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mlr-org / mlr3proba

Probabilistic Learning for mlr3
https://mlr3proba.mlr-org.com
GNU Lesser General Public License v3.0
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Issue with "Neural Networks for Survival Analysis in R" article #370

Closed cxie19 closed 6 months ago

cxie19 commented 6 months ago

Hi,

I was following the example presented on Neural Networks for Survival Analysis in R article, but I encountered an error:

The code is shown as below:

# install.packages(c("ggplot2", "mlr3benchmark", "mlr3pipelines", "mlr3proba","mlr3tuning", "survivalmodels"))
# remotes::install_github("mlr-org/mlr3extralearners")

library(survivalmodels)

# install_pycox(pip = TRUE, install_torch = TRUE)
# install_keras(pip = TRUE, install_tensorflow = TRUE)

set_seed(1234)

library(mlr3)
library(mlr3proba)

## get the `whas` task from mlr3proba
whas <- tsk("whas")

## create our own task from the rats dataset
rats_data <- survival::rats

## convert characters to factors
rats_data$sex <- factor(rats_data$sex, levels = c("f", "m"))
rats <- TaskSurv$new("rats", rats_data, time = "time", event = "status")

## combine in list
tasks <- list(whas, rats)

library(paradox)

search_space <- ps(
  ## p_dbl for numeric valued parameters
  dropout = p_dbl(lower = 0, upper = 1),
  weight_decay = p_dbl(lower = 0, upper = 0.5),
  learning_rate = p_dbl(lower = 0, upper = 1),

  ## p_int for integer valued parameters
  nodes = p_int(lower = 1, upper = 32),
  k = p_int(lower = 1, upper = 4)
)

search_space$extra_trafo <- function(x, param_set) {
  x$num_nodes = rep(x$nodes, x$k)
  x$nodes = x$k = NULL
  return(x)
}

library(mlr3tuning)

create_autotuner <- function(learner) {
  AutoTuner$new(
    learner = learner,
    search_space = search_space,
    resampling = rsmp("holdout"),
    measure = msr("surv.cindex"),
    terminator = trm("evals", n_evals = 2),
    tuner = tnr("random_search")
  )
}

## learners are stored in mlr3extralearners
library(mlr3extralearners)

## load learners
learners <- lrns(
  paste0("surv.", c("coxtime", "deephit", "deepsurv", "loghaz", "pchazard")),
  frac = 0.3, early_stopping = TRUE, epochs = 10, optimizer = "adam"
)

# apply our function
learners <- lapply(learners, create_autotuner)

library(mlr3pipelines)

create_pipeops <- function(learner) {
  po("encode") %>>% po("scale") %>>% po("learner", learner)
}

## apply our function
learners <- lapply(learners, create_pipeops)
#> Error in isTRUE(lhs): object 'ParamFct' not found

Created on 2024-03-06 by the reprex package (v2.0.1)

bblodfon commented 6 months ago

Thanks! Without actually looking at this in detail, have you installed the most recent version of mlr3extralearners and paradox? there was some recent updates from how the various parameters are set with R6 and now ParamFct$new(...) has been replaced with another syntax in the newest versions

cxie19 commented 6 months ago

Thanks! Without actually looking at this in detail, have you installed the most recent version of mlr3extralearners and paradox? there was some recent updates from how the various parameters are set with R6 and now ParamFct$new(...) has been replaced with another syntax in the newest versions

Yes, I have installed the most recent version of mlr3extralearners and paradox.

packageVersion("mlr3extralearners")
#> [1] '0.7.1.9000'
packageVersion("paradox")
#> [1] '1.0.0'

Created on 2024-03-07 by the reprex package (v2.0.1)

bblodfon commented 6 months ago

So it seems the issue is related with the recent paradox update, thanks for reporting, we will solve it soon.

bblodfon commented 6 months ago

@cxie19 Do a clean installation of the latest versions of:

and it will work

cxie19 commented 6 months ago

@bblodfon Could you provide me the latest version numbers of these packages? I still have the same issue after removing and reinstalling these packages.

bblodfon commented 6 months ago

Sure, you can also see the specific commit numbers below (since some developing packages from GitHub may have not updated to a new version yet and changes "accumulate" in the same development version): 

library(survivalmodels)
set_seed(1234)
library(mlr3)
library(mlr3proba)

## get the `whas` task from mlr3proba
whas <- tsk("whas")

## create our own task from the rats dataset
rats_data <- survival::rats

## convert characters to factors
rats_data$sex <- factor(rats_data$sex, levels = c("f", "m"))
rats <- TaskSurv$new("rats", rats_data, time = "time", event = "status")

## combine in list
tasks <- list(whas, rats)

library(paradox)

search_space <- ps(
  ## p_dbl for numeric valued parameters
  dropout = p_dbl(lower = 0, upper = 1),
  weight_decay = p_dbl(lower = 0, upper = 0.5),
  learning_rate = p_dbl(lower = 0, upper = 1),

  ## p_int for integer valued parameters
  nodes = p_int(lower = 1, upper = 32),
  k = p_int(lower = 1, upper = 4)
)

search_space$extra_trafo <- function(x, param_set) {
  x$num_nodes = rep(x$nodes, x$k)
  x$nodes = x$k = NULL
  return(x)
}

library(mlr3tuning)

create_autotuner <- function(learner) {
  AutoTuner$new(
    learner = learner,
    search_space = search_space,
    resampling = rsmp("holdout"),
    measure = msr("surv.cindex"),
    terminator = trm("evals", n_evals = 2),
    tuner = tnr("random_search")
  )
}

## learners are stored in mlr3extralearners
library(mlr3extralearners)

## load learners
learners <- lrns(
  paste0("surv.", c("coxtime", "deephit", "deepsurv", "loghaz", "pchazard")),
  frac = 0.3, early_stopping = TRUE, epochs = 10, optimizer = "adam"
)

# apply our function
learners <- lapply(learners, create_autotuner)

library(mlr3pipelines)

create_pipeops <- function(learner) {
  po("encode") %>>% po("scale") %>>% po("learner", learner)
}

## apply our function
learners <- lapply(learners, create_pipeops)
learners[[1]]
#> Graph with 3 PipeOps:
#>                  ID         State           sccssors prdcssors
#>              <char>        <char>             <char>    <char>
#>              encode <<UNTRAINED>>              scale          
#>               scale <<UNTRAINED>> surv.coxtime.tuned    encode
#>  surv.coxtime.tuned <<UNTRAINED>>                        scale

devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.1 (2022-06-23)
#>  os       Ubuntu 20.04.6 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Europe/Oslo
#>  date     2024-03-13
#>  pandoc   3.1.1 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package           * version    date (UTC) lib source
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#> 
#>  [1] /opt/R/4.2.1/lib/R/library
#> 
#> ─ Python configuration ───────────────────────────────────────────────────────
#>  python:         /usr/bin/python3
#>  libpython:      /usr/lib/python3.8/config-3.8-x86_64-linux-gnu/libpython3.8.so
#>  pythonhome:     //usr://usr
#>  version:        3.8.10 (default, Nov 22 2023, 10:22:35)  [GCC 9.4.0]
#>  numpy:          /usr/lib/python3/dist-packages/numpy
#>  numpy_version:  1.17.4
#>  numpy:          /usr/lib/python3/dist-packages/numpy
#>  
#>  NOTE: Python version was forced by RETICULATE_PYTHON_FALLBACK
#> 
#> ──────────────────────────────────────────────────────────────────────────────

Created on 2024-03-13 with reprex v2.0.2

bblodfon commented 6 months ago

@cxie19 solved?

cxie19 commented 6 months ago

Yes, thank you @bblodfon. The problem is solved.