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import argparse
import os
import os.path as osp
import random
import time
from logging import getLogger
import openai
from utils import get_res_batch, load_json, intention_prompt, preference_prompt_1, preference_prompt_2, amazon18_dataset2fullname, write_json_file
import json
def get_intention_train(args, inters, item2feature, reviews, api_info):
intention_train_output_file = os.path.join(args.root,"intention_train.json")
# Suggest modifying the prompt based on different datasets
prompt = intention_prompt
dataset_full_name = amazon18_dataset2fullname[args.dataset]
dataset_full_name = dataset_full_name.replace("_", " ").lower()
print(dataset_full_name)
prompt_list = []
inter_data = []
for (user,item_list) in inters.items():
user = int(user)
item = int(item_list[-3])
history = item_list[:-3]
inter_data.append((user,item,history))
review = reviews[str((user, item))]["review"]
item_title = item2feature[str(item)]["title"]
input_prompt = prompt.format(item_title=item_title,dataset_full_name=dataset_full_name,review=review)
prompt_list.append(input_prompt)
st = 0
with open(intention_train_output_file, mode='a') as f:
while st < len(prompt_list):
# while st < 3:
print(st)
# if st < 25631:
# st += args.batchsize
# continue
res = get_res_batch(args.model_name, prompt_list[st:st+args.batchsize], args.max_tokens, api_info)
for i, answer in enumerate(res):
user, item, history = inter_data[st+i]
# print(answer)
# print("=============")
if answer == '':
print("answer null error")
answer = "I enjoy high-quality item."
if answer.strip().count('\n') != 1:
if 'haracteristics:' in answer:
answer = answer.strip().split("The item's characteristics:")
else:
answer = answer.strip().split("The item's characteristic:")
else:
answer = answer.strip().split('\n')
if '' in answer:
answer.remove('')
if len(answer) == 1:
print(answer)
user_preference = item_character = answer[0]
elif len(answer) >= 3:
print(answer)
answer = answer[-1]
user_preference = item_character = answer
else:
user_preference, item_character = answer
if ':' in user_preference:
idx = user_preference.index(':')
user_preference = user_preference[idx+1:]
user_preference = user_preference.strip().replace('}','')
user_preference = user_preference.replace('\n','')
if ':' in item_character:
idx = item_character.index(':')
item_character = item_character[idx+1:]
item_character = item_character.strip().replace('}','')
item_character = item_character.replace('\n','')
dict = {"user":user, "item":item, "inters": history,
"user_related_intention":user_preference, "item_related_intention": item_character}
json.dump(dict, f)
f.write("\n")
st += args.batchsize
return intention_train_output_file
def get_intention_test(args, inters, item2feature, reviews, api_info):
intention_test_output_file = os.path.join(args.root,"intention_test.json")
# Suggest modifying the prompt based on different datasets
prompt = intention_prompt
dataset_full_name = amazon18_dataset2fullname[args.dataset]
dataset_full_name = dataset_full_name.replace("_", " ").lower()
print(dataset_full_name)
prompt_list = []
inter_data = []
for (user,item_list) in inters.items():
user = int(user)
item = int(item_list[-1])
history = item_list[:-1]
inter_data.append((user,item,history))
review = reviews[str((user, item))]["review"]
item_title = item2feature[str(item)]["title"]
input_prompt = prompt.format(item_title=item_title,dataset_full_name=dataset_full_name,review=review)
prompt_list.append(input_prompt)
st = 0
with open(intention_test_output_file, mode='a') as f:
while st < len(prompt_list):
# while st < 3:
print(st)
# if st < 4623:
# st += args.batchsize
# continue
res = get_res_batch(args.model_name, prompt_list[st:st+args.batchsize], args.max_tokens, api_info)
for i, answer in enumerate(res):
user, item, history = inter_data[st+i]
if answer == '':
print("answer null error")
answer = "I enjoy high-quality item."
if answer.strip().count('\n') != 1:
if 'haracteristics:' in answer:
answer = answer.strip().split("The item's characteristics:")
else:
answer = answer.strip().split("The item's characteristic:")
else:
answer = answer.strip().split('\n')
if '' in answer:
answer.remove('')
if len(answer) == 1:
print(answer)
user_preference = item_character = answer[0]
elif len(answer) >= 3:
print(answer)
answer = answer[-1]
user_preference = item_character = answer
else:
user_preference, item_character = answer
if ':' in user_preference:
idx = user_preference.index(':')
user_preference = user_preference[idx+1:]
user_preference = user_preference.strip().replace('}','')
user_preference = user_preference.replace('\n','')
if ':' in item_character:
idx = item_character.index(':')
item_character = item_character[idx+1:]
item_character = item_character.strip().replace('}','')
item_character = item_character.replace('\n','')
dict = {"user":user, "item":item, "inters": history,
"user_related_intention":user_preference, "item_related_intention": item_character}
json.dump(dict, f)
f.write("\n")
st += args.batchsize
return intention_test_output_file
def get_user_preference(args, inters, item2feature, reviews, api_info):
preference_output_file = os.path.join(args.root,"user_preference.json")
# Suggest modifying the prompt based on different datasets
prompt_1 = preference_prompt_1
prompt_2 = preference_prompt_2
dataset_full_name = amazon18_dataset2fullname[args.dataset]
dataset_full_name = dataset_full_name.replace("_", " ").lower()
print(dataset_full_name)
prompt_list_1 = []
prompt_list_2 = []
users = []
for (user,item_list) in inters.items():
users.append(user)
history = item_list[:-3]
item_titles = []
for j, item in enumerate(history):
item_titles.append(str(j+1) + '.' + item2feature[str(item)]["title"])
if len(item_titles) > args.max_his_len:
item_titles = item_titles[-args.max_his_len:]
item_titles = ", ".join(item_titles)
input_prompt_1 = prompt_1.format(dataset_full_name=dataset_full_name, item_titles=item_titles)
input_prompt_2 = prompt_2.format(dataset_full_name=dataset_full_name, item_titles=item_titles)
prompt_list_1.append(input_prompt_1)
prompt_list_2.append(input_prompt_2)
st = 0
with open(preference_output_file, mode='a') as f:
while st < len(prompt_list_1):
# while st < 3:
print(st)
# if st < 22895:
# st += args.batchsize
# continue
res_1 = get_res_batch(args.model_name, prompt_list_1[st:st + args.batchsize], args.max_tokens, api_info)
res_2 = get_res_batch(args.model_name, prompt_list_2[st:st + args.batchsize], args.max_tokens, api_info)
for i, answers in enumerate(zip(res_1, res_2)):
user = users[st + i]
answer_1, answer_2 = answers
# print(answers)
# print("=============")
if answer_1 == '':
print("answer null error")
answer_1 = "I enjoy high-quality item."
if answer_2 == '':
print("answer null error")
answer_2 = "I enjoy high-quality item."
if answer_2.strip().count('\n') != 1:
if 'references:' in answer_2:
answer_2 = answer_2.strip().split("Short-term preferences:")
else:
answer_2 = answer_2.strip().split("Short-term preference:")
else:
answer_2 = answer_2.strip().split('\n')
if '' in answer_2:
answer_2.remove('')
if len(answer_2) == 1:
print(answer_2)
long_preference = short_preference = answer_2[0]
elif len(answer_2) >= 3:
print(answer_2)
answer_2 = answer_2[-1]
long_preference = short_preference = answer_2
else:
long_preference, short_preference = answer_2
if ':' in long_preference:
idx = long_preference.index(':')
long_preference = long_preference[idx+1:]
long_preference = long_preference.strip().replace('}','')
long_preference = long_preference.replace('\n','')
if ':' in short_preference:
idx = short_preference.index(':')
short_preference = short_preference[idx+1:]
short_preference = short_preference.strip().replace('}','')
short_preference = short_preference.replace('\n','')
dict = {"user":user,"user_preference":[answer_1, long_preference, short_preference]}
# print(dict)
json.dump(dict, f)
f.write("\n")
st += args.batchsize
return preference_output_file
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='Instruments', help='Instruments / Arts / Games')
parser.add_argument('--root', type=str, default='')
parser.add_argument('--api_info', type=str, default='./api_info.json')
parser.add_argument('--model_name', type=str, default='text-davinci-003')
parser.add_argument('--max_tokens', type=int, default=512)
parser.add_argument('--batchsize', type=int, default=16)
parser.add_argument('--max_his_len', type=int, default=20)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
args.root = os.path.join(args.root, args.dataset)
api_info = load_json(args.api_info)
openai.api_key = api_info["api_key_list"].pop()
inter_path = os.path.join(args.root, f'{args.dataset}.inter.json')
inters = load_json(inter_path)
item2feature_path = os.path.join(args.root, f'{args.dataset}.item.json')
item2feature = load_json(item2feature_path)
reviews_path = os.path.join(args.root, f'{args.dataset}.review.json')
reviews = load_json(reviews_path)
intention_train_output_file = get_intention_train(args, inters, item2feature, reviews, api_info)
intention_test_output_file = get_intention_test(args, inters, item2feature, reviews ,api_info)
preference_output_file = get_user_preference(args, inters, item2feature, reviews, api_info)
intention_train = {}
intention_test = {}
user_preference = {}
with open(intention_train_output_file, "r") as f:
for line in f:
# print(line)
content = json.loads(line)
if content["user"] not in intention_train:
intention_train[content["user"]] = {"item":content["item"],
"inters":content["inters"],
"querys":[ content["user_related_intention"], content["item_related_intention"] ]}
with open(intention_test_output_file, "r") as f:
for line in f:
content = json.loads(line)
if content["user"] not in intention_train:
intention_test[content["user"]] = {"item":content["item"],
"inters":content["inters"],
"querys":[ content["user_related_intention"], content["item_related_intention"] ]}
with open(preference_output_file, "r") as f:
for line in f:
content = json.loads(line)
user_preference[content["user"]] = content["user_preference"]
user_dict = {
"user_explicit_preference": user_preference,
"user_vague_intention": {"train": intention_train, "test": intention_test},
}
write_json_file(user_dict, os.path.join(args.root, f'{args.dataset}.user.json'))
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