Python DialogueActConfusionNetwork.add方法代码示例

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    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))

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def _resolve_user_da_in_context(self, user_da, system_da):
        """Resolves and converts meaning of some user dialogue acts
        given the context."""
        old_user_da = deepcopy(user_da)
        new_user_da = DialogueActConfusionNetwork()

        if isinstance(system_da, DialogueAct):
            for system_dai in system_da:
                for prob, user_dai in user_da:
                    new_user_dai = None

                    if system_dai.dat == "confirm" and user_dai.dat == "affirm":
                        new_user_dai = DialogueActItem("inform", system_dai.name, system_dai.value)

                    elif system_dai.dat == "confirm" and user_dai.dat == "negate":
                        new_user_dai = DialogueActItem("deny", system_dai.name, system_dai.value)

                    elif system_dai.dat == "request" and user_dai.dat == "inform" and \
                                    user_dai.name in self.ontology['context_resolution'] and \
                                    system_dai.name in self.ontology['context_resolution'][user_dai.name] and \
                                    user_dai.value == "dontcare":
                        new_user_dai = DialogueActItem("inform", system_dai.name, system_dai.value)

                    elif system_dai.dat == "request" and user_dai.dat == "inform" and \
                                    user_dai.name in self.ontology['context_resolution'] and \
                                    system_dai.name in self.ontology['context_resolution'][user_dai.name] and \
                                    self.ontology.slot_has_value(system_dai.name, user_dai.value):
                        new_user_dai = DialogueActItem("inform", system_dai.name, user_dai.value)

                    elif system_dai.dat == "request" and system_dai.name != "" and \
                                    user_dai.dat == "affirm" and self.ontology.slot_is_binary(system_dai.name):
                        new_user_dai = DialogueActItem("inform", system_dai.name, "true")

                    elif system_dai.dat == "request" and system_dai.name != "" and \
                                    user_dai.dat == "negate" and self.ontology.slot_is_binary(system_dai.name):
                        new_user_dai = DialogueActItem("inform", system_dai.name, "false")

                    if new_user_dai:
                        new_user_da.add(prob, new_user_dai)

        old_user_da.merge(new_user_da, combine='max')

        return old_user_da

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def _infer_last_talked_about_slots(self, user_da, system_da):
        """This adds dialogue act items to support inference of the last slots the user talked about."""
        old_user_da = deepcopy(user_da)
        new_user_da = DialogueActConfusionNetwork()

        colliding_slots = {}
        done_slots = set()

        for prob, user_dai in user_da:
            new_user_dais = []
            lta_tsvs = self.ontology.last_talked_about(user_dai.dat, user_dai.name, user_dai.value)

            for name, value in lta_tsvs:
                new_user_dais.append(DialogueActItem("inform", name, value))
                if name in done_slots:
                    if not name in colliding_slots:
                        colliding_slots[name] = set()
                    colliding_slots[name].add(value)
                else:
                    done_slots.add(name)

            if new_user_dais:
                for nudai in new_user_dais:
                    if not nudai in new_user_da:
                        new_user_da.add(prob, nudai)

        # In case of collisions, prefer the current last talked about values if it is one of the colliding values.
        # If there is a collision and the current last talked about value is not among the colliding values, do not
        # consider the colliding DA's at all.
        invalid_das = set()
        for prob, da in set(new_user_da):
            if da.name in colliding_slots and self[da.name].mpv() in colliding_slots[da.name]:
                if not da.value == self[da.name].mpv():
                    invalid_das.add(da)
            elif da.name in colliding_slots:
                invalid_das.add(da)

        for invalid_da in invalid_das:
            new_user_da.remove(invalid_da)

        old_user_da.merge(new_user_da, combine='max')

        return old_user_da

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def last_talked_about(self, user_da, system_da):
        """This adds dialogue act items to support inference of the last slots the user talked about."""
        old_user_da = deepcopy(user_da)
        new_user_da = DialogueActConfusionNetwork()

        for prob, user_dai in user_da:
            new_user_dais = []
            lta_tsvs = self.ontology.last_talked_about(user_dai.dat, user_dai.name, user_dai.value)

            for name, value in lta_tsvs:
                new_user_dais.append(DialogueActItem("inform", name, value))

            if new_user_dais:
                for nudai in new_user_dais:
                    new_user_da.add(prob, nudai)

        old_user_da.extend(new_user_da)

        return old_user_da

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    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

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def test_get_prob(self):
        dacn = DialogueActConfusionNetwork()
        dacn.add(0.2, DialogueActItem(dai='inform(food=chinese)'))
        dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
        dacn.add(0.1, DialogueActItem(dai='inform(food=russian)'))

        self.assertAlmostEqual(dacn._get_prob([0, 1, 1]), 0.2 * 0.3 * 0.9)
        self.assertAlmostEqual(dacn._get_prob([0, 0, 0]), 0.2 * 0.7 * 0.1)

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    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)

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def test_merge(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)'))

        dacn.merge(dacn, combine='max')

        # Russian food should be pruned.
        dacn.sort().prune()
        self.assertTrue(not DialogueActItem(dai='inform(food=russian)') in dacn)

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def test_sort(self):
        dacn = DialogueActConfusionNetwork()
        dacn.add(0.05, DialogueActItem(dai='inform(food=chinese)'))
        dacn.add(1.0, DialogueActItem(dai='inform(food=czech)'))
        dacn.add(0.00005, DialogueActItem(dai='inform(food=russian)'))

        dacn.sort()

        cn = list(dacn)
        self.assertEqual(cn[0][1], DialogueActItem(dai='inform(food=czech)'))
        self.assertEqual(cn[1][1], DialogueActItem(dai='inform(food=chinese)'))
        self.assertEqual(cn[2][1], DialogueActItem(dai='inform(food=russian)'))

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
def main():
    # initialize tracker and state
    slots = ["food", "location"]
    tr = DSTCTracker(slots)
    state = DSTCState(slots)
    state.pprint()

    # try to update state with some information
    print '---'
    cn = DialogueActConfusionNetwork()
    cn.add(0.3, DialogueActItem("inform", "food", "chinese"))
    cn.add(0.1, DialogueActItem("inform", "food", "indian"))
    tr.update_state(state, cn)
    state.pprint()

    # try to deny some information
    print '---'
    cn.add(0.9, DialogueActItem("deny", "food", "chinese"))
    cn.add(0.1, DialogueActItem("deny", "food", "indian"))
    tr.update_state(state, cn)
    state.pprint()

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def test_get_platform_res_da(self):
        hdc_policy = self._build_policy()

        state = DeterministicDiscriminativeDialogueState(self.cfg, self.ontology)

        system_input = DialogueActConfusionNetwork()

        res = hdc_policy.get_da(state)

        user_input = DialogueActConfusionNetwork()
        user_input.add(1.0, DialogueActItem(dai='info(task=find_platform)'))
        user_input.add(1.0, DialogueActItem(dai='inform(from_stop=Praha)'))
        user_input.add(1.0, DialogueActItem(dai='inform(to_stop=Brno)'))

        state.update(user_input, system_input)
        res = hdc_policy.get_da(state)

        self.assert_('inform(not_supported)' in res)

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def _build_user_input(self, *args):
        user_input = DialogueActConfusionNetwork()
        for arg in args:
            user_input.add(1.0, DialogueActItem(dai=arg))

        return user_input

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def test_get_best_nonnull_da(self):
        dacn = DialogueActConfusionNetwork()
        dacn.add(0.2, DialogueActItem(dai='inform(food=chinese)'))
        dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
        dacn.add(0.1, DialogueActItem(dai='inform(food=russian)'))

        da_nn = dacn.get_best_nonnull_da()
        self.assertEqual(len(da_nn), 1)
        self.assertEqual(da_nn.dais[0], DialogueActItem(dai='inform(food=czech)'))

        dacn = DialogueActConfusionNetwork()
        dacn.add(0.075, DialogueActItem(dai='inform(food=chinese)'))
        dacn.add(0.7, DialogueActItem(dai='null()'))
        dacn.add(0.15, DialogueActItem(dai='inform(food=czech)'))
        dacn.add(0.075, DialogueActItem(dai='inform(food=russian)'))

        da_nn = dacn.get_best_nonnull_da()
        self.assertEqual(len(da_nn), 1)

        self.assertEqual(da_nn.dais[0], DialogueActItem(dai='inform(food=czech)'))

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    def test_get_best_da_hyp(self):
        # Test case when only one dai should be included in the hyp.
        dacn = DialogueActConfusionNetwork()
        dacn.add(0.2, DialogueActItem(dai='inform(food=chinese)'))
        dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
        dacn.add(0.1, DialogueActItem(dai='inform(food=russian)'))

        best_hyp = dacn.get_best_da_hyp(use_log=False)
        self.assertAlmostEqual(best_hyp.prob, 0.8 * 0.7 * 0.9)
        self.assertEqual(len(best_hyp.da), 1)

        # Test case when 2 dais should be included in the hyp.
        dacn = DialogueActConfusionNetwork()
        dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
        dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
        dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))

        best_hyp = dacn.get_best_da_hyp(use_log=False)
        self.assertAlmostEqual(best_hyp.prob, 0.9 * 0.7 * 0.9)
        self.assertEqual(len(best_hyp.da), 2)

        # Test the case with logarithms.
        dacn = DialogueActConfusionNetwork()
        dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
        dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
        dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))

        best_hyp = dacn.get_best_da_hyp(use_log=True)
        self.assertAlmostEqual(best_hyp.prob, math.log(0.9 * 0.7 * 0.9))
        self.assertEqual(len(best_hyp.da), 2)

        # Test the case with manual thresholds.
        dacn = DialogueActConfusionNetwork()
        dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
        dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
        dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))

        best_hyp = dacn.get_best_da_hyp(
            use_log=True, threshold=0.1, thresholds={
                DialogueActItem(dai='inform(food=chinese)'): 0.5,
                DialogueActItem(dai='inform(food=czech)'): 0.9,
                DialogueActItem(dai='inform(food=russian)'): 0.5
            })
        # Test food=czech should NOT be included.
        self.assertAlmostEqual(best_hyp.prob, math.log(0.9 * 0.3 * 0.9))
        self.assertEqual(len(best_hyp.da), 1)
        self.assertTrue(not DialogueActItem(dai='inform(food=czech)') in best_hyp.da)

        dacn = DialogueActConfusionNetwork()
        dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
        dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
        dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))

        best_hyp = dacn.get_best_da_hyp(
            use_log=True, threshold=0.1, thresholds={
                DialogueActItem(dai='inform(food=chinese)'): 0.5,
                DialogueActItem(dai='inform(food=czech)'): 0.5,
                DialogueActItem(dai='inform(food=russian)'): 0.5
            })
        # Test food=czech should be included.
        self.assertAlmostEqual(best_hyp.prob, math.log(0.9 * 0.7 * 0.9))
        self.assertEqual(len(best_hyp.da), 2)
        self.assertTrue(DialogueActItem(dai='inform(food=czech)') in best_hyp.da)

# 需要导入模块: from alex.components.slu.da import DialogueActConfusionNetwork [as 别名]
# 或者: from alex.components.slu.da.DialogueActConfusionNetwork import add [as 别名]
    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(p[0][1], dai)
            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

                da_confnet.add(p[0][1], self.parsed_classifiers[clser])

        da_confnet.sort().merge().prune()

        return da_confnet