def preprocess_bubs_format(bubs, output):
"""Convert grammar from bubs-parser into ldp-friendly csv format. The result is
an equivalent grammar, which is much faster to load because it has been
integerized.
Given a gzipped grammar from bubs-parser, e.g. `eng.M2.gr.gz`, this function
will generate four files:
- eng.M2.gr.csv: grammar rules
- eng.M2.lex.csv: lexical rules
- eng.M2.lex.alphabet: mapping from terminals to integers
- eng.M2.sym.alphabet: mapping from syms to integers
"""
sym = Alphabet()
lex = Alphabet()
import gzip
lines = gzip.open(bubs, 'rb').readlines()
reading_lex = False
l = []
f = []
for line in iterview(lines[1:]): # drop first line
if line.startswith('===== LEXICON'):
reading_lex = True
continue
x = line.strip().split()
if not x:
continue
lhs = x[0]
rhs = tuple(b for b in x[2:-1])
score = x[-1]
if len(rhs) == 1:
rhs = (rhs[0], '')
y, z = rhs
lhs = sym[lhs]
y = lex[y] if reading_lex else sym[y]
z = sym[z] if z else -1
if reading_lex:
l.append({'score': score, 'head': lhs, 'left': y})
else:
f.append({'score': score, 'head': lhs, 'left': y, 'right': z})
# non-gzipped loads faster.
#DataFrame(f).to_csv(gzip.open(output + '.gr.csv.gz', 'wb'))
#DataFrame(l).to_csv(gzip.open(output + '.lex.csv.gz', 'wb'))
DataFrame(f).to_csv(output + '.gr.csv')
DataFrame(l).to_csv(output + '.lex.csv')
sym.save(output + '.sym.alphabet')
lex.save(output + '.lex.alphabet')
def preprocess_berkeley_format(input_prefix, output, coarsen=False):
"""
Preprocessing: convert PTB grammar into simple tsv format.
"""
def g(x):
if coarsen:
x = x.split('^')[0]
x = x.split('_')[0]
return x
sym = Alphabet()
lex = Alphabet()
lexical_rules = []
for x in file(input_prefix + '.lexicon'):
[(x, y, s)] = re.findall(r'(\S+)\s+(\S+)\s*\[(.*?)\]', x)
s = float(s)
x = g(x)
y = g(y)
lexical_rules.append({'score': log(s), 'head': sym[x], 'left': lex[y]})
rules = []
for x in file(input_prefix + '.grammar'):
x, y = x.split(' -> ')
y = y.split()
if len(y) == 2:
y, s = y
s = float(s)
z = -1
else:
assert len(y) == 3
y, z, s = y
s = float(s)
x = g(x)
y = g(y)
if x == y and z == -1:
continue
x = sym[x]
y = sym[y]
if z != -1:
z = g(z)
z = sym[z]
rules.append({'score': log(s), 'head': x, 'left': y, 'right': z})
DataFrame(rules).to_csv(output + '.gr.csv')
DataFrame(lexical_rules).to_csv(output + '.lex.csv')
sym.save(output + '.sym.alphabet')
lex.save(output + '.lex.alphabet')
class Segmenter(object):
""" Segmenter """
def __init__(self,
train,
dev,
test,
decode_type,
split_num,
log_fname=None,
segmenter_type='tree',
G=3,
weights='weights',
alphabet=None,
T_L=2,
T_eta=1.0,
T_C=0.0000001,
S_L=2,
S_eta=1.0,
S_C=0.00000001):
# set up the logging system
log = logging.getLogger('')
log.setLevel(logging.INFO)
format = logging.Formatter("%(asctime)s<>%(levelname)s<>%(message)s")
ch = logging.StreamHandler(sys.stdout)
ch.setFormatter(format)
log.addHandler(ch)
fh = logging.handlers.RotatingFileHandler(log_fname,
maxBytes=(1048576 * 5),
backupCount=7)
fh.setFormatter(format)
log.addHandler(fh)
self.G = G
self.split_num = split_num
self.segmenter_type = segmenter_type
self.decode_type = decode_type
self.S_L, self.S_eta, self.S_C = S_L, S_eta, S_C
self.T_L, self.T_eta, self.T_C = T_L, T_eta, T_C
self.sig = "split={0},L={1},C={2},eta={3},type={4}".format(
*(self.split_num, self.T_L, self.T_C, self.T_eta,
self.decode_type))
logging.info("Transducer Regularizer Type: L={0}".format(T_L))
logging.info("Transducer Regularizer Coefficient: C={0}".format(T_C))
logging.info("Transducer Learning Rate: eta={0}".format(T_eta))
logging.info("Segmenter Regularizer Type: L={0}".format(S_L))
logging.info("Segmenter Regularizer Coefficient: C={0}".format(S_C))
logging.info("Segmenter Learning Rate: eta={0}".format(S_eta))
self.weights = weights
self.Sigma = Alphabet()
self.Sigma.add("") # add epsilon at 0
self.Sigma.add("o")
self.Sigma.add("n")
self.Sigma.add("y")
self.Sigma.add("s")
self.Sigma.add("e")
if alphabet is not None:
self.Sigma = self.Sigma.load(alphabet)
# processs the data
# TODO: modularize
self.train = self.process(train, 100000)
self.dev = self.process(dev, 1000)
self.test = self.process(test, 1000)
# dump the alphabet
self.Sigma.save("alphabets/sigma-{0}.alphabet".format(self.split_num))
# create model
self.segmenter = None
logging.info("Segmenter Type: {0}".format(self.segmenter_type))
logging.info("Decoder Type: {0}".format(self.decode_type))
if self.segmenter_type == TREE:
self.segmenter = TreeSegmenter(G)
self.features = TreeFeatures(self.G, 1000)
elif self.segmenter_type == CHUNK:
self.segmenter = ChunkSegmenter(G)
self.features = ChunkFeatures(self.G, 1000)
else:
raise Exception('Illicit Model Type')
# transducer
self.transducer = TransducerModel(self.train,
self.dev,
self.test,
self.Sigma,
L=self.T_L,
eta=self.T_eta,
C=self.T_C)
# extract features
self.features.featurize(self.train, 'train')
self.features.featurize(self.dev, 'dev')
self.features.featurize(self.test, 'test')
# dimension of data
self.d = 2**22 + self.features.offset
self.updater = LazyRegularizedAdagrad(self.d,
L=2,
C=self.S_C,
eta=0.1,
fudge=1e-4)
self.updater.w[0] = 10
self.updater.w[1] = 10
def save_transducer(self, directory, i):
""" save the transducer weights """
np.save(directory + "/transducer-{0}-{1}.npy".format(*(self.sig, i)),
array(self.transducer.updater.w))
def save_segmenter(self, directory, i):
np.save(directory + "/segmenter-{0}-{1}.npy".format(*(self.sig, i)),
array(self.updater.w))
def save(self, directory):
self.save_transducer(directory, 'final')
self.save_segmenter(directory, 'final')
def optimize(self,
t=None,
load=False,
transducer=None,
segmenter=None,
iterations=20):
""" optimize the model """
if load:
assert transducer is not None
#assert segmenter is not None
self.load_weights(transducer, segmenter)
if t is None:
return
elif t == JOINT:
self.optimize_joint(iterations)
elif t == PIPE:
self.optimize_pipeline(iterations)
def load_weights(self, transducer, segmenter):
""" load weights """
self.transducer.updater.w = np.load(transducer)
self.updater.w = np.load(segmenter)
def optimize_pipeline(self, iterations=10):
""" optimize """
for i in xrange(iterations):
self.transducer.optimize(1, i)
train_acc = self.transducer.evaluate(self.train)
dev_acc = self.transducer.evaluate(self.dev)
test_acc = self.transducer.evaluate(self.test)
logging.info(
"transducer epoch {0} train acc: {1}".format(*(i, train_acc)))
logging.info(
"transducer epoch {0} dev acc: {1}".format(*(i, dev_acc)))
logging.info(
"transducer epoch {0} test acc: {1}".format(*(i, test_acc)))
self.save_transducer(self.weights, i)
print self.transducer.evaluate(self.dev)
if self.segmenter_type == TREE:
self.optimize_tree(iterations)
elif self.segmenter_type == CHUNK:
self.optimize_chunk(iterations)
def optimize_chunk(self, iterations):
""" optimize the model """
for i in xrange(iterations):
for tree in iterview(self.train,
colored('Pass %s' % (i + 1), 'blue')):
gt0, gt, ge = self.features.potentials_catchup(
tree, self.updater.w, self.updater)
dgt0, dgt, dge = zeros_like(gt0), zeros_like(gt), zeros_like(
ge)
self.segmenter.dll(tree, ge, gt0, gt, dge, dgt0, dgt)
self.features.update(tree, dge, dgt0, dgt, self.updater)
self.updater.step += 1
self.save_segmenter(self.weights, i)
self.decode(self.train, VITERBI)
self.decode(self.dev, VITERBI)
test_acc, test_f1 = self.decode(self.test, VITERBI)
logging.info("chunk epoch {0} train acc: {1}".format(*(i,
train_acc)))
logging.info("chunk epoch {0} dev acc: {1}".format(*(i, dev_acc)))
logging.info("chunk epoch {0} test acc: {1}".format(*(i,
test_acc)))
logging.info("chunk epoch {0} train f1: {1}".format(*(i,
train_f1)))
logging.info("chunk epoch {0} dev f1: {1}".format(*(i, dev_f1)))
logging.info("chunk epoch {0} test f1: {1}".format(*(i, dev_f1)))
def optimize_tree(self, iterations):
""" optimize the model """
for i in xrange(iterations):
for tree in iterview(self.train,
colored('Pass %s' % (i + 1), 'blue')):
psi = self.features.potentials_catchup(tree, self.updater.w,
self.updater)
dpsi = self.segmenter.dll(tree, psi)
self.features.update(tree, dpsi, self.updater)
self.updater.step += 1
self.save_segmenter(self.weights, i)
self.decode(self.train, VITERBI)
self.decode(self.dev, VITERBI)
test_acc, test_f1 = self.decode(self.test, VITERBI)
logging.info("tree epoch {0} train acc: {1}".format(*(i,
train_acc)))
logging.info("tree epoch {0} dev acc: {1}".format(*(i, dev_acc)))
logging.info("tree epoch {0} test acc: {1}".format(*(i, test_acc)))
logging.info("tree epoch {0} train f1: {1}".format(*(i, train_f1)))
logging.info("tree epoch {0} dev f1: {1}".format(*(i, dev_f1)))
logging.info("tree epoch {0} test f1: {1}".format(*(i, test_f1)))
def optimize_joint(self, iterations, num_samples=10, eta1=0.0, eta2=0.0):
""" optimize jointly using importance sampling """
# TODO: unit test
self.updater.eta = eta1
for i in xrange(iterations):
samples = self.transducer.sample(self.train, num=num_samples)
for tree, sample in iterview(zip(self.train, samples),
colored('Pass %s' % (i + 1), 'blue')):
# compute approximate partition function
logZ = NINF
strings, weights = [], []
for (ur, count) in sample.items():
score = self.transducer.ll(tree, ur)
if self.segmenter_type == CHUNK:
score += self.score_chunk(tree, ur)
elif self.segmenter_type == TREE:
score += self.score_tree(tree, ur)
# TODO: double check
logZ = logaddexp(logZ, score)
weights.append(score)
strings.append(ur)
#TODO: make more elegant
tmp = []
for weight in weights:
tmp.append(weight - logZ) # TODO: double check
weights = tmp
# take a tranducer weight gradient step with the importance sampling
self.transducer.step_is(tree, strings, weights, eta=eta2)
# take a segmenter weight gradient step with the importance sampling
for ur, weight in zip(sample, weights):
if self.segmenter_type == CHUNK:
self.is_chunk(tree, ur, weight)
elif self.segmenter_type == TREE:
self.is_tree(tree, ur, weight)
self.updater.step += 1
def is_chunk(self, tree, ur, weight):
""" importance sampling gradient step tree """
tree.update_ur(ur)
self.features.featurize_instance(tree)
gt0, gt, ge = self.features.potentials_catchup(tree, self.updater.w,
self.updater)
dgt0, dgt, dge = zeros_like(gt0), zeros_like(gt), zeros_like(ge)
self.segmenter.dll(tree, ge, gt0, gt, dge, dgt0, dgt)
dgt0 *= weight
dgt *= weight
dge *= weight
self.features.update(tree, dge, dgt0, dgt, self.updater)
def is_tree(self, tree, ur, weight):
""" importance sampling gradient step chunk """
tree.update_ur(ur)
self.features.featurize_instance(tree)
psi = self.features.potentials_catchup(tree, self.updater.w,
self.updater)
dpsi = self.segmenter.dll(tree, psi)
dpsi *= weight
self.features.update(tree, dpsi, self.updater)
def baseline_ur(self, data):
""" baseline ur """
for tree in iterview(data, colored('Updating Baseline UR', 'red')):
tree.ur_samples = []
tree.ur_samples.append(tree.sr)
def decode_ur(self, data):
""" decodes the UR """
for tree in iterview(data, colored('Updating Viterbi UR', 'red')):
tree.ur_samples = []
viterbi_ur = self.transducer.decode(tree)[1]
tree.ur_samples.append(viterbi_ur)
def oracle_ur(self, data):
""" uses the oracle UR """
for tree in iterview(data, colored('Updating Oracle UR', 'red')):
tree.ur_samples = []
tree.ur_samples.append(tree.ur_gold)
def sample_ur(self, data, num_samples=1000):
""" samples the UR """
samples = self.transducer.sample(data, num=num_samples)
for tree, samples in iterview(zip(data, samples),
colored('Sampling', 'red')):
tree.ur_samples = []
viterbi_ur = self.transducer.decode(tree)[1]
tree.ur_samples.append(viterbi_ur)
for sample, count in samples.items():
tree.ur_samples.append(sample)
def decode_chunk(self, tree, ur):
""" decodes a chunk """
tree.update_ur(ur)
self.features.featurize_instance(tree)
gt0, gt, ge = self.features.potentials_catchup(tree, self.updater.w,
self.updater)
best, segments, labels = self.segmenter.decode(tree, ge, gt0, gt)
truth = [tree.ur[i:j] for i, j in tree.indices]
guess = [tree.ur[i:j] for i, j in segments]
return truth, guess
def decode_tree(self, tree, ur):
""" decodes a tree """
tree.update_ur(ur)
self.features.featurize_instance(tree)
psi = self.features.potentials_catchup(tree, self.updater.w,
self.updater)
max_score, tree_string, max_spans = self.segmenter.argmax(
tree.M, self.G, psi, tree.ur)
gold_spans = set(tree.spans)
guess_spans = set(tree.spans)
p, r = 0.0, 0.0
for span in gold_spans:
if span in guess_spans:
p += 1.0
p /= len(gold_spans)
for span in guess_spans:
if span in gold_spans:
r += 1.0
r /= len(guess_spans)
f1 = (2 * p * r) / (p + r)
# TODO: horrible hack
segmentation = tree_string.replace("(",
"").replace(")",
"").replace(" ", "")
for i in xrange(100):
segmentation = segmentation.replace(str(i), "")
segmentation = segmentation.split(":")
guess = segmentation[:-1]
truth = [x[0] for x in to_segmentation(tree.root)]
return truth, guess, f1
def score_chunk(self, tree, ur):
""" scores a chunk """
tree.update_ur(ur)
self.features.featurize_instance(tree)
M = tree.M
gt0, gt, ge = self.features.potentials_catchup(tree, self.updater.w,
self.updater)
return self.segmenter.logZ(ge, gt0, gt, M)
def score_tree(self, tree, ur):
""" scores a tree """
tree.update_ur(ur)
self.features.featurize_instance(tree)
M = tree.M
psi = self.features.potentials_catchup(tree, self.updater.w,
self.updater)
return self.segmenter.logZ(M, self.G, psi)
def decode(self, data, data_type, decode_type=None, sep=u"#"):
""" decode the chunker """
if decode_type is None:
decode_type = self.decode_type
if decode_type == ORACLE:
self.oracle_ur(data)
elif decode_type == BASELINE:
self.baseline_ur(data)
elif decode_type == VITERBI:
self.decode_ur(data)
elif decode_type == SAMPLE:
self.sample_ur(data)
else:
raise Exception('Illicit Decode Type')
ur_correct, ur_total = 0, 0
correct, f1, tree_f1, lev, total = 0, 0, 0, 0, 0
for tree in iterview(data, colored('Decoding', 'red')):
max_ur, max_score = None, NINF
counter = 0
for ur in tree.ur_samples:
tree.update_ur(ur)
counter += 1
score = 0.0
score = self.transducer.ll(tree, ur)
#print
#print "LL", self.transducer.ll(tree, ur)
if self.segmenter_type == CHUNK:
score += self.score_chunk(tree, ur)
#print "SCORE", self.score_chunk(tree, ur)
#print ur
#raw_input()
elif self.segmenter_type == TREE:
score += self.score_tree(tree, ur)
# take the best importance sample
if score >= max_score:
max_score = score
max_ur = ur
#print "counter", counter
if max_ur == tree.ur_gold:
ur_correct += 1
ur_total += 1
truth, guess, tree_f1_tmp = None, None, None
if self.segmenter_type == CHUNK:
truth, guess = self.decode_chunk(tree, max_ur)
elif self.segmenter_type == TREE:
truth, guess, tree_f1_tmp = self.decode_tree(tree, max_ur)
tree_f1 += tree_f1_tmp
# ACCURACY
if truth == guess:
correct += 1
# LEVENSHTEIN
lev += Levenshtein.distance(sep.join(truth), sep.join(guess))
# F1
set1, set2 = set(guess), set(truth)
p, r = 0, 0
for e in set1:
if e in set2:
p += 1
for e in set2:
if e in set1:
r += 1
p /= len(set1)
r /= len(set2)
if p + r > 0:
f1 += 2 * p * r / (p + r)
total += 1
logging.info("decoder type: {0}".format(decode_type))
logging.info("{0} ur acc: {1}".format(*(data_type,
ur_correct / total)))
logging.info("{0} seg acc: {1}".format(*(data_type, correct / total)))
logging.info("{0} f1: {1}".format(*(data_type, f1 / total)))
logging.info("{0} edit: {1}".format(*(data_type, lev / total)))
if self.segmenter_type == TREE:
logging.info("{0} tree f1: {1}".format(*(data_type,
tree_f1 / total)))
def process(self, fname, maximum=100):
"""
Put the string data into the data structures
necessary for training and decoding the model.
"""
processed = []
data = Data(fname)
for counter, (sr, (tree, (indices, index_labels))) in enumerate(data):
if counter == maximum:
break
ur = to_string(tree)
for s in list(sr):
self.Sigma.add(s)
for s in list(ur):
self.Sigma.add(s)
spans, labels = [], []
for node in walk(tree):
spans.append((node.i, node.j))
labels.append(node.label)
t = Tree(self.G, sr, ur, spans, labels, indices, index_labels,
len(spans), tree)
processed.append(t)
return processed
