def _build_confusion_network(self, sampled_da_items):
confusion_net = DialogueActConfusionNetwork()
for da_items, probs in sampled_da_items:
for dai, prob in zip(da_items, probs):
confusion_net.add_merge(prob, dai)
return confusion_net
def _build_confusion_network(self, sampled_da_items):
'''Build confusion network from a list containing DialgoueActItem and their observation probability.'''
confusion_net = DialogueActConfusionNetwork()
for da_items, probs in sampled_da_items:
for dai, prob in zip(da_items, probs):
confusion_net.add_merge(prob, dai)
return confusion_net
def test_add_merge(self):
dai = DialogueActItem(dai='inform(food=chinese)')
dacn = DialogueActConfusionNetwork()
dacn.add_merge(0.5, dai, combine='add')
self.assertEqual(dacn._get_prob([0]), 0.5)
dacn.add_merge(0.5, dai, combine='add')
self.assertEqual(dacn._get_prob([0]), 1.0)
def test_add_merge(self):
dai = DialogueActItem(dai='inform(food=chinese)')
dacn = DialogueActConfusionNetwork()
dacn.add_merge(0.5, dai, combine='add')
self.assertEqual(dacn._get_prob([0]), 0.5)
dacn.add_merge(0.5, dai, combine='add')
self.assertEqual(dacn._get_prob([0]), 1.0)
def test_merge_slu_confnets(self):
confnet1 = DialogueActConfusionNetwork()
confnet1.add(0.7, DialogueActItem('hello'))
confnet1.add(0.2, DialogueActItem('bye'))
confnet2 = DialogueActConfusionNetwork()
confnet2.add(0.6, DialogueActItem('hello'))
confnet2.add(0.3, DialogueActItem('restart'))
confnets = [[0.7, confnet1], [0.3, confnet2]]
merged_confnets = merge_slu_confnets(confnets)
correct_merged_confnet = DialogueActConfusionNetwork()
correct_merged_confnet.add_merge(0.7 * 0.7, DialogueActItem('hello'),
combine='add')
correct_merged_confnet.add_merge(0.7 * 0.2, DialogueActItem('bye'),
combine='add')
correct_merged_confnet.add_merge(0.3 * 0.6, DialogueActItem('hello'),
combine='add')
correct_merged_confnet.add_merge(0.3 * 0.3, DialogueActItem('restart'),
combine='add')
s = []
s.append("")
s.append("Merged confnets:")
s.append(unicode(merged_confnets))
s.append("")
s.append("Correct merged results:")
s.append(unicode(correct_merged_confnet))
s.append("")
self.assertEqual(unicode(merged_confnets), unicode(correct_merged_confnet))
def test_merge_slu_confnets(self):
confnet1 = DialogueActConfusionNetwork()
confnet1.add(0.7, DialogueActItem('hello'))
confnet1.add(0.2, DialogueActItem('bye'))
confnet2 = DialogueActConfusionNetwork()
confnet2.add(0.6, DialogueActItem('hello'))
confnet2.add(0.3, DialogueActItem('restart'))
confnets = [[0.7, confnet1], [0.3, confnet2]]
merged_confnets = merge_slu_confnets(confnets)
correct_merged_confnet = DialogueActConfusionNetwork()
correct_merged_confnet.add_merge(0.7 * 0.7,
DialogueActItem('hello'),
combine='add')
correct_merged_confnet.add_merge(0.7 * 0.2,
DialogueActItem('bye'),
combine='add')
correct_merged_confnet.add_merge(0.3 * 0.6,
DialogueActItem('hello'),
combine='add')
correct_merged_confnet.add_merge(0.3 * 0.3,
DialogueActItem('restart'),
combine='add')
s = []
s.append("")
s.append("Merged confnets:")
s.append(unicode(merged_confnets))
s.append("")
s.append("Correct merged results:")
s.append(unicode(correct_merged_confnet))
s.append("")
print '\n'.join(s)
self.assertEqual(unicode(merged_confnets),
unicode(correct_merged_confnet))
def parse_X(self, utterance, verbose=False):
if verbose:
print '='*120
print 'Parsing X'
print '-'*120
print unicode(utterance)
if self.preprocessing:
utterance = self.preprocessing.normalise(utterance)
utterance_fvcs = self.get_fvc(utterance)
if verbose:
print unicode(utterance)
print unicode(utterance_fvcs)
da_confnet = DialogueActConfusionNetwork()
for clser in self.trained_classifiers:
if verbose:
print "Using classifier: ", unicode(clser)
if self.parsed_classifiers[clser].value and self.parsed_classifiers[clser].value.startswith('CL_'):
# process abstracted classifiers
for f, v, c in utterance_fvcs:
cc = "CL_" + c.upper()
if self.parsed_classifiers[clser].value == cc:
#print clser, f, v, c
classifiers_features = self.get_features(utterance, (f, v, cc), utterance_fvcs)
classifiers_inputs = np.zeros((1, len(self.classifiers_features_mapping[clser])))
classifiers_inputs[0] = classifiers_features.get_feature_vector(self.classifiers_features_mapping[clser])
#if verbose:
# print classifiers_features
# print self.classifiers_features_mapping[clser]
p = self.trained_classifiers[clser].predict_proba(classifiers_inputs)
if verbose:
print ' Probability:', p
dai = DialogueActItem(self.parsed_classifiers[clser].dat, self.parsed_classifiers[clser].name, v)
da_confnet.add_merge(p[0][1], dai, combine='max')
else:
# process concrete classifiers
classifiers_features = self.get_features(utterance, (None, None, None), utterance_fvcs)
classifiers_inputs = np.zeros((1, len(self.classifiers_features_mapping[clser])))
classifiers_inputs[0] = classifiers_features.get_feature_vector(self.classifiers_features_mapping[clser])
#if verbose:
# print classifiers_features
# print self.classifiers_features_mapping[clser]
p = self.trained_classifiers[clser].predict_proba(classifiers_inputs)
if verbose:
print ' Probability:', p
dai = self.parsed_classifiers[clser]
da_confnet.add_merge(p[0][1], dai, combine='max')
da_confnet.sort().prune()
return da_confnet
def parse_X(self, utterance, verbose=False):
if verbose:
print '=' * 120
print 'Parsing X'
print '-' * 120
print unicode(utterance)
if self.preprocessing:
utterance = self.preprocessing.normalise(utterance)
utterance_fvcs = self.get_fvc(utterance)
if verbose:
print unicode(utterance)
print unicode(utterance_fvcs)
da_confnet = DialogueActConfusionNetwork()
for clser in self.trained_classifiers:
if verbose:
print "Using classifier: ", unicode(clser)
if self.parsed_classifiers[clser].value and self.parsed_classifiers[
clser].value.startswith('CL_'):
# process abstracted classifiers
for f, v, c in utterance_fvcs:
cc = "CL_" + c.upper()
if self.parsed_classifiers[clser].value == cc:
#print clser, f, v, c
classifiers_features = self.get_features(
utterance, (f, v, cc), utterance_fvcs)
classifiers_inputs = np.zeros(
(1, len(self.classifiers_features_mapping[clser])))
classifiers_inputs[
0] = classifiers_features.get_feature_vector(
self.classifiers_features_mapping[clser])
#if verbose:
# print classifiers_features
# print self.classifiers_features_mapping[clser]
p = self.trained_classifiers[clser].predict_proba(
classifiers_inputs)
if verbose:
print ' Probability:', p
dai = DialogueActItem(
self.parsed_classifiers[clser].dat,
self.parsed_classifiers[clser].name, v)
da_confnet.add_merge(p[0][1], dai, combine='max')
else:
# process concrete classifiers
classifiers_features = self.get_features(
utterance, (None, None, None), utterance_fvcs)
classifiers_inputs = np.zeros(
(1, len(self.classifiers_features_mapping[clser])))
classifiers_inputs[
0] = classifiers_features.get_feature_vector(
self.classifiers_features_mapping[clser])
#if verbose:
# print classifiers_features
# print self.classifiers_features_mapping[clser]
p = self.trained_classifiers[clser].predict_proba(
classifiers_inputs)
if verbose:
print ' Probability:', p
dai = self.parsed_classifiers[clser]
da_confnet.add_merge(p[0][1], dai, combine='max')
da_confnet.sort().prune()
return da_confnet
def parse_1_best(self, obs, verbose=False, *args, **kwargs):
"""Parse an utterance into a dialogue act.
:rtype DialogueActConfusionNetwork
"""
utterance = obs['utt']
if isinstance(utterance, UtteranceHyp):
# Parse just the utterance and ignore the confidence score.
utterance = utterance.utterance
if verbose:
print 'Parsing utterance "{utt}".'.format(utt=utterance)
res_cn = DialogueActConfusionNetwork()
dict_da = self.utt2da.get(unicode(utterance), None)
if dict_da:
for dai in DialogueAct(dict_da):
res_cn.add_merge(1.0, dai)
return res_cn
utterance = self.preprocessing.normalise_utterance(utterance)
abutterance, category_labels, abutterance_lenghts = self.abstract_utterance(utterance)
if verbose:
print 'After preprocessing: "{utt}".'.format(utt=abutterance)
print category_labels
self.parse_non_speech_events(utterance, res_cn)
utterance = utterance.replace_all(['_noise_'], '').replace_all(['_laugh_'], '').replace_all(['_ehm_hmm_'], '').replace_all(['_inhale_'], '')
abutterance = abutterance.replace_all(['_noise_'], '').replace_all(['_laugh_'], '').replace_all(['_ehm_hmm_'], '').replace_all(['_inhale_'], '')
abutterance = self.handle_false_abstractions(abutterance)
category_labels.add('CITY')
category_labels.add('VEHICLE')
category_labels.add('NUMBER')
if len(res_cn) == 0:
if 'STOP' in category_labels:
self.parse_stop(abutterance, res_cn)
if 'CITY' in category_labels:
self.parse_city(abutterance, res_cn)
if 'NUMBER' in category_labels:
self.parse_number(abutterance)
if any([word.startswith("TIME") for word in abutterance]):
category_labels.add('TIME')
if 'TIME' in category_labels:
self.parse_time(abutterance, res_cn)
if 'DATE_REL' in category_labels:
self.parse_date_rel(abutterance, res_cn)
if 'AMPM' in category_labels:
self.parse_ampm(abutterance, res_cn)
if 'VEHICLE' in category_labels:
self.parse_vehicle(abutterance, res_cn)
if 'TASK' in category_labels:
self.parse_task(abutterance, res_cn)
if 'TRAIN_NAME' in category_labels:
self.parse_train_name(abutterance, res_cn)
self.parse_meta(utterance, abutterance_lenghts, res_cn)
return res_cn
