mirror of
https://github.com/explosion/spaCy.git
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941a591f3c
* Pass excludes when serializing vocab Additional minor bug fix: * Deserialize vocab in `EntityLinker.from_disk` * Add test for excluding strings on load * Fix formatting
289 lines
10 KiB
Python
289 lines
10 KiB
Python
import pytest
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from spacy import registry, Vocab, load
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from spacy.pipeline import Tagger, DependencyParser, EntityRecognizer
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from spacy.pipeline import TextCategorizer, SentenceRecognizer, TrainablePipe
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from spacy.pipeline.dep_parser import DEFAULT_PARSER_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.pipeline.senter import DEFAULT_SENTER_MODEL
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from spacy.lang.en import English
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from thinc.api import Linear
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import spacy
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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.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)
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assert tagger1_d.to_bytes() == tagger2_d.to_bytes()
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def test_serialize_tagger_strings(en_vocab, de_vocab, taggers):
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label = "SomeWeirdLabel"
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assert label not in en_vocab.strings
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assert label not in de_vocab.strings
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tagger = taggers[0]
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assert label not in tagger.vocab.strings
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with make_tempdir() as d:
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# check that custom labels are serialized as part of the component's strings.jsonl
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tagger.add_label(label)
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assert label in tagger.vocab.strings
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file_path = d / "tagger1"
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tagger.to_disk(file_path)
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# ensure that the custom strings are loaded back in when using the tagger in another pipeline
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cfg = {"model": DEFAULT_TAGGER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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tagger2 = Tagger(de_vocab, model).from_disk(file_path)
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assert label in tagger2.vocab.strings
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def test_serialize_textcat_empty(en_vocab):
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# See issue #1105
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cfg = {"model": DEFAULT_SINGLE_TEXTCAT_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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textcat = TextCategorizer(en_vocab, model, threshold=0.5)
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textcat.to_bytes(exclude=["vocab"])
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@pytest.mark.parametrize("Parser", test_parsers)
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def test_serialize_pipe_exclude(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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def get_new_parser():
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new_parser = Parser(en_vocab, model)
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return new_parser
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parser = Parser(en_vocab, model)
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parser.cfg["foo"] = "bar"
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new_parser = get_new_parser().from_bytes(parser.to_bytes(exclude=["vocab"]))
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assert "foo" in new_parser.cfg
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new_parser = get_new_parser().from_bytes(
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parser.to_bytes(exclude=["vocab"]), exclude=["cfg"]
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)
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assert "foo" not in new_parser.cfg
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new_parser = get_new_parser().from_bytes(
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parser.to_bytes(exclude=["cfg"]), exclude=["vocab"]
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)
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assert "foo" not in new_parser.cfg
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def test_serialize_sentencerecognizer(en_vocab):
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cfg = {"model": DEFAULT_SENTER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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sr = SentenceRecognizer(en_vocab, model)
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sr_b = sr.to_bytes()
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sr_d = SentenceRecognizer(en_vocab, model).from_bytes(sr_b)
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assert sr.to_bytes() == sr_d.to_bytes()
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def test_serialize_pipeline_disable_enable():
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nlp = English()
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nlp.add_pipe("ner")
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nlp.add_pipe("tagger")
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nlp.disable_pipe("tagger")
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assert nlp.config["nlp"]["disabled"] == ["tagger"]
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config = nlp.config.copy()
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nlp2 = English.from_config(config)
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assert nlp2.pipe_names == ["ner"]
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assert nlp2.component_names == ["ner", "tagger"]
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assert nlp2.disabled == ["tagger"]
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assert nlp2.config["nlp"]["disabled"] == ["tagger"]
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with make_tempdir() as d:
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nlp2.to_disk(d)
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nlp3 = spacy.load(d)
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assert nlp3.pipe_names == ["ner"]
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assert nlp3.component_names == ["ner", "tagger"]
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with make_tempdir() as d:
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nlp3.to_disk(d)
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nlp4 = spacy.load(d, disable=["ner"])
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assert nlp4.pipe_names == []
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assert nlp4.component_names == ["ner", "tagger"]
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assert nlp4.disabled == ["ner", "tagger"]
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with make_tempdir() as d:
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nlp.to_disk(d)
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nlp5 = spacy.load(d, exclude=["tagger"])
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assert nlp5.pipe_names == ["ner"]
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assert nlp5.component_names == ["ner"]
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assert nlp5.disabled == []
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def test_serialize_custom_trainable_pipe():
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class BadCustomPipe1(TrainablePipe):
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def __init__(self, vocab):
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pass
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class BadCustomPipe2(TrainablePipe):
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def __init__(self, vocab):
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self.vocab = vocab
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self.model = None
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class CustomPipe(TrainablePipe):
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def __init__(self, vocab, model):
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self.vocab = vocab
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self.model = model
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pipe = BadCustomPipe1(Vocab())
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with pytest.raises(ValueError):
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pipe.to_bytes()
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with make_tempdir() as d:
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with pytest.raises(ValueError):
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pipe.to_disk(d)
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pipe = BadCustomPipe2(Vocab())
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with pytest.raises(ValueError):
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pipe.to_bytes()
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with make_tempdir() as d:
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with pytest.raises(ValueError):
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pipe.to_disk(d)
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pipe = CustomPipe(Vocab(), Linear())
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pipe_bytes = pipe.to_bytes()
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new_pipe = CustomPipe(Vocab(), Linear()).from_bytes(pipe_bytes)
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assert new_pipe.to_bytes() == pipe_bytes
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with make_tempdir() as d:
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pipe.to_disk(d)
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new_pipe = CustomPipe(Vocab(), Linear()).from_disk(d)
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assert new_pipe.to_bytes() == pipe_bytes
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def test_load_without_strings():
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nlp = spacy.blank("en")
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orig_strings_length = len(nlp.vocab.strings)
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word = "unlikely_word_" * 20
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nlp.vocab.strings.add(word)
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assert len(nlp.vocab.strings) == orig_strings_length + 1
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with make_tempdir() as d:
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nlp.to_disk(d)
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# reload with strings
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reloaded_nlp = load(d)
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assert len(nlp.vocab.strings) == len(reloaded_nlp.vocab.strings)
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assert word in reloaded_nlp.vocab.strings
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# reload without strings
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reloaded_nlp = load(d, exclude=["strings"])
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assert orig_strings_length == len(reloaded_nlp.vocab.strings)
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assert word not in reloaded_nlp.vocab.strings
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