Kaggle competition: Titanic - Machine Learning from Disaster
<SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<set_options>
%matplotlib inline
plt.rcParams['figure.figsize'] = [18, 9]
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 )<define_variables>
train = pd.read_csv("/kaggle/input/titanic/train.csv", index_col=0)
 test = pd.read_csv("/kaggle/input/titanic/test.csv", index_col=0 )
SAMPLE = False
N_SAMPLE_TRAIN = 500
N_SAMPLE_TEST = 100<define_variables>
Y = train["Survived"].values
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<load_pretrained>
train = train.drop(columns=["Name", "Ticket", "Cabin"] )
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<compute_test_metric>
one_hot_sex = OneHotEncoder(sparse=False)
 sex_encoded = one_hot_sex.fit_transform(train["Sex"].values.reshape(-1, 1))
X_categorical = sex_encoded
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<statistical_test>
train["Pclass"].describe()
train["Pclass"].hist()
train["Pclass"].isnull().sum()
one_hot_pclass = OneHotEncoder(sparse=False)
 pclass_encoded = one_hot_pclass.fit_transform(train["Pclass"].values.reshape(-1, 1))
X_categorical = np.hstack(( X_categorical, pclass_encoded))
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 )<train_model>
train["Embarked"].isnull().sum()
train["Embarked"].loc[train["Embarked"].isnull() ] = train["Embarked"].value_counts().idxmax()
train["Embarked"].isnull().sum()
train["Embarked"].hist()
one_hot_embarked = OneHotEncoder(sparse=False)
 embarked_encoded = one_hot_embarked.fit_transform(train["Embarked"].values.reshape(-1, 1))
X_categorical = np.hstack(( X_categorical, embarked_encoded))
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 )<choose_model_class>
scaler = StandardScaler()
X_numerical = scaler.fit_transform(X_numerical)
 X = np.hstack(( X_numerical, X_categorical))
batch_size = 32
n_splits = 10
num_epochs = 3
skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed )<load_from_csv>
kf = KFold(20, shuffle=True, random_state=41)
 classifier = GradientBoostingClassifier()
classifier_accuracy = []
for train_index, test_index in kf.split(train):
train_X, train_Y = X[train_index], Y[train_index]
test_X, test_Y = X[test_index], Y[test_index]
classifier.fit(train_X, train_Y)
 predictions = classifier.predict(test_X)
 accuracy = accuracy_score(test_Y, predictions)
 classifier_accuracy.append(accuracy)
 print(sum(classifier_accuracy)/len(classifier_accuracy))
%%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' )<set_options>
test_sex_encoded = one_hot_sex.transform(test["Sex"].values.reshape(-1, 1))
test["Age"].loc[test["Age"].isnull() ] = mean_age
test_pclass_encoded = one_hot_pclass.transform(test["Pclass"].values.reshape(-1, 1))
test["Embarked"].loc[test["Embarked"].isnull() ] = train["Embarked"].value_counts().idxmax()
test_embarked_encoded = one_hot_embarked.transform(test["Embarked"].values.reshape(-1, 1))
test["Fare"].loc[test["Fare"].isnull() ] = train["Fare"].mean()
test_X_categorical = np.hstack(( test_sex_encoded, test_pclass_encoded, test_embarked_encoded))
test_X_numerical = np.hstack(( test["Age"].values.reshape(-1, 1), test["SibSp"].values.reshape(-1, 1), test["Parch"].values.reshape(-1, 1), test["Fare"].values.reshape(-1, 1)))
 test_X_numerical = scaler.transform(test_X_numerical)
 test_X = np.hstack(( test_X_numerical, test_X_categorical))
!nvidia-smi<load_from_csv>
classifier.fit(train_X, train_Y)
 predictions = classifier.predict(test_X )
<feature_engineering><EOS> 
data = []
for(passenger_id, passenger_data), prediction in zip(test.iterrows() , predictions):
data.append([passenger_id, prediction])
 series = pd.DataFrame(data, columns=["PassengerID", "Survived"] ).set_index("PassengerID")
 series.to_csv("predictions.csv" )