old_contents: import os
  import numpy as np
  import pandas as pd
  from serenata_toolbox.ceap_dataset import CEAPDataset
  from monthly_subquota_limit_classifier import MonthlySubquotaLimitClassifier
  
  DATA_PATH = '/tmp/serenata-data'
  DATASET_KEYS = ['applicant_id', 'year', 'document_id']
  
  def update_datasets(self):
      os.makedirs(self.DATA_PATH, exist_ok=True)
      ceap = CEAPDataset(self.DATA_PATH)
      ceap.fetch()
      ceap.convert_to_csv()
      ceap.translate()
      ceap.clean()
  
  
  def run_classifiers(data):
      irregularities = data[DATASET_KEYS].copy()
      model = MonthlySubquotaLimitClassifier()
      model.fit_transform(data)
      irregularities['over_monthly_subquota_limit'] = model.predict(data)
      irregularities.to_csv(os.path.join(DATA_PATH, 'irregularities.xz'),
                            compression='xz',
                            encoding='utf-8',
                            index=False)
  
  
  
  if __name__ == '__main__':
      update_datasets()
  
      dataset = pd.read_csv(os.path.join(DATA_PATH, 'reimbursements.xz'),
                            dtype={'applicant_id': np.str,
                                   'subquota_number': np.str},
                            low_memory=False)
      run_classifiers(dataset)
  
new_contents: import os
  import numpy as np
  import pandas as pd
  from serenata_toolbox.ceap_dataset import CEAPDataset
  from monthly_subquota_limit_classifier import MonthlySubquotaLimitClassifier
  
  DATA_PATH = '/tmp/serenata-data'
  DATASET_KEYS = ['applicant_id', 'year', 'document_id']
  
  
  def update_datasets():
      os.makedirs(DATA_PATH, exist_ok=True)
      ceap = CEAPDataset(DATA_PATH)
      ceap.fetch()
      ceap.convert_to_csv()
      ceap.translate()
      ceap.clean()
  
  
  def run_classifiers(data):
      irregularities = data[DATASET_KEYS].copy()
      model = MonthlySubquotaLimitClassifier()
      model.fit_transform(data)
      irregularities['over_monthly_subquota_limit'] = model.predict(data)
      irregularities.to_csv(os.path.join(DATA_PATH, 'irregularities.xz'),
                            compression='xz',
                            encoding='utf-8',
                            index=False)
  
  
  if __name__ == '__main__':
      update_datasets()
  
      dataset = pd.read_csv(os.path.join(DATA_PATH, 'reimbursements.xz'),
                            dtype={'applicant_id': np.str,
                                   'subquota_number': np.str},
                            low_memory=False)
      run_classifiers(dataset)
  
subject: Fix DATA_PATH attribute