requirements.txt

@@ -2,5 +2,5 @@ flair==0.13.1
 conllu==4.5.3
 pandas==1.5.3
 numpy==1.26.4
-torch==2.3.0
+torch==1.13
 convlab==3.0.2a0

src/service/convlab/policy/policy.py

@@ -1,24 +0,0 @@
-"""Policy Interface"""
-from convlab.util.module import Module
-
-
-class Policy(Module):
-    """Policy module interface."""
-
-    def predict(self, state):
-        """Predict the next agent action given dialog state.
-
-        Args:
-            state (dict or list of list):
-                when the policy takes dialogue state as input, the type is dict.
-                else when the policy takes dialogue act as input, the type is list of list.
-        Returns:
-            action (list of list or str):
-                when the policy outputs dialogue act, the type is list of list.
-                else when the policy outputs utterance directly, the type is str.
-        """
-        return []
-
-    def update_memory(self, utterance_list, state_list, action_list, reward_list):
-        pass
-

src/service/convlab/util/module.py

@@ -1,25 +0,0 @@
-"""module interface."""
-from abc import ABC
-
-
-class Module(ABC):
-
-    def train(self, *args, **kwargs):
-        """Model training entry point"""
-        pass
-
-    def test(self, *args, **kwargs):
-        """Model testing entry point"""
-        pass
-
-    def from_cache(self, *args, **kwargs):
-        """restore internal state for multi-turn dialog"""
-        return None
-
-    def to_cache(self, *args, **kwargs):
-        """save internal state for multi-turn dialog"""
-        return None
-
-    def init_session(self):
-        """Init the class variables for a new session."""
-        pass

src/service/convlab/util/restaurant/dbquery.py

@@ -1,38 +0,0 @@
-"""
-"""
-import json
-import os
-import random
-from fuzzywuzzy import fuzz
-from itertools import chain
-from copy import deepcopy
-
-
-class Database(object):
-    def __init__(self):
-        super(Database, self).__init__()
-        # loading databases
-        domains = ['menu', 'pizza', 'drink', 'size']
-        self.dbs = {}
-        for domain in domains:
-            with open(os.path.join(os.path.dirname(
-                    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))),
-                    'data/restaurant/db/{}_db.json'.format(domain))) as f:
-                self.dbs[domain] = json.load(f)
-
-    def query(self, domain):
-        """Returns the list of entities for a given domain
-        based on the annotation of the belief state"""
-        # query the db
-        if domain == 'pizza':
-            return [{'Name': random.choice(self.dbs[domain]['name'])}]
-        if domain == 'menu':
-            return deepcopy(self.dbs[domain])
-        if domain == 'drink':
-            return [{'Name': random.choice(self.dbs[domain]['name'])}]
-        if domain == 'size':
-            return [{'Size': random.choice(self.dbs[domain]['size'])}]
-
-
-if __name__ == '__main__':
-    db = Database()

src/service/dbquery.py

@@ -0,0 +1,91 @@
+"""
+"""
+import json
+import os
+import random
+from fuzzywuzzy import fuzz
+from itertools import chain
+from copy import deepcopy
+
+
+class Database(object):
+    def __init__(self):
+        super(Database, self).__init__()
+        # loading databases
+        domains = ['restaurant', 'hotel', 'attraction', 'train', 'hospital', 'taxi', 'police']
+        self.dbs = {}
+        for domain in domains:
+            with open(os.path.join(os.path.dirname(
+                    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))),
+                    'data/restaurant/db/{}_db.json'.format(domain))) as f:
+                self.dbs[domain] = json.load(f)
+
+    def query(self, domain, constraints, ignore_open=False, soft_contraints=(), fuzzy_match_ratio=60):
+        """Returns the list of entities for a given domain
+        based on the annotation of the belief state"""
+        # query the db
+        if domain == 'taxi':
+            return [{'taxi_colors': random.choice(self.dbs[domain]['taxi_colors']),
+            'taxi_types': random.choice(self.dbs[domain]['taxi_types']),
+            'taxi_phone': ''.join([str(random.randint(1, 9)) for _ in range(11)])}]
+        if domain == 'police':
+            return deepcopy(self.dbs['police'])
+        if domain == 'hospital':
+            department = None
+            for key, val in constraints:
+                if key == 'department':
+                    department = val
+            if not department:
+                return deepcopy(self.dbs['hospital'])
+            else:
+                return [deepcopy(x) for x in self.dbs['hospital'] if x['department'].lower() == department.strip().lower()]
+        constraints = list(map(lambda ele: ele if not(ele[0] == 'area' and ele[1] == 'center') else ('area', 'centre'), constraints))
+
+        found = []
+        for i, record in enumerate(self.dbs[domain]):
+            constraints_iterator = zip(constraints, [False] * len(constraints))
+            soft_contraints_iterator = zip(soft_contraints, [True] * len(soft_contraints))
+            for (key, val), fuzzy_match in chain(constraints_iterator, soft_contraints_iterator):
+                if val == "" or val == "dont care" or val == 'not mentioned' or val == "don't care" or val == "dontcare" or val == "do n't care":
+                    pass
+                else:
+                    try:
+                        record_keys = [k.lower() for k in record]
+                        if key.lower() not in record_keys:
+                            continue
+                        if key == 'leaveAt':
+                            val1 = int(val.split(':')[0]) * 100 + int(val.split(':')[1])
+                            val2 = int(record['leaveAt'].split(':')[0]) * 100 + int(record['leaveAt'].split(':')[1])
+                            if val1 > val2:
+                                break
+                        elif key == 'arriveBy':
+                            val1 = int(val.split(':')[0]) * 100 + int(val.split(':')[1])
+                            val2 = int(record['arriveBy'].split(':')[0]) * 100 + int(record['arriveBy'].split(':')[1])
+                            if val1 < val2:
+                                break
+                        # elif ignore_open and key in ['destination', 'departure', 'name']:
+                        elif ignore_open and key in ['destination', 'departure']:
+                            continue
+                        elif record[key].strip() == '?':
+                            # '?' matches any constraint
+                            continue
+                        else:
+                            if not fuzzy_match:
+                                if val.strip().lower() != record[key].strip().lower():
+                                    break
+                            else:
+                                if fuzz.partial_ratio(val.strip().lower(), record[key].strip().lower()) < fuzzy_match_ratio:
+                                    break
+                    except:
+                        continue
+            else:
+                res = deepcopy(record)
+                res['Ref'] = '{0:08d}'.format(i)
+                found.append(res)
+
+        return found
+
+
+if __name__ == '__main__':
+    db = Database()
+    print(db.query("train", [['departure', 'cambridge'], ['destination','peterborough'], ['day', 'tuesday'], ['arriveBy', '11:15']]))

src/service/dialog_policy.py

@@ -4,7 +4,7 @@ import json
 from copy import deepcopy
 
 from convlab.policy.policy import Policy
-from convlab.util.restaurant.dbquery import Database
+from dbquery import Database
 
 class SimpleRulePolicy(Policy):
     def __init__(self):
@@ -23,9 +23,9 @@ class SimpleRulePolicy(Policy):
             self.update_system_action(user_act, user_action, state, system_action)
 
         # Reguła 3
-        if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['pizza', 'size', 'drink']):
+        if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['book stay', 'book day', 'book people']):
             if self.results:
-                system_action = {('Ordering', 'Order'): [["Ref", self.results[0].get('Ref', 'N/A')]]}
+                system_action = {('Booking', 'Book'): [["Ref", self.results[0].get('Ref', 'N/A')]]}
 
         system_acts = [[intent, domain, slot, value] for (domain, intent), slots in system_action.items() for slot, value in slots]
         state['system_action'] = system_acts
@@ -53,7 +53,7 @@ class SimpleRulePolicy(Policy):
                 system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])
                 choice = self.results[0]
 
-                if domain in ["pizza", "drink"]:
+                if domain in ["hotel", "attraction", "police", "restaurant"]:
                     system_action[(domain, 'Recommend')].append(['Name', choice['name']])
-                if domain in ["size"]:
-                    system_action[(domain, 'Recommend')].append(['Size', choice['size']])
+
+dialogPolicy = SimpleRulePolicy()