Kaggle competition: Riiid Answer Correctness Prediction
train.isnull().sum()<count_missing_values>
import gc
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
import torch.nn.utils.rnn as rnn_utils
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
test.isnull().sum()<count_missing_values>
MAX_SEQ = 160
test.isnull().sum()<count_values>
%%time
dtype = {'timestamp':'int64',
'user_id':'int32' ,
'content_id':'int16',
'content_type_id':'int8',
'answered_correctly':'int8'}
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', usecols=[1, 2, 3, 4, 7], dtype=dtype)
 train_df.head()
train["Embarked"].value_counts()<data_type_conversions>
train_df = train_df[train_df.content_type_id == False]
train_df = train_df.sort_values(['timestamp'], ascending=True ).reset_index(drop = True )
train = train.fillna({"Embarked": "S"} )<data_type_conversions>
skills = train_df["content_id"].unique()
n_skill = len(skills)
 print("number skills", len(skills))
test = test.fillna({"Embarked": "S"} )<count_missing_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()
train.isnull().sum()<count_missing_values>
random.seed(1)
train.isnull().sum()<count_missing_values>
class SAKTDataset(Dataset):
def __init__(self, group, n_skill, max_seq=MAX_SEQ):
super(SAKTDataset, self ).__init__()
self.max_seq = max_seq
self.n_skill = n_skill
self.samples = group
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 random.random() >0.1:
start = random.randint(0,(seq_len-self.max_seq))
end = start + self.max_seq
q[:] = q_[start:end]
qa[:] = qa_[start:end]
else:
q[:] = q_[-self.max_seq:]
qa[:] = qa_[-self.max_seq:]
else:
if random.random() >0.1:
start = 0
end = random.randint(2,seq_len)
 seq_len = end - start
q[-seq_len:] = q_[0:seq_len]
qa[-seq_len:] = qa_[0:seq_len]
else:
q[-seq_len:] = q_
qa[-seq_len:] = qa_
target_id = q[1:]
label = qa[1:]
x = np.zeros(self.max_seq-1, dtype=int)
 x = q[:-1].copy()
x +=(qa[:-1] == 1)* self.n_skill
return x, target_id, label
test.isnull().sum()<count_missing_values>
dataset = SAKTDataset(group, n_skill)
 dataloader = DataLoader(dataset, batch_size=2048, shuffle=True, num_workers=8)
 item = dataset.__getitem__(5)
test.isnull().sum()<filter>
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):
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=8, 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
test[test["Fare"].isnull() ]<groupby>
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = SAKTModel(n_skill, embed_dim=256)
 optimizer = torch.optim.Adam(model.parameters() , lr=1.5e-3)
 criterion = nn.BCEWithLogitsLoss()
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 8e-3, eta_min=0, last_epoch=-1)
 model.to(device)
 criterion.to(device )
test[["Pclass","Fare"]].groupby("Pclass" ).mean()<feature_engineering>
def train_epoch(model, train_iterator, optim, criterion, device="cpu"):
model.train()
train_loss = []
num_corrects = 0
num_total = 0
labels = []
outs = []
tbar = tqdm(train_iterator)
 for item in tbar:
x = item[0].to(device ).long()
target_id = item[1].to(device ).long()
label = item[2].to(device ).float()
optim.zero_grad()
output, atten_weight = model(x, target_id)
 loss = criterion(output, label)
 loss.backward()
optim.step()
train_loss.append(loss.item())
 output = output[:, -1]
label = label[:, -1]
pred =(torch.sigmoid(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())
 tbar.set_description('loss - {:.4f}'.format(loss))
scheduler.step()
acc = num_corrects / num_total
auc = roc_auc_score(labels, outs)
 loss = np.mean(train_loss)
 return loss, acc, auc
test["Fare"] = test["Fare"].fillna(12 )<count_missing_values>
epochs = 35
for epoch in range(epochs):
loss, acc, auc = train_epoch(model, dataloader, optimizer, criterion, device)
 print("epoch - {} train_loss - {:.2f} acc - {:.3f} auc - {:.3f}".format(epoch, loss, acc, auc))
test["Fare"].isnull().sum()<categorify>
torch.save(model.state_dict() , "SAKT-HDKIM.pt" )
sex_mapping = {"male": 0, "female": 1}
train['Sex'] = train['Sex'].map(sex_mapping)
 test['Sex'] = test['Sex'].map(sex_mapping)
 train.head()<categorify>
del dataset
gc.collect()
lbe = preprocessing.LabelEncoder()
train["Embarked"] = lbe.fit_transform(train["Embarked"])
 test["Embarked"] = lbe.fit_transform(test["Embarked"])
 train.head() 
<categorify>
env = riiideducation.make_env()
iter_test = env.iter_test()
<drop_column><EOS> 
model.eval()
prev_test_df = None
for(test_df, sample_prediction_df)in tqdm(iter_test):
if(prev_test_df is not None)&(psutil.virtual_memory().percent<90):
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, skills)
 test_dataloader = DataLoader(test_dataset, batch_size=51200, shuffle=False)
 outs = []
for item in tqdm(test_dataloader):
x = item[0].to(device ).long()
target_id = item[1].to(device ).long()
with torch.no_grad() :
output, att_weight = 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']] )