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https://github.com/explosion/spaCy.git
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Merge test_misc and test_util
This commit is contained in:
Ines Montani
parent
e3acad6264
commit
20f2a17a09
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@ -7,6 +7,15 @@ from spacy import util
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from spacy import prefer_gpu, require_gpu
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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 PrecomputableAffine
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from spacy.ml._precomputable_affine import _backprop_precomputable_affine_padding
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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 .util import get_random_doc
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@pytest.fixture
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@pytest.fixture
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@ -157,3 +166,128 @@ def test_dot_to_dict(dot_notation, expected):
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result = util.dot_to_dict(dot_notation)
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result = util.dot_to_dict(dot_notation)
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assert result == expected
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assert result == expected
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assert util.dict_to_dot(result) == dot_notation
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assert util.dict_to_dot(result) == dot_notation
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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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@ -1,137 +0,0 @@
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import pytest
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from spacy import util
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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 .util import get_random_doc
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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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