| import pandas as pd
|
| import json
|
| import random
|
| import pandas as pd
|
| import tqdm
|
| import numpy as np
|
| import os
|
|
|
| DATA_DIR = "./data"
|
| DATA_ASS_DIR = "./"
|
| DATA_SAVE_DIR = "./data"
|
|
|
|
|
| df = pd.read_csv(DATA_DIR+"/raw.csv", dtype=object, encoding="utf-8")
|
|
|
|
|
| df=df.drop(index = df[(df.status == "NO_ANSWER")].index.tolist())
|
|
|
| answer=[]
|
|
|
| for row in df.itertuples(index=True, name='hxd'):
|
| if row.status=="ACCEPTED":
|
| answer.append(1)
|
| else:
|
| answer.append(0)
|
|
|
| df['status']=answer
|
|
|
|
|
| _users = df.groupby(["user_id"], as_index=True)
|
| delete_users = []
|
| for j in tqdm.tqdm(_users):
|
| if len(j[1]) < 5:
|
| delete_users.append(j[0])
|
| print(f"del less than 5, del{len(delete_users)}totally")
|
| df = df[~df['user_id'].isin(delete_users)]
|
|
|
|
|
| psp = json.load(open(DATA_ASS_DIR + "/psp_problem.json", encoding="utf-8"))
|
| problemsetproblemid=df["problem_set_problem_id"]
|
| problem_id_list=[]
|
|
|
| for psp_id in problemsetproblemid:
|
| try:
|
| problem_id_list.append(psp[str(psp_id)])
|
| except:
|
| problem_id_list.append(0)
|
| continue
|
|
|
|
|
|
|
| df["problem_id"]=problem_id_list
|
| df=df.drop(index = df[(df.problem_id == 0)].index.tolist())
|
|
|
|
|
| dif = json.load(open(DATA_ASS_DIR + "/problem_difficulty.json", encoding="utf-8"))
|
| skill = json.load(open(DATA_ASS_DIR + "/problem_knowledge.json", encoding="utf-8"))
|
| ref = json.load(open(DATA_ASS_DIR + "/problem_reference_count.json", encoding="utf-8"))
|
|
|
| pid=df["problem_id"]
|
|
|
| dif_list=[]
|
| skill_list=[]
|
| ref_list=[]
|
| for psp_id in tqdm.tqdm(pid):
|
| try:
|
| skill_list.append(skill[str(psp_id)])
|
| dif_list.append(dif[str(psp_id)])
|
| ref_list.append(ref[str(psp_id)])
|
| except:
|
| continue
|
|
|
| df["reference_count"]=ref_list
|
| df["skill_id"]=skill_list
|
| df["difficulty"]=dif_list
|
|
|
| df.to_csv(DATA_SAVE_DIR + "/jisuan.csv",index=0)
|
|
|
|
|
