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spaCy/spacy/tests/test_scorer.py

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from numpy.testing import assert_almost_equal, assert_array_almost_equal
import pytest
from pytest import approx
from spacy.gold import Example
from spacy.gold.iob_utils import biluo_tags_from_offsets
from spacy.scorer import Scorer, ROCAUCScore
from spacy.scorer import _roc_auc_score, _roc_curve
from .util import get_doc
from spacy.lang.en import English
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].morph_ = morphs[i]
doc.is_tagged = True
return doc
def test_las_per_type(en_vocab):
# Gold and Doc are identical
scorer = Scorer()
for input_, annot in test_las_apple:
doc = get_doc(
en_vocab,
words=input_.split(" "),
heads=([h - i for i, h in enumerate(annot["heads"])]),
deps=annot["deps"],
)
gold = {"heads": annot["heads"], "deps": annot["deps"]}
example = Example.from_dict(doc, gold)
scorer.score(example)
results = scorer.scores
assert results["uas"] == 100
assert results["las"] == 100
assert results["las_per_type"]["nsubj"]["p"] == 100
assert results["las_per_type"]["nsubj"]["r"] == 100
assert results["las_per_type"]["nsubj"]["f"] == 100
assert results["las_per_type"]["compound"]["p"] == 100
assert results["las_per_type"]["compound"]["r"] == 100
assert results["las_per_type"]["compound"]["f"] == 100
# One dep is incorrect in Doc
scorer = Scorer()
for input_, annot in test_las_apple:
doc = get_doc(
en_vocab,
words=input_.split(" "),
heads=([h - i for i, h in enumerate(annot["heads"])]),
deps=annot["deps"],
)
gold = {"heads": annot["heads"], "deps": annot["deps"]}
doc[0].dep_ = "compound"
example = Example.from_dict(doc, gold)
scorer.score(example)
results = scorer.scores
assert results["uas"] == 100
assert_almost_equal(results["las"], 90.9090909)
assert results["las_per_type"]["nsubj"]["p"] == 0
assert results["las_per_type"]["nsubj"]["r"] == 0
assert results["las_per_type"]["nsubj"]["f"] == 0
assert_almost_equal(results["las_per_type"]["compound"]["p"], 66.6666666)
assert results["las_per_type"]["compound"]["r"] == 100
assert results["las_per_type"]["compound"]["f"] == 80
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"]],
)
entities = biluo_tags_from_offsets(doc, annot["entities"])
ex = Example.from_dict(doc, {"entities": entities})
scorer.score(ex)
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"]],
)
entities = biluo_tags_from_offsets(doc, annot["entities"])
ex = Example.from_dict(doc, {"entities": entities})
scorer.score(ex)
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)
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": [t.morph_ for t in tagged_doc],
}
example = Example.from_dict(tagged_doc, gold)
scorer.score(example)
results = scorer.scores
assert results["tags_acc"] == 100
assert results["pos_acc"] == 100
assert results["morphs_acc"] == 100
assert results["morphs_per_type"]["NounType"]["f"] == 100
# Gold and Doc are identical
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 = [t.morph_ for t in tagged_doc]
morphs[1] = "Number=sing"
morphs[2] = "Number=plur"
gold = {"tags": tags, "pos": pos, "morphs": morphs}
example = Example.from_dict(tagged_doc, gold)
scorer.score(example)
results = scorer.scores
assert results["tags_acc"] == 90
assert results["pos_acc"] == 90
assert results["morphs_acc"] == approx(80)
assert results["morphs_per_type"]["Poss"]["f"] == 0.0
assert results["morphs_per_type"]["Number"]["f"] == approx(72.727272)
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)
assert score.score == -float("inf")
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)
assert score.score == -float("inf")
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