def __init__(self, base, coarse=True):
self.base = base
self.coarse = coarse
self.Y = Alphabet() # tag set
self.V, self.V_freq = Alphabet(), {} # vocabulary
self.V2Y, self.Y2V = dd(set), dd(set)
self.train, self.dev, self.test = [], [], []
self.prefixes, self.suffixes = {}, {}
self.prefix2int, self.suffix2int = {}, {}
# Read data and create standard splits according to
# http://aclweb.org/aclwiki/index.php?title=POS_Tagging_(State_of_the_art)
#
# train split [0,18]
for sectionid in xrange(19):
read = self.read_section(sectionid)
for sentence in read:
#for tag, word in sentence:
# if tag == self.Y["BAD"]:
# break
self.train.append(sentence)
for y, w in sentence:
self.V.add(w)
self.V2Y[w].add(self.Y.lookup(y))
self.Y2V[self.Y.lookup(y)].add(w)
if w not in self.V_freq:
self.V_freq[w] = 0
self.V_freq[w] += 1
for prefix in self.extract_prefixes(w):
if prefix not in self.prefixes:
self.prefixes[prefix] = 0
self.prefixes[prefix] += 1
for suffix in self.extract_suffixes(w):
if suffix not in self.suffixes:
self.suffixes[suffix] = 0
self.suffixes[suffix] += 1
# dev split [19,21]
for sectionid in xrange(19, 22):
read = self.read_section(sectionid)
for sentence in read:
#for tag, word in sentence:
# if tag == self.Y["BAD"]:
# break
self.dev.append(sentence)
# test split [22,24]
for sectionid in xrange(22, 25):
#for tag, word in sentence:
# if tag == self.Y["BAD"]:
# break
self.test.extend(self.read_section(sectionid))
self.Y.freeze()
def integerize(data):
"""
Integerize dataset
returns a triple (label alphabet, feature alphabet, integerized dataset)
"""
F = Alphabet()
L = Alphabet()
I = [(L[label], fromiter(F.map(features), dtype=int32)) for label, features in data]
return (L, F, I)
def __init__(self, filename):
self.Y = Alphabet()
data = list(
fromSGML(filename, linegrouper="<NEW.*?>", bioencoding=False))
np.random.shuffle(data)
super(CoraCitations, self).__init__(train=data[len(data) // 5:],
dev=data[:len(data) // 5],
test=[])
self.train = self.make_instances('train', Instance)
self.dev = self.make_instances('dev', Instance)
def integerize(data):
"""
Integerize dataset
returns a triple (label alphabet, feature alphabet, integerized dataset)
"""
F = Alphabet()
L = Alphabet()
I = [(L[label], fromiter(F.map(features), dtype=int32))
for label, features in data]
return (L, F, I)
class Dataset(object):
def __init__(self, train, dev, test):
self.train = train
self.dev = dev
self.test = test
# indexes will be populated by `_index`.
self.Y = Alphabet() # tag set
self.V = Alphabet() # vocabulary
self.V_freq = Counter() # token unigram counts
self.V2Y = defaultdict(set) # tag dictionary
self.prefixes = Counter()
self.suffixes = Counter()
self._index(self.train)
def _index(self, data):
"frequency tables, etc."
for sentence in data:
for y, w in sentence:
self.Y.add(y)
self.V.add(w)
self.V2Y[w].add(y)
self.V_freq[w] += 1
for prefix in prefixes(w):
self.prefixes[prefix] += 1
for suffix in suffixes(w):
self.suffixes[suffix] += 1
def make_instances(self, fold, cls):
"Convert tuples in data `fold` to instances of `cls`."
data = []
for x in iterview(getattr(self, fold), msg='Features (%s)' % fold):
tags, tokens = zip(*x)
data.append(cls(tokens, self.Y.map(tags), self))
return data
def tag_ngram_counts(self, n):
"Returns tag ngram count for subsequences of length n."
# Y = self.Y
def tag_sequences():
"""Iterate over tag sequence (as `str` instead of `int`, which is how they are
stored.).
"""
for e in self.train:
y, _ = zip(*e)
# assert all(isinstance(yy, int) for yy in y), y
# yield tuple(Y.lookup_many(y))
yield y
return ngram_counts(tag_sequences(), n)
def __init__(self, train, dev, test):
self.train = train
self.dev = dev
self.test = test
# indexes will be populated by `_index`.
self.Y = Alphabet() # tag set
self.V = Alphabet() # vocabulary
self.V_freq = Counter() # token unigram counts
self.V2Y = defaultdict(set) # tag dictionary
self.prefixes = Counter()
self.suffixes = Counter()
self._index(self.train)
def build_domain(data):
"""
Do feature extraction to determine the set of *supported* featues, i.e.
those active in the ground truth configuration and active labels. This
function will each features and label an integer.
"""
L = Alphabet()
A = Alphabet()
for x in data:
L.add_many(x.truth)
A.add_many(f for token in x.sequence for f in token.attributes)
# domains are now ready
L.freeze()
A.stop_growth()
return (L, A)
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')
class CoraCitations(Dataset):
def __init__(self, filename):
self.Y = Alphabet()
data = list(
fromSGML(filename, linegrouper="<NEW.*?>", bioencoding=False))
np.random.shuffle(data)
super(CoraCitations, self).__init__(train=data[len(data) // 5:],
dev=data[:len(data) // 5],
test=[])
self.train = self.make_instances('train', Instance)
self.dev = self.make_instances('dev', Instance)
def evaluate(self, predict, data, name, verbosity=1):
if not data:
return
if verbosity:
print()
print('Phrase-based F1:', name)
f1 = F1()
for i, x in enumerate(iterview(data, msg='Eval %s' % name)):
pred = extract_contiguous(predict(x))
gold = extract_contiguous(self.Y.lookup_many(x.tags))
# (i,begin,end) uniquely identifies the span
for (label, begins, ends) in gold:
f1.add_relevant(label, (i, begins, ends))
for (label, begins, ends) in pred:
f1.add_retrieved(label, (i, begins, ends))
if verbosity:
print()
return f1.scores(verbose=verbosity >= 1)
def __init__(self, train, dev, test, Sigma, IL=6, L=2, eta=0.01, C=0.0001):
self.train = train
self.dev = dev
self.test = test
self.Sigma = Sigma
assert self.Sigma[""] == 0
self.IL = IL
self.C = C
self.L = L
self.eta = eta
# X and Y
self.X, self.Y = Alphabet(), Alphabet()
self.X.add(""); self.Y.add("")
for s, si in self.Sigma.items():
if si == 0:
continue
self.X.add(s)
for s, si in self.Sigma.items():
if si == 0:
continue
self.Y.add(s)
self.X.freeze(); self.Y.freeze()
# first order (possibly extend)
self.P = Alphabet()
self.P.add("")
for s, si in self.Sigma.items():
if si == 0:
continue
self.P.add(s)
self.P.add("oo")
self.P.add("nn")
self.P.add("yy")
self.P.add("ss")
self.P.add("ee")
self.P.freeze()
# create Z
self.Z = Alphabet()
self.Z[""] = 0
for p, pi in self.P.items():
for o, oi in self.Y.items():
z = p+o
self.Z.add(z)
self.Z.freeze()
# model
self.model = Transducer(self.Sigma, self.X, self.Y, self.P, self.Z, IL = self.IL)
self.features = TransducerFeatures(self.X, self.Y, self.P)
self.features.featurize(self.train, 'train')
self.features.featurize(self.dev, 'dev')
self.features.featurize(self.test, 'test')
self.d = 2**22 + self.features.offset
self.updater = LazyRegularizedAdagrad(self.d, L=self.L, C=self.C, eta=self.eta, fudge=1e-4)
self.updater.w[0] = 10.0
self.updater.w[1] = -10.0
def __init__(self, fin, source_lang, target_lang):
# variables
self.source_lang = source_lang
self.target_lang = target_lang
# intern the variables
self.source = Alphabet()
self.target = Alphabet()
self.store = {}
with codecs.open(fin, encoding="utf-8") as f:
for line in f:
line = line.strip()
source, target, score = line.split(" ")
#if "Buch" in source or "Stuhl" in source:
score = float(score)
self.store[(source, target)] = score
self.source.add(source)
self.target.add(target)
def integerize(data):
"""
Integerize dataset
returns a triple (label alphabet, feature alphabet, integerized dataset)
"""
if do_label_count:
label_count = defaultdict(int)
for label, features in data:
label_count[label] += 1
label_count = label_count.items()
label_count.sort(key=lambda x: -x[1]) # sort by count
print 'label count'
for k,v in label_count:
print '%20s => %s' % (k, v)
sys.exit(0)
F = Alphabet()
L = Alphabet()
I = [(L[label], fromiter(F.map(features), dtype=int32)) for label, features in data]
return (L, F, I)
def integerize(data):
"""
Integerize dataset
returns a triple (label alphabet, feature alphabet, integerized dataset)
"""
if do_label_count:
label_count = defaultdict(int)
for label, features in data:
label_count[label] += 1
label_count = label_count.items()
label_count.sort(key=lambda x: -x[1]) # sort by count
print 'label count'
for k, v in label_count:
print '%20s => %s' % (k, v)
sys.exit(0)
F = Alphabet()
L = Alphabet()
I = [(L[label], fromiter(F.map(features), dtype=int32))
for label, features in data]
return (L, F, I)
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')
def vectorize(self, segmentations, maxn=200, threshold=5):
""" vectorize to features for theano """
lookup = {'11': 0, '22': 1}
index = Alphabet()
count = dd(int)
for segmentation in segmentations:
for segment in segmentation:
if segment not in lookup:
lookup[segment] = len(lookup)
count[segment] += 1
# create vectors
self.N = threshold
for k, v in count.items():
if v > threshold:
index.add(k)
self.N += 1
seg2vec = {}
for seg, i in lookup.items():
if i < 2:
continue
vec = zeros((self.N))
if (self.d.check(seg)
or self.d.check(seg.title())) and len(seg) > 3:
vec[0] = 1.0
elif len(seg) > 3:
vec[1] = 1.0
if count[seg] > threshold:
vec[index[seg] + 2] = 1.0
seg2vec[seg] = vec
# segmentation2vec
self.segmentation2vec = {}
for segmentation in segmentations:
f = zeros((self.N))
for segment in segmentation:
f += seg2vec[segment]
self.segmentation2vec[' '.join(segmentation)] = f
class StringFeatures(object):
""" String features """
def __init__(self, prefix_length=0, suffix_length=4):
self.prefix_length, self.suffix_length = prefix_length, suffix_length
self.attributes = Alphabet()
self.word2attributes = {}
self.words = Alphabet()
def get_attributes(self, word, extract=False):
""" extract the features """
lst = []
for i in xrange(1, self.prefix_length+1):
if i > len(word):
break
prefix = word[:i]
name = "PREFIX: "+prefix
if extract:
self.attributes.add(name)
if name in self.attributes:
lst.append(self.attributes[name])
for i in xrange(1, self.suffix_length+1):
if i < 0:
break
suffix = word[-i:]
name = "SUFFIX: "+suffix
if extract:
self.attributes.add(name)
if name in self.attributes:
lst.append(self.attributes[name])
return lst
def store(self, word):
""" store the features """
self.words.add(word)
i = self.words[word]
self.word2attributes[i] = self.get_attributes(word, True)
def __len__(self):
return len(self.attributes)
def __getitem__(self, word):
if word in self.words:
i = self.words[word]
return self.word2attributes[i]
# don't extract
return self.get_attributes(word, False)
def __init__(self, fin, atts, vals, av, avs):
# probably redundant...
# but not optimizing for space so who cares
self.atts, self.vals, self.av, self.avs = atts, vals, av, avs
self.lexicon = dd(list)
self.words = Alphabet()
with codecs.open(fin, encoding="utf-8") as f:
for line in f:
line = line.strip()
if line == "":
continue
word, lemma, tags = line.split(" ")
self.words.add(word)
tags = tags.split(",")
for tag in tags:
if len(tag.split("=")) != 2:
print line
print tag
a, v = tag.split("=")
self.av[a].add(v)
self.atts.add(a)
self.vals.add(v)
self.lexicon[word].append((lemma, tags))
# get rid of default dict wrapper
self.lexicon = dict(self.lexicon)
self.av = dict(self.av)
for a, s in self.av.items():
for v in s:
self.avs.add((a, v))
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
psi = self.features.potentials_catchup(instance, self.updater.w, self.updater)
ur1 = instance.ur
results = self.model.decode(instance.sr, psi, minx=MINX, miny=MINY)
ll = self.model.ll(instance.sr, ur1, psi, minx=MINX, miny=MINY)
score, ur2 = results[0], results[1]
if ur1 == ur2:
correct += 1
print ur1, ur2
total += 1
counter += 1
print
return float(correct) / total
def ll(self, tree, ur):
""" gets the log-likelihood """
psi = self.features.potentials_catchup(tree, self.updater.w, self.updater)
return self.model.ll(tree.sr, ur, psi, minx=MINX, miny=MINY)
if __name__ == "__main__":
data = [("hablar", "hablando"), ("comer", "comiendo")]
Sigma = Alphabet()
Sigma.add("")
for (x, y) in data:
for c in list(x):
Sigma.add(c)
for c in list(y):
Sigma.add(c)
tm = TransductionModel(Sigma, data)
profile.runctx("tm.train()", locals(), globals())
class TransducerModel(object):
""" Transducer model """
def __init__(self, train, dev, test, Sigma, IL=6, L=2, eta=0.01, C=0.0001):
self.train = train
self.dev = dev
self.test = test
self.Sigma = Sigma
assert self.Sigma[""] == 0
self.IL = IL
self.C = C
self.L = L
self.eta = eta
# X and Y
self.X, self.Y = Alphabet(), Alphabet()
self.X.add(""); self.Y.add("")
for s, si in self.Sigma.items():
if si == 0:
continue
self.X.add(s)
for s, si in self.Sigma.items():
if si == 0:
continue
self.Y.add(s)
self.X.freeze(); self.Y.freeze()
# first order (possibly extend)
self.P = Alphabet()
self.P.add("")
for s, si in self.Sigma.items():
if si == 0:
continue
self.P.add(s)
self.P.add("oo")
self.P.add("nn")
self.P.add("yy")
self.P.add("ss")
self.P.add("ee")
self.P.freeze()
# create Z
self.Z = Alphabet()
self.Z[""] = 0
for p, pi in self.P.items():
for o, oi in self.Y.items():
z = p+o
self.Z.add(z)
self.Z.freeze()
# model
self.model = Transducer(self.Sigma, self.X, self.Y, self.P, self.Z, IL = self.IL)
self.features = TransducerFeatures(self.X, self.Y, self.P)
self.features.featurize(self.train, 'train')
self.features.featurize(self.dev, 'dev')
self.features.featurize(self.test, 'test')
self.d = 2**22 + self.features.offset
self.updater = LazyRegularizedAdagrad(self.d, L=self.L, C=self.C, eta=self.eta, fudge=1e-4)
self.updater.w[0] = 10.0
self.updater.w[1] = -10.0
def optimize(self, iterations=10, start=0):
""" optimize the model """
#np.random.shuffle(self.train)
for i in xrange(iterations):
for instance in iterview(self.train, colored('Pass %s' % (i+1+start), 'blue')):
psi = self.features.potentials_catchup(instance, self.updater.w, self.updater)
dpsi = zeros_like(psi)
x, y = instance.sr, instance.ur
#print "LL", self.model.ll(x, y, psi, minx=MINX, miny=MINY)
dpsi = self.model.dll(x, y, psi, minx=MINX, miny=MINY)
self.features.update(instance, dpsi, self.updater)
self.updater.step += 1
def step_is(self, tree, strings, weights, eta=0.0):
""" optimize the model """
self.updater.eta = eta
psi = self.features.potentials_catchup(tree, self.updater.w, self.updater)
dpsi = zeros_like(psi)
dpsi = self.model.dll_is(tree.sr, tree.ur, strings, weights, psi, minx=MINX, miny=MINY)
self.features.update(tree, dpsi, self.updater)
self.updater.step += 1
def sample(self, data, num=1000):
""" sample """
samples = []
inside = 0
correct1, correct2, total = 0, 0, 0
for instance in iterview(data, colored('Sampling', 'green')):
psi = self.features.potentials_catchup(instance, self.updater.w, self.updater)
sr = instance.sr
dist = {}
for s in self.model.sample(sr, psi, num=num):
output = ""
for x, y in s:
output += y
if output not in dist:
dist[output] = 0
dist[output] += 1
count = dist[instance.ur_gold] if instance.ur_gold in dist else 0
decoded = self.decode(instance)[1]
if decoded != instance.ur_gold and count > 0:
inside += 1
if decoded == instance.ur_gold:
correct1 += 1
if instance.ur_gold in dist:
correct2 += 1
total += 1
samples.append(dist)
# TODO: put into log
#print ; print inside
#print correct1 / total, correct2 / total
return samples
def decode(self, instance):
""" Decodes an instance """
psi = self.features.potentials_catchup(instance, self.updater.w, self.updater)
ur1 = instance.ur
results = self.model.decode(instance.sr, psi, minx=MINX, miny=MINY)
return results
def evaluate(self, data, maximum=100000000):
""" decode the model """
correct, total = 0, 0
counter = 0
for instance in iterview(data, colored('Decoding', 'red')):
if counter == maximum:
break
psi = self.features.potentials_catchup(instance, self.updater.w, self.updater)
ur1 = instance.ur
results = self.model.decode(instance.sr, psi, minx=MINX, miny=MINY)
ll = self.model.ll(instance.sr, ur1, psi, minx=MINX, miny=MINY)
score, ur2 = results[0], results[1]
if ur1 == ur2:
correct += 1
print ur1, ur2
total += 1
counter += 1
print
return float(correct) / total
def ll(self, tree, ur):
""" gets the log-likelihood """
psi = self.features.potentials_catchup(tree, self.updater.w, self.updater)
return self.model.ll(tree.sr, ur, psi, minx=MINX, miny=MINY)
from arsenal.alphabet import Alphabet
from arsenal.iterview import progress
from arsenal.terminal import colors
from collections import Counter, defaultdict
from grafl.test import make_model_func
from grafl.dataset.edge_dataset import BWD_dataset
np.set_printoptions(precision=4)
L = {
0: 'coordinate',
1: 'hypernym',
2: 'hyponym',
}
A = Alphabet()
A.map([x.strip().split()[1] for i, x in enumerate(file('res/bowman_wordnet_longer_shuffled_synset_relations.map')) if i > 2])
tst = BWD_dataset('test').data
trn = BWD_dataset('train').data
trn_x = trn[0]
trn_y = trn[1]
seen = set(trn_x.flatten()) | set(trn_y.flatten())
X,Y,_ = tst
X = list(A.lookup_many(X.flatten()))
Y = list(A.lookup_many(Y.flatten()))
#D = np.array([X,Y,L.flatten()]).T
model_file = 'res/experiments/BWD-projection-Softmax_best.pkl'
def build_domain(data):
"""
Do feature extraction to determine the set of *supported* featues, i.e.
those active in the ground truth configuration and active labels. This
function will each features and label an integer.
"""
L = Alphabet()
A = Alphabet()
for x in data:
L.add_many(x.truth)
A.add_many(f for token in x.sequence for f in token.attributes)
# domains are now ready
L.freeze()
A.stop_growth()
return (L, A)
def build_domain(data):
"""
Do feature extraction to determine the set of *supported* featues, i.e.
those active in the ground truth configuration and active labels. This
function will each features and label an integer.
"""
L = Alphabet()
A = Alphabet()
for x in data:
L.add_many(x.truth) # add labels to label domain
# extract features of the target path
F = x.F
path = x.truth
A.add_many(F(0, None, path[0]))
A.add_many(k for t in xrange(1, x.N) for k in F(t, path[t-1], path[t]))
# domains are now ready
L.freeze()
A.stop_growth()
return (L, A)
def __init__(self, prefix_length=0, suffix_length=4):
self.prefix_length, self.suffix_length = prefix_length, suffix_length
self.attributes = Alphabet()
self.word2attributes = {}
self.words = Alphabet()
def build_domain(data):
"""
Do feature extraction to determine the set of *supported* featues, i.e.
those active in the ground truth configuration and active labels. This
function will each features and label an integer.
"""
L = Alphabet()
A = Alphabet()
for x in data:
L.add_many(x.truth) # add labels to label domain
# extract features of the target path
F = x.F
path = x.truth
A.add_many(F(0, None, path[0]))
A.add_many(k for t in xrange(1, x.N)
for k in F(t, path[t - 1], path[t]))
# domains are now ready
L.freeze()
A.stop_growth()
return (L, A)
class Lexicon(object):
""" Reads in the universal morpholigcal lexicon """
def __init__(self, fin, atts, vals, av, avs):
# probably redundant...
# but not optimizing for space so who cares
self.atts, self.vals, self.av, self.avs = atts, vals, av, avs
self.lexicon = dd(list)
self.words = Alphabet()
with codecs.open(fin, encoding="utf-8") as f:
for line in f:
line = line.strip()
if line == "":
continue
word, lemma, tags = line.split(" ")
self.words.add(word)
tags = tags.split(",")
for tag in tags:
if len(tag.split("=")) != 2:
print line
print tag
a, v = tag.split("=")
self.av[a].add(v)
self.atts.add(a)
self.vals.add(v)
self.lexicon[word].append((lemma, tags))
# get rid of default dict wrapper
self.lexicon = dict(self.lexicon)
self.av = dict(self.av)
for a, s in self.av.items():
for v in s:
self.avs.add((a, v))
def create_vectors(self):
self.N = len(self.avs)
self.W = zeros((len(self.lexicon), self.N))
# use Manaal's encoding (http://arxiv.org/abs/1512.05030)
for w, lst in self.lexicon.items():
vec = zeros((self.N))
for l, ts in lst:
for tag in ts:
a, v = tag.split("=")
#if a != "pos":
# continue
j = self.avs[(a, v)]
vec[j] = 1.0
i = self.words[w]
self.W[i] = vec
def pp(self, word):
""" pretty print the morphological tag of a word """
i = self.words[word]
lst = []
for n in xrange(self.N):
if self.W[i, n] > 0:
lst.append("=".join(self.avs.lookup(n)))
return word, ",".join(lst)
def __getitem__(self, word):
i = self.words[word]
return self.W[i]
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
class PTB(object):
"Load the POS-tagged Penn Treebank."
def __init__(self, base, coarse=True):
self.base = base
self.coarse = coarse
self.Y = Alphabet() # tag set
self.V, self.V_freq = Alphabet(), {} # vocabulary
self.V2Y, self.Y2V = dd(set), dd(set)
self.train, self.dev, self.test = [], [], []
self.prefixes, self.suffixes = {}, {}
self.prefix2int, self.suffix2int = {}, {}
# Read data and create standard splits according to
# http://aclweb.org/aclwiki/index.php?title=POS_Tagging_(State_of_the_art)
#
# train split [0,18]
for sectionid in range(19):
read = self.read_section(sectionid)
for sentence in read:
#for tag, word in sentence:
# if tag == self.Y["BAD"]:
# break
self.train.append(sentence)
for y, w in sentence:
self.V.add(w)
self.V2Y[w].add(self.Y.lookup(y))
self.Y2V[self.Y.lookup(y)].add(w)
if w not in self.V_freq:
self.V_freq[w] = 0
self.V_freq[w] += 1
for prefix in self.extract_prefixes(w):
if prefix not in self.prefixes:
self.prefixes[prefix] = 0
self.prefixes[prefix] += 1
for suffix in self.extract_suffixes(w):
if suffix not in self.suffixes:
self.suffixes[suffix] = 0
self.suffixes[suffix] += 1
# dev split [19,21]
for sectionid in range(19, 22):
read = self.read_section(sectionid)
for sentence in read:
#for tag, word in sentence:
# if tag == self.Y["BAD"]:
# break
self.dev.append(sentence)
# test split [22,24]
for sectionid in range(22, 25):
#for tag, word in sentence:
# if tag == self.Y["BAD"]:
# break
self.test.extend(self.read_section(sectionid))
self.Y.freeze()
def extract_prefixes(self, w, n=10):
""" gets prefixes up to length n """
prefixes = []
for i in range(1, min(len(w)+1, n+1)):
segment = w[:i]
if segment not in self.prefix2int:
self.prefix2int[segment] = len(self.prefix2int)
prefixes.append(w[:i])
return prefixes
def extract_suffixes(self, w, n=10):
""" gets suffixes up to lenght n """
suffixes = []
for i in range(1, min(len(w)+1, n+1)):
segment = w[-i:]
if segment not in self.suffix2int:
self.suffix2int[segment] = len(self.suffix2int)
suffixes.append(w[-i:])
return suffixes
def tag_bigrams(self):
""" extract all tag bigrams """
bigram2count = {}
for sentence in self.train:
for (tag1, _), (tag2, _) in zip(sentence, sentence[1:]):
key = (tag1, tag2)
if key not in bigram2count:
bigram2count[key] = 0
bigram2count[key] += 1
return bigram2count
def read_section(self, sectionid):
"Read a section number `sectionid` from the PTB."
root = os.path.join(self.base, str(sectionid).zfill(2))
for fname in os.listdir(root):
if not fname.endswith('pos.gz'):
continue
with gzip.open(os.path.join(root, fname), 'rb') as f:
for chunk in f.read().split('======================================'):
if chunk.strip():
if self.coarse:
# STUPID BIO ENCODING
#yield [(self.Y["NNP"] if "NNP" in y else self.Y["OTHER"], w) for w, y in re_tagged.findall(chunk)]
# Note: clean up punc reduction
yield [(self.Y["PUNC"] if y in PUNC else self.Y[y[0]], w) for w, y in re_tagged.findall(chunk)]
else:
# TODO: what to do able bars in the tags?
# FIND OUT AND CLEAN UP
yield [(self.Y["PUNC"] if y in PUNC else self.Y[y.split("|")[0]] if "|" in y else self.Y[y], w) for w, y in re_tagged.findall(chunk)]
def pp(self, sentence):
"Pretty print."
return ' '.join('%s/%s' % (w, self.Y.lookup(t)) for (t, w) in sentence)
from arsenal.alphabet import Alphabet
from arsenal.iterview import progress
from arsenal.terminal import colors
from collections import Counter, defaultdict
from grafl.test import make_model_func
from grafl.dataset.edge_dataset import BWD_dataset
np.set_printoptions(precision=4)
L = {
0: 'coordinate',
1: 'hypernym',
2: 'hyponym',
}
A = Alphabet()
A.map([
x.strip().split()[1] for i, x in enumerate(
file('res/bowman_wordnet_longer_shuffled_synset_relations.map'))
if i > 2
])
tst = BWD_dataset('test').data
trn = BWD_dataset('train').data
trn_x = trn[0]
trn_y = trn[1]
seen = set(trn_x.flatten()) | set(trn_y.flatten())
X, Y, _ = tst
X = list(A.lookup_many(X.flatten()))
def main():
datafile = sys.argv[1]
train, test = traintest(datafile)
print 'train: %s, test: %s' % (len(train), len(test))
from scipy.sparse import dok_matrix
from sklearn import linear_model
from sklearn.svm import SVC
from arsenal.alphabet import Alphabet
N_FEATURES = 100000
alphabet = Alphabet(random_int=N_FEATURES)
def _f1(name, data, c, verbose=True):
if verbose:
print
print name
f = F1()
for (i, x) in enumerate(data):
phi = dok_matrix((1, N_FEATURES))
for k in x.features:
phi[0, alphabet[k] % N_FEATURES] = 1.0
[y] = c.predict(phi)
f.report(i, y, x.label)
f.scores(verbose=verbose)
return f
X = dok_matrix((len(train), N_FEATURES))
M = len(train)
Y = []
X = dok_matrix((M, N_FEATURES))
for i, x in enumerate(train):
# binary features
for k in x.features:
X[i, alphabet[k] % N_FEATURES] = 1.0
Y.append(x.label)
X = X.tocsc()
c = SVC(class_weight={
'author': 1000,
'title': 1000,
'other': 1.0
},
verbose=1)
c.fit(X, Y)
_f1('train', train, c)
ff = _f1('test', test, c, verbose=1)
if 0:
import numpy as np
import matplotlib.pyplot as pl
from mpl_toolkits.mplot3d import Axes3D
ax = pl.figure().add_subplot(111, projection='3d')
pl.ion()
data = []
for (author_weight,
title_weight) in iterview(np.random.uniform(1, 10,
size=(100, 2))):
print
print 'params:', (author_weight, title_weight)
c = SVC(class_weight={
'author': author_weight,
'title': title_weight,
'other': 1.0
},
verbose=1)
#c = linear_model.SGDClassifier()
c.fit(X, Y)
#_f1('train', train, c)
ff = _f1('test', test, c, verbose=1)
score = sum(x for (_, _, _, _, x) in ff.scores(verbose=0))
data.append((author_weight, title_weight, score))
print 'score:', score
x, y, z = zip(*data)
ax.clear()
ax.scatter(x, y, z)
ax.figure.canvas.draw()
print 'done'
pl.ioff()
pl.show()
def main():
from arsenal.alphabet import Alphabet
from arsenal.maths import spherical
D = 100
alphabet = Alphabet(random_int=D)
weights = spherical(D)
direction = spherical(D)
#sentence = 'Papa ate the caviar with the spoon in the park .'.split()
sentence = 'Papa ate the caviar with the spoon .'.split()
if 0:
grammar = """
S S .
S NP VP
NP D N
NP NP PP
VP V NP
VP VP PP
PP P NP
NP Papa
N caviar
N spoon
N park
V ate
P with
P in
D the
"""
else:
grammar = """
S X .
X X X
X Papa
X ate
X the
X caviar
X with
X spoon
X in
X park
"""
rhs = load_grammar(grammar)
if 1:
# This code branch enumerates all (exponentially many) valid
# derivations.
root = semiring_enumeration(sentence, rhs)
for d in root.x:
print(post_process(d))
assert len(root.x) == len(set(root.x))
def binary_features(sentence,X,Y,Z,i,j,k):
return alphabet.map(['%s -> %s %s [%s,%s,%s]' % (X,Y,Z,i,j,k)])
def unary_features(sentence,X,Y,i,k):
return alphabet.map(['%s -> %s [%s,%s]' % (X,Y,i,k)])
root = semiring_mert(sentence, rhs, weights, direction, binary_features, unary_features)
mert_derivations = [] #set()
for x in root.points:
print(x)
d = x.derivation()
mert_derivations.append(d)
assert len(mert_derivations) == len(set(mert_derivations))
#root.draw()
# Compare the set of derivations found by the MERT semiring to 'brute force'
# linesearch. Note: Linesearch might only find a subset of derivations found
# by MERT if the grid isn't fine enough.
brute_derivations = set()
for step in np.linspace(-20,20,1000):
root = semiring_linesearch(sentence, rhs, weights, step, direction, binary_features, unary_features)
d = root.derivation()
brute_derivations.add(d)
# NOTE: need to take upper hull of mert (so it's currently an over estimate)
print('mert:', len(mert_derivations))
print('brute:', len(brute_derivations))
assert brute_derivations.issubset(mert_derivations)