import xgboost as xgb

import seaborn as sns

def xgb_mape(preds, dtrain):
   labels = dtrain.get_label()
   return('mape', np.mean(np.abs((labels - preds) / (labels+1))))

mpg = sns.load_dataset('mpg')

mpg = mpg.sample(frac = 1)

n = int(mpg.shape[0] * 0.7)

mpg_train = mpg.iloc[:n, :7]

mpg_test = mpg.iloc[n:, :7]

mpg_train_y = mpg_train.iloc[:, 0].values

mpg_test_y = mpg_test.iloc[:, 0].values

mpg_train_X = mpg_train.iloc[:, 1:].values

mpg_test_X = mpg_test.iloc[:, 1:].values

xgb_model_mpg = xgb.XGBRegressor(max_depth= 10, learning_rate=0.1, n_estimators=1000, silent=True,
                                 objective='reg:linear',
                 booster='gbtree', subsample= 0.6, colsample_bytree= 0.9, colsample_bylevel= 1, reg_lambda= 20 ,
                 random_state=1 , seed= 1, importance_type='gain')

xgb_model_mpg.fit(mpg_train_X ,mpg_train_y , eval_set= [(mpg_test_X , mpg_test_y )], eval_metric= xgb_mape,
              early_stopping_rounds= 20)
[...]
82] validation_0-rmse:3.41167   validation_0-mape:0.085761
[83]    validation_0-rmse:3.40828   validation_0-mape:0.085618
[84]    validation_0-rmse:3.40087   validation_0-mape:0.085519
[85]    validation_0-rmse:3.403 validation_0-mape:0.085631
[86]    validation_0-rmse:3.39977   validation_0-mape:0.085711
[87]    validation_0-rmse:3.39626   validation_0-mape:0.085739
[88]    validation_0-rmse:3.40048   validation_0-mape:0.085727
[89]    validation_0-rmse:3.40356   validation_0-mape:0.085883
[90]    validation_0-rmse:3.40341   validation_0-mape:0.085664
Stopping. Best iteration:
[70]    validation_0-rmse:3.42186   validation_0-mape:0.085076

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
       colsample_bytree=0.9, gamma=0, importance_type='gain',
       learning_rate=0.1, max_delta_step=0, max_depth=10,
       min_child_weight=1, missing=None, n_estimators=1000, n_jobs=1,
       nthread=None, objective='reg:linear', random_state=1, reg_alpha=0,
       reg_lambda=20, scale_pos_weight=1, seed=1, silent=True,
       subsample=0.6)

y_pred = xgb_model_mpg.predict(mpg_test_X)

results = pd.DataFrame({'actual':mpg_test_y, 'predictions' : y_pred})

results['Absolute_Percent_Error'] = 100 * np.abs(results['actual'] - results['predictions'])/results['actual']

MAPE = results['Absolute_Percent_Error'].mean()

MAPE
8.982732737486339