[python] Move python files to avoid pythonpath conflicts

5 files changed, 0 insertions, 264 deletions

diff --git a/python/cdec/sa/__init__.py b/python/cdec/sa/__init__.py
deleted file mode 100644
index 8645e837..00000000
--- a/python/cdec/sa/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from _sa import sym_fromstring,\
-        SuffixArray, DataArray, LCP, Precomputation, Alignment, BiLex,\
-        HieroCachingRuleFactory, Sampler
-from extractor import GrammarExtractor
diff --git a/python/cdec/sa/compile.py b/python/cdec/sa/compile.py
deleted file mode 100644
index 30e605a6..00000000
--- a/python/cdec/sa/compile.py
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/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('-o', '--output', required=True,
-                        help='Output path')
-    parser.add_argument('-f', '--source', required=True,
-                        help='Source language corpus')
-    parser.add_argument('-e', '--target', required=True,
-                        help='Target language corpus')
-    parser.add_argument('-a', '--alignment', required=True,
-                        help='Bitext word alignment')
-    args = parser.parse_args()
-
-    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')
-    f_sa = cdec.sa.SuffixArray(from_text=args.source)
-    f_sa.write_binary(f_sa_bin)
-
-    logger.info('Compiling target data array')
-    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/cdec/sa/extract.py b/python/cdec/sa/extract.py
deleted file mode 100644
index 918aa3bb..00000000
--- a/python/cdec/sa/extract.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/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/cdec/sa/extractor.py b/python/cdec/sa/extractor.py
deleted file mode 100644
index bb912e16..00000000
--- a/python/cdec/sa/extractor.py
+++ /dev/null
@@ -1,78 +0,0 @@
-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/cdec/sa/features.py b/python/cdec/sa/features.py
deleted file mode 100644
index 325b9e13..00000000
--- a/python/cdec/sa/features.py
+++ /dev/null
@@ -1,57 +0,0 @@
-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)