spaCy/examples/chainer_sentiment.py

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'''WIP --- Doesn't work well yet'''
import plac
import random
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import six
import pathlib
import cPickle as pickle
from itertools import izip
import spacy
import cytoolz
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import cupy as xp
import cupy.cuda
import chainer.cuda
import chainer.links as L
import chainer.functions as F
from chainer import Chain, Variable, report
import chainer.training
import chainer.optimizers
from chainer.training import extensions
from chainer.iterators import SerialIterator
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from chainer.datasets import TupleDataset
class SentimentAnalyser(object):
@classmethod
def load(cls, path, nlp, max_length=100):
raise NotImplementedError
#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, max_length=max_length)
def __init__(self, model, max_length=100):
self._model = model
self.max_length = max_length
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 cytoolz.partition_all(batch_size, docs):
minibatch = list(minibatch)
sentences = []
for doc in minibatch:
sentences.extend(doc.sents)
Xs = get_features(sentences, self.max_length)
ys = self._model.predict(Xs)
for sent, label in zip(sentences, ys):
sent.doc.sentiment += label - 0.5
for doc in minibatch:
yield doc
def set_sentiment(self, doc, 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
class Classifier(Chain):
def __init__(self, predictor):
super(Classifier, self).__init__(predictor=predictor)
def __call__(self, x, t):
y = self.predictor(x)
loss = F.softmax_cross_entropy(y, t)
accuracy = F.accuracy(y, t)
report({'loss': loss, 'accuracy': accuracy}, self)
return loss
class SentimentModel(Chain):
def __init__(self, shape, **settings):
Chain.__init__(self,
embed=_Embed(shape['nr_vector'], shape['nr_dim'], shape['nr_hidden']),
encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
predict=_Predict(shape['nr_hidden'], shape['nr_class']))
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self.to_gpu(0)
def __call__(self, sentence):
return self.predict(
self.attend(
self.encode(
self.embed(sentence))))
class _Embed(Chain):
def __init__(self, nr_vector, nr_dim, nr_out):
Chain.__init__(self,
embed=L.EmbedID(nr_vector, nr_dim),
project=L.Linear(None, nr_out, nobias=True))
#self.embed.unchain_backward()
def __call__(self, sentence):
return [self.project(self.embed(ts)) for ts in F.transpose(sentence)]
class _Encode(Chain):
def __init__(self, nr_in, nr_out):
Chain.__init__(self,
fwd=L.LSTM(nr_in, nr_out),
bwd=L.LSTM(nr_in, nr_out),
mix=L.Bilinear(nr_out, nr_out, nr_out))
def __call__(self, sentence):
self.fwd.reset_state()
fwds = map(self.fwd, sentence)
self.bwd.reset_state()
bwds = reversed(map(self.bwd, reversed(sentence)))
return [F.elu(self.mix(f, b)) for f, b in zip(fwds, bwds)]
class _Attend(Chain):
def __init__(self, nr_in, nr_out):
Chain.__init__(self)
def __call__(self, sentence):
sent = sum(sentence)
return sent
class _Predict(Chain):
def __init__(self, nr_in, nr_out):
Chain.__init__(self,
l1=L.Linear(nr_in, nr_in),
l2=L.Linear(nr_in, nr_out))
def __call__(self, vector):
vector = self.l1(vector)
vector = F.elu(vector)
vector = self.l2(vector)
return vector
class SentenceDataset(TupleDataset):
def __init__(self, nlp, texts, labels, max_length):
self.max_length = max_length
sents, labels = self._get_labelled_sentences(
nlp.pipe(texts, batch_size=5000, n_threads=3),
labels)
TupleDataset.__init__(self,
get_features(sents, max_length),
labels)
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def __getitem__(self, index):
batches = [dataset[index] for dataset in self._datasets]
if isinstance(index, slice):
length = len(batches[0])
returns = [tuple([batch[i] for batch in batches])
for i in six.moves.range(length)]
return returns
else:
return tuple(batches)
def _get_labelled_sentences(self, docs, doc_labels):
labels = []
sentences = []
for doc, y in izip(docs, doc_labels):
for sent in doc.sents:
sentences.append(sent)
labels.append(y)
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return sentences, xp.asarray(labels, dtype='i')
class DocDataset(TupleDataset):
def __init__(self, nlp, texts, labels):
self.max_length = max_length
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DatasetMixin.__init__(self,
get_features(
nlp.pipe(texts, batch_size=5000, n_threads=3), self.max_length),
labels)
def read_data(data_dir, limit=0):
examples = []
for subdir, label in (('pos', 1), ('neg', 0)):
for filename in (data_dir / subdir).iterdir():
with filename.open() as file_:
text = file_.read()
examples.append((text, label))
random.shuffle(examples)
if limit >= 1:
examples = examples[:limit]
return zip(*examples) # Unzips into two lists
def get_features(docs, max_length):
docs = list(docs)
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Xs = xp.zeros((len(docs), max_length), dtype='i')
for i, doc in enumerate(docs):
j = 0
for token in doc:
if token.has_vector and not token.is_punct and not token.is_space:
Xs[i, j] = token.norm
j += 1
if j >= max_length:
break
return Xs
def get_embeddings(vocab, max_rank=1000):
if max_rank is None:
max_rank = max(lex.rank+1 for lex in vocab if lex.has_vector)
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vectors = xp.ndarray((max_rank+1, vocab.vectors_length), dtype='f')
for lex in vocab:
if lex.has_vector and lex.rank < max_rank:
lex.norm = lex.rank+1
vectors[lex.rank + 1] = lex.vector
else:
lex.norm = 0
return vectors
def train(train_texts, train_labels, dev_texts, dev_labels,
lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5,
by_sentence=True):
nlp = spacy.load('en', entity=False)
for lex in nlp.vocab:
if lex.rank >= (lstm_shape['nr_vector'] - 1):
lex.norm = 0
else:
lex.norm = lex.rank+1
print("Make model")
model = Classifier(SentimentModel(lstm_shape, **lstm_settings))
print("Parsing texts...")
if by_sentence:
train_data = SentenceDataset(nlp, train_texts, train_labels, lstm_shape['max_length'])
dev_data = SentenceDataset(nlp, dev_texts, dev_labels, lstm_shape['max_length'])
else:
train_data = DocDataset(nlp, train_texts, train_labels)
dev_data = DocDataset(nlp, dev_texts, dev_labels)
train_iter = SerialIterator(train_data, batch_size=batch_size,
shuffle=True, repeat=True)
dev_iter = SerialIterator(dev_data, batch_size=batch_size,
shuffle=False, repeat=False)
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
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updater = chainer.training.StandardUpdater(train_iter, optimizer, device=0)
trainer = chainer.training.Trainer(updater, (20, 'epoch'), out='result')
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trainer.extend(extensions.Evaluator(dev_iter, model, device=0))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport([
'epoch', 'main/accuracy', 'validation/main/accuracy']))
trainer.extend(extensions.ProgressBar())
trainer.run()
def evaluate(model_dir, texts, labels, max_length=100):
def create_pipeline(nlp):
'''
This could be a lambda, but named functions are easier to read in Python.
'''
return [nlp.tagger, nlp.parser, SentimentAnalyser.load(model_dir, nlp,
max_length=max_length)]
nlp = spacy.load('en')
nlp.pipeline = create_pipeline(nlp)
correct = 0
i = 0
for doc in nlp.pipe(texts, batch_size=1000, n_threads=4):
correct += bool(doc.sentiment >= 0.5) == bool(labels[i])
i += 1
return float(correct) / i
@plac.annotations(
train_dir=("Location of training file or directory"),
dev_dir=("Location of development file or directory"),
model_dir=("Location of output model directory",),
is_runtime=("Demonstrate run-time usage", "flag", "r", bool),
nr_hidden=("Number of hidden units", "option", "H", int),
max_length=("Maximum sentence length", "option", "L", int),
dropout=("Dropout", "option", "d", float),
learn_rate=("Learn rate", "option", "e", float),
nb_epoch=("Number of training epochs", "option", "i", int),
batch_size=("Size of minibatches for training LSTM", "option", "b", int),
nr_examples=("Limit to N examples", "option", "n", int)
)
def main(model_dir, train_dir, dev_dir,
is_runtime=False,
nr_hidden=64, max_length=100, # Shape
dropout=0.5, learn_rate=0.001, # General NN config
nb_epoch=5, batch_size=32, nr_examples=-1): # Training params
model_dir = pathlib.Path(model_dir)
train_dir = pathlib.Path(train_dir)
dev_dir = pathlib.Path(dev_dir)
if is_runtime:
dev_texts, dev_labels = read_data(dev_dir)
acc = evaluate(model_dir, dev_texts, dev_labels, max_length=max_length)
print(acc)
else:
print("Read data")
train_texts, train_labels = read_data(train_dir, limit=nr_examples)
dev_texts, dev_labels = read_data(dev_dir, limit=nr_examples)
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print("Using GPU 0")
#chainer.cuda.get_device(0).use()
train_labels = xp.asarray(train_labels, dtype='i')
dev_labels = xp.asarray(dev_labels, dtype='i')
lstm = train(train_texts, train_labels, dev_texts, dev_labels,
{'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 2,
'nr_vector': 2000, 'nr_dim': 32},
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{'dropout': 0.5, 'lr': learn_rate},
{},
nb_epoch=nb_epoch, batch_size=batch_size)
if __name__ == '__main__':
plac.call(main)