Kaggle competition: Tweet Sentiment Extraction
<SOS> metric: Jaccard Kaggle data source: tweet-sentiment-extraction<data_type_conversions>
warnings.filterwarnings('ignore' )
housing_df['MSSubClass'] = housing_df['MSSubClass'].astype('object')
 housing_df['OverallQual'] = housing_df['OverallQual'].astype('object')
 housing_df['OverallCond'] = housing_df['OverallCond'].astype('object')
 housing_df['BsmtFullBath'] = housing_df['BsmtFullBath'].astype('object')
 housing_df['BsmtHalfBath'] = housing_df['BsmtHalfBath'].astype('object')
 housing_df['FullBath'] = housing_df['FullBath'].astype('object')
 housing_df['HalfBath'] = housing_df['HalfBath'].astype('object')
 housing_df['BedroomAbvGr'] = housing_df['BedroomAbvGr'].astype('object')
 housing_df['KitchenAbvGr'] = housing_df['KitchenAbvGr'].astype('object')
 housing_df['TotRmsAbvGrd'] = housing_df['TotRmsAbvGrd'].astype('object')
 housing_df['Fireplaces'] = housing_df['Fireplaces'].astype('object')
 housing_df['GarageCars'] = housing_df['GarageCars'].astype('object' )<count_unique_values>
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 = 8888
seed_everything(seed )
col_cat=housing_df.nunique()
binary_cols=col_cat[col_cat<3]
binary_cols.index<drop_column>
SAMPLE = False
N_SAMPLE_TRAIN = 500
N_SAMPLE_TEST = 100
housing_df.drop(['LowQualFinSF','3SsnPorch','PoolArea'],axis=1,inplace=True )<categorify>
tokenizer = tokenizers.ByteLevelBPETokenizer(
 vocab_file='.. /input/robertabase/vocab.json',
merges_file='.. /input/robertabase/merges.txt',
lowercase=True,
add_prefix_space=True)
 num_workers = 2
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
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 = tokenizer.encode(tweet)
 sentiment_id = 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=num_workers,
drop_last=True)
 val_loader = torch.utils.data.DataLoader(
 TweetDataset(val_df),
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
 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=num_workers)
 return loader
housing_df['Street']=housing_df['Street'].map({'Pave': 1, 'Grvl': 0})
 housing_df['Utilities']=housing_df['Utilities'].map({'AllPub': 1,'NoSeWa':0})
 housing_df['CentralAir']=housing_df['CentralAir'].map({'Y': 1, "N": 0} )<count_unique_values>
class TweetModel(nn.Module):
def __init__(self):
super(TweetModel, self ).__init__()
config = RobertaConfig.from_pretrained(
 '.. /input/robertabase/config.json', output_hidden_states=True)
 self.roberta = RobertaModel.from_pretrained(
 '.. /input/robertabase/pytorch_model.bin', config=config)
 self.dropout = nn.Dropout(0.5)
 self.fc = nn.Linear(config.hidden_size, 2)
 nn.init.normal_(self.fc.weight, std=0.02)
 nn.init.normal_(self.fc.bias, 0)
 def forward(self, input_ids, attention_mask):
_, _, hs = self.roberta(input_ids, attention_mask)
 x = torch.stack([hs[-1], hs[-2], hs[-3]])
 x = torch.mean(x, 0)
 x = self.dropout(x)
 x = self.fc(x)
 start_logits, end_logits = x.split(1, dim=-1)
 start_logits = start_logits.squeeze(-1)
 end_logits = end_logits.squeeze(-1)
 return start_logits, end_logits
categorical_fields=housing_df.select_dtypes(include='object')
 categorical_columns=categorical_fields.nunique().sort_values(ascending=False ).index
categorical_columns<categorify>
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
dummy_vars = pd.get_dummies(housing_df[categorical_columns], drop_first=True)
 dummy_vars.head()<concatenate>
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 )
housing_df = pd.concat([housing_df, dummy_vars], axis=1)
 housing_df = housing_df.drop(categorical_columns, axis = 1)
 housing_df.shape<feature_engineering>
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 )
df_train = housing_df.iloc[:1460]
df_test = housing_df.iloc[1460:]
df_train['LotFrontage']=train_df['LotFrontage']
df_train['SalePrice'] = train_df['SalePrice']
df_test['LotFrontage']=test_df['LotFrontage']
df_test['SalePrice'] = 0<drop_column>
batch_size = 32
n_splits = 10
num_epochs = 3
skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed )
df_train.drop('Id',axis=1,inplace=True )<prepare_x_and_y>
%%time
torch.cuda.empty_cache()
train_df = pd.read_csv('.. /input/tweet-sentiment-extraction/train.csv')
 if SAMPLE:
train_df = pd.read_csv('.. /input/tweet-sentiment-extraction/train.csv' ).sample(N_SAMPLE_TRAIN)
 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' )
y_train = df_train.pop('SalePrice')
 X_train = df_train
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train.values)
 X_train = pd.DataFrame(X_train_scaled, index=X_train.index, columns=X_train.columns)
 X_train.head()<choose_model_class>
!nvidia-smi
params = {'alpha': [0.0001, 0.001, 0.01, 0.05, 0.1,
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0,
4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 20, 50, 100]}
ridge = Ridge()
folds = 5
model_cv = GridSearchCV(estimator = ridge,
param_grid = params,
scoring= 'neg_mean_absolute_error',
cv = folds,
return_train_score=True,
verbose = 1)
 model_cv.fit(X_train, y_train)
 print(model_cv.best_params_)
 print(model_cv.best_score_ )<train_model>
%%time
test_df = pd.read_csv('.. /input/tweet-sentiment-extraction/test.csv')
 if SAMPLE:
test_df = pd.read_csv('.. /input/tweet-sentiment-extraction/test.csv' ).sample(N_SAMPLE_TEST)
 test_df['text'] = test_df['text'].astype(str)
 test_loader = get_test_loader(test_df, batch_size=batch_size)
 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 )
<compute_train_metric><EOS> 
sub_df = pd.read_csv('.. /input/tweet-sentiment-extraction/sample_submission.csv')
 sub_df['selected_text'] = predictions
sub_df = sub_df.merge(test_df, how="inner", on="textID")
 sub_df['selected_text'] = sub_df['selected_text'].apply(lambda x: x.replace('!!!!', '!')if len(x.split())==1 else x)
 sub_df['selected_text'] = sub_df['selected_text'].apply(lambda x: x.replace('.. ', '.')if len(x.split())==1 else x)
 sub_df['selected_text'] = sub_df['selected_text'].apply(lambda x: x.replace('...', '.')if len(x.split())==1 else x)
 sub_df['selected_text'] = [row["text"] if row["sentiment"]=="neutral" else row["selected_text"] for idx, row in sub_df.iterrows() ]
sub_df[["textID","selected_text"]].to_csv('submission.csv', index=False)
 sub_df.head()