Generating one parameter per factor level of categorical predictors #28
Description
When trying to setup a deepregression::deepregression model with one parameter per level of a factor, I ran into the problems illustrated below.
Running
library(deepregression)
set.seed(42)
n <- 100
y <- rnorm(n)
x <- gl(10, 10)
d <- data.frame(x)
m_dr_1 <- deepregression(y = y,
list_of_formulas = list(
location = ~ x,
scale = ~ 1),
data = d)
seems to work fine and generates a model with 1 intercept and 9 additional coefficients for each of the non-reference levels:
coef(m_dr_1)
# $x
# [,1]
# [1,] -0.47599542
# [2,] -0.73255873
# [3,] -0.62333083
# [4,] -0.40610617
# [5,] -0.43092820
# [6,] -0.54474354
# [7,] 0.67287409
# [8,] 0.73756588
# [9,] 0.09474695
#
# $`(Intercept)`
# [,1]
# [1,] 1.244688
But with the usual ~ 0 + x or -1 + x syntax, we only get 9 coefficients instead of the usual 10 (one per factor level) coefficients that I expected:
m_dr_2 <- deepregression(y = y,
list_of_formulas = list(
location = ~ 0 + x,
scale = ~ 1),
data = d)
coef(m_dr_2)
# $x
# [,1]
# [1,] -0.47599542
# [2,] -0.73255873
# [3,] -0.62333083
# [4,] -0.40610617
# [5,] -0.43092820
# [6,] -0.54474354
# [7,] 0.67287409
# [8,] 0.73756588
# [9,] 0.09474695
m_dr_3 <- deepregression(y = y,
list_of_formulas = list(
location = ~ -1 + x,
scale = ~ 1),
data = d)
coef(m_dr_3)
# $x
# [,1]
# [1,] -0.47599542
# [2,] -0.73255873
# [3,] -0.62333083
# [4,] -0.40610617
# [5,] -0.43092820
# [6,] -0.54474354
# [7,] 0.67287409
# [8,] 0.73756588
# [9,] 0.09474695
Setting check_form = FALSE in the formula_options leads to the following errors:
m_dr_4 <- deepregression(y = y,
list_of_formulas = list(
location = ~ 0 + x,
scale = ~ 1),
formula_options = list(precalculate_gamparts = TRUE,
check_form = FALSE),
data = d)
# Error in py_call_impl(callable, call_args$unnamed, call_args$named) :
# ValueError: The name "input__Intercept__2" is used 2 times in the model. All layer names should be unique.
m_dr_5 <- deepregression(y = y,
list_of_formulas = list(
location = ~ -1 + x,
scale = ~ 1),
formula_options = list(precalculate_gamparts = TRUE,
check_form = FALSE),
data = d)
# Error in py_call_impl(callable, call_args$unnamed, call_args$named) :
# ValueError: '-1_1/' is not a valid root scope name. A root scope name has to match the following pattern: ^[A-Za-z0-9.][A-Za-z0-9_.\\/>-]*$
The problems seem to be caused by the formula processing in deepregression:::process_terms and deepregression:::separate_define_relation, but I didn't feel comfortable editing the code in a way that respects the current structure and so didn't create a pull request.
For completeness, this is the corresponding sessionInfo()
R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C
[5] LC_TIME=German_Germany.utf8
time zone: Europe/Berlin
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] deepregression_2.0.0 keras_2.13.0 tfprobability_0.15.1 tensorflow_2.14.0
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 utf8_1.2.4 generics_0.1.3 lattice_0.21-9 hms_1.1.3 magrittr_2.0.3 grid_4.3.2 rprojroot_2.0.4
[9] jsonlite_1.8.7 Matrix_1.6-2 processx_3.8.2 progress_1.2.2 whisker_0.4.1 torch_0.11.0 ps_1.7.5 tfruns_1.5.1
[17] mgcv_1.9-0 fansi_1.0.5 coro_1.0.3 cli_3.6.1 rlang_1.1.2 crayon_1.5.2 bit64_4.0.5 splines_4.3.2
[25] withr_2.5.2 base64enc_0.1-3 luz_0.4.0 tools_4.3.2 dplyr_1.1.3 zeallot_0.1.0 here_1.0.1 reticulate_1.34.0
[33] vctrs_0.6.4 R6_2.5.1 png_0.1-8 lifecycle_1.0.4 fs_1.6.3 bit_4.0.5 pkgconfig_2.0.3 callr_3.7.3
[41] pillar_1.9.0 torchvision_0.5.1 glue_1.6.2 Rcpp_1.0.11 tibble_3.2.1 tidyselect_1.2.0 rstudioapi_0.15.0 nlme_3.1-163