def test_prune(self):
dacn = DialogueActConfusionNetwork()
dacn.add(0.05, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.00005, DialogueActItem(dai='inform(food=russian)'))
# Russian food should be pruned.
self.assertEqual(len(dacn), 3)
dacn.prune()
self.assertEqual(len(dacn), 2)
self.assertTrue(not DialogueActItem(dai='inform(food=russian)') in dacn)
def test_prune(self):
dacn = DialogueActConfusionNetwork()
dacn.add(0.05, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.00005, DialogueActItem(dai='inform(food=russian)'))
# Russian food should be pruned.
self.assertEqual(len(dacn), 3)
dacn.prune()
self.assertEqual(len(dacn), 2)
self.assertTrue(not DialogueActItem(
dai='inform(food=russian)') in dacn)
def parse_nblist(self, obs, *args, **kwargs):
"""
Parses an observation featuring an utterance n-best list using the
parse_1_best method.
Arguments:
obs -- a dictionary of observations
:: observation type -> observed value
where observation type is one of values for `obs_type' used in
`ft_props', and observed value is the corresponding observed
value for the input
args -- further positional arguments that should be passed to the
`parse_1_best' method call
kwargs -- further keyword arguments that should be passed to the
`parse_1_best' method call
"""
nblist = obs['utt_nbl']
if len(nblist) == 0:
return DialogueActConfusionNetwork()
obs_wo_nblist = copy.deepcopy(obs)
del obs_wo_nblist['utt_nbl']
dacn_list = []
for prob, utt in nblist:
if "_other_" == utt:
dacn = DialogueActConfusionNetwork()
dacn.add(1.0, DialogueActItem("other"))
elif "_silence_" == utt:
dacn = DialogueActConfusionNetwork()
dacn.add(1.0, DialogueActItem("silence"))
else:
obs_wo_nblist['utt'] = utt
dacn = self.parse_1_best(obs_wo_nblist, *args, **kwargs)
dacn_list.append((prob, dacn))
dacn = merge_slu_confnets(dacn_list)
dacn.prune()
dacn.sort()
return dacn
def parse_nblist(self, obs, *args, **kwargs):
"""
Parses an observation featuring an utterance n-best list using the
parse_1_best method.
Arguments:
obs -- a dictionary of observations
:: observation type -> observed value
where observation type is one of values for `obs_type' used in
`ft_props', and observed value is the corresponding observed
value for the input
args -- further positional arguments that should be passed to the
`parse_1_best' method call
kwargs -- further keyword arguments that should be passed to the
`parse_1_best' method call
"""
nblist = obs['utt_nbl']
if len(nblist) == 0:
return DialogueActConfusionNetwork()
obs_wo_nblist = copy.deepcopy(obs)
del obs_wo_nblist['utt_nbl']
dacn_list = []
for prob, utt in nblist:
if "_other_" == utt:
dacn = DialogueActConfusionNetwork()
dacn.add(1.0, DialogueActItem("other"))
elif "_silence_" == utt:
dacn = DialogueActConfusionNetwork()
dacn.add(1.0, DialogueActItem("silence"))
else:
obs_wo_nblist['utt'] = utt
dacn = self.parse_1_best(obs_wo_nblist, *args, **kwargs)
dacn_list.append((prob, dacn))
dacn = merge_slu_confnets(dacn_list)
dacn.prune()
dacn.sort()
return dacn