Kaggle competition: Riiid Answer Correctness Prediction
<SOS> metric: AUC Kaggle data source: riiid-answer-correctness-prediction<count_values>
import gc
import psutil
import numpy as np
import pandas as pd
import random
from tqdm import tqdm
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
import seaborn as sns
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
merged['Parch'].value_counts()<count_values>
random.seed(42)
 MAX_SEQ = 150
WORKERS = 4
VAL_BATCH_SIZE = 2048
n_skill = 13523
VALID = True
model_file = '.. /input/riiid-models/sakt_layer_1_head_8_embed_128_seq_150_auc_0.7605.pt'
merged['Family_size'] = merged.SibSp + merged.Parch + 1
merged['Family_size'].value_counts()<categorify>
%%time
TRAIN_DTYPES = {'timestamp':'int64',
'user_id':'int32' ,
'content_id':'int16',
'content_type_id':'int8',
'answered_correctly':'int8'}
TRAIN_COLS = TRAIN_DTYPES.keys()
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv',
usecols=[1, 2, 3, 4, 7], dtype=TRAIN_DTYPES)
 train_df = train_df[TRAIN_COLS].astype(TRAIN_DTYPES)
 train_df = train_df[train_df["content_type_id"] == False]
train_df = train_df.sort_values(['timestamp'], ascending=True ).reset_index(drop = True )
merged['Family_size'].replace(to_replace = [1], value = 'single', inplace = True)
 merged['Family_size'].replace(to_replace = [2,3], value = 'small', inplace = True)
 merged['Family_size'].replace(to_replace = [4,5], value = 'medium', inplace = True)
 merged['Family_size'].replace(to_replace = [6, 7, 8, 11], value = 'large', inplace = True )<count_values>
group = train_df[['user_id', 'content_id', 'answered_correctly']].groupby('user_id' ).apply(lambda r:(
 r['content_id'].values,
r['answered_correctly'].values))
del train_df
gc.collect() ;
merged['Family_size'].value_counts()<feature_engineering>
class FFN(nn.Module):
def __init__(self, state_size=200):
super(FFN, self ).__init__()
self.state_size = state_size
self.lr1 = nn.Linear(state_size, state_size)
 self.relu = nn.ReLU()
self.lr2 = nn.Linear(state_size, state_size)
 self.dropout = nn.Dropout(0.2)
 def forward(self, x):
x = self.lr1(x)
 x = self.relu(x)
 x = self.lr2(x)
 return self.dropout(x)
 def future_mask(seq_length):
future_mask = np.triu(np.ones(( seq_length, seq_length)) , k=1 ).astype('bool')
 return torch.from_numpy(future_mask)
 class SAKTModel(nn.Module):
def __init__(self, n_skill, max_seq=MAX_SEQ, embed_dim=128, num_heads=8):
super(SAKTModel, self ).__init__()
self.n_skill = n_skill
self.embed_dim = embed_dim
self.embedding = nn.Embedding(2*n_skill+1, embed_dim)
 self.pos_embedding = nn.Embedding(max_seq-1, embed_dim)
 self.e_embedding = nn.Embedding(n_skill+1, embed_dim)
 self.multi_att = nn.MultiheadAttention(embed_dim=embed_dim, num_heads=num_heads, dropout=0.2)
 self.dropout = nn.Dropout(0.2)
 self.layer_normal = nn.LayerNorm(embed_dim)
 self.ffn = FFN(embed_dim)
 self.pred = nn.Linear(embed_dim, 1)
 def forward(self, x, question_ids):
device = x.device
x = self.embedding(x)
 pos_id = torch.arange(x.size(1)).unsqueeze(0 ).to(device)
 pos_x = self.pos_embedding(pos_id)
 x = x + pos_x
e = self.e_embedding(question_ids)
 x = x.permute(1, 0, 2)
 e = e.permute(1, 0, 2)
 att_mask = future_mask(x.size(0)).to(device)
 att_output, att_weight = self.multi_att(e, x, x, attn_mask=att_mask)
 att_output = self.layer_normal(att_output + e)
 att_output = att_output.permute(1, 0, 2)
 x = self.ffn(att_output)
 x = self.layer_normal(x+att_output)
 x = self.pred(x)
 return x.squeeze(-1), att_weight
ticket = []
for x in list(merged['Ticket']):
if x.isdigit() :
ticket.append('N')
 else:
ticket.append(x.replace('.','' ).replace('/','' ).strip().split(' ')[0])
 merged['Ticket'] = ticket<count_values>
class SAKTDataset(Dataset):
def __init__(self, group, n_skill, subset="train", max_seq=MAX_SEQ):
super(SAKTDataset, self ).__init__()
self.max_seq = max_seq
self.n_skill = n_skill
self.samples = group
self.subset = subset
self.user_ids = []
for user_id in group.index:

 q, qa = group[user_id]
if len(q)< 2:
continue
self.user_ids.append(user_id)
 def __len__(self):
return len(self.user_ids)
 def __getitem__(self, index):
user_id = self.user_ids[index]
q_, qa_ = self.samples[user_id]
seq_len = len(q_)
 q = np.zeros(self.max_seq, dtype=int)
 qa = np.zeros(self.max_seq, dtype=int)
 if seq_len >= self.max_seq:
if self.subset == "train":
if random.random() > 0.1:
random_start_index = random.randint(0, seq_len - self.max_seq)

end_index = random_start_index + self.max_seq
q[:] = q_[random_start_index:end_index]
qa[:] = qa_[random_start_index:end_index]
else:
q[:] = q_[-self.max_seq:]
qa[:] = qa_[-self.max_seq:]
else:
q[:] = q_[-self.max_seq:]
qa[:] = qa_[-self.max_seq:]
else:
if random.random() >0.1:
seq_len = random.randint(2,seq_len)
 q[-seq_len:] = q_[:seq_len]
qa[-seq_len:] = qa_[:seq_len]
else:
q[-seq_len:] = q_
qa[-seq_len:] = qa_
target_id = q[1:]
label = qa[1:]
x = q[:-1].copy()
x +=(qa[:-1] == 1)* self.n_skill
return x, target_id, label
def valid_epoch(model, valid_iterator, criterion, device="cuda"):
model.eval()
valid_loss = []
num_corrects = 0
num_total = 0
labels = []
outs = []
len_dataset = len(valid_iterator)
 with tqdm(total=len_dataset)as pbar:
for idx, item in enumerate(valid_iterator):
x = item[0].to(device ).long()
target_id = item[1].to(device ).long()
label = item[2].to(device ).float()
with torch.no_grad() :
output, _ = model(x, target_id)
 loss = criterion(output, label)
 valid_loss.append(loss.item())
 output = output[:, -1]
output = torch.sigmoid(output)
 label = label[:, -1]
pred =(output >= 0.5 ).long()
num_corrects +=(pred == label ).sum().item()
num_total += len(label)
 labels.extend(label.view(-1 ).data.cpu().numpy())
 outs.extend(output.view(-1 ).data.cpu().numpy())
 pbar.set_description(f'val loss batch - {valid_loss[-1]:.4f}')
 pbar.update(1)
 acc = num_corrects / num_total
auc = roc_auc_score(labels, outs)
 loss = np.mean(valid_loss)
 return loss, acc, auc
merged['Ticket'].value_counts()<count_values>
if VALID:
valid_df = pd.read_parquet('.. /input/cv-strategy-in-the-kaggle-environment/cv3_valid.parquet')
 valid_df = valid_df[TRAIN_COLS]
valid_df = valid_df[valid_df["content_type_id"] == False]
valid_group = valid_df[['user_id', 'content_id', 'answered_correctly']].groupby('user_id' ).apply(lambda r:(
 r['content_id'].values,
r['answered_correctly'].values))
del valid_df
gc.collect() ;
merged['Ticket'] = merged['Ticket'].apply(lambda x: x[0])
 merged['Ticket'].value_counts()<count_values>
if VALID:
valid_dataset = SAKTDataset(valid_group, n_skill, subset="valid")
 val_loader = DataLoader(valid_dataset,
batch_size=VAL_BATCH_SIZE,
shuffle=False)
 criterion = nn.BCEWithLogitsLoss()
criterion.to(device)
 val_loss, val_acc, val_auc = valid_epoch(model, val_loader, criterion, device=device)
 print(f"Valid: loss - {val_loss:.2f} acc - {val_acc:.4f} auc - {val_auc:.4f}" )
outliers(merged['Fare'] )<count_missing_values>
env = riiideducation.make_env()
iter_test = env.iter_test()
merged.isnull().sum()<count_values>
model.eval()
prev_test_df = None
for(test_df, sample_prediction_df)in iter_test:
if(prev_test_df is not None)&(psutil.virtual_memory().percent<95):
print(psutil.virtual_memory().percent)
 prev_test_df['answered_correctly'] = eval(test_df['prior_group_answers_correct'].iloc[0])
 prev_test_df = prev_test_df[prev_test_df.content_type_id == False]
prev_group = prev_test_df[['user_id', 'content_id', 'answered_correctly']].groupby('user_id' ).apply(lambda r:(
 r['content_id'].values,
r['answered_correctly'].values))
for prev_user_id in prev_group.index:
prev_group_content = prev_group[prev_user_id][0]
prev_group_ac = prev_group[prev_user_id][1]
if prev_user_id in group.index:
group[prev_user_id] =(np.append(group[prev_user_id][0],prev_group_content),
np.append(group[prev_user_id][1],prev_group_ac))
else:
group[prev_user_id] =(prev_group_content,prev_group_ac)
 if len(group[prev_user_id][0])>MAX_SEQ:
new_group_content = group[prev_user_id][0][-MAX_SEQ:]
new_group_ac = group[prev_user_id][1][-MAX_SEQ:]
group[prev_user_id] =(new_group_content,new_group_ac)
 prev_test_df = test_df.copy()
test_df = test_df[test_df.content_type_id == False]
test_dataset = TestDataset(group, test_df, n_skill)
 test_dataloader = DataLoader(test_dataset, batch_size=51200, shuffle=False)
 outs = []
for item in test_dataloader:
x = item[0].to(device ).long()
target_id = item[1].to(device ).long()
with torch.no_grad() :
output, _ = model(x, target_id)
 output = torch.sigmoid(output)
 output = output[:, -1]
outs.extend(output.view(-1 ).data.cpu().numpy())
 test_df['answered_correctly'] = outs
env.predict(test_df.loc[test_df['content_type_id'] == 0, ['row_id', 'answered_correctly']] )
<categorify><EOS> 
sns.set()
sub = pd.read_csv('.. /working/submission.csv')
 sub['answered_correctly'].hist() ;