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import torch
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
import tqdm
import random
from .vocab import Vocab
import pickle
import copy
import os
class TokenizerDataset(Dataset):
"""
Class name: TokenizerDataset
Tokenize the data in the dataset
Feat length: 17
"""
def __init__(self, dataset_path, label_path, vocab, seq_len=30):
self.dataset_path = dataset_path
self.label_path = label_path
self.vocab = vocab # Vocab object
# Related to input dataset file
self.lines = []
self.labels = []
self.feats = []
if self.label_path:
self.label_file = open(self.label_path, "r")
for line in self.label_file:
if line:
line = line.strip()
if not line:
continue
self.labels.append(int(line))
self.label_file.close()
# Comment this section if you are not using feat attribute
try:
j = 0
dataset_info_file = open(self.label_path.replace("label", "info"), "r")
for line in dataset_info_file:
if line:
line = line.strip()
if not line:
continue
# # highGRschool_w_prior
# feat_vec = [float(i) for i in line.split(",")[-3].split("\t")]
# highGRschool_w_prior_w_diffskill_wo_fa
feat_vec = [float(i) for i in line.split(",")[-3].split("\t")]
feat2 = [float(i) for i in line.split(",")[-2].split("\t")]
feat_vec.extend(feat2[1:])
if j == 0:
print(len(feat_vec))
j+=1
self.feats.append(feat_vec)
dataset_info_file.close()
except Exception as e:
print(e)
self.file = open(self.dataset_path, "r")
for line in self.file:
if line:
line = line.strip()
if line:
self.lines.append(line)
self.file.close()
self.len = len(self.lines)
self.seq_len = seq_len
print("Sequence length set at ", self.seq_len, len(self.lines), len(self.labels) if self.label_path else 0)
def __len__(self):
return self.len
def __getitem__(self, item):
org_line = self.lines[item].split("\t")
dup_line = []
opt = False
for l in org_line:
if l in ["OptionalTask_1", "EquationAnswer", "NumeratorFactor", "DenominatorFactor", "OptionalTask_2", "FirstRow1:1", "FirstRow1:2", "FirstRow2:1", "FirstRow2:2", "SecondRow", "ThirdRow"]:
opt = True
if opt and 'FinalAnswer-' in l:
dup_line.append('[UNK]')
else:
dup_line.append(l)
dup_line = "\t".join(dup_line)
# print(dup_line)
s1 = self.vocab.to_seq(dup_line, self.seq_len) # This is like tokenizer and adds [CLS] and [SEP].
s1_label = self.labels[item] if self.label_path else 0
segment_label = [1 for _ in range(len(s1))]
s1_feat = self.feats[item] if len(self.feats)>0 else 0
padding = [self.vocab.vocab['[PAD]'] for _ in range(self.seq_len - len(s1))]
s1.extend(padding), segment_label.extend(padding)
output = {'input': s1,
'label': s1_label,
'feat': s1_feat,
'segment_label': segment_label}
return {key: torch.tensor(value) for key, value in output.items()}
class TokenizerwSkillsDataset(Dataset):
"""
Feature length: 17
"""
def __init__(self, dataset_path, label_path, vocab, seq_len=30):
print(f"dataset_path: {dataset_path}")
print(f"label_path: {label_path}")
self.dataset_path = dataset_path
self.label_path = label_path
self.vocab = vocab # Vocab object
self.seq_len = seq_len
# Related to input dataset file
self.lines = []
self.labels = []
self.feats = []
selected_lines = []
print("TokenizerwSkillsDataset...............................")
if self.label_path:
# Comment this section if you are not using feat attribute
dataset_info_file = open(self.label_path.replace("label", "info"), "r").readlines()
print(">>>>>>>>>>>>>>>>>", len(dataset_info_file))
j = 0
for idex, line in enumerate(dataset_info_file):
try:
if line:
line = line.strip()
if not line:
continue
feat_vec = [float(i) for i in line.split(",")[-9].split("\t")]
feat2 = [float(i) for i in line.split(",")[-8].split("\t")]
feat_vec.extend(feat2[1:])
if j == 0:
print(";;;;", len(feat_vec), feat_vec)
j+=1
self.feats.append(feat_vec)
selected_lines.append(idex)
except Exception as e:
print("................>")
print(e)
print("Error at index: ", idex)
self.label_file = open(self.label_path, "r")
for idex, line in enumerate(self.label_file):
if line:
line = line.strip()
if not line:
continue
if idex in selected_lines:
self.labels.append(int(line))
# self.labels.append(int(line))
self.label_file.close()
self.file = open(self.dataset_path, "r")
for idex, line in enumerate(self.file):
if line:
line = line.strip()
if line:
if idex in selected_lines:
self.lines.append(line)
# self.lines.append(line)
self.file.close()
self.len = len(self.lines)
print("Sequence length set at ", self.seq_len, len(self.lines), len(self.labels) if self.label_path else 0)
def __len__(self):
return self.len
def __getitem__(self, item):
org_line = self.lines[item].split("\t")
dup_line = []
opt = False
for l in org_line:
if l in ["OptionalTask_1", "EquationAnswer", "NumeratorFactor", "DenominatorFactor", "OptionalTask_2", "FirstRow1:1", "FirstRow1:2", "FirstRow2:1", "FirstRow2:2", "SecondRow", "ThirdRow"]:
opt = True
if opt and 'FinalAnswer-' in l:
dup_line.append('[UNK]')
else:
dup_line.append(l)
dup_line = "\t".join(dup_line)
# print(dup_line)
s1 = self.vocab.to_seq(dup_line, self.seq_len) # This is like tokenizer and adds [CLS] and [SEP].
s1_label = self.labels[item] if self.label_path else 0
segment_label = [1 for _ in range(len(s1))]
s1_feat = self.feats[item] if len(self.feats)>0 else 0
padding = [self.vocab.vocab['[PAD]'] for _ in range(self.seq_len - len(s1))]
s1.extend(padding), segment_label.extend(padding)
# print(s1_feat)
output = {'input': s1,
'label': s1_label,
'feat': s1_feat,
'segment_label': segment_label}
return {key: torch.tensor(value) for key, value in output.items()}
class TokenizerwTimeDataset(Dataset):
"""
Feature length: 4
"""
def __init__(self, dataset_path, label_path, vocab, seq_len=30):
print(f"dataset_path: {dataset_path}")
print(f"label_path: {label_path}")
self.dataset_path = dataset_path
self.label_path = label_path
self.vocab = vocab # Vocab object
self.seq_len = seq_len
# Related to input dataset file
self.lines = []
self.labels = []
self.feats = []
selected_lines = []
print("TokenizerwTimeDataset...............................")
time_df = pickle.load(open("ratio_proportion_change3_2223/sch_largest_100-coded/time_info/full_data_normalized_time.pkl", "rb"))
print("time: ?? ", time_df.shape)
if self.label_path:
# Comment this section if you are not using feat attribute
dataset_info_file = open(self.label_path.replace("label", "info"), "r").readlines()
print(">>>>>>>>>>>>>>>>>", len(dataset_info_file))
j = 0
for idex, line in enumerate(dataset_info_file):
try:
if line:
line = line.strip()
if not line:
continue
feat_vec = []
sch = line.split(",")[0]
stu = line.split(",")[2]
progress = line.split(",")[3]
prob_id = line.split(",")[4]
total_time = time_df.loc[(sch, stu, progress, prob_id)]['total_time'].item()
faopt_time = time_df.loc[(sch, stu, progress, prob_id)]['faopt_time'].item()
opt_time = time_df.loc[(sch, stu, progress, prob_id)]['opt_time'].item()
nonopt_time = time_df.loc[(sch, stu, progress, prob_id)]['nonopt_time'].item()
feat_vec.append(faopt_time)
feat_vec.append(total_time)
feat_vec.append(opt_time)
feat_vec.append(nonopt_time)
if j == 0:
print(";;;;", len(feat_vec), feat_vec)
j+=1
self.feats.append(feat_vec)
selected_lines.append(idex)
except Exception as e:
print("................>")
print(e)
print("Error at index: ", idex)
self.label_file = open(self.label_path, "r")
for idex, line in enumerate(self.label_file):
if line:
line = line.strip()
if not line:
continue
if idex in selected_lines:
self.labels.append(int(line))
# self.labels.append(int(line))
self.label_file.close()
self.file = open(self.dataset_path, "r")
for idex, line in enumerate(self.file):
if line:
line = line.strip()
if line:
if idex in selected_lines:
self.lines.append(line)
# self.lines.append(line)
self.file.close()
self.len = len(self.lines)
print("Sequence length set at ", self.seq_len, len(self.lines), len(self.labels) if self.label_path else 0)
def __len__(self):
return self.len
def __getitem__(self, item):
org_line = self.lines[item].split("\t")
dup_line = []
opt = False
for l in org_line:
if l in ["OptionalTask_1", "EquationAnswer", "NumeratorFactor", "DenominatorFactor", "OptionalTask_2", "FirstRow1:1", "FirstRow1:2", "FirstRow2:1", "FirstRow2:2", "SecondRow", "ThirdRow"]:
opt = True
if opt and 'FinalAnswer-' in l:
dup_line.append('[UNK]')
else:
dup_line.append(l)
dup_line = "\t".join(dup_line)
# print(dup_line)
s1 = self.vocab.to_seq(dup_line, self.seq_len) # This is like tokenizer and adds [CLS] and [SEP].
s1_label = self.labels[item] if self.label_path else 0
segment_label = [1 for _ in range(len(s1))]
s1_feat = self.feats[item] if len(self.feats)>0 else 0
padding = [self.vocab.vocab['[PAD]'] for _ in range(self.seq_len - len(s1))]
s1.extend(padding), segment_label.extend(padding)
# print(s1_feat)
output = {'input': s1,
'label': s1_label,
'feat': s1_feat,
'segment_label': segment_label}
return {key: torch.tensor(value) for key, value in output.items()}
class TokenizerwSkillsTimeDataset(Dataset):
"""
Feature length: 17+4 = 21
"""
def __init__(self, dataset_path, label_path, vocab, seq_len=30):
print(f"dataset_path: {dataset_path}")
print(f"label_path: {label_path}")
self.dataset_path = dataset_path
self.label_path = label_path
self.vocab = vocab # Vocab object
self.seq_len = seq_len
# Related to input dataset file
self.lines = []
self.labels = []
self.feats = []
selected_lines = []
print("TokenizerwSkillsTimeDataset...............................")
time_df = pickle.load(open("ratio_proportion_change3_2223/sch_largest_100-coded/time_info/full_data_normalized_time.pkl", "rb"))
print("time: ", time_df.shape)
if self.label_path:
# Comment this section if you are not using feat attribute
dataset_info_file = open(self.label_path.replace("label", "info"), "r").readlines()
print(">>>>>>>>>>>>>>>>>", len(dataset_info_file))
j = 0
for idex, line in enumerate(dataset_info_file):
try:
if line:
line = line.strip()
if not line:
continue
feat_vec = [float(i) for i in line.split(",")[-9].split("\t")]
feat2 = [float(i) for i in line.split(",")[-8].split("\t")]
feat_vec.extend(feat2[1:])
sch = line.split(",")[0]
stu = line.split(",")[2]
progress = line.split(",")[3]
prob_id = line.split(",")[4]
total_time = time_df.loc[(sch, stu, progress, prob_id)]['total_time'].item()
faopt_time = time_df.loc[(sch, stu, progress, prob_id)]['faopt_time'].item()
opt_time = time_df.loc[(sch, stu, progress, prob_id)]['opt_time'].item()
nonopt_time = time_df.loc[(sch, stu, progress, prob_id)]['nonopt_time'].item()
feat_vec.append(faopt_time)
feat_vec.append(total_time)
feat_vec.append(opt_time)
feat_vec.append(nonopt_time)
if j == 0:
print(";;;;", len(feat_vec), feat_vec)
j+=1
self.feats.append(feat_vec)
selected_lines.append(idex)
except Exception as e:
print("................>")
print(e)
print("Error at index: ", idex)
self.label_file = open(self.label_path, "r")
for idex, line in enumerate(self.label_file):
if line:
line = line.strip()
if not line:
continue
if idex in selected_lines:
self.labels.append(int(line))
# self.labels.append(int(line))
self.label_file.close()
self.file = open(self.dataset_path, "r")
for idex, line in enumerate(self.file):
if line:
line = line.strip()
if line:
if idex in selected_lines:
self.lines.append(line)
# self.lines.append(line)
self.file.close()
self.len = len(self.lines)
print("Sequence length set at ", self.seq_len, len(self.lines), len(self.labels) if self.label_path else 0)
def __len__(self):
return self.len
def __getitem__(self, item):
org_line = self.lines[item].split("\t")
dup_line = []
opt = False
for l in org_line:
if l in ["OptionalTask_1", "EquationAnswer", "NumeratorFactor", "DenominatorFactor", "OptionalTask_2", "FirstRow1:1", "FirstRow1:2", "FirstRow2:1", "FirstRow2:2", "SecondRow", "ThirdRow"]:
opt = True
if opt and 'FinalAnswer-' in l:
dup_line.append('[UNK]')
else:
dup_line.append(l)
dup_line = "\t".join(dup_line)
# print(dup_line)
s1 = self.vocab.to_seq(dup_line, self.seq_len) # This is like tokenizer and adds [CLS] and [SEP].
s1_label = self.labels[item] if self.label_path else 0
segment_label = [1 for _ in range(len(s1))]
s1_feat = self.feats[item] if len(self.feats)>0 else 0
padding = [self.vocab.vocab['[PAD]'] for _ in range(self.seq_len - len(s1))]
s1.extend(padding), segment_label.extend(padding)
# print(s1_feat)
output = {'input': s1,
'label': s1_label,
'feat': s1_feat,
'segment_label': segment_label}
return {key: torch.tensor(value) for key, value in output.items()} |