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34146750d4
We don't want to break backwards compatibility too much but we also want to provide the best possible UX
125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
import pytest
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from .util import get_random_doc
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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
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from spacy.gold.batchers import minibatch_by_words
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from ..lang.en import English
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from ..lang.nl import Dutch
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from ..language import DEFAULT_CONFIG_PATH
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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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load_vocab_data = false
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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, en_config = 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, nl_config = 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 not en_config["nlp"]["load_vocab_data"]
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assert nl_config["nlp"]["load_vocab_data"] # default value True
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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_config, "nlp.pipeline.tagger")
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with pytest.raises(KeyError):
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dot_to_object(en_config, "nlp.unknownattribute")
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assert not dot_to_object(en_config, "nlp.load_vocab_data")
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assert dot_to_object(nl_config, "nlp.load_vocab_data")
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assert isinstance(dot_to_object(nl_config, "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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