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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.
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import pytest
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from spacy.language import Language
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from spacy.tokens import Span
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from ..util import get_doc
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@pytest.fixture
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def doc(en_tokenizer):
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text = 'I like New York in Autumn.'
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heads = [1, 0, 1, -2, -3, -1, -5]
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tags = ['PRP', 'IN', 'NNP', 'NNP', 'IN', 'NNP', '.']
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pos = ['PRON', 'VERB', 'PROPN', 'PROPN', 'ADP', 'PROPN', 'PUNCT']
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deps = ['ROOT', 'prep', 'compound', 'pobj', 'prep', 'pobj', 'punct']
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads,
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tags=tags, pos=pos, deps=deps)
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doc.ents = [Span(doc, 2, 4, doc.vocab.strings['GPE'])]
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doc.is_parsed = True
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doc.is_tagged = True
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return doc
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def test_factories_merge_noun_chunks(doc):
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assert len(doc) == 7
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nlp = Language()
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merge_noun_chunks = nlp.create_pipe('merge_noun_chunks')
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merge_noun_chunks(doc)
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assert len(doc) == 6
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assert doc[2].text == 'New York'
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def test_factories_merge_ents(doc):
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assert len(doc) == 7
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assert len(list(doc.ents)) == 1
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nlp = Language()
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merge_entities = nlp.create_pipe('merge_entities')
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merge_entities(doc)
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assert len(doc) == 6
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assert len(list(doc.ents)) == 1
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assert doc[2].text == 'New York'
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