mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 09:56:28 +03:00
256 lines
8.3 KiB
Python
256 lines
8.3 KiB
Python
# coding: utf-8
|
|
from __future__ import unicode_literals
|
|
|
|
import pytest
|
|
from spacy.attrs import ORTH, LENGTH
|
|
from spacy.tokens import Doc, Span
|
|
from spacy.vocab import Vocab
|
|
from spacy.errors import ModelsWarning
|
|
from spacy.util import filter_spans
|
|
|
|
from ..util import get_doc
|
|
|
|
|
|
@pytest.fixture
|
|
def doc(en_tokenizer):
|
|
# fmt: off
|
|
text = "This is a sentence. This is another sentence. And a third."
|
|
heads = [1, 0, 1, -2, -3, 1, 0, 1, -2, -3, 0, 1, -2, -1]
|
|
deps = ["nsubj", "ROOT", "det", "attr", "punct", "nsubj", "ROOT", "det",
|
|
"attr", "punct", "ROOT", "det", "npadvmod", "punct"]
|
|
# fmt: on
|
|
tokens = en_tokenizer(text)
|
|
return get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps)
|
|
|
|
|
|
@pytest.fixture
|
|
def doc_not_parsed(en_tokenizer):
|
|
text = "This is a sentence. This is another sentence. And a third."
|
|
tokens = en_tokenizer(text)
|
|
doc = Doc(tokens.vocab, words=[t.text for t in tokens])
|
|
doc.is_parsed = False
|
|
return doc
|
|
|
|
|
|
def test_spans_sent_spans(doc):
|
|
sents = list(doc.sents)
|
|
assert sents[0].start == 0
|
|
assert sents[0].end == 5
|
|
assert len(sents) == 3
|
|
assert sum(len(sent) for sent in sents) == len(doc)
|
|
|
|
|
|
def test_spans_root(doc):
|
|
span = doc[2:4]
|
|
assert len(span) == 2
|
|
assert span.text == "a sentence"
|
|
assert span.root.text == "sentence"
|
|
assert span.root.head.text == "is"
|
|
|
|
|
|
def test_spans_string_fn(doc):
|
|
span = doc[0:4]
|
|
assert len(span) == 4
|
|
assert span.text == "This is a sentence"
|
|
assert span.upper_ == "THIS IS A SENTENCE"
|
|
assert span.lower_ == "this is a sentence"
|
|
|
|
|
|
def test_spans_root2(en_tokenizer):
|
|
text = "through North and South Carolina"
|
|
heads = [0, 3, -1, -2, -4]
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
|
|
assert doc[-2:].root.text == "Carolina"
|
|
|
|
|
|
def test_spans_span_sent(doc, doc_not_parsed):
|
|
"""Test span.sent property"""
|
|
assert len(list(doc.sents))
|
|
assert doc[:2].sent.root.text == "is"
|
|
assert doc[:2].sent.text == "This is a sentence ."
|
|
assert doc[6:7].sent.root.left_edge.text == "This"
|
|
# test on manual sbd
|
|
doc_not_parsed[0].is_sent_start = True
|
|
doc_not_parsed[5].is_sent_start = True
|
|
assert doc_not_parsed[1:3].sent == doc_not_parsed[0:5]
|
|
assert doc_not_parsed[10:14].sent == doc_not_parsed[5:]
|
|
|
|
|
|
def test_spans_lca_matrix(en_tokenizer):
|
|
"""Test span's lca matrix generation"""
|
|
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[:2].get_lca_matrix()
|
|
assert lca.shape == (2, 2)
|
|
assert lca[0, 0] == 0 # the & the -> the
|
|
assert lca[0, 1] == -1 # the & lazy -> dog (out of span)
|
|
assert lca[1, 0] == -1 # lazy & the -> dog (out of span)
|
|
assert lca[1, 1] == 1 # lazy & lazy -> lazy
|
|
|
|
lca = doc[1:].get_lca_matrix()
|
|
assert lca.shape == (3, 3)
|
|
assert lca[0, 0] == 0 # lazy & lazy -> lazy
|
|
assert lca[0, 1] == 1 # lazy & dog -> dog
|
|
assert lca[0, 2] == 2 # lazy & slept -> slept
|
|
|
|
lca = doc[2:].get_lca_matrix()
|
|
assert lca.shape == (2, 2)
|
|
assert lca[0, 0] == 0 # dog & dog -> dog
|
|
assert lca[0, 1] == 1 # dog & slept -> slept
|
|
assert lca[1, 0] == 1 # slept & dog -> slept
|
|
assert lca[1, 1] == 1 # slept & slept -> slept
|
|
|
|
|
|
def test_span_similarity_match():
|
|
doc = Doc(Vocab(), words=["a", "b", "a", "b"])
|
|
span1 = doc[:2]
|
|
span2 = doc[2:]
|
|
with pytest.warns(ModelsWarning):
|
|
assert span1.similarity(span2) == 1.0
|
|
assert span1.similarity(doc) == 0.0
|
|
assert span1[:1].similarity(doc.vocab["a"]) == 1.0
|
|
|
|
|
|
def test_spans_default_sentiment(en_tokenizer):
|
|
"""Test span.sentiment property's default averaging behaviour"""
|
|
text = "good stuff bad stuff"
|
|
tokens = en_tokenizer(text)
|
|
tokens.vocab[tokens[0].text].sentiment = 3.0
|
|
tokens.vocab[tokens[2].text].sentiment = -2.0
|
|
doc = Doc(tokens.vocab, words=[t.text for t in tokens])
|
|
assert doc[:2].sentiment == 3.0 / 2
|
|
assert doc[-2:].sentiment == -2.0 / 2
|
|
assert doc[:-1].sentiment == (3.0 + -2) / 3.0
|
|
|
|
|
|
def test_spans_override_sentiment(en_tokenizer):
|
|
"""Test span.sentiment property's default averaging behaviour"""
|
|
text = "good stuff bad stuff"
|
|
tokens = en_tokenizer(text)
|
|
tokens.vocab[tokens[0].text].sentiment = 3.0
|
|
tokens.vocab[tokens[2].text].sentiment = -2.0
|
|
doc = Doc(tokens.vocab, words=[t.text for t in tokens])
|
|
doc.user_span_hooks["sentiment"] = lambda span: 10.0
|
|
assert doc[:2].sentiment == 10.0
|
|
assert doc[-2:].sentiment == 10.0
|
|
assert doc[:-1].sentiment == 10.0
|
|
|
|
|
|
def test_spans_are_hashable(en_tokenizer):
|
|
"""Test spans can be hashed."""
|
|
text = "good stuff bad stuff"
|
|
tokens = en_tokenizer(text)
|
|
span1 = tokens[:2]
|
|
span2 = tokens[2:4]
|
|
assert hash(span1) != hash(span2)
|
|
span3 = tokens[0:2]
|
|
assert hash(span3) == hash(span1)
|
|
|
|
|
|
def test_spans_by_character(doc):
|
|
span1 = doc[1:-2]
|
|
span2 = doc.char_span(span1.start_char, span1.end_char, label="GPE")
|
|
assert span1.start_char == span2.start_char
|
|
assert span1.end_char == span2.end_char
|
|
assert span2.label_ == "GPE"
|
|
|
|
|
|
def test_span_to_array(doc):
|
|
span = doc[1:-2]
|
|
arr = span.to_array([ORTH, LENGTH])
|
|
assert arr.shape == (len(span), 2)
|
|
assert arr[0, 0] == span[0].orth
|
|
assert arr[0, 1] == len(span[0])
|
|
|
|
|
|
def test_span_as_doc(doc):
|
|
span = doc[4:10]
|
|
span_doc = span.as_doc()
|
|
assert span.text == span_doc.text.strip()
|
|
assert isinstance(span_doc, doc.__class__)
|
|
assert span_doc is not doc
|
|
assert span_doc[0].idx == 0
|
|
|
|
|
|
def test_span_as_doc_user_data(doc):
|
|
"""Test that the user_data can be preserved (but not by default). """
|
|
my_key = "my_info"
|
|
my_value = 342
|
|
doc.user_data[my_key] = my_value
|
|
|
|
span = doc[4:10]
|
|
span_doc_with = span.as_doc(copy_user_data=True)
|
|
span_doc_without = span.as_doc()
|
|
|
|
assert doc.user_data.get(my_key, None) is my_value
|
|
assert span_doc_with.user_data.get(my_key, None) is my_value
|
|
assert span_doc_without.user_data.get(my_key, None) is None
|
|
|
|
|
|
def test_span_string_label_kb_id(doc):
|
|
span = Span(doc, 0, 1, label="hello", kb_id="Q342")
|
|
assert span.label_ == "hello"
|
|
assert span.label == doc.vocab.strings["hello"]
|
|
assert span.kb_id_ == "Q342"
|
|
assert span.kb_id == doc.vocab.strings["Q342"]
|
|
|
|
|
|
def test_span_label_readonly(doc):
|
|
span = Span(doc, 0, 1)
|
|
with pytest.raises(NotImplementedError):
|
|
span.label_ = "hello"
|
|
|
|
|
|
def test_span_kb_id_readonly(doc):
|
|
span = Span(doc, 0, 1)
|
|
with pytest.raises(NotImplementedError):
|
|
span.kb_id_ = "Q342"
|
|
|
|
|
|
def test_span_ents_property(doc):
|
|
"""Test span.ents for the """
|
|
doc.ents = [
|
|
(doc.vocab.strings["PRODUCT"], 0, 1),
|
|
(doc.vocab.strings["PRODUCT"], 7, 8),
|
|
(doc.vocab.strings["PRODUCT"], 11, 14),
|
|
]
|
|
assert len(list(doc.ents)) == 3
|
|
sentences = list(doc.sents)
|
|
assert len(sentences) == 3
|
|
assert len(sentences[0].ents) == 1
|
|
# First sentence, also tests start of sentence
|
|
assert sentences[0].ents[0].text == "This"
|
|
assert sentences[0].ents[0].label_ == "PRODUCT"
|
|
assert sentences[0].ents[0].start == 0
|
|
assert sentences[0].ents[0].end == 1
|
|
# Second sentence
|
|
assert len(sentences[1].ents) == 1
|
|
assert sentences[1].ents[0].text == "another"
|
|
assert sentences[1].ents[0].label_ == "PRODUCT"
|
|
assert sentences[1].ents[0].start == 7
|
|
assert sentences[1].ents[0].end == 8
|
|
# Third sentence ents, Also tests end of sentence
|
|
assert sentences[2].ents[0].text == "a third ."
|
|
assert sentences[2].ents[0].label_ == "PRODUCT"
|
|
assert sentences[2].ents[0].start == 11
|
|
assert sentences[2].ents[0].end == 14
|
|
|
|
|
|
def test_filter_spans(doc):
|
|
# Test filtering duplicates
|
|
spans = [doc[1:4], doc[6:8], doc[1:4], doc[10:14]]
|
|
filtered = filter_spans(spans)
|
|
assert len(filtered) == 3
|
|
assert filtered[0].start == 1 and filtered[0].end == 4
|
|
assert filtered[1].start == 6 and filtered[1].end == 8
|
|
assert filtered[2].start == 10 and filtered[2].end == 14
|
|
# Test filtering overlaps with longest preference
|
|
spans = [doc[1:4], doc[1:3], doc[5:10], doc[7:9], doc[1:4]]
|
|
filtered = filter_spans(spans)
|
|
assert len(filtered) == 2
|
|
assert len(filtered[0]) == 3
|
|
assert len(filtered[1]) == 5
|
|
assert filtered[0].start == 1 and filtered[0].end == 4
|
|
assert filtered[1].start == 5 and filtered[1].end == 10
|