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delete outdated tests

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svlandeg 2021-05-27 13:54:31 +02:00
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spacy/tests/pipeline/test_coref.py
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import pytest
import spacy
from spacy.matcher import PhraseMatcher
from spacy.training import Example
from spacy.lang.en import English
from spacy.tests.util import make_tempdir
from spacy.tokens import Doc
from spacy.pipeline.coref import DEFAULT_CLUSTERS_PREFIX
from spacy.pipeline.coref_er import DEFAULT_MENTIONS
# fmt: off
TRAIN_DATA = [
(
"John Smith told Laura that he was running late and asked her whether she could pick up their kids.",
{
"spans": {
DEFAULT_MENTIONS: [
(0, 10, "MENTION"),
(16, 21, "MENTION"),
(27, 29, "MENTION"),
(57, 60, "MENTION"),
(69, 72, "MENTION"),
(87, 92, "MENTION"),
(87, 97, "MENTION"),
],
f"{DEFAULT_CLUSTERS_PREFIX}_1": [
(0, 10, "MENTION"), # John
(27, 29, "MENTION"),
(87, 92, "MENTION"), # 'their' refers to John and Laur
],
f"{DEFAULT_CLUSTERS_PREFIX}_2": [
(16, 21, "MENTION"), # Laura
(57, 60, "MENTION"),
(69, 72, "MENTION"),
(87, 92, "MENTION"), # 'their' refers to John and Laura
],
}
},
),
(
"Yes, I noticed that many friends around me received it. It seems that almost everyone received this SMS.",
{
"spans": {
DEFAULT_MENTIONS: [
(5, 6, "MENTION"),
(40, 42, "MENTION"),
(52, 54, "MENTION"),
(95, 103, "MENTION"),
],
f"{DEFAULT_CLUSTERS_PREFIX}_1": [
(5, 6, "MENTION"), # I
(40, 42, "MENTION"),
],
f"{DEFAULT_CLUSTERS_PREFIX}_2": [
(52, 54, "MENTION"), # SMS
(95, 103, "MENTION"),
]
}
},
),
]
# fmt: on
@pytest.fixture
def nlp():
return English()
@pytest.fixture
def examples(nlp):
examples = []
for text, annot in TRAIN_DATA:
# eg = Example.from_dict(nlp.make_doc(text), annot)
# if PR #7197 is merged, replace below with above line
ref_doc = nlp.make_doc(text)
for key, span_list in annot["spans"].items():
spans = []
for span_tuple in span_list:
start_char = span_tuple[0]
end_char = span_tuple[1]
label = span_tuple[2]
span = ref_doc.char_span(start_char, end_char, label=label)
spans.append(span)
ref_doc.spans[key] = spans
eg = Example(nlp.make_doc(text), ref_doc)
examples.append(eg)
return examples
def test_coref_er_no_POS(nlp):
doc = nlp("The police woman talked to him.")
coref_er = nlp.add_pipe("coref_er", last=True)
with pytest.raises(ValueError):
coref_er(doc)
def test_coref_er_with_POS(nlp):
words = ["The", "police", "woman", "talked", "to", "him", "."]
pos = ["DET", "NOUN", "NOUN", "VERB", "ADP", "PRON", "PUNCT"]
doc = Doc(nlp.vocab, words=words, pos=pos)
coref_er = nlp.add_pipe("coref_er", last=True)
coref_er(doc)
assert len(doc.spans[coref_er.span_mentions]) == 1
mention = doc.spans[coref_er.span_mentions][0]
assert (mention.text, mention.start, mention.end) == ("him", 5, 6)
def test_coref_er_custom_POS(nlp):
words = ["The", "police", "woman", "talked", "to", "him", "."]
pos = ["DET", "NOUN", "NOUN", "VERB", "ADP", "PRON", "PUNCT"]
doc = Doc(nlp.vocab, words=words, pos=pos)
config = {"matcher_key": "POS", "matcher_values": ["NOUN"]}
coref_er = nlp.add_pipe("coref_er", last=True, config=config)
coref_er(doc)
assert len(doc.spans[coref_er.span_mentions]) == 1
mention = doc.spans[coref_er.span_mentions][0]
assert (mention.text, mention.start, mention.end) == ("police woman", 1, 3)
def test_coref_clusters(nlp, examples):
coref_er = nlp.add_pipe("coref_er", last=True)
coref = nlp.add_pipe("coref", last=True)
coref.initialize(lambda: examples)
words = ["Laura", "walked", "her", "dog", "."]
pos = ["PROPN", "VERB", "PRON", "NOUN", "PUNCT"]
doc = Doc(nlp.vocab, words=words, pos=pos)
coref_er(doc)
coref(doc)
assert len(doc.spans[coref_er.span_mentions]) > 0
found_clusters = 0
for name, spans in doc.spans.items():
if name.startswith(coref.span_cluster_prefix):
found_clusters += 1
assert found_clusters > 0
def test_coref_er_score(nlp, examples):
config = {"matcher_key": "POS", "matcher_values": []}
coref_er = nlp.add_pipe("coref_er", last=True, config=config)
coref = nlp.add_pipe("coref", last=True)
coref.initialize(lambda: examples)
mentions_key = coref_er.span_mentions
cluster_prefix_key = coref.span_cluster_prefix
matcher = PhraseMatcher(nlp.vocab)
terms_1 = ["Laura", "her", "she"]
terms_2 = ["it", "this SMS"]
matcher.add("A", [nlp.make_doc(text) for text in terms_1])
matcher.add("B", [nlp.make_doc(text) for text in terms_2])
for eg in examples:
pred = eg.predicted
matches = matcher(pred, as_spans=True)
pred.set_ents(matches)
coref_er(pred)
coref(pred)
eg.predicted = pred
# TODO: if #7209 is merged, experiment with 'include_label'
scores = coref_er.score([eg])
assert f"{mentions_key}_f" in scores
scores = coref.score([eg])
assert f"{cluster_prefix_key}_f" in scores
def test_coref_serialization(nlp):
# Test that the coref component can be serialized
config_er = {"matcher_key": "TAG", "matcher_values": ["NN"]}
nlp.add_pipe("coref_er", last=True, config=config_er)
nlp.add_pipe("coref", last=True)
assert "coref_er" in nlp.pipe_names
assert "coref" in nlp.pipe_names
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = spacy.load(tmp_dir)
assert "coref_er" in nlp2.pipe_names
assert "coref" in nlp2.pipe_names
coref_er_2 = nlp2.get_pipe("coref_er")
assert coref_er_2.matcher_key == "TAG"