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61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from spacy.vocab import Vocab
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from spacy.language import Language
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from spacy.tokens import Doc
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from spacy.gold import GoldParse
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@pytest.fixture
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def nlp():
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nlp = Language(Vocab())
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textcat = nlp.create_pipe("textcat")
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for label in ("POSITIVE", "NEGATIVE"):
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textcat.add_label(label)
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nlp.add_pipe(textcat)
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nlp.begin_training()
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return nlp
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def test_language_update(nlp):
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text = "hello world"
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annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
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wrongkeyannots = {"LABEL": True}
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doc = Doc(nlp.vocab, words=text.split(" "))
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gold = GoldParse(doc, **annots)
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# Update with doc and gold objects
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nlp.update([doc], [gold])
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# Update with text and dict
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nlp.update([text], [annots])
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# Update with doc object and dict
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nlp.update([doc], [annots])
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# Update with text and gold object
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nlp.update([text], [gold])
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# Update badly
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with pytest.raises(IndexError):
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nlp.update([doc], [])
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with pytest.raises(IndexError):
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nlp.update([], [gold])
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with pytest.raises(ValueError):
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nlp.update([text], [wrongkeyannots])
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def test_language_evaluate(nlp):
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text = "hello world"
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annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
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doc = Doc(nlp.vocab, words=text.split(" "))
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gold = GoldParse(doc, **annots)
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# Evaluate with doc and gold objects
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nlp.evaluate([(doc, gold)])
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# Evaluate with text and dict
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nlp.evaluate([(text, annots)])
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# Evaluate with doc object and dict
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nlp.evaluate([(doc, annots)])
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# Evaluate with text and gold object
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nlp.evaluate([(text, gold)])
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# Evaluate badly
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with pytest.raises(Exception):
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nlp.evaluate([text, gold])
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