from __future__ import print_function, unicode_literals, division, absolute_import
import colibricore
from colibrimt.alignmentmodel import FeaturedAlignmentModel
sourceencoder = colibricore.ClassEncoder()
targetencoder = colibricore.ClassEncoder()
s1 = sourceencoder.buildpattern("het grote huis", False, True)
s2 = sourceencoder.buildpattern("het paleis", False, True)
t1 = targetencoder.buildpattern("the big house", False, True)
t2 = targetencoder.buildpattern("the grand house", False, True)
t3 = targetencoder.buildpattern("the palace", False, True)
sourceencoder.save('/tmp/s.cls')
targetencoder.save('/tmp/t.cls')
sd = colibricore.ClassDecoder('/tmp/s.cls')
td = colibricore.ClassDecoder('/tmp/t.cls')
model = FeaturedAlignmentModel()
model.add(s1,t1,[1,0,1,0])
model.add(s1,t2,[1,0,1,0])
model.add(s2,t2,[1,0,1,0])
model.add(s2,t3,[1,0,1,0])
model.normalize('s-t-')
for source, target,scores in model:
print(source.tostring(sd)+"\t"+target.tostring(td)+"\t" + " ".join([str(x) for x in scores]))
from __future__ import print_function, unicode_literals, division, absolute_import
import colibricore
from colibrimt.alignmentmodel import FeaturedAlignmentModel
sourceencoder = colibricore.ClassEncoder()
targetencoder = colibricore.ClassEncoder()
s1 = sourceencoder.buildpattern("het grote huis", False, True)
s2 = sourceencoder.buildpattern("het paleis", False, True)
t1 = targetencoder.buildpattern("the big house", False, True)
t2 = targetencoder.buildpattern("the grand house", False, True)
t3 = targetencoder.buildpattern("the palace", False, True)
sourceencoder.save('/tmp/s.cls')
targetencoder.save('/tmp/t.cls')
sd = colibricore.ClassDecoder('/tmp/s.cls')
td = colibricore.ClassDecoder('/tmp/t.cls')
model = FeaturedAlignmentModel()
model.add(s1, t1, [1, 0, 1, 0])
model.add(s1, t2, [1, 0, 1, 0])
model.add(s2, t2, [1, 0, 1, 0])
model.add(s2, t3, [1, 0, 1, 0])
model.normalize('s-t-')
for source, target, scores in model:
print(
source.tostring(sd) + "\t" + target.tostring(td) + "\t" +
" ".join([str(x) for x in scores]))
def main():
parser = argparse.ArgumentParser(
description="Extract skipgrams from a Moses phrasetable",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-t',
'--minskiptypes',
type=int,
help="Minimal skip types",
action='store',
default=2,
required=False)
parser.add_argument(
'-i',
'--inputfile',
type=str,
help=
"Input alignment model (file prefix without .colibri.alignmodel-* extension) or moses phrasetable ",
action='store',
required=True)
parser.add_argument(
'-o',
'--outputfile',
type=str,
help=
"Output alignment model (file prefix without .colibri.alignmodel-* extension). Same as input if not specified!",
default="",
action='store',
required=False)
parser.add_argument('-l',
'--maxlength',
type=int,
help="Maximum length",
action='store',
default=8,
required=False)
parser.add_argument('-W',
'--tmpdir',
type=str,
help="Temporary work directory",
action='store',
default="./",
required=False)
parser.add_argument('-S',
'--sourceclassfile',
type=str,
help="Source class file",
action='store',
required=True)
parser.add_argument('-T',
'--targetclassfile',
type=str,
help="Target class file",
action='store',
required=True)
parser.add_argument(
'-s',
'--constrainskipgrams',
help=
"Strictly constrain skipgrams: only skipgrams present in the constrain models (-m and -M) will be considered",
action='store_true',
required=False)
parser.add_argument(
'-m',
'--constrainsourcemodel',
type=str,
help="Source patternmodel, used to constrain possible patterns",
action='store',
required=False)
parser.add_argument(
'-M',
'--constraintargetmodel',
type=str,
help="Target patternmodel, used to constrain possible patterns",
action='store',
required=False)
parser.add_argument(
'-p',
'--pts',
type=float,
help=
"Minimum probability p(t|s) for skipgram consideration (set to a high number)",
default=0.75,
action='store',
required=False)
parser.add_argument(
'-P',
'--pst',
type=float,
help=
"Minimum probability p(s|t) for skipgram consideration (set to a high number)",
default=0.75,
action='store',
required=False)
parser.add_argument('-D',
'--debug',
help="Enable debug mode",
action='store_true',
required=False)
args = parser.parse_args()
#args.storeconst, args.dataset, args.num, args.bar
if args.constrainsourcemodel:
print("Loading source model for constraints", file=sys.stderr)
if args.constrainskipgrams:
constrainsourcemodel = colibricore.IndexedPatternModel(
args.constrainsourcemodel)
else:
constrainsourcemodel = colibricore.UnindexedPatternModel(
args.constrainsourcemodel)
else:
constrainsourcemodel = None
if args.constraintargetmodel:
print("Loading target model for constraints", file=sys.stderr)
if args.constrainskipgrams:
constraintargetmodel = colibricore.IndexedPatternModel(
args.constraintargetmodel)
else:
constraintargetmodel = colibricore.UnindexedPatternModel(
args.constraintargetmodel)
else:
constraintargetmodel = None
alignmodel = FeaturedAlignmentModel()
if os.path.exists(args.inputfile + '.colibri.alignmodel-keys'):
print("Loading colibri alignment model", file=sys.stderr)
alignmodel.load(args.inputfile)
else:
print("Loading class encoders", file=sys.stderr)
sourceencoder = colibricore.ClassEncoder(args.sourceclassfile)
targetencoder = colibricore.ClassEncoder(args.targetclassfile)
print("Loading moses phrase table", file=sys.stderr)
alignmodel.loadmosesphrasetable(args.inputfile, sourceencoder,
targetencoder)
if args.debug:
debug = (colibricore.ClassDecoder(args.sourceclassfile),
colibricore.ClassDecoder(args.targetclassfile))
else:
debug = False
scorefilter = lambda features: features[0] >= args.pst and features[
2] >= args.pts
extractskipgrams(alignmodel, args.maxlength, args.minskiptypes,
args.tmpdir, constrainsourcemodel, constraintargetmodel,
args.constrainskipgrams, scorefilter, False, debug)
if args.outputfile:
outfile = args.outputfile
else:
outfile = os.path.basename(args.inputfile)
if outfile[-3:] == '.gz': outfile = outfile[:-3]
if outfile[-4:] == '.bz2': outfile = outfile[:-4]
if outfile[-11:] == '.phrasetable': outfile = outfile[:-11]
if outfile[-12:] == '.phrase-table': outfile = outfile[:-12]
print("Saving alignment model to " + outfile, file=sys.stderr)
alignmodel.save(outfile) #extensions will be added automatically
def main():
parser = argparse.ArgumentParser(description="Extract skipgrams from a Moses phrasetable", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-t','--minskiptypes', type=int,help="Minimal skip types", action='store',default=2,required=False)
parser.add_argument('-i','--inputfile',type=str,help="Input alignment model (file prefix without .colibri.alignmodel-* extension) or moses phrasetable ", action='store',required=True)
parser.add_argument('-o','--outputfile',type=str,help="Output alignment model (file prefix without .colibri.alignmodel-* extension). Same as input if not specified!", default="", action='store',required=False)
parser.add_argument('-l','--maxlength',type=int,help="Maximum length", action='store',default=8,required=False)
parser.add_argument('-W','--tmpdir',type=str,help="Temporary work directory", action='store',default="./",required=False)
parser.add_argument('-S','--sourceclassfile',type=str,help="Source class file", action='store',required=True)
parser.add_argument('-T','--targetclassfile',type=str,help="Target class file", action='store',required=True)
parser.add_argument('-s','--constrainskipgrams',help="Strictly constrain skipgrams: only skipgrams present in the constrain models (-m and -M) will be considered", action='store_true',required=False)
parser.add_argument('-m','--constrainsourcemodel',type=str,help="Source patternmodel, used to constrain possible patterns", action='store',required=False)
parser.add_argument('-M','--constraintargetmodel',type=str,help="Target patternmodel, used to constrain possible patterns", action='store',required=False)
parser.add_argument('-p','--pts',type=float,help="Minimum probability p(t|s) for skipgram consideration (set to a high number)",default=0.75, action='store',required=False)
parser.add_argument('-P','--pst',type=float,help="Minimum probability p(s|t) for skipgram consideration (set to a high number)", default=0.75,action='store',required=False)
parser.add_argument('-D','--debug',help="Enable debug mode", action='store_true',required=False)
args = parser.parse_args()
#args.storeconst, args.dataset, args.num, args.bar
if args.constrainsourcemodel:
print("Loading source model for constraints",file=sys.stderr)
if args.constrainskipgrams:
constrainsourcemodel = colibricore.IndexedPatternModel(args.constrainsourcemodel)
else:
constrainsourcemodel = colibricore.UnindexedPatternModel(args.constrainsourcemodel)
else:
constrainsourcemodel = None
if args.constraintargetmodel:
print("Loading target model for constraints",file=sys.stderr)
if args.constrainskipgrams:
constraintargetmodel = colibricore.IndexedPatternModel(args.constraintargetmodel)
else:
constraintargetmodel = colibricore.UnindexedPatternModel(args.constraintargetmodel)
else:
constraintargetmodel = None
alignmodel = FeaturedAlignmentModel()
if os.path.exists(args.inputfile + '.colibri.alignmodel-keys'):
print("Loading colibri alignment model",file=sys.stderr)
alignmodel.load(args.inputfile)
else:
print("Loading class encoders",file=sys.stderr)
sourceencoder = colibricore.ClassEncoder(args.sourceclassfile)
targetencoder = colibricore.ClassEncoder(args.targetclassfile)
print("Loading moses phrase table",file=sys.stderr)
alignmodel.loadmosesphrasetable(args.inputfile, sourceencoder, targetencoder)
if args.debug:
debug = (colibricore.ClassDecoder(args.sourceclassfile), colibricore.ClassDecoder(args.targetclassfile))
else:
debug = False
scorefilter = lambda features: features[0] >= args.pst and features[2] >= args.pts
extractskipgrams(alignmodel, args.maxlength, args.minskiptypes, args.tmpdir, constrainsourcemodel, constraintargetmodel,args.constrainskipgrams,scorefilter,False, debug)
if args.outputfile:
outfile = args.outputfile
else:
outfile = os.path.basename(args.inputfile)
if outfile[-3:] == '.gz': outfile = outfile[:-3]
if outfile[-4:] == '.bz2': outfile = outfile[:-4]
if outfile[-11:] == '.phrasetable': outfile = outfile[:-11]
if outfile[-12:] == '.phrase-table': outfile = outfile[:-12]
print("Saving alignment model to " + outfile,file=sys.stderr)
alignmodel.save(outfile) #extensions will be added automatically
