mirror of
https://github.com/explosion/spaCy.git
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226 lines
7.2 KiB
Python
226 lines
7.2 KiB
Python
import copy
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import pickle
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import numpy
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import pytest
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from spacy.attrs import DEP, HEAD
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from spacy.lang.en import English
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from spacy.language import Language
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from spacy.matcher import Matcher, PhraseMatcher
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from spacy.tokens import Doc
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from spacy.vectors import Vectors
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from spacy.vocab import Vocab
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from ..util import make_tempdir
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@pytest.mark.issue(1727)
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def test_issue1727():
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"""Test that models with no pretrained vectors can be deserialized
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correctly after vectors are added."""
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nlp = Language(Vocab())
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data = numpy.ones((3, 300), dtype="f")
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vectors = Vectors(data=data, keys=["I", "am", "Matt"])
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tagger = nlp.create_pipe("tagger")
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tagger.add_label("PRP")
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assert tagger.cfg.get("pretrained_dims", 0) == 0
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tagger.vocab.vectors = vectors
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with make_tempdir() as path:
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tagger.to_disk(path)
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tagger = nlp.create_pipe("tagger").from_disk(path)
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assert tagger.cfg.get("pretrained_dims", 0) == 0
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@pytest.mark.issue(1799)
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def test_issue1799():
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"""Test sentence boundaries are deserialized correctly, even for
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non-projective sentences."""
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heads_deps = numpy.asarray(
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[
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[1, 397],
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[4, 436],
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[2, 426],
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[1, 402],
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[0, 8206900633647566924],
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[18446744073709551615, 440],
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[18446744073709551614, 442],
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],
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dtype="uint64",
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)
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doc = Doc(Vocab(), words="Just what I was looking for .".split())
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doc.vocab.strings.add("ROOT")
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doc = doc.from_array([HEAD, DEP], heads_deps)
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assert len(list(doc.sents)) == 1
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@pytest.mark.issue(1834)
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def test_issue1834():
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"""Test that sentence boundaries & parse/tag flags are not lost
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during serialization."""
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words = ["This", "is", "a", "first", "sentence", ".", "And", "another", "one"]
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doc = Doc(Vocab(), words=words)
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doc[6].is_sent_start = True
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new_doc = Doc(doc.vocab).from_bytes(doc.to_bytes())
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assert new_doc[6].sent_start
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assert not new_doc.has_annotation("DEP")
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assert not new_doc.has_annotation("TAG")
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doc = Doc(
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Vocab(),
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words=words,
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tags=["TAG"] * len(words),
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heads=[0, 0, 0, 0, 0, 0, 6, 6, 6],
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deps=["dep"] * len(words),
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)
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new_doc = Doc(doc.vocab).from_bytes(doc.to_bytes())
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assert new_doc[6].sent_start
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assert new_doc.has_annotation("DEP")
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assert new_doc.has_annotation("TAG")
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@pytest.mark.issue(1883)
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def test_issue1883():
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matcher = Matcher(Vocab())
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matcher.add("pat1", [[{"orth": "hello"}]])
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doc = Doc(matcher.vocab, words=["hello"])
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assert len(matcher(doc)) == 1
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new_matcher = copy.deepcopy(matcher)
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new_doc = Doc(new_matcher.vocab, words=["hello"])
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assert len(new_matcher(new_doc)) == 1
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@pytest.mark.issue(2564)
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def test_issue2564():
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"""Test the tagger sets has_annotation("TAG") correctly when used via Language.pipe."""
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nlp = Language()
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tagger = nlp.add_pipe("tagger")
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tagger.add_label("A")
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nlp.initialize()
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doc = nlp("hello world")
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assert doc.has_annotation("TAG")
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docs = nlp.pipe(["hello", "world"])
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piped_doc = next(docs)
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assert piped_doc.has_annotation("TAG")
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@pytest.mark.issue(3248)
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def test_issue3248_2():
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"""Test that the PhraseMatcher can be pickled correctly."""
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nlp = English()
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matcher = PhraseMatcher(nlp.vocab)
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matcher.add("TEST1", [nlp("a"), nlp("b"), nlp("c")])
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matcher.add("TEST2", [nlp("d")])
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data = pickle.dumps(matcher)
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new_matcher = pickle.loads(data)
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assert len(new_matcher) == len(matcher)
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@pytest.mark.issue(3289)
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def test_issue3289():
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"""Test that Language.to_bytes handles serializing a pipeline component
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with an uninitialized model."""
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nlp = English()
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nlp.add_pipe("textcat")
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bytes_data = nlp.to_bytes()
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new_nlp = English()
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new_nlp.add_pipe("textcat")
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new_nlp.from_bytes(bytes_data)
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@pytest.mark.issue(3468)
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def test_issue3468():
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"""Test that sentence boundaries are set correctly so Doc.has_annotation("SENT_START") can
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be restored after serialization."""
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nlp = English()
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nlp.add_pipe("sentencizer")
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doc = nlp("Hello world")
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assert doc[0].is_sent_start
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assert doc.has_annotation("SENT_START")
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assert len(list(doc.sents)) == 1
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doc_bytes = doc.to_bytes()
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new_doc = Doc(nlp.vocab).from_bytes(doc_bytes)
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assert new_doc[0].is_sent_start
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assert new_doc.has_annotation("SENT_START")
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assert len(list(new_doc.sents)) == 1
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@pytest.mark.issue(3959)
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def test_issue3959():
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"""Ensure that a modified pos attribute is serialized correctly."""
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nlp = English()
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doc = nlp(
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"displaCy uses JavaScript, SVG and CSS to show you how computers understand language"
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)
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assert doc[0].pos_ == ""
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doc[0].pos_ = "NOUN"
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assert doc[0].pos_ == "NOUN"
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# usually this is already True when starting from proper models instead of blank English
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with make_tempdir() as tmp_dir:
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file_path = tmp_dir / "my_doc"
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doc.to_disk(file_path)
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doc2 = nlp("")
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doc2.from_disk(file_path)
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assert doc2[0].pos_ == "NOUN"
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def test_serialize_empty_doc(en_vocab):
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doc = Doc(en_vocab)
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data = doc.to_bytes()
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doc2 = Doc(en_vocab)
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doc2.from_bytes(data)
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assert len(doc) == len(doc2)
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for token1, token2 in zip(doc, doc2):
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assert token1.text == token2.text
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def test_serialize_doc_roundtrip_bytes(en_vocab):
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doc = Doc(en_vocab, words=["hello", "world"])
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doc.cats = {"A": 0.5}
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doc_b = doc.to_bytes()
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new_doc = Doc(en_vocab).from_bytes(doc_b)
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assert new_doc.to_bytes() == doc_b
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def test_serialize_doc_roundtrip_disk(en_vocab):
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doc = Doc(en_vocab, words=["hello", "world"])
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with make_tempdir() as d:
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file_path = d / "doc"
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doc.to_disk(file_path)
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doc_d = Doc(en_vocab).from_disk(file_path)
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assert doc.to_bytes() == doc_d.to_bytes()
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def test_serialize_doc_roundtrip_disk_str_path(en_vocab):
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doc = Doc(en_vocab, words=["hello", "world"])
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with make_tempdir() as d:
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file_path = d / "doc"
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file_path = str(file_path)
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doc.to_disk(file_path)
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doc_d = Doc(en_vocab).from_disk(file_path)
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assert doc.to_bytes() == doc_d.to_bytes()
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def test_serialize_doc_exclude(en_vocab):
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doc = Doc(en_vocab, words=["hello", "world"])
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doc.user_data["foo"] = "bar"
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new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
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assert new_doc.user_data["foo"] == "bar"
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new_doc = Doc(en_vocab).from_bytes(doc.to_bytes(), exclude=["user_data"])
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assert not new_doc.user_data
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new_doc = Doc(en_vocab).from_bytes(doc.to_bytes(exclude=["user_data"]))
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assert not new_doc.user_data
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def test_serialize_doc_span_groups(en_vocab):
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doc = Doc(en_vocab, words=["hello", "world", "!"])
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span = doc[0:2]
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span.label_ = "test_serialize_doc_span_groups_label"
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span.id_ = "test_serialize_doc_span_groups_id"
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span.kb_id_ = "test_serialize_doc_span_groups_kb_id"
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doc.spans["content"] = [span]
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new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
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assert len(new_doc.spans["content"]) == 1
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assert new_doc.spans["content"][0].label_ == "test_serialize_doc_span_groups_label"
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assert new_doc.spans["content"][0].id_ == "test_serialize_doc_span_groups_id"
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assert new_doc.spans["content"][0].kb_id_ == "test_serialize_doc_span_groups_kb_id"
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