old_contents: import numpy as np
  import pandas as pd
  import sys
  import os
  from sklearn.externals import joblib
  import xgboost as xgb
  
  scriptpath = os.path.dirname(os.path.realpath(sys.argv[0])) + '/../'
  sys.path.append(os.path.abspath(scriptpath))
  import utils
  
  train = joblib.load(utils.processed_data_path + 'train_is_booking_all_top_5_cw_0.05_year_all.pkl')
  
  X_train = train.ix[:,2:]
  y_train = pd.DataFrame(train['hotel_cluster'].astype(int))
  
  
  print "train XGBClassifier..."
  cxgb = xgb.XGBClassifier(max_depth=15, n_estimators=100, learning_rate=0.02, colsample_bytree=0.5, min_child_weight=5, verbose=1)
  
  cxgb.fit(X_train, y_train.ravel())
  joblib.dump(cxgb, utils.model_path + 'cxgb_all_without_time_top_5_cw_0.05_year_all.pkl')
  
  
  
  
  
  
new_contents: import numpy as np
  import pandas as pd
  import sys
  import os
  from sklearn.externals import joblib
  import xgboost as xgb
  
  scriptpath = os.path.dirname(os.path.realpath(sys.argv[0])) + '/../'
  sys.path.append(os.path.abspath(scriptpath))
  import utils
  
  train = joblib.load(utils.processed_data_path + 'train_is_booking_all_top_5_cw_0.05_year_all.pkl')
  
  X_train = train.ix[:,2:]
  y_train = pd.DataFrame(train['hotel_cluster'].astype(int))
  
  
  print "train XGBClassifier..."
  cxgb = xgb.XGBClassifier(max_depth=15, n_estimators=100, learning_rate=0.02, colsample_bytree=0.5, min_child_weight=5)
  
  cxgb.fit(X_train, y_train.ravel(), verbose=True)
  joblib.dump(cxgb, utils.model_path + 'cxgb_all_without_time_top_5_cw_0.05_year_all.pkl')
  
  
  
  
  
  
subject: Fix bugs in rf and xgb models