model.save_model(f'../data/lgb{i}.model')
#models = []
#for i in range(LOOP):
# model = lgb.Booster(model_file=f'../data/lgb{i}.model')
# models.append(model)
imp = ex.getImp(models)
imp['split'] /= imp['split'].max()
imp['gain'] /= imp['gain'].max()
imp['total'] = imp['split'] + imp['gain']
imp.sort_values('total', ascending=False, inplace=True)
imp.reset_index(drop=True, inplace=True)
imp.to_csv(f'LOG/imp_{__file__}.csv', index=False)
utils.savefig_imp(imp, f'LOG/imp_{__file__}.png', x='total')
RESULT_DICT['nfold'] = NFOLD
RESULT_DICT['seed'] = SEED
RESULT_DICT['eta'] = param['learning_rate']
RESULT_DICT['best NROUND'] = len(ret['auc-mean'])
RESULT_DICT['train AUC'] = ret['auc-mean'][-1]
del dtrain, X_train, y_train
gc.collect()
# =============================================================================
# test
# =============================================================================
files_te = sorted(glob('../data/test_f*.f'))
result = f"CV wloss: {np.mean(wloss_list)} + {np.std(wloss_list)}"
print(result)
imp = ex.getImp(model_all)
imp['split'] /= imp['split'].max()
imp['gain'] /= imp['gain'].max()
imp['total'] = imp['split'] + imp['gain']
imp.sort_values('total', ascending=False, inplace=True)
imp.reset_index(drop=True, inplace=True)
imp.to_csv(f'LOG/imp_{__file__}.csv', index=False)
png = f'LOG/imp_{__file__}.png'
utils.savefig_imp(imp, png, x='total', title=f'{__file__}')
utils.send_line(result, png)
for i,y_pred in enumerate(y_preds):
y_pred = pd.DataFrame(utils_metric.softmax(y_pred.astype(float).values))
if i==0:
tmp = y_pred
else:
tmp += y_pred
tmp /= len(y_preds)
y_preds = tmp.copy().values.astype(float)
a_score = utils_metric.akiyama_metric(y.values, y_preds)
print('akiyama_metric:', a_score)