mirror of
https://github.com/explosion/spaCy.git
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209 lines
7.5 KiB
Python
209 lines
7.5 KiB
Python
import plac
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import collections
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import random
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import pathlib
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import cytoolz
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import numpy
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from keras.models import Sequential, model_from_json
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from keras.layers import LSTM, Dense, Embedding, Dropout, Bidirectional
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from keras.layers import TimeDistributed
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from keras.optimizers import Adam
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import cPickle as pickle
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import spacy
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class SentimentAnalyser(object):
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@classmethod
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def load(cls, path, nlp, max_length=100):
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with (path / 'config.json').open() as file_:
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model = model_from_json(file_.read())
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with (path / 'model').open('rb') as file_:
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lstm_weights = pickle.load(file_)
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embeddings = get_embeddings(nlp.vocab)
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model.set_weights([embeddings] + lstm_weights)
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return cls(model, max_length=max_length)
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def __init__(self, model, max_length=100):
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self._model = model
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self.max_length = max_length
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def __call__(self, doc):
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X = get_features([doc], self.max_length)
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y = self._model.predict(X)
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self.set_sentiment(doc, y)
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def pipe(self, docs, batch_size=1000, n_threads=2):
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for minibatch in cytoolz.partition_all(batch_size, docs):
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minibatch = list(minibatch)
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sentences = []
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for doc in minibatch:
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sentences.extend(doc.sents)
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Xs = get_features(sentences, self.max_length)
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ys = self._model.predict(Xs)
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for sent, label in zip(sentences, ys):
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sent.doc.sentiment += label - 0.5
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for doc in minibatch:
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yield doc
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def set_sentiment(self, doc, y):
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doc.sentiment = float(y[0])
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# Sentiment has a native slot for a single float.
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# For arbitrary data storage, there's:
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# doc.user_data['my_data'] = y
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def get_labelled_sentences(docs, doc_labels):
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labels = []
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sentences = []
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for doc, y in zip(docs, doc_labels):
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for sent in doc.sents:
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sentences.append(sent)
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labels.append(y)
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return sentences, numpy.asarray(labels, dtype='int32')
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def get_features(docs, max_length):
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docs = list(docs)
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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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j = 0
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for token in doc:
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if token.has_vector and not token.is_punct and not token.is_space:
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Xs[i, j] = token.rank + 1
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j += 1
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if j >= max_length:
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break
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return Xs
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def train(train_texts, train_labels, dev_texts, dev_labels,
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lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5,
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by_sentence=True):
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print("Loading spaCy")
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nlp = spacy.load('en', entity=False)
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embeddings = get_embeddings(nlp.vocab)
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model = compile_lstm(embeddings, lstm_shape, lstm_settings)
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print("Parsing texts...")
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train_docs = list(nlp.pipe(train_texts, batch_size=5000, n_threads=3))
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dev_docs = list(nlp.pipe(dev_texts, batch_size=5000, n_threads=3))
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if by_sentence:
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train_docs, train_labels = get_labelled_sentences(train_docs, train_labels)
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dev_docs, dev_labels = get_labelled_sentences(dev_docs, dev_labels)
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train_X = get_features(train_docs, lstm_shape['max_length'])
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dev_X = get_features(dev_docs, lstm_shape['max_length'])
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model.fit(train_X, train_labels, validation_data=(dev_X, dev_labels),
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nb_epoch=nb_epoch, batch_size=batch_size)
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return model
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def compile_lstm(embeddings, shape, settings):
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model = Sequential()
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model.add(
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Embedding(
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embeddings.shape[0],
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embeddings.shape[1],
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input_length=shape['max_length'],
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trainable=False,
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weights=[embeddings],
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mask_zero=True
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)
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)
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model.add(TimeDistributed(Dense(shape['nr_hidden'], bias=False)))
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model.add(Bidirectional(LSTM(shape['nr_hidden'], dropout_U=settings['dropout'],
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dropout_W=settings['dropout'])))
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model.add(Dense(shape['nr_class'], activation='sigmoid'))
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model.compile(optimizer=Adam(lr=settings['lr']), loss='binary_crossentropy',
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metrics=['accuracy'])
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return model
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def get_embeddings(vocab):
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max_rank = max(lex.rank+1 for lex in vocab if lex.has_vector)
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vectors = numpy.ndarray((max_rank+1, 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
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return vectors
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def evaluate(model_dir, texts, labels, max_length=100):
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def create_pipeline(nlp):
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'''
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This could be a lambda, but named functions are easier to read in Python.
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'''
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return [nlp.tagger, nlp.parser, SentimentAnalyser.load(model_dir, nlp,
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max_length=max_length)]
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nlp = spacy.load('en')
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nlp.pipeline = create_pipeline(nlp)
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correct = 0
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i = 0
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for doc in nlp.pipe(texts, batch_size=1000, n_threads=4):
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correct += bool(doc.sentiment >= 0.5) == bool(labels[i])
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i += 1
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return float(correct) / i
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def read_data(data_dir, limit=0):
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examples = []
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for subdir, label in (('pos', 1), ('neg', 0)):
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for filename in (data_dir / subdir).iterdir():
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with filename.open() as file_:
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text = file_.read()
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examples.append((text, label))
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random.shuffle(examples)
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if limit >= 1:
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examples = examples[:limit]
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return zip(*examples) # Unzips into two lists
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@plac.annotations(
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train_dir=("Location of training file or directory"),
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dev_dir=("Location of development file or directory"),
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model_dir=("Location of output model directory",),
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is_runtime=("Demonstrate run-time usage", "flag", "r", bool),
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nr_hidden=("Number of hidden units", "option", "H", int),
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max_length=("Maximum sentence length", "option", "L", int),
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dropout=("Dropout", "option", "d", float),
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learn_rate=("Learn rate", "option", "e", float),
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nb_epoch=("Number of training epochs", "option", "i", int),
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batch_size=("Size of minibatches for training LSTM", "option", "b", int),
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nr_examples=("Limit to N examples", "option", "n", int)
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)
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def main(model_dir, train_dir, dev_dir,
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is_runtime=False,
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nr_hidden=64, max_length=100, # Shape
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dropout=0.5, learn_rate=0.001, # General NN config
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nb_epoch=5, batch_size=100, nr_examples=-1): # Training params
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model_dir = pathlib.Path(model_dir)
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train_dir = pathlib.Path(train_dir)
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dev_dir = pathlib.Path(dev_dir)
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if is_runtime:
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dev_texts, dev_labels = read_data(dev_dir)
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acc = evaluate(model_dir, dev_texts, dev_labels, max_length=max_length)
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print(acc)
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else:
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print("Read data")
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train_texts, train_labels = read_data(train_dir, limit=nr_examples)
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dev_texts, dev_labels = read_data(dev_dir, limit=nr_examples)
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train_labels = numpy.asarray(train_labels, dtype='int32')
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dev_labels = numpy.asarray(dev_labels, dtype='int32')
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lstm = train(train_texts, train_labels, dev_texts, dev_labels,
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{'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 1},
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{'dropout': dropout, 'lr': learn_rate},
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{},
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nb_epoch=nb_epoch, batch_size=batch_size)
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weights = lstm.get_weights()
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with (model_dir / 'model').open('wb') as file_:
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pickle.dump(weights[1:], file_)
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with (model_dir / 'config.json').open('wb') as file_:
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file_.write(lstm.to_json())
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if __name__ == '__main__':
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plac.call(main)
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