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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.
30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import numpy
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from spacy.attrs import HEAD, DEP
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from spacy.symbols import nsubj, dobj, amod, nmod, conj, cc, root
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from spacy.lang.en.syntax_iterators import SYNTAX_ITERATORS
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from ...util import get_doc
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def test_en_noun_chunks_not_nested(en_tokenizer):
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text = "Peter has chronic command and control issues"
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heads = [1, 0, 4, 3, -1, -2, -5]
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deps = ['nsubj', 'ROOT', 'amod', 'nmod', 'cc', 'conj', 'dobj']
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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, deps=deps)
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tokens.from_array(
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[HEAD, DEP],
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numpy.asarray([[1, nsubj], [0, root], [4, amod], [3, nmod], [-1, cc],
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[-2, conj], [-5, dobj]], dtype='uint64'))
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tokens.noun_chunks_iterator = SYNTAX_ITERATORS['noun_chunks']
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word_occurred = {}
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for chunk in tokens.noun_chunks:
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for word in chunk:
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word_occurred.setdefault(word.text, 0)
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word_occurred[word.text] += 1
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for word, freq in word_occurred.items():
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assert freq == 1, (word, [chunk.text for chunk in tokens.noun_chunks])
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