Source code for pykt.preprocess.assist2012_preprocess

# _*_ coding:utf-8 _*_

import pandas as pd
from .utils import sta_infos, write_txt,format_list2str,change2timestamp
#ref https://sites.google.com/site/assistmentsdata/datasets/2012-13-school-data-with-affect

KEYS = ["user_id", "skill_id", "problem_id"]

[docs]def read_data_from_csv(read_file, write_file): stares = [] # load data df = pd.read_csv(read_file, low_memory=False, usecols=[ "user_id", "skill_id", "start_time", "problem_id", "correct","ms_first_response"]) df['correct'] = df['correct'].apply(int) ins, us, qs, cs, avgins, avgcq, na = sta_infos(df, KEYS, stares) print(f"original interaction num: {ins}, user num: {us}, question num: {qs}, concept num: {cs}, avg(ins) per s: {avgins}, avg(c) per q: {avgcq}, na: {na}") df['tmp_index'] = range(len(df)) df = df.dropna(subset=["user_id", "skill_id", "start_time","problem_id", "correct","ms_first_response"]) df = df[df['correct'].isin([0,1])]#filter responses # add timestamp and duration df['start_timestamp'] = df['start_time'].apply(lambda x:change2timestamp(x,hasf='.' in x)) ins, us, qs, cs, avgins, avgcq, na = sta_infos(df, KEYS, stares) print(f"after drop interaction num: {ins}, user num: {us}, question num: {qs}, concept num: {cs}, avg(ins) per s: {avgins}, avg(c) per q: {avgcq}, na: {na}") user_inters = [] for user, group in df.groupby(['user_id'], sort=False): group = group.sort_values(['start_timestamp','tmp_index']) seq_skills = group['skill_id'].tolist() seq_ans = group['correct'].tolist() seq_response_cost = group['ms_first_response'].tolist() seq_start_time = group['start_timestamp'].tolist() seq_problems = group['problem_id'].tolist() seq_len = len(group) user_inters.append( [[str(user), str(seq_len)], format_list2str(seq_problems), format_list2str(seq_skills), format_list2str(seq_ans), format_list2str(seq_start_time), format_list2str(seq_response_cost)]) write_txt(write_file, user_inters) print("\n".join(stares)) return