254 lines
8.1 KiB
Plaintext
254 lines
8.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 202,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import os\n",
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"\n",
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"\n",
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"class DST():\n",
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" def __init__(self):\n",
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" self.state = json.load(open('dictionary.json'))\n",
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"\n",
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" def update(self, user_act=None):\n",
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" for intent, domain, slot, value in user_act:\n",
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" domain = domain.lower()\n",
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" intent = intent.lower()\n",
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" slot = slot.lower()\n",
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" \n",
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" if domain not in self.state['belief_state']:\n",
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" continue\n",
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"\n",
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" if intent == 'inform':\n",
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" if slot == 'none' or slot == '':\n",
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" continue\n",
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"\n",
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" domain_dic = self.state['belief_state'][domain]\n",
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"\n",
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" if slot in domain_dic:\n",
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" self.state['belief_state'][domain][slot] = value\n",
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"\n",
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" elif intent == 'request':\n",
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" if domain not in self.state['request_state']:\n",
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" self.state['request_state'][domain] = {}\n",
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" if slot not in self.state['request_state'][domain]:\n",
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" self.state['request_state'][domain][slot] = 0\n",
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" \n",
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" self.state['user_act'] = user_act\n",
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" return self.state\n",
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" def init_session(self):\n",
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" self.state = json.load(open('dictionary.json'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 203,
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"metadata": {},
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"outputs": [],
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"source": [
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"dst = DST()\n",
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"user_act = [('inform', 'payment', 'type', 'karta'), ('inform', 'delivery', 'type','paczkomat'), ('inform', 'product', 'type', 'telefon'), ('request', 'product', 'type', '?')]\n",
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"state = dst.update(user_act)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 204,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'payment': {'type': 'karta', 'amount': '', 'loyalty_card': ''}, 'delivery': {'type': 'paczkomat', 'address': '', 'time': ''}, 'product': {'name': '', 'type': 'telefon', 'brand': '', 'price_range': '', 'price': '', 'quantity': '', 'quality': ''}}\n",
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"{'product': {'type': 0}}\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'user_act': [('inform', 'payment', 'type', 'karta'),\n",
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" ('inform', 'delivery', 'type', 'paczkomat'),\n",
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" ('inform', 'product', 'type', 'telefon'),\n",
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" ('request', 'product', 'type', '?')],\n",
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" 'system_act': [],\n",
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" 'belief_state': {'payment': {'type': 'karta',\n",
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" 'amount': '',\n",
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" 'loyalty_card': ''},\n",
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" 'delivery': {'type': 'paczkomat', 'address': '', 'time': ''},\n",
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" 'product': {'name': '',\n",
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" 'type': 'telefon',\n",
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" 'brand': '',\n",
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" 'price_range': '',\n",
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" 'price': '',\n",
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" 'quantity': '',\n",
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" 'quality': ''}},\n",
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" 'request_state': {'product': {'type': 0}},\n",
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" 'terminated': False,\n",
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" 'history': []}"
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]
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},
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"execution_count": 204,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"print(state['belief_state'])\n",
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"print(state['request_state'])\n",
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"dst.state"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 223,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import defaultdict\n",
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"from convlab.policy.policy import Policy\n",
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"import json\n",
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"\n",
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"class SimpleRulePolicy(Policy):\n",
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" def __init__(self):\n",
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" Policy.__init__(self)\n",
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" self.db = json.load(open('product_db.json'))\n",
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"\n",
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" def predict(self, state):\n",
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" self.results = []\n",
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" system_action = defaultdict(list)\n",
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" user_action = defaultdict(list)\n",
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" system_acts = []\n",
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" for intent, domain, slot, value in state['user_act']:\n",
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" user_action[(domain.lower(), intent.lower())].append((slot.lower(), value))\n",
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" for user_act in user_action:\n",
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" self.update_system_action(user_act, user_action, state, system_action)\n",
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" system_acts = [[intent, domain, slot, value] for (domain, intent), slots in system_action.items() for slot, value in slots]\n",
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" state['system_act'] = system_acts\n",
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" return system_acts\n",
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"\n",
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"\n",
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" def update_system_action(self, user_act, user_action, state, system_action):\n",
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" domain, intent = user_act\n",
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" constraints = [(slot, value) for slot, value in state['belief_state'][domain.lower()].items() if value != '']\n",
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" self.results = self.db['database'][domain]\n",
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"\n",
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" # Reguła 1\n",
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" if intent == 'request':\n",
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" if len(self.results) == 0:\n",
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" system_action[(domain, 'NoOffer')] = []\n",
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" else:\n",
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" for slot in user_action[user_act]:\n",
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" if self.results and slot[0] in self.results[0]:\n",
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" system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(slot[0], 'unknown')])\n",
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"\n",
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" # Reguła 2\n",
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" elif intent == 'inform':\n",
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" if len(self.results) == 0:\n",
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" system_action[(domain, 'NoOffer')] = []\n",
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" else:\n",
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" system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])\n",
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" for product in self.results:\n",
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" if all(product.get(slot, '').lower() == value.lower() for slot, value in constraints):\n",
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" system_action[(domain, 'Recommend')].append(['Name', product['name']])\n",
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" break\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 224,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[['Inform', 'product', 'Choice', '11'],\n",
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" ['Recommend', 'product', 'Name', 'RedBull']]"
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]
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},
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"execution_count": 224,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dst = DST()\n",
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"user_act = [('inform', 'product', 'type', 'energol')]\n",
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"state = dst.update(user_act)\n",
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"policy = SimpleRulePolicy()\n",
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"policy.predict(state)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 225,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:root:nlu info_dict is not initialized\n",
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"WARNING:root:dst info_dict is not initialized\n",
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"WARNING:root:policy info_dict is not initialized\n",
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"WARNING:root:nlg info_dict is not initialized\n"
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]
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}
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],
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"source": [
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"from convlab.dialog_agent import PipelineAgent\n",
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"policy = SimpleRulePolicy()\n",
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"agent = PipelineAgent(nlu=None, dst=dst, policy=policy, nlg=None, name='sys')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 228,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[['Inform', 'product', 'Choice', '11'],\n",
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" ['Recommend', 'product', 'Name', 'pomidor']]"
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]
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},
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"execution_count": 228,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.response([('inform', 'product', 'type', 'warzywo'), ('inform', 'product', 'price_range', 'tani'), ('inform', 'product', 'quality', 'exquisite')])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "py38",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.16"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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