I encountered an error when trying to access model coefficients using mlr3. Can you help me understand how to do this properly?
library(haven)
library(tidyverse)
library(survival)
library(penalized)
#> Welcome to penalized. For extended examples, see vignette("penalized").
library(mlr3)
library(distr6)
#>
#> Attaching package: 'distr6'
#> The following object is masked from 'package:stats':
#>
#> qqplot
#> The following object is masked from 'package:base':
#>
#> truncate
library(mlr3verse)
library(mlr3proba)
library(mlr3learners)
library(mlr3pipelines)
library(mlr3extralearners)
data <- read.csv("C:/Users/Click_32235414/Documents/liver30.csv")
#convert data to tibble
tb <- as_tibble(data)
# delete variable with a lot of missing
tb <- subset(tb, select = -c(alcohol,smoking,micro_vesicular_steatosis_donor_liver_biopsy,
macro_vesicular_steatosis_donor_liver_biopsy,hdl_pre,hdl_post,phos_pre,
mg_pre,tg_pre,tg_post,ldh_pre,ldh_post,
ldl_pre,ldl_post,gamma_gt_pre,gamma_gt_post,t_chol_pre,t_chol_post,
micro_vesicular_steatosis_number_donor_liver_biopsy
,reperfusion_syndrome,fibrosis_donor_liver_biopsy,
macro_vesicular_steatosis_number_donor_liver_biopsy))
tb <- subset(tb, select = -c(bun_pre,bun_post,pt_pre,pt_post))
tb<- tb[!(is.na(tb$time_to_death)), ]
attach(tb)
## Task Definition
tsk_s <- as_task_surv(tb, time = "time_to_death", event = "status", type = "right")
# imputes values based on histogram
imputer_hist = po("imputehist",
affect_columns = selector_name(c("meld_peld_score", "alk_pre", "alk_post","alb_pre", "alb_post"
,"creatinine_pre","creatinine_post","na_pre","na_post"
,"direct_billi_pre","direct_billi_post","total_billi_pre","total_billi_post"
,"inr_pre","inr_post","phos_post","mg_post","ppt_pre","ptt_post","bmi"
,"DeRitis_ratio_pre","DeRitis_ratio_post","cold_time","warm_time"
,"graft_weight","max_Tacrolimus")))
# imputes values using the mod
imputer_mode = po("imputemode",
affect_columns = selector_name(c("biliary_anastomosis", "vasopressors","acute_kidney_disease_pm_history"
,"diabetes_mellitus_pm_history","hypertension_pm_history","infections_pm_history"
,"sbp_pm_history","cardiovascular_pm_history","previous_hospitalization_pm_history"
,"Sirolimus")))
imputer_hist$train(list(tsk_s))[[1]]$missings()
#> time_to_death status
#> 0 0
#> Sirolimus acute_kidney_disease_pm_history
#> 19 107
#> age biliary_anastomosis
#> 0 101
#> cardiovascular_pm_history diabetes_mellitus_pm_history
#> 107 107
#> gender hypertension_pm_history
#> 0 107
#> infections_pm_history previous_hospitalization_pm_history
#> 107 475
#> sbp_pm_history vasopressors
#> 107 37
#> DeRitis_ratio_post DeRitis_ratio_pre
#> 0 0
#> alb_post alb_pre
#> 0 0
#> alk_post alk_pre
#> 0 0
#> bmi cold_time
#> 0 0
#> creatinine_post creatinine_pre
#> 0 0
#> direct_billi_post direct_billi_pre
#> 0 0
#> graft_weight inr_post
#> 0 0
#> inr_pre max_Tacrolimus
#> 0 0
#> meld_peld_score mg_post
#> 0 0
#> na_post na_pre
#> 0 0
#> phos_post ppt_pre
#> 0 0
#> ptt_post total_billi_post
#> 0 0
#> total_billi_pre warm_time
#> 0 0
imputer_mode$train(list(tsk_s))[[1]]$missings()
#> time_to_death status
#> 0 0
#> DeRitis_ratio_post DeRitis_ratio_pre
#> 156 234
#> age alb_post
#> 0 52
#> alb_pre alk_post
#> 258 64
#> alk_pre bmi
#> 155 137
#> cold_time creatinine_post
#> 129 32
#> creatinine_pre direct_billi_post
#> 201 67
#> direct_billi_pre gender
#> 185 0
#> graft_weight inr_post
#> 118 79
#> inr_pre max_Tacrolimus
#> 210 481
#> meld_peld_score mg_post
#> 135 136
#> na_post na_pre
#> 33 206
#> phos_post ppt_pre
#> 91 463
#> ptt_post total_billi_post
#> 116 66
#> total_billi_pre warm_time
#> 163 128
#> Sirolimus acute_kidney_disease_pm_history
#> 0 0
#> biliary_anastomosis cardiovascular_pm_history
#> 0 0
#> diabetes_mellitus_pm_history hypertension_pm_history
#> 0 0
#> infections_pm_history previous_hospitalization_pm_history
#> 0 0
#> sbp_pm_history vasopressors
#> 0 0
impute_graph = imputer_hist %>>% imputer_mode
surv_task = impute_graph$train(tsk_s)[[1]]
set.seed(42)
part = partition(surv_task, ratio = 0.8)
learner_penalized=lrn("surv.penalized", lambda1=25.8)
learner =
ppl("crankcompositor",
learner = learner_penalized,
response = TRUE, method = "median", overwrite = FALSE) |>
as_learner()
learner$train(surv_task, part$train)
#> # nonzero coefficients: 38# nonzero coefficients: 25 # nonzero coefficients: 27 # nonzero coefficients: 28 # nonzero coefficients: 27 # nonzero coefficients: 27 # nonzero coefficients: 26 # nonzero coefficients: 25 # nonzero coefficients: 25 # nonzero coefficients: 25 # nonzero coefficients: 25 # nonzero coefficients: 24 # nonzero coefficients: 24 # nonzero coefficients: 24 # nonzero coefficients: 24 # nonzero coefficients: 24 # nonzero coefficients: 25 # nonzero coefficients: 25 # nonzero coefficients: 24 # nonzero coefficients: 25 # nonzero coefficients: 25 # nonzero coefficients: 24 # nonzero coefficients: 24 # nonzero coefficients: 24 # nonzero coefficients: 24 # nonzero coefficients: 24 # nonzero coefficients: 24 # nonzero coefficients: 24
learner::coef(learner$model)
#> Error in loadNamespace(x): there is no package called 'learner'
learner$selected_features()
#> Error in eval(expr, envir, enclos): attempt to apply non-function
Created on 2024-09-10 with reprex v2.1.0
This is similar to the question here. You have to use:
penalized::coef(learner$model)
instead of
learner::coef(learner$model)
the $selected_features() function is WIP, see PR.
Also we now have a better pipeline to estimate the response (survival time) , check out the response composition pipeline