diff --git a/examples/deep_learning_keras.py b/examples/deep_learning_keras.py index 368126da6..fc672da75 100644 --- a/examples/deep_learning_keras.py +++ b/examples/deep_learning_keras.py @@ -1,5 +1,13 @@ +import plac +import collections +import random + +import cytoolz import numpy -from collections import defaultdict +from keras.layers import Sequential, LSTM, Dense, Embedding, Dropout +from keras.wrappers import Bidirectional +from keras import model_from_json +import cPickle as pickle import spacy @@ -7,35 +15,58 @@ import spacy class SentimentAnalyser(object): @classmethod def load(cls, path, nlp): - pass + with (path / 'config.json').open() as file_: + + model = model_from_json(file_.read()) + with (path / 'model').open('rb') as file_: + lstm_weights = pickle.load(file_) + embeddings = get_embeddings(nlp.vocab) + model.set_weights([embeddings] + lstm_weights) + return cls(model) def __init__(self, model): self._model = model - + def __call__(self, doc): X = get_features([doc], self.max_length) y = self._model.predict(X) self.set_sentiment(doc, y) def pipe(self, docs, batch_size=1000, n_threads=2): - for minibatch in partition_all(batch_size, docs): - Xs = _get_features(minibatch) - ys = self._model.predict(X) + for minibatch in cytoolz.partition_all(batch_size, docs): + Xs = get_features(minibatch, self.max_length) + ys = self._model.predict(Xs) for i, doc in enumerate(minibatch): doc.user_data['sentiment'] = ys[i] def set_sentiment(self, doc, y): - doc.user_data['sentiment'] = y + doc.sentiment = float(y[0]) + # Sentiment has a native slot for a single float. + # For arbitrary data storage, there's: + # doc.user_data['my_data'] = y def get_features(docs, max_length): Xs = numpy.zeros(len(docs), max_length, dtype='int32') - for i, doc in enumerate(minibatch): + for i, doc in enumerate(docs): for j, token in enumerate(doc[:max_length]): Xs[i, j] = token.rank if token.has_vector else 0 return Xs - -def compile_lstm(embeddings, shape, settings, optimizer): + + +def train(train_texts, train_labels, dev_texts, dev_labels, + lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5): + nlp = spacy.load('en', parser=False, tagger=False, entity=False) + embeddings = get_embeddings(nlp.vocab) + model = compile_lstm(embeddings, lstm_shape, lstm_settings) + train_X = get_features(nlp.pipe(train_texts), lstm_shape['max_length']) + dev_X = get_features(nlp.pipe(dev_texts), lstm_shape['max_length']) + model.fit(train_X, train_labels, dev_X, dev_labels, + nb_epoch=nb_epoch, batch_size=batch_size) + return model + + +def compile_lstm(embeddings, shape, settings): model = Sequential() model.add( Embedding( @@ -53,42 +84,14 @@ def compile_lstm(embeddings, shape, settings, optimizer): def get_embeddings(vocab): - ''' - Get a numpy vector of the word embeddings. The Lexeme.rank attribute will - be the index into the table. We're going to be "decadent" here and use - 1m vectors, because we're not going to fine-tune them. - ''' - max_rank = max(lex.rank for lex in nlp.vocab if lex.has_vector) - vectors = numpy.ndarray((max_rank+1, nlp.vocab.vectors_length), dtype='float32') + max_rank = max(lex.rank 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] = lex.vector return vectors -def train(train_texts, train_labels, dev_texts, dev_labels, - lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5): - nlp = spacy.load('en', parser=False, tagger=False, entity=False) - model = _compile_model( - _get_embeddings( - nlp.vocab), - lstm_shape, - lstm_settings, - lstm_optimizer) - model.fit( - _get_features( - nlp.pipe( - train_texts)), - train_ys, - _get_features( - nlp.pipe( - dev_texts)), - dev_ys, - nb_epoch=nb_epoch, - batch_size=batch_size) - model.save(model_dir) - - def demonstrate_runtime(model_dir, texts): '''Demonstrate runtime usage of the custom sentiment model with spaCy. @@ -102,16 +105,11 @@ def demonstrate_runtime(model_dir, texts): return [nlp.tagger, nlp.entity, SentimentAnalyser.load(model_dir, nlp)] nlp = spacy.load('en', create_pipeline=create_pipeline) - entity_sentiments = defaultdict(float) - entity_freqs = defaultdict(int) + + entity_sentiments = collections.Counter(float) for doc in nlp.pipe(texts, batch_size=1000, n_threads=4): - sentiment = doc.user_data['sentiment'] for ent in doc.ents: - entity_sentiments[ent.text] += sentiment - entity_freqs[ent.text] += 1 - # Compute estimate of P(sentiment | entity) - for entity, sentiment in entity_freqs.items(): - entity_sentiments[entity] /= entity_freqs[entity] + entity_sentiments[ent.text] += doc.sentiment return entity_sentiments @@ -120,7 +118,7 @@ def read_data(data_dir, limit=0): for subdir, label in (('pos', 1), ('neg', 0)): for filename in (data_dir / subdir).iterdir(): with filename.open() as file_: - text = filename.read() + text = file_.read() examples.append((text, label)) random.shuffle(examples) if limit >= 1: @@ -147,16 +145,19 @@ def main(model_dir, train_dir, dev_dir, dropout=0.5, # General NN config nb_epoch=5, batch_size=100, nr_examples=-1): # Training params if is_runtime: - dev_texts, dev_labels = read_dev(dev_dir) + dev_texts, dev_labels = read_data(dev_dir) demonstrate_runtime(model_dir, dev_texts) else: train_texts, train_labels = read_data(train_dir, limit=nr_examples) - dev_texts, dev_labels = read_dev(dev_dir) + dev_texts, dev_labels = read_data(dev_dir) lstm = train(train_texts, train_labels, dev_texts, dev_labels, {'nr_hidden': nr_hidden, 'max_length': max_length}, {'dropout': 0.5}, {}, nb_epoch=nb_epoch, batch_size=batch_size) + weights = lstm.get_weights() + with (model_dir / 'model').open('wb') as file_: + pickle.dump(file_, weights[1:]) if __name__ == '__main__':