old_contents: 
  import commons, sys, os
  import logging as log
  import pandas as pd
  import xgboost as xgb
  import numpy as np
  from sklearn.externals import joblib
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix
  
  def add_activations(df):
      for ind in commons.indicators:
          log.info("Adding activations for {}".format(ind))
          ind_prev = ind + "_1"
          res = df[ind].sub(df[ind_prev])
          res[res < 0] = 0
          df["act_" + ind] = res.fillna(0)
      return df
  
  df = df[~df['is_test_data']]
  df.drop(['is_test_data'], inplace=True, axis=1)
  df.drop(commons.indicators_ignored, inplace=True, axis=1)
  
  df = add_activations(df)
  df.drop(commons.indicators ,inplace=True,axis=1)
  
  #Drop excessive months. Keep 2015-06, 2016-03/05.
  
  df = df[~df['fecha_dato'].isin(pd.Period('2015-06'), pd.Period('2016-03'), pd.Period('2016-04'), pd.Period('2016-05'))]
  #df.drop(['ncodpers', 'fecha_dato'], inplace=True, axis=1)
  df.drop(['ncodpers'], inplace=True, axis=1)
  
  activation_columns=["act_" + i for i in commons.indicators]
  
  for ind in commons.indicators:
      log.info("Building model for ".format(ind))
  
new_contents: 
  import commons, sys, os
  import logging as log
  import pandas as pd
  import xgboost as xgb
  import numpy as np
  from sklearn.externals import joblib
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix
  
  def add_activations(df):
      for ind in commons.indicators:
          log.info("Adding activations for {}".format(ind))
          ind_prev = ind + "_1"
          res = df[ind].sub(df[ind_prev])
          res[res < 0] = 0
          df["act_" + ind] = res.fillna(0)
      return df
  
  df = df[~df['is_test_data']]
  df.drop(['is_test_data'], inplace=True, axis=1)
  df.drop(commons.indicators_ignored, inplace=True, axis=1)
  
  df = add_activations(df)
  df.drop(commons.indicators ,inplace=True,axis=1)
  
  #Drop excessive months. Keep 2015-06, 2016-03/05.
  
  log.info("Before: {}".format(df.shape))
  df = df[~df['fecha_dato'].isin(pd.Period('2015-06'), pd.Period('2016-03'), pd.Period('2016-04'), pd.Period('2016-05'))]
  log.info("After: {}".format(df.shape))
  #df.drop(['ncodpers', 'fecha_dato'], inplace=True, axis=1)
  df.drop(['ncodpers'], inplace=True, axis=1)
  
  activation_columns=["act_" + i for i in commons.indicators]
  
  for ind in commons.indicators:
      log.info("Building model for ".format(ind))
  
subject: Update 02-02_cleanse.py