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71f525ec07
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flair==0.13.1
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conllu==4.5.3
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pandas==1.5.3
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numpy==1.26.4
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numpy==1.24.4
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torch==2.3.0
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fuzzywuzzy==0.18.0
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24
src/service/convlab/policy/policy.py
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24
src/service/convlab/policy/policy.py
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"""Policy Interface"""
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from convlab.util.module import Module
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class Policy(Module):
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"""Policy module interface."""
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def predict(self, state):
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"""Predict the next agent action given dialog state.
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Args:
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state (dict or list of list):
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when the policy takes dialogue state as input, the type is dict.
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else when the policy takes dialogue act as input, the type is list of list.
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Returns:
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action (list of list or str):
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when the policy outputs dialogue act, the type is list of list.
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else when the policy outputs utterance directly, the type is str.
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"""
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return []
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def update_memory(self, utterance_list, state_list, action_list, reward_list):
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pass
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25
src/service/convlab/util/module.py
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25
src/service/convlab/util/module.py
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"""module interface."""
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from abc import ABC
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class Module(ABC):
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def train(self, *args, **kwargs):
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"""Model training entry point"""
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pass
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def test(self, *args, **kwargs):
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"""Model testing entry point"""
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pass
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def from_cache(self, *args, **kwargs):
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"""restore internal state for multi-turn dialog"""
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return None
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def to_cache(self, *args, **kwargs):
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"""save internal state for multi-turn dialog"""
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return None
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def init_session(self):
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"""Init the class variables for a new session."""
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pass
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38
src/service/convlab/util/restaurant/dbquery.py
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38
src/service/convlab/util/restaurant/dbquery.py
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"""
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"""
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import json
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import os
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import random
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from fuzzywuzzy import fuzz
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from itertools import chain
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from copy import deepcopy
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class Database(object):
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def __init__(self):
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super(Database, self).__init__()
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# loading databases
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domains = ['menu', 'pizza', 'drink', 'size']
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self.dbs = {}
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for domain in domains:
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with open(os.path.join(os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))),
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'data/restaurant/db/{}_db.json'.format(domain))) as f:
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self.dbs[domain] = json.load(f)
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def query(self, domain):
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"""Returns the list of entities for a given domain
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based on the annotation of the belief state"""
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# query the db
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if domain == 'pizza':
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return [{'Name': random.choice(self.dbs[domain]['name'])}]
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if domain == 'menu':
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return deepcopy(self.dbs[domain])
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if domain == 'drink':
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return [{'Name': random.choice(self.dbs[domain]['name'])}]
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if domain == 'size':
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return [{'Size': random.choice(self.dbs[domain]['size'])}]
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if __name__ == '__main__':
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db = Database()
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4
src/service/data/restaurant/db/confirm_db.json
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4
src/service/data/restaurant/db/confirm_db.json
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[
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"true",
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"false"
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]
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5
src/service/data/restaurant/db/dough_db.json
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5
src/service/data/restaurant/db/dough_db.json
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[
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"pepsi",
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"cola",
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"water"
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]
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11
src/service/data/restaurant/db/drink_db.json
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src/service/data/restaurant/db/drink_db.json
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[
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{
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"name":"pepsi"
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},
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{
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"name":"cola"
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},
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{
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"name":"water"
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}
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]
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3
src/service/data/restaurant/db/food_db.json
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3
src/service/data/restaurant/db/food_db.json
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[
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"pizza"
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]
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5
src/service/data/restaurant/db/meat_db.json
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5
src/service/data/restaurant/db/meat_db.json
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[
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"chicken",
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"ham",
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"tuna"
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]
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7
src/service/data/restaurant/db/menu_db.json
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7
src/service/data/restaurant/db/menu_db.json
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[
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"capri",
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"margarita",
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"hawajska",
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"barcelona",
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"tuna"
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]
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51
src/service/data/restaurant/db/pizza_db.json
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src/service/data/restaurant/db/pizza_db.json
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[
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{
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"name": "capri",
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"ingredient": [
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"tomato",
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"ham",
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"mushrooms",
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"cheese"
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],
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"price": 25
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},
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{
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"name": "margarita",
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"ingredient": [
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"tomato",
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"cheese"
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],
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"price": 20
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},
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{
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"name": "hawajska",
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"ingredient": [
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"tomato",
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"pineapple",
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"chicken",
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"cheese"
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],
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"price": 30
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},
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{
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"name": "barcelona",
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"ingredient": [
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"tomato",
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"onion",
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"ham",
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"pepper",
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"cheese"
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],
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"price": 40
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},
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{
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"name": "tuna",
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"ingredient": [
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"tomato",
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"tuna",
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"onion",
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"cheese"
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],
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"price": 40
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}
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]
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4
src/service/data/restaurant/db/sauce_db.json
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4
src/service/data/restaurant/db/sauce_db.json
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[
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"garlic",
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"1000w"
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]
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14
src/service/data/restaurant/db/size_db.json
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src/service/data/restaurant/db/size_db.json
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[
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{
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"size": "m",
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"price_multiplier": 1
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},
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{
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"size": "l",
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"price_multiplier": 1.2
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},
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{
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"size": "xl",
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"price_multiplier": 1.4
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}
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]
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@ -1,8 +1,61 @@
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from model.frame import Frame
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from collections import defaultdict
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import copy
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import json
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from copy import deepcopy
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class DialogPolicy:
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def next_dialogue_act(self, frames: list[Frame]) -> Frame:
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if frames[-1].act == "welcomemsg":
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return Frame("system", "welcomemsg", [])
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else:
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return Frame("system", "canthelp", [])
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from convlab.policy.policy import Policy
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from convlab.util.restaurant.dbquery import Database
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class SimpleRulePolicy(Policy):
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def __init__(self):
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Policy.__init__(self)
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self.db = Database()
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def predict(self, state):
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self.results = []
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system_action = defaultdict(list)
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user_action = defaultdict(list)
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for intent, domain, slot, value in state['user_action']:
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user_action[(domain.lower(), intent.lower())].append((slot.lower(), value))
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for user_act in user_action:
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self.update_system_action(user_act, user_action, state, system_action)
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# Reguła 3
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if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['pizza', 'size', 'drink']):
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if self.results:
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system_action = {('Ordering', 'Order'): [["Ref", self.results[0].get('Ref', 'N/A')]]}
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system_acts = [[intent, domain, slot, value] for (domain, intent), slots in system_action.items() for slot, value in slots]
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state['system_action'] = system_acts
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return system_acts
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def update_system_action(self, user_act, user_action, state, system_action):
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domain, intent = user_act
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constraints = [(slot, value) for slot, value in state['belief_state'][domain.lower()].items() if value != '']
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self.results = deepcopy(self.db.query(domain.lower(), constraints))
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# Reguła 1
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if intent == 'request':
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if len(self.results) == 0:
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system_action[(domain, 'NoOffer')] = []
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else:
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for slot in user_action[user_act]:
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if slot[0] in self.results[0]:
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system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(slot[0], 'unknown')])
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# Reguła 2
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elif intent == 'inform':
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if len(self.results) == 0:
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system_action[(domain, 'NoOffer')] = []
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else:
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system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])
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choice = self.results[0]
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if domain in ["pizza", "drink"]:
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system_action[(domain, 'Recommend')].append(['Name', choice['name']])
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if domain in ["size"]:
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system_action[(domain, 'Recommend')].append(['Size', choice['size']])
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dialogPolicy = SimpleRulePolicy()
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