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Improve NER per type scoring (#4052)

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* Improve NER per type scoring

* include all gold labels in per type scoring, not only when recall > 0
* improve efficiency of per type scoring

* Create Scorer tests, initially with NER tests

* move regression test #3968 (per type NER scoring) to Scorer tests

* add new test for per type NER scoring with imperfect P/R/F and per
type P/R/F including a case where R == 0.0
This commit is contained in:
adrianeboyd 2019-08-01 17:15:36 +02:00 committed by Ines Montani
parent f7d950de6d
commit 925a852bb6
3 changed files with 88 additions and 45 deletions
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26
spacy/scorer.py
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@ -159,12 +159,19 @@ class Scorer(object):
else:
cand_deps.add((gold_i, gold_head, token.dep_.lower()))
if "-" not in [token[-1] for token in gold.orig_annot]:
# Find all NER labels in gold and doc
ent_labels = set([x[0] for x in gold_ents]
+ [k.label_ for k in doc.ents])
# Set up all labels for per type scoring and prepare gold per type
gold_per_ents = {ent_label: set() for ent_label in ent_labels}
for ent_label in ent_labels:
if ent_label not in self.ner_per_ents:
self.ner_per_ents[ent_label] = PRFScore()
gold_per_ents[ent_label].update([x for x in gold_ents if x[0] == ent_label])
# Find all candidate labels, for all and per type
cand_ents = set()
current_ent = {k.label_: set() for k in doc.ents}
current_gold = {k.label_: set() for k in doc.ents}
cand_per_ents = {ent_label: set() for ent_label in ent_labels}
for ent in doc.ents:
if ent.label_ not in self.ner_per_ents:
self.ner_per_ents[ent.label_] = PRFScore()
first = gold.cand_to_gold[ent.start]
last = gold.cand_to_gold[ent.end - 1]
if first is None or last is None:
@ -172,14 +179,11 @@ class Scorer(object):
self.ner_per_ents[ent.label_].fp += 1
else:
cand_ents.add((ent.label_, first, last))
current_ent[ent.label_].update([x for x in cand_ents if x[0] == ent.label_])
current_gold[ent.label_].update([x for x in gold_ents if x[0] == ent.label_])
cand_per_ents[ent.label_].add((ent.label_, first, last))
# Scores per ent
[
v.score_set(current_ent[k], current_gold[k])
for k, v in self.ner_per_ents.items()
if k in current_ent
]
for k, v in self.ner_per_ents.items():
if k in cand_per_ents:
v.score_set(cand_per_ents[k], gold_per_ents[k])
# Score for all ents
self.ner.score_set(cand_ents, gold_ents)
self.tags.score_set(cand_tags, gold_tags)

34
spacy/tests/regression/test_issue3968.py
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@ -1,34 +0,0 @@
# coding: utf-8
from __future__ import unicode_literals
from spacy.gold import GoldParse
from spacy.scorer import Scorer
from ..util import get_doc
test_samples = [
[
"100 - 200",
{
"entities": [
[0, 3, "CARDINAL"],
[6, 9, "CARDINAL"]
]
}
]
]
def test_issue3625(en_vocab):
scorer = Scorer()
for input_, annot in test_samples:
doc = get_doc(en_vocab, words = input_.split(' '), ents = [[0,1,'CARDINAL'], [2,3,'CARDINAL']]);
gold = GoldParse(doc, entities = annot['entities'])
scorer.score(doc, gold)
results = scorer.scores
# Expects total accuracy and accuracy for each each entity to be 100%
assert results['ents_p'] == 100
assert results['ents_f'] == 100
assert results['ents_r'] == 100
assert results['ents_per_type']['CARDINAL']['p'] == 100
assert results['ents_per_type']['CARDINAL']['f'] == 100
assert results['ents_per_type']['CARDINAL']['r'] == 100

73
spacy/tests/test_scorer.py Normal file
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@ -0,0 +1,73 @@
# coding: utf-8
from __future__ import unicode_literals
from pytest import approx
from spacy.gold import GoldParse
from spacy.scorer import Scorer
from .util import get_doc
test_ner_cardinal = [
[
"100 - 200",
{
"entities": [
[0, 3, "CARDINAL"],
[6, 9, "CARDINAL"]
]
}
]
]
test_ner_apple = [
[
"Apple is looking at buying U.K. startup for $1 billion",
{
"entities": [
(0, 5, "ORG"),
(27, 31, "GPE"),
(44, 54, "MONEY"),
]
}
]
]
def test_ner_per_type(en_vocab):
# Gold and Doc are identical
scorer = Scorer()
for input_, annot in test_ner_cardinal:
doc = get_doc(en_vocab, words = input_.split(' '), ents = [[0, 1, 'CARDINAL'], [2, 3, 'CARDINAL']])
gold = GoldParse(doc, entities = annot['entities'])
scorer.score(doc, gold)
results = scorer.scores
assert results['ents_p'] == 100
assert results['ents_f'] == 100
assert results['ents_r'] == 100
assert results['ents_per_type']['CARDINAL']['p'] == 100
assert results['ents_per_type']['CARDINAL']['f'] == 100
assert results['ents_per_type']['CARDINAL']['r'] == 100
# Doc has one missing and one extra entity
# Entity type MONEY is not present in Doc
scorer = Scorer()
for input_, annot in test_ner_apple:
doc = get_doc(en_vocab, words = input_.split(' '), ents = [[0, 1, 'ORG'], [5, 6, 'GPE'], [6, 7, 'ORG']])
gold = GoldParse(doc, entities = annot['entities'])
scorer.score(doc, gold)
results = scorer.scores
assert results['ents_p'] == approx(66.66666)
assert results['ents_r'] == approx(66.66666)
assert results['ents_f'] == approx(66.66666)
assert 'GPE' in results['ents_per_type']
assert 'MONEY' in results['ents_per_type']
assert 'ORG' in results['ents_per_type']
assert results['ents_per_type']['GPE']['p'] == 100
assert results['ents_per_type']['GPE']['r'] == 100
assert results['ents_per_type']['GPE']['f'] == 100
assert results['ents_per_type']['MONEY']['p'] == 0
assert results['ents_per_type']['MONEY']['r'] == 0
assert results['ents_per_type']['MONEY']['f'] == 0
assert results['ents_per_type']['ORG']['p'] == 50
assert results['ents_per_type']['ORG']['r'] == 100
assert results['ents_per_type']['ORG']['f'] == approx(66.66666)