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111 lines
3.6 KiB
Python
111 lines
3.6 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 minibatch_by_words, dot_to_object
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from thinc.api import Config, Optimizer
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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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assert en_nlp.get_pipe("textcat").model.attrs["multi_label"] == False # not exclusive_classes
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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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obj = dot_to_object(en_config, "nlp.pipeline.tagger")
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with pytest.raises(KeyError):
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obj = 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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