Kaggle competition: LANL Earthquake Prediction
preds_xgb0=extr_test_pred(tst_preds_xgb0 )<compute_test_metric>
xgb = pd.read_csv(".. /input/final-subs/xgb_final.csv")
 print(xgb.shape)
 lgb = pd.read_csv(".. /input/lgbm-tunning-bayes-search/clean_submission.csv")
 print(lgb.shape)
 cat = pd.read_csv(".. /input/final-subs/cat_boost.csv")
 print(cat.shape)
 xgb.head()
accuracy_score(target['surface'], oof_preds_xgb0 )<train_model>
comb = cat
comb.rename(columns={'time_to_failure':'cat'},inplace=True)
 comb.head()
%%time
model, oof_preds_xgb, tst_preds_xgb, perf = xgb_train_no_groups(xgb_params, X,y,T )<predict_on_test>
comb['lgb'] = lgb.time_to_failure
comb.head()
preds_xgb=extr_test_pred(tst_preds_xgb)
 preds_xgb<compute_test_metric>
comb['xgb'] = xgb.time_to_failure
comb.head()
accuracy_score(target['surface'], oof_preds_xgb )<prepare_output>
comb['time_to_failure'] = comb.mean(numeric_only=True, axis=1)
 comb.head()
preds=preds_rf0 
<categorify>
sub = comb.loc[:,['seg_id','time_to_failure']]
sub.head()
<create_dataframe><EOS> 
sub.to_csv('submission.csv',index=False )