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master
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feature/di
Author | SHA1 | Date | |
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8b55dbf73b | |||
ee1d7e45d4 |
@ -2,5 +2,5 @@ flair==0.13.1
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conllu==4.5.3
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pandas==1.5.3
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numpy==1.26.4
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torch==2.3.0
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torch==1.13
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convlab==3.0.2a0
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4
src/service/data/restaurant/db/confirm_db.json
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4
src/service/data/restaurant/db/confirm_db.json
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@ -0,0 +1,4 @@
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[
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"true",
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"false"
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]
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5
src/service/data/restaurant/db/dough_db.json
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5
src/service/data/restaurant/db/dough_db.json
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@ -0,0 +1,5 @@
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[
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"pepsi",
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"cola",
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"water"
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]
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11
src/service/data/restaurant/db/drink_db.json
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11
src/service/data/restaurant/db/drink_db.json
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@ -0,0 +1,11 @@
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[
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{
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"name":"pepsi"
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},
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{
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"name":"cola"
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},
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{
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"name":"water"
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}
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]
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3
src/service/data/restaurant/db/food_db.json
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3
src/service/data/restaurant/db/food_db.json
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@ -0,0 +1,3 @@
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[
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"pizza"
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]
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5
src/service/data/restaurant/db/meat_db.json
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5
src/service/data/restaurant/db/meat_db.json
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@ -0,0 +1,5 @@
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[
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"chicken",
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"ham",
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"tuna"
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]
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7
src/service/data/restaurant/db/menu_db.json
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7
src/service/data/restaurant/db/menu_db.json
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@ -0,0 +1,7 @@
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[
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"capri",
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"margarita",
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"hawajska",
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"barcelona",
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"tuna"
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]
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51
src/service/data/restaurant/db/pizza_db.json
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51
src/service/data/restaurant/db/pizza_db.json
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@ -0,0 +1,51 @@
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[
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{
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"name": "capri",
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"ingredient": [
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"tomato",
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"ham",
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"mushrooms",
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"cheese"
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],
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"price": 25
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},
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{
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"name": "margarita",
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"ingredient": [
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"tomato",
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"cheese"
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],
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"price": 20
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},
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{
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"name": "hawajska",
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"ingredient": [
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"tomato",
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"pineapple",
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"chicken",
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"cheese"
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],
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"price": 30
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},
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{
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"name": "barcelona",
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"ingredient": [
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"tomato",
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"onion",
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"ham",
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"pepper",
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"cheese"
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],
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"price": 40
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},
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{
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"name": "tuna",
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"ingredient": [
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"tomato",
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"tuna",
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"onion",
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"cheese"
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],
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"price": 40
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}
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]
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4
src/service/data/restaurant/db/sauce_db.json
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4
src/service/data/restaurant/db/sauce_db.json
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@ -0,0 +1,4 @@
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[
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"garlic",
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"1000w"
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]
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14
src/service/data/restaurant/db/size_db.json
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14
src/service/data/restaurant/db/size_db.json
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@ -0,0 +1,14 @@
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[
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{
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"size": "m",
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"price_multiplier": 1
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},
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{
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"size": "l",
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"price_multiplier": 1.2
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},
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{
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"size": "xl",
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"price_multiplier": 1.4
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}
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]
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91
src/service/dbquery.py
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91
src/service/dbquery.py
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@ -0,0 +1,91 @@
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"""
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"""
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import json
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import os
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import random
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from fuzzywuzzy import fuzz
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from itertools import chain
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from copy import deepcopy
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class Database(object):
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def __init__(self):
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super(Database, self).__init__()
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# loading databases
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domains = ['restaurant', 'hotel', 'attraction', 'train', 'hospital', 'taxi', 'police']
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self.dbs = {}
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for domain in domains:
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with open(os.path.join(os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))),
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'data/restaurant/db/{}_db.json'.format(domain))) as f:
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self.dbs[domain] = json.load(f)
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def query(self, domain, constraints, ignore_open=False, soft_contraints=(), fuzzy_match_ratio=60):
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"""Returns the list of entities for a given domain
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based on the annotation of the belief state"""
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# query the db
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if domain == 'taxi':
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return [{'taxi_colors': random.choice(self.dbs[domain]['taxi_colors']),
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'taxi_types': random.choice(self.dbs[domain]['taxi_types']),
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'taxi_phone': ''.join([str(random.randint(1, 9)) for _ in range(11)])}]
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if domain == 'police':
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return deepcopy(self.dbs['police'])
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if domain == 'hospital':
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department = None
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for key, val in constraints:
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if key == 'department':
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department = val
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if not department:
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return deepcopy(self.dbs['hospital'])
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else:
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return [deepcopy(x) for x in self.dbs['hospital'] if x['department'].lower() == department.strip().lower()]
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constraints = list(map(lambda ele: ele if not(ele[0] == 'area' and ele[1] == 'center') else ('area', 'centre'), constraints))
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found = []
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for i, record in enumerate(self.dbs[domain]):
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constraints_iterator = zip(constraints, [False] * len(constraints))
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soft_contraints_iterator = zip(soft_contraints, [True] * len(soft_contraints))
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for (key, val), fuzzy_match in chain(constraints_iterator, soft_contraints_iterator):
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if val == "" or val == "dont care" or val == 'not mentioned' or val == "don't care" or val == "dontcare" or val == "do n't care":
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pass
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else:
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try:
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record_keys = [k.lower() for k in record]
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if key.lower() not in record_keys:
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continue
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if key == 'leaveAt':
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val1 = int(val.split(':')[0]) * 100 + int(val.split(':')[1])
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val2 = int(record['leaveAt'].split(':')[0]) * 100 + int(record['leaveAt'].split(':')[1])
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if val1 > val2:
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break
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elif key == 'arriveBy':
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val1 = int(val.split(':')[0]) * 100 + int(val.split(':')[1])
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val2 = int(record['arriveBy'].split(':')[0]) * 100 + int(record['arriveBy'].split(':')[1])
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if val1 < val2:
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break
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# elif ignore_open and key in ['destination', 'departure', 'name']:
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elif ignore_open and key in ['destination', 'departure']:
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continue
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elif record[key].strip() == '?':
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# '?' matches any constraint
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continue
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else:
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if not fuzzy_match:
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if val.strip().lower() != record[key].strip().lower():
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break
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else:
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if fuzz.partial_ratio(val.strip().lower(), record[key].strip().lower()) < fuzzy_match_ratio:
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break
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except:
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continue
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else:
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res = deepcopy(record)
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res['Ref'] = '{0:08d}'.format(i)
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found.append(res)
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return found
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if __name__ == '__main__':
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db = Database()
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print(db.query("train", [['departure', 'cambridge'], ['destination','peterborough'], ['day', 'tuesday'], ['arriveBy', '11:15']]))
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@ -1,8 +1,59 @@
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from model.frame import Frame
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from collections import defaultdict
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import copy
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import json
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from copy import deepcopy
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class DialogPolicy:
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def next_dialogue_act(self, frames: list[Frame]) -> Frame:
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if frames[-1].act == "welcomemsg":
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return Frame("system", "welcomemsg", [])
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else:
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return Frame("system", "canthelp", [])
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from convlab.policy.policy import Policy
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from dbquery import Database
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class SimpleRulePolicy(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.lower(), intent.lower())].append((slot.lower(), 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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# Reguła 3
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if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['book stay', 'book day', 'book people']):
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if self.results:
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system_action = {('Booking', 'Book'): [["Ref", self.results[0].get('Ref', 'N/A')]]}
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system_acts = [[intent, domain, slot, value] for (domain, intent), slots in system_action.items() for slot, value in slots]
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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 = [(slot, value) for slot, value in state['belief_state'][domain.lower()].items() if value != '']
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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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if slot[0] in self.results[0]:
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system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(slot[0], 'unknown')])
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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(['Choice', str(len(self.results))])
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choice = self.results[0]
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if domain in ["hotel", "attraction", "police", "restaurant"]:
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system_action[(domain, 'Recommend')].append(['Name', choice['name']])
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dialogPolicy = SimpleRulePolicy()
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