mirror of
https://github.com/explosion/spaCy.git
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43b960c01b
* Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
160 lines
4.5 KiB
Python
160 lines
4.5 KiB
Python
import pytest
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import os
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import ctypes
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from pathlib import Path
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from spacy.about import __version__ as spacy_version
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from spacy import util
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from spacy import prefer_gpu, require_gpu
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from spacy.ml._precomputable_affine import PrecomputableAffine
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from spacy.ml._precomputable_affine import _backprop_precomputable_affine_padding
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@pytest.fixture
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def is_admin():
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"""Determine if the tests are run as admin or not."""
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try:
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admin = os.getuid() == 0
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except AttributeError:
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admin = ctypes.windll.shell32.IsUserAnAdmin() != 0
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return admin
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@pytest.mark.parametrize("text", ["hello/world", "hello world"])
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def test_util_ensure_path_succeeds(text):
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path = util.ensure_path(text)
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assert isinstance(path, Path)
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@pytest.mark.parametrize(
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"package,result", [("numpy", True), ("sfkodskfosdkfpsdpofkspdof", False)]
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)
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def test_util_is_package(package, result):
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"""Test that an installed package via pip is recognised by util.is_package."""
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assert util.is_package(package) is result
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@pytest.mark.parametrize("package", ["thinc"])
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def test_util_get_package_path(package):
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"""Test that a Path object is returned for a package name."""
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path = util.get_package_path(package)
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assert isinstance(path, Path)
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def test_PrecomputableAffine(nO=4, nI=5, nF=3, nP=2):
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model = PrecomputableAffine(nO=nO, nI=nI, nF=nF, nP=nP).initialize()
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assert model.get_param("W").shape == (nF, nO, nP, nI)
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tensor = model.ops.alloc((10, nI))
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Y, get_dX = model.begin_update(tensor)
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assert Y.shape == (tensor.shape[0] + 1, nF, nO, nP)
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dY = model.ops.alloc((15, nO, nP))
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ids = model.ops.alloc((15, nF))
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ids[1, 2] = -1
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dY[1] = 1
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assert not model.has_grad("pad")
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d_pad = _backprop_precomputable_affine_padding(model, dY, ids)
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assert d_pad[0, 2, 0, 0] == 1.0
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ids.fill(0.0)
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dY.fill(0.0)
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dY[0] = 0
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ids[1, 2] = 0
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ids[1, 1] = -1
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ids[1, 0] = -1
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dY[1] = 1
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ids[2, 0] = -1
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dY[2] = 5
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d_pad = _backprop_precomputable_affine_padding(model, dY, ids)
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assert d_pad[0, 0, 0, 0] == 6
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assert d_pad[0, 1, 0, 0] == 1
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assert d_pad[0, 2, 0, 0] == 0
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def test_prefer_gpu():
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try:
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import cupy # noqa: F401
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except ImportError:
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assert not prefer_gpu()
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def test_require_gpu():
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try:
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import cupy # noqa: F401
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except ImportError:
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with pytest.raises(ValueError):
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require_gpu()
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def test_ascii_filenames():
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"""Test that all filenames in the project are ASCII.
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See: https://twitter.com/_inesmontani/status/1177941471632211968
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"""
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root = Path(__file__).parent.parent
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for path in root.glob("**/*"):
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assert all(ord(c) < 128 for c in path.name), path.name
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def test_load_model_blank_shortcut():
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"""Test that using a model name like "blank:en" works as a shortcut for
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spacy.blank("en").
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"""
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nlp = util.load_model("blank:en")
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assert nlp.lang == "en"
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assert nlp.pipeline == []
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with pytest.raises(ImportError):
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util.load_model("blank:fjsfijsdof")
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@pytest.mark.parametrize(
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"version,constraint,compatible",
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[
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(spacy_version, spacy_version, True),
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(spacy_version, f">={spacy_version}", True),
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("3.0.0", "2.0.0", False),
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("3.2.1", ">=2.0.0", True),
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("2.2.10a1", ">=1.0.0,<2.1.1", False),
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("3.0.0.dev3", ">=1.2.3,<4.5.6", True),
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("n/a", ">=1.2.3,<4.5.6", None),
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("1.2.3", "n/a", None),
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("n/a", "n/a", None),
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],
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)
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def test_is_compatible_version(version, constraint, compatible):
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assert util.is_compatible_version(version, constraint) is compatible
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@pytest.mark.parametrize(
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"constraint,expected",
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[
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("3.0.0", False),
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("==3.0.0", False),
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(">=2.3.0", True),
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(">2.0.0", True),
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("<=2.0.0", True),
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(">2.0.0,<3.0.0", False),
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(">=2.0.0,<3.0.0", False),
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("!=1.1,>=1.0,~=1.0", True),
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("n/a", None),
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],
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)
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def test_is_unconstrained_version(constraint, expected):
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assert util.is_unconstrained_version(constraint) is expected
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@pytest.mark.parametrize(
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"dot_notation,expected",
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[
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(
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{"token.pos": True, "token._.xyz": True},
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{"token": {"pos": True, "_": {"xyz": True}}},
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),
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(
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{"training.batch_size": 128, "training.optimizer.learn_rate": 0.01},
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{"training": {"batch_size": 128, "optimizer": {"learn_rate": 0.01}}},
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),
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],
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)
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def test_dot_to_dict(dot_notation, expected):
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result = util.dot_to_dict(dot_notation)
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assert result == expected
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assert util.dict_to_dot(result) == dot_notation
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