def cook_expected_ref(refs,n=4,p_refs=None,eff_ref_len="average"):
'''same input as bleu.cook_refs, but maxcounts is avg. rather than max.
p_refs is a parallel (to refs) list of weights. uniform 1/n will be used if
None'''
if p_refs == None:
l=len(refs)
p_refs=[1.0/l]*l
assert isclose(sum(p_refs),1)
reflen = []
# ecount=collections.defaultdict(float)
ecount = {}
for ref,p in itertools.izip(refs,p_refs):
rl, counts = bleu.precook(ref, n)
reflen.append(rl)
for (ngram,count) in counts.iteritems():
#ecounts[ngram] += p*count
ecount[ngram] = ecount.get(ngram,0)+p*count
return collapse_reflen(reflen,eff_ref_len),ecount
def cook_expected_ref(refs, n=4, p_refs=None, eff_ref_len="average"):
'''same input as bleu.cook_refs, but maxcounts is avg. rather than max.
p_refs is a parallel (to refs) list of weights. uniform 1/n will be used if
None'''
if p_refs == None:
l = len(refs)
p_refs = [1.0 / l] * l
assert isclose(sum(p_refs), 1)
reflen = []
# ecount=collections.defaultdict(float)
ecount = {}
for ref, p in itertools.izip(refs, p_refs):
rl, counts = bleu.precook(ref, n)
reflen.append(rl)
for (ngram, count) in counts.iteritems():
#ecounts[ngram] += p*count
ecount[ngram] = ecount.get(ngram, 0) + p * count
return collapse_reflen(reflen, eff_ref_len), ecount
def mbr_best(lines, nbest, cost_weighting, rank_limit, rank_weight, sys_weights, sys_cost_bases=None,fast=False,addprec=1,eff_ref_len="average",n=4,cost_base=None,normalize_cost_base=False,per_system_norm=True):
if sys_weights == None or len(sys_weights)==0:
no_syswt=True
sys_weights={}
else:
no_syswt=False
sumsw=sum(sys_weights.itervalues())
sys_weights=dict((s,(w/sumsw)) for s,w in sys_weights.iteritems())
use_cost_base = (cost_base != None or sys_cost_bases != None)
if use_cost_base and sys_cost_bases == None:
sys_cost_bases = {}
if nbest:
start = 6 # added system also to beginning
else:
start = 1
entries_per_system = {}
best_system_score = {} # these are set by first/last in input, rather than assuming more positive -> better
worst_system_score = {}
max_system_cost = {}
# cookedrefs = []
ref_probs = []
ref_syss = []
splits=[line.split(None,start) for line in lines]
hyps=[bleu.precook(s[start]) for s in splits]
sump_sys = {}
for split_ref in splits:
sysname = split_ref[0]
entries_per_system[sysname] = entries_per_system.get(sysname, 0) + 1
if nbest:
score = float(split_ref[5])
cost = -score
if sysname not in best_system_score:
best_system_score[sysname]=score
worst_system_score[sysname]=score
if max_system_cost.get(sysname, 0) < cost:
max_system_cost[sysname] = cost
# diff_system_score = dict((s,w-best_system_score[s]) for s,w in worst_system_score.iteritems())
sump=0
for split_ref in splits:
p = 1.0
sysname = split_ref[0]
if not per_system_norm and sysname in sys_weights:
# pdb.set_trace()
p *= sys_weights[sysname]
if nbest:
if rank_weight != None:
rank = int(split_ref[4])
p *= 1./(rank_weight + rank)
if use_cost_base and sysname in best_system_score:
bss=best_system_score[sysname]
score = float(split_ref[5])-bss
if normalize_cost_base:
diff=bss-worst_system_score[sysname]
#diff=diff_system_score[sysname]
if diff!=0:
score /= diff
p *= math.pow(sys_cost_bases.get(sysname,cost_base),score)
if cost_weighting and max_system_cost.get(sysname, 0) > 0.0:
cost = -float(split_ref[5])
p *= cost/max_system_cost[sysname]
sump+=p
sump_sys[sysname]=sump_sys.get(sysname,0)+p
ref_probs.append(p)
ref_syss.append(sysname)
if per_system_norm:
if no_syswt:
wsys=1./len(sump_sys)
sys_weights=dict.fromkeys(sump_sys.iterkeys(),wsys)
# dump(sys_weights)
assert isclose(sum(sys_weights.itervalues()),1)
mult_sys=dict((n,(sys_weights[n]/s)) for n,s
in sump_sys.iteritems());
# dump([(sum([p for n,p in zip(ref_syss,ref_probs) if n==sn]),sn,sump_sys[sn],mult_sys[sn]) for sn in sump_sys.keys()])
ref_probs=[mult_sys[sn]*p for sn,p in itertools.izip(ref_syss,ref_probs)]
dump(sum(ref_probs))
elif sump != 1.0:
oos=1./sump
ref_probs=[p*oos for p in ref_probs]
if fast:
expected_ref=cook_expected_ref(hyps,n=n,p_refs=ref_probs,eff_ref_len=eff_ref_len)
else:
cookedrefs = [ bleu.cook_refs([s[start]],n=n) for s in splits ]
max_items = []
avg_bleu = []
N=len(hyps)
for test in xrange(N):
split_test = splits[test]
avg_test_bleu = 0.0
if nbest and rank_limit != None:
test_rank = int(split_test[4])
if test_rank > rank_limit:
avg_bleu.append(avg_test_bleu)
continue
if fast:
avg_test_bleu = score_vs_cooked(hyps[test],expected_ref,n=n,addprec=addprec)
else:
for ref in xrange(N):
split_ref = splits[ref]
factor = ref_probs[ref]
# score=1.
if ref != test: # each system gets to vote for itself
score=score_vs_cooked(hyps[test],cookedrefs[ref],n=n,addprec=addprec)
else:
score=1.
avg_test_bleu += ref_probs[ref]*score
avg_bleu.append(avg_test_bleu)
if len(max_items) == 0 or avg_test_bleu == avg_bleu[max_items[0]]:
max_items.append(test)
elif avg_test_bleu > avg_bleu[max_items[0]]:
max_items = []
max_items.append(test)
dump(avg_bleu)
# dump([x/avg_bleu[0] for x in avg_bleu])
return max_items,avg_bleu
def mbr_best(lines,
nbest,
cost_weighting,
rank_limit,
rank_weight,
sys_weights,
sys_cost_bases=None,
fast=False,
addprec=1,
eff_ref_len="average",
n=4,
cost_base=None,
normalize_cost_base=False,
per_system_norm=True):
if sys_weights == None or len(sys_weights) == 0:
no_syswt = True
sys_weights = {}
else:
no_syswt = False
sumsw = sum(sys_weights.itervalues())
sys_weights = dict(
(s, (w / sumsw)) for s, w in sys_weights.iteritems())
use_cost_base = (cost_base != None or sys_cost_bases != None)
if use_cost_base and sys_cost_bases == None:
sys_cost_bases = {}
if nbest:
start = 6 # added system also to beginning
else:
start = 1
entries_per_system = {}
best_system_score = {
} # these are set by first/last in input, rather than assuming more positive -> better
worst_system_score = {}
max_system_cost = {}
# cookedrefs = []
ref_probs = []
ref_syss = []
splits = [line.split(None, start) for line in lines]
hyps = [bleu.precook(s[start]) for s in splits]
sump_sys = {}
for split_ref in splits:
sysname = split_ref[0]
entries_per_system[sysname] = entries_per_system.get(sysname, 0) + 1
if nbest:
score = float(split_ref[5])
cost = -score
if sysname not in best_system_score:
best_system_score[sysname] = score
worst_system_score[sysname] = score
if max_system_cost.get(sysname, 0) < cost:
max_system_cost[sysname] = cost
# diff_system_score = dict((s,w-best_system_score[s]) for s,w in worst_system_score.iteritems())
sump = 0
for split_ref in splits:
p = 1.0
sysname = split_ref[0]
if not per_system_norm and sysname in sys_weights:
# pdb.set_trace()
p *= sys_weights[sysname]
if nbest:
if rank_weight != None:
rank = int(split_ref[4])
p *= 1. / (rank_weight + rank)
if use_cost_base and sysname in best_system_score:
bss = best_system_score[sysname]
score = float(split_ref[5]) - bss
if normalize_cost_base:
diff = bss - worst_system_score[sysname]
#diff=diff_system_score[sysname]
if diff != 0:
score /= diff
p *= math.pow(sys_cost_bases.get(sysname, cost_base), score)
if cost_weighting and max_system_cost.get(sysname, 0) > 0.0:
cost = -float(split_ref[5])
p *= cost / max_system_cost[sysname]
sump += p
sump_sys[sysname] = sump_sys.get(sysname, 0) + p
ref_probs.append(p)
ref_syss.append(sysname)
if per_system_norm:
if no_syswt:
wsys = 1. / len(sump_sys)
sys_weights = dict.fromkeys(sump_sys.iterkeys(), wsys)
# dump(sys_weights)
assert isclose(sum(sys_weights.itervalues()), 1)
mult_sys = dict(
(n, (sys_weights[n] / s)) for n, s in sump_sys.iteritems())
# dump([(sum([p for n,p in zip(ref_syss,ref_probs) if n==sn]),sn,sump_sys[sn],mult_sys[sn]) for sn in sump_sys.keys()])
ref_probs = [
mult_sys[sn] * p for sn, p in itertools.izip(ref_syss, ref_probs)
]
dump(sum(ref_probs))
elif sump != 1.0:
oos = 1. / sump
ref_probs = [p * oos for p in ref_probs]
if fast:
expected_ref = cook_expected_ref(hyps,
n=n,
p_refs=ref_probs,
eff_ref_len=eff_ref_len)
else:
cookedrefs = [bleu.cook_refs([s[start]], n=n) for s in splits]
max_items = []
avg_bleu = []
N = len(hyps)
for test in xrange(N):
split_test = splits[test]
avg_test_bleu = 0.0
if nbest and rank_limit != None:
test_rank = int(split_test[4])
if test_rank > rank_limit:
avg_bleu.append(avg_test_bleu)
continue
if fast:
avg_test_bleu = score_vs_cooked(hyps[test],
expected_ref,
n=n,
addprec=addprec)
else:
for ref in xrange(N):
split_ref = splits[ref]
factor = ref_probs[ref]
# score=1.
if ref != test: # each system gets to vote for itself
score = score_vs_cooked(hyps[test],
cookedrefs[ref],
n=n,
addprec=addprec)
else:
score = 1.
avg_test_bleu += ref_probs[ref] * score
avg_bleu.append(avg_test_bleu)
if len(max_items) == 0 or avg_test_bleu == avg_bleu[max_items[0]]:
max_items.append(test)
elif avg_test_bleu > avg_bleu[max_items[0]]:
max_items = []
max_items.append(test)
dump(avg_bleu)
# dump([x/avg_bleu[0] for x in avg_bleu])
return max_items, avg_bleu
