importargparsefromtimeimporttimefromtypingimportDict,List,Tupleimportnumpyasnpfrommatplotlibimportpyplotaspltimportxgboostasxgb# shape of generated data.kRows=4096kCols=16kOutlier=10000# mean of generated outlierskNumberOfOutliers=64kRatio=0.7kSeed=1994kBoostRound=20np.random.seed(seed=kSeed)defgenerate_data()->Tuple[xgb.DMatrix,xgb.DMatrix]:'''Generate data containing outliers.'''x=np.random.randn(kRows,kCols)y=np.random.randn(kRows)y+=np.abs(np.min(y))# Create outliersforiinrange(0,kNumberOfOutliers):ind=np.random.randint(0,len(y)-1)y[ind]+=np.random.randint(0,kOutlier)train_portion=int(kRows*kRatio)# rmsle requires all label be greater than -1.assertnp.all(y>-1.0)train_x:np.ndarray=x[:train_portion]train_y:np.ndarray=y[:train_portion]dtrain=xgb.DMatrix(train_x,label=train_y)test_x=x[train_portion:]test_y=y[train_portion:]dtest=xgb.DMatrix(test_x,label=test_y)returndtrain,dtestdefnative_rmse(dtrain:xgb.DMatrix,dtest:xgb.DMatrix)->Dict[str,Dict[str,List[float]]]:'''Train using native implementation of Root Mean Squared Loss.'''print('Squared Error')squared_error={'objective':'reg:squarederror','eval_metric':'rmse','tree_method':'hist','seed':kSeed}start=time()results:Dict[str,Dict[str,List[float]]]={}xgb.train(squared_error,dtrain=dtrain,num_boost_round=kBoostRound,evals=[(dtrain,'dtrain'),(dtest,'dtest')],evals_result=results)print('Finished Squared Error in:',time()-start,'\n')returnresultsdefnative_rmsle(dtrain:xgb.DMatrix,dtest:xgb.DMatrix)->Dict[str,Dict[str,List[float]]]:'''Train using native implementation of Squared Log Error.'''print('Squared Log Error')results:Dict[str,Dict[str,List[float]]]={}squared_log_error={'objective':'reg:squaredlogerror','eval_metric':'rmsle','tree_method':'hist','seed':kSeed}start=time()xgb.train(squared_log_error,dtrain=dtrain,num_boost_round=kBoostRound,evals=[(dtrain,'dtrain'),(dtest,'dtest')],evals_result=results)print('Finished Squared Log Error in:',time()-start)returnresultsdefpy_rmsle(dtrain:xgb.DMatrix,dtest:xgb.DMatrix)->Dict:'''Train using Python implementation of Squared Log Error.'''defgradient(predt:np.ndarray,dtrain:xgb.DMatrix)->np.ndarray:'''Compute the gradient squared log error.'''y=dtrain.get_label()return(np.log1p(predt)-np.log1p(y))/(predt+1)defhessian(predt:np.ndarray,dtrain:xgb.DMatrix)->np.ndarray:'''Compute the hessian for squared log error.'''y=dtrain.get_label()return((-np.log1p(predt)+np.log1p(y)+1)/np.power(predt+1,2))defsquared_log(predt:np.ndarray,dtrain:xgb.DMatrix)->Tuple[np.ndarray,np.ndarray]:'''Squared Log Error objective. A simplified version for RMSLE used as        objective function.        :math:`\frac{1}{2}[log(pred + 1) - log(label + 1)]^2`        '''predt[predt<-1]=-1+1e-6grad=gradient(predt,dtrain)hess=hessian(predt,dtrain)returngrad,hessdefrmsle(predt:np.ndarray,dtrain:xgb.DMatrix)->Tuple[str,float]:''' Root mean squared log error metric.        :math:`\\sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}`        '''y=dtrain.get_label()predt[predt<-1]=-1+1e-6elements=np.power(np.log1p(y)-np.log1p(predt),2)return'PyRMSLE',float(np.sqrt(np.sum(elements)/len(y)))results:Dict[str,Dict[str,List[float]]]={}xgb.train({'tree_method':'hist','seed':kSeed,'disable_default_eval_metric':1},dtrain=dtrain,num_boost_round=kBoostRound,obj=squared_log,custom_metric=rmsle,evals=[(dtrain,'dtrain'),(dtest,'dtest')],evals_result=results)returnresultsdefplot_history(rmse_evals:Dict[str,Dict],rmsle_evals:Dict[str,Dict],py_rmsle_evals:Dict[str,Dict])->None:fig,axs=plt.subplots(3,1)assertisinstance(axs,np.ndarray)ax0=axs[0]ax1=axs[1]ax2=axs[2]x=np.arange(0,kBoostRound,1)ax0.plot(x,rmse_evals['dtrain']['rmse'],label='train-RMSE')ax0.plot(x,rmse_evals['dtest']['rmse'],label='test-RMSE')ax0.legend()ax1.plot(x,rmsle_evals['dtrain']['rmsle'],label='train-native-RMSLE')ax1.plot(x,rmsle_evals['dtest']['rmsle'],label='test-native-RMSLE')ax1.legend()ax2.plot(x,py_rmsle_evals['dtrain']['PyRMSLE'],label='train-PyRMSLE')ax2.plot(x,py_rmsle_evals['dtest']['PyRMSLE'],label='test-PyRMSLE')ax2.legend()defmain(args:argparse.Namespace)->None:dtrain,dtest=generate_data()rmse_evals=native_rmse(dtrain,dtest)rmsle_evals=native_rmsle(dtrain,dtest)py_rmsle_evals=py_rmsle(dtrain,dtest)ifargs.plot!=0:plot_history(rmse_evals,rmsle_evals,py_rmsle_evals)plt.show()if__name__=="__main__":parser=argparse.ArgumentParser(description='Arguments for custom RMSLE objective function demo.')parser.add_argument('--plot',type=int,default=1,help='Set to 0 to disable plotting the evaluation history.')args=parser.parse_args()main(args)

DownloadJupyternotebook:custom_rmsle.ipynb

DownloadPythonsourcecode:custom_rmsle.py

Downloadzipped:custom_rmsle.zip