mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
350 lines
11 KiB
Python
350 lines
11 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, require_cpu
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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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from spacy.util import dot_to_object, SimpleFrozenList
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from thinc.api import Config, Optimizer, ConfigValidationError
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from spacy.training.batchers import minibatch_by_words
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from spacy.lang.en import English
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from spacy.lang.nl import Dutch
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from spacy.language import DEFAULT_CONFIG_PATH
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from spacy.schemas import ConfigSchemaTraining
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from thinc.api import get_current_ops, NumpyOps, CupyOps
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from .util import get_random_doc
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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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prefer_gpu()
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assert isinstance(get_current_ops(), CupyOps)
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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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require_gpu()
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assert isinstance(get_current_ops(), CupyOps)
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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_require_cpu():
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require_cpu()
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assert isinstance(get_current_ops(), NumpyOps)
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try:
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import cupy # noqa: F401
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require_gpu()
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assert isinstance(get_current_ops(), CupyOps)
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except ImportError:
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pass
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require_cpu()
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assert isinstance(get_current_ops(), NumpyOps)
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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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"a1,a2,b1,b2,is_match",
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[
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("3.0.0", "3.0", "3.0.1", "3.0", True),
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("3.1.0", "3.1", "3.2.1", "3.2", False),
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("xxx", None, "1.2.3.dev0", "1.2", False),
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],
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)
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def test_minor_version(a1, a2, b1, b2, is_match):
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assert util.get_minor_version(a1) == a2
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assert util.get_minor_version(b1) == b2
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assert util.is_minor_version_match(a1, b1) is is_match
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assert util.is_minor_version_match(a2, b2) is is_match
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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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def test_set_dot_to_object():
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config = {"foo": {"bar": 1, "baz": {"x": "y"}}, "test": {"a": {"b": "c"}}}
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with pytest.raises(KeyError):
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util.set_dot_to_object(config, "foo.bar.baz", 100)
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with pytest.raises(KeyError):
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util.set_dot_to_object(config, "hello.world", 100)
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with pytest.raises(KeyError):
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util.set_dot_to_object(config, "test.a.b.c", 100)
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util.set_dot_to_object(config, "foo.bar", 100)
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assert config["foo"]["bar"] == 100
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util.set_dot_to_object(config, "foo.baz.x", {"hello": "world"})
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assert config["foo"]["baz"]["x"]["hello"] == "world"
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assert config["test"]["a"]["b"] == "c"
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util.set_dot_to_object(config, "foo", 123)
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assert config["foo"] == 123
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util.set_dot_to_object(config, "test", "hello")
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assert dict(config) == {"foo": 123, "test": "hello"}
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@pytest.mark.parametrize(
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"doc_sizes, expected_batches",
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[
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([400, 400, 199], [3]),
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([400, 400, 199, 3], [4]),
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([400, 400, 199, 3, 200], [3, 2]),
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([400, 400, 199, 3, 1], [5]),
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([400, 400, 199, 3, 1, 1500], [5]), # 1500 will be discarded
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([400, 400, 199, 3, 1, 200], [3, 3]),
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([400, 400, 199, 3, 1, 999], [3, 3]),
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([400, 400, 199, 3, 1, 999, 999], [3, 2, 1, 1]),
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([1, 2, 999], [3]),
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([1, 2, 999, 1], [4]),
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([1, 200, 999, 1], [2, 2]),
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([1, 999, 200, 1], [2, 2]),
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],
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)
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def test_util_minibatch(doc_sizes, expected_batches):
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docs = [get_random_doc(doc_size) for doc_size in doc_sizes]
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tol = 0.2
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batch_size = 1000
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batches = list(
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minibatch_by_words(docs, size=batch_size, tolerance=tol, discard_oversize=True)
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)
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assert [len(batch) for batch in batches] == expected_batches
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max_size = batch_size + batch_size * tol
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for batch in batches:
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assert sum([len(doc) for doc in batch]) < max_size
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@pytest.mark.parametrize(
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"doc_sizes, expected_batches",
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[
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([400, 4000, 199], [1, 2]),
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([400, 400, 199, 3000, 200], [1, 4]),
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([400, 400, 199, 3, 1, 1500], [1, 5]),
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([400, 400, 199, 3000, 2000, 200, 200], [1, 1, 3, 2]),
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([1, 2, 9999], [1, 2]),
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([2000, 1, 2000, 1, 1, 1, 2000], [1, 1, 1, 4]),
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],
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)
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def test_util_minibatch_oversize(doc_sizes, expected_batches):
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""" Test that oversized documents are returned in their own batch"""
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docs = [get_random_doc(doc_size) for doc_size in doc_sizes]
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tol = 0.2
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batch_size = 1000
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batches = list(
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minibatch_by_words(docs, size=batch_size, tolerance=tol, discard_oversize=False)
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)
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assert [len(batch) for batch in batches] == expected_batches
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def test_util_dot_section():
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cfg_string = """
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[nlp]
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lang = "en"
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pipeline = ["textcat"]
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[components]
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[components.textcat]
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factory = "textcat"
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[components.textcat.model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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"""
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nlp_config = Config().from_str(cfg_string)
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en_nlp = util.load_model_from_config(nlp_config, auto_fill=True)
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default_config = Config().from_disk(DEFAULT_CONFIG_PATH)
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default_config["nlp"]["lang"] = "nl"
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nl_nlp = util.load_model_from_config(default_config, auto_fill=True)
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# Test that creation went OK
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assert isinstance(en_nlp, English)
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assert isinstance(nl_nlp, Dutch)
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assert nl_nlp.pipe_names == []
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assert en_nlp.pipe_names == ["textcat"]
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# not exclusive_classes
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assert en_nlp.get_pipe("textcat").model.attrs["multi_label"] is False
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# Test that default values got overwritten
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assert en_nlp.config["nlp"]["pipeline"] == ["textcat"]
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assert nl_nlp.config["nlp"]["pipeline"] == [] # default value []
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# Test proper functioning of 'dot_to_object'
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with pytest.raises(KeyError):
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dot_to_object(en_nlp.config, "nlp.pipeline.tagger")
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with pytest.raises(KeyError):
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dot_to_object(en_nlp.config, "nlp.unknownattribute")
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T = util.registry.resolve(nl_nlp.config["training"], schema=ConfigSchemaTraining)
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assert isinstance(dot_to_object({"training": T}, "training.optimizer"), Optimizer)
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def test_simple_frozen_list():
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t = SimpleFrozenList(["foo", "bar"])
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assert t == ["foo", "bar"]
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assert t.index("bar") == 1 # okay method
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with pytest.raises(NotImplementedError):
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t.append("baz")
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with pytest.raises(NotImplementedError):
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t.sort()
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with pytest.raises(NotImplementedError):
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t.extend(["baz"])
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with pytest.raises(NotImplementedError):
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t.pop()
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t = SimpleFrozenList(["foo", "bar"], error="Error!")
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with pytest.raises(NotImplementedError):
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t.append("baz")
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def test_resolve_dot_names():
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config = {
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"training": {"optimizer": {"@optimizers": "Adam.v1"}},
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"foo": {"bar": "training.optimizer", "baz": "training.xyz"},
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}
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result = util.resolve_dot_names(config, ["training.optimizer"])
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assert isinstance(result[0], Optimizer)
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with pytest.raises(ConfigValidationError) as e:
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util.resolve_dot_names(config, ["training.xyz", "training.optimizer"])
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errors = e.value.errors
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assert len(errors) == 1
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assert errors[0]["loc"] == ["training", "xyz"]
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