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	## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
		
			
				
	
	
		
			89 lines
		
	
	
		
			2.6 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			89 lines
		
	
	
		
			2.6 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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| 
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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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|     ]
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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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|     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) == 3
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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_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) == 3
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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(ruler) == len(patterns)
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|     assert len(ruler.labels) == 3
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