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d105771 Nov 13, 2016
@kylepjohnson @syllog1sm
282 lines (235 sloc) 9.34 KB
"""This script expects something like a binary sentiment data set, such as
that available here: `http://www.cs.cornell.edu/people/pabo/movie-review-data/`
It expects a directory structure like: `data_dir/train/{pos|neg}`
and `data_dir/test/{pos|neg}`. Put (say) 90% of the files in the former
and the remainder in the latter.
"""
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from collections import defaultdict
from pathlib import Path
import numpy
import plac
import spacy.en
def read_data(nlp, data_dir):
for subdir, label in (('pos', 1), ('neg', 0)):
for filename in (data_dir / subdir).iterdir():
text = filename.open().read()
doc = nlp(text)
if len(doc) >= 1:
yield doc, label
def partition(examples, split_size):
examples = list(examples)
numpy.random.shuffle(examples)
n_docs = len(examples)
split = int(n_docs * split_size)
return examples[:split], examples[split:]
def minibatch(data, bs=24):
for i in range(0, len(data), bs):
yield data[i:i+bs]
class Extractor(object):
def __init__(self, nlp, vector_length, dropout=0.3):
self.nlp = nlp
self.dropout = dropout
self.vector = numpy.zeros((vector_length, ))
def doc2bow(self, doc, dropout=None):
if dropout is None:
dropout = self.dropout
bow = defaultdict(int)
all_words = defaultdict(int)
for word in doc:
if numpy.random.random() >= dropout and not word.is_punct:
bow[word.lower] += 1
all_words[word.lower] += 1
if sum(bow.values()) >= 1:
return bow
else:
return all_words
def bow2vec(self, bow, E):
self.vector.fill(0)
n = 0
for orth_id, freq in bow.items():
self.vector += self.nlp.vocab[self.nlp.vocab.strings[orth_id]].vector * freq
# Apply the fine-tuning we've learned
if orth_id < E.shape[0]:
self.vector += E[orth_id] * freq
n += freq
return self.vector / n
class NeuralNetwork(object):
def __init__(self, depth, width, n_classes, n_vocab, extracter, optimizer):
self.depth = depth
self.width = width
self.n_classes = n_classes
self.weights = Params.random(depth, width, width, n_classes, n_vocab)
self.doc2bow = extracter.doc2bow
self.bow2vec = extracter.bow2vec
self.optimizer = optimizer
self._gradient = Params.zero(depth, width, width, n_classes, n_vocab)
self._activity = numpy.zeros((depth, width))
def train(self, batch):
activity = self._activity
gradient = self._gradient
activity.fill(0)
gradient.data.fill(0)
loss = 0
word_freqs = defaultdict(int)
for doc, label in batch:
word_ids = self.doc2bow(doc)
vector = self.bow2vec(word_ids, self.weights.E)
self.forward(activity, vector)
loss += self.backprop(vector, gradient, activity, word_ids, label)
for w, freq in word_ids.items():
word_freqs[w] += freq
self.optimizer(self.weights, gradient, len(batch), word_freqs)
return loss
def predict(self, doc):
actv = self._activity
actv.fill(0)
W = self.weights.W
b = self.weights.b
E = self.weights.E
vector = self.bow2vec(self.doc2bow(doc, dropout=0.0), E)
self.forward(actv, vector)
return numpy.argmax(softmax(actv[-1], W[-1], b[-1]))
def forward(self, actv, in_):
actv.fill(0)
W = self.weights.W; b = self.weights.b
actv[0] = relu(in_, W[0], b[0])
for i in range(1, self.depth):
actv[i] = relu(actv[i-1], W[i], b[i])
def backprop(self, input_vector, gradient, activity, ids, label):
W = self.weights.W
b = self.weights.b
target = numpy.zeros(self.n_classes)
target[label] = 1.0
pred = softmax(activity[-1], W[-1], b[-1])
delta = pred - target
for i in range(self.depth, 0, -1):
gradient.b[i] += delta
gradient.W[i] += numpy.outer(delta, activity[i-1])
delta = d_relu(activity[i-1]) * W[i].T.dot(delta)
gradient.b[0] += delta
gradient.W[0] += numpy.outer(delta, input_vector)
tuning = W[0].T.dot(delta).reshape((self.width,)) / len(ids)
for w, freq in ids.items():
if w < gradient.E.shape[0]:
gradient.E[w] += tuning * freq
return -sum(target * numpy.log(pred))
def softmax(actvn, W, b):
w = W.dot(actvn) + b
ew = numpy.exp(w - max(w))
return (ew / sum(ew)).ravel()
def relu(actvn, W, b):
x = W.dot(actvn) + b
return x * (x > 0)
def d_relu(x):
return x > 0
class Adagrad(object):
def __init__(self, lr, rho):
self.eps = 1e-3
# initial learning rate
self.learning_rate = lr
self.rho = rho
# stores sum of squared gradients
#self.h = numpy.zeros(self.dim)
#self._curr_rate = numpy.zeros(self.h.shape)
self.h = None
self._curr_rate = None
def __call__(self, weights, gradient, batch_size, word_freqs):
if self.h is None:
self.h = numpy.zeros(gradient.data.shape)
self._curr_rate = numpy.zeros(gradient.data.shape)
self.L2_penalty(gradient, weights, word_freqs)
update = self.rescale(gradient.data / batch_size)
weights.data -= update
def rescale(self, gradient):
if self.h is None:
self.h = numpy.zeros(gradient.data.shape)
self._curr_rate = numpy.zeros(gradient.data.shape)
self._curr_rate.fill(0)
self.h += gradient ** 2
self._curr_rate = self.learning_rate / (numpy.sqrt(self.h) + self.eps)
return self._curr_rate * gradient
def L2_penalty(self, gradient, weights, word_freqs):
# L2 Regularization
for i in range(len(weights.W)):
gradient.W[i] += weights.W[i] * self.rho
gradient.b[i] += weights.b[i] * self.rho
for w, freq in word_freqs.items():
if w < gradient.E.shape[0]:
gradient.E[w] += weights.E[w] * self.rho
class Params(object):
@classmethod
def zero(cls, depth, n_embed, n_hidden, n_labels, n_vocab):
return cls(depth, n_embed, n_hidden, n_labels, n_vocab, lambda x: numpy.zeros((x,)))
@classmethod
def random(cls, depth, nE, nH, nL, nV):
return cls(depth, nE, nH, nL, nV, lambda x: (numpy.random.rand(x) * 2 - 1) * 0.08)
def __init__(self, depth, n_embed, n_hidden, n_labels, n_vocab, initializer):
nE = n_embed; nH = n_hidden; nL = n_labels; nV = n_vocab
n_weights = sum([
(nE * nH) + nH,
(nH * nH + nH) * depth,
(nH * nL) + nL,
(nV * nE)
])
self.data = initializer(n_weights)
self.W = []
self.b = []
i = self._add_layer(0, nE, nH)
for _ in range(1, depth):
i = self._add_layer(i, nH, nH)
i = self._add_layer(i, nL, nH)
self.E = self.data[i : i + (nV * nE)].reshape((nV, nE))
self.E.fill(0)
def _add_layer(self, start, x, y):
end = start + (x * y)
self.W.append(self.data[start : end].reshape((x, y)))
self.b.append(self.data[end : end + x].reshape((x, )))
return end + x
@plac.annotations(
data_dir=("Data directory", "positional", None, Path),
n_iter=("Number of iterations (epochs)", "option", "i", int),
width=("Size of hidden layers", "option", "H", int),
depth=("Depth", "option", "d", int),
dropout=("Drop-out rate", "option", "r", float),
rho=("Regularization penalty", "option", "p", float),
eta=("Learning rate", "option", "e", float),
batch_size=("Batch size", "option", "b", int),
vocab_size=("Number of words to fine-tune", "option", "w", int),
)
def main(data_dir, depth=3, width=300, n_iter=5, vocab_size=40000,
batch_size=24, dropout=0.3, rho=1e-5, eta=0.005):
n_classes = 2
print("Loading")
nlp = spacy.en.English(parser=False)
train_data, dev_data = partition(read_data(nlp, data_dir / 'train'), 0.8)
print("Begin training")
extracter = Extractor(nlp, width, dropout=0.3)
optimizer = Adagrad(eta, rho)
model = NeuralNetwork(depth, width, n_classes, vocab_size, extracter, optimizer)
prev_best = 0
best_weights = None
for epoch in range(n_iter):
numpy.random.shuffle(train_data)
train_loss = 0.0
for batch in minibatch(train_data, bs=batch_size):
train_loss += model.train(batch)
n_correct = sum(model.predict(x) == y for x, y in dev_data)
print(epoch, train_loss, n_correct / len(dev_data))
if n_correct >= prev_best:
best_weights = model.weights.data.copy()
prev_best = n_correct
model.weights.data = best_weights
print("Evaluating")
eval_data = list(read_data(nlp, data_dir / 'test'))
n_correct = sum(model.predict(x) == y for x, y in eval_data)
print(n_correct / len(eval_data))
if __name__ == '__main__':
#import cProfile
#import pstats
#cProfile.runctx("main(Path('data/aclImdb'))", globals(), locals(), "Profile.prof")
#s = pstats.Stats("Profile.prof")
#s.strip_dirs().sort_stats("time").print_stats(100)
plac.call(main)