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move preset_spans_suggester test to spancat tests
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@ -1,6 +1,5 @@
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import pytest
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from thinc.api import Config
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from thinc.types import Ragged
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from spacy.language import Language
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from spacy.lang.en import English
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@ -193,23 +192,6 @@ def test_set_annotations_span_lengths(min_length, max_length, span_count):
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assert all(min_length <= len(span) <= max_length for span in doc.spans[SPANS_KEY])
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def test_span_finder_suggester():
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nlp = Language()
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docs = [nlp("This is an example."), nlp("This is the second example.")]
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docs[0].spans[SPANS_KEY] = [docs[0][3:4]]
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docs[1].spans[SPANS_KEY] = [docs[1][0:4], docs[1][3:5]]
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suggester = registry.misc.get("spacy.preset_spans_suggester.v1")(
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spans_key=SPANS_KEY
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)
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candidates = suggester(docs)
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assert type(candidates) == Ragged
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assert len(candidates) == 2
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assert list(candidates.dataXd[0]) == [3, 4]
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assert list(candidates.dataXd[1]) == [0, 4]
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assert list(candidates.dataXd[2]) == [3, 5]
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assert list(candidates.lengths) == [1, 2]
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the span_finder component - ensuring the ML models work correctly
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fix_random_seed(0)
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@ -406,6 +406,23 @@ def test_ngram_sizes(en_tokenizer):
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assert_array_equal(OPS.to_numpy(ngrams_3.lengths), [0, 1, 3, 6, 9])
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def test_preset_spans_suggester():
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nlp = Language()
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docs = [nlp("This is an example."), nlp("This is the second example.")]
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docs[0].spans[SPAN_KEY] = [docs[0][3:4]]
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docs[1].spans[SPAN_KEY] = [docs[1][0:4], docs[1][3:5]]
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suggester = registry.misc.get("spacy.preset_spans_suggester.v1")(
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spans_key=SPAN_KEY
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)
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candidates = suggester(docs)
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assert type(candidates) == Ragged
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assert len(candidates) == 2
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assert list(candidates.dataXd[0]) == [3, 4]
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assert list(candidates.dataXd[1]) == [0, 4]
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assert list(candidates.dataXd[2]) == [3, 5]
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assert list(candidates.lengths) == [1, 2]
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the spancat component - ensuring the ML models work correctly
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fix_random_seed(0)
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