spaCy/spacy/tests/doc/test_doc_api.py
Adriane Boyd 455f089c9b
Support exclude in Doc.from_docs (#10689)
* Support exclude in Doc.from_docs

* Update API docs

* Add new tag to docs
2022-04-25 18:19:03 +02:00

967 lines
34 KiB
Python

import weakref
import numpy
from numpy.testing import assert_array_equal
import pytest
from thinc.api import NumpyOps, get_current_ops
from spacy.attrs import DEP, ENT_IOB, ENT_TYPE, HEAD, IS_ALPHA, MORPH, POS
from spacy.attrs import SENT_START, TAG
from spacy.lang.en import English
from spacy.lang.xx import MultiLanguage
from spacy.language import Language
from spacy.lexeme import Lexeme
from spacy.tokens import Doc, Span, Token
from spacy.vocab import Vocab
from .test_underscore import clean_underscore # noqa: F401
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.issue(1547)
def test_issue1547():
"""Test that entity labels still match after merging tokens."""
words = ["\n", "worda", ".", "\n", "wordb", "-", "Biosphere", "2", "-", " \n"]
doc = Doc(Vocab(), words=words)
doc.ents = [Span(doc, 6, 8, label=doc.vocab.strings["PRODUCT"])]
with doc.retokenize() as retokenizer:
retokenizer.merge(doc[5:7])
assert [ent.text for ent in doc.ents]
@pytest.mark.issue(1757)
def test_issue1757():
"""Test comparison against None doesn't cause segfault."""
doc = Doc(Vocab(), words=["a", "b", "c"])
assert not doc[0] < None
assert not doc[0] is None
assert doc[0] >= None
assert not doc[:2] < None
assert not doc[:2] is None
assert doc[:2] >= None
assert not doc.vocab["a"] is None
assert not doc.vocab["a"] < None
@pytest.mark.issue(2396)
def test_issue2396(en_vocab):
words = ["She", "created", "a", "test", "for", "spacy"]
heads = [1, 1, 3, 1, 3, 4]
deps = ["dep"] * len(heads)
matrix = numpy.array(
[
[0, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 2, 3, 3, 3],
[1, 1, 3, 3, 3, 3],
[1, 1, 3, 3, 4, 4],
[1, 1, 3, 3, 4, 5],
],
dtype=numpy.int32,
)
doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
span = doc[:]
assert (doc.get_lca_matrix() == matrix).all()
assert (span.get_lca_matrix() == matrix).all()
@pytest.mark.parametrize("text", ["-0.23", "+123,456", "±1"])
@pytest.mark.parametrize("lang_cls", [English, MultiLanguage])
@pytest.mark.issue(2782)
def test_issue2782(text, lang_cls):
"""Check that like_num handles + and - before number."""
nlp = lang_cls()
doc = nlp(text)
assert len(doc) == 1
assert doc[0].like_num
@pytest.mark.parametrize(
"sentence",
[
"The story was to the effect that a young American student recently called on Professor Christlieb with a letter of introduction.",
"The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale's #1.",
"The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale's number one",
"Indeed, making the one who remains do all the work has installed him into a position of such insolent tyranny, it will take a month at least to reduce him to his proper proportions.",
"It was a missed assignment, but it shouldn't have resulted in a turnover ...",
],
)
@pytest.mark.issue(3869)
def test_issue3869(sentence):
"""Test that the Doc's count_by function works consistently"""
nlp = English()
doc = nlp(sentence)
count = 0
for token in doc:
count += token.is_alpha
assert count == doc.count_by(IS_ALPHA).get(1, 0)
@pytest.mark.issue(3962)
def test_issue3962(en_vocab):
"""Ensure that as_doc does not result in out-of-bound access of tokens.
This is achieved by setting the head to itself if it would lie out of the span otherwise."""
# fmt: off
words = ["He", "jests", "at", "scars", ",", "that", "never", "felt", "a", "wound", "."]
heads = [1, 7, 1, 2, 7, 7, 7, 7, 9, 7, 7]
deps = ["nsubj", "ccomp", "prep", "pobj", "punct", "nsubj", "neg", "ROOT", "det", "dobj", "punct"]
# fmt: on
doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
span2 = doc[1:5] # "jests at scars ,"
doc2 = span2.as_doc()
doc2_json = doc2.to_json()
assert doc2_json
# head set to itself, being the new artificial root
assert doc2[0].head.text == "jests"
assert doc2[0].dep_ == "dep"
assert doc2[1].head.text == "jests"
assert doc2[1].dep_ == "prep"
assert doc2[2].head.text == "at"
assert doc2[2].dep_ == "pobj"
assert doc2[3].head.text == "jests" # head set to the new artificial root
assert doc2[3].dep_ == "dep"
# We should still have 1 sentence
assert len(list(doc2.sents)) == 1
span3 = doc[6:9] # "never felt a"
doc3 = span3.as_doc()
doc3_json = doc3.to_json()
assert doc3_json
assert doc3[0].head.text == "felt"
assert doc3[0].dep_ == "neg"
assert doc3[1].head.text == "felt"
assert doc3[1].dep_ == "ROOT"
assert doc3[2].head.text == "felt" # head set to ancestor
assert doc3[2].dep_ == "dep"
# We should still have 1 sentence as "a" can be attached to "felt" instead of "wound"
assert len(list(doc3.sents)) == 1
@pytest.mark.issue(3962)
def test_issue3962_long(en_vocab):
"""Ensure that as_doc does not result in out-of-bound access of tokens.
This is achieved by setting the head to itself if it would lie out of the span otherwise."""
# fmt: off
words = ["He", "jests", "at", "scars", ".", "They", "never", "felt", "a", "wound", "."]
heads = [1, 1, 1, 2, 1, 7, 7, 7, 9, 7, 7]
deps = ["nsubj", "ROOT", "prep", "pobj", "punct", "nsubj", "neg", "ROOT", "det", "dobj", "punct"]
# fmt: on
two_sent_doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
span2 = two_sent_doc[1:7] # "jests at scars. They never"
doc2 = span2.as_doc()
doc2_json = doc2.to_json()
assert doc2_json
# head set to itself, being the new artificial root (in sentence 1)
assert doc2[0].head.text == "jests"
assert doc2[0].dep_ == "ROOT"
assert doc2[1].head.text == "jests"
assert doc2[1].dep_ == "prep"
assert doc2[2].head.text == "at"
assert doc2[2].dep_ == "pobj"
assert doc2[3].head.text == "jests"
assert doc2[3].dep_ == "punct"
# head set to itself, being the new artificial root (in sentence 2)
assert doc2[4].head.text == "They"
assert doc2[4].dep_ == "dep"
# head set to the new artificial head (in sentence 2)
assert doc2[4].head.text == "They"
assert doc2[4].dep_ == "dep"
# We should still have 2 sentences
sents = list(doc2.sents)
assert len(sents) == 2
assert sents[0].text == "jests at scars ."
assert sents[1].text == "They never"
@Language.factory("my_pipe")
class CustomPipe:
def __init__(self, nlp, name="my_pipe"):
self.name = name
Span.set_extension("my_ext", getter=self._get_my_ext)
Doc.set_extension("my_ext", default=None)
def __call__(self, doc):
gathered_ext = []
for sent in doc.sents:
sent_ext = self._get_my_ext(sent)
sent._.set("my_ext", sent_ext)
gathered_ext.append(sent_ext)
doc._.set("my_ext", "\n".join(gathered_ext))
return doc
@staticmethod
def _get_my_ext(span):
return str(span.end)
@pytest.mark.issue(4903)
def test_issue4903():
"""Ensure that this runs correctly and doesn't hang or crash on Windows /
macOS."""
nlp = English()
nlp.add_pipe("sentencizer")
nlp.add_pipe("my_pipe", after="sentencizer")
text = ["I like bananas.", "Do you like them?", "No, I prefer wasabi."]
if isinstance(get_current_ops(), NumpyOps):
docs = list(nlp.pipe(text, n_process=2))
assert docs[0].text == "I like bananas."
assert docs[1].text == "Do you like them?"
assert docs[2].text == "No, I prefer wasabi."
@pytest.mark.issue(5048)
def test_issue5048(en_vocab):
words = ["This", "is", "a", "sentence"]
pos_s = ["DET", "VERB", "DET", "NOUN"]
spaces = [" ", " ", " ", ""]
deps_s = ["dep", "adj", "nn", "atm"]
tags_s = ["DT", "VBZ", "DT", "NN"]
strings = en_vocab.strings
for w in words:
strings.add(w)
deps = [strings.add(d) for d in deps_s]
pos = [strings.add(p) for p in pos_s]
tags = [strings.add(t) for t in tags_s]
attrs = [POS, DEP, TAG]
array = numpy.array(list(zip(pos, deps, tags)), dtype="uint64")
doc = Doc(en_vocab, words=words, spaces=spaces)
doc.from_array(attrs, array)
v1 = [(token.text, token.pos_, token.tag_) for token in doc]
doc2 = Doc(en_vocab, words=words, pos=pos_s, deps=deps_s, tags=tags_s)
v2 = [(token.text, token.pos_, token.tag_) for token in doc2]
assert v1 == v2
@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"
tokens[0].ent_id_ = "ent_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"
assert new_tokens[0].ent_id_ == "ent_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 inner_func(d1, d2):
return "hello!"
_ = tokens.to_bytes() # noqa: F841
with pytest.warns(UserWarning):
tokens.user_hooks["similarity"] = inner_func
_ = tokens.to_bytes() # noqa: F841
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]
@pytest.mark.usefixtures("clean_underscore")
def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
en_texts = [
"Merging the docs is fun.",
"",
"They don't think alike. ",
"",
"Another doc.",
]
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]
en_docs[0].spans["group"] = [en_docs[0][1:4]]
en_docs[2].spans["group"] = [en_docs[2][1:4]]
en_docs[4].spans["group"] = [en_docs[4][0:1]]
span_group_texts = sorted(
[en_docs[0][1:4].text, en_docs[2][1:4].text, en_docs[4][0:1].text]
)
de_doc = de_tokenizer(de_text)
Token.set_extension("is_ambiguous", default=False)
en_docs[0][2]._.is_ambiguous = True # docs
en_docs[2][3]._.is_ambiguous = True # think
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(m_doc.text) > len(en_texts[0]) + len(en_texts[1])
assert m_doc.text == " ".join([t.strip() for t in 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[2]._.is_ambiguous is True
assert m_doc[9].idx == think_idx
assert m_doc[9]._.is_ambiguous is True
assert not any([t._.is_ambiguous for t in m_doc[3:8]])
assert "group" in m_doc.spans
assert span_group_texts == sorted([s.text for s in m_doc.spans["group"]])
assert bool(m_doc[11].whitespace_)
m_doc = Doc.from_docs(en_docs, ensure_whitespace=False)
assert len(en_texts_without_empty) == len(list(m_doc.sents))
assert len(m_doc.text) == sum(len(t) for t in en_texts)
assert m_doc.text == "".join(en_texts_without_empty)
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
assert "group" in m_doc.spans
assert span_group_texts == sorted([s.text for s in m_doc.spans["group"]])
assert bool(m_doc[11].whitespace_)
m_doc = Doc.from_docs(en_docs, attrs=["lemma", "length", "pos"])
assert len(m_doc.text) > len(en_texts[0]) + len(en_texts[1])
# space delimiter considered, although spacy attribute was missing
assert m_doc.text == " ".join([t.strip() for t in 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
assert "group" in m_doc.spans
assert span_group_texts == sorted([s.text for s in m_doc.spans["group"]])
# can exclude spans
m_doc = Doc.from_docs(en_docs, exclude=["spans"])
assert "group" not in m_doc.spans
# can exclude user_data
m_doc = Doc.from_docs(en_docs, exclude=["user_data"])
assert m_doc.user_data == {}
# can merge empty docs
doc = Doc.from_docs([en_tokenizer("")] * 10)
# empty but set spans keys are preserved
en_docs = [en_tokenizer(text) for text in en_texts]
m_doc = Doc.from_docs(en_docs)
assert "group" not in m_doc.spans
for doc in en_docs:
doc.spans["group"] = []
m_doc = Doc.from_docs(en_docs)
assert "group" in m_doc.spans
assert len(m_doc.spans["group"]) == 0
# with tensor
ops = get_current_ops()
for doc in en_docs:
doc.tensor = ops.asarray([[len(t.text), 0.0] for t in doc])
m_doc = Doc.from_docs(en_docs)
assert_array_equal(
ops.to_numpy(m_doc.tensor),
ops.to_numpy(ops.xp.vstack([doc.tensor for doc in en_docs if len(doc)])),
)
# can exclude tensor
m_doc = Doc.from_docs(en_docs, exclude=["tensor"])
assert m_doc.tensor.shape == (0,)
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)
assert not doc.has_annotation(attr, require_complete=True)
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_has_annotation_sents(en_vocab):
doc = Doc(en_vocab, words=["Hello", "beautiful", "world"])
attrs = ("SENT_START", "IS_SENT_START", "IS_SENT_END")
for attr in attrs:
assert not doc.has_annotation(attr)
assert not doc.has_annotation(attr, require_complete=True)
# The first token (index 0) is always assumed to be a sentence start,
# and ignored by the check in doc.has_annotation
doc[1].is_sent_start = False
for attr in attrs:
assert doc.has_annotation(attr)
assert not doc.has_annotation(attr, require_complete=True)
doc[2].is_sent_start = False
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)
def test_doc_set_ents_invalid_spans(en_tokenizer):
doc = en_tokenizer("Some text about Colombia and the Czech Republic")
spans = [Span(doc, 3, 4, label="GPE"), Span(doc, 6, 8, label="GPE")]
with doc.retokenize() as retokenizer:
for span in spans:
retokenizer.merge(span)
with pytest.raises(IndexError):
doc.ents = spans
def test_doc_noun_chunks_not_implemented():
"""Test that a language without noun_chunk iterator, throws a NotImplementedError"""
text = "Může data vytvářet a spravovat, ale především je dokáže analyzovat, najít v nich nové vztahy a vše přehledně vizualizovat."
nlp = MultiLanguage()
doc = nlp(text)
with pytest.raises(NotImplementedError):
_ = list(doc.noun_chunks) # noqa: F841
def test_span_groups(en_tokenizer):
doc = en_tokenizer("Some text about Colombia and the Czech Republic")
doc.spans["hi"] = [Span(doc, 3, 4, label="bye")]
assert "hi" in doc.spans
assert "bye" not in doc.spans
assert len(doc.spans["hi"]) == 1
assert doc.spans["hi"][0].label_ == "bye"
doc.spans["hi"].append(doc[0:3])
assert len(doc.spans["hi"]) == 2
assert doc.spans["hi"][1].text == "Some text about"
assert [span.text for span in doc.spans["hi"]] == ["Colombia", "Some text about"]
assert not doc.spans["hi"].has_overlap
doc.ents = [Span(doc, 3, 4, label="GPE"), Span(doc, 6, 8, label="GPE")]
doc.spans["hi"].extend(doc.ents)
assert len(doc.spans["hi"]) == 4
assert [span.label_ for span in doc.spans["hi"]] == ["bye", "", "GPE", "GPE"]
assert doc.spans["hi"].has_overlap
del doc.spans["hi"]
assert "hi" not in doc.spans
def test_doc_spans_copy(en_tokenizer):
doc1 = en_tokenizer("Some text about Colombia and the Czech Republic")
assert weakref.ref(doc1) == doc1.spans.doc_ref
doc2 = doc1.copy()
assert weakref.ref(doc2) == doc2.spans.doc_ref