mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-16 06:37:04 +03:00
12974bf4d9
* Add micro PRF for morph scoring For pipelines where morph features are added by more than one component and a reference training corpus may not contain all features, a micro PRF score is more flexible than a simple accuracy score. An example is the reading and inflection features added by the Japanese tokenizer. * Use `morph_micro_f` as the default morph score for Japanese morphologizers. * Update docstring * Fix typo in docstring * Update Scorer API docs * Fix results type * Organize score list by attribute prefix
477 lines
15 KiB
Python
477 lines
15 KiB
Python
from numpy.testing import assert_almost_equal, assert_array_almost_equal
|
|
import pytest
|
|
from pytest import approx
|
|
from spacy.training import Example
|
|
from spacy.training.iob_utils import offsets_to_biluo_tags
|
|
from spacy.scorer import Scorer, ROCAUCScore, PRFScore
|
|
from spacy.scorer import _roc_auc_score, _roc_curve
|
|
from spacy.lang.en import English
|
|
from spacy.tokens import Doc, Span
|
|
|
|
|
|
test_las_apple = [
|
|
[
|
|
"Apple is looking at buying U.K. startup for $ 1 billion",
|
|
{
|
|
"heads": [2, 2, 2, 2, 3, 6, 4, 4, 10, 10, 7],
|
|
"deps": [
|
|
"nsubj",
|
|
"aux",
|
|
"ROOT",
|
|
"prep",
|
|
"pcomp",
|
|
"compound",
|
|
"dobj",
|
|
"prep",
|
|
"quantmod",
|
|
"compound",
|
|
"pobj",
|
|
],
|
|
},
|
|
]
|
|
]
|
|
|
|
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")]},
|
|
]
|
|
]
|
|
|
|
|
|
@pytest.fixture
|
|
def tagged_doc():
|
|
text = "Sarah's sister flew to Silicon Valley via London."
|
|
tags = ["NNP", "POS", "NN", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
|
|
pos = [
|
|
"PROPN",
|
|
"PART",
|
|
"NOUN",
|
|
"VERB",
|
|
"ADP",
|
|
"PROPN",
|
|
"PROPN",
|
|
"ADP",
|
|
"PROPN",
|
|
"PUNCT",
|
|
]
|
|
morphs = [
|
|
"NounType=prop|Number=sing",
|
|
"Poss=yes",
|
|
"Number=sing",
|
|
"Tense=past|VerbForm=fin",
|
|
"",
|
|
"NounType=prop|Number=sing",
|
|
"NounType=prop|Number=sing",
|
|
"",
|
|
"NounType=prop|Number=sing",
|
|
"PunctType=peri",
|
|
]
|
|
nlp = English()
|
|
doc = nlp(text)
|
|
for i in range(len(tags)):
|
|
doc[i].tag_ = tags[i]
|
|
doc[i].pos_ = pos[i]
|
|
doc[i].set_morph(morphs[i])
|
|
if i > 0:
|
|
doc[i].is_sent_start = False
|
|
return doc
|
|
|
|
|
|
@pytest.fixture
|
|
def sented_doc():
|
|
text = "One sentence. Two sentences. Three sentences."
|
|
nlp = English()
|
|
doc = nlp(text)
|
|
for i in range(len(doc)):
|
|
if i % 3 == 0:
|
|
doc[i].is_sent_start = True
|
|
else:
|
|
doc[i].is_sent_start = False
|
|
return doc
|
|
|
|
|
|
def test_tokenization(sented_doc):
|
|
scorer = Scorer()
|
|
gold = {"sent_starts": [t.sent_start for t in sented_doc]}
|
|
example = Example.from_dict(sented_doc, gold)
|
|
scores = scorer.score([example])
|
|
assert scores["token_acc"] == 1.0
|
|
|
|
nlp = English()
|
|
example.predicted = Doc(
|
|
nlp.vocab,
|
|
words=["One", "sentence.", "Two", "sentences.", "Three", "sentences."],
|
|
spaces=[True, True, True, True, True, False],
|
|
)
|
|
example.predicted[1].is_sent_start = False
|
|
scores = scorer.score([example])
|
|
assert scores["token_acc"] == approx(0.66666666)
|
|
assert scores["token_p"] == 0.5
|
|
assert scores["token_r"] == approx(0.33333333)
|
|
assert scores["token_f"] == 0.4
|
|
|
|
|
|
def test_sents(sented_doc):
|
|
scorer = Scorer()
|
|
gold = {"sent_starts": [t.sent_start for t in sented_doc]}
|
|
example = Example.from_dict(sented_doc, gold)
|
|
scores = scorer.score([example])
|
|
assert scores["sents_f"] == 1.0
|
|
|
|
# One sentence start is moved
|
|
gold["sent_starts"][3] = 0
|
|
gold["sent_starts"][4] = 1
|
|
example = Example.from_dict(sented_doc, gold)
|
|
scores = scorer.score([example])
|
|
assert scores["sents_f"] == approx(0.3333333)
|
|
|
|
|
|
def test_las_per_type(en_vocab):
|
|
# Gold and Doc are identical
|
|
scorer = Scorer()
|
|
examples = []
|
|
for input_, annot in test_las_apple:
|
|
doc = Doc(
|
|
en_vocab, words=input_.split(" "), heads=annot["heads"], deps=annot["deps"]
|
|
)
|
|
gold = {"heads": annot["heads"], "deps": annot["deps"]}
|
|
example = Example.from_dict(doc, gold)
|
|
examples.append(example)
|
|
results = scorer.score(examples)
|
|
|
|
assert results["dep_uas"] == 1.0
|
|
assert results["dep_las"] == 1.0
|
|
assert results["dep_las_per_type"]["nsubj"]["p"] == 1.0
|
|
assert results["dep_las_per_type"]["nsubj"]["r"] == 1.0
|
|
assert results["dep_las_per_type"]["nsubj"]["f"] == 1.0
|
|
assert results["dep_las_per_type"]["compound"]["p"] == 1.0
|
|
assert results["dep_las_per_type"]["compound"]["r"] == 1.0
|
|
assert results["dep_las_per_type"]["compound"]["f"] == 1.0
|
|
|
|
# One dep is incorrect in Doc
|
|
scorer = Scorer()
|
|
examples = []
|
|
for input_, annot in test_las_apple:
|
|
doc = Doc(
|
|
en_vocab, words=input_.split(" "), heads=annot["heads"], deps=annot["deps"]
|
|
)
|
|
gold = {"heads": annot["heads"], "deps": annot["deps"]}
|
|
doc[0].dep_ = "compound"
|
|
example = Example.from_dict(doc, gold)
|
|
examples.append(example)
|
|
results = scorer.score(examples)
|
|
|
|
assert results["dep_uas"] == 1.0
|
|
assert_almost_equal(results["dep_las"], 0.9090909)
|
|
assert results["dep_las_per_type"]["nsubj"]["p"] == 0
|
|
assert results["dep_las_per_type"]["nsubj"]["r"] == 0
|
|
assert results["dep_las_per_type"]["nsubj"]["f"] == 0
|
|
assert_almost_equal(results["dep_las_per_type"]["compound"]["p"], 0.666666666)
|
|
assert results["dep_las_per_type"]["compound"]["r"] == 1.0
|
|
assert results["dep_las_per_type"]["compound"]["f"] == 0.8
|
|
|
|
|
|
def test_ner_per_type(en_vocab):
|
|
# Gold and Doc are identical
|
|
scorer = Scorer()
|
|
examples = []
|
|
for input_, annot in test_ner_cardinal:
|
|
doc = Doc(
|
|
en_vocab, words=input_.split(" "), ents=["B-CARDINAL", "O", "B-CARDINAL"]
|
|
)
|
|
entities = offsets_to_biluo_tags(doc, annot["entities"])
|
|
example = Example.from_dict(doc, {"entities": entities})
|
|
# a hack for sentence boundaries
|
|
example.predicted[1].is_sent_start = False
|
|
example.reference[1].is_sent_start = False
|
|
examples.append(example)
|
|
results = scorer.score(examples)
|
|
|
|
assert results["ents_p"] == 1.0
|
|
assert results["ents_r"] == 1.0
|
|
assert results["ents_f"] == 1.0
|
|
assert results["ents_per_type"]["CARDINAL"]["p"] == 1.0
|
|
assert results["ents_per_type"]["CARDINAL"]["r"] == 1.0
|
|
assert results["ents_per_type"]["CARDINAL"]["f"] == 1.0
|
|
|
|
# Doc has one missing and one extra entity
|
|
# Entity type MONEY is not present in Doc
|
|
scorer = Scorer()
|
|
examples = []
|
|
for input_, annot in test_ner_apple:
|
|
doc = Doc(
|
|
en_vocab,
|
|
words=input_.split(" "),
|
|
ents=["B-ORG", "O", "O", "O", "O", "B-GPE", "B-ORG", "O", "O", "O"],
|
|
)
|
|
entities = offsets_to_biluo_tags(doc, annot["entities"])
|
|
example = Example.from_dict(doc, {"entities": entities})
|
|
# a hack for sentence boundaries
|
|
example.predicted[1].is_sent_start = False
|
|
example.reference[1].is_sent_start = False
|
|
examples.append(example)
|
|
results = scorer.score(examples)
|
|
|
|
assert results["ents_p"] == approx(0.6666666)
|
|
assert results["ents_r"] == approx(0.6666666)
|
|
assert results["ents_f"] == approx(0.6666666)
|
|
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"] == 1.0
|
|
assert results["ents_per_type"]["GPE"]["r"] == 1.0
|
|
assert results["ents_per_type"]["GPE"]["f"] == 1.0
|
|
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"] == 0.5
|
|
assert results["ents_per_type"]["ORG"]["r"] == 1.0
|
|
assert results["ents_per_type"]["ORG"]["f"] == approx(0.6666666)
|
|
|
|
|
|
def test_tag_score(tagged_doc):
|
|
# Gold and Doc are identical
|
|
scorer = Scorer()
|
|
gold = {
|
|
"tags": [t.tag_ for t in tagged_doc],
|
|
"pos": [t.pos_ for t in tagged_doc],
|
|
"morphs": [str(t.morph) for t in tagged_doc],
|
|
"sent_starts": [1 if t.is_sent_start else -1 for t in tagged_doc],
|
|
}
|
|
example = Example.from_dict(tagged_doc, gold)
|
|
results = scorer.score([example])
|
|
|
|
assert results["tag_acc"] == 1.0
|
|
assert results["pos_acc"] == 1.0
|
|
assert results["morph_acc"] == 1.0
|
|
assert results["morph_micro_f"] == 1.0
|
|
assert results["morph_per_feat"]["NounType"]["f"] == 1.0
|
|
|
|
# Gold annotation is modified
|
|
scorer = Scorer()
|
|
tags = [t.tag_ for t in tagged_doc]
|
|
tags[0] = "NN"
|
|
pos = [t.pos_ for t in tagged_doc]
|
|
pos[1] = "X"
|
|
morphs = [str(t.morph) for t in tagged_doc]
|
|
morphs[1] = "Number=sing"
|
|
morphs[2] = "Number=plur"
|
|
gold = {
|
|
"tags": tags,
|
|
"pos": pos,
|
|
"morphs": morphs,
|
|
"sent_starts": gold["sent_starts"],
|
|
}
|
|
example = Example.from_dict(tagged_doc, gold)
|
|
results = scorer.score([example])
|
|
|
|
assert results["tag_acc"] == 0.9
|
|
assert results["pos_acc"] == 0.9
|
|
assert results["morph_acc"] == approx(0.8)
|
|
assert results["morph_micro_f"] == approx(0.8461538)
|
|
assert results["morph_per_feat"]["NounType"]["f"] == 1.0
|
|
assert results["morph_per_feat"]["Poss"]["f"] == 0.0
|
|
assert results["morph_per_feat"]["Number"]["f"] == approx(0.72727272)
|
|
|
|
|
|
def test_partial_annotation(en_tokenizer):
|
|
pred_doc = en_tokenizer("a b c d e")
|
|
pred_doc[0].tag_ = "A"
|
|
pred_doc[0].pos_ = "X"
|
|
pred_doc[0].set_morph("Feat=Val")
|
|
pred_doc[0].dep_ = "dep"
|
|
|
|
# unannotated reference
|
|
ref_doc = en_tokenizer("a b c d e")
|
|
ref_doc.has_unknown_spaces = True
|
|
example = Example(pred_doc, ref_doc)
|
|
scorer = Scorer()
|
|
scores = scorer.score([example])
|
|
for key in scores:
|
|
# cats doesn't have an unset state
|
|
if key.startswith("cats"):
|
|
continue
|
|
assert scores[key] is None
|
|
|
|
# partially annotated reference, not overlapping with predicted annotation
|
|
ref_doc = en_tokenizer("a b c d e")
|
|
ref_doc.has_unknown_spaces = True
|
|
ref_doc[1].tag_ = "A"
|
|
ref_doc[1].pos_ = "X"
|
|
ref_doc[1].set_morph("Feat=Val")
|
|
ref_doc[1].dep_ = "dep"
|
|
example = Example(pred_doc, ref_doc)
|
|
scorer = Scorer()
|
|
scores = scorer.score([example])
|
|
assert scores["token_acc"] is None
|
|
assert scores["tag_acc"] == 0.0
|
|
assert scores["pos_acc"] == 0.0
|
|
assert scores["morph_acc"] == 0.0
|
|
assert scores["dep_uas"] == 1.0
|
|
assert scores["dep_las"] == 0.0
|
|
assert scores["sents_f"] is None
|
|
|
|
# partially annotated reference, overlapping with predicted annotation
|
|
ref_doc = en_tokenizer("a b c d e")
|
|
ref_doc.has_unknown_spaces = True
|
|
ref_doc[0].tag_ = "A"
|
|
ref_doc[0].pos_ = "X"
|
|
ref_doc[1].set_morph("Feat=Val")
|
|
ref_doc[1].dep_ = "dep"
|
|
example = Example(pred_doc, ref_doc)
|
|
scorer = Scorer()
|
|
scores = scorer.score([example])
|
|
assert scores["token_acc"] is None
|
|
assert scores["tag_acc"] == 1.0
|
|
assert scores["pos_acc"] == 1.0
|
|
assert scores["morph_acc"] == 0.0
|
|
assert scores["dep_uas"] == 1.0
|
|
assert scores["dep_las"] == 0.0
|
|
assert scores["sents_f"] is None
|
|
|
|
|
|
def test_roc_auc_score():
|
|
# Binary classification, toy tests from scikit-learn test suite
|
|
y_true = [0, 1]
|
|
y_score = [0, 1]
|
|
tpr, fpr, _ = _roc_curve(y_true, y_score)
|
|
roc_auc = _roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 0, 1])
|
|
assert_array_almost_equal(fpr, [0, 1, 1])
|
|
assert_almost_equal(roc_auc, 1.0)
|
|
|
|
y_true = [0, 1]
|
|
y_score = [1, 0]
|
|
tpr, fpr, _ = _roc_curve(y_true, y_score)
|
|
roc_auc = _roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 1, 1])
|
|
assert_array_almost_equal(fpr, [0, 0, 1])
|
|
assert_almost_equal(roc_auc, 0.0)
|
|
|
|
y_true = [1, 0]
|
|
y_score = [1, 1]
|
|
tpr, fpr, _ = _roc_curve(y_true, y_score)
|
|
roc_auc = _roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 1])
|
|
assert_array_almost_equal(fpr, [0, 1])
|
|
assert_almost_equal(roc_auc, 0.5)
|
|
|
|
y_true = [1, 0]
|
|
y_score = [1, 0]
|
|
tpr, fpr, _ = _roc_curve(y_true, y_score)
|
|
roc_auc = _roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 0, 1])
|
|
assert_array_almost_equal(fpr, [0, 1, 1])
|
|
assert_almost_equal(roc_auc, 1.0)
|
|
|
|
y_true = [1, 0]
|
|
y_score = [0.5, 0.5]
|
|
tpr, fpr, _ = _roc_curve(y_true, y_score)
|
|
roc_auc = _roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 1])
|
|
assert_array_almost_equal(fpr, [0, 1])
|
|
assert_almost_equal(roc_auc, 0.5)
|
|
|
|
# same result as above with ROCAUCScore wrapper
|
|
score = ROCAUCScore()
|
|
score.score_set(0.5, 1)
|
|
score.score_set(0.5, 0)
|
|
assert_almost_equal(score.score, 0.5)
|
|
|
|
# check that errors are raised in undefined cases and score is -inf
|
|
y_true = [0, 0]
|
|
y_score = [0.25, 0.75]
|
|
with pytest.raises(ValueError):
|
|
_roc_auc_score(y_true, y_score)
|
|
|
|
score = ROCAUCScore()
|
|
score.score_set(0.25, 0)
|
|
score.score_set(0.75, 0)
|
|
with pytest.raises(ValueError):
|
|
_ = score.score # noqa: F841
|
|
|
|
y_true = [1, 1]
|
|
y_score = [0.25, 0.75]
|
|
with pytest.raises(ValueError):
|
|
_roc_auc_score(y_true, y_score)
|
|
|
|
score = ROCAUCScore()
|
|
score.score_set(0.25, 1)
|
|
score.score_set(0.75, 1)
|
|
with pytest.raises(ValueError):
|
|
_ = score.score # noqa: F841
|
|
|
|
|
|
def test_score_spans():
|
|
nlp = English()
|
|
text = "This is just a random sentence."
|
|
key = "my_spans"
|
|
gold = nlp.make_doc(text)
|
|
pred = nlp.make_doc(text)
|
|
spans = []
|
|
spans.append(gold.char_span(0, 4, label="PERSON"))
|
|
spans.append(gold.char_span(0, 7, label="ORG"))
|
|
spans.append(gold.char_span(8, 12, label="ORG"))
|
|
gold.spans[key] = spans
|
|
|
|
def span_getter(doc, span_key):
|
|
return doc.spans[span_key]
|
|
|
|
# Predict exactly the same, but overlapping spans will be discarded
|
|
pred.spans[key] = spans
|
|
eg = Example(pred, gold)
|
|
scores = Scorer.score_spans([eg], attr=key, getter=span_getter)
|
|
assert scores[f"{key}_p"] == 1.0
|
|
assert scores[f"{key}_r"] < 1.0
|
|
|
|
# Allow overlapping, now both precision and recall should be 100%
|
|
pred.spans[key] = spans
|
|
eg = Example(pred, gold)
|
|
scores = Scorer.score_spans([eg], attr=key, getter=span_getter, allow_overlap=True)
|
|
assert scores[f"{key}_p"] == 1.0
|
|
assert scores[f"{key}_r"] == 1.0
|
|
|
|
# Change the predicted labels
|
|
new_spans = [Span(pred, span.start, span.end, label="WRONG") for span in spans]
|
|
pred.spans[key] = new_spans
|
|
eg = Example(pred, gold)
|
|
scores = Scorer.score_spans([eg], attr=key, getter=span_getter, allow_overlap=True)
|
|
assert scores[f"{key}_p"] == 0.0
|
|
assert scores[f"{key}_r"] == 0.0
|
|
assert f"{key}_per_type" in scores
|
|
|
|
# Discard labels from the evaluation
|
|
scores = Scorer.score_spans(
|
|
[eg], attr=key, getter=span_getter, allow_overlap=True, labeled=False
|
|
)
|
|
assert scores[f"{key}_p"] == 1.0
|
|
assert scores[f"{key}_r"] == 1.0
|
|
assert f"{key}_per_type" not in scores
|
|
|
|
|
|
def test_prf_score():
|
|
cand = {"hi", "ho"}
|
|
gold1 = {"yo", "hi"}
|
|
gold2 = set()
|
|
|
|
a = PRFScore()
|
|
a.score_set(cand=cand, gold=gold1)
|
|
assert (a.precision, a.recall, a.fscore) == approx((0.5, 0.5, 0.5))
|
|
|
|
b = PRFScore()
|
|
b.score_set(cand=cand, gold=gold2)
|
|
assert (b.precision, b.recall, b.fscore) == approx((0.0, 0.0, 0.0))
|
|
|
|
c = a + b
|
|
assert (c.precision, c.recall, c.fscore) == approx((0.25, 0.5, 0.33333333))
|
|
|
|
a += b
|
|
assert (a.precision, a.recall, a.fscore) == approx(
|
|
(c.precision, c.recall, c.fscore)
|
|
)
|