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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.
18 lines
639 B
Python
18 lines
639 B
Python
# coding: utf8
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from __future__ import unicode_literals
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import pytest
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NB_TOKEN_EXCEPTION_TESTS = [
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('Smørsausen brukes bl.a. til fisk', ['Smørsausen', 'brukes', 'bl.a.', 'til', 'fisk']),
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('Jeg kommer først kl. 13 pga. diverse forsinkelser', ['Jeg', 'kommer', 'først', 'kl.', '13', 'pga.', 'diverse', 'forsinkelser'])
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]
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@pytest.mark.parametrize('text,expected_tokens', NB_TOKEN_EXCEPTION_TESTS)
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def test_nb_tokenizer_handles_exception_cases(nb_tokenizer, text, expected_tokens):
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tokens = nb_tokenizer(text)
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token_list = [token.text for token in tokens if not token.is_space]
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assert expected_tokens == token_list
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