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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.
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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import numpy
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from spacy.tokens import Doc
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from ..util import get_cosine, add_vecs_to_vocab
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@pytest.fixture
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def vectors():
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return [("apple", [1, 2, 3]), ("orange", [-1, -2, -3])]
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@pytest.fixture()
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def vocab(en_vocab, vectors):
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add_vecs_to_vocab(en_vocab, vectors)
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return en_vocab
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def test_vectors_similarity_LL(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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lex1 = vocab[word1]
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lex2 = vocab[word2]
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assert lex1.has_vector
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assert lex2.has_vector
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assert lex1.vector_norm != 0
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assert lex2.vector_norm != 0
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assert lex1.vector[0] != lex2.vector[0] and lex1.vector[1] != lex2.vector[1]
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assert numpy.isclose(lex1.similarity(lex2), get_cosine(vec1, vec2))
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assert numpy.isclose(lex2.similarity(lex2), lex1.similarity(lex1))
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def test_vectors_similarity_TT(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(vocab, words=[word1, word2])
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assert doc[0].has_vector
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assert doc[1].has_vector
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assert doc[0].vector_norm != 0
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assert doc[1].vector_norm != 0
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assert doc[0].vector[0] != doc[1].vector[0] and doc[0].vector[1] != doc[1].vector[1]
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assert numpy.isclose(doc[0].similarity(doc[1]), get_cosine(vec1, vec2))
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assert numpy.isclose(doc[1].similarity(doc[0]), doc[0].similarity(doc[1]))
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def test_vectors_similarity_TD(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(vocab, words=[word1, word2])
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with pytest.warns(None):
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assert doc.similarity(doc[0]) == doc[0].similarity(doc)
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def test_vectors_similarity_DS(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(vocab, words=[word1, word2])
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assert doc.similarity(doc[:2]) == doc[:2].similarity(doc)
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def test_vectors_similarity_TS(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(vocab, words=[word1, word2])
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with pytest.warns(None):
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assert doc[:2].similarity(doc[0]) == doc[0].similarity(doc[:2])
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