mirror of
https://github.com/explosion/spaCy.git
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316 lines
11 KiB
Python
316 lines
11 KiB
Python
'''WIP --- Doesn't work well yet'''
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import plac
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import random
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import six
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import pathlib
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import cPickle as pickle
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from itertools import izip
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import spacy
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import cytoolz
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import cupy as xp
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import cupy.cuda
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import chainer.cuda
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import chainer.links as L
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import chainer.functions as F
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from chainer import Chain, Variable, report
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import chainer.training
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import chainer.optimizers
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from chainer.training import extensions
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from chainer.iterators import SerialIterator
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from chainer.datasets import TupleDataset
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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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raise NotImplementedError
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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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class Classifier(Chain):
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def __init__(self, predictor):
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super(Classifier, self).__init__(predictor=predictor)
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def __call__(self, x, t):
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y = self.predictor(x)
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loss = F.softmax_cross_entropy(y, t)
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accuracy = F.accuracy(y, t)
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report({'loss': loss, 'accuracy': accuracy}, self)
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return loss
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class SentimentModel(Chain):
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def __init__(self, nlp, shape, **settings):
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Chain.__init__(self,
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embed=_Embed(shape['nr_vector'], shape['nr_dim'], shape['nr_hidden'],
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initialW=lambda arr: set_vectors(arr, nlp.vocab)),
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encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
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attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
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predict=_Predict(shape['nr_hidden'], shape['nr_class']))
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self.to_gpu(0)
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def __call__(self, sentence):
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return self.predict(
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self.attend(
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self.encode(
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self.embed(sentence))))
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class _Embed(Chain):
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def __init__(self, nr_vector, nr_dim, nr_out):
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Chain.__init__(self,
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embed=L.EmbedID(nr_vector, nr_dim),
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project=L.Linear(None, nr_out, nobias=True))
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#self.embed.unchain_backward()
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def __call__(self, sentence):
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return [self.project(self.embed(ts)) for ts in F.transpose(sentence)]
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class _Encode(Chain):
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def __init__(self, nr_in, nr_out):
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Chain.__init__(self,
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fwd=L.LSTM(nr_in, nr_out),
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bwd=L.LSTM(nr_in, nr_out),
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mix=L.Bilinear(nr_out, nr_out, nr_out))
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def __call__(self, sentence):
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self.fwd.reset_state()
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fwds = map(self.fwd, sentence)
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self.bwd.reset_state()
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bwds = reversed(map(self.bwd, reversed(sentence)))
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return [F.elu(self.mix(f, b)) for f, b in zip(fwds, bwds)]
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class _Attend(Chain):
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def __init__(self, nr_in, nr_out):
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Chain.__init__(self)
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def __call__(self, sentence):
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sent = sum(sentence)
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return sent
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class _Predict(Chain):
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def __init__(self, nr_in, nr_out):
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Chain.__init__(self,
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l1=L.Linear(nr_in, nr_in),
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l2=L.Linear(nr_in, nr_out))
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def __call__(self, vector):
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vector = self.l1(vector)
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vector = F.elu(vector)
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vector = self.l2(vector)
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return vector
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class SentenceDataset(TupleDataset):
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def __init__(self, nlp, texts, labels, max_length):
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self.max_length = max_length
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sents, labels = self._get_labelled_sentences(
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nlp.pipe(texts, batch_size=5000, n_threads=3),
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labels)
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TupleDataset.__init__(self,
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get_features(sents, max_length),
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labels)
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def __getitem__(self, index):
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batches = [dataset[index] for dataset in self._datasets]
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if isinstance(index, slice):
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length = len(batches[0])
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returns = [tuple([batch[i] for batch in batches])
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for i in six.moves.range(length)]
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return returns
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else:
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return tuple(batches)
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def _get_labelled_sentences(self, docs, doc_labels):
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labels = []
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sentences = []
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for doc, y in izip(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, xp.asarray(labels, dtype='i')
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class DocDataset(TupleDataset):
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def __init__(self, nlp, texts, labels):
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self.max_length = max_length
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DatasetMixin.__init__(self,
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get_features(
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nlp.pipe(texts, batch_size=5000, n_threads=3), self.max_length),
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labels)
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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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def get_features(docs, max_length):
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docs = list(docs)
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Xs = xp.zeros((len(docs), max_length), dtype='i')
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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.norm
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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 set_vectors(vectors, vocab):
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for lex in vocab:
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if lex.has_vector and (lex.rank+1) < vectors.shape[0]:
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lex.norm = lex.rank+1
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vectors[lex.rank + 1] = lex.vector
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else:
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lex.norm = 0
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vectors.unchain_backwards()
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return vectors
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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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nlp = spacy.load('en', entity=False)
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if 'nr_vector' not in lstm_shape:
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lstm_shape['nr_vector'] = max(lex.rank+1 for lex in vocab if lex.has_vector)
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print("Make model")
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model = Classifier(SentimentModel(nlp, lstm_shape, **lstm_settings))
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print("Parsing texts...")
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if by_sentence:
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train_data = SentenceDataset(nlp, train_texts, train_labels, lstm_shape['max_length'])
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dev_data = SentenceDataset(nlp, dev_texts, dev_labels, lstm_shape['max_length'])
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else:
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train_data = DocDataset(nlp, train_texts, train_labels)
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dev_data = DocDataset(nlp, dev_texts, dev_labels)
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train_iter = SerialIterator(train_data, batch_size=batch_size,
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shuffle=True, repeat=True)
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dev_iter = SerialIterator(dev_data, batch_size=batch_size,
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shuffle=False, repeat=False)
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optimizer = chainer.optimizers.Adam()
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optimizer.setup(model)
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updater = chainer.training.StandardUpdater(train_iter, optimizer, device=0)
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trainer = chainer.training.Trainer(updater, (20, 'epoch'), out='result')
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trainer.extend(extensions.Evaluator(dev_iter, model, device=0))
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trainer.extend(extensions.LogReport())
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trainer.extend(extensions.PrintReport([
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'epoch', 'main/accuracy', 'validation/main/accuracy']))
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trainer.extend(extensions.ProgressBar())
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trainer.run()
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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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@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=32, 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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print("Using GPU 0")
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#chainer.cuda.get_device(0).use()
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train_labels = xp.asarray(train_labels, dtype='i')
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dev_labels = xp.asarray(dev_labels, dtype='i')
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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': 2,
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'nr_vector': 2000, 'nr_dim': 32},
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{'dropout': 0.5, 'lr': learn_rate},
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{},
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nb_epoch=nb_epoch, batch_size=batch_size)
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if __name__ == '__main__':
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plac.call(main)
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