from itertools import chain import os, sys import cdec.configobj from cdec.sa.features import EgivenFCoherent, SampleCountF, CountEF,\ MaxLexEgivenF, MaxLexFgivenE, IsSingletonF, IsSingletonFE,\ IsSupportedOnline import cdec.sa # maximum span of a grammar rule in TEST DATA MAX_INITIAL_SIZE = 15 class GrammarExtractor: def __init__(self, config, online=False, 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: clean this up extended_features = [] if online: extended_features.append(IsSupportedOnline) # TODO: use @cdec.sa.features decorator for standard features too # + add a mask to disable features for f in cdec.sa._SA_FEATURES: extended_features.append(f) scorer = cdec.sa.Scorer(EgivenFCoherent, SampleCountF, CountEF, MaxLexFgivenE(tt), MaxLexEgivenF(tt), IsSingletonF, IsSingletonFE, *extended_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, ctx_name=None): 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, ctx_name) # Add training instance to data def add_instance(self, sentence, reference, alignment, ctx_name=None): 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, ctx_name) # Remove all incremental data for a context def drop_ctx(self, ctx_name=None): self.factory.drop_ctx(ctx_name)