Question

我对学习整洁模型感兴趣，并尝试将其应用于Appied Predictive Modeling中的一些练习。这是练习6.2。我想为渗透率数据集指定偏最小二乘（PLS）模型。

我有以下代码可以一直运行到调优网格。我已经使用tidymodels和The Office found here对Julia Juliage的-Lasso回归进行了建模。

您可以在下面看到我的脚本和tune_grid错误消息。

library(tidymodels)
library(tidyverse)
library(skimr)
library(plsmod)
library(caret)

library(AppliedPredictiveModeling)
data(permeability)

dim(fingerprints)
fingerprints <- fingerprints[, -nearZeroVar(fingerprints)]
dim(fingerprints)
df <- cbind(fingerprints, permeability)
df <- as_tibble(df)

perm_split <- initial_split(df)
perm_train <- training(perm_split)
perm_test <- testing(perm_split)

perm_rec<- recipe(permeability ~ ., data=perm_train) %>% 
  step_center(all_numeric(),-all_outcomes()) %>% 
  step_scale(all_numeric(),-all_outcomes()) 
  
perm_prep <- perm_rec %>% 
  prep()


perm_prep


pls_spec <- pls(num_comp = 4) %>% 
  set_mode("regression") %>% 
  set_engine("mixOmics")

wf <- workflow() %>% 
  add_recipe(perm_prep) 
  
pls_fit <- wf %>% 
  add_model(pls_spec) %>% 
  fit(data=perm_train)

pls_fit %>%  
  pull_workflow_fit() %>% 
  tidy()

set.seed(123)
perm_folds <-  vfold_cv(perm_train, v=10)

pls_tune_spec <- pls(num_comp = tune()) %>% 
  set_mode("regression") %>% 
  set_engine("mixOmics")

comp_grid <- expand.grid(num_comp = seq(from = 1, to = 20, by = 1))

doParallel::registerDoParallel()

set.seed(4763)

pls_grid <- tune_grid(
  wf %>% add_model(pls_tune_spec),
  resamples = perm_folds,
  grid = comp_grid
)

目前，我收到以下错误：所有模型都在tune_grid（）中失败。请参阅.notes列。

两个问题：

为什么我的调优网格出现故障，我该如何解决？
人们如何看到.note列。

Answer 1

我猜您可能正在使用Windows计算机，因为当前在Windows并行处理的CRAN版本的tune中存在一个错误。尝试之一：

顺序训练而无需并行处理，或者
通过devtools::install_github("tidymodels/tune")

您应该看到如下结果：

library(tidymodels)
library(plsmod)
library(AppliedPredictiveModeling)
data(permeability)

df <- cbind(fingerprints, permeability)
df <- as_tibble(df)

set.seed(123)
perm_split <- initial_split(df)
perm_train <- training(perm_split)
perm_test <- testing(perm_split)

set.seed(234)
perm_folds <-  vfold_cv(perm_train, v=10)

perm_rec <- recipe(permeability ~ ., data = perm_train) %>%
  step_nzv(all_predictors()) %>%
  step_center(all_numeric(), -all_outcomes()) %>% 
  step_scale(all_numeric(), -all_outcomes())

pls_spec <- pls(num_comp = tune()) %>% 
  set_mode("regression") %>% 
  set_engine("mixOmics")

comp_grid <- tibble(num_comp = seq(from = 1, to = 20, by = 5))

doParallel::registerDoParallel()

workflow() %>% 
  add_recipe(perm_rec) %>%
  add_model(pls_spec) %>%
  tune_grid(
    resamples = perm_folds,
    grid = comp_grid
  )
#> 
#> Attaching package: 'rlang'
#> The following objects are masked from 'package:purrr':
#> 
#>     %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
#>     flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
#>     splice
#> 
#> Attaching package: 'vctrs'
#> The following object is masked from 'package:tibble':
#> 
#>     data_frame
#> The following object is masked from 'package:dplyr':
#> 
#>     data_frame
#> Loading required package: MASS
#> 
#> Attaching package: 'MASS'
#> The following object is masked from 'package:dplyr':
#> 
#>     select
#> Loading required package: lattice
#> 
#> Loaded mixOmics 6.12.2
#> Thank you for using mixOmics!
#> Tutorials: http://mixomics.org
#> Bookdown vignette: https://mixomicsteam.github.io/Bookdown
#> Questions, issues: Follow the prompts at http://mixomics.org/contact-us
#> Cite us:  citation('mixOmics')
#> 
#> Attaching package: 'mixOmics'
#> The following object is masked from 'package:plsmod':
#> 
#>     pls
#> The following object is masked from 'package:tune':
#> 
#>     tune
#> The following object is masked from 'package:purrr':
#> 
#>     map
#> # Tuning results
#> # 10-fold cross-validation 
#> # A tibble: 10 x 4
#>    splits           id     .metrics         .notes          
#>    <list>           <chr>  <list>           <list>          
#>  1 <split [111/13]> Fold01 <tibble [8 × 5]> <tibble [0 × 1]>
#>  2 <split [111/13]> Fold02 <tibble [8 × 5]> <tibble [0 × 1]>
#>  3 <split [111/13]> Fold03 <tibble [8 × 5]> <tibble [0 × 1]>
#>  4 <split [111/13]> Fold04 <tibble [8 × 5]> <tibble [0 × 1]>
#>  5 <split [112/12]> Fold05 <tibble [8 × 5]> <tibble [0 × 1]>
#>  6 <split [112/12]> Fold06 <tibble [8 × 5]> <tibble [0 × 1]>
#>  7 <split [112/12]> Fold07 <tibble [8 × 5]> <tibble [0 × 1]>
#>  8 <split [112/12]> Fold08 <tibble [8 × 5]> <tibble [0 × 1]>
#>  9 <split [112/12]> Fold09 <tibble [8 × 5]> <tibble [0 × 1]>
#> 10 <split [112/12]> Fold10 <tibble [8 × 5]> <tibble [0 × 1]>

^{由reprex package（v0.3.0.9001）于2020-11-12创建}

如果您有一个带有注释的pls_grid之类的对象，则应该能够通过pls_grid$.notes进入该列，或通过pls_grid$.notes[[1]]来查看第一个示例。

如何在整齐模型中指定PLS模型

1 个答案: