Kaggle competition: Tweet Sentiment Extraction
rmse = np.sqrt(mean_squared_error(val_target, val_predictions))
print("RMSE: %f" %(rmse))
print("MAE: " + str(mean_absolute_error(val_predictions, val_target)))
 print("R2:" + str(r2_score(val_target, val_predictions)) )<save_to_csv>
warnings.filterwarnings('ignore' )
test_df = pd.read_csv('.. /input/titanic/test.csv')
 predict_Y = model.predict(predict_X)
 submissionRF = pd.DataFrame({
"PassengerId": test_df["PassengerId"],
"Survived": predict_Y
})
 submissionRF.to_csv('gender_submission.csv', index=False )<train_on_grid>
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 )
def algorithm_pipeline(X_train_data, X_test_data, y_train_data, y_test_data,
model, param_grid, cv=10, scoring_fit='neg_mean_squared_error',
do_probabilities = False):
gs = GridSearchCV(
 estimator=model,
param_grid=param_grid,
cv=cv,
n_jobs=-1,
scoring=scoring_fit,
verbose=2 
)
 fitted_model = gs.fit(X_train_data, y_train_data)
 if do_probabilities:
pred = fitted_model.predict_proba(X_test_data)
 else:
pred = fitted_model.predict(X_test_data)
 return fitted_model, pred<choose_model_class>
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='.. /input/roberta-base/vocab.json',
merges_file='.. /input/roberta-base/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
model = RandomForestClassifier()
parameters = {
'min_samples_split': [4,8],
'min_samples_leaf': [2,4,8],
'criterion': ['gini', 'entropy'],
'n_estimators': [100]}
model, pred = algorithm_pipeline(train_data, val_data, train_target, val_target, model,
parameters, cv=5)
 print(model.best_params_)
 rmse = np.sqrt(-model.best_score_)
 print("RMSE: %f" %(rmse))<import_modules>
class TweetModel(nn.Module):
def __init__(self):
super(TweetModel, self ).__init__()
config = RobertaConfig.from_pretrained(
 '.. /input/roberta-base/config.json', output_hidden_states=True)
 self.roberta = RobertaModel.from_pretrained(
 '.. /input/roberta-base/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
classifiers = [
KNeighborsClassifier() ,
SVC(probability=True),
DecisionTreeClassifier() ,
RandomForestClassifier() ,
AdaBoostClassifier() ,
GradientBoostingClassifier() ,
GaussianNB() ,
LinearDiscriminantAnalysis() ,
QuadraticDiscriminantAnalysis() ,
LogisticRegression() ]
log_cols = ["Classifier", "Accuracy"]
log 	 = pd.DataFrame(columns=log_cols)
 sss = StratifiedShuffleSplit(n_splits=8, test_size=0.5, random_state=1)
 X = features_X.values
y = target_Y.values
acc_dict = {}
for train_index, test_index in sss.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
for clf in classifiers:
name = clf.__class__.__name__
clf.fit(X_train, y_train)
 train_predictions = clf.predict(X_test)
 acc = accuracy_score(y_test, train_predictions)
 if name in acc_dict:
acc_dict[name] += acc
else:
acc_dict[name] = acc
for clf in acc_dict:
acc_dict[clf] = acc_dict[clf] / 10.0
log_entry = pd.DataFrame([[clf, acc_dict[clf]]], columns=log_cols)
 log = log.append(log_entry)
 log.sort_values('Accuracy', ascending=False )<predict_on_test>
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
model = GradientBoostingClassifier(learning_rate=0.005,n_estimators=1000, min_samples_split=2, min_samples_leaf=2)
 model.fit(train_data, train_target)
 val_predictions = model.predict(val_data)
 accuracy_score(val_target, val_predictions )<compute_test_metric>
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 )
rmse = np.sqrt(mean_squared_error(val_target, val_predictions))
print("RMSE: %f" %(rmse))
print("MAE: " + str(mean_absolute_error(val_predictions, val_target)))
 print("R2:" + str(r2_score(val_target, val_predictions)) )<save_to_csv>
num_epochs = 10
batch_size = 16
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed )
<train_model>
%%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'/kaggle/input/tweet-sentiment-submission-file/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 )
model = Sequential()
model.add(Dense(units = 7, kernel_initializer = 'uniform', activation = 'relu', input_dim = 7))
model.add(Dense(units = 9, kernel_initializer = 'uniform', activation = 'relu'))
model.add(Dense(units = 5, kernel_initializer = 'uniform', activation = 'relu'))
model.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
keras.optimizers.Adam(learning_rate=0.0009)
 model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
 model.fit(features_X, target_Y, batch_size = 32, epochs = 200)
<predict_on_test>
y_pred = model.predict(features_X)
 y_pred =(y_pred > 0.5 ).astype(int ).reshape(features_X.shape[0])
 accuracy_score(target_Y, y_pred )<predict_on_test>
sub_df = pd.read_csv('.. /input/tweet-sentiment-extraction/sample_submission.csv')
 sub_df['selected_text'] = predictions
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.to_csv('submission.csv', index=False)
 sub_df.head()