Merge branch 'master' of github.com:redpony/cdec

5 files changed, 276 insertions, 0 deletions

diff --git a/python/pkg/cdec/sa/__init__.py b/python/pkg/cdec/sa/__init__.py
new file mode 100644
index 00000000..fd4a4148
--- /dev/null
+++ b/python/pkg/cdec/sa/__init__.py
@@ -0,0 +1,4 @@
+from cdec.sa._sa import sym_fromstring,\
+        SuffixArray, DataArray, LCP, Precomputation, Alignment, BiLex,\
+        HieroCachingRuleFactory, Sampler
+from cdec.sa.extractor import GrammarExtractor
diff --git a/python/pkg/cdec/sa/compile.py b/python/pkg/cdec/sa/compile.py
new file mode 100644
index 00000000..2a89243b
--- /dev/null
+++ b/python/pkg/cdec/sa/compile.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+import argparse
+import os
+import logging
+import cdec.configobj
+import cdec.sa
+
+MAX_PHRASE_LENGTH = 4
+def precompute(f_sa, max_len, max_nt, max_size, min_gap, rank1, rank2):
+    lcp = cdec.sa.LCP(f_sa)
+    stats = sorted(lcp.compute_stats(MAX_PHRASE_LENGTH), reverse=True)
+    precomp = cdec.sa.Precomputation(from_stats=stats,
+            fsarray=f_sa,
+            precompute_rank=rank1,
+            precompute_secondary_rank=rank2,
+            max_length=max_len,
+            max_nonterminals=max_nt,
+            train_max_initial_size=max_size,
+            train_min_gap_size=min_gap)
+    return precomp
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+    logger = logging.getLogger('cdec.sa.compile')
+    parser = argparse.ArgumentParser(description='Compile a corpus into a suffix array.')
+    parser.add_argument('--maxnt', '-n', type=int, default=2,
+                        help='Maximum number of non-terminal symbols')
+    parser.add_argument('--maxlen', '-l', type=int, default=5,
+                        help='Maximum number of terminals')
+    parser.add_argument('--maxsize', '-s', type=int, default=15,
+                        help='Maximum rule span')
+    parser.add_argument('--mingap', '-g', type=int, default=1,
+                        help='Minimum gap size')
+    parser.add_argument('--rank1', '-r1', type=int, default=100,
+                        help='Number of pre-computed frequent patterns')
+    parser.add_argument('--rank2', '-r2', type=int, default=10,
+                        help='Number of pre-computed super-frequent patterns)')
+    parser.add_argument('-c', '--config', default='/dev/stdout',
+                        help='Output configuration')
+    parser.add_argument('-f', '--source',
+                        help='Source language corpus')
+    parser.add_argument('-e', '--target',
+                        help='Target language corpus')
+    parser.add_argument('-b', '--bitext',
+                        help='Parallel text (source ||| target)')
+    parser.add_argument('-a', '--alignment', required=True,
+                        help='Bitext word alignment')
+    parser.add_argument('-o', '--output', required=True,
+                        help='Output path')
+    args = parser.parse_args()
+
+    if not ((args.source and args.target) or args.bitext):
+        parser.error('a parallel corpus is required\n'
+        '\tuse -f (source) with -e (target) or -b (bitext)')
+
+    param_names = ("max_len", "max_nt", "max_size", "min_gap", "rank1", "rank2")
+    params = (args.maxlen, args.maxnt, args.maxsize, args.mingap, args.rank1, args.rank2)
+
+    if not os.path.exists(args.output):
+        os.mkdir(args.output)
+
+    f_sa_bin = os.path.join(args.output, 'f.sa.bin')
+    e_bin = os.path.join(args.output, 'e.bin')
+    precomp_file = 'precomp.{0}.{1}.{2}.{3}.{4}.{5}.bin'.format(*params)
+    precomp_bin = os.path.join(args.output, precomp_file)
+    a_bin = os.path.join(args.output, 'a.bin')
+    lex_bin = os.path.join(args.output, 'lex.bin')
+
+    logger.info('Compiling source suffix array')
+    if args.bitext:
+        f_sa = cdec.sa.SuffixArray(from_text=args.bitext, side='source')
+    else:
+        f_sa = cdec.sa.SuffixArray(from_text=args.source)
+    f_sa.write_binary(f_sa_bin)
+
+    logger.info('Compiling target data array')
+    if args.bitext:
+        e = cdec.sa.DataArray(from_text=args.bitext, side='target')
+    else:
+        e = cdec.sa.DataArray(from_text=args.target)
+    e.write_binary(e_bin)
+
+    logger.info('Precomputing frequent phrases')
+    precompute(f_sa, *params).write_binary(precomp_bin)
+
+    logger.info('Compiling alignment')
+    a = cdec.sa.Alignment(from_text=args.alignment)
+    a.write_binary(a_bin)
+
+    logger.info('Compiling bilexical dictionary')
+    lex = cdec.sa.BiLex(from_data=True, alignment=a, earray=e, fsarray=f_sa)
+    lex.write_binary(lex_bin)
+    
+    # Write configuration
+    config = cdec.configobj.ConfigObj(args.config, unrepr=True)
+    config['f_sa_file'] = f_sa_bin
+    config['e_file'] = e_bin
+    config['a_file'] = a_bin
+    config['lex_file'] = lex_bin
+    config['precompute_file'] = precomp_bin
+    for name, value in zip(param_names, params):
+        config[name] = value
+    config.write()
+
+if __name__ == '__main__':
+    main()
diff --git a/python/pkg/cdec/sa/extract.py b/python/pkg/cdec/sa/extract.py
new file mode 100644
index 00000000..918aa3bb
--- /dev/null
+++ b/python/pkg/cdec/sa/extract.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python
+import sys
+import os
+import argparse
+import logging
+import cdec.sa
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+    parser = argparse.ArgumentParser(description='Extract grammars from a compiled corpus.')
+    parser.add_argument('-c', '--config', required=True,
+                        help='Extractor configuration')
+    parser.add_argument('-g', '--grammars', required=True,
+                        help='Grammar output path')
+    args = parser.parse_args()
+
+    if not os.path.exists(args.grammars):
+        os.mkdir(args.grammars)
+
+    extractor = cdec.sa.GrammarExtractor(args.config)
+    for i, sentence in enumerate(sys.stdin):
+        sentence = sentence[:-1]
+        grammar_file = os.path.join(args.grammars, 'grammar.{0}'.format(i))
+        with open(grammar_file, 'w') as output:
+            for rule in extractor.grammar(sentence):
+                output.write(str(rule)+'\n')
+        grammar_file = os.path.abspath(grammar_file)
+        print('<seg grammar="{0}">{1}</seg>'.format(grammar_file, sentence))
+
+if __name__ == '__main__':
+    main()
diff --git a/python/pkg/cdec/sa/extractor.py b/python/pkg/cdec/sa/extractor.py
new file mode 100644
index 00000000..bb912e16
--- /dev/null
+++ b/python/pkg/cdec/sa/extractor.py
@@ -0,0 +1,78 @@
+from itertools import chain
+import os
+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):
+        if isinstance(config, str) or isinstance(config, unicode):
+            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'],
+                # True if phrases should be tight, False otherwise (better but slower)
+                tight_phrases=True,
+                )
+
+        # lexical weighting tables
+        tt = cdec.sa.BiLex(from_binary=config['lex_file'])
+
+        self.models = (EgivenFCoherent, SampleCountF, CountEF, 
+                MaxLexFgivenE(tt), MaxLexEgivenF(tt), IsSingletonF, IsSingletonFE)
+
+        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)
+
+    def grammar(self, sentence):
+        if isinstance(sentence, unicode):
+            sentence = sentence.encode('utf8')
+        cnet = chain(('<s>',), sentence.split(), ('</s>',))
+        cnet = (cdec.sa.sym_fromstring(word, terminal=True) for word in cnet)
+        cnet = tuple(((word, None, 1), ) for word in cnet)
+        return self.factory.input(cnet, self.models)
diff --git a/python/pkg/cdec/sa/features.py b/python/pkg/cdec/sa/features.py
new file mode 100644
index 00000000..325b9e13
--- /dev/null
+++ b/python/pkg/cdec/sa/features.py
@@ -0,0 +1,57 @@
+from __future__ import division
+import math
+
+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 = fphrase.words
+        fwords.append('NULL')
+        def score():
+            for e in ephrase.words:
+              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):
+        ewords = ephrase.words
+        ewords.append('NULL')
+        def score():
+            for f in fphrase.words:
+              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)