Package: regressionmetrics
Regression MetricsInstallationTo install the package from the PyPi repository you can execute the following
command:pipinstallregressionmetricsIf you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:gitclonehttps://github.com/ashishpatel26/regressionmetrics.gitcdregressionmetrics
pipinstall.MetricsFull FormInterpretationSklearnKerasmaeMean Absolute ErrorSmaller is better (Best value is 0)☑️☑️mseMean Sqaured ErrorSmaller is better(Best value is 0)☑️☑️rmseRoot Mean Square ErrorSmaller is better(Best value is 0)☑️☑️rmsleRoot Mean Square Log ErrorSmaller is better(Best value is 0)☑️☑️rmsle_with_negvalRoot Mean Square Log Error with neg. valueSmaller is better(Best value is 0)☑️r2 scorecoefficient of determinationBest possible score is 1☑️☑️Adjusted r2 scoreAdjusted R2 scoreBest possible score is 1☑️☑️mapeMean Absolute Percentage ErrorSmaller is better(Best value is 0)☑️☑️msleMean Sqaured Logarithm ErrorSmaller is better(Best value is 0)☑️☑️smapeSymmetric mean absolute percentage errorSmaller is better(Best value is 0)☑️nrmseNormalized Root Mean Square Error.☑️☑️nrmsleNormalized Root Mean Squared Logarithmic Error☑️medianAEMedian Absolute ErrorSmaller is better(Best value is 0)☑️mreMean Relative ErrorSmaller is better(Best value is 0)☑️maapeMean Arctangent Absolute Percentage ErrorSmaller is better(Best value is 0)☑️nseNash-Sutcliffe Efficiency CoefficientLarger is better (Best = 1)☑️willmott_index_of_agreementWillmott IndexLarger is better (Best = 1)☑️UsageUsage with scikit learn :fromregressionmetrics.metricsimport*y_true=np.array([3,0.5,2,7])y_pred=np.array([2.5,0.0,2,-8])print("R2Score: ",r2(y_true,y_pred))print("Adjusted_R2_Score:",adj_r2(y_true,y_pred))print("RMSE:",rmse(y_true,y_pred))print("MAE:",mae(y_true,y_pred))print("RMSLE with Neg Value:",rmsle_with_negval(y_true,y_pred))print("MSE:",mse(y_true,y_pred))print("MAPE: ",mape(y_true,y_pred))Usage with Tensorflow keras:fromregressionmetrics.kerasimport*importpandasaspdimportnumpyasnpimporttensorflowastffromtensorflowimportkerasfromtensorflow.kerasimportlayers(x_train,y_train),(x_test,y_test)=tf.keras.datasets.boston_housing.load_data(path="boston_housing.npz",test_split=0.2,seed=113)model=keras.Sequential([layers.Dense(64,activation='relu',input_shape=(x_train.shape[1],)),layers.Dense(64,activation='relu'),layers.Dense(1)])model.compile(optimizer='rmsprop',loss='mse',metrics=[r2,mae,mse,rmse,mape,rmsle,nrmse])model.fit(x_train,y_train,epochs=10,batch_size=32,validation_data=(x_test,y_test))Epoch1/101/13[=>............................]-ETA:7s-loss:1574.7567-r2:0.6597-mae:37.1803-mse:1574.7567-rmse:37.1802-mape:159.261313/13[==============================]-1s15ms/step-loss:270.0653-r2:0.9472-mae:11.5427-mse:270.0653-rmse:11.5427-mape:57.3519-rmsle:0.6445-nrmse:0.5735-val_loss:88.6351-val_r2:0.9727-val_mae:6.6028-val_mse:88.6351-val_rmse:6.6028-val_mape:29.6502-val_rmsle:0.3161-val_nrmse:0.2965
Epoch2/101/13[=>............................]-ETA:0s-loss:74.6623-r2:0.9913-mae:5.5958-mse:74.6623-rmse:5.5958-mape:25.3655-rmsl13/13[==============================]-0s3ms/step-loss:87.1876-r2:0.9856-mae:6.9466-mse:87.1876-rmse:6.9466-mape:33.4256-rmsle:0.3057-nrmse:0.3343-val_loss:81.7884-val_r2:0.9712-val_mae:6.6424-val_mse:81.7884-val_rmse:6.6424-val_mape:28.8687-val_rmsle:0.3334-val_nrmse:0.2887
Epoch3/101/13[=>............................]-ETA:0s-loss:41.2790-r2:0.9722-mae:5.3798-mse:41.2790-rmse:5.3798-mape:28.7497-rmsl13/13[==============================]-0s3ms/step-loss:103.6462-r2:0.9825-mae:7.1041-mse:103.6462-rmse:7.1041-mape:34.6278-rmsle:0.3231-nrmse:0.3463-val_loss:71.7539-val_r2:0.9769-val_mae:6.1455-val_mse:71.7539-val_rmse:6.1455-val_mape:27.5078-val_rmsle:0.2893-val_nrmse:0.2751
Epoch4/101/13[=>............................]-ETA:0s-loss:113.6758-r2:0.9917-mae:6.6575-mse:113.6758-rmse:6.6575-mape:20.8683-rm13/13[==============================]-0s3ms/step-loss:88.1601-r2:0.9823-mae:6.8479-mse:88.1601-rmse:6.8479-mape:32.5867-rmsle:0.3080-nrmse:0.3259-val_loss:63.3707-val_r2:0.9829-val_mae:6.0845-val_mse:63.3707-val_rmse:6.0845-val_mape:33.1628-val_rmsle:0.2747-val_nrmse:0.3316
Epoch5/101/13[=>............................]-ETA:0s-loss:85.8188-r2:0.9893-mae:7.0097-mse:85.8188-rmse:7.0097-mape:34.8362-rmsl13/13[==============================]-0s3ms/step-loss:82.3233-r2:0.9860-mae:6.5795-mse:82.3233-rmse:6.5795-mape:32.5198-rmsle:0.3105-nrmse:0.3252-val_loss:74.4783-val_r2:0.9813-val_mae:6.8936-val_mse:74.4783-val_rmse:6.8936-val_mape:41.9492-val_rmsle:0.3067-val_nrmse:0.4195
Epoch7/101/13[=>............................]-ETA:0s-loss:105.6430-r2:0.9658-mae:9.4737-mse:105.6430-rmse:9.4737-mape:53.0854-rm13/13[==============================]-0s3ms/step-loss:76.0740-r2:0.9856-mae:6.4234-mse:76.0740-rmse:6.4234-mape:31.8728-rmsle:0.2828-nrmse:0.3187-val_loss:104.1779-val_r2:0.9679-val_mae:7.5539-val_mse:104.1779-val_rmse:7.5539-val_mape:30.9401-val_rmsle:0.3692-val_nrmse:0.3094
Epoch8/101/13[=>............................]-ETA:0s-loss:100.0114-r2:0.9833-mae:6.8492-mse:100.0114-rmse:6.8492-mape:27.9621-rm13/13[==============================]-0s4ms/step-loss:68.4268-r2:0.9892-mae:5.9540-mse:68.4268-rmse:5.9540-mape:29.7586-rmsle:0.2623-nrmse:0.2976-val_loss:171.7968-val_r2:0.9412-val_mae:10.5855-val_mse:171.7968-val_rmse:10.5855-val_mape:47.9010-val_rmsle:0.7561-val_nrmse:0.4790
Epoch9/101/13[=>............................]-ETA:0s-loss:291.8670-r2:0.9725-mae:13.9899-mse:291.8670-rmse:13.9899-mape:61.3658-13/13[==============================]-0s3ms/step-loss:92.3889-r2:0.9796-mae:6.8932-mse:92.3889-rmse:6.8932-mape:33.2856-rmsle:0.3333-nrmse:0.3329-val_loss:67.2208-val_r2:0.9808-val_mae:5.8498-val_mse:67.2208-val_rmse:5.8498-val_mape:26.4504-val_rmsle:0.2680-val_nrmse:0.2645
Epoch10/101/13[=>............................]-ETA:0s-loss:97.0853-r2:0.9923-mae:5.9866-mse:97.0853-rmse:5.9866-mape:24.9878-rmsl13/13[==============================]-0s3ms/step-loss:78.3823-r2:0.9856-mae:6.5958-mse:78.3823-rmse:6.5958-mape:32.8136-rmsle:0.3025-nrmse:0.3281-val_loss:69.5314-val_r2:0.9787-val_mae:6.8302-val_mse:69.5314-val_rmse:6.8302-val_mape:37.3933-val_rmsle:0.2974-val_nrmse:0.3739:smiley: Thanks for reading and forking.