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from itertools import chain
import os, sys
import cdec.configobj
from cdec.sa.features import EgivenFCoherent, SampleCountF, CountEF,\
MaxLexEgivenF, MaxLexFgivenE, IsSingletonF, IsSingletonFE
import cdec.sa
# maximum span of a grammar rule in TEST DATA
MAX_INITIAL_SIZE = 15
class GrammarExtractor:
def __init__(self, config, features=None):
if isinstance(config, basestring):
if not os.path.exists(config):
raise IOError('cannot read configuration from {0}'.format(config))
config = cdec.configobj.ConfigObj(config, unrepr=True)
alignment = cdec.sa.Alignment(from_binary=config['a_file'])
self.factory = cdec.sa.HieroCachingRuleFactory(
# compiled alignment object (REQUIRED)
alignment,
# name of generic nonterminal used by Hiero
category="[X]",
# maximum number of contiguous chunks of terminal symbols in RHS of a rule
max_chunks=config['max_nt']+1,
# maximum span of a grammar rule in TEST DATA
max_initial_size=MAX_INITIAL_SIZE,
# maximum number of symbols (both T and NT) allowed in a rule
max_length=config['max_len'],
# maximum number of nonterminals allowed in a rule (set >2 at your own risk)
max_nonterminals=config['max_nt'],
# maximum number of contiguous chunks of terminal symbols
# in target-side RHS of a rule.
max_target_chunks=config['max_nt']+1,
# maximum number of target side symbols (both T and NT) allowed in a rule.
max_target_length=MAX_INITIAL_SIZE,
# minimum span of a nonterminal in the RHS of a rule in TEST DATA
min_gap_size=1,
# filename of file containing precomputed collocations
precompute_file=config['precompute_file'],
# maximum frequency rank of patterns used to compute triples (< 20)
precompute_secondary_rank=config['rank2'],
# maximum frequency rank of patterns used to compute collocations (< 300)
precompute_rank=config['rank1'],
# require extracted rules to have at least one aligned word
require_aligned_terminal=True,
# require each contiguous chunk of extracted rules
# to have at least one aligned word
require_aligned_chunks=False,
# maximum span of a grammar rule extracted from TRAINING DATA
train_max_initial_size=config['max_size'],
# minimum span of an RHS nonterminal in a rule extracted from TRAINING DATA
train_min_gap_size=config['min_gap'],
# False if phrases should be loose (better but slower), True otherwise
tight_phrases=config.get('tight_phrases', True),
)
# lexical weighting tables
tt = cdec.sa.BiLex(from_binary=config['lex_file'])
# TODO: use @cdec.sa.features decorator for standard features too
# + add a mask to disable features
scorer = cdec.sa.Scorer(EgivenFCoherent, SampleCountF, CountEF,
MaxLexFgivenE(tt), MaxLexEgivenF(tt), IsSingletonF, IsSingletonFE,
*cdec.sa._SA_FEATURES)
fsarray = cdec.sa.SuffixArray(from_binary=config['f_sa_file'])
edarray = cdec.sa.DataArray(from_binary=config['e_file'])
# lower=faster, higher=better; improvements level off above 200-300 range,
# -1 = don't sample, use all data (VERY SLOW!)
sampler = cdec.sa.Sampler(300, fsarray)
self.factory.configure(fsarray, edarray, sampler, scorer)
# Initialize feature definitions with configuration
for fn in cdec.sa._SA_CONFIGURE:
fn(config)
def grammar(self, sentence):
if isinstance(sentence, unicode):
sentence = sentence.encode('utf8')
words = tuple(chain(('<s>',), sentence.split(), ('</s>',)))
meta = cdec.sa.annotate(words)
cnet = cdec.sa.make_lattice(words)
return self.factory.input(cnet, meta)
# Add training instance to data
def add_instance(self, sentence, reference, alignment):
f_words = cdec.sa.encode_words(sentence.split())
e_words = cdec.sa.encode_words(reference.split())
al = sorted(tuple(int(i) for i in pair.split('-')) for pair in alignment.split())
self.factory.add_instance(f_words, e_words, al)
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