spaCy/spacy/tests/doc/test_span.py
Adriane Boyd 7e4cd7575c
Refactor Docs.is_ flags (#6044)
* Refactor Docs.is_ flags

* Add derived `Doc.has_annotation` method

  * `Doc.has_annotation(attr)` returns `True` for partial annotation

  * `Doc.has_annotation(attr, require_complete=True)` returns `True` for
    complete annotation

* Add deprecation warnings to `is_tagged`, `is_parsed`, `is_sentenced`
and `is_nered`

* Add `Doc._get_array_attrs()`, which returns a full list of `Doc` attrs
for use with `Doc.to_array`, `Doc.to_bytes` and `Doc.from_docs`. The
list is the `DocBin` attributes list plus `SPACY` and `LENGTH`.

Notes on `Doc.has_annotation`:

* `HEAD` is converted to `DEP` because heads don't have an unset state

* Accept `IS_SENT_START` as a synonym of `SENT_START`

Additional changes:

* Add `NORM`, `ENT_ID` and `SENT_START` to default attributes for
`DocBin`

* In `Doc.from_array()` the presence of `DEP` causes `HEAD` to override
`SENT_START`

* In `Doc.from_array()` using `attrs` other than
`Doc._get_array_attrs()` (i.e., a user's custom list rather than our
default internal list) with both `HEAD` and `SENT_START` shows a warning
that `HEAD` will override `SENT_START`

* `set_children_from_heads` does not require dependency labels to set
sentence boundaries and sets `sent_start` for all non-sentence starts to
`-1`

* Fix call to set_children_form_heads

Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-09-17 00:14:01 +02:00

322 lines
10 KiB
Python

import pytest
from spacy.attrs import ORTH, LENGTH
from spacy.tokens import Doc, Span
from spacy.vocab import Vocab
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])
return doc
@pytest.mark.parametrize(
"i_sent,i,j,text",
[
(0, 0, len("This is a"), "This is a"),
(1, 0, len("This is another"), "This is another"),
(2, len("And "), len("And ") + len("a third"), "a third"),
(0, 1, 2, None),
],
)
def test_char_span(doc, i_sent, i, j, text):
sents = list(doc.sents)
span = sents[i_sent].char_span(i, j)
if not text:
assert not span
else:
assert span.text == text
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"
def test_spans_root2(en_tokenizer):
text = "through North and South Carolina"
heads = [0, 3, -1, -2, -4]
deps = ["dep"] * len(heads)
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps)
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], deps=["dep"] * 4)
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(UserWarning):
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]
# default and specified alignment mode "strict"
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"
span2 = doc.char_span(
span1.start_char, span1.end_char, label="GPE", alignment_mode="strict"
)
assert span1.start_char == span2.start_char
assert span1.end_char == span2.end_char
assert span2.label_ == "GPE"
# alignment mode "contract"
span2 = doc.char_span(
span1.start_char - 3, span1.end_char, label="GPE", alignment_mode="contract"
)
assert span1.start_char == span2.start_char
assert span1.end_char == span2.end_char
assert span2.label_ == "GPE"
# alignment mode "expand"
span2 = doc.char_span(
span1.start_char + 1, span1.end_char, label="GPE", alignment_mode="expand"
)
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
# Test filtering overlaps with earlier preference for identical length
spans = [doc[1:4], doc[2:5], 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
def test_span_eq_hash(doc, doc_not_parsed):
assert doc[0:2] == doc[0:2]
assert doc[0:2] != doc[1:3]
assert doc[0:2] != doc_not_parsed[0:2]
assert hash(doc[0:2]) == hash(doc[0:2])
assert hash(doc[0:2]) != hash(doc[1:3])
assert hash(doc[0:2]) != hash(doc_not_parsed[0:2])
def test_span_boundaries(doc):
start = 1
end = 5
span = doc[start:end]
for i in range(start, end):
assert span[i - start] == doc[i]
with pytest.raises(IndexError):
span[-5]
with pytest.raises(IndexError):
span[5]