[python] Fork of the suffix-array extractor with surface improvements

Available as the cdec.sa module, with commande-line helpers:
python -m cdec.sa.compile -f ... -e ... -a ... -o sa-out/ -c extract.ini
python -m cdec.sa.extract -c extract.ini -g grammars-out/ < input.txt > input.sgml

+ renamed cdec.scfg -> cdec.sa
+ Python README

3 files changed, 0 insertions, 183 deletions

diff --git a/python/cdec/scfg/__init__.py b/python/cdec/scfg/__init__.py
deleted file mode 100644
index 6eb2f88f..00000000
--- a/python/cdec/scfg/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from extractor import GrammarExtractor
diff --git a/python/cdec/scfg/extractor.py b/python/cdec/scfg/extractor.py
deleted file mode 100644
index 1dfa2421..00000000
--- a/python/cdec/scfg/extractor.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import sys, os
-import re
-import StringIO
-from itertools import chain
-
-import clex
-import rulefactory
-import calignment
-import csuf
-import cdat
-import sym
-import log
-
-from features import EgivenFCoherent, SampleCountF, CountEF,\
-        MaxLexEgivenF, MaxLexFgivenE, IsSingletonF, IsSingletonFE
-from features import contextless
-
-log.level = -1
-
-class Output(StringIO.StringIO):
-    def close(self):
-        pass
-
-    def __str__(self):
-        return self.getvalue()
-
-def get_cn(sentence):
-    sentence = chain(('<s>',), sentence.split(), ('</s>',))
-    sentence = (sym.fromstring(word, terminal=True) for word in sentence)
-    return tuple(((word, None, 1), ) for word in sentence)
-
-class PhonyGrammar:
-    def add(self, thing):
-        pass
-
-class GrammarExtractor:
-    def __init__(self, cfg):
-        if isinstance(cfg, dict):
-            config = cfg
-        elif isinstance(cfg, str):
-            cfg_file = os.path.basename(cfg)
-            if not re.match(r'^\w+\.py$', cfg_file):
-                raise ValueError('Config must be a *.py file')
-            sys.path.append(os.path.dirname(cfg))
-            config =  __import__(cfg_file.replace('.py', '')).__dict__
-            sys.path.pop()
-        alignment = calignment.Alignment(config['a_file'], from_binary=True)
-        self.factory = rulefactory.HieroCachingRuleFactory(
-                # compiled alignment object (REQUIRED)
-                alignment=alignment,
-                # name of generic nonterminal used by Hiero
-                category="[X]",
-                # do not change for extraction
-                grammar=PhonyGrammar(), # TODO: set to None?
-                # maximum number of contiguous chunks of terminal symbols in RHS of a rule. If None, defaults to max_nonterminals+1
-                max_chunks=None,
-                # maximum span of a grammar rule in TEST DATA
-                max_initial_size=15,
-                # 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. If None, defaults to max_nonterminals+1
-                max_target_chunks=None,
-                # maximum number of target side symbols (both T and NT) allowed in a rule. If None, defaults to max_initial_size
-                max_target_length=None,
-                # 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 (don't set higher than 20).
-                precompute_secondary_rank=config['rank2'],
-                # maximum frequency rank of patterns used to compute collocations (no need to set higher than maybe 200-300)
-                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,
-                # generate a complete grammar for each input sentence
-                per_sentence_grammar=True,
-                # 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'],
-                # True if phrases should be tight, False otherwise (False seems to give better results but is slower)
-                tight_phrases=True,
-                )
-        self.fsarray = csuf.SuffixArray(config['f_sa_file'], from_binary=True)
-        self.edarray = cdat.DataArray(config['e_file'], from_binary=True)
-
-        self.factory.registerContext(self)
-
-        # lower=faster, higher=better; improvements level off above 200-300 range, -1 = don't sample, use all data (VERY SLOW!)
-        self.sampler = rulefactory.Sampler(300)
-        self.sampler.registerContext(self)
-
-        # lexical weighting tables
-        tt = clex.CLex(config['lex_file'], from_binary=True)
-
-        self.models = (EgivenFCoherent, SampleCountF, CountEF, 
-                MaxLexFgivenE(tt), MaxLexEgivenF(tt), IsSingletonF, IsSingletonFE)
-        self.models = tuple(contextless(feature) for feature in self.models)
-
-    def grammar(self, sentence):
-        if isinstance(sentence, unicode):
-            sentence = sentence.encode('utf8')
-        out = Output()
-        cn = get_cn(sentence)
-        self.factory.input(cn, output=out)
-        return str(out)
-
-def main(config):
-    extractor = GrammarExtractor(config)
-    sys.stdout.write(extractor.grammar(next(sys.stdin)))
-
-if __name__ == '__main__':
-    if len(sys.argv) != 2 or not sys.argv[1].endswith('.py'):
-        sys.stderr.write('Usage: %s config.py\n' % sys.argv[0])
-        sys.exit(1)
-    main(*sys.argv[1:])
diff --git a/python/cdec/scfg/features.py b/python/cdec/scfg/features.py
deleted file mode 100644
index 6419cdd8..00000000
--- a/python/cdec/scfg/features.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from __future__ import division
-import math
-import sym
-
-def contextless(feature):
-    feature.compute_contextless_score = feature
-    return feature
-
-MAXSCORE = 99
-
-def EgivenF(fphrase, ephrase, paircount, fcount, fsample_count): # p(e|f)
-    return -math.log10(paircount/fcount)
-
-def CountEF(fphrase, ephrase, paircount, fcount, fsample_count):
-    return math.log10(1 + paircount)
-
-def SampleCountF(fphrase, ephrase, paircount, fcount, fsample_count):
-    return math.log10(1 + fsample_count)
-
-def EgivenFCoherent(fphrase, ephrase, paircount, fcount, fsample_count):
-    prob = paircount/fsample_count
-    return -math.log10(prob) if prob > 0 else MAXSCORE
-
-def CoherenceProb(fphrase, ephrase, paircount, fcount, fsample_count):
-    return -math.log10(fcount/fsample_count)
-
-def MaxLexEgivenF(ttable):
-    def feature(fphrase, ephrase, paircount, fcount, fsample_count):
-        fwords = [sym.tostring(w) for w in fphrase if not sym.isvar(w)] + ['NULL']
-        ewords = (sym.tostring(w) for w in ephrase if not sym.isvar(w))
-        def score():
-            for e in ewords:
-              maxScore = max(ttable.get_score(f, e, 0) for f in fwords)
-              yield -math.log10(maxScore) if maxScore > 0 else MAXSCORE
-        return sum(score())
-    return feature
-
-def MaxLexFgivenE(ttable):
-    def feature(fphrase, ephrase, paircount, fcount, fsample_count):
-        fwords = (sym.tostring(w) for w in fphrase if not sym.isvar(w))
-        ewords = [sym.tostring(w) for w in ephrase if not sym.isvar(w)] + ['NULL']
-        def score():
-            for f in fwords:
-              maxScore = max(ttable.get_score(f, e, 1) for e in ewords)
-              yield -math.log10(maxScore) if maxScore > 0 else MAXSCORE
-        return sum(score())
-    return feature
-
-def IsSingletonF(fphrase, ephrase, paircount, fcount, fsample_count):
-    return (fcount == 1)
-
-def IsSingletonFE(fphrase, ephrase, paircount, fcount, fsample_count):
-    return (paircount == 1)
-
-def IsNotSingletonF(fphrase, ephrase, paircount, fcount, fsample_count):
-    return (fcount > 1)
-
-def IsNotSingletonFE(fphrase, ephrase, paircount, fcount, fsample_count):
-    return (paircount > 1)
-
-def IsFEGreaterThanZero(fphrase, ephrase, paircount, fcount, fsample_count):
-    return (paircount > 0.01)