Models.py 文件源码

def Second_Model_KRR(Scaled_Input_Data, Output_Data):
    T0 = time.time()
    n = len(Scaled_Input_Data)
    Grid_Dict = {"alpha": [1e0, 1e-1, 1e-2],"gamma": np.logspace(-2, 1, 3)}
    krr_Tuned = GridSearchCV(KernelRidge(kernel='rbf', gamma=0.1), cv=5 ,param_grid=Grid_Dict, scoring="mean_absolute_error")
    krr_Tuned.fit(Scaled_Input_Data, Output_Data)
    KRR_MSE = KernelRidge(kernel='rbf', alpha=krr_Tuned.best_params_['alpha'], gamma=krr_Tuned.best_params_['gamma'])
    KRR_Time = time.time() - T0
    print('The computational time of Kernel Ridge Regression for ', n, ' examples is: ', KRR_Time)
    MSEs_KRR = cross_validation.cross_val_score(KRR_MSE, Scaled_Input_Data, Output_Data, cv=cross_validation.LeaveOneOut(n), scoring="mean_absolute_error")
    MeanMSE_KRR = np.mean(list(MSEs_KRR))
    print('The average MSE of Kernel Ridge Regression for ', n, ' examples is: ', (-1*MeanMSE_KRR))
    return(MeanMSE_KRR, krr_Tuned)