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g3_level_api.py
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g3_level_api.py
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import argparse
import json
import os
import random
from enum import auto, Enum
from typing import List, Any, Dict
import numpy as np
import pandas as pd
import re
input_data = "../instruction/G3_query.json"
sample_ids= "../test_query_ids/G3_instruction_test_query_ids.json"
gpt4_instructions="../category/coarse_instruction/api_instruction_5000.json"
raw_data = json.load(open(input_data, "r"))
output_file="G3_query_100_level_api.json"
#output_file="G3_query_100_opendomain_cut_apiStand.json"
output_file1="G3_query_100_cut_api.json"
#output_file2="G3_query_100_ground_cut.json"
#input_data = "multi-tool/train_multi.csv"
#output_file="builddata/cut0901/train_multi.txt"
#output_csv="builddata/cut0901/train_multi.csv"
data_ori_gen={}
data_key_dict={}
with open(gpt4_instructions, 'r') as f1:
# 逐行写入json对象
#({"query":raw_data[0],"new_instruction":output,\
#"original_query":raw_data[1],"type":raw_data[2],"path":raw_key})
for item in f1:
data_=json.loads(item)
data_ori_gen[data_["original_query"]]=data_["new_instruction"]
data_key_dict[data_["path"]]=data_
print("###########################################")
print("dataset num",len(data_ori_gen))
def standardize_category(category):
save_category = category.replace(" ", "_").replace(",", "_").replace("/", "_")
while " " in save_category or "," in save_category:
save_category = save_category.replace(" ", "_").replace(",", "_")
save_category = save_category.replace("__", "_")
return save_category
def standardize(string):
res = re.compile("[^\\u4e00-\\u9fa5^a-z^A-Z^0-9^_]")
string = res.sub("_", string)
string = re.sub(r"(_)\1+","_", string).lower()
while True:
if len(string) == 0:
return string
if string[0] == "_":
string = string[1:]
else:
break
while True:
if len(string) == 0:
return string
if string[-1] == "_":
string = string[:-1]
else:
break
if string[0].isdigit():
string = "get_" + string
return string
def change_name(name):
change_list = ["from", "class", "return", "false", "true", "id", "and"]
if name in change_list:
name = "is_" + name
return name
count=0
out_list=[]
data=[]
cuttask=[]
test_list1=[]
test_list2=[]
test_list=[]
sample_data = json.load(open(sample_ids, "r"))
ids_list=[]
for ids in sample_data:
ids_list.append(ids)
print(ids_list)
for index, raw in enumerate(raw_data):
query_id=raw["query_id"]
category_list=[]
tool_list=[]
api_list1=[]
if str(query_id) in ids_list:
print("###############################")
print(query_id)
print(raw)
print(raw.keys())
query=raw["query"]
query_id=raw["query_id"]
api_list=raw["api_list"]
relevantAPIs=raw["relevant APIs"]
for api_identity in relevantAPIs:
tool_name=api_identity[0]
api_name=api_identity[1]
for api in api_list:
if api['tool_name']==tool_name:
category_name=api['category_name']
break
tool_name = standardize(tool_name)
tool_list.append(tool_name)
api_name = change_name(standardize(api_name))
api_list1.append(api_name)
if len(category_name)>0:
category_name = standardize_category(category_name)
category_list.append(category_name)
list1=[]
for api in api_list:
newapi={"category_name":api["category_name"],"tool_name":api["tool_name"],"api_name":api["api_name"]}
list1.append(newapi)
#newquery=query.split(".")[0]+"."
if query not in data_ori_gen:
print(query)
print(query_id)
query_id_str=str(query_id)+"_"
for key in data_key_dict:
if key.startswith(query_id_str):
print(key)
print(data_key_dict[key])
print("**********************")
newquery=data_ori_gen[query]
#newplan=".".join(query.split(".")[1:])
#toolnames=", ".join(api_list1)+"."
#newquery=newquery+"\nAPI: "+toolnames
test_list.append({"query":newquery,"original_query":query,"query_id":query_id})
test_list1.append({"query":newquery,"original_query":query,"query_id":query_id,"api_list":list1})
test_list2.append({"query":newquery,"original_query":query,"query_id":query_id,"relevant APIs":relevantAPIs,"api_list":list1})
json.dump(test_list, open(output_file,"w"), indent=4, ensure_ascii=False)