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47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
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# coding: utf8
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from __future__ import unicode_literals
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import numpy as np
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from spacy.lang.en import English
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from spacy.pipeline import EntityRuler
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def test_issue5082():
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# Ensure the 'merge_entities' pipeline does something sensible for the vectors of the merged tokens
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nlp = English()
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vocab = nlp.vocab
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array1 = np.asarray([0.1, 0.5, 0.8], dtype=np.float32)
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array2 = np.asarray([-0.2, -0.6, -0.9], dtype=np.float32)
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array3 = np.asarray([0.3, -0.1, 0.7], dtype=np.float32)
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array4 = np.asarray([0.5, 0, 0.3], dtype=np.float32)
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array34 = np.asarray([0.4, -0.05, 0.5], dtype=np.float32)
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vocab.set_vector("I", array1)
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vocab.set_vector("like", array2)
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vocab.set_vector("David", array3)
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vocab.set_vector("Bowie", array4)
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text = "I like David Bowie"
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ruler = EntityRuler(nlp)
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patterns = [
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{"label": "PERSON", "pattern": [{"LOWER": "david"}, {"LOWER": "bowie"}]}
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]
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ruler.add_patterns(patterns)
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nlp.add_pipe(ruler)
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parsed_vectors_1 = [t.vector for t in nlp(text)]
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assert len(parsed_vectors_1) == 4
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np.testing.assert_array_equal(parsed_vectors_1[0], array1)
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np.testing.assert_array_equal(parsed_vectors_1[1], array2)
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np.testing.assert_array_equal(parsed_vectors_1[2], array3)
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np.testing.assert_array_equal(parsed_vectors_1[3], array4)
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merge_ents = nlp.create_pipe("merge_entities")
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nlp.add_pipe(merge_ents)
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parsed_vectors_2 = [t.vector for t in nlp(text)]
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assert len(parsed_vectors_2) == 3
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np.testing.assert_array_equal(parsed_vectors_2[0], array1)
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np.testing.assert_array_equal(parsed_vectors_2[1], array2)
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np.testing.assert_array_equal(parsed_vectors_2[2], array34)
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