spaCy/spacy/tests/doc/test_doc_api.py
2020-04-28 13:37:37 +02:00

310 lines
10 KiB
Python

import pytest
import numpy
from spacy.tokens import Doc, Span
from spacy.vocab import Vocab
from spacy.attrs import ENT_TYPE, ENT_IOB, SENT_START, HEAD, DEP
from ..util import get_doc
@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)
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]
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] == [0, 0, 3, 1, 0, 0, 0, 0]
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("")
doc.is_parsed = True
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 = get_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_tokenizer):
"""Test for bug occurring from Unshift action, causing incorrect right edge"""
# fmt: off
text = "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, 1, 0, -1, -1, -3, 15, 1, -2, -1, 1, -3, -1, -1, 1, -2, -1, 1,
-2, -7, 1, -19, 1, -2, -3, 2, 1, -3, -26]
# fmt: on
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
assert doc[6].text == "for"
subtree = [w.text for w in doc[6].subtree]
assert subtree == [
"for",
"the",
"sake",
"of",
"such",
"as",
"live",
"under",
"the",
"government",
"of",
"the",
"Romans",
",",
]
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(
"sentence,heads,lca_matrix",
[
(
"the lazy dog slept",
[2, 1, 1, 0],
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, 1, 1, 0, -1, 2, 1, 1, 0],
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_tokenizer, sentence, heads, lca_matrix):
tokens = en_tokenizer(sentence)
doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=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.is_nered
doc.ents = [Span(doc, 3, 5, label="GPE")]
assert doc.is_nered
# 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.is_nered
# Test serialization
new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
assert new_doc.is_nered
def test_doc_from_array_sent_starts(en_vocab):
words = ["I", "live", "in", "New", "York", ".", "I", "like", "cats", "."]
heads = [0, 0, 0, 0, 0, 0, 6, 6, 6, 6]
# fmt: off
deps = ["ROOT", "dep", "dep", "dep", "dep", "dep", "ROOT", "dep", "dep", "dep", "dep"]
# fmt: on
doc = Doc(en_vocab, words=words)
for i, (dep, head) in enumerate(zip(deps, heads)):
doc[i].dep_ = dep
doc[i].head = doc[head]
if head == i:
doc[i].is_sent_start = True
doc.is_parsed
attrs = [SENT_START, HEAD]
arr = doc.to_array(attrs)
new_doc = Doc(en_vocab, words=words)
with pytest.raises(ValueError):
new_doc.from_array(attrs, arr)
attrs = [SENT_START, 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 not new_doc.is_parsed
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.is_parsed
def test_doc_lang(en_vocab):
doc = Doc(en_vocab, words=["Hello", "world"])
assert doc.lang_ == "en"
assert doc.lang == en_vocab.strings["en"]