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Fresh implementation aft and cox #260

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Fresh implementation aft and cox #260

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I have fixed the bug with the previous implementation it was a parse vs rlang thing

library(tidymodels)
#> Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
#> had status 1
library(censored)
#> Loading required package: survival
library(tidyverse)
library(survival)

data(cancer)

lung <- lung %>% drop_na()
lung_train <- lung[-c(1:5), ]
lung_test <- lung[1:5, ]


test_aft <-
  boost_tree()|> set_engine('xgboost') |> set_mode('censored regression')

test_aft |>
  translate()
#> Boosted Tree Model Specification (censored regression)
#> 
#> Computational engine: xgboost 
#> 
#> Model fit template:
#> censored::xgb_train_censored(x = missing_arg(), y = missing_arg(), 
#>     nthread = 1, verbose = 0, objective = "survival:aft")

set.seed(1)
bt_fit <- test_aft %>% fit(Surv(time, status) ~ ., data = lung_train)
bt_fit
#> parsnip model object
#> 
#> ##### xgb.Booster
#> raw: 36.3 Kb 
#> call:
#>   xgboost::xgb.train(params = list(eta = 0.3, max_depth = 6, gamma = 0, 
#>     colsample_bytree = 1, colsample_bynode = 1, min_child_weight = 1, 
#>     subsample = 1, objective = "survival:aft"), data = x$data, 
#>     nrounds = 15, watchlist = x$watchlist, verbose = 0, nthread = 1)
#> params (as set within xgb.train):
#>   eta = "0.3", max_depth = "6", gamma = "0", colsample_bytree = "1", colsample_bynode = "1", min_child_weight = "1", subsample = "1", objective = "survival:aft", nthread = "1", validate_parameters = "TRUE"
#> xgb.attributes:
#>   niter
#> callbacks:
#>   cb.evaluation.log()
#> # of features: 8 
#> niter: 15
#> nfeatures : 8 
#> evaluation_log:
#>     iter training_aft_nloglik
#>        1            14.698676
#>        2             9.883017
#> ---                          
#>       14             4.564666
#>       15             4.553461

predict(
  bt_fit,
  lung_test,
  type = "linear_pred",
)
#> Error in `object$spec$method$pred$linear_pred$pre()`:
#> ! The objective should be survival:cox not survival:aft
#> Backtrace:
#>     ▆
#>  1. ├─stats::predict(bt_fit, lung_test, type = "linear_pred", )
#>  2. └─parsnip::predict.model_fit(...)
#>  3.   ├─parsnip::predict_linear_pred(...)
#>  4.   └─parsnip::predict_linear_pred.model_fit(...)
#>  5.     └─object$spec$method$pred$linear_pred$pre(new_data, object)
#>  6.       └─rlang::abort(glue::glue("The objective should be survival:cox not {object$fit$params$objective}")) at censored/R/boost_tree-data.R:280:8

predict(bt_fit,lung_test,type = 'time')
#> # A tibble: 5 × 1
#>   .pred_time
#>        <dbl>
#> 1      420. 
#> 2      239. 
#> 3      120. 
#> 4       78.7
#> 5      350.

test_cox <-
  boost_tree()|> set_engine('xgboost',objective = 'survival:cox')  |> set_mode('censored regression')

test_cox |>
  translate()
#> Boosted Tree Model Specification (censored regression)
#> 
#> Engine-Specific Arguments:
#>   objective = survival:cox
#> 
#> Computational engine: xgboost 
#> 
#> Model fit template:
#> censored::xgb_train_censored(x = missing_arg(), y = missing_arg(), 
#>     objective = "survival:cox", nthread = 1, verbose = 0)

set.seed(1)
bt_fit <- test_cox %>% fit(Surv(time, status) ~ ., data = lung_train)
bt_fit
#> parsnip model object
#> 
#> ##### xgb.Booster
#> raw: 40.5 Kb 
#> call:
#>   xgboost::xgb.train(params = list(eta = 0.3, max_depth = 6, gamma = 0, 
#>     colsample_bytree = 1, colsample_bynode = 1, min_child_weight = 1, 
#>     subsample = 1, objective = "survival:cox"), data = x$data, 
#>     nrounds = 15, watchlist = x$watchlist, verbose = 0, nthread = 1)
#> params (as set within xgb.train):
#>   eta = "0.3", max_depth = "6", gamma = "0", colsample_bytree = "1", colsample_bynode = "1", min_child_weight = "1", subsample = "1", objective = "survival:cox", nthread = "1", validate_parameters = "TRUE"
#> xgb.attributes:
#>   niter
#> callbacks:
#>   cb.evaluation.log()
#> # of features: 8 
#> niter: 15
#> nfeatures : 8 
#> evaluation_log:
#>     iter training_cox_nloglik
#>        1             3.967019
#>        2             3.840237
#> ---                          
#>       14             3.095573
#>       15             3.054746


predict(bt_fit, lung_test, type = 'time')
#> Error in `object$spec$method$pred$time$pre()`:
#> ! The objective should be survival:aft not survival:cox
#> Backtrace:
#>     ▆
#>  1. ├─stats::predict(bt_fit, lung_test, type = "time")
#>  2. └─parsnip::predict.model_fit(bt_fit, lung_test, type = "time")
#>  3.   ├─parsnip::predict_time(object = object, new_data = new_data, ...)
#>  4.   └─parsnip::predict_time.model_fit(...)
#>  5.     └─object$spec$method$pred$time$pre(new_data, object)
#>  6.       └─rlang::abort(glue::glue("The objective should be survival:aft not {object$fit$params$objective}")) at censored/R/boost_tree-data.R:256:8

predict(bt_fit,
        lung_test,
        type = "linear_pred")
#> # A tibble: 5 × 1
#>   .pred_linear_pred
#>               <dbl>
#> 1             0.351
#> 2             4.41 
#> 3             2.23 
#> 4             4.50 
#> 5             2.36

Created on 2023-04-13 with reprex v2.0.2

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.3 (2023-03-15)
#>  os       Ubuntu 22.04.2 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  C.UTF-8
#>  ctype    C.UTF-8
#>  tz       America/Sao_Paulo
#>  date     2023-04-13
#>  pandoc   2.19.2 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package      * version    date (UTC) lib source
#>  backports      1.4.1      2021-12-13 [1] CRAN (R 4.2.3)
#>  broom        * 1.0.4      2023-03-11 [1] CRAN (R 4.2.3)
#>  censored     * 0.1.1.9003 2023-04-14 [1] local
#>  class          7.3-21     2023-01-23 [4] CRAN (R 4.2.2)
#>  cli            3.6.1      2023-03-23 [1] CRAN (R 4.2.3)
#>  codetools      0.2-19     2023-02-01 [4] CRAN (R 4.2.2)
#>  colorspace     2.1-0      2023-01-23 [1] CRAN (R 4.2.3)
#>  data.table     1.14.8     2023-02-17 [1] CRAN (R 4.2.3)
#>  dials        * 1.2.0      2023-04-03 [1] CRAN (R 4.2.3)
#>  DiceDesign     1.9        2021-02-13 [1] CRAN (R 4.2.3)
#>  digest         0.6.31     2022-12-11 [1] CRAN (R 4.2.3)
#>  dplyr        * 1.1.1      2023-03-22 [1] CRAN (R 4.2.3)
#>  evaluate       0.20       2023-01-17 [1] CRAN (R 4.2.3)
#>  fansi          1.0.4      2023-01-22 [1] CRAN (R 4.2.3)
#>  fastmap        1.1.1      2023-02-24 [1] CRAN (R 4.2.3)
#>  forcats      * 1.0.0      2023-01-29 [1] CRAN (R 4.2.3)
#>  foreach        1.5.2      2022-02-02 [1] CRAN (R 4.2.3)
#>  fs             1.6.1      2023-02-06 [1] CRAN (R 4.2.3)
#>  furrr          0.3.1      2022-08-15 [1] CRAN (R 4.2.3)
#>  future         1.32.0     2023-03-07 [1] CRAN (R 4.2.3)
#>  future.apply   1.10.0     2022-11-05 [1] CRAN (R 4.2.3)
#>  generics       0.1.3      2022-07-05 [1] CRAN (R 4.2.3)
#>  ggplot2      * 3.4.2      2023-04-03 [1] CRAN (R 4.2.3)
#>  globals        0.16.2     2022-11-21 [1] CRAN (R 4.2.3)
#>  glue           1.6.2      2022-02-24 [1] CRAN (R 4.2.3)
#>  gower          1.0.1      2022-12-22 [1] CRAN (R 4.2.3)
#>  GPfit          1.0-8      2019-02-08 [1] CRAN (R 4.2.3)
#>  gtable         0.3.3      2023-03-21 [1] CRAN (R 4.2.3)
#>  hardhat        1.3.0      2023-03-30 [1] CRAN (R 4.2.3)
#>  hms            1.1.3      2023-03-21 [1] CRAN (R 4.2.3)
#>  htmltools      0.5.5      2023-03-23 [1] CRAN (R 4.2.3)
#>  infer        * 1.0.4      2022-12-02 [1] CRAN (R 4.2.3)
#>  ipred          0.9-14     2023-03-09 [1] CRAN (R 4.2.3)
#>  iterators      1.0.14     2022-02-05 [1] CRAN (R 4.2.3)
#>  jsonlite       1.8.4      2022-12-06 [1] CRAN (R 4.2.3)
#>  knitr          1.42       2023-01-25 [1] CRAN (R 4.2.3)
#>  lattice        0.20-45    2021-09-22 [4] CRAN (R 4.2.0)
#>  lava           1.7.2.1    2023-02-27 [1] CRAN (R 4.2.3)
#>  lhs            1.1.6      2022-12-17 [1] CRAN (R 4.2.3)
#>  lifecycle      1.0.3      2022-10-07 [1] CRAN (R 4.2.3)
#>  listenv        0.9.0      2022-12-16 [1] CRAN (R 4.2.3)
#>  lubridate    * 1.9.2      2023-02-10 [1] CRAN (R 4.2.3)
#>  magrittr       2.0.3      2022-03-30 [1] CRAN (R 4.2.3)
#>  MASS           7.3-58.3   2023-03-07 [4] CRAN (R 4.2.3)
#>  Matrix         1.5-1      2022-09-13 [4] CRAN (R 4.2.1)
#>  modeldata    * 1.1.0      2023-01-25 [1] CRAN (R 4.2.3)
#>  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.2.3)
#>  nnet           7.3-18     2022-09-28 [4] CRAN (R 4.2.1)
#>  parallelly     1.35.0     2023-03-23 [1] CRAN (R 4.2.3)
#>  parsnip      * 1.1.0      2023-04-12 [1] CRAN (R 4.2.3)
#>  pillar         1.9.0      2023-03-22 [1] CRAN (R 4.2.3)
#>  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.2.3)
#>  prodlim        2023.03.31 2023-04-02 [1] CRAN (R 4.2.3)
#>  purrr        * 1.0.1      2023-01-10 [1] CRAN (R 4.2.3)
#>  R6             2.5.1      2021-08-19 [1] CRAN (R 4.2.3)
#>  Rcpp           1.0.10     2023-01-22 [1] CRAN (R 4.2.3)
#>  readr        * 2.1.4      2023-02-10 [1] CRAN (R 4.2.3)
#>  recipes      * 1.0.5      2023-02-20 [1] CRAN (R 4.2.3)
#>  reprex         2.0.2      2022-08-17 [1] CRAN (R 4.2.3)
#>  rlang          1.1.0      2023-03-14 [1] CRAN (R 4.2.3)
#>  rmarkdown      2.21       2023-03-26 [1] CRAN (R 4.2.3)
#>  rpart          4.1.19     2022-10-21 [4] CRAN (R 4.2.1)
#>  rsample      * 1.1.1      2022-12-07 [1] CRAN (R 4.2.3)
#>  rstudioapi     0.14       2022-08-22 [1] CRAN (R 4.2.3)
#>  scales       * 1.2.1      2022-08-20 [1] CRAN (R 4.2.3)
#>  sessioninfo    1.2.2      2021-12-06 [1] CRAN (R 4.2.3)
#>  stringi        1.7.12     2023-01-11 [1] CRAN (R 4.2.3)
#>  stringr      * 1.5.0      2022-12-02 [1] CRAN (R 4.2.3)
#>  survival     * 3.5-3      2023-02-12 [4] CRAN (R 4.2.2)
#>  tibble       * 3.2.1      2023-03-20 [1] CRAN (R 4.2.3)
#>  tidymodels   * 1.0.0      2022-07-13 [1] CRAN (R 4.2.3)
#>  tidyr        * 1.3.0      2023-01-24 [1] CRAN (R 4.2.3)
#>  tidyselect     1.2.0      2022-10-10 [1] CRAN (R 4.2.3)
#>  tidyverse    * 2.0.0      2023-02-22 [1] CRAN (R 4.2.3)
#>  timechange     0.2.0      2023-01-11 [1] CRAN (R 4.2.3)
#>  timeDate       4022.108   2023-01-07 [1] CRAN (R 4.2.3)
#>  tune         * 1.1.1      2023-04-11 [1] CRAN (R 4.2.3)
#>  tzdb           0.3.0      2022-03-28 [1] CRAN (R 4.2.3)
#>  utf8           1.2.3      2023-01-31 [1] CRAN (R 4.2.3)
#>  vctrs          0.6.1      2023-03-22 [1] CRAN (R 4.2.3)
#>  withr          2.5.0      2022-03-03 [1] CRAN (R 4.2.3)
#>  workflows    * 1.1.3      2023-02-22 [1] CRAN (R 4.2.3)
#>  workflowsets * 1.0.1      2023-04-06 [1] CRAN (R 4.2.3)
#>  xfun           0.38       2023-03-24 [1] CRAN (R 4.2.3)
#>  xgboost        1.7.5.1    2023-03-30 [1] CRAN (R 4.2.3)
#>  yaml           2.3.7      2023-01-23 [1] CRAN (R 4.2.3)
#>  yardstick    * 1.1.0      2022-09-07 [1] CRAN (R 4.2.3)
#> 
#>  [1] /home/bcarlin/R/x86_64-pc-linux-gnu-library/4.2
#>  [2] /usr/local/lib/R/site-library
#>  [3] /usr/lib/R/site-library
#>  [4] /usr/lib/R/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

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@brunocarlin