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	* Fix typo in rule-based matching docs * Improve token pattern checking without validation Add more detailed token pattern checks without full JSON pattern validation and provide more detailed error messages. Addresses #4070 (also related: #4063, #4100). * Check whether top-level attributes in patterns and attr for PhraseMatcher are in token pattern schema * Check whether attribute value types are supported in general (as opposed to per attribute with full validation) * Report various internal error types (OverflowError, AttributeError, KeyError) as ValueError with standard error messages * Check for tagger/parser in PhraseMatcher pipeline for attributes TAG, POS, LEMMA, and DEP * Add error messages with relevant details on how to use validate=True or nlp() instead of nlp.make_doc() * Support attr=TEXT for PhraseMatcher * Add NORM to schema * Expand tests for pattern validation, Matcher, PhraseMatcher, and EntityRuler * Remove unnecessary .keys() * Rephrase error messages * Add another type check to Matcher Add another type check to Matcher for more understandable error messages in some rare cases. * Support phrase_matcher_attr=TEXT for EntityRuler * Don't use spacy.errors in examples and bin scripts * Fix error code * Auto-format Also try get Azure pipelines to finally start a build :( * Update errors.py Co-authored-by: Ines Montani <ines@ines.io> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
		
			
				
	
	
		
			150 lines
		
	
	
		
			4.8 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			150 lines
		
	
	
		
			4.8 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| # coding: utf8
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| from __future__ import unicode_literals
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| 
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| 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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| 
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| 
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| @pytest.fixture
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| def nlp():
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|     return Language()
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| 
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| 
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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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|     ]
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| 
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| 
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| @pytest.fixture
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| def add_ent():
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|     def add_ent_component(doc):
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|         doc.ents = [Span(doc, 0, 3, label=doc.vocab.strings["ORG"])]
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|         return doc
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| 
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|     return add_ent_component
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| 
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| 
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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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|     nlp.add_pipe(ruler)
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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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| 
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| 
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| def test_entity_ruler_existing(nlp, patterns, add_ent):
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|     ruler = EntityRuler(nlp, patterns=patterns)
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|     nlp.add_pipe(add_ent)
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|     nlp.add_pipe(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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| 
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| 
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| def test_entity_ruler_existing_overwrite(nlp, patterns, add_ent):
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|     ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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|     nlp.add_pipe(add_ent)
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|     nlp.add_pipe(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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| 
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| 
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| def test_entity_ruler_existing_complex(nlp, patterns, add_ent):
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|     ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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|     nlp.add_pipe(add_ent)
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|     nlp.add_pipe(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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| 
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| 
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| def test_entity_ruler_entity_id(nlp, patterns):
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|     ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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|     nlp.add_pipe(ruler)
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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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| 
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| 
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| def test_entity_ruler_cfg_ent_id_sep(nlp, patterns):
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|     ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True, ent_id_sep="**")
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|     assert "TECH_ORG**a1" in ruler.phrase_patterns
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|     nlp.add_pipe(ruler)
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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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| 
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| 
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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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| 
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| 
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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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| 
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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