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https://github.com/explosion/spaCy.git
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51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
from __future__ import unicode_literals
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import pytest
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import gc
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from spacy.en import English
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# Let this have its own instances, as we have to be careful about memory here
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# that's the point, after all
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def get_orphan_token(text, i):
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nlp = English(load_vectors=False)
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tokens = nlp(text)
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gc.collect()
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token = tokens[i]
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del tokens
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return token
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def test_orphan():
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orphan = get_orphan_token('An orphan token', 1)
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gc.collect()
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dummy = get_orphan_token('Load and flush the memory', 0)
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dummy = get_orphan_token('Load again...', 0)
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assert orphan.orth_ == 'orphan'
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assert orphan.pos_ == 'NOUN'
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assert orphan.head.orth_ == 'token'
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def _orphan_from_list(toks):
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''' Take the tokens from nlp(), append them to a list, return the list '''
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lst = []
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for tok in toks:
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lst.append(tok)
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return lst
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def test_list_orphans():
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# Test case from NSchrading
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nlp = English(load_vectors=False)
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samples = ["a", "test blah wat okay"]
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lst = []
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for sample in samples:
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# Go through all the samples, call nlp() on each to get tokens,
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# pass those tokens to the _orphan_from_list() function, get a list back
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# and put all results in another list
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lst.extend(_orphan_from_list(nlp(sample)))
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# go through the list of all tokens and try to print orth_
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orths = ['a', 'test', 'blah', 'wat', 'okay']
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for i, l in enumerate(lst):
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assert l.orth_ == orths[i]
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