def test_matching(self):
filename = os.path.join(DATA, "track_000000")
wordalign = self._alignerio.read_aligned( filename )[1]
self.assertEqual(len(wordalign), 21)
ref = [u"好", u"感", u"啦 @", u"好似", u"梁", u"安", u"琪", u"咁", u"係", u"啦", u"係", u"我", u"以前", u"都", u"聽", u"過", u"佢", u"", u"節目", u"喀", u"覺得", u"佢", u"講", u"", u"非常之", u"膚", u"淺", u"及", u"幼稚", u"呀", u"哦", u"即", u"khut6", u"嘩", u"咁"]
hyp = [(token,score) for (start,end,token,score) in wordalign]
pattern = Patterns()
pattern.set_ngram(3)
m3 = pattern.ngram_matchings( ref,hyp )
# Search for the lowest value in ref:
minr = min( [ v[0] for v in m3 ] )
maxr = max( [ v[0] for v in m3 ] )
newref = ref[minr:maxr+1]
minh = min( [ v[1] for v in m3 ] )
maxh = max( [ v[1] for v in m3 ] )
newhyp = hyp[minh:maxh+1]
pattern = Patterns()
pattern.set_ngram(3)
newm3 = pattern.ngram_alignments( newref,newhyp )
newm3 = [ (v[0]+minr,v[1]+minh) for v in newm3 ]
self.assertEqual(newm3,[(5, 5), (6, 6), (7, 7), (17, 16), (18, 17), (19, 18)])
pattern = Patterns()
pattern.set_score(0.6)
pattern.set_ngram(1)
m1 = pattern.ngram_alignments( newref,newhyp )
newm1 = [ (v[0]+minr,v[1]+minh) for v in m1 ]
self.assertEqual(newm1,[(6, 6)])
class TestPatterns(unittest.TestCase):
def setUp(self):
self._patterns = Patterns()
def test_set_score(self):
self.assertEqual( self._patterns.get_score(), 1. )
self._patterns.set_score(0.9)
self.assertEqual( self._patterns.get_score(), 0.9 )
with self.assertRaises(ValueError):
self._patterns.set_score(-1.)
self._patterns.set_score(2.)
def test_set_ngram(self):
self.assertEqual( self._patterns.get_ngram(), 3 )
self._patterns.set_ngram(2)
self.assertEqual( self._patterns.get_ngram(), 2 )
with self.assertRaises(ValueError):
self._patterns.set_score(0)
self._patterns.set_score(5)
def test_set_gap(self):
self.assertEqual( self._patterns.get_gap(), 2 )
self._patterns.set_gap(1)
self.assertEqual( self._patterns.get_gap(), 1 )
with self.assertRaises(ValueError):
self._patterns.set_gap(-1)
self._patterns.set_gap(5)
def test_ngram_alignments(self):
ref = [ "w0", "w1", "w2", "w3", "w4", "w5", "w6", "w7", "w8", "w9", "w10", "w11", "w12" ]
hyp = [ ("w0",0.8), ("w1",1), ("w2",0.7), ("wX",0.9), ("w3",1), ("w5",0.4), ("w6",0.95), ("wX",1), ("w9",1) ]
self._patterns.set_ngram(3)
self._patterns.set_gap(1)
self.assertEqual([ (0,0), (1,1), (2,2) ], self._patterns.ngram_alignments(ref,hyp))
self._patterns.set_ngram(2)
self._patterns.set_gap(1)
self.assertEqual([(0,0), (1,1), (2,2), (5,5), (6,6)], self._patterns.ngram_alignments(ref,hyp))
self._patterns.set_ngram(1)
self._patterns.set_score(0.9)
self._patterns.set_gap(0)
self.assertEqual([(1,1), (6,6)], self._patterns.ngram_alignments(ref,hyp))
self._patterns.set_gap(1)
self.assertEqual([(1,1), (3,4), (6,6), (9,8)], self._patterns.ngram_alignments(ref,hyp))
self._patterns.set_gap(2)
self.assertEqual([(1,1), (3,4), (6,6), (9,8)], self._patterns.ngram_alignments(ref,hyp))
self._patterns.set_score(0.5)
self._patterns.set_gap(1)
self.assertEqual([(0,0), (1,1), (2,2), (3,4), (6,6), (9,8)], self._patterns.ngram_alignments(ref,hyp))
def test_ngram_alignments_repeats(self):
ref = [ "喀", "早晨", "係", "係", "係", "喀", "我" ]
hyp = [ ("兩", 0.207), ("兩", 0.369), ("兩", 0.536), ("係", 0.165), ("係", 0.201), ("係", 0.193), ("係", 0.172), ("係", 0.182)]
self._patterns.set_gap(2)
self._patterns.set_ngram(3)
#print self._patterns.ngram_alignments(ref,hyp)
self._patterns.set_ngram(2)
#print self._patterns.ngram_alignments(ref,hyp)
def test_ngram_matchings(self):
ref = [ "wa", "wb", "w0", "wa", "wX", "w0", "w1", "w2", "w3", "w4", "w5", "w6", "w7", "w8", "w9", "w10", "w11", "w12", "wX", "wX" ]
hyp = [ ("w0",0.8), ("w1",1), ("w2",0.7), ("wX",0.9), ("w3",1), ("w5",0.4), ("w6",0.95), ("wX",1), ("w9",1) ]
self.assertEqual([(5, 0), (6, 1), (7, 2)] , self._patterns.ngram_matchings(ref,hyp))
def setUp(self):
self._patterns = Patterns()
def _fix_matchings_list(self, ref, hyp, N ):
"""
Create the list of matches between ref and hyp.
"""
pattern = Patterns()
pattern.set_ngram( N )
m3 = pattern.ngram_matchings( ref,hyp )
if len( m3 ) == 0:
return []
# Search for the lowest/highest values in ref/hyp:
listref = [ v[0] for v in m3 ]
listhyp = [ v[1] for v in m3 ]
minr = min( listref )
maxr = max( listref )
newref = ref[minr:maxr+1]
minh = min( listhyp )
maxh = max( listhyp )
newhyp = hyp[minh:maxh+1]
# Do some hypothesis were found several times in the reference?
if len(listhyp) > len(list(set(listhyp))):
pattern.set_ngram( N+2 )
newm3 = pattern.ngram_matchings( ref,hyp )
listref = [ v[0] for v in m3 ]
listhyp = [ v[1] for v in m3 ]
if len(listhyp) > len(list(set(listhyp))):
newm3 = []
else:
newm3 = m3
m1 = []
if len(hyp) < N:
pattern = Patterns()
pattern.set_score(0.9)
pattern.set_ngram(1)
pattern.set_gap(1)
m1 = pattern.ngram_alignments( newref,newhyp )
return sorted(list(set(m1+newm3)))
