How to determine feature importance while using xgboost in pipeline?
As I found, there are two ways to determine feature importance:
First: 

print(grid_search.best_estimator_.named_steps["clf"].feature_importances_)


result:

[ 0.14582562  0.08367272  0.06409663  0.07631433  0.08705109  0.03827286
  0.0592836   0.05025916  0.07076083  0.0699278   0.04993521  0.07756387
  0.05095335  0.07608293]


Second:

print(grid_search.best_estimator_.named_steps["clf"].booster().get_fscore())


result:

{'f2': 1385, 'f11': 1676, 'f12': 1101, 'f6': 1281, 'f9': 1511, 'f7': 1086, 'f5': 827, 'f0': 3151, 'f10': 1079, 'f1': 1808, 'f3': 1649, 'f13': 1644, 'f8': 1529, 'f4': 1881}


Third:

print(grid_search.best_estimator_.named_steps["clf"].get_booster().get_fscore())