xgb_params['eta'] = 0.02 xgb_params['seed'] = SEED xgb_params['silent'] = True # does help xgb_params['verbose_eval'] = False xgb_params['nrounds'] = 5000 xgb_params['early_stopping_rounds'] = 100 xgb_params['max_depth'] = 6 xgb_params['min_child_weight'] = 1 xgb_params['colsample_bytree'] = 0.724 xgb_params['subsample'] = 0.925 xgb_params['gamma'] = 0.512 xgb_params['alpha'] = 8.6 xgb_params['lambda'] = 1 full_data, ntrain, ntest = data_preparation() xgb_clf = XgbWrapper(seed=SEED, params=xgb_params) results = cross_validate( full_data=full_data, clf=xgb_clf, seed=SEED, ntrain=ntrain, ntest=ntest, features=FEATURES, target=TARGET, nfolds=4, ) sub, v06, v33, oof_score = results sub_to_csv(sub, v06, v33, oof_score[0], oof_score[1], os.path.basename(sys.argv[0]))
xgb_params['seed'] = SEED xgb_params['silent'] = True # does help xgb_params['verbose_eval'] = False xgb_params['nrounds'] = 2000 xgb_params['early_stopping_rounds'] = 100 xgb_params['max_depth'] = 5 xgb_params['min_child_weight'] = 1.91 xgb_params['colsample_bytree'] = 0.920 xgb_params['subsample'] = 0.856 xgb_params['gamma'] = 0.718 xgb_params['alpha'] = 1.83 xgb_params['lambda'] = 9.79 xgb_params['rate_drop'] = 0.262 full_data, ntrain, ntest = data_preparation() xgb_clf = XgbWrapper(seed=SEED, params=xgb_params) results = cross_validate( full_data=full_data, clf=xgb_clf, seed=SEED, ntrain=ntrain, ntest=ntest, features=FEATURES, target=TARGET, nfolds=4, ) sub, v06, v33, oof_score = results sub_to_csv(sub, v06, v33, oof_score[0], oof_score[1], "xgb_f2_cv4")
full_data, ntrain, ntest, FEATURES, CAT_FEATS = prepare_data() # Get parameters try: exe_type = str(sys.argv[1]) opt_type = int(sys.argv[2]) except: exe_type = 'test' opt_type = 0 opt_path = '../tuning/' + "_".join(MODEL_NAME.split('_')[:1]) + ".csv" xgb_turn_params = dict(pd.read_csv(opt_type).iloc[opt_type, :]) xgb_params = {**xgb_pick_params[exe_type], **xgb_turn_params} # Define model and get results xgb_clf = XgbWrapper(seed=SEED, params=xgb_params) results = cross_validate( full_data=full_data, clf=xgb_clf, seed=SEED, ntrain=ntrain, ntest=ntest, features=FEATURES, target=TARGET, nfolds=4, ) sub, v06, v33, oof_score = results # Save submission to file sub_to_csv(sub, v06, v33, oof_score[0], oof_score[1], MODEL_NAME)