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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.
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from spacy.pipeline import EntityRecognizer
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from spacy.vocab import Vocab
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from spacy.syntax.ner import BiluoPushDown
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from spacy.gold import GoldParse
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from spacy.tokens import Doc
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@pytest.fixture
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def vocab():
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return Vocab()
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@pytest.fixture
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def doc(vocab):
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return Doc(vocab, words=['Casey', 'went', 'to', 'New', 'York', '.'])
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@pytest.fixture
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def entity_annots(doc):
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casey = doc[0:1]
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ny = doc[3:5]
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return [(casey.start_char, casey.end_char, 'PERSON'),
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(ny.start_char, ny.end_char, 'GPE')]
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@pytest.fixture
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def entity_types(entity_annots):
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return sorted(set([label for (s, e, label) in entity_annots]))
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@pytest.fixture
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def tsys(vocab, entity_types):
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actions = BiluoPushDown.get_actions(entity_types=entity_types)
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return BiluoPushDown(vocab.strings, actions)
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def test_get_oracle_moves(tsys, doc, entity_annots):
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gold = GoldParse(doc, entities=entity_annots)
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tsys.preprocess_gold(gold)
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act_classes = tsys.get_oracle_sequence(doc, gold)
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names = [tsys.get_class_name(act) for act in act_classes]
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assert names == ['U-PERSON', 'O', 'O', 'B-GPE', 'L-GPE', 'O']
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def test_get_oracle_moves_negative_entities(tsys, doc, entity_annots):
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entity_annots = [(s, e, '!' + label) for s, e, label in entity_annots]
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gold = GoldParse(doc, entities=entity_annots)
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for i, tag in enumerate(gold.ner):
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if tag == 'L-!GPE':
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gold.ner[i] = '-'
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tsys.preprocess_gold(gold)
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act_classes = tsys.get_oracle_sequence(doc, gold)
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names = [tsys.get_class_name(act) for act in act_classes]
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def test_get_oracle_moves_negative_entities2(tsys, vocab):
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doc = Doc(vocab, words=['A', 'B', 'C', 'D'])
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gold = GoldParse(doc, entities=[])
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gold.ner = ['B-!PERSON', 'L-!PERSON', 'B-!PERSON', 'L-!PERSON']
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tsys.preprocess_gold(gold)
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act_classes = tsys.get_oracle_sequence(doc, gold)
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names = [tsys.get_class_name(act) for act in act_classes]
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def test_get_oracle_moves_negative_O(tsys, vocab):
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doc = Doc(vocab, words=['A', 'B', 'C', 'D'])
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gold = GoldParse(doc, entities=[])
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gold.ner = ['O', '!O', 'O', '!O']
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tsys.preprocess_gold(gold)
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act_classes = tsys.get_oracle_sequence(doc, gold)
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names = [tsys.get_class_name(act) for act in act_classes]
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def test_doc_add_entities_set_ents_iob(en_vocab):
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doc = Doc(en_vocab, words=["This", "is", "a", "lion"])
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ner = EntityRecognizer(en_vocab)
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ner.begin_training([])
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ner(doc)
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assert len(list(doc.ents)) == 0
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assert [w.ent_iob_ for w in doc] == (['O'] * len(doc))
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doc.ents = [(doc.vocab.strings['ANIMAL'], 3, 4)]
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assert [w.ent_iob_ for w in doc] == ['', '', '', 'B']
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doc.ents = [(doc.vocab.strings['WORD'], 0, 2)]
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assert [w.ent_iob_ for w in doc] == ['B', 'I', '', '']
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