboard.metrics.f1_score.F1ScoreConfig(
name='F1', average='macro'
).to_metric()
true = ['a', 'b', 'a', 'b', 'a', 'a', 'c', 'c']
pred = ['a', 'b', 'a', 'b', 'b', 'a', 'c', 'a']
sklearn_f1 = sklearn.metrics.f1_score(true, pred, average='macro')
result = metric.evaluate(true, pred, conf_value=None)
self.assertAlmostEqual(result.value, sklearn_f1)
def test_ner_f1(self):
true = [
['O', 'O', 'B-MISC', 'I-MISC', 'B-MISC', 'O', 'O'],
['B-PER', 'I-PER', 'O'],
]
pred = [
['O', 'O', 'B-MISC', 'I-MISC', 'B-MISC', 'I-MISC', 'O'],
['B-PER', 'I-PER', 'O'],
]
metric = explainaboard.metrics.f1_score.SeqF1ScoreConfig(
name='MicroF1', average='micro', tag_schema='bio'
).to_metric()
result = metric.evaluate(true, pred, conf_value=None)
self.assertAlmostEqual(result.value, 2.0 / 3.0)
metric = explainaboard.metrics.f1_score.SeqF1ScoreConfig(
name='MacroF1', average='macro', tag_schema='bio'
).to_metric()
result = metric.evaluate(true, pred, conf_value=None)
self.assertAlmostEqual(result.value, 3.0 / 4.0)
def test_hits(self):
metric = explainaboard.metrics.ranking.HitsConfig(name='Hits').to_metric()
true = ['a', 'b', 'a', 'b', 'a', 'b']
pred = [['a', 'b'], ['c', 'd'], ['c', 'a'], ['a', 'c'], ['b', 'a'], ['a', 'b']]
result = metric.evaluate(true, pred, conf_value=0.05)
self.assertAlmostEqual(result.value, 4.0 / 6.0)