added hotels data

This commit is contained in:
464962 2024-05-27 20:53:07 +02:00
parent a7e1d271e9
commit c71bbc071d
2 changed files with 361 additions and 178 deletions

254
hotels_data.json Normal file
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@ -0,0 +1,254 @@
[
{
"name": "Hotel Marriott",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Hotel Cambridge",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Hotel Belfry",
"area": "suburbs",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Aylesbray Guest House",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "guesthouse"
},
{
"name": "University Arms Hotel",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Lensfield Hotel",
"area": "north",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Autumn House Hotel",
"area": "east",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "guesthouse"
},
{
"name": "Finches Bed and Breakfast",
"area": "west",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "bed and breakfast"
},
{
"name": "Arbury Lodge Guest House",
"area": "north",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "guesthouse"
},
{
"name": "Royal Cambridge Hotel",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Hilton Hotel",
"area": "suburbs",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Holiday Inn Hotel",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Radisson Hotel",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Regent Guest House",
"area": "north",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "guesthouse"
},
{
"name": "Travelodge Hotel",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Premier Inn Hotel",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Ibis Hotel",
"area": "suburbs",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Novotel Hotel",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Mercure Hotel",
"area": "suburbs",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Crowne Plaza Hotel",
"area": "centre",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Best Western Hotel",
"area": "north",
"parking": "yes",
"price range": "cheap",
"stars": "4",
"internet": "yes",
"type": "hotel"
},
{
"name": "Marriott Hotel",
"area": "west",
"parking": "yes",
"price range": "expensive",
"stars": "5",
"internet": "yes",
"type": "hotel"
},
{
"name": "Hyatt Regency Hotel",
"area": "south",
"parking": "yes",
"price range": "expensive",
"stars": "5",
"internet": "yes",
"type": "hotel"
},
{
"name": "Four Seasons Hotel",
"area": "centre",
"parking": "yes",
"price range": "expensive",
"stars": "5",
"internet": "yes",
"type": "hotel"
},
{
"name": "The Ritz Hotel",
"area": "centre",
"parking": "yes",
"price range": "expensive",
"stars": "5",
"internet": "yes",
"type": "hotel"
},
{
"name": "The Savoy Hotel",
"area": "centre",
"parking": "yes",
"price range": "expensive",
"stars": "5",
"internet": "yes",
"type": "hotel"
},
{
"name": "Mandarin Oriental Hotel",
"area": "suburbs",
"parking": "yes",
"price range": "expensive",
"stars": "5",
"internet": "yes",
"type": "hotel"
},
{
"name": "Shangri-La Hotel",
"area": "centre",
"parking": "yes",
"price range": "expensive",
"stars": "5",
"internet": "yes",
"type": "hotel"
}
]

View File

@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 212,
"id": "706dd5e1-57ee-416b-a77c-5d15df8dbdc8",
"metadata": {},
"outputs": [],
@ -43,63 +43,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"id": "423f0821-000a-4aaa-b400-2e7554866175",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'user_action': [],\n",
" 'system_action': [],\n",
" 'belief_state': {'attraction': {'type': '', 'name': '', 'area': ''},\n",
" 'hotel': {'name': '',\n",
" 'area': '',\n",
" 'parking': '',\n",
" 'price range': '',\n",
" 'stars': '4',\n",
" 'internet': 'yes',\n",
" 'type': 'hotel',\n",
" 'book stay': '',\n",
" 'book day': '',\n",
" 'book people': ''},\n",
" 'restaurant': {'food': '',\n",
" 'price range': '',\n",
" 'name': '',\n",
" 'area': '',\n",
" 'book time': '',\n",
" 'book day': '',\n",
" 'book people': ''},\n",
" 'taxi': {'leave at': '',\n",
" 'destination': '',\n",
" 'departure': '',\n",
" 'arrive by': ''},\n",
" 'train': {'leave at': '',\n",
" 'destination': '',\n",
" 'day': '',\n",
" 'arrive by': '',\n",
" 'departure': '',\n",
" 'book people': ''},\n",
" 'hospital': {'department': ''}},\n",
" 'booked': {},\n",
" 'request_state': {},\n",
" 'terminated': False,\n",
" 'history': []}"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from convlab.util.multiwoz.state import default_state\n",
"default_state()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 213,
"id": "06926543-cab1-48e7-8e82-0560fc0fa16a",
"metadata": {},
"outputs": [],
@ -109,11 +53,42 @@
"from convlab.dst.dst import DST\n",
"from convlab.dst.rule.multiwoz.dst_util import normalize_value\n",
"\n",
"class SimpleRuleDST(DST):\n",
"\n",
"def default_state():\n",
" return {\n",
" 'belief_state': {\n",
" 'hotel': {\n",
" 'info': {\n",
" 'name': '',\n",
" 'area': '',\n",
" 'parking': '',\n",
" 'price range': '',\n",
" 'stars': '',\n",
" 'internet': '',\n",
" 'type': ''\n",
" },\n",
" 'booking': {\n",
" 'book stay': '',\n",
" 'book day': '',\n",
" 'book people': ''\n",
" }\n",
" }\n",
" },\n",
" 'request_state': {},\n",
" 'history': [],\n",
" 'user_action': [],\n",
" 'system_action': [],\n",
" 'terminated': False,\n",
" 'booked': []\n",
" }\n",
"\n",
"\n",
"class DialogueStateTracker(DST):\n",
" def __init__(self):\n",
" DST.__init__(self)\n",
" self.state = default_state()\n",
" self.value_dict = json.load(open('value_dict.json'))\n",
" with open('./hotels_data.json') as f:\n",
" self.value_dict = json.load(f)\n",
"\n",
" def update(self, user_act=None):\n",
" for intent, domain, slot, value in user_act:\n",
@ -125,14 +100,14 @@
" continue\n",
"\n",
" if intent == 'inform':\n",
" if slot == 'none' or slot == '':\n",
" if slot == 'none' or slot == '' or value == 'dontcare':\n",
" continue\n",
"\n",
" domain_dic = self.state['belief_state'][domain]\n",
" domain_dic = self.state['belief_state'][domain]['info']\n",
"\n",
" if slot in domain_dic:\n",
" nvalue = normalize_value(self.value_dict, domain, slot, value)\n",
" self.state['belief_state'][domain][slot] = nvalue\n",
" nvalue = self.normalize_value(self.value_dict, domain, slot, value)\n",
" self.state['belief_state'][domain]['info'][slot] = nvalue\n",
"\n",
" elif intent == 'request':\n",
" if domain not in self.state['request_state']:\n",
@ -142,55 +117,21 @@
"\n",
" return self.state\n",
"\n",
" def normalize_value(self, value_dict, domain, slot, value):\n",
" normalized_value = value.lower().strip()\n",
" if domain in value_dict and slot in value_dict[domain]:\n",
" possible_values = value_dict[domain][slot]\n",
" if isinstance(possible_values, dict) and normalized_value in possible_values:\n",
" return possible_values[normalized_value]\n",
" return value\n",
"\n",
" def init_session(self):\n",
" self.state = default_state()"
" self.state = default_state()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b1d42d5f-e923-4c46-a930-48da9b72d77b",
"metadata": {},
"outputs": [],
"source": [
"dst = SimpleRuleDST()\n",
"dst.state"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "749e3a90-17c3-4a3e-acd7-856560445eaf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': '',\n",
" 'area': '',\n",
" 'parking': 'yes',\n",
" 'price range': 'cheap',\n",
" 'stars': '4',\n",
" 'internet': 'yes',\n",
" 'type': 'hotel',\n",
" 'book stay': '',\n",
" 'book day': '',\n",
" 'book people': ''}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dst.update([['Inform', 'Hotel', 'Price Range', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])\n",
"dst.state['belief_state']['hotel']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 214,
"id": "a7f3d067-3a95-4ef5-b216-be5840bc8831",
"metadata": {},
"outputs": [],
@ -203,11 +144,27 @@
"from convlab.policy.policy import Policy\n",
"from convlab.util.multiwoz.dbquery import Database\n",
"\n",
"db_path = './hotels_data.json'\n",
"\n",
"class SimpleRulePolicy(Policy):\n",
"class DialoguePolicy(Policy):\n",
" def __init__(self):\n",
" Policy.__init__(self)\n",
" self.db = Database()\n",
" self.db = self.load_database(db_path)\n",
"\n",
" def load_database(self, db_path):\n",
" with open(db_path, 'r', encoding='utf-8') as f:\n",
" return json.load(f)\n",
"\n",
" def query(self, domain, constraints):\n",
" if domain != 'hotel':\n",
" return []\n",
" \n",
" results = []\n",
" for entry in self.db:\n",
" match = all(entry.get(key) == value for key, value in constraints)\n",
" if match:\n",
" results.append(entry)\n",
" return results\n",
"\n",
" def predict(self, state):\n",
" self.results = []\n",
@ -220,7 +177,6 @@
" for user_act in user_action:\n",
" self.update_system_action(user_act, user_action, state, system_action)\n",
"\n",
" # Reguła 3\n",
" if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['book stay', 'book day', 'book people']):\n",
" if self.results:\n",
" system_action = {('Booking', 'Book'): [[\"Ref\", self.results[0].get('Ref', 'N/A')]]}\n",
@ -231,10 +187,11 @@
"\n",
" def update_system_action(self, user_act, user_action, state, system_action):\n",
" domain, intent = user_act\n",
" constraints = [(slot, value) for slot, value in state['belief_state'][domain.lower()].items() if value != '']\n",
" self.results = deepcopy(self.db.query(domain.lower(), constraints))\n",
" constraints = [(slot, value) for slot, value in state['belief_state'][domain]['info'].items() if value != '']\n",
" print(f\"Constraints: {constraints}\")\n",
" self.results = deepcopy(self.query(domain.lower(), constraints))\n",
" print(f\"Query results: {self.results}\")\n",
"\n",
" # Reguła 1\n",
" if intent == 'request':\n",
" if len(self.results) == 0:\n",
" system_action[(domain, 'NoOffer')] = []\n",
@ -243,7 +200,6 @@
" if slot[0] in self.results[0]:\n",
" system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(slot[0], 'unknown')])\n",
"\n",
" # Reguła 2\n",
" elif intent == 'inform':\n",
" if len(self.results) == 0:\n",
" system_action[(domain, 'NoOffer')] = []\n",
@ -251,60 +207,17 @@
" system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])\n",
" choice = self.results[0]\n",
"\n",
" if domain in [\"hotel\", \"attraction\", \"police\", \"restaurant\"]:\n",
" system_action[(domain, 'Recommend')].append(['Name', choice['name']])"
" if domain in [\"hotel\"]:\n",
" system_action[(domain, 'Recommend')].append(['Name', choice['name']])\n",
" for slot in state['belief_state'][domain]['info']:\n",
" if choice.get(slot):\n",
" state['belief_state'][domain]['info'][slot] = choice[slot]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "089dbfa8-d34a-457c-9084-ef335372ea05",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:root:nlu info_dict is not initialized\n",
"WARNING:root:dst info_dict is not initialized\n",
"WARNING:root:policy info_dict is not initialized\n",
"WARNING:root:nlg info_dict is not initialized\n"
]
}
],
"source": [
"from convlab.dialog_agent import PipelineAgent\n",
"dst.init_session()\n",
"policy = SimpleRulePolicy()\n",
"agent = PipelineAgent(nlu=None, dst=dst, policy=policy, nlg=None, name='sys')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "5ac57cc8-6650-4a1b-a87e-2cda67d9b0f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[['Inform', 'hotel', 'Choice', '3'],\n",
" ['Recommend', 'hotel', 'Name', 'huntingdon marriott hotel']]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.response([['Inform', 'Hotel', 'Price Range', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "eaeca7b0-08d5-4db0-9eb3-3aceda24f987",
"execution_count": 218,
"id": "11f34b20-c5b0-4752-8610-21f5eef4b569",
"metadata": {},
"outputs": [
{
@ -326,34 +239,50 @@
}
],
"source": [
"from convlab.base_models.t5.nlu import T5NLU\n",
"from convlab.nlg.template.multiwoz import TemplateNLG\n",
"from convlab.dialog_agent import PipelineAgent\n",
"\n",
"# nlu = T5NLU(speaker='user', context_window_size=0, model_name_or_path='ConvLab/t5-small-nlu-multiwoz21')\n",
"nlu = NaturalLanguageAnalyzer()\n",
"dst = DialogueStateTracker()\n",
"policy = DialoguePolicy()\n",
"nlg = TemplateNLG(is_user=False)\n",
"\n",
"agent = PipelineAgent(nlu=nlu, dst=dst, policy=policy, nlg=nlg, name='sys')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "b559fcd3-861b-49d7-ac2b-d3160d4c5a1d",
"execution_count": 219,
"id": "faf05778-2bca-4044-97a7-d6facf853e10",
"metadata": {},
"outputs": [],
"source": [
"# nla = NaturalLanguageAnalyzer()\n",
"# nla_response = nla.predict(\"chciałbym zarezerwować drogi hotel bez parkingu 1 stycznia w Warszawie w centrum\")\n",
"# print(nla_response)\n",
"# response = agent.response(nla_response)\n",
"# print(response)"
]
},
{
"cell_type": "code",
"execution_count": 220,
"id": "6c837788-e7d5-483e-b873-00061f118619",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'We have 3 such places . Would huntingdon marriott hotel work for you ?'"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
"name": "stdout",
"output_type": "stream",
"text": [
"Constraints: [('area', 'centre'), ('parking', 'yes'), ('price range', 'expensive'), ('type', 'hotel')]\n",
"Query results: [{'name': 'Four Seasons Hotel', 'area': 'centre', 'parking': 'yes', 'price range': 'expensive', 'stars': '5', 'internet': 'yes', 'type': 'hotel'}, {'name': 'The Ritz Hotel', 'area': 'centre', 'parking': 'yes', 'price range': 'expensive', 'stars': '5', 'internet': 'yes', 'type': 'hotel'}, {'name': 'The Savoy Hotel', 'area': 'centre', 'parking': 'yes', 'price range': 'expensive', 'stars': '5', 'internet': 'yes', 'type': 'hotel'}, {'name': 'Shangri-La Hotel', 'area': 'centre', 'parking': 'yes', 'price range': 'expensive', 'stars': '5', 'internet': 'yes', 'type': 'hotel'}]\n",
"We have 4 such places . Four Seasons Hotel looks like it would be a good choice .\n"
]
}
],
"source": [
"agent.response(\"I need a cheap hotel with free parking .\")"
"response = agent.response(\"chciałbym zarezerwować drogi hotel z parkingiem 1 stycznia w Warszawie w centrum\")\n",
"print(response)"
]
},
{