content: import os
  import glob
  import shutil
  import datetime
  import numpy as np
  import pandas as pd
  
  # Dataset
  PROJECT_NAME = "TalkingData AdTracking Fraud Detection"
  PROJECT_FOLDER_PATH = os.path.join(os.path.expanduser("~"), "Documents/Dataset",
                                     PROJECT_NAME)
  
  # Submission
  TEAM_NAME = "Aurora"
  SUBMISSION_FOLDER_PATH = os.path.join(PROJECT_FOLDER_PATH, "submission")
  os.makedirs(SUBMISSION_FOLDER_PATH, exist_ok=True)
  
  # Ensembling
  WORKSPACE_FOLDER_PATH = os.path.join(PROJECT_FOLDER_PATH, "script/Mar_25_3")
  KEYWORD = "DL"
  
  # Generate a zip archive for a file
  create_zip_archive = lambda file_path: shutil.make_archive(
      file_path[:file_path.rindex(".")], "zip",
      os.path.abspath(os.path.join(file_path, "..")), os.path.basename(file_path))
  
  
  def run():
      print("Searching for submissions with keyword {} at {} ...".format(
          KEYWORD, WORKSPACE_FOLDER_PATH))
      submission_file_path_list = sorted(
          glob.glob(os.path.join(WORKSPACE_FOLDER_PATH, "*{}*".format(KEYWORD))))
      assert len(submission_file_path_list) != 0
  
      ranking_array_list = []
      for submission_file_path in submission_file_path_list:
          print("Loading {} ...".format(submission_file_path))
          submission_df = pd.read_csv(submission_file_path)
  
          print("Ranking the entries ...")
          index_series = submission_df["is_attributed"].argsort()
          ranking_array = np.zeros(index_series.shape, dtype=np.uint32)
          ranking_array[index_series] = np.arange(len(index_series))
          ranking_array_list.append(ranking_array)
  
      ensemble_df = submission_df.copy()
      ensemble_prediction_array = np.mean(ranking_array_list, axis=0)
      apply_normalization = lambda data_array: 1.0 * (data_array - np.min(
          data_array)) / (np.max(data_array) - np.min(data_array))
      ensemble_df["is_attributed"] = apply_normalization(
          ensemble_prediction_array)
      ensemble_file_path = os.path.join(
          SUBMISSION_FOLDER_PATH, "{} {} {}.csv".format(
              TEAM_NAME, KEYWORD,
              str(datetime.datetime.now()).split(".")[0]).replace(" ", "_"))
      print("Saving submission to {} ...".format(ensemble_file_path))
      ensemble_df.to_csv(ensemble_file_path, float_format="%.6f", index=False)
      compressed_ensemble_file_path = create_zip_archive(ensemble_file_path)
      print("Saving compressed submission to {} ...".format(
          compressed_ensemble_file_path))
  
      print("All done!")
  
  
  if __name__ == "__main__":
      run()
  