mirror of https://github.com/explosion/spaCy.git
344 lines
11 KiB
Python
344 lines
11 KiB
Python
# coding: utf-8
|
|
from __future__ import unicode_literals
|
|
|
|
|
|
import pytest
|
|
import numpy
|
|
from spacy.tokens import Doc
|
|
from spacy.vocab import Vocab
|
|
from spacy.attrs import LEMMA
|
|
|
|
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(tensor=False), tensor=False
|
|
)
|
|
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(sentiment=False), sentiment=False
|
|
)
|
|
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_merge(en_tokenizer):
|
|
text = "WKRO played songs by the beach boys all night"
|
|
|
|
# merge 'The Beach Boys'
|
|
doc = en_tokenizer(text)
|
|
assert len(doc) == 9
|
|
doc.merge(
|
|
doc[4].idx,
|
|
doc[6].idx + len(doc[6]),
|
|
tag="NAMED",
|
|
lemma="LEMMA",
|
|
ent_type="TYPE",
|
|
)
|
|
assert len(doc) == 7
|
|
assert doc[4].text == "the beach boys"
|
|
assert doc[4].text_with_ws == "the beach boys "
|
|
assert doc[4].tag_ == "NAMED"
|
|
|
|
# merge 'all night'
|
|
doc = en_tokenizer(text)
|
|
assert len(doc) == 9
|
|
doc.merge(
|
|
doc[7].idx,
|
|
doc[8].idx + len(doc[8]),
|
|
tag="NAMED",
|
|
lemma="LEMMA",
|
|
ent_type="TYPE",
|
|
)
|
|
assert len(doc) == 8
|
|
assert doc[7].text == "all night"
|
|
assert doc[7].text_with_ws == "all night"
|
|
|
|
# merge both with bulk merge
|
|
doc = en_tokenizer(text)
|
|
assert len(doc) == 9
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.merge(
|
|
doc[4:7], attrs={"tag": "NAMED", "lemma": "LEMMA", "ent_type": "TYPE"}
|
|
)
|
|
retokenizer.merge(
|
|
doc[7:9], attrs={"tag": "NAMED", "lemma": "LEMMA", "ent_type": "TYPE"}
|
|
)
|
|
|
|
assert len(doc) == 6
|
|
assert doc[4].text == "the beach boys"
|
|
assert doc[4].text_with_ws == "the beach boys "
|
|
assert doc[4].tag_ == "NAMED"
|
|
assert doc[5].text == "all night"
|
|
assert doc[5].text_with_ws == "all night"
|
|
assert doc[5].tag_ == "NAMED"
|
|
|
|
|
|
def test_doc_api_merge_children(en_tokenizer):
|
|
"""Test that attachments work correctly after merging."""
|
|
text = "WKRO played songs by the beach boys all night"
|
|
doc = en_tokenizer(text)
|
|
assert len(doc) == 9
|
|
doc.merge(
|
|
doc[4].idx,
|
|
doc[6].idx + len(doc[6]),
|
|
tag="NAMED",
|
|
lemma="LEMMA",
|
|
ent_type="TYPE",
|
|
)
|
|
|
|
for word in doc:
|
|
if word.i < word.head.i:
|
|
assert word in list(word.head.lefts)
|
|
elif word.i > word.head.i:
|
|
assert word in list(word.head.rights)
|
|
|
|
|
|
def test_doc_api_merge_hang(en_tokenizer):
|
|
text = "through North and South Carolina"
|
|
doc = en_tokenizer(text)
|
|
doc.merge(18, 32, tag="", lemma="", ent_type="ORG")
|
|
doc.merge(8, 32, tag="", lemma="", ent_type="ORG")
|
|
|
|
|
|
def test_doc_api_retokenizer(en_tokenizer):
|
|
doc = en_tokenizer("WKRO played songs by the beach boys all night")
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.merge(doc[4:7])
|
|
assert len(doc) == 7
|
|
assert doc[4].text == "the beach boys"
|
|
|
|
|
|
def test_doc_api_retokenizer_attrs(en_tokenizer):
|
|
doc = en_tokenizer("WKRO played songs by the beach boys all night")
|
|
# test both string and integer attributes and values
|
|
attrs = {LEMMA: "boys", "ENT_TYPE": doc.vocab.strings["ORG"]}
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.merge(doc[4:7], attrs=attrs)
|
|
assert len(doc) == 7
|
|
assert doc[4].text == "the beach boys"
|
|
assert doc[4].lemma_ == "boys"
|
|
assert doc[4].ent_type_ == "ORG"
|
|
|
|
|
|
@pytest.mark.xfail
|
|
def test_doc_api_retokenizer_lex_attrs(en_tokenizer):
|
|
"""Test that lexical attributes can be changed (see #2390)."""
|
|
doc = en_tokenizer("WKRO played beach boys songs")
|
|
assert not any(token.is_stop for token in doc)
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.merge(doc[2:4], attrs={"LEMMA": "boys", "IS_STOP": True})
|
|
assert doc[2].text == "beach boys"
|
|
assert doc[2].lemma_ == "boys"
|
|
assert doc[2].is_stop
|
|
new_doc = Doc(doc.vocab, words=["beach boys"])
|
|
assert new_doc[0].is_stop
|
|
|
|
|
|
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 = ["nsubj", "prep", "amod", "pobj", "ROOT", "amod", "attr", "",
|
|
"nummod", "prep", "det", "amod", "pobj", "acl", "prep", "prep",
|
|
"pobj", "", "nummod", "prep", "det", "amod", "pobj", "aux", "neg",
|
|
"ROOT", "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.start_char, np.end_char, np.root.tag_, np.text, np.root.ent_type_)
|
|
)
|
|
for np in nps:
|
|
start, end, tag, lemma, ent_type = np
|
|
doc.merge(start, end, tag=tag, lemma=lemma, ent_type=ent_type)
|
|
|
|
|
|
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"])
|
|
with pytest.warns(None):
|
|
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(None):
|
|
assert doc.similarity(doc2[:1]) == 1.0
|
|
assert doc.similarity(doc2) == 0.0
|
|
|
|
|
|
def test_lowest_common_ancestor(en_tokenizer):
|
|
tokens = en_tokenizer("the lazy dog slept")
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=[2, 1, 1, 0])
|
|
lca = doc.get_lca_matrix()
|
|
assert lca[1, 1] == 1
|
|
assert lca[0, 1] == 2
|
|
assert lca[1, 2] == 2
|