478 lines
16 KiB
Python
478 lines
16 KiB
Python
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import pickle
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import pytest
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import srsly
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from thinc.api import Linear
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import spacy
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from spacy import Vocab, load, registry
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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.pipeline import (
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DependencyParser,
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EntityRecognizer,
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EntityRuler,
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SentenceRecognizer,
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Tagger,
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TextCategorizer,
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TrainablePipe,
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)
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from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
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from spacy.pipeline.senter import DEFAULT_SENTER_MODEL
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from spacy.pipeline.tagger import DEFAULT_TAGGER_MODEL
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from spacy.pipeline.textcat import DEFAULT_SINGLE_TEXTCAT_MODEL
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from spacy.tokens import Span
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from spacy.util import ensure_path, load_model
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from ..util import make_tempdir
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test_parsers = [DependencyParser, EntityRecognizer]
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@pytest.fixture
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def parser(en_vocab):
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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"beam_width": 1,
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"beam_update_prob": 1.0,
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"beam_density": 0.0,
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}
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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parser = DependencyParser(en_vocab, model, **config)
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parser.add_label("nsubj")
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return parser
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@pytest.fixture
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def blank_parser(en_vocab):
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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"beam_width": 1,
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"beam_update_prob": 1.0,
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"beam_density": 0.0,
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}
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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parser = DependencyParser(en_vocab, model, **config)
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return parser
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@pytest.fixture
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def taggers(en_vocab):
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cfg = {"model": DEFAULT_TAGGER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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tagger1 = Tagger(en_vocab, model)
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tagger2 = Tagger(en_vocab, model)
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return tagger1, tagger2
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@pytest.mark.issue(3456)
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def test_issue3456():
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# this crashed because of a padding error in layer.ops.unflatten in thinc
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nlp = English()
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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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list(nlp.pipe(["hi", ""]))
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@pytest.mark.issue(3526)
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def test_issue_3526_1(en_vocab):
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patterns = [
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{"label": "HELLO", "pattern": "hello world"},
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{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
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{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
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{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
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{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
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]
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nlp = Language(vocab=en_vocab)
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ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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ruler_bytes = ruler.to_bytes()
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assert len(ruler) == len(patterns)
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assert len(ruler.labels) == 4
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assert ruler.overwrite
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new_ruler = EntityRuler(nlp)
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new_ruler = new_ruler.from_bytes(ruler_bytes)
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assert len(new_ruler) == len(ruler)
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assert len(new_ruler.labels) == 4
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assert new_ruler.overwrite == ruler.overwrite
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assert new_ruler.ent_id_sep == ruler.ent_id_sep
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@pytest.mark.issue(3526)
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def test_issue_3526_2(en_vocab):
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patterns = [
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{"label": "HELLO", "pattern": "hello world"},
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{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
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{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
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{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
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{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
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]
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nlp = Language(vocab=en_vocab)
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ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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bytes_old_style = srsly.msgpack_dumps(ruler.patterns)
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new_ruler = EntityRuler(nlp)
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new_ruler = new_ruler.from_bytes(bytes_old_style)
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assert len(new_ruler) == len(ruler)
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for pattern in ruler.patterns:
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assert pattern in new_ruler.patterns
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assert new_ruler.overwrite is not ruler.overwrite
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@pytest.mark.issue(3526)
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def test_issue_3526_3(en_vocab):
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patterns = [
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{"label": "HELLO", "pattern": "hello world"},
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{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
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{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
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{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
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{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
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]
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nlp = Language(vocab=en_vocab)
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ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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with make_tempdir() as tmpdir:
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out_file = tmpdir / "entity_ruler"
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srsly.write_jsonl(out_file.with_suffix(".jsonl"), ruler.patterns)
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new_ruler = EntityRuler(nlp).from_disk(out_file)
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for pattern in ruler.patterns:
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assert pattern in new_ruler.patterns
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assert len(new_ruler) == len(ruler)
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assert new_ruler.overwrite is not ruler.overwrite
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@pytest.mark.issue(3526)
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def test_issue_3526_4(en_vocab):
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nlp = Language(vocab=en_vocab)
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patterns = [{"label": "ORG", "pattern": "Apple"}]
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config = {"overwrite_ents": True}
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ruler = nlp.add_pipe("entity_ruler", config=config)
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ruler.add_patterns(patterns)
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with make_tempdir() as tmpdir:
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nlp.to_disk(tmpdir)
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ruler = nlp.get_pipe("entity_ruler")
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assert ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
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assert ruler.overwrite is True
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nlp2 = load(tmpdir)
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new_ruler = nlp2.get_pipe("entity_ruler")
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assert new_ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
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assert new_ruler.overwrite is True
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@pytest.mark.issue(4042)
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def test_issue4042():
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"""Test that serialization of an EntityRuler before NER works fine."""
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nlp = English()
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# add ner pipe
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ner = nlp.add_pipe("ner")
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ner.add_label("SOME_LABEL")
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nlp.initialize()
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# Add entity ruler
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patterns = [
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{"label": "MY_ORG", "pattern": "Apple"},
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{"label": "MY_GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]},
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]
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# works fine with "after"
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ruler = nlp.add_pipe("entity_ruler", before="ner")
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ruler.add_patterns(patterns)
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doc1 = nlp("What do you think about Apple ?")
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assert doc1.ents[0].label_ == "MY_ORG"
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with make_tempdir() as d:
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output_dir = ensure_path(d)
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if not output_dir.exists():
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output_dir.mkdir()
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nlp.to_disk(output_dir)
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nlp2 = load_model(output_dir)
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doc2 = nlp2("What do you think about Apple ?")
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assert doc2.ents[0].label_ == "MY_ORG"
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@pytest.mark.issue(4042)
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def test_issue4042_bug2():
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"""
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Test that serialization of an NER works fine when new labels were added.
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This is the second bug of two bugs underlying the issue 4042.
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"""
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nlp1 = English()
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# add ner pipe
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ner1 = nlp1.add_pipe("ner")
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ner1.add_label("SOME_LABEL")
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nlp1.initialize()
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# add a new label to the doc
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doc1 = nlp1("What do you think about Apple ?")
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assert len(ner1.labels) == 1
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assert "SOME_LABEL" in ner1.labels
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apple_ent = Span(doc1, 5, 6, label="MY_ORG")
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doc1.ents = list(doc1.ents) + [apple_ent]
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# Add the label explicitly. Previously we didn't require this.
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ner1.add_label("MY_ORG")
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ner1(doc1)
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assert len(ner1.labels) == 2
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assert "SOME_LABEL" in ner1.labels
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assert "MY_ORG" in ner1.labels
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with make_tempdir() as d:
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# assert IO goes fine
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output_dir = ensure_path(d)
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if not output_dir.exists():
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output_dir.mkdir()
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ner1.to_disk(output_dir)
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config = {}
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ner2 = nlp1.create_pipe("ner", config=config)
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ner2.from_disk(output_dir)
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assert len(ner2.labels) == 2
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@pytest.mark.issue(4725)
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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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@pytest.mark.parametrize("Parser", test_parsers)
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def test_serialize_parser_roundtrip_bytes(en_vocab, Parser):
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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parser = Parser(en_vocab, model)
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new_parser = Parser(en_vocab, model)
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new_parser = new_parser.from_bytes(parser.to_bytes(exclude=["vocab"]))
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bytes_2 = new_parser.to_bytes(exclude=["vocab"])
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bytes_3 = parser.to_bytes(exclude=["vocab"])
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assert len(bytes_2) == len(bytes_3)
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assert bytes_2 == bytes_3
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@pytest.mark.parametrize("Parser", test_parsers)
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def test_serialize_parser_strings(Parser):
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vocab1 = Vocab()
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label = "FunnyLabel"
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assert label not in vocab1.strings
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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parser1 = Parser(vocab1, model)
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parser1.add_label(label)
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assert label in parser1.vocab.strings
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vocab2 = Vocab()
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assert label not in vocab2.strings
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parser2 = Parser(vocab2, model)
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parser2 = parser2.from_bytes(parser1.to_bytes(exclude=["vocab"]))
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assert label in parser2.vocab.strings
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@pytest.mark.parametrize("Parser", test_parsers)
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def test_serialize_parser_roundtrip_disk(en_vocab, Parser):
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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parser = Parser(en_vocab, model)
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with make_tempdir() as d:
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file_path = d / "parser"
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parser.to_disk(file_path)
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parser_d = Parser(en_vocab, model)
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parser_d = parser_d.from_disk(file_path)
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parser_bytes = parser.to_bytes(exclude=["model", "vocab"])
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parser_d_bytes = parser_d.to_bytes(exclude=["model", "vocab"])
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assert len(parser_bytes) == len(parser_d_bytes)
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assert parser_bytes == parser_d_bytes
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def test_to_from_bytes(parser, blank_parser):
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assert parser.model is not True
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assert blank_parser.model is not True
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assert blank_parser.moves.n_moves != parser.moves.n_moves
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bytes_data = parser.to_bytes(exclude=["vocab"])
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# the blank parser needs to be resized before we can call from_bytes
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blank_parser.model.attrs["resize_output"](blank_parser.model, parser.moves.n_moves)
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blank_parser.from_bytes(bytes_data)
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assert blank_parser.model is not True
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assert blank_parser.moves.n_moves == parser.moves.n_moves
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def test_serialize_tagger_roundtrip_bytes(en_vocab, taggers):
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tagger1 = taggers[0]
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tagger1_b = tagger1.to_bytes()
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tagger1 = tagger1.from_bytes(tagger1_b)
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assert tagger1.to_bytes() == tagger1_b
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cfg = {"model": DEFAULT_TAGGER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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new_tagger1 = Tagger(en_vocab, model).from_bytes(tagger1_b)
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new_tagger1_b = new_tagger1.to_bytes()
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assert len(new_tagger1_b) == len(tagger1_b)
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assert new_tagger1_b == tagger1_b
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def test_serialize_tagger_roundtrip_disk(en_vocab, taggers):
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tagger1, tagger2 = taggers
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with make_tempdir() as d:
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file_path1 = d / "tagger1"
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file_path2 = d / "tagger2"
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tagger1.to_disk(file_path1)
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tagger2.to_disk(file_path2)
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cfg = {"model": DEFAULT_TAGGER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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tagger1_d = Tagger(en_vocab, model).from_disk(file_path1)
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tagger2_d = Tagger(en_vocab, model).from_disk(file_path2)
|
||
|
|
assert tagger1_d.to_bytes() == tagger2_d.to_bytes()
|
||
|
|
|
||
|
|
|
||
|
|
def test_serialize_tagger_strings(en_vocab, de_vocab, taggers):
|
||
|
|
label = "SomeWeirdLabel"
|
||
|
|
assert label not in en_vocab.strings
|
||
|
|
assert label not in de_vocab.strings
|
||
|
|
tagger = taggers[0]
|
||
|
|
assert label not in tagger.vocab.strings
|
||
|
|
with make_tempdir() as d:
|
||
|
|
# check that custom labels are serialized as part of the component's strings.jsonl
|
||
|
|
tagger.add_label(label)
|
||
|
|
assert label in tagger.vocab.strings
|
||
|
|
file_path = d / "tagger1"
|
||
|
|
tagger.to_disk(file_path)
|
||
|
|
# ensure that the custom strings are loaded back in when using the tagger in another pipeline
|
||
|
|
cfg = {"model": DEFAULT_TAGGER_MODEL}
|
||
|
|
model = registry.resolve(cfg, validate=True)["model"]
|
||
|
|
tagger2 = Tagger(de_vocab, model).from_disk(file_path)
|
||
|
|
assert label in tagger2.vocab.strings
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.issue(1105)
|
||
|
|
def test_serialize_textcat_empty(en_vocab):
|
||
|
|
# See issue #1105
|
||
|
|
cfg = {"model": DEFAULT_SINGLE_TEXTCAT_MODEL}
|
||
|
|
model = registry.resolve(cfg, validate=True)["model"]
|
||
|
|
textcat = TextCategorizer(en_vocab, model, threshold=0.5)
|
||
|
|
textcat.to_bytes(exclude=["vocab"])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("Parser", test_parsers)
|
||
|
|
def test_serialize_pipe_exclude(en_vocab, Parser):
|
||
|
|
cfg = {"model": DEFAULT_PARSER_MODEL}
|
||
|
|
model = registry.resolve(cfg, validate=True)["model"]
|
||
|
|
|
||
|
|
def get_new_parser():
|
||
|
|
new_parser = Parser(en_vocab, model)
|
||
|
|
return new_parser
|
||
|
|
|
||
|
|
parser = Parser(en_vocab, model)
|
||
|
|
parser.cfg["foo"] = "bar"
|
||
|
|
new_parser = get_new_parser().from_bytes(parser.to_bytes(exclude=["vocab"]))
|
||
|
|
assert "foo" in new_parser.cfg
|
||
|
|
new_parser = get_new_parser().from_bytes(
|
||
|
|
parser.to_bytes(exclude=["vocab"]), exclude=["cfg"]
|
||
|
|
)
|
||
|
|
assert "foo" not in new_parser.cfg
|
||
|
|
new_parser = get_new_parser().from_bytes(
|
||
|
|
parser.to_bytes(exclude=["cfg"]), exclude=["vocab"]
|
||
|
|
)
|
||
|
|
assert "foo" not in new_parser.cfg
|
||
|
|
|
||
|
|
|
||
|
|
def test_serialize_sentencerecognizer(en_vocab):
|
||
|
|
cfg = {"model": DEFAULT_SENTER_MODEL}
|
||
|
|
model = registry.resolve(cfg, validate=True)["model"]
|
||
|
|
sr = SentenceRecognizer(en_vocab, model)
|
||
|
|
sr_b = sr.to_bytes()
|
||
|
|
sr_d = SentenceRecognizer(en_vocab, model).from_bytes(sr_b)
|
||
|
|
assert sr.to_bytes() == sr_d.to_bytes()
|
||
|
|
|
||
|
|
|
||
|
|
def test_serialize_pipeline_disable_enable():
|
||
|
|
nlp = English()
|
||
|
|
nlp.add_pipe("ner")
|
||
|
|
nlp.add_pipe("tagger")
|
||
|
|
nlp.disable_pipe("tagger")
|
||
|
|
assert nlp.config["nlp"]["disabled"] == ["tagger"]
|
||
|
|
config = nlp.config.copy()
|
||
|
|
nlp2 = English.from_config(config)
|
||
|
|
assert nlp2.pipe_names == ["ner"]
|
||
|
|
assert nlp2.component_names == ["ner", "tagger"]
|
||
|
|
assert nlp2.disabled == ["tagger"]
|
||
|
|
assert nlp2.config["nlp"]["disabled"] == ["tagger"]
|
||
|
|
with make_tempdir() as d:
|
||
|
|
nlp2.to_disk(d)
|
||
|
|
nlp3 = spacy.load(d)
|
||
|
|
assert nlp3.pipe_names == ["ner"]
|
||
|
|
assert nlp3.component_names == ["ner", "tagger"]
|
||
|
|
with make_tempdir() as d:
|
||
|
|
nlp3.to_disk(d)
|
||
|
|
nlp4 = spacy.load(d, disable=["ner"])
|
||
|
|
assert nlp4.pipe_names == []
|
||
|
|
assert nlp4.component_names == ["ner", "tagger"]
|
||
|
|
assert nlp4.disabled == ["ner", "tagger"]
|
||
|
|
with make_tempdir() as d:
|
||
|
|
nlp.to_disk(d)
|
||
|
|
nlp5 = spacy.load(d, exclude=["tagger"])
|
||
|
|
assert nlp5.pipe_names == ["ner"]
|
||
|
|
assert nlp5.component_names == ["ner"]
|
||
|
|
assert nlp5.disabled == []
|
||
|
|
|
||
|
|
|
||
|
|
def test_serialize_custom_trainable_pipe():
|
||
|
|
class BadCustomPipe1(TrainablePipe):
|
||
|
|
def __init__(self, vocab):
|
||
|
|
pass
|
||
|
|
|
||
|
|
class BadCustomPipe2(TrainablePipe):
|
||
|
|
def __init__(self, vocab):
|
||
|
|
self.vocab = vocab
|
||
|
|
self.model = None
|
||
|
|
|
||
|
|
class CustomPipe(TrainablePipe):
|
||
|
|
def __init__(self, vocab, model):
|
||
|
|
self.vocab = vocab
|
||
|
|
self.model = model
|
||
|
|
|
||
|
|
pipe = BadCustomPipe1(Vocab())
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
pipe.to_bytes()
|
||
|
|
with make_tempdir() as d:
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
pipe.to_disk(d)
|
||
|
|
pipe = BadCustomPipe2(Vocab())
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
pipe.to_bytes()
|
||
|
|
with make_tempdir() as d:
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
pipe.to_disk(d)
|
||
|
|
pipe = CustomPipe(Vocab(), Linear())
|
||
|
|
pipe_bytes = pipe.to_bytes()
|
||
|
|
new_pipe = CustomPipe(Vocab(), Linear()).from_bytes(pipe_bytes)
|
||
|
|
assert new_pipe.to_bytes() == pipe_bytes
|
||
|
|
with make_tempdir() as d:
|
||
|
|
pipe.to_disk(d)
|
||
|
|
new_pipe = CustomPipe(Vocab(), Linear()).from_disk(d)
|
||
|
|
assert new_pipe.to_bytes() == pipe_bytes
|
||
|
|
|
||
|
|
|
||
|
|
def test_load_without_strings():
|
||
|
|
nlp = spacy.blank("en")
|
||
|
|
orig_strings_length = len(nlp.vocab.strings)
|
||
|
|
word = "unlikely_word_" * 20
|
||
|
|
nlp.vocab.strings.add(word)
|
||
|
|
assert len(nlp.vocab.strings) == orig_strings_length + 1
|
||
|
|
with make_tempdir() as d:
|
||
|
|
nlp.to_disk(d)
|
||
|
|
# reload with strings
|
||
|
|
reloaded_nlp = load(d)
|
||
|
|
assert len(nlp.vocab.strings) == len(reloaded_nlp.vocab.strings)
|
||
|
|
assert word in reloaded_nlp.vocab.strings
|
||
|
|
# reload without strings
|
||
|
|
reloaded_nlp = load(d, exclude=["strings"])
|
||
|
|
assert orig_strings_length == len(reloaded_nlp.vocab.strings)
|
||
|
|
assert word not in reloaded_nlp.vocab.strings
|