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963ea5e8d0
* fixing vector and lemma attributes after retokenizer.split * fixing unit test with mockup tensor * xp instead of numpy
45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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from spacy.tokens import Doc
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import numpy as np
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def test_issue3540(en_vocab):
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words = ["I", "live", "in", "NewYork", "right", "now"]
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tensor = np.asarray([[1.0, 1.1], [2.0, 2.1], [3.0, 3.1], [4.0, 4.1], [5.0, 5.1], [6.0, 6.1]], dtype="f")
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doc = Doc(en_vocab, words=words)
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doc.tensor = tensor
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gold_text = ["I", "live", "in", "NewYork", "right", "now"]
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assert [token.text for token in doc] == gold_text
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gold_lemma = ["I", "live", "in", "NewYork", "right", "now"]
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assert [token.lemma_ for token in doc] == gold_lemma
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vectors_1 = [token.vector for token in doc]
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assert len(vectors_1) == len(doc)
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with doc.retokenize() as retokenizer:
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heads = [(doc[3], 1), doc[2]]
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attrs = {"POS": ["PROPN", "PROPN"], "DEP": ["pobj", "compound"]}
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retokenizer.split(doc[3], [u"New", u"York"], heads=heads, attrs=attrs)
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gold_text = ["I", "live", "in", "New", "York", "right", "now"]
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assert [token.text for token in doc] == gold_text
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gold_lemma = ["I", "live", "in", "New", "York", "right", "now"]
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assert [token.lemma_ for token in doc] == gold_lemma
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vectors_2 = [token.vector for token in doc]
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assert len(vectors_2) == len(doc)
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assert vectors_1[0].tolist() == vectors_2[0].tolist()
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assert vectors_1[1].tolist() == vectors_2[1].tolist()
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assert vectors_1[2].tolist() == vectors_2[2].tolist()
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assert vectors_1[4].tolist() == vectors_2[5].tolist()
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assert vectors_1[5].tolist() == vectors_2[6].tolist()
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