DST (start)
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Anna Nowak
parent
2227204dc4
commit
2eca0a043d
4
.gitignore
vendored
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4
.gitignore
vendored
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@ -0,0 +1,4 @@
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*frame-model*
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*slot-model*
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.venv*
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env*
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File diff suppressed because it is too large
Load Diff
@ -2,7 +2,7 @@ import jsgf
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from Modules.NLG_module import NLG
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from Modules.DP_module import DP
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from Modules.DST_module import DST
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from Modules.DST_module import Rules_DST
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from Modules.Book_NLU_module import Book_NLU
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from Modules.ML_NLU_module import ML_NLU
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@ -20,26 +20,15 @@ if torch.cuda.is_available():
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class Janet:
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def __init__(self):
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self.acts={
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0: "greetings",
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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.nlg = NLG(self.acts, self.arguments)
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self.dp = DP(self.acts, self.arguments)
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self.dst = DST(self.acts, self.arguments)
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self.nlu = Book_NLU(self.acts, self.arguments, jsgf.parse_grammar_file('book.jsgf'))
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self.nlu_v2 = ML_NLU(self.acts, self.arguments)
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def test(self, command):
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out = self.nlu_v2.test_nlu(command)
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return out
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self.nlg = NLG()
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self.dp = DP()
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self.dst = Rules_DST()
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self.nlu = Book_NLU(jsgf.parse_grammar_file('book.jsgf'))
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self.nlu_v2 = ML_NLU()
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def process(self, command):
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act = self.nlu.analyze(command)
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self.dst.store(act)
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act = self.nlu_v2.test_nlu(command)
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self.dst.update(act)
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dest_act = self.dp.choose_tactic(self.dst.transfer())
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return self.nlg.change_to_text(dest_act)
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@ -49,7 +38,7 @@ def main():
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while(1):
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print('\n')
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text = input("Wpisz tekst: ")
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print(janet.test(text))
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print(janet.process(text))
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if __name__ == "__main__":
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main()
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@ -8,9 +8,7 @@ class Book_NLU: #Natural Language Understanding
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Wyjście: Akt użytkownika (rama)
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"""
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def __init__(self, acts, arguments, book_grammar):
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self.acts = acts
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self.arguments = arguments
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def __init__(self, book_grammar):
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self.book_grammar = book_grammar
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def get_dialog_act(self, rule):
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@ -6,10 +6,8 @@ class DP:
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Wyjście: Akt systemu (rama)
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"""
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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 __init__(self):
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pass
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def choose_tactic(self, frame_list):
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"""
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@ -1,4 +1,9 @@
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class DST: #Dialogue State Tracker
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import json
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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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class Rules_DST(DST): #Dialogue State Tracker
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"""
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Moduł odpowiedzialny za śledzenie stanu dialogu. Przechowuje informacje o tym jakie dane zostały uzyskane od użytkownika w toku prowadzonej konwersacji.
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@ -6,22 +11,38 @@ class DST: #Dialogue State Tracker
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Wyjście: Reprezentacja stanu dialogu (rama)
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"""
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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.frame_list= []
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def __init__(self):
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DST.__init__(self)
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self.state = json.load(open('default_state.json'))
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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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slots = user_act["slots"]
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intent = user_act["act"]
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domain = user_act["act"].split('/')[0]
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def store(self, rama):
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"""
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Dodanie nowego aktu do listy
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"""
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print("\nDodanie do listy nowej ramy: ")
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print(rama)
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self.frame_list.append(rama)
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if domain in ['password', 'name', 'email', 'enter_email', 'enter_name']:
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return
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def transfer(self):
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print("Przekazanie dalej listy ram: ")
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print(self.frame_list)
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return self.frame_list
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if 'appointment' in intent:
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for full_slot in slots:
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slot = full_slot[1]
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value = full_slot[1]
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k = self.value_dict[domain.lower()].get(slot, slot)
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if k is None:
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return
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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 == 'end_conversation':
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self.state = {}
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return self.state
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@ -5,9 +5,7 @@ from flair.datasets import SentenceDataset
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from flair.models import SequenceTagger
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class ML_NLU:
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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 __init__(self):
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self.slot_model, self.frame_model = self.setup()
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def nolabel2o(self, line, i):
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@ -6,10 +6,8 @@ class NLG:
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Wyjście: Tekst
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"""
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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 __init__(self):
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pass
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def change_to_text(self, act_vector):
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"""
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@ -46,6 +46,9 @@ frame_corpus = Corpus(train=conllu2flair(frame_trainset, 'frame'), test=conllu2f
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slot_tag_dictionary = slot_corpus.make_tag_dictionary(tag_type='slot')
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frame_tag_dictionary = frame_corpus.make_tag_dictionary(tag_type='frame')
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print(slot_tag_dictionary)
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print(frame_tag_dictionary)
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embedding_types = [
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WordEmbeddings('pl'),
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@ -62,7 +65,7 @@ frame_tagger = SequenceTagger(hidden_size=256, embeddings=embeddings,
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tag_dictionary=frame_tag_dictionary,
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tag_type='frame', use_crf=True)
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# slot_trainer = ModelTrainer(slot_tagger, slot_corpus)
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slot_trainer = ModelTrainer(slot_tagger, slot_corpus)
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# slot_trainer.train('slot-model',
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# learning_rate=0.1,
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# mini_batch_size=32,
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@ -828,8 +828,8 @@
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# intent: request_information/doctors
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# slots:
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1 jacy request_information/doctors NoLabel
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2 lekarze request_information/doctors B-appoinment/doctor
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3 specjaliści request_information/doctors B-appoinment/doctor
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2 lekarze request_information/doctors B-appointment/doctor
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3 specjaliści request_information/doctors B-appointment/doctor
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4 przyjmują request_information/doctors NoLabel
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5 w request_information/doctors NoLabel
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6 państwa request_information/doctors NoLabel
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@ -840,7 +840,7 @@
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# slots:
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1 chciałbym appointment/create_appointment NoLabel
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2 umówić appointment/create_appointment NoLabel
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3 wizytę appointment/create_appointment B-appoinment
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3 wizytę appointment/create_appointment B-appointment
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4 do appointment/create_appointment NoLabel
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5 doktora appointment/create_appointment B-appointment/doctor
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6 kolano appointment/create_appointment I-appointment/doctor
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@ -891,7 +891,7 @@
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5 okulisty appointment/create_appointment B-appointment/doctor
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6 ile request_information/cost NoLabel
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7 kosztuje request_information/cost NoLabel
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8 wizyta request_information/cost B-appoinment
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8 wizyta request_information/cost B-appointment
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# text: Nie ten jest idealny
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# intent: deny
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17
default_state.json
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17
default_state.json
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{
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"user_action": [],
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"system_action": [],
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"belief_state": {
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"appointment": {
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"prescription": {
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"url": {},
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"type": {}
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},
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"doctor": {},
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"where": {}
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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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}
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110
value_dict.json
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110
value_dict.json
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{
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"appointment": {
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"hour": [
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"08:00",
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"08:15",
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"08:30",
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"08:45",
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"09:00",
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"09:15",
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"09:30",
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"09:45",
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"10:00",
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"10:15",
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"10:30",
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"10:45",
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"11:00",
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"11:15",
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"11:30",
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"11:45",
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"12:00",
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"12:15",
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"12:30",
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"12:45",
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"13:00",
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"13:15",
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"13:30",
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"13:45",
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"14:00",
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"14:15",
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"14:30",
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"14:45",
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"15:00",
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"15:15",
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"15:30",
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"15:45",
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"16:00",
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"16:15",
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"16:30",
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"16:45",
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"17:00",
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"17:15",
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"17:30",
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"17:45",
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"18:00",
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"18:15",
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"18:30",
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"18:45",
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"19:00",
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"19:15",
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"19:30",
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"19:45"
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],
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"date": [
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"poniedziałek",
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"wtorek",
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"środa",
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"środę",
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"czwartek",
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"piątek",
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"sobota",
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"sobotę",
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"niedziela",
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"niedzielę",
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"01.05.2021",
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"02.05.2021",
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"03.05.2021",
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"04.05.2021",
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"05.05.2021",
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"06.05.2021",
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"07.05.2021",
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"08.05.2021",
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"09.05.2021",
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"10.05.2021",
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"11.05.2021",
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"12.05.2021",
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"13.05.2021",
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"14.05.2021",
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"15.05.2021",
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"16.05.2021",
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"17.05.2021",
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"18.05.2021",
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"19.05.2021",
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"20.05.2021",
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"21.05.2021",
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"22.05.2021",
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"23.05.2021",
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"24.05.2021",
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"25.05.2021",
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"26.05.2021",
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"27.05.2021",
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"28.05.2021",
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"29.05.2021",
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"20.05.2021"
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],
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"doctor": [
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"internista",
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"okulista",
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"rodzinny",
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"ginekolog",
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"dr. Kolano",
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"dr. Kowalska"
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]
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},
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"prescritpion": {
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"type": [
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"recepta",
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"e-recepta"
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]
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}
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}
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