Source code for pykt.preprocess.assist2015_preprocess

import pandas as pd
from .utils import sta_infos, write_txt

KEYS = ["user_id", "sequence_id"]

[docs]def read_data_from_csv(read_file, write_file): stares = [] df = pd.read_csv(read_file) 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["index"] = range(df.shape[0]) df = df.dropna(subset=["user_id", "log_id", "sequence_id", "correct"]) df = df[df['correct'].isin([0,1])]#filter responses df['correct'] = df['correct'].astype(int) 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}") ui_df = df.groupby(['user_id'], sort=False) user_inters = [] for ui in ui_df: user, tmp_inter = ui[0], ui[1] tmp_inter = tmp_inter.sort_values(by=["log_id", "index"]) seq_len = len(tmp_inter) seq_skills = tmp_inter['sequence_id'].astype(str) seq_ans = tmp_inter['correct'].astype(str) seq_problems = ["NA"] seq_start_time = ["NA"] seq_response_cost = ["NA"] assert seq_len == len(seq_skills) == len(seq_ans) user_inters.append( [[str(user), str(seq_len)], seq_problems, seq_skills, seq_ans, seq_start_time, seq_response_cost]) write_txt(write_file, user_inters) print("\n".join(stares)) return