mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-01 00:17:44 +03:00
Merge vectors.pyx doc strings
This commit is contained in:
Matthew Honnibal
commit
d90cc917fa
|
|
@ -1,322 +0,0 @@
|
|||
'''WIP --- Doesn't work well yet'''
|
||||
import plac
|
||||
import random
|
||||
import six
|
||||
|
||||
import cProfile
|
||||
import pstats
|
||||
|
||||
import pathlib
|
||||
import cPickle as pickle
|
||||
from itertools import izip
|
||||
|
||||
import spacy
|
||||
|
||||
import cytoolz
|
||||
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
|
||||
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, nlp, shape, **settings):
|
||||
Chain.__init__(self,
|
||||
embed=_Embed(shape['nr_vector'], shape['nr_dim'], shape['nr_hidden'],
|
||||
set_vectors=lambda arr: set_vectors(arr, nlp.vocab)),
|
||||
encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
|
||||
attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
|
||||
predict=_Predict(shape['nr_hidden'], shape['nr_class']))
|
||||
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, set_vectors=None):
|
||||
Chain.__init__(self,
|
||||
embed=L.EmbedID(nr_vector, nr_dim, initialW=set_vectors),
|
||||
project=L.Linear(None, nr_out, nobias=True))
|
||||
self.embed.W.volatile = False
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
return sentences, xp.asarray(labels, dtype='i')
|
||||
|
||||
|
||||
class DocDataset(TupleDataset):
|
||||
def __init__(self, nlp, texts, labels):
|
||||
self.max_length = max_length
|
||||
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)
|
||||
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 set_vectors(vectors, vocab):
|
||||
for lex in vocab:
|
||||
if lex.has_vector and (lex.rank+1) < vectors.shape[0]:
|
||||
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)
|
||||
if 'nr_vector' not in lstm_shape:
|
||||
lstm_shape['nr_vector'] = max(lex.rank+1 for lex in nlp.vocab if lex.has_vector)
|
||||
if 'nr_dim' not in lstm_shape:
|
||||
lstm_shape['nr_dim'] = nlp.vocab.vectors_length
|
||||
print("Make model")
|
||||
model = Classifier(SentimentModel(nlp, 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)
|
||||
updater = chainer.training.StandardUpdater(train_iter, optimizer, device=0)
|
||||
trainer = chainer.training.Trainer(updater, (1, 'epoch'), out='result')
|
||||
|
||||
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)
|
||||
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': 5000},
|
||||
{'dropout': 0.5, 'lr': learn_rate},
|
||||
{},
|
||||
nb_epoch=nb_epoch, batch_size=batch_size)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
#cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
|
||||
#s = pstats.Stats("Profile.prof")
|
||||
#s.strip_dirs().sort_stats("time").print_stats()
|
||||
plac.call(main)
|
||||
30
examples/vectors_fast_text.py
Normal file
30
examples/vectors_fast_text.py
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
'''Load vectors for a language trained using FastText
|
||||
|
||||
https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md
|
||||
'''
|
||||
from __future__ import unicode_literals
|
||||
import plac
|
||||
import numpy
|
||||
|
||||
import spacy.language
|
||||
|
||||
|
||||
def main(vectors_loc):
|
||||
nlp = spacy.language.Language()
|
||||
|
||||
with open(vectors_loc, 'rb') as file_:
|
||||
header = file_.readline()
|
||||
nr_row, nr_dim = header.split()
|
||||
nlp.vocab.clear_vectors(int(nr_dim))
|
||||
for line in file_:
|
||||
line = line.decode('utf8')
|
||||
pieces = line.split()
|
||||
word = pieces[0]
|
||||
vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
|
||||
nlp.vocab.set_vector(word, vector)
|
||||
doc = nlp(u'class colspan')
|
||||
print(doc[0].similarity(doc[1]))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
plac.call(main)
|
||||
|
|
@ -16,7 +16,16 @@ from .compat import basestring_
|
|||
|
||||
|
||||
cdef class Vectors:
|
||||
'''Store, save and load word vectors.'''
|
||||
'''Store, save and load word vectors.
|
||||
|
||||
Vectors data is kept in the vectors.data attribute, which should be an
|
||||
instance of numpy.ndarray (for CPU vectors)
|
||||
or cupy.ndarray (for GPU vectors).
|
||||
|
||||
vectors.key2row is a dictionary mapping word hashes to rows
|
||||
in the vectors.data table. The array `vectors.keys` keeps
|
||||
the keys in order, such that keys[vectors.key2row[key]] == key.
|
||||
'''
|
||||
cdef public object data
|
||||
cdef readonly StringStore strings
|
||||
cdef public object key2row
|
||||
|
|
@ -44,6 +53,11 @@ cdef class Vectors:
|
|||
return (Vectors, (self.strings, self.data))
|
||||
|
||||
def __getitem__(self, key):
|
||||
'''Get a vector by key. If key is a string, it is hashed
|
||||
to an integer ID using the vectors.strings table.
|
||||
|
||||
If the integer key is not found in the table, a KeyError is raised.
|
||||
'''
|
||||
if isinstance(key, basestring):
|
||||
key = self.strings[key]
|
||||
i = self.key2row[key]
|
||||
|
|
@ -53,23 +67,30 @@ cdef class Vectors:
|
|||
return self.data[i]
|
||||
|
||||
def __setitem__(self, key, vector):
|
||||
'''Set a vector for the given key. If key is a string, it is hashed
|
||||
to an integer ID using the vectors.strings table.
|
||||
'''
|
||||
if isinstance(key, basestring):
|
||||
key = self.strings.add(key)
|
||||
i = self.key2row[key]
|
||||
self.data[i] = vector
|
||||
|
||||
def __iter__(self):
|
||||
'''Yield vectors from the table.'''
|
||||
yield from self.data
|
||||
|
||||
def __len__(self):
|
||||
'''Return the number of vectors that have been assigned.'''
|
||||
return self.i
|
||||
|
||||
def __contains__(self, key):
|
||||
'''Check whether a key has a vector entry in the table.'''
|
||||
if isinstance(key, basestring_):
|
||||
key = self.strings[key]
|
||||
return key in self.key2row
|
||||
|
||||
def add(self, key, vector=None):
|
||||
'''Add a key to the table, optionally setting a vector value as well.'''
|
||||
if isinstance(key, basestring_):
|
||||
key = self.strings.add(key)
|
||||
if key not in self.key2row:
|
||||
|
|
@ -87,6 +108,7 @@ cdef class Vectors:
|
|||
return i
|
||||
|
||||
def items(self):
|
||||
'''Iterate over (string key, vector) pairs, in order.'''
|
||||
for i, key in enumerate(self.keys):
|
||||
string = self.strings[key]
|
||||
yield string, self.data[i]
|
||||
|
|
|
|||
|
|
@ -262,7 +262,7 @@ cdef class Vocab:
|
|||
Words can be looked up by string or int ID.
|
||||
|
||||
RETURNS:
|
||||
A word vector. Size and shape determed by the
|
||||
A word vector. Size and shape determined by the
|
||||
vocab.vectors instance. Usually, a numpy ndarray
|
||||
of shape (300,) and dtype float32.
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user