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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.
72 lines
2.4 KiB
Python
72 lines
2.4 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.attrs import IS_ALPHA, IS_DIGIT
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@pytest.mark.parametrize('text1,prob1,text2,prob2', [("NOUN", -1, "opera", -2)])
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def test_vocab_lexeme_lt(en_vocab, text1, text2, prob1, prob2):
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"""More frequent is l.t. less frequent"""
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lex1 = en_vocab[text1]
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lex1.prob = prob1
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lex2 = en_vocab[text2]
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lex2.prob = prob2
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assert lex1 < lex2
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assert lex2 > lex1
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@pytest.mark.parametrize('text1,text2', [("phantom", "opera")])
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def test_vocab_lexeme_hash(en_vocab, text1, text2):
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"""Test that lexemes are hashable."""
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lex1 = en_vocab[text1]
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lex2 = en_vocab[text2]
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lexes = {lex1: lex1, lex2: lex2}
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assert lexes[lex1].orth_ == text1
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assert lexes[lex2].orth_ == text2
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def test_vocab_lexeme_is_alpha(en_vocab):
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assert en_vocab['the'].flags & (1 << IS_ALPHA)
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assert not en_vocab['1999'].flags & (1 << IS_ALPHA)
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assert not en_vocab['hello1'].flags & (1 << IS_ALPHA)
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def test_vocab_lexeme_is_digit(en_vocab):
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assert not en_vocab['the'].flags & (1 << IS_DIGIT)
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assert en_vocab['1999'].flags & (1 << IS_DIGIT)
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assert not en_vocab['hello1'].flags & (1 << IS_DIGIT)
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def test_vocab_lexeme_add_flag_auto_id(en_vocab):
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is_len4 = en_vocab.add_flag(lambda string: len(string) == 4)
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assert en_vocab['1999'].check_flag(is_len4) == True
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assert en_vocab['1999'].check_flag(IS_DIGIT) == True
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assert en_vocab['199'].check_flag(is_len4) == False
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assert en_vocab['199'].check_flag(IS_DIGIT) == True
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assert en_vocab['the'].check_flag(is_len4) == False
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assert en_vocab['dogs'].check_flag(is_len4) == True
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def test_vocab_lexeme_add_flag_provided_id(en_vocab):
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is_len4 = en_vocab.add_flag(lambda string: len(string) == 4, flag_id=IS_DIGIT)
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assert en_vocab['1999'].check_flag(is_len4) == True
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assert en_vocab['199'].check_flag(is_len4) == False
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assert en_vocab['199'].check_flag(IS_DIGIT) == False
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assert en_vocab['the'].check_flag(is_len4) == False
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assert en_vocab['dogs'].check_flag(is_len4) == True
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def test_lexeme_bytes_roundtrip(en_vocab):
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one = en_vocab['one']
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alpha = en_vocab['alpha']
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assert one.orth != alpha.orth
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assert one.lower != alpha.lower
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alpha.from_bytes(one.to_bytes())
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assert one.orth_ == alpha.orth_
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assert one.orth == alpha.orth
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assert one.lower == alpha.lower
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assert one.lower_ == alpha.lower_
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