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78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
from keras.models import model_from_json
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import numpy
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import numpy.random
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class KerasSimilarityShim(object):
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@classmethod
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def load(cls, path, nlp, get_features=None):
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if get_features is None:
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get_features = doc2ids
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with (path / 'config.json').open() as file_:
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config = json.load(file_)
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model = model_from_json(config['model'])
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with (path / 'model').open('rb') as file_:
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weights = pickle.load(file_)
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embeddings = get_embeddings(nlp.vocab)
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model.set_weights([embeddings] + weights)
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return cls(model, get_features=get_features)
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def __init__(self, model, get_features=None):
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self.model = model
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self.get_features = get_features
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def __call__(self, doc):
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doc.user_hooks['similarity'] = self.predict
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doc.user_span_hooks['similarity'] = self.predict
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def predict(self, doc1, doc2):
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x1 = self.get_features(doc1)
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x2 = self.get_features(doc2)
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scores = self.model.predict([x1, x2])
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return scores[0]
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def get_embeddings(vocab, nr_unk=100):
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nr_vector = max(lex.rank for lex in vocab) + 1
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vectors = numpy.zeros((nr_vector+nr_unk+2, vocab.vectors_length), dtype='float32')
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for lex in vocab:
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if lex.has_vector:
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vectors[lex.rank+1] = lex.vector / lex.vector_norm
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return vectors
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def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100, nr_unk=100):
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Xs = numpy.zeros((len(docs), max_length), dtype='int32')
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for i, doc in enumerate(docs):
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if tree_truncate:
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queue = [sent.root for sent in doc.sents]
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else:
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queue = list(doc)
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words = []
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while len(words) <= max_length and queue:
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word = queue.pop(0)
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if rnn_encode or (not word.is_punct and not word.is_space):
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words.append(word)
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if tree_truncate:
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queue.extend(list(word.lefts))
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queue.extend(list(word.rights))
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words.sort()
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for j, token in enumerate(words):
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if token.has_vector:
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Xs[i, j] = token.rank+1
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else:
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Xs[i, j] = (token.shape % (nr_unk-1))+2
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j += 1
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if j >= max_length:
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break
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else:
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Xs[i, len(words)] = 1
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return Xs
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def create_similarity_pipeline(nlp):
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return [SimilarityModel.load(
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nlp.path / 'similarity',
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nlp,
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feature_extracter=get_features)]
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