mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
86c3ec9c2b
* Refactor Token morph setting * Remove `Token.morph_` * Add `Token.set_morph()` * `0` resets `token.c.morph` to unset * Any other values are passed to `Morphology.add` * Add token.morph setter to set from MorphAnalysis
609 lines
21 KiB
Python
609 lines
21 KiB
Python
import pytest
|
|
import numpy
|
|
from spacy.tokens import Doc, Span
|
|
from spacy.vocab import Vocab
|
|
from spacy.lexeme import Lexeme
|
|
from spacy.lang.en import English
|
|
from spacy.attrs import ENT_TYPE, ENT_IOB, SENT_START, HEAD, DEP, MORPH
|
|
|
|
|
|
def test_doc_api_init(en_vocab):
|
|
words = ["a", "b", "c", "d"]
|
|
heads = [0, 0, 2, 2]
|
|
# set sent_start by sent_starts
|
|
doc = Doc(en_vocab, words=words, sent_starts=[True, False, True, False])
|
|
assert [t.is_sent_start for t in doc] == [True, False, True, False]
|
|
|
|
# set sent_start by heads
|
|
doc = Doc(en_vocab, words=words, heads=heads, deps=["dep"] * 4)
|
|
assert [t.is_sent_start for t in doc] == [True, False, True, False]
|
|
# heads override sent_starts
|
|
doc = Doc(
|
|
en_vocab, words=words, sent_starts=[True] * 4, heads=heads, deps=["dep"] * 4
|
|
)
|
|
assert [t.is_sent_start for t in doc] == [True, False, True, False]
|
|
|
|
|
|
@pytest.mark.parametrize("text", [["one", "two", "three"]])
|
|
def test_doc_api_compare_by_string_position(en_vocab, text):
|
|
doc = Doc(en_vocab, words=text)
|
|
# Get the tokens in this order, so their ID ordering doesn't match the idx
|
|
token3 = doc[-1]
|
|
token2 = doc[-2]
|
|
token1 = doc[-1]
|
|
token1, token2, token3 = doc
|
|
assert token1 < token2 < token3
|
|
assert not token1 > token2
|
|
assert token2 > token1
|
|
assert token2 <= token3
|
|
assert token3 >= token1
|
|
|
|
|
|
def test_doc_api_getitem(en_tokenizer):
|
|
text = "Give it back! He pleaded."
|
|
tokens = en_tokenizer(text)
|
|
assert tokens[0].text == "Give"
|
|
assert tokens[-1].text == "."
|
|
with pytest.raises(IndexError):
|
|
tokens[len(tokens)]
|
|
|
|
def to_str(span):
|
|
return "/".join(token.text for token in span)
|
|
|
|
span = tokens[1:1]
|
|
assert not to_str(span)
|
|
span = tokens[1:4]
|
|
assert to_str(span) == "it/back/!"
|
|
span = tokens[1:4:1]
|
|
assert to_str(span) == "it/back/!"
|
|
with pytest.raises(ValueError):
|
|
tokens[1:4:2]
|
|
with pytest.raises(ValueError):
|
|
tokens[1:4:-1]
|
|
|
|
span = tokens[-3:6]
|
|
assert to_str(span) == "He/pleaded"
|
|
span = tokens[4:-1]
|
|
assert to_str(span) == "He/pleaded"
|
|
span = tokens[-5:-3]
|
|
assert to_str(span) == "back/!"
|
|
span = tokens[5:4]
|
|
assert span.start == span.end == 5 and not to_str(span)
|
|
span = tokens[4:-3]
|
|
assert span.start == span.end == 4 and not to_str(span)
|
|
|
|
span = tokens[:]
|
|
assert to_str(span) == "Give/it/back/!/He/pleaded/."
|
|
span = tokens[4:]
|
|
assert to_str(span) == "He/pleaded/."
|
|
span = tokens[:4]
|
|
assert to_str(span) == "Give/it/back/!"
|
|
span = tokens[:-3]
|
|
assert to_str(span) == "Give/it/back/!"
|
|
span = tokens[-3:]
|
|
assert to_str(span) == "He/pleaded/."
|
|
|
|
span = tokens[4:50]
|
|
assert to_str(span) == "He/pleaded/."
|
|
span = tokens[-50:4]
|
|
assert to_str(span) == "Give/it/back/!"
|
|
span = tokens[-50:-40]
|
|
assert span.start == span.end == 0 and not to_str(span)
|
|
span = tokens[40:50]
|
|
assert span.start == span.end == 7 and not to_str(span)
|
|
|
|
span = tokens[1:4]
|
|
assert span[0].orth_ == "it"
|
|
subspan = span[:]
|
|
assert to_str(subspan) == "it/back/!"
|
|
subspan = span[:2]
|
|
assert to_str(subspan) == "it/back"
|
|
subspan = span[1:]
|
|
assert to_str(subspan) == "back/!"
|
|
subspan = span[:-1]
|
|
assert to_str(subspan) == "it/back"
|
|
subspan = span[-2:]
|
|
assert to_str(subspan) == "back/!"
|
|
subspan = span[1:2]
|
|
assert to_str(subspan) == "back"
|
|
subspan = span[-2:-1]
|
|
assert to_str(subspan) == "back"
|
|
subspan = span[-50:50]
|
|
assert to_str(subspan) == "it/back/!"
|
|
subspan = span[50:-50]
|
|
assert subspan.start == subspan.end == 4 and not to_str(subspan)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"text", ["Give it back! He pleaded.", " Give it back! He pleaded. "]
|
|
)
|
|
def test_doc_api_serialize(en_tokenizer, text):
|
|
tokens = en_tokenizer(text)
|
|
tokens[0].lemma_ = "lemma"
|
|
tokens[0].norm_ = "norm"
|
|
tokens.ents = [(tokens.vocab.strings["PRODUCT"], 0, 1)]
|
|
tokens[0].ent_kb_id_ = "ent_kb_id"
|
|
new_tokens = Doc(tokens.vocab).from_bytes(tokens.to_bytes())
|
|
assert tokens.text == new_tokens.text
|
|
assert [t.text for t in tokens] == [t.text for t in new_tokens]
|
|
assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
|
|
assert new_tokens[0].lemma_ == "lemma"
|
|
assert new_tokens[0].norm_ == "norm"
|
|
assert new_tokens[0].ent_kb_id_ == "ent_kb_id"
|
|
|
|
new_tokens = Doc(tokens.vocab).from_bytes(
|
|
tokens.to_bytes(exclude=["tensor"]), exclude=["tensor"]
|
|
)
|
|
assert tokens.text == new_tokens.text
|
|
assert [t.text for t in tokens] == [t.text for t in new_tokens]
|
|
assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
|
|
|
|
new_tokens = Doc(tokens.vocab).from_bytes(
|
|
tokens.to_bytes(exclude=["sentiment"]), exclude=["sentiment"]
|
|
)
|
|
assert tokens.text == new_tokens.text
|
|
assert [t.text for t in tokens] == [t.text for t in new_tokens]
|
|
assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
|
|
|
|
|
|
def test_doc_api_set_ents(en_tokenizer):
|
|
text = "I use goggle chrone to surf the web"
|
|
tokens = en_tokenizer(text)
|
|
assert len(tokens.ents) == 0
|
|
tokens.ents = [(tokens.vocab.strings["PRODUCT"], 2, 4)]
|
|
assert len(list(tokens.ents)) == 1
|
|
assert [t.ent_iob for t in tokens] == [2, 2, 3, 1, 2, 2, 2, 2]
|
|
assert tokens.ents[0].label_ == "PRODUCT"
|
|
assert tokens.ents[0].start == 2
|
|
assert tokens.ents[0].end == 4
|
|
|
|
|
|
def test_doc_api_sents_empty_string(en_tokenizer):
|
|
doc = en_tokenizer("")
|
|
sents = list(doc.sents)
|
|
assert len(sents) == 0
|
|
|
|
|
|
def test_doc_api_runtime_error(en_tokenizer):
|
|
# Example that caused run-time error while parsing Reddit
|
|
# fmt: off
|
|
text = "67% of black households are single parent \n\n72% of all black babies born out of wedlock \n\n50% of all black kids don\u2019t finish high school"
|
|
deps = ["nummod", "nsubj", "prep", "amod", "pobj", "ROOT", "amod", "attr", "", "nummod", "appos", "prep", "det",
|
|
"amod", "pobj", "acl", "prep", "prep", "pobj",
|
|
"", "nummod", "nsubj", "prep", "det", "amod", "pobj", "aux", "neg", "ccomp", "amod", "dobj"]
|
|
# fmt: on
|
|
tokens = en_tokenizer(text)
|
|
doc = Doc(tokens.vocab, words=[t.text for t in tokens], deps=deps)
|
|
nps = []
|
|
for np in doc.noun_chunks:
|
|
while len(np) > 1 and np[0].dep_ not in ("advmod", "amod", "compound"):
|
|
np = np[1:]
|
|
if len(np) > 1:
|
|
nps.append(np)
|
|
with doc.retokenize() as retokenizer:
|
|
for np in nps:
|
|
attrs = {
|
|
"tag": np.root.tag_,
|
|
"lemma": np.text,
|
|
"ent_type": np.root.ent_type_,
|
|
}
|
|
retokenizer.merge(np, attrs=attrs)
|
|
|
|
|
|
def test_doc_api_right_edge(en_vocab):
|
|
"""Test for bug occurring from Unshift action, causing incorrect right edge"""
|
|
# fmt: off
|
|
words = [
|
|
"I", "have", "proposed", "to", "myself", ",", "for", "the", "sake",
|
|
"of", "such", "as", "live", "under", "the", "government", "of", "the",
|
|
"Romans", ",", "to", "translate", "those", "books", "into", "the",
|
|
"Greek", "tongue", "."
|
|
]
|
|
heads = [2, 2, 2, 2, 3, 2, 21, 8, 6, 8, 11, 8, 11, 12, 15, 13, 15, 18, 16, 12, 21, 2, 23, 21, 21, 27, 27, 24, 2]
|
|
deps = ["dep"] * len(heads)
|
|
# fmt: on
|
|
doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
|
|
assert doc[6].text == "for"
|
|
subtree = [w.text for w in doc[6].subtree]
|
|
# fmt: off
|
|
assert subtree == ["for", "the", "sake", "of", "such", "as", "live", "under", "the", "government", "of", "the", "Romans", ","]
|
|
# fmt: on
|
|
assert doc[6].right_edge.text == ","
|
|
|
|
|
|
def test_doc_api_has_vector():
|
|
vocab = Vocab()
|
|
vocab.reset_vectors(width=2)
|
|
vocab.set_vector("kitten", vector=numpy.asarray([0.0, 2.0], dtype="f"))
|
|
doc = Doc(vocab, words=["kitten"])
|
|
assert doc.has_vector
|
|
|
|
|
|
def test_doc_api_similarity_match():
|
|
doc = Doc(Vocab(), words=["a"])
|
|
assert doc.similarity(doc[0]) == 1.0
|
|
assert doc.similarity(doc.vocab["a"]) == 1.0
|
|
doc2 = Doc(doc.vocab, words=["a", "b", "c"])
|
|
with pytest.warns(UserWarning):
|
|
assert doc.similarity(doc2[:1]) == 1.0
|
|
assert doc.similarity(doc2) == 0.0
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"words,heads,lca_matrix",
|
|
[
|
|
(
|
|
["the", "lazy", "dog", "slept"],
|
|
[2, 2, 3, 3],
|
|
numpy.array([[0, 2, 2, 3], [2, 1, 2, 3], [2, 2, 2, 3], [3, 3, 3, 3]]),
|
|
),
|
|
(
|
|
["The", "lazy", "dog", "slept", ".", "The", "quick", "fox", "jumped"],
|
|
[2, 2, 3, 3, 3, 7, 7, 8, 8],
|
|
numpy.array(
|
|
[
|
|
[0, 2, 2, 3, 3, -1, -1, -1, -1],
|
|
[2, 1, 2, 3, 3, -1, -1, -1, -1],
|
|
[2, 2, 2, 3, 3, -1, -1, -1, -1],
|
|
[3, 3, 3, 3, 3, -1, -1, -1, -1],
|
|
[3, 3, 3, 3, 4, -1, -1, -1, -1],
|
|
[-1, -1, -1, -1, -1, 5, 7, 7, 8],
|
|
[-1, -1, -1, -1, -1, 7, 6, 7, 8],
|
|
[-1, -1, -1, -1, -1, 7, 7, 7, 8],
|
|
[-1, -1, -1, -1, -1, 8, 8, 8, 8],
|
|
]
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_lowest_common_ancestor(en_vocab, words, heads, lca_matrix):
|
|
doc = Doc(en_vocab, words, heads=heads, deps=["dep"] * len(heads))
|
|
lca = doc.get_lca_matrix()
|
|
assert (lca == lca_matrix).all()
|
|
assert lca[1, 1] == 1
|
|
assert lca[0, 1] == 2
|
|
assert lca[1, 2] == 2
|
|
|
|
|
|
def test_doc_is_nered(en_vocab):
|
|
words = ["I", "live", "in", "New", "York"]
|
|
doc = Doc(en_vocab, words=words)
|
|
assert not doc.has_annotation("ENT_IOB")
|
|
doc.ents = [Span(doc, 3, 5, label="GPE")]
|
|
assert doc.has_annotation("ENT_IOB")
|
|
# Test creating doc from array with unknown values
|
|
arr = numpy.array([[0, 0], [0, 0], [0, 0], [384, 3], [384, 1]], dtype="uint64")
|
|
doc = Doc(en_vocab, words=words).from_array([ENT_TYPE, ENT_IOB], arr)
|
|
assert doc.has_annotation("ENT_IOB")
|
|
# Test serialization
|
|
new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
|
|
assert new_doc.has_annotation("ENT_IOB")
|
|
|
|
|
|
def test_doc_from_array_sent_starts(en_vocab):
|
|
# fmt: off
|
|
words = ["I", "live", "in", "New", "York", ".", "I", "like", "cats", "."]
|
|
heads = [0, 0, 0, 0, 0, 0, 6, 6, 6, 6]
|
|
deps = ["ROOT", "dep", "dep", "dep", "dep", "dep", "ROOT", "dep", "dep", "dep"]
|
|
# fmt: on
|
|
doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
|
|
# HEAD overrides SENT_START without warning
|
|
attrs = [SENT_START, HEAD]
|
|
arr = doc.to_array(attrs)
|
|
new_doc = Doc(en_vocab, words=words)
|
|
new_doc.from_array(attrs, arr)
|
|
# no warning using default attrs
|
|
attrs = doc._get_array_attrs()
|
|
arr = doc.to_array(attrs)
|
|
with pytest.warns(None) as record:
|
|
new_doc.from_array(attrs, arr)
|
|
assert len(record) == 0
|
|
# only SENT_START uses SENT_START
|
|
attrs = [SENT_START]
|
|
arr = doc.to_array(attrs)
|
|
new_doc = Doc(en_vocab, words=words)
|
|
new_doc.from_array(attrs, arr)
|
|
assert [t.is_sent_start for t in doc] == [t.is_sent_start for t in new_doc]
|
|
assert not new_doc.has_annotation("DEP")
|
|
# only HEAD uses HEAD
|
|
attrs = [HEAD, DEP]
|
|
arr = doc.to_array(attrs)
|
|
new_doc = Doc(en_vocab, words=words)
|
|
new_doc.from_array(attrs, arr)
|
|
assert [t.is_sent_start for t in doc] == [t.is_sent_start for t in new_doc]
|
|
assert new_doc.has_annotation("DEP")
|
|
|
|
|
|
def test_doc_from_array_morph(en_vocab):
|
|
# fmt: off
|
|
words = ["I", "live", "in", "New", "York", "."]
|
|
morphs = ["Feat1=A", "Feat1=B", "Feat1=C", "Feat1=A|Feat2=D", "Feat2=E", "Feat3=F"]
|
|
# fmt: on
|
|
doc = Doc(en_vocab, words=words, morphs=morphs)
|
|
attrs = [MORPH]
|
|
arr = doc.to_array(attrs)
|
|
new_doc = Doc(en_vocab, words=words)
|
|
new_doc.from_array(attrs, arr)
|
|
assert [str(t.morph) for t in new_doc] == morphs
|
|
assert [str(t.morph) for t in doc] == [str(t.morph) for t in new_doc]
|
|
|
|
|
|
def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
|
|
en_texts = ["Merging the docs is fun.", "", "They don't think alike."]
|
|
en_texts_without_empty = [t for t in en_texts if len(t)]
|
|
de_text = "Wie war die Frage?"
|
|
en_docs = [en_tokenizer(text) for text in en_texts]
|
|
docs_idx = en_texts[0].index("docs")
|
|
de_doc = de_tokenizer(de_text)
|
|
expected = (True, None, None, None)
|
|
en_docs[0].user_data[("._.", "is_ambiguous", docs_idx, None)] = expected
|
|
assert Doc.from_docs([]) is None
|
|
assert de_doc is not Doc.from_docs([de_doc])
|
|
assert str(de_doc) == str(Doc.from_docs([de_doc]))
|
|
|
|
with pytest.raises(ValueError):
|
|
Doc.from_docs(en_docs + [de_doc])
|
|
|
|
m_doc = Doc.from_docs(en_docs)
|
|
assert len(en_texts_without_empty) == len(list(m_doc.sents))
|
|
assert len(str(m_doc)) > len(en_texts[0]) + len(en_texts[1])
|
|
assert str(m_doc) == " ".join(en_texts_without_empty)
|
|
p_token = m_doc[len(en_docs[0]) - 1]
|
|
assert p_token.text == "." and bool(p_token.whitespace_)
|
|
en_docs_tokens = [t for doc in en_docs for t in doc]
|
|
assert len(m_doc) == len(en_docs_tokens)
|
|
think_idx = len(en_texts[0]) + 1 + en_texts[2].index("think")
|
|
assert m_doc[9].idx == think_idx
|
|
with pytest.raises(AttributeError):
|
|
# not callable, because it was not set via set_extension
|
|
m_doc[2]._.is_ambiguous
|
|
assert len(m_doc.user_data) == len(en_docs[0].user_data) # but it's there
|
|
|
|
m_doc = Doc.from_docs(en_docs, ensure_whitespace=False)
|
|
assert len(en_texts_without_empty) == len(list(m_doc.sents))
|
|
assert len(str(m_doc)) == sum(len(t) for t in en_texts)
|
|
assert str(m_doc) == "".join(en_texts)
|
|
p_token = m_doc[len(en_docs[0]) - 1]
|
|
assert p_token.text == "." and not bool(p_token.whitespace_)
|
|
en_docs_tokens = [t for doc in en_docs for t in doc]
|
|
assert len(m_doc) == len(en_docs_tokens)
|
|
think_idx = len(en_texts[0]) + 0 + en_texts[2].index("think")
|
|
assert m_doc[9].idx == think_idx
|
|
|
|
m_doc = Doc.from_docs(en_docs, attrs=["lemma", "length", "pos"])
|
|
assert len(str(m_doc)) > len(en_texts[0]) + len(en_texts[1])
|
|
# space delimiter considered, although spacy attribute was missing
|
|
assert str(m_doc) == " ".join(en_texts_without_empty)
|
|
p_token = m_doc[len(en_docs[0]) - 1]
|
|
assert p_token.text == "." and bool(p_token.whitespace_)
|
|
en_docs_tokens = [t for doc in en_docs for t in doc]
|
|
assert len(m_doc) == len(en_docs_tokens)
|
|
think_idx = len(en_texts[0]) + 1 + en_texts[2].index("think")
|
|
assert m_doc[9].idx == think_idx
|
|
|
|
|
|
def test_doc_api_from_docs_ents(en_tokenizer):
|
|
texts = ["Merging the docs is fun.", "They don't think alike."]
|
|
docs = [en_tokenizer(t) for t in texts]
|
|
docs[0].ents = ()
|
|
docs[1].ents = (Span(docs[1], 0, 1, label="foo"),)
|
|
doc = Doc.from_docs(docs)
|
|
assert len(doc.ents) == 1
|
|
|
|
|
|
def test_doc_lang(en_vocab):
|
|
doc = Doc(en_vocab, words=["Hello", "world"])
|
|
assert doc.lang_ == "en"
|
|
assert doc.lang == en_vocab.strings["en"]
|
|
assert doc[0].lang_ == "en"
|
|
assert doc[0].lang == en_vocab.strings["en"]
|
|
nlp = English()
|
|
doc = nlp("Hello world")
|
|
assert doc.lang_ == "en"
|
|
assert doc.lang == en_vocab.strings["en"]
|
|
assert doc[0].lang_ == "en"
|
|
assert doc[0].lang == en_vocab.strings["en"]
|
|
|
|
|
|
def test_token_lexeme(en_vocab):
|
|
"""Test that tokens expose their lexeme."""
|
|
token = Doc(en_vocab, words=["Hello", "world"])[0]
|
|
assert isinstance(token.lex, Lexeme)
|
|
assert token.lex.text == token.text
|
|
assert en_vocab[token.orth] == token.lex
|
|
|
|
|
|
def test_has_annotation(en_vocab):
|
|
doc = Doc(en_vocab, words=["Hello", "world"])
|
|
attrs = ("TAG", "POS", "MORPH", "LEMMA", "DEP", "HEAD", "ENT_IOB", "ENT_TYPE")
|
|
for attr in attrs:
|
|
assert not doc.has_annotation(attr)
|
|
|
|
doc[0].tag_ = "A"
|
|
doc[0].pos_ = "X"
|
|
doc[0].set_morph("Feat=Val")
|
|
doc[0].lemma_ = "a"
|
|
doc[0].dep_ = "dep"
|
|
doc[0].head = doc[1]
|
|
doc.set_ents([Span(doc, 0, 1, label="HELLO")], default="missing")
|
|
|
|
for attr in attrs:
|
|
assert doc.has_annotation(attr)
|
|
assert not doc.has_annotation(attr, require_complete=True)
|
|
|
|
doc[1].tag_ = "A"
|
|
doc[1].pos_ = "X"
|
|
doc[1].set_morph("")
|
|
doc[1].lemma_ = "a"
|
|
doc[1].dep_ = "dep"
|
|
doc.ents = [Span(doc, 0, 2, label="HELLO")]
|
|
|
|
for attr in attrs:
|
|
assert doc.has_annotation(attr)
|
|
assert doc.has_annotation(attr, require_complete=True)
|
|
|
|
|
|
def test_is_flags_deprecated(en_tokenizer):
|
|
doc = en_tokenizer("test")
|
|
with pytest.deprecated_call():
|
|
doc.is_tagged
|
|
with pytest.deprecated_call():
|
|
doc.is_parsed
|
|
with pytest.deprecated_call():
|
|
doc.is_nered
|
|
with pytest.deprecated_call():
|
|
doc.is_sentenced
|
|
|
|
|
|
def test_doc_set_ents(en_tokenizer):
|
|
# set ents
|
|
doc = en_tokenizer("a b c d e")
|
|
doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)])
|
|
assert [t.ent_iob for t in doc] == [3, 3, 1, 2, 2]
|
|
assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]
|
|
|
|
# add ents, invalid IOB repaired
|
|
doc = en_tokenizer("a b c d e")
|
|
doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)])
|
|
doc.set_ents([Span(doc, 0, 2, 12)], default="unmodified")
|
|
assert [t.ent_iob for t in doc] == [3, 1, 3, 2, 2]
|
|
assert [t.ent_type for t in doc] == [12, 12, 11, 0, 0]
|
|
|
|
# missing ents
|
|
doc = en_tokenizer("a b c d e")
|
|
doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)], missing=[doc[4:5]])
|
|
assert [t.ent_iob for t in doc] == [3, 3, 1, 2, 0]
|
|
assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]
|
|
|
|
# outside ents
|
|
doc = en_tokenizer("a b c d e")
|
|
doc.set_ents(
|
|
[Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)],
|
|
outside=[doc[4:5]],
|
|
default="missing",
|
|
)
|
|
assert [t.ent_iob for t in doc] == [3, 3, 1, 0, 2]
|
|
assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]
|
|
|
|
# blocked ents
|
|
doc = en_tokenizer("a b c d e")
|
|
doc.set_ents([], blocked=[doc[1:2], doc[3:5]], default="unmodified")
|
|
assert [t.ent_iob for t in doc] == [0, 3, 0, 3, 3]
|
|
assert [t.ent_type for t in doc] == [0, 0, 0, 0, 0]
|
|
assert doc.ents == tuple()
|
|
|
|
# invalid IOB repaired after blocked
|
|
doc.ents = [Span(doc, 3, 5, "ENT")]
|
|
assert [t.ent_iob for t in doc] == [2, 2, 2, 3, 1]
|
|
doc.set_ents([], blocked=[doc[3:4]], default="unmodified")
|
|
assert [t.ent_iob for t in doc] == [2, 2, 2, 3, 3]
|
|
|
|
# all types
|
|
doc = en_tokenizer("a b c d e")
|
|
doc.set_ents(
|
|
[Span(doc, 0, 1, 10)],
|
|
blocked=[doc[1:2]],
|
|
missing=[doc[2:3]],
|
|
outside=[doc[3:4]],
|
|
default="unmodified",
|
|
)
|
|
assert [t.ent_iob for t in doc] == [3, 3, 0, 2, 0]
|
|
assert [t.ent_type for t in doc] == [10, 0, 0, 0, 0]
|
|
|
|
doc = en_tokenizer("a b c d e")
|
|
# single span instead of a list
|
|
with pytest.raises(ValueError):
|
|
doc.set_ents([], missing=doc[1:2])
|
|
# invalid default mode
|
|
with pytest.raises(ValueError):
|
|
doc.set_ents([], missing=[doc[1:2]], default="none")
|
|
# conflicting/overlapping specifications
|
|
with pytest.raises(ValueError):
|
|
doc.set_ents([], missing=[doc[1:2]], outside=[doc[1:2]])
|
|
|
|
|
|
def test_doc_ents_setter():
|
|
"""Test that both strings and integers can be used to set entities in
|
|
tuple format via doc.ents."""
|
|
words = ["a", "b", "c", "d", "e"]
|
|
doc = Doc(Vocab(), words=words)
|
|
doc.ents = [("HELLO", 0, 2), (doc.vocab.strings.add("WORLD"), 3, 5)]
|
|
assert [e.label_ for e in doc.ents] == ["HELLO", "WORLD"]
|
|
vocab = Vocab()
|
|
ents = [("HELLO", 0, 2), (vocab.strings.add("WORLD"), 3, 5)]
|
|
ents = ["B-HELLO", "I-HELLO", "O", "B-WORLD", "I-WORLD"]
|
|
doc = Doc(vocab, words=words, ents=ents)
|
|
assert [e.label_ for e in doc.ents] == ["HELLO", "WORLD"]
|
|
|
|
|
|
def test_doc_morph_setter(en_tokenizer, de_tokenizer):
|
|
doc1 = en_tokenizer("a b")
|
|
doc1b = en_tokenizer("c d")
|
|
doc2 = de_tokenizer("a b")
|
|
|
|
# unset values can be copied
|
|
doc1[0].morph = doc1[1].morph
|
|
assert doc1[0].morph.key == 0
|
|
assert doc1[1].morph.key == 0
|
|
|
|
# morph values from the same vocab can be copied
|
|
doc1[0].set_morph("Feat=Val")
|
|
doc1[1].morph = doc1[0].morph
|
|
assert doc1[0].morph == doc1[1].morph
|
|
|
|
# ... also across docs
|
|
doc1b[0].morph = doc1[0].morph
|
|
assert doc1[0].morph == doc1b[0].morph
|
|
|
|
doc2[0].set_morph("Feat2=Val2")
|
|
|
|
# the morph value must come from the same vocab
|
|
with pytest.raises(ValueError):
|
|
doc1[0].morph = doc2[0].morph
|
|
|
|
|
|
def test_doc_init_iob():
|
|
"""Test ents validation/normalization in Doc.__init__"""
|
|
words = ["a", "b", "c", "d", "e"]
|
|
ents = ["O"] * len(words)
|
|
doc = Doc(Vocab(), words=words, ents=ents)
|
|
assert doc.ents == ()
|
|
|
|
ents = ["B-PERSON", "I-PERSON", "O", "I-PERSON", "I-PERSON"]
|
|
doc = Doc(Vocab(), words=words, ents=ents)
|
|
assert len(doc.ents) == 2
|
|
|
|
ents = ["B-PERSON", "I-PERSON", "O", "I-PERSON", "I-GPE"]
|
|
doc = Doc(Vocab(), words=words, ents=ents)
|
|
assert len(doc.ents) == 3
|
|
|
|
# None is missing
|
|
ents = ["B-PERSON", "I-PERSON", "O", None, "I-GPE"]
|
|
doc = Doc(Vocab(), words=words, ents=ents)
|
|
assert len(doc.ents) == 2
|
|
|
|
# empty tag is missing
|
|
ents = ["", "B-PERSON", "O", "B-PERSON", "I-PERSON"]
|
|
doc = Doc(Vocab(), words=words, ents=ents)
|
|
assert len(doc.ents) == 2
|
|
|
|
# invalid IOB
|
|
ents = ["Q-PERSON", "I-PERSON", "O", "I-PERSON", "I-GPE"]
|
|
with pytest.raises(ValueError):
|
|
doc = Doc(Vocab(), words=words, ents=ents)
|
|
|
|
# no dash
|
|
ents = ["OPERSON", "I-PERSON", "O", "I-PERSON", "I-GPE"]
|
|
with pytest.raises(ValueError):
|
|
doc = Doc(Vocab(), words=words, ents=ents)
|
|
|
|
# no ent type
|
|
ents = ["O", "B-", "O", "I-PERSON", "I-GPE"]
|
|
with pytest.raises(ValueError):
|
|
doc = Doc(Vocab(), words=words, ents=ents)
|
|
|
|
# not strings or None
|
|
ents = [0, "B-", "O", "I-PERSON", "I-GPE"]
|
|
with pytest.raises(ValueError):
|
|
doc = Doc(Vocab(), words=words, ents=ents)
|