mirror of
https://github.com/explosion/spaCy.git
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256 lines
8.9 KiB
Python
256 lines
8.9 KiB
Python
import pytest
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from spacy.tokens import Doc, Span, DocBin
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from spacy.training import Example
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from spacy.training.converters.conllu_to_docs import conllu_to_docs
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from spacy.lang.en import English
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from spacy.kb import KnowledgeBase
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from spacy.vocab import Vocab
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from spacy.language import Language
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from spacy.util import ensure_path, load_model_from_path
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import numpy
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import pickle
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from thinc.api import NumpyOps, get_current_ops
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from ..util import make_tempdir
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def test_issue4528(en_vocab):
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"""Test that user_data is correctly serialized in DocBin."""
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doc = Doc(en_vocab, words=["hello", "world"])
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doc.user_data["foo"] = "bar"
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# This is how extension attribute values are stored in the user data
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doc.user_data[("._.", "foo", None, None)] = "bar"
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doc_bin = DocBin(store_user_data=True)
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doc_bin.add(doc)
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doc_bin_bytes = doc_bin.to_bytes()
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new_doc_bin = DocBin(store_user_data=True).from_bytes(doc_bin_bytes)
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new_doc = list(new_doc_bin.get_docs(en_vocab))[0]
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assert new_doc.user_data["foo"] == "bar"
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assert new_doc.user_data[("._.", "foo", None, None)] == "bar"
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@pytest.mark.parametrize(
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"text,words", [("A'B C", ["A", "'", "B", "C"]), ("A-B", ["A-B"])]
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)
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def test_gold_misaligned(en_tokenizer, text, words):
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doc = en_tokenizer(text)
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Example.from_dict(doc, {"words": words})
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def test_issue4651_with_phrase_matcher_attr():
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"""Test that the EntityRuler PhraseMatcher is deserialized correctly using
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the method from_disk when the EntityRuler argument phrase_matcher_attr is
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specified.
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"""
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text = "Spacy is a python library for nlp"
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nlp = English()
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patterns = [{"label": "PYTHON_LIB", "pattern": "spacy", "id": "spaCy"}]
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ruler = nlp.add_pipe("entity_ruler", config={"phrase_matcher_attr": "LOWER"})
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ruler.add_patterns(patterns)
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doc = nlp(text)
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res = [(ent.text, ent.label_, ent.ent_id_) for ent in doc.ents]
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nlp_reloaded = English()
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with make_tempdir() as d:
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file_path = d / "entityruler"
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ruler.to_disk(file_path)
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nlp_reloaded.add_pipe("entity_ruler").from_disk(file_path)
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doc_reloaded = nlp_reloaded(text)
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res_reloaded = [(ent.text, ent.label_, ent.ent_id_) for ent in doc_reloaded.ents]
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assert res == res_reloaded
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def test_issue4651_without_phrase_matcher_attr():
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"""Test that the EntityRuler PhraseMatcher is deserialized correctly using
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the method from_disk when the EntityRuler argument phrase_matcher_attr is
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not specified.
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"""
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text = "Spacy is a python library for nlp"
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nlp = English()
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patterns = [{"label": "PYTHON_LIB", "pattern": "spacy", "id": "spaCy"}]
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ruler = nlp.add_pipe("entity_ruler")
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ruler.add_patterns(patterns)
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doc = nlp(text)
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res = [(ent.text, ent.label_, ent.ent_id_) for ent in doc.ents]
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nlp_reloaded = English()
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with make_tempdir() as d:
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file_path = d / "entityruler"
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ruler.to_disk(file_path)
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nlp_reloaded.add_pipe("entity_ruler").from_disk(file_path)
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doc_reloaded = nlp_reloaded(text)
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res_reloaded = [(ent.text, ent.label_, ent.ent_id_) for ent in doc_reloaded.ents]
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assert res == res_reloaded
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def test_issue4665():
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"""
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conllu_to_docs should not raise an exception if the HEAD column contains an
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underscore
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"""
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input_data = """
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1 [ _ PUNCT -LRB- _ _ punct _ _
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2 This _ DET DT _ _ det _ _
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3 killing _ NOUN NN _ _ nsubj _ _
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4 of _ ADP IN _ _ case _ _
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5 a _ DET DT _ _ det _ _
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6 respected _ ADJ JJ _ _ amod _ _
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7 cleric _ NOUN NN _ _ nmod _ _
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8 will _ AUX MD _ _ aux _ _
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9 be _ AUX VB _ _ aux _ _
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10 causing _ VERB VBG _ _ root _ _
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11 us _ PRON PRP _ _ iobj _ _
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12 trouble _ NOUN NN _ _ dobj _ _
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13 for _ ADP IN _ _ case _ _
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14 years _ NOUN NNS _ _ nmod _ _
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15 to _ PART TO _ _ mark _ _
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16 come _ VERB VB _ _ acl _ _
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17 . _ PUNCT . _ _ punct _ _
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18 ] _ PUNCT -RRB- _ _ punct _ _
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"""
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conllu_to_docs(input_data)
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def test_issue4674():
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"""Test that setting entities with overlapping identifiers does not mess up IO"""
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nlp = English()
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kb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
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vector1 = [0.9, 1.1, 1.01]
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vector2 = [1.8, 2.25, 2.01]
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with pytest.warns(UserWarning):
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kb.set_entities(
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entity_list=["Q1", "Q1"],
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freq_list=[32, 111],
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vector_list=[vector1, vector2],
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)
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assert kb.get_size_entities() == 1
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# dumping to file & loading back in
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with make_tempdir() as d:
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dir_path = ensure_path(d)
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if not dir_path.exists():
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dir_path.mkdir()
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file_path = dir_path / "kb"
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kb.to_disk(str(file_path))
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kb2 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
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kb2.from_disk(str(file_path))
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assert kb2.get_size_entities() == 1
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@pytest.mark.skip(reason="API change: disable just disables, new exclude arg")
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def test_issue4707():
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"""Tests that disabled component names are also excluded from nlp.from_disk
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by default when loading a model.
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"""
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nlp = English()
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nlp.add_pipe("sentencizer")
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nlp.add_pipe("entity_ruler")
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assert nlp.pipe_names == ["sentencizer", "entity_ruler"]
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exclude = ["tokenizer", "sentencizer"]
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with make_tempdir() as tmpdir:
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nlp.to_disk(tmpdir, exclude=exclude)
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new_nlp = load_model_from_path(tmpdir, disable=exclude)
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assert "sentencizer" not in new_nlp.pipe_names
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assert "entity_ruler" in new_nlp.pipe_names
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def test_issue4725_1():
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"""Ensure the pickling of the NER goes well"""
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vocab = Vocab(vectors_name="test_vocab_add_vector")
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nlp = English(vocab=vocab)
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config = {
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"update_with_oracle_cut_size": 111,
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}
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ner = nlp.create_pipe("ner", config=config)
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with make_tempdir() as tmp_path:
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with (tmp_path / "ner.pkl").open("wb") as file_:
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pickle.dump(ner, file_)
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assert ner.cfg["update_with_oracle_cut_size"] == 111
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with (tmp_path / "ner.pkl").open("rb") as file_:
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ner2 = pickle.load(file_)
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assert ner2.cfg["update_with_oracle_cut_size"] == 111
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def test_issue4725_2():
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if isinstance(get_current_ops, NumpyOps):
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# ensures that this runs correctly and doesn't hang or crash because of the global vectors
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# if it does crash, it's usually because of calling 'spawn' for multiprocessing (e.g. on Windows),
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# or because of issues with pickling the NER (cf test_issue4725_1)
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vocab = Vocab(vectors_name="test_vocab_add_vector")
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data = numpy.ndarray((5, 3), dtype="f")
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data[0] = 1.0
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data[1] = 2.0
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vocab.set_vector("cat", data[0])
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vocab.set_vector("dog", data[1])
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nlp = English(vocab=vocab)
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nlp.add_pipe("ner")
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nlp.initialize()
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docs = ["Kurt is in London."] * 10
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for _ in nlp.pipe(docs, batch_size=2, n_process=2):
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pass
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def test_issue4849():
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nlp = English()
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patterns = [
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{"label": "PERSON", "pattern": "joe biden", "id": "joe-biden"},
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{"label": "PERSON", "pattern": "bernie sanders", "id": "bernie-sanders"},
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]
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ruler = nlp.add_pipe("entity_ruler", config={"phrase_matcher_attr": "LOWER"})
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ruler.add_patterns(patterns)
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text = """
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The left is starting to take aim at Democratic front-runner Joe Biden.
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Sen. Bernie Sanders joined in her criticism: "There is no 'middle ground' when it comes to climate policy."
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"""
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# USING 1 PROCESS
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count_ents = 0
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for doc in nlp.pipe([text], n_process=1):
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count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
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assert count_ents == 2
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# USING 2 PROCESSES
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if isinstance(get_current_ops, NumpyOps):
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count_ents = 0
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for doc in nlp.pipe([text], n_process=2):
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count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
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assert count_ents == 2
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@Language.factory("my_pipe")
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class CustomPipe:
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def __init__(self, nlp, name="my_pipe"):
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self.name = name
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Span.set_extension("my_ext", getter=self._get_my_ext)
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Doc.set_extension("my_ext", default=None)
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def __call__(self, doc):
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gathered_ext = []
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for sent in doc.sents:
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sent_ext = self._get_my_ext(sent)
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sent._.set("my_ext", sent_ext)
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gathered_ext.append(sent_ext)
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doc._.set("my_ext", "\n".join(gathered_ext))
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return doc
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@staticmethod
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def _get_my_ext(span):
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return str(span.end)
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def test_issue4903():
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"""Ensure that this runs correctly and doesn't hang or crash on Windows /
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macOS."""
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nlp = English()
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nlp.add_pipe("sentencizer")
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nlp.add_pipe("my_pipe", after="sentencizer")
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text = ["I like bananas.", "Do you like them?", "No, I prefer wasabi."]
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if isinstance(get_current_ops(), NumpyOps):
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docs = list(nlp.pipe(text, n_process=2))
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assert docs[0].text == "I like bananas."
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assert docs[1].text == "Do you like them?"
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assert docs[2].text == "No, I prefer wasabi."
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def test_issue4924():
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nlp = Language()
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example = Example.from_dict(nlp.make_doc(""), {})
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nlp.evaluate([example])
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