Policy and DST
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vendored
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@ -1 +1,3 @@
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__pycache__
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*.log
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*.ipynb
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255
Modules.py
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Modules.py
@ -1,19 +1,29 @@
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from convlab2.dst.dst import DST
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from convlab2.dst.rule.multiwoz.dst_util import normalize_value
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from collections import defaultdict
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from convlab2.policy.policy import Policy
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from convlab2.util.multiwoz.dbquery import Database
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import copy
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from copy import deepcopy
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import json
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import os
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import jsgf
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# Natural Language Understanding
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class NLU:
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def __init__(self):
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self.grammars = [jsgf.parse_grammar_file(f'JSGFs/{file_name}') for file_name in os.listdir('JSGFs')]
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self.grammars = [
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jsgf.parse_grammar_file(f"JSGFs/{file_name}")
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for file_name in os.listdir("JSGFs")
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]
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def get_dialog_act(self, rule):
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slots = []
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self.get_slots(rule.expansion, slots)
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return {'act': rule.grammar.name, 'slots': slots}
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return {"act": rule.grammar.name, "slots": slots}
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def get_slots(self, expansion, slots):
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if expansion.tag != '':
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if expansion.tag != "":
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slots.append((expansion.tag, expansion.current_match))
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return
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@ -24,43 +34,217 @@ class NLU:
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self.get_slots(expansion.referenced_rule.expansion, slots)
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def match(self, utterance):
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list_of_illegal_character = [',', '.', "'", '?', '!', ':', '-', '/']
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list_of_illegal_character = [",", ".", "'", "?", "!", ":", "-", "/"]
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for illegal_character in list_of_illegal_character[:-2]:
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utterance = utterance.replace(f'{illegal_character}','')
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utterance = utterance.replace(f"{illegal_character}", "")
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for illegal_character in list_of_illegal_character[-2:]:
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utterance = utterance.replace(f'{illegal_character}',' ')
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utterance = utterance.replace(f"{illegal_character}", " ")
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for grammar in self.grammars:
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matched = grammar.find_matching_rules(utterance)
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if matched:
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return self.get_dialog_act(matched[0])
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return {'act': 'null', 'slots': []}
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return {"act": "null", "slots": []}
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#Dialogue policy
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class DP:
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#Module decide what act takes next
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def __init__(self, acts, arguments):
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self.acts = acts
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self.arguments = arguments
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def tacticChoice(self, frame_list):
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actVector = [0, 0]
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return actVector
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class DP(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, intent)].append((slot, 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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system_acts = [
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[intent, domain, slot, value]
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for (domain, intent), slots in system_action.items()
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for slot, value in slots
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]
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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 = [
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(slot, value)
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for slot, value in state["belief_state"][domain.lower()]["semi"].items()
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if value != ""
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]
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self.db.dbs = {
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"book": [
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{
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"author": "autor",
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"title": "krew",
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"edition": "2020",
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"lang": "polski",
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},
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{
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"author": "Marcin Bruczkowski",
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"title": "Bezsenność w Tokio",
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"genre": "reportaż",
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"publisher": "Społeczny Instytut Wydawniczy Znak",
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"edition": "2004",
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"lang": "polski",
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},
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{
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"author": "Harari Yuval Noah",
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"title": "Sapiens Od zwierząt do bogów",
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"edition": "2011",
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"lang": "polski",
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},
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{
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"author": "Haruki Murakami",
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"title": "1Q84",
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"edition": "2009",
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"lang": "polski",
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},
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{
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"author": "Fiodor Dostojewski",
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"title": "Zbrodnia i Kara",
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"publisher": "Wydawnictwo Mg",
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"edition": "2015",
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"lang": "polski",
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},
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]
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}
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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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kb_slot_name = ref[domain].get(slot[0], slot[0])
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if kb_slot_name in self.results[0]:
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system_action[(domain, "Inform")].append(
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[slot[0], self.results[0].get(kb_slot_name, "unknown")]
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)
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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(
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["Choice", str(len(self.results))]
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)
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choice = self.results[0]
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if domain in ["Book"]:
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system_action[(domain, "Recommend")].append(
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["Title", choice["title"]]
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)
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# Dialogue State Tracker
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class DST:
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#Contain informations about state of the dialogue and data taken from user
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def __init__(self, acts, arguments):
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self.acts = acts
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self.arguments = arguments
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self.frameList= []
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class SDST(DST):
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def __init__(self):
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DST.__init__(self)
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self.state = {
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"user_action": [],
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"system_action": [],
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"belief_state": {
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"books": {
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"reserve": {"reservation": []},
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"semi": {
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"title": "",
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"author": "",
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"genre": "",
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"publisher": "",
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"edition": "",
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"lang": "",
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},
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},
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"library": {
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"semi": {
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"location": "",
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"status": "",
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"events": "",
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"days": "",
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"phone number": "",
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}
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},
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"card": {"semi": {"lost": "", "destroyed": "", "new": ""}},
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"date": {"semi": {"day": "", "month": "", "year": ""}},
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},
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"request_state": {},
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"terminated": False,
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"history": [],
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}
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self.ref = {
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"Books": {
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"Title": "title",
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"Author": "author",
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"Genre": "genre",
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"Publisher": "publisher",
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"Edition": "edition",
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"Lang": "lang",
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"None": "none",
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},
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"Library": {
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"Location": "location",
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"Status": "status",
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"Events": "events",
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"Days": "days",
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"Phone number": "phone number",
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"None": "none",
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},
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"Card": {
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"Lost": "lost",
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"Destroyed": "destroyed",
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"New": "new",
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"None": "none",
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},
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"Date": {"Day": "day", "Month": "month", "Year": "year", "None": "none"},
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}
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self.value_dict = json.load(open("value_dict.json"))
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def update(self, user_act=None):
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for intent, domain, slot, value in user_act:
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domain = domain.lower()
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intent = intent.lower()
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if domain in ["unk", "general", "booking"]:
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continue
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if intent == "inform":
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k = self.ref[domain.capitalize()].get(slot, slot)
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if k is None:
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continue
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domain_dic = self.state["belief_state"][domain]
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if k in domain_dic["semi"]:
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nvalue = normalize_value(self.value_dict, domain, k, value)
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self.state["belief_state"][domain]["semi"][k] = nvalue
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elif k in domain_dic["book"]:
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self.state["belief_state"][domain]["book"][k] = value
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elif k.lower() in domain_dic["book"]:
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self.state["belief_state"][domain]["book"][k.lower()] = value
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elif intent == "request":
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k = self.ref[domain.capitalize()].get(slot, slot)
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if domain not in self.state["request_state"]:
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self.state["request_state"][domain] = {}
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if k not in self.state["request_state"][domain]:
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self.state["request_state"][domain][k] = 0
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return self.state
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def init_session(self):
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self.state = self_state
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#store new act into frame
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def store(self, frame):
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self.frameList.append(frame)
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def transfer(self):
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return self.frameList
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# Natural Language Generator
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class NLG:
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def __init__(self, acts, arguments):
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@ -68,7 +252,7 @@ class NLG:
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self.arguments = arguments
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def vectorToText(self, actVector):
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if(actVector == [0, 0]):
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if actVector == [0, 0]:
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return "Witaj, nazywam się Mateusz."
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else:
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return "Przykro mi, nie zrozumiałem Cię"
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@ -80,9 +264,7 @@ class Run:
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0: "hello",
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1: "request",
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}
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self.arguments={
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0: "name"
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}
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self.arguments = {0: "name"}
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self.nlu = NLU()
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self.dp = DP(self.acts, self.arguments)
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@ -98,15 +280,8 @@ class Run:
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return self.nlg.vectorToText(basic_act)
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# run = Run()
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# while(1):
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# message = input("Napisz coś: ")
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# print(run.inputProcessing(message))
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12
evaluate.py
12
evaluate.py
@ -4,7 +4,7 @@ import pandas as pd
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import numpy as np
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from Modules import NLU
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PATTERN = r'[^(]*'
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PATTERN = r"[^(]*"
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# Algorytm sprawdzający
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@ -13,17 +13,17 @@ hits = 0
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nlu = NLU()
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for file_name in os.listdir('data'):
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df = pd.read_csv(f'data/{file_name}', sep='\t', names=['user', 'sentence', 'acts'])
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df = df[df.user == 'user']
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for file_name in os.listdir("data"):
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df = pd.read_csv(f"data/{file_name}", sep="\t", names=["user", "sentence", "acts"])
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df = df[df.user == "user"]
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data = np.array(df)
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for row in data:
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rows += 1
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sentence = row[1]
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user_acts = row[2].split('&')
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user_acts = row[2].split("&")
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nlu_match = nlu.match(sentence)
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if nlu_match['act'] in user_acts:
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if nlu_match["act"] in user_acts:
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hits += 1
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