kfold

三阶段定义：

阶段	用途	模型参数是否更新	谁来”看”这部分数据
train	拟合模型参数	✅ 更新	优化器（梯度下降）
valid	调超参、选模型、早停	❌ 不更新	人/调参逻辑
test	评估最终泛化性能	❌ 不更新	只看一次（最后）

Installation

You can install the development version of kfold like so:

remotes::install_github("rpkgs/kfold")

Example

library(kfold)
#> Registered S3 method overwritten by 'Ipaper':#> method from #> print.data.table data.table#> Registered S3 method overwritten by 'kfold':#> method from #> predict.ranger rangerdevtools::load_all()
#> ℹ Loading kfold

set.seed(1)
# train + validX<-matrix(rnorm(300), 100, 3); colnames(X) <- c("a", "b", "c")
Y<-X%*% c(1, -2, 0.5) + rnorm(100)
# testXt<-matrix(rnorm(60), 20, 3); colnames(Xt) <- c("a", "b", "c")
Yt<-Xt%*% c(1, -2, 0.5) + rnorm(20)
m<- kfold_xgboost(X, Y, kfold=5, nrounds=30)
# %% 
GOF(m) #> [data.table]: #> # A data frame: 12 × 12#> kfold mode R2 NSE KGE RMSE MAE Bias Bias_perc n_sim R#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 1 train 1.000 1.000 0.996 0.0249 0.0193 3.65e-4 0.00150 80 1.000#> 2 2 train 1.000 1.000 0.994 0.0400 0.0297 7.37e-4 0.00307 80 1.000#> 3 3 train 1.000 1.000 0.996 0.0329 0.0243 2.16e-4 0.000857 80 1.000#> 4 4 train 1.000 1.000 0.995 0.0337 0.0229 1.01e-4 0.000409 80 1.000#> 5 5 train 1.000 1.000 0.995 0.0314 0.0226 6.71e-4 0.00247 80 1.000#> 6 ensemble train 1.000 1.000 0.995 0.0285 0.0209 4.18e-4 0.00167 100 1.000#> 7 1 valid 0.589 0.537 -0.842 1.72 1.57 5.18e-1 1.82 20 0.767#> 8 2 valid 0.578 0.548 0.181 1.60 1.33 2.30e-1 0.777 20 0.761#> 9 3 valid 0.641 0.629 0.154 1.50 1.20 1.99e-1 0.814 20 0.801#> 10 4 valid 0.793 0.790 0.594 1.16 0.985 9.49e-2 0.361 20 0.890#> # ℹ 2 more rows#> # ℹ 1 more variable: pvalue <dbl>
GOF(m, test=list(X=Xt, Y=Yt))
#> [data.table]: #> # A data frame: 6 × 12#> kfold mode R2 NSE KGE RMSE MAE Bias Bias_perc n_sim R#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 1 test 0.767 0.750 0.872 1.32 1.06 -0.0193 -0.0298 20 0.876#> 2 2 test 0.714 0.708 0.690 1.43 1.17 0.161 0.248 20 0.845#> 3 3 test 0.728 0.674 0.823 1.51 1.25 -0.0319 -0.0492 20 0.853#> 4 4 test 0.767 0.742 0.828 1.34 1.07 -0.0746 -0.115 20 0.876#> 5 5 test 0.800 0.771 0.784 1.27 1.05 0.116 0.179 20 0.894#> 6 ensemble test 0.787 0.775 0.878 1.25 1.03 0.0303 0.0467 20 0.887#> # ℹ 1 more variable: pvalue <dbl>
predict(m, mode="train") %>% str()
#> List of 6#> $ 1 : num [1:100] 1.686 -0.11 NA 0.708 NA ...#> $ 2 : num [1:100] 1.6937 -0.0751 3.6643 0.7084 2.0931 ...#> $ 3 : num [1:100] 1.68 NA 3.7 0.67 2.1 ...#> $ 4 : num [1:100] NA -0.0845 3.6729 0.6744 2.1429 ...#> $ 5 : num [1:100] 1.6608 -0.0694 3.6919 NA 2.113 ...#> $ ensemble: num [1:100] 1.6795 -0.0846 3.6823 0.6902 2.1114 ...
predict(m, Xt, mode="test") %>% str()
#> List of 6#> $ 1 : num [1:20] -4.085 3.533 -0.494 4.409 -0.566 ...#> $ 2 : num [1:20] -2.465 3.558 -0.174 3.618 0.292 ...#> $ 3 : num [1:20] -1.469 3.627 0.293 4.674 -0.813 ...#> $ 4 : num [1:20] -4.156 3.765 0.241 4.036 -0.14 ...#> $ 5 : num [1:20] -3.74 4.579 0.236 4.263 -1.121 ...#> $ ensemble: num [1:20] -3.1829 3.8123 0.0205 4.2002 -0.4694 ...