old_contents: #!/usr/bin/env python
  
  import pandas as pd
  from sklearn.preprocessing import LabelEncoder
  from xgboost.sklearn import XGBClassifier
  from utils.io import generate_submission
  
  
  def main():
      path = '../data/processed/'
      train_users = pd.read_csv(path + 'ohe_count_processed_train_users.csv')
      test_users = pd.read_csv(path + 'ohe_count_processed_test_users.csv')
  
      y_train = train_users['country_destination']
      train_users.drop(['country_destination', 'id'], axis=1, inplace=True)
      train_users = train_users.fillna(-1)
      x_train = train_users.values
      label_encoder = LabelEncoder()
      encoded_y_train = label_encoder.fit_transform(y_train)
  
      test_users_ids = test_users['id']
      test_users.drop('id', axis=1, inplace=True)
      test_users = test_users.fillna(-1)
      x_test = test_users.values
  
      clf = XGBClassifier(
          max_depth=7,
          learning_rate=0.18,
          n_estimators=80,
          gamma=0,
          min_child_weight=1,
          max_delta_step=0,
          subsample=1,
          colsample_bytree=1,
          colsample_bylevel=1,
          reg_alpha=0,
          reg_lambda=1,
          scale_pos_weight=1,
          base_score=0.5,
          missing=None,
          silent=True,
          nthread=-1,
          seed=42
      )
  
      clf.fit(x_train, encoded_y_train)
      y_pred = clf.predict_proba(x_test)
  
      generate_submission(y_pred, test_users_ids, label_encoder)
  
  
  if __name__ == '__main__':
      main()
  
new_contents: #!/usr/bin/env python
  
  import pandas as pd
  from sklearn.preprocessing import LabelEncoder
  from xgboost.sklearn import XGBClassifier
  from utils.io import generate_submission
  
  
  def main():
      path = '../data/processed/'
      train_users = pd.read_csv(path + 'ohe_count_processed_train_users.csv')
      test_users = pd.read_csv(path + 'ohe_count_processed_test_users.csv')
  
      y_train = train_users['country_destination']
      train_users.drop(['country_destination', 'id'], axis=1, inplace=True)
      train_users = train_users.fillna(-1)
      x_train = train_users.values
      label_encoder = LabelEncoder()
      encoded_y_train = label_encoder.fit_transform(y_train)
  
      test_users_ids = test_users['id']
      test_users.drop('id', axis=1, inplace=True)
      test_users = test_users.fillna(-1)
      x_test = test_users.values
  
      clf = XGBClassifier(
          max_depth=7,
          learning_rate=0.18,
          n_estimators=80,
          gamma=0,
          min_child_weight=1,
          max_delta_step=0,
          subsample=1,
          colsample_bytree=1,
          colsample_bylevel=1,
          reg_alpha=0,
          reg_lambda=1,
          scale_pos_weight=1,
          base_score=0.5,
          missing=None,
          silent=True,
          nthread=-1,
          seed=42
      )
  
      clf.fit(x_train, encoded_y_train)
      y_pred = clf.predict_proba(x_test)
  
      generate_submission(y_pred, test_users_ids, label_encoder, name='gb')
  
  
  if __name__ == '__main__':
      main()
  
subject: Change submission name