spaCy/examples/keras_parikh_entailment/spacy_hook.py
Matthew Honnibal 389e8b700e Fix conflict
2016-11-13 08:52:20 -06:00

72 lines
2.3 KiB
Python

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