{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4af8e091", "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "class Rules_DST(): \n", "\n", " def __init__(self):\n", " self.state = json.load(open('data.json'))\n", "\n", " def update_user(self, user_acts=None):\n", " for intent, domain, slot, value in user_acts:\n", " domain = domain.lower()\n", " intent = intent.lower()\n", " slot = slot.lower()\n", " if intent == 'start_conversation':\n", " continue\n", "\n", " elif intent == 'end_conversation':\n", " self.state = json.load(open('data.json'))\n", " elif domain not in self.state['belief_state']:\n", " continue\n", " \n", " \n", " elif 'inform' in intent:\n", " if (slot == 'inform'):\n", " continue\n", " \n", " if(domain in slot):\n", " slot.replace(domain + \"/\", '')\n", "\n", " domain_dic = self.state['belief_state'][domain]\n", " if slot in domain_dic:\n", " self.state['belief_state'][domain][slot] = value\n", " \n", " \n", " elif intent == 'request':\n", " if domain not in self.state['request_state']:\n", " self.state['request_state'][domain] = {}\n", " if slot not in self.state['request_state'][domain]:\n", " self.state['request_state'][domain][slot] = 0\n", " else:\n", " self.state['request_state'][domain][slot] = value\n", " \n", " elif intent == 'start_conversation':\n", " self.state[\"user_action\"].append([intent, domain, slot, value])\n", " continue\n", "\n", " elif intent == 'end_conversation':\n", " self.state = json.load(open('data.json'))\n", " \n", " self.state[\"user_action\"].append([intent, domain, slot, value])\n", " return self.state" ] }, { "cell_type": "code", "execution_count": 2, "id": "09903205", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'user_action': [],\n", " 'system_action': [],\n", " 'belief_state': {'food': {'name': '',\n", " 'type': '',\n", " 'price_range': '',\n", " 'size': '',\n", " 'ingredients': ''},\n", " 'drink': {'name': '', 'price_range': '', 'size': ''},\n", " 'sauce': {'name': '', 'price_range': '', 'size': ''},\n", " 'order': {'type': '',\n", " 'price_range': '',\n", " 'restaurant_name': '',\n", " 'area': '',\n", " 'book_time': '',\n", " 'book_day': ''},\n", " 'booking': {'restaurant_name': '',\n", " 'area': '',\n", " 'book_time': '',\n", " 'book_day': '',\n", " 'book_people': ''},\n", " 'payment': {'type': '', 'amount': '', 'vat': ''}},\n", " 'request_state': {},\n", " 'terminated': False,\n", " 'history': []}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dst = Rules_DST()\n", "dst.state" ] }, { "cell_type": "code", "execution_count": 3, "id": "ec2b40d2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dst.state['user_action']" ] }, { "cell_type": "code", "execution_count": 4, "id": "ca5ec2f3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': '', 'type': '', 'price_range': '', 'size': '', 'ingredients': ''}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dst.update_user([['star_conversation',\"\",\"\",\"\"], ['inform', 'drink', 'size', 'duża']])\n", "dst.state['belief_state']['food']" ] }, { "cell_type": "code", "execution_count": 5, "id": "325a39f3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': '', 'price_range': '', 'size': 'duża'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dst.state['belief_state']['drink']" ] }, { "cell_type": "code", "execution_count": 6, "id": "2a36fa8c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['inform', 'drink', 'size', 'duża']]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dst.state['user_action']" ] }, { "cell_type": "code", "execution_count": 7, "id": "67fd77b2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'drink': {'price range': 0}}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dst.update_user([['request', 'drink', 'price range', '?']])\n", "dst.state['request_state']" ] }, { "cell_type": "code", "execution_count": 8, "id": "834ebb03", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': '',\n", " 'type': 'pizza',\n", " 'price_range': '',\n", " 'size': 'duża',\n", " 'ingredients': ''}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dst.update_user([['inform', 'food', 'type', 'pizza'], ['inform', 'food', 'size', 'duża']])\n", "dst.state['belief_state']['food']" ] }, { "cell_type": "code", "execution_count": 9, "id": "802c9fe4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': '', 'price_range': 'średnia', 'size': 'standardowa'}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dst.update_user([['inform', 'sauce', 'size', 'standardowa'], ['inform', 'sauce', 'price_range', 'średnia']])\n", "dst.state['belief_state']['sauce']" ] }, { "cell_type": "code", "execution_count": 10, "id": "4b61083c", "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict\n", "import jmespath\n", "\n", "class DP():\n", " def __init__(self):\n", " with open('database.json', encoding='utf-8-sig') as json_file:\n", " self.db = json.load(json_file)\n", " \n", "\n", " def predict(self, state):\n", " self.results = []\n", " system_action = defaultdict(list)\n", " user_action = defaultdict(list)\n", " system_acts = []\n", " for idx in range(len(state['user_action'])):\n", " intent, domain, slot, value = state['user_action'][idx]\n", " user_action[(domain, intent)].append((slot, value))\n", "\n", " for user_act in user_action:\n", " system_acts.append(self.update_system_action(user_act, user_action, state, system_action))\n", " state['system_action'] = system_acts\n", " return system_acts[-1]\n", "\n", "\n", " def update_system_action(self, user_act, user_action, state, system_action):\n", " \n", " domain, intent = user_act \n", " \n", " #Reguła 3\n", " if intent == 'end_conversation':\n", " return None\n", " \n", " constraints = [(slot, value) for slot, value in state['belief_state'][domain].items() if value != '']\n", " \n", " # Reguła 1\n", " if intent == 'request':\n", " if len(self.results) == 0:\n", " system_action[(domain, 'NoOffer')] = []\n", " else:\n", " for slot in user_action[user_act]: \n", " 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(constraints)>1:\n", " arg=f\"{constraints[0]}\".replace(f\"\\'{constraints[0][0]}\\'\",f\"{constraints[0][0]}\")\n", " arg = arg.replace(\"[\",\"\").replace(\"]\",\"\")\n", " for cons in constraints[1:]:\n", " arg+=f\" && contains{cons}\".replace(f\"\\'{cons[0]}\\'\",f\"{cons[0]}\").replace(\"[\",\"\").replace(\"]\",\"\")\n", " else:\n", " arg=f\"{constraints}\".replace(f\"\\'{constraints[0]}\\'\",f\"{constraints[0]}\").replace(\"[\",\"\").replace(\"]\",\"\").replace(\"(\\'\",\"(\").replace(\"\\',\",\",\") \n", " print(arg)\n", " print(jmespath.search(f\"database.{domain}[?contains{arg} == `true` ]\", self.db) )\n", " self.results = jmespath.search(f\"database.{domain}[?contains{arg} == `true` ]\", self.db) \n", " if len(self.results) == 0:\n", " system_action[(domain, 'NoOffer')] = []\n", " else:\n", " system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])\n", " choice = self.results[0]\n", "\n", " if domain in [\"food\", \"drink\", \"sauce\"]:\n", " system_action[(domain, 'Recommend')].append(['Name', choice['name']])\n", " elif domain in [\"order\", \"booking\", \"payment\"]:\n", " system_action[(domain, 'Recommend')].append(['Type', choice['type']])\n", " return system_action\n", " \n" ] }, { "cell_type": "code", "execution_count": 11, "id": "e587661a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(size, 'duża')\n", "[{'name': 'lemoniada', 'price_range': 'średnia', 'size': 'duża'}]\n", "(type, 'pizza') && contains(size, 'duża')\n", "[{'name': 'pizza margherita', 'type': 'pizza', 'price_range': 'średnia', 'size': 'duża', 'ingredients': 'sos pomidorowy, ser mozzarella, bazylia'}, {'name': 'pizza vegetariana', 'type': 'pizza', 'price_range': 'wysoka', 'size': 'duża', 'ingredients': 'sos pomidorowy, mozzarella, warzywa (papryka, cebula, pomidory, pieczarki), oregano'}, {'name': 'pizza hawajska', 'type': 'pizza', 'price_range': 'wysoka', 'size': 'duża', 'ingredients': 'sos pomidorowy, mozzarella, szynka, ananas, oregano'}, {'name': 'pizza capricciosa', 'type': 'pizza', 'price_range': 'wysoka', 'size': 'duża', 'ingredients': 'sos pomidorowy, mozzarella, szynka, pieczarki, oregano'}]\n", "(price_range, 'średnia') && contains(size, 'standardowa')\n", "[]\n" ] }, { "data": { "text/plain": [ "defaultdict(list,\n", " {('drink', 'Inform'): [['Choice', '1']],\n", " ('drink', 'Recommend'): [['Name', 'lemoniada']],\n", " ('food', 'Inform'): [['Choice', '4']],\n", " ('food', 'Recommend'): [['Name', 'pizza margherita']],\n", " ('sauce', 'NoOffer'): []})" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dp= DP()\n", "dp.predict(dst.state)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 5 }