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cimport mteval
cdef SufficientStats as_stats(x, y):
if isinstance(x, SufficientStats):
return x
elif x == 0 and isinstance(y, SufficientStats):
stats = SufficientStats()
stats.stats = new mteval.SufficientStats()
stats.metric = (<SufficientStats> y).metric
return stats
cdef class Candidate:
cdef mteval.const_Candidate* candidate
cdef public float score
property words:
def __get__(self):
return unicode(GetString(self.candidate.ewords).c_str(), encoding='utf8')
property fmap:
def __get__(self):
cdef SparseVector fmap = SparseVector.__new__(SparseVector)
fmap.vector = new FastSparseVector[weight_t](self.candidate.fmap)
return fmap
cdef class SufficientStats:
cdef mteval.SufficientStats* stats
cdef mteval.EvaluationMetric* metric
def __cinit__(self):
self.stats = new mteval.SufficientStats()
def __dealloc__(self):
del self.stats
property score:
def __get__(self):
return self.metric.ComputeScore(self.stats[0])
property detail:
def __get__(self):
return str(self.metric.DetailedScore(self.stats[0]).c_str())
def __len__(self):
return self.stats.size()
def __iter__(self):
for i in range(len(self)):
yield self[i]
def __getitem__(self, int index):
if not 0 <= index < len(self):
raise IndexError('sufficient stats vector index out of range')
return self.stats[0][index]
def __iadd__(SufficientStats self, SufficientStats other):
self.stats[0] += other.stats[0]
return self
def __add__(x, y):
cdef SufficientStats sx = as_stats(x, y)
cdef SufficientStats sy = as_stats(y, x)
cdef SufficientStats result = SufficientStats()
result.stats = new mteval.SufficientStats(mteval.add(sx.stats[0], sy.stats[0]))
result.metric = sx.metric
return result
cdef class CandidateSet:
cdef shared_ptr[mteval.SegmentEvaluator]* scorer
cdef mteval.EvaluationMetric* metric
cdef mteval.CandidateSet* cs
def __cinit__(self, SegmentEvaluator evaluator):
self.scorer = new shared_ptr[mteval.SegmentEvaluator](evaluator.scorer[0])
self.metric = evaluator.metric
self.cs = new mteval.CandidateSet()
def __dealloc__(self):
del self.scorer
del self.cs
def __len__(self):
return self.cs.size()
def __getitem__(self,int k):
if not 0 <= k < self.cs.size():
raise IndexError('candidate set index out of range')
cdef Candidate candidate = Candidate()
candidate.candidate = &self.cs[0][k]
candidate.score = self.metric.ComputeScore(self.cs[0][k].eval_feats)
return candidate
def __iter__(self):
cdef unsigned i
for i in range(len(self)):
yield self[i]
def add_kbest(self, Hypergraph hypergraph, unsigned k):
"""cs.add_kbest(Hypergraph hypergraph, int k) -> Extract K-best hypotheses
from the hypergraph and add them to the candidate set."""
self.cs.AddKBestCandidates(hypergraph.hg[0], k, self.scorer.get())
cdef class SegmentEvaluator:
cdef shared_ptr[mteval.SegmentEvaluator]* scorer
cdef mteval.EvaluationMetric* metric
def __dealloc__(self):
del self.scorer
def evaluate(self, sentence):
"""se.evaluate(sentence) -> SufficientStats for the given hypothesis."""
cdef vector[WordID] hyp
cdef SufficientStats sf = SufficientStats()
sf.metric = self.metric
sf.stats = new mteval.SufficientStats()
ConvertSentence(as_str(sentence.strip()), &hyp)
self.scorer.get().Evaluate(hyp, sf.stats)
return sf
def candidate_set(self):
"""se.candidate_set() -> Candidate set using this segment evaluator for scoring."""
return CandidateSet(self)
cdef class Scorer:
cdef string* name
cdef mteval.EvaluationMetric* metric
def __cinit__(self, bytes name=None):
if name:
self.name = new string(name)
self.metric = mteval.MetricInstance(self.name[0])
def __dealloc__(self):
del self.name
def __call__(self, refs):
if isinstance(refs, basestring):
refs = [refs]
cdef vector[vector[WordID]]* refsv = new vector[vector[WordID]]()
cdef vector[WordID]* refv
for ref in refs:
refv = new vector[WordID]()
ConvertSentence(as_str(ref.strip()), refv)
refsv.push_back(refv[0])
del refv
cdef unsigned i
cdef SegmentEvaluator evaluator = SegmentEvaluator()
evaluator.metric = self.metric
evaluator.scorer = new shared_ptr[mteval.SegmentEvaluator](
self.metric.CreateSegmentEvaluator(refsv[0]))
del refsv # in theory should not delete but store in SegmentEvaluator
return evaluator
def __str__(self):
return str(self.name.c_str())
cdef float _compute_score(void* metric_, mteval.SufficientStats* stats):
cdef Metric metric = <Metric> metric_
cdef list ss = []
cdef unsigned i
for i in range(stats.size()):
ss.append(stats[0][i])
return metric.score(ss)
cdef void _compute_sufficient_stats(void* metric_,
string* hyp,
vector[string]* refs,
mteval.SufficientStats* out):
cdef Metric metric = <Metric> metric_
cdef list refs_ = []
cdef unsigned i
for i in range(refs.size()):
refs_.append(str(refs[0][i].c_str()))
cdef list ss = metric.evaluate(str(hyp.c_str()), refs_)
out.fields.resize(len(ss))
for i in range(len(ss)):
out.fields[i] = ss[i]
cdef class Metric:
cdef Scorer scorer
def __cinit__(self):
self.scorer = Scorer()
cdef bytes class_name = self.__class__.__name__
self.scorer.name = new string(class_name)
self.scorer.metric = mteval.PyMetricInstance(self.scorer.name[0],
<void*> self, _compute_sufficient_stats, _compute_score)
def __call__(self, refs):
return self.scorer(refs)
def score(SufficientStats stats):
return 0
def evaluate(self, hyp, refs):
return []
BLEU = Scorer('IBM_BLEU')
QCRI = Scorer('QCRI_BLEU')
TER = Scorer('TER')
CER = Scorer('CER')
SSK = Scorer('SSK')
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