mirror of https://github.com/explosion/spaCy.git
363 lines
12 KiB
Python
363 lines
12 KiB
Python
import pytest
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from spacy.attrs import ORTH, LENGTH
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from spacy.tokens import Doc, Span, Token
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from spacy.vocab import Vocab
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from spacy.util import filter_spans
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from .test_underscore import clean_underscore # noqa: F401
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@pytest.fixture
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def doc(en_tokenizer):
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# fmt: off
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text = "This is a sentence. This is another sentence. And a third."
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heads = [1, 1, 3, 1, 1, 6, 6, 8, 6, 6, 12, 12, 12, 12]
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deps = ["nsubj", "ROOT", "det", "attr", "punct", "nsubj", "ROOT", "det",
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"attr", "punct", "ROOT", "det", "npadvmod", "punct"]
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ents = ["O", "O", "B-ENT", "I-ENT", "I-ENT", "I-ENT", "I-ENT", "O", "O",
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"O", "O", "O", "O", "O"]
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# fmt: on
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tokens = en_tokenizer(text)
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return Doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps, ents=ents)
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@pytest.fixture
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def doc_not_parsed(en_tokenizer):
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text = "This is a sentence. This is another sentence. And a third."
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tokens = en_tokenizer(text)
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doc = Doc(tokens.vocab, words=[t.text for t in tokens])
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return doc
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@pytest.mark.parametrize(
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"i_sent,i,j,text",
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[
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(0, 0, len("This is a"), "This is a"),
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(1, 0, len("This is another"), "This is another"),
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(2, len("And "), len("And ") + len("a third"), "a third"),
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(0, 1, 2, None),
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],
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)
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def test_char_span(doc, i_sent, i, j, text):
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sents = list(doc.sents)
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span = sents[i_sent].char_span(i, j)
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if not text:
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assert not span
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else:
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assert span.text == text
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def test_spans_sent_spans(doc):
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sents = list(doc.sents)
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assert sents[0].start == 0
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assert sents[0].end == 5
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assert len(sents) == 3
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assert sum(len(sent) for sent in sents) == len(doc)
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def test_spans_root(doc):
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span = doc[2:4]
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assert len(span) == 2
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assert span.text == "a sentence"
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assert span.root.text == "sentence"
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assert span.root.head.text == "is"
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def test_spans_string_fn(doc):
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span = doc[0:4]
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assert len(span) == 4
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assert span.text == "This is a sentence"
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def test_spans_root2(en_tokenizer):
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text = "through North and South Carolina"
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heads = [0, 4, 1, 1, 0]
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deps = ["dep"] * len(heads)
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tokens = en_tokenizer(text)
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doc = Doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps)
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assert doc[-2:].root.text == "Carolina"
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def test_spans_span_sent(doc, doc_not_parsed):
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"""Test span.sent property"""
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assert len(list(doc.sents))
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assert doc[:2].sent.root.text == "is"
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assert doc[:2].sent.text == "This is a sentence ."
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assert doc[6:7].sent.root.left_edge.text == "This"
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# test on manual sbd
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doc_not_parsed[0].is_sent_start = True
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doc_not_parsed[5].is_sent_start = True
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assert doc_not_parsed[1:3].sent == doc_not_parsed[0:5]
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assert doc_not_parsed[10:14].sent == doc_not_parsed[5:]
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def test_spans_lca_matrix(en_tokenizer):
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"""Test span's lca matrix generation"""
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tokens = en_tokenizer("the lazy dog slept")
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doc = Doc(
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tokens.vocab,
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words=[t.text for t in tokens],
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heads=[2, 2, 3, 3],
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deps=["dep"] * 4,
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)
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lca = doc[:2].get_lca_matrix()
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assert lca.shape == (2, 2)
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assert lca[0, 0] == 0 # the & the -> the
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assert lca[0, 1] == -1 # the & lazy -> dog (out of span)
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assert lca[1, 0] == -1 # lazy & the -> dog (out of span)
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assert lca[1, 1] == 1 # lazy & lazy -> lazy
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lca = doc[1:].get_lca_matrix()
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assert lca.shape == (3, 3)
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assert lca[0, 0] == 0 # lazy & lazy -> lazy
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assert lca[0, 1] == 1 # lazy & dog -> dog
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assert lca[0, 2] == 2 # lazy & slept -> slept
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lca = doc[2:].get_lca_matrix()
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assert lca.shape == (2, 2)
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assert lca[0, 0] == 0 # dog & dog -> dog
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assert lca[0, 1] == 1 # dog & slept -> slept
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assert lca[1, 0] == 1 # slept & dog -> slept
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assert lca[1, 1] == 1 # slept & slept -> slept
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def test_span_similarity_match():
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doc = Doc(Vocab(), words=["a", "b", "a", "b"])
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span1 = doc[:2]
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span2 = doc[2:]
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with pytest.warns(UserWarning):
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assert span1.similarity(span2) == 1.0
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assert span1.similarity(doc) == 0.0
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assert span1[:1].similarity(doc.vocab["a"]) == 1.0
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def test_spans_default_sentiment(en_tokenizer):
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"""Test span.sentiment property's default averaging behaviour"""
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text = "good stuff bad stuff"
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tokens = en_tokenizer(text)
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tokens.vocab[tokens[0].text].sentiment = 3.0
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tokens.vocab[tokens[2].text].sentiment = -2.0
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doc = Doc(tokens.vocab, words=[t.text for t in tokens])
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assert doc[:2].sentiment == 3.0 / 2
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assert doc[-2:].sentiment == -2.0 / 2
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assert doc[:-1].sentiment == (3.0 + -2) / 3.0
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def test_spans_override_sentiment(en_tokenizer):
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"""Test span.sentiment property's default averaging behaviour"""
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text = "good stuff bad stuff"
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tokens = en_tokenizer(text)
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tokens.vocab[tokens[0].text].sentiment = 3.0
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tokens.vocab[tokens[2].text].sentiment = -2.0
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doc = Doc(tokens.vocab, words=[t.text for t in tokens])
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doc.user_span_hooks["sentiment"] = lambda span: 10.0
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assert doc[:2].sentiment == 10.0
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assert doc[-2:].sentiment == 10.0
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assert doc[:-1].sentiment == 10.0
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def test_spans_are_hashable(en_tokenizer):
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"""Test spans can be hashed."""
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text = "good stuff bad stuff"
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tokens = en_tokenizer(text)
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span1 = tokens[:2]
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span2 = tokens[2:4]
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assert hash(span1) != hash(span2)
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span3 = tokens[0:2]
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assert hash(span3) == hash(span1)
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def test_spans_by_character(doc):
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span1 = doc[1:-2]
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# default and specified alignment mode "strict"
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span2 = doc.char_span(span1.start_char, span1.end_char, label="GPE")
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assert span1.start_char == span2.start_char
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assert span1.end_char == span2.end_char
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assert span2.label_ == "GPE"
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span2 = doc.char_span(
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span1.start_char, span1.end_char, label="GPE", alignment_mode="strict"
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)
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assert span1.start_char == span2.start_char
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assert span1.end_char == span2.end_char
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assert span2.label_ == "GPE"
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# alignment mode "contract"
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span2 = doc.char_span(
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span1.start_char - 3, span1.end_char, label="GPE", alignment_mode="contract"
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)
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assert span1.start_char == span2.start_char
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assert span1.end_char == span2.end_char
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assert span2.label_ == "GPE"
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# alignment mode "expand"
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span2 = doc.char_span(
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span1.start_char + 1, span1.end_char, label="GPE", alignment_mode="expand"
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)
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assert span1.start_char == span2.start_char
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assert span1.end_char == span2.end_char
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assert span2.label_ == "GPE"
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# unsupported alignment mode
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with pytest.raises(ValueError):
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span2 = doc.char_span(
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span1.start_char + 1, span1.end_char, label="GPE", alignment_mode="unk"
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)
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def test_span_to_array(doc):
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span = doc[1:-2]
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arr = span.to_array([ORTH, LENGTH])
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assert arr.shape == (len(span), 2)
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assert arr[0, 0] == span[0].orth
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assert arr[0, 1] == len(span[0])
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def test_span_as_doc(doc):
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span = doc[4:10]
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span_doc = span.as_doc()
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assert span.text == span_doc.text.strip()
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assert isinstance(span_doc, doc.__class__)
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assert span_doc is not doc
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assert span_doc[0].idx == 0
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# partial initial entity is removed
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assert len(span_doc.ents) == 0
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# full entity is preserved
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span_doc = doc[2:10].as_doc()
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assert len(span_doc.ents) == 1
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# partial final entity is removed
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span_doc = doc[0:5].as_doc()
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assert len(span_doc.ents) == 0
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@pytest.mark.usefixtures("clean_underscore")
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def test_span_as_doc_user_data(doc):
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"""Test that the user_data can be preserved (but not by default). """
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my_key = "my_info"
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my_value = 342
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doc.user_data[my_key] = my_value
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Token.set_extension("is_x", default=False)
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doc[7]._.is_x = True
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span = doc[4:10]
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span_doc_with = span.as_doc(copy_user_data=True)
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span_doc_without = span.as_doc()
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assert doc.user_data.get(my_key, None) is my_value
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assert span_doc_with.user_data.get(my_key, None) is my_value
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assert span_doc_without.user_data.get(my_key, None) is None
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for i in range(len(span_doc_with)):
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if i != 3:
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assert span_doc_with[i]._.is_x is False
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else:
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assert span_doc_with[i]._.is_x is True
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assert not any([t._.is_x for t in span_doc_without])
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def test_span_string_label_kb_id(doc):
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span = Span(doc, 0, 1, label="hello", kb_id="Q342")
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assert span.label_ == "hello"
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assert span.label == doc.vocab.strings["hello"]
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assert span.kb_id_ == "Q342"
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assert span.kb_id == doc.vocab.strings["Q342"]
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def test_span_label_readonly(doc):
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span = Span(doc, 0, 1)
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with pytest.raises(NotImplementedError):
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span.label_ = "hello"
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def test_span_kb_id_readonly(doc):
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span = Span(doc, 0, 1)
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with pytest.raises(NotImplementedError):
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span.kb_id_ = "Q342"
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def test_span_ents_property(doc):
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"""Test span.ents for the """
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doc.ents = [
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(doc.vocab.strings["PRODUCT"], 0, 1),
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(doc.vocab.strings["PRODUCT"], 7, 8),
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(doc.vocab.strings["PRODUCT"], 11, 14),
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]
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assert len(list(doc.ents)) == 3
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sentences = list(doc.sents)
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assert len(sentences) == 3
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assert len(sentences[0].ents) == 1
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# First sentence, also tests start of sentence
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assert sentences[0].ents[0].text == "This"
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assert sentences[0].ents[0].label_ == "PRODUCT"
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assert sentences[0].ents[0].start == 0
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assert sentences[0].ents[0].end == 1
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# Second sentence
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assert len(sentences[1].ents) == 1
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assert sentences[1].ents[0].text == "another"
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assert sentences[1].ents[0].label_ == "PRODUCT"
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assert sentences[1].ents[0].start == 7
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assert sentences[1].ents[0].end == 8
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# Third sentence ents, Also tests end of sentence
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assert sentences[2].ents[0].text == "a third ."
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assert sentences[2].ents[0].label_ == "PRODUCT"
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assert sentences[2].ents[0].start == 11
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assert sentences[2].ents[0].end == 14
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def test_filter_spans(doc):
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# Test filtering duplicates
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spans = [doc[1:4], doc[6:8], doc[1:4], doc[10:14]]
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filtered = filter_spans(spans)
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assert len(filtered) == 3
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assert filtered[0].start == 1 and filtered[0].end == 4
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assert filtered[1].start == 6 and filtered[1].end == 8
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assert filtered[2].start == 10 and filtered[2].end == 14
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# Test filtering overlaps with longest preference
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spans = [doc[1:4], doc[1:3], doc[5:10], doc[7:9], doc[1:4]]
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filtered = filter_spans(spans)
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assert len(filtered) == 2
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assert len(filtered[0]) == 3
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assert len(filtered[1]) == 5
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assert filtered[0].start == 1 and filtered[0].end == 4
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assert filtered[1].start == 5 and filtered[1].end == 10
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# Test filtering overlaps with earlier preference for identical length
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spans = [doc[1:4], doc[2:5], doc[5:10], doc[7:9], doc[1:4]]
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filtered = filter_spans(spans)
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assert len(filtered) == 2
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assert len(filtered[0]) == 3
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assert len(filtered[1]) == 5
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assert filtered[0].start == 1 and filtered[0].end == 4
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assert filtered[1].start == 5 and filtered[1].end == 10
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def test_span_eq_hash(doc, doc_not_parsed):
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assert doc[0:2] == doc[0:2]
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assert doc[0:2] != doc[1:3]
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assert doc[0:2] != doc_not_parsed[0:2]
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assert hash(doc[0:2]) == hash(doc[0:2])
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assert hash(doc[0:2]) != hash(doc[1:3])
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assert hash(doc[0:2]) != hash(doc_not_parsed[0:2])
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def test_span_boundaries(doc):
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start = 1
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end = 5
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span = doc[start:end]
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for i in range(start, end):
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assert span[i - start] == doc[i]
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with pytest.raises(IndexError):
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span[-5]
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with pytest.raises(IndexError):
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span[5]
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def test_sent(en_tokenizer):
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doc = en_tokenizer("Check span.sent raises error if doc is not sentencized.")
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span = doc[1:3]
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assert not span.doc.has_annotation("SENT_START")
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with pytest.raises(ValueError):
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span.sent
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