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45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
from __future__ import unicode_literals
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import random
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import numpy.random
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from ..pipeline import TextCategorizer
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from ..lang.en import English
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from ..vocab import Vocab
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from ..tokens import Doc
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from ..gold import GoldParse
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def test_textcat_learns_multilabel():
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random.seed(5)
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numpy.random.seed(5)
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docs = []
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nlp = English()
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vocab = nlp.vocab
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letters = ['a', 'b', 'c']
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for w1 in letters:
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for w2 in letters:
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cats = {letter: float(w2==letter) for letter in letters}
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docs.append((Doc(vocab, words=['d']*3 + [w1, w2] + ['d']*3), cats))
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random.shuffle(docs)
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model = TextCategorizer(vocab, width=8)
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for letter in letters:
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model.add_label(letter)
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optimizer = model.begin_training()
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for i in range(30):
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losses = {}
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Ys = [GoldParse(doc, cats=cats) for doc, cats in docs]
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Xs = [doc for doc, cats in docs]
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model.update(Xs, Ys, sgd=optimizer, losses=losses)
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random.shuffle(docs)
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for w1 in letters:
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for w2 in letters:
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doc = Doc(vocab, words=['d']*3 + [w1, w2] + ['d']*3)
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truth = {letter: w2==letter for letter in letters}
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model(doc)
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for cat, score in doc.cats.items():
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if not truth[cat]:
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assert score < 0.5
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else:
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assert score > 0.5
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