mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-25 17:36:30 +03:00
58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
|
# coding: utf-8
|
||
|
|
from __future__ import unicode_literals
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
from spacy.vocab import Vocab
|
||
|
|
from spacy.language import Language
|
||
|
|
from spacy.tokens import Doc
|
||
|
|
from spacy.gold import GoldParse
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture
|
||
|
|
def nlp():
|
||
|
|
nlp = Language(Vocab())
|
||
|
|
textcat = nlp.create_pipe("textcat")
|
||
|
|
for label in ("POSITIVE", "NEGATIVE"):
|
||
|
|
textcat.add_label(label)
|
||
|
|
nlp.add_pipe(textcat)
|
||
|
|
nlp.begin_training()
|
||
|
|
return nlp
|
||
|
|
|
||
|
|
|
||
|
|
def test_language_update(nlp):
|
||
|
|
text = "hello world"
|
||
|
|
annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
|
||
|
|
doc = Doc(nlp.vocab, words=text.split(" "))
|
||
|
|
gold = GoldParse(doc, **annots)
|
||
|
|
# Update with doc and gold objects
|
||
|
|
nlp.update([doc], [gold])
|
||
|
|
# Update with text and dict
|
||
|
|
nlp.update([text], [annots])
|
||
|
|
# Update with doc object and dict
|
||
|
|
nlp.update([doc], [annots])
|
||
|
|
# Update with text and gold object
|
||
|
|
nlp.update([text], [gold])
|
||
|
|
# Update badly
|
||
|
|
with pytest.raises(IndexError):
|
||
|
|
nlp.update([doc], [])
|
||
|
|
with pytest.raises(IndexError):
|
||
|
|
nlp.update([], [gold])
|
||
|
|
|
||
|
|
|
||
|
|
def test_language_evaluate(nlp):
|
||
|
|
text = "hello world"
|
||
|
|
annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
|
||
|
|
doc = Doc(nlp.vocab, words=text.split(" "))
|
||
|
|
gold = GoldParse(doc, **annots)
|
||
|
|
# Evaluate with doc and gold objects
|
||
|
|
nlp.evaluate([(doc, gold)])
|
||
|
|
# Evaluate with text and dict
|
||
|
|
nlp.evaluate([(text, annots)])
|
||
|
|
# Evaluate with doc object and dict
|
||
|
|
nlp.evaluate([(doc, annots)])
|
||
|
|
# Evaluate with text and gold object
|
||
|
|
nlp.evaluate([(text, gold)])
|
||
|
|
# Evaluate badly
|
||
|
|
with pytest.raises(Exception):
|
||
|
|
nlp.evaluate([text, gold])
|