finetune下載finetune原始碼下載

finetune

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finetune 1.2.0

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finetune包含一些用於模型調整的額外函數，這些函數擴展了tune套件中目前的功能。您可以使用以下程式碼安裝 CRAN 版本的軟體包：

install.packages( " finetune " )

若要安裝該套件的開發版本，請執行：

 # install.packages("pak")
pak :: pak( " tidymodels/finetune " )

軟體包中有兩套主要工具：模擬退火和競賽。

透過模擬退火最佳化進行調整是一種迭代搜尋工具，用於尋找好的值：

library( tidymodels )
library( finetune )

# Syntax very similar to `tune_grid()` or `tune_bayes()`: 

# # -----------------------------------------------------------------------------

data( two_class_dat , package = " modeldata " )

set.seed( 1 )
rs <- bootstraps( two_class_dat , times = 10 ) # more resamples usually needed

# Optimize a regularized discriminant analysis model
library( discrim )
rda_spec <-
  discrim_regularized( frac_common_cov = tune(), frac_identity = tune()) % > %
  set_engine( " klaR " )

# # -----------------------------------------------------------------------------

set.seed( 2 )
sa_res <- 
  rda_spec % > % 
  tune_sim_anneal( Class ~ . , resamples = rs , iter = 20 , initial = 4 )
# > Optimizing roc_auc
# > Initial best: 0.86480
# > 1 ♥ new best           roc_auc=0.87327 (+/-0.004592)
# > 2 ♥ new best           roc_auc=0.87915 (+/-0.003864)
# > 3 ◯ accept suboptimal  roc_auc=0.87029 (+/-0.004994)
# > 4 + better suboptimal  roc_auc=0.87171 (+/-0.004717)
# > 5 ◯ accept suboptimal  roc_auc=0.86944 (+/-0.005081)
# > 6 ◯ accept suboptimal  roc_auc=0.86812 (+/-0.0052)
# > 7 ♥ new best           roc_auc=0.88172 (+/-0.003647)
# > 8 ◯ accept suboptimal  roc_auc=0.87678 (+/-0.004276)
# > 9 ◯ accept suboptimal  roc_auc=0.8627 (+/-0.005784)
# > 10 + better suboptimal  roc_auc=0.87003 (+/-0.005106)
# > 11 + better suboptimal  roc_auc=0.87088 (+/-0.004962)
# > 12 ◯ accept suboptimal  roc_auc=0.86803 (+/-0.005195)
# > 13 ◯ accept suboptimal  roc_auc=0.85294 (+/-0.006498)
# > 14 ─ discard suboptimal roc_auc=0.84689 (+/-0.006867)
# > 15 ✖ restart from best  roc_auc=0.85021 (+/-0.006623)
# > 16 ◯ accept suboptimal  roc_auc=0.87607 (+/-0.004318)
# > 17 ◯ accept suboptimal  roc_auc=0.87245 (+/-0.004799)
# > 18 + better suboptimal  roc_auc=0.87706 (+/-0.004131)
# > 19 ◯ accept suboptimal  roc_auc=0.87213 (+/-0.004791)
# > 20 ◯ accept suboptimal  roc_auc=0.86218 (+/-0.005773)
show_best( sa_res , metric = " roc_auc " , n = 2 )
# > # A tibble: 2 × 9
# >   frac_common_cov frac_identity .metric .estimator  mean     n std_err .config
# >             <dbl>         <dbl> <chr>   <chr>      <dbl> <int>   <dbl> <chr>  
# > 1           0.308        0.0166 roc_auc binary     0.882    10 0.00365 Iter7  
# > 2           0.121        0.0474 roc_auc binary     0.879    10 0.00386 Iter2  
# > # ℹ 1 more variable: .iter <int>

第二組方法是針對賽車的。我們首先對所有網格點進行一組重新採樣，然後進行統計測試，看看哪些網格點應該被丟棄或進行更多調查。這裡的兩種方法是基於 Kuhn (2014) 中的方法。

例如，使用 ANOVA 類型分析來篩選參數組合：

set.seed( 3 )
grid <-
  rda_spec % > %
  extract_parameter_set_dials() % > %
  grid_max_entropy( size = 20 )

ctrl <- control_race( verbose_elim = TRUE )

set.seed( 4 )
grid_anova <- 
  rda_spec % > % 
  tune_race_anova( Class ~ . , resamples = rs , grid = grid , control = ctrl )
# > ℹ Evaluating against the initial 3 burn-in resamples.
# > ℹ Racing will maximize the roc_auc metric.
# > ℹ Resamples are analyzed in a random order.
# > ℹ Bootstrap10: 14 eliminated; 6 candidates remain.
# > 
# > ℹ Bootstrap04: 2 eliminated; 4 candidates remain.
# > 
# > ℹ Bootstrap03: All but one parameter combination were eliminated.

show_best( grid_anova , metric = " roc_auc " , n = 2 )
# > # A tibble: 1 × 8
# >   frac_common_cov frac_identity .metric .estimator  mean     n std_err .config  
# >             <dbl>         <dbl> <chr>   <chr>      <dbl> <int>   <dbl> <chr>    
# > 1           0.831        0.0207 roc_auc binary     0.881    10 0.00386 Preproce…

也可以使用tune_race_win_loss() 。它將調整參數視為錦標賽中的運動隊併計算輸贏統計數據。

set.seed( 4 )
grid_win_loss <- 
  rda_spec % > % 
  tune_race_win_loss( Class ~ . , resamples = rs , grid = grid , control = ctrl )
# > ℹ Racing will maximize the roc_auc metric.
# > ℹ Resamples are analyzed in a random order.
# > ℹ Bootstrap10: 3 eliminated; 17 candidates remain.
# > 
# > ℹ Bootstrap04: 2 eliminated; 15 candidates remain.
# > 
# > ℹ Bootstrap03: 2 eliminated; 13 candidates remain.
# > 
# > ℹ Bootstrap01: 1 eliminated; 12 candidates remain.
# > 
# > ℹ Bootstrap07: 1 eliminated; 11 candidates remain.
# > 
# > ℹ Bootstrap05: 1 eliminated; 10 candidates remain.
# > 
# > ℹ Bootstrap08: 1 eliminated; 9 candidates remain.

show_best( grid_win_loss , metric = " roc_auc " , n = 2 )
# > # A tibble: 2 × 8
# >   frac_common_cov frac_identity .metric .estimator  mean     n std_err .config  
# >             <dbl>         <dbl> <chr>   <chr>      <dbl> <int>   <dbl> <chr>    
# > 1           0.831        0.0207 roc_auc binary     0.881    10 0.00386 Preproce…
# > 2           0.119        0.0470 roc_auc binary     0.879    10 0.00387 Preproce…