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* Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
153 lines
5.1 KiB
Python
153 lines
5.1 KiB
Python
import pytest
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from spacy.tokens import Span
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from spacy.language import Language
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from spacy.pipeline import EntityRuler
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from spacy.errors import MatchPatternError
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@pytest.fixture
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def nlp():
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return Language()
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@pytest.fixture
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def patterns():
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return [
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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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{"label": "TECH_ORG", "pattern": "Microsoft", "id": "a2"},
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]
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@Language.component("add_ent")
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def add_ent_component(doc):
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doc.ents = [Span(doc, 0, 3, label="ORG")]
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return doc
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def test_entity_ruler_init(nlp, patterns):
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ruler = EntityRuler(nlp, patterns=patterns)
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assert len(ruler) == len(patterns)
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assert len(ruler.labels) == 4
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assert "HELLO" in ruler
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assert "BYE" in ruler
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ruler = nlp.add_pipe("entity_ruler")
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ruler.add_patterns(patterns)
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doc = nlp("hello world bye bye")
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assert len(doc.ents) == 2
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assert doc.ents[0].label_ == "HELLO"
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assert doc.ents[1].label_ == "BYE"
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def test_entity_ruler_existing(nlp, patterns):
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ruler = nlp.add_pipe("entity_ruler")
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ruler.add_patterns(patterns)
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nlp.add_pipe("add_ent", before="entity_ruler")
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doc = nlp("OH HELLO WORLD bye bye")
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assert len(doc.ents) == 2
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assert doc.ents[0].label_ == "ORG"
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assert doc.ents[1].label_ == "BYE"
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def test_entity_ruler_existing_overwrite(nlp, patterns):
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ruler = nlp.add_pipe("entity_ruler", config={"overwrite_ents": True})
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ruler.add_patterns(patterns)
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nlp.add_pipe("add_ent", before="entity_ruler")
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doc = nlp("OH HELLO WORLD bye bye")
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assert len(doc.ents) == 2
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assert doc.ents[0].label_ == "HELLO"
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assert doc.ents[0].text == "HELLO"
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assert doc.ents[1].label_ == "BYE"
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def test_entity_ruler_existing_complex(nlp, patterns):
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ruler = nlp.add_pipe("entity_ruler", config={"overwrite_ents": True})
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ruler.add_patterns(patterns)
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nlp.add_pipe("add_ent", before="entity_ruler")
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doc = nlp("foo foo bye bye")
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assert len(doc.ents) == 2
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assert doc.ents[0].label_ == "COMPLEX"
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assert doc.ents[1].label_ == "BYE"
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assert len(doc.ents[0]) == 2
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assert len(doc.ents[1]) == 2
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def test_entity_ruler_entity_id(nlp, patterns):
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ruler = nlp.add_pipe("entity_ruler", config={"overwrite_ents": True})
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ruler.add_patterns(patterns)
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doc = nlp("Apple is a technology company")
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assert len(doc.ents) == 1
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assert doc.ents[0].label_ == "TECH_ORG"
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assert doc.ents[0].ent_id_ == "a1"
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def test_entity_ruler_cfg_ent_id_sep(nlp, patterns):
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config = {"overwrite_ents": True, "ent_id_sep": "**"}
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ruler = nlp.add_pipe("entity_ruler", config=config)
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ruler.add_patterns(patterns)
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assert "TECH_ORG**a1" in ruler.phrase_patterns
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doc = nlp("Apple is a technology company")
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assert len(doc.ents) == 1
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assert doc.ents[0].label_ == "TECH_ORG"
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assert doc.ents[0].ent_id_ == "a1"
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def test_entity_ruler_serialize_bytes(nlp, patterns):
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ruler = EntityRuler(nlp, patterns=patterns)
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assert len(ruler) == len(patterns)
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assert len(ruler.labels) == 4
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ruler_bytes = ruler.to_bytes()
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new_ruler = EntityRuler(nlp)
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assert len(new_ruler) == 0
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assert len(new_ruler.labels) == 0
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new_ruler = new_ruler.from_bytes(ruler_bytes)
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assert len(new_ruler) == len(patterns)
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assert len(new_ruler.labels) == 4
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assert len(new_ruler.patterns) == len(ruler.patterns)
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for pattern in ruler.patterns:
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assert pattern in new_ruler.patterns
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assert sorted(new_ruler.labels) == sorted(ruler.labels)
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def test_entity_ruler_serialize_phrase_matcher_attr_bytes(nlp, patterns):
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ruler = EntityRuler(nlp, phrase_matcher_attr="LOWER", patterns=patterns)
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assert len(ruler) == len(patterns)
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assert len(ruler.labels) == 4
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ruler_bytes = ruler.to_bytes()
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new_ruler = EntityRuler(nlp)
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assert len(new_ruler) == 0
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assert len(new_ruler.labels) == 0
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assert new_ruler.phrase_matcher_attr is None
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new_ruler = new_ruler.from_bytes(ruler_bytes)
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assert len(new_ruler) == len(patterns)
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assert len(new_ruler.labels) == 4
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assert new_ruler.phrase_matcher_attr == "LOWER"
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def test_entity_ruler_validate(nlp):
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ruler = EntityRuler(nlp)
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validated_ruler = EntityRuler(nlp, validate=True)
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valid_pattern = {"label": "HELLO", "pattern": [{"LOWER": "HELLO"}]}
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invalid_pattern = {"label": "HELLO", "pattern": [{"ASDF": "HELLO"}]}
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# invalid pattern raises error without validate
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with pytest.raises(ValueError):
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ruler.add_patterns([invalid_pattern])
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# valid pattern is added without errors with validate
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validated_ruler.add_patterns([valid_pattern])
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# invalid pattern raises error with validate
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with pytest.raises(MatchPatternError):
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validated_ruler.add_patterns([invalid_pattern])
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def test_entity_ruler_properties(nlp, patterns):
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ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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assert sorted(ruler.labels) == sorted(["HELLO", "BYE", "COMPLEX", "TECH_ORG"])
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assert sorted(ruler.ent_ids) == ["a1", "a2"]
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