Kaggle competition: Tweet Sentiment Extraction
<SOS> metric: Jaccard Kaggle data source: tweet-sentiment-extraction<create_dataframe>
warnings.filterwarnings('ignore' )
pred_df = pd.DataFrame(pred, columns=['Survived'])
 result = pd.concat([PassengerId,pred_df],axis =1)
<save_to_csv>
def seed_everything(seed_value):
random.seed(seed_value)
 np.random.seed(seed_value)
 torch.manual_seed(seed_value)
 os.environ['PYTHONHASHSEED'] = str(seed_value)
 if torch.cuda.is_available() :
torch.cuda.manual_seed(seed_value)
 torch.cuda.manual_seed_all(seed_value)
 torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
seed = 42
seed_everything(seed )
result.to_csv('result.csv', index=False )<set_options>
roberta_folder = 'fine-tuning-roberta/roberta_finetuned/'
%matplotlib inline
print("Setup Complete")
 warnings.filterwarnings('ignore' )<load_from_csv>
class TweetDataset(torch.utils.data.Dataset):
def __init__(self, df, max_len=96):
self.df = df
self.max_len = max_len
self.labeled = 'selected_text' in df
self.tokenizer = tokenizers.ByteLevelBPETokenizer(
 vocab_file=f'.. /input/{roberta_folder}/vocab.json',
merges_file=f'.. /input/{roberta_folder}/merges.txt',
lowercase=True,
add_prefix_space=True)
 def __getitem__(self, index):
data = {}
row = self.df.iloc[index]
ids, masks, tweet, offsets = self.get_input_data(row)
 data['ids'] = ids
data['masks'] = masks
data['tweet'] = tweet
data['offsets'] = offsets
if self.labeled:
start_idx, end_idx = self.get_target_idx(row, tweet, offsets)
 data['start_idx'] = start_idx
data['end_idx'] = end_idx
return data
def __len__(self):
return len(self.df)
 def get_input_data(self, row):
tweet = " " + " ".join(row.text.lower().split())
 encoding = self.tokenizer.encode(tweet)
 sentiment_id = self.tokenizer.encode(row.sentiment ).ids
ids = [0] + sentiment_id + [2, 2] + encoding.ids + [2]
offsets = [(0, 0)] * 4 + encoding.offsets + [(0, 0)]
pad_len = self.max_len - len(ids)
 if pad_len > 0:
ids += [1] * pad_len
offsets += [(0, 0)] * pad_len
ids = torch.tensor(ids)
 masks = torch.where(ids != 1, torch.tensor(1), torch.tensor(0))
offsets = torch.tensor(offsets)
 return ids, masks, tweet, offsets
def get_target_idx(self, row, tweet, offsets):
selected_text = " " + " ".join(row.selected_text.lower().split())
 len_st = len(selected_text)- 1
idx0 = None
idx1 = None
for ind in(i for i, e in enumerate(tweet)if e == selected_text[1]):
if " " + tweet[ind: ind+len_st] == selected_text:
idx0 = ind
idx1 = ind + len_st - 1
break
char_targets = [0] * len(tweet)
 if idx0 != None and idx1 != None:
for ct in range(idx0, idx1 + 1):
char_targets[ct] = 1
target_idx = []
for j,(offset1, offset2)in enumerate(offsets):
if sum(char_targets[offset1: offset2])> 0:
target_idx.append(j)
 start_idx = target_idx[0]
end_idx = target_idx[-1]
return start_idx, end_idx
def get_train_val_loaders(df, train_idx, val_idx, batch_size=8):
train_df = df.iloc[train_idx]
val_df = df.iloc[val_idx]
train_loader = torch.utils.data.DataLoader(
 TweetDataset(train_df),
batch_size=batch_size,
shuffle=True,
num_workers=2,
drop_last=True)
 val_loader = torch.utils.data.DataLoader(
 TweetDataset(val_df),
batch_size=batch_size,
shuffle=False,
num_workers=2)
 dataloaders_dict = {"train": train_loader, "val": val_loader}
return dataloaders_dict
def get_test_loader(df, batch_size=32):
loader = torch.utils.data.DataLoader(
 TweetDataset(df),
batch_size=batch_size,
shuffle=False,
num_workers=2)
 return loader
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv',na_values='nan')
 df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv',na_values='nan')
 print("Train size:",df_train.shape)
 print("Test size:",df_test.shape)
 train_ID =df_train['Id']
test_ID =df_test['Id']
df_train.drop("Id", axis = 1, inplace = True)
 df_test.drop("Id", axis = 1, inplace = True)
 print("Size of train data after dropping Id: {}".format(df_train.shape))
print("Size of test data after dropping Id: {}".format(df_test.shape)) 
<drop_column>
class TweetModel(nn.Module):
def __init__(self):
super(TweetModel, self ).__init__()
config = RobertaConfig.from_pretrained(
 f'.. /input/{roberta_folder}/config.json', output_hidden_states=True)
 self.roberta = RobertaModel.from_pretrained(
 f'.. /input/{roberta_folder}/pytorch_model.bin', config=config)
 self.dropout = nn.Dropout(0.15)
 m_size = 128
self.qa_outputs1c1 = torch.nn.Conv1d(config.hidden_size, m_size, 2)
 self.qa_outputs1c2 = torch.nn.Conv1d(m_size, 64, 2)
 self.qa_outputs1 = nn.Linear(64, 1)
 self.qa_outputs2c1 = torch.nn.Conv1d(config.hidden_size, m_size, 2)
 self.qa_outputs2c2 = torch.nn.Conv1d(m_size, 64, 2)
 self.qa_outputs2 = nn.Linear(64, 1)
 self.dropout = nn.Dropout(0.15)
 def forward(self, input_ids, attention_mask):
_, _, hs = self.roberta(input_ids, attention_mask)
 x = torch.stack([hs[-1], hs[-2], hs[-3], hs[-4]])
 x = torch.mean(x, 0)
 x = self.dropout(x)
 x = torch.nn.functional.pad(x.transpose(1,2),(1, 0))
out1 = self.qa_outputs1c1(x)
 out1 = F.relu(out1)
 out1 = self.qa_outputs1c2(out1 ).transpose(1,2)
 start_logits = self.qa_outputs1(self.dropout(out1)).squeeze(-1)
 out2 = self.qa_outputs2c1(x)
 out2 = F.relu(out2)
 out2 = self.qa_outputs2c2(out2 ).transpose(1,2)
 end_logits = self.qa_outputs2(self.dropout(out2)).squeeze(-1)
 return start_logits, end_logits
train = df_train.drop(df_train[(df_train['GrLivArea']>4000)&(df_train['SalePrice']<300000)].index )<sort_values>
def loss_fn(start_logits, end_logits, start_positions, end_positions):
ce_loss = nn.CrossEntropyLoss()
start_loss = ce_loss(start_logits, start_positions)
 end_loss = ce_loss(end_logits, end_positions)
 total_loss = start_loss + end_loss
return total_loss
p_df=[]
for col in df_train_num.columns:
stat, p = shapiro(df_train[col])
 p_df.append(p)
 p_df_=pd.DataFrame(p_df,df_train_num.columns ).sort_values(by=0,ascending=True)
 p_df_=p_df_.loc[p_df_[0]>0.05]
p_df_<prepare_x_and_y>
def get_selected_text(text, start_idx, end_idx, offsets):
selected_text = ""
for ix in range(start_idx, end_idx + 1):
selected_text += text[offsets[ix][0]: offsets[ix][1]]
if(ix + 1)< len(offsets)and offsets[ix][1] < offsets[ix + 1][0]:
selected_text += " "
return selected_text
def jaccard(str1, str2):
a = set(str1.lower().split())
 b = set(str2.lower().split())
 c = a.intersection(b)
 return float(len(c)) /(len(a)+ len(b)- len(c))
def compute_jaccard_score(text, start_idx, end_idx, start_logits, end_logits, offsets):
start_pred = np.argmax(start_logits)
 end_pred = np.argmax(end_logits)
 if start_pred > end_pred:
pred = text
else:
pred = get_selected_text(text, start_pred, end_pred, offsets)
 true = get_selected_text(text, start_idx, end_idx, offsets)
 return jaccard(true, pred )
ntrain = train.shape[0]
ntest = df_test.shape[0]
all_data = pd.concat(( train,df_test)).reset_index(drop=True)
 all_data.drop(['SalePrice'], axis=1, inplace=True)
 print("all_data size is : {}".format(all_data.shape)) 
<create_dataframe>
def train_model(model, dataloaders_dict, criterion, optimizer, num_epochs, filename):
model.cuda()
for epoch in range(num_epochs):
for phase in ['train', 'val']:
if phase == 'train':
model.train()
else:
model.eval()
epoch_loss = 0.0
epoch_jaccard = 0.0
for data in(dataloaders_dict[phase]):
ids = data['ids'].cuda()
masks = data['masks'].cuda()
tweet = data['tweet']
offsets = data['offsets'].numpy()
start_idx = data['start_idx'].cuda()
end_idx = data['end_idx'].cuda()
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
start_logits, end_logits = model(ids, masks)
 loss = criterion(start_logits, end_logits, start_idx, end_idx)
 if phase == 'train':
loss.backward()
optimizer.step()
epoch_loss += loss.item() * len(ids)
 start_idx = start_idx.cpu().detach().numpy()
end_idx = end_idx.cpu().detach().numpy()
start_logits = torch.softmax(start_logits, dim=1 ).cpu().detach().numpy()
end_logits = torch.softmax(end_logits, dim=1 ).cpu().detach().numpy()
for i in range(len(ids)) :
jaccard_score = compute_jaccard_score(
 tweet[i],
start_idx[i],
end_idx[i],
start_logits[i],
end_logits[i],
offsets[i])
 epoch_jaccard += jaccard_score
epoch_loss = epoch_loss / len(dataloaders_dict[phase].dataset)
 epoch_jaccard = epoch_jaccard / len(dataloaders_dict[phase].dataset)
 print('Epoch {}/{} | {:^5} | Loss: {:.4f} | Jaccard: {:.4f}'.format(
 epoch + 1, num_epochs, phase, epoch_loss, epoch_jaccard))
torch.save(model.state_dict() , filename )
all_data_na =(all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index ).sort_values(ascending=False)[:30]
missing_data = pd.DataFrame({'Missing Ratio' :all_data_na} )<categorify>
num_epochs = 3
batch_size = 32
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed )
all_data_rep=all_data.copy()
all_data_rep["PoolQC"] = all_data_rep["PoolQC"].fillna(0)
 all_data_rep["MiscFeature"] = all_data_rep["MiscFeature"].fillna(0)
 all_data_rep["Fence"] = all_data_rep["Fence"].fillna(0)
 all_data_rep["FireplaceQu"] = all_data_rep["FireplaceQu"].fillna(0)
 all_data_rep["LotFrontage"] = all_data_rep.groupby('Neighborhood')["LotFrontage"].transform(
 lambda x:x.fillna(x.median()))
grd_col=["GarageQual","GarageCond","GarageType","GarageFinish","GarageArea","GarageCars","GarageYrBlt"]
for col in grd_col:
all_data_rep[col] = all_data_rep[col].fillna(0)
 grd_col=["BsmtExposure","BsmtCond","BsmtQual","BsmtFinType1","BsmtFinType2","BsmtUnfSF","BsmtFinSF1","BsmtFinSF2"]
for col in grd_col:
all_data_rep[col] = all_data_rep[col].fillna(0)
 all_data_rep["TotalBsmtSF"] = all_data_rep["TotalBsmtSF"].fillna(0)
 all_data_rep["MasVnrArea"] = all_data_rep["MasVnrArea"].fillna(0)
 all_data_rep["MasVnrType"] = all_data_rep["MasVnrType"].fillna(0)
 all_data_rep['MSZoning']=all_data_rep['MSZoning'].fillna(all_data_rep['MSZoning'].mode() [0])
 all_data_rep["BsmtFullBath"] = all_data_rep["BsmtFullBath"].fillna(0)
 all_data_rep["BsmtHalfBath"] = all_data_rep["BsmtHalfBath"].fillna(0)
 all_data_rep["Functional"] =all_data_rep["Functional"].fillna("Typ")
 all_data_rep["Alley"] = all_data_rep["Alley"].fillna(0)
 mode_col = ['Electrical','KitchenQual', 'Exterior1st', 'Exterior2nd', 'SaleType']
for col in mode_col:
all_data_rep[col] = all_data_rep[col].fillna(all_data_rep[col].mode() [0])
 all_data_rep["MoSold"] = all_data_rep["MoSold"].fillna(0 )<count_missing_values>
import gc
all_data_rep.isnull().sum().sort_values(ascending=False)[:3]<feature_engineering>
%%time
train_df = pd.read_csv('.. /input/tweet-sentiment-extraction/train.csv')
 train_df['text'] = train_df['text'].astype(str)
 train_df['selected_text'] = train_df['selected_text'].astype(str)
 for fold,(train_idx, val_idx)in enumerate(skf.split(train_df, train_df.sentiment), start=1):
print(f'Fold: {fold}')
 model = TweetModel()
optimizer = optim.AdamW(model.parameters() , lr=3e-5, betas=(0.9, 0.999))
criterion = loss_fn
dataloaders_dict = get_train_val_loaders(train_df, train_idx, val_idx, batch_size)
 train_model(
 model,
dataloaders_dict,
criterion,
optimizer,
num_epochs,
f'roberta_fold{fold}.pth')
 gc.collect()
all_data_rep['TotalSF'] = all_data_rep['TotalBsmtSF'] + all_data_rep['1stFlrSF'] + all_data_rep['2ndFlrSF']<prepare_output>
%%time
test_df = pd.read_csv('.. /input/tweet-sentiment-extraction/test.csv')
 test_df['text'] = test_df['text'].astype(str)
 test_loader = get_test_loader(test_df)
 predictions = []
models = []
for fold in range(skf.n_splits):
model = TweetModel()
model.cuda()
model.load_state_dict(torch.load(f'roberta_fold{fold+1}.pth'))
model.eval()
models.append(model)
 for data in test_loader:
ids = data['ids'].cuda()
masks = data['masks'].cuda()
tweet = data['tweet']
offsets = data['offsets'].numpy()
start_logits = []
end_logits = []
for model in models:
with torch.no_grad() :
output = model(ids, masks)
 start_logits.append(torch.softmax(output[0], dim=1 ).cpu().detach().numpy())
 end_logits.append(torch.softmax(output[1], dim=1 ).cpu().detach().numpy())
 start_logits = np.mean(start_logits, axis=0)
 end_logits = np.mean(end_logits, axis=0)
 for i in range(len(ids)) :
start_pred = np.argmax(start_logits[i])
 end_pred = np.argmax(end_logits[i])
 if start_pred > end_pred:
pred = tweet[i]
else:
pred = get_selected_text(tweet[i], start_pred, end_pred, offsets[i])
 predictions.append(pred)
all_data_rep_enc_1=all_data_rep.copy()<create_dataframe>
sub_df = pd.read_csv('.. /input/tweet-sentiment-extraction/sample_submission.csv')
 sub_df['selected_text'] = predictions
sub_df.to_csv('submission.csv', index=False)
 sub_df.head()