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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.
101 lines
2.1 KiB
Python
101 lines
2.1 KiB
Python
# coding: utf8
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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.vocab import Vocab
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from spacy.language import Language
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from spacy.pipeline import DependencyParser
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from spacy.syntax.arc_eager import ArcEager
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from spacy.tokens import Doc
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from spacy.syntax._beam_utils import ParserBeam
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from spacy.syntax.stateclass import StateClass
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from spacy.gold import GoldParse
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@pytest.fixture
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def vocab():
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return Vocab()
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@pytest.fixture
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def moves(vocab):
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aeager = ArcEager(vocab.strings, {})
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aeager.add_action(2, 'nsubj')
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aeager.add_action(3, 'dobj')
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aeager.add_action(2, 'aux')
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return aeager
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@pytest.fixture
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def docs(vocab):
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return [Doc(vocab, words=['Rats', 'bite', 'things'])]
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@pytest.fixture
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def states(docs):
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return [StateClass(doc) for doc in docs]
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@pytest.fixture
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def tokvecs(docs, vector_size):
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output = []
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for doc in docs:
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vec = numpy.random.uniform(-0.1, 0.1, (len(doc), vector_size))
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output.append(numpy.asarray(vec))
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return output
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@pytest.fixture
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def golds(docs):
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return [GoldParse(doc) for doc in docs]
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@pytest.fixture
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def batch_size(docs):
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return len(docs)
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@pytest.fixture
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def beam_width():
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return 4
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@pytest.fixture
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def vector_size():
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return 6
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@pytest.fixture
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def beam(moves, states, golds, beam_width):
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return ParserBeam(moves, states, golds, width=beam_width, density=0.0)
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@pytest.fixture
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def scores(moves, batch_size, beam_width):
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return [
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numpy.asarray(
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numpy.random.uniform(-0.1, 0.1, (batch_size, moves.n_moves)),
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dtype='f')
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for _ in range(batch_size)]
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def test_create_beam(beam):
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pass
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def test_beam_advance(beam, scores):
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beam.advance(scores)
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def test_beam_advance_too_few_scores(beam, scores):
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with pytest.raises(IndexError):
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beam.advance(scores[:-1])
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def test_beam_parse():
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nlp = Language()
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nlp.add_pipe(DependencyParser(nlp.vocab), name='parser')
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nlp.parser.add_label('nsubj')
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nlp.parser.begin_training([], token_vector_width=8, hidden_width=8)
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doc = nlp.make_doc('Australia is a country')
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nlp.parser(doc, beam_width=2)
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