Error: UQE() can only be used within a quasiquoted argument

[osofr]

After installing the latest versions of sl3/delayed/origami, every attempt to train a super learner in sl3 crashes and burns on my system. Not sure why its running fine on travis. Any ideas / clues where to look?

This is just running one of the tests from https://github.com/jeremyrcoyle/sl3/blob/f40c784d56e603b2b9df41b5edef0b801fb2df66/tests/testthat/test_sl.R#L4-L21

The error is the same regardless of what code I run.

R version 3.4.2 (2017-09-28) -- "Short Summer"
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Platform: x86_64-apple-darwin15.6.0 (64-bit)

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> options(sl3.verbose = TRUE)
> library(sl3)
library(origami)
library(SuperLearner)

data(cpp_imputed)
covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
outcome <- "haz"
task <- sl3_Task$new(data.table::copy(cpp_imputed), covariates = covars, outcome = outcome)
task2 <- sl3_Task$new(data.table::copy(cpp_imputed), covariates = covars, outcome = outcome)

glm_learner <- Lrnr_glm$new()
glmnet_learner <- Lrnr_pkg_SuperLearner$new("SL.glmnet")
subset_apgar <- Lrnr_subset_covariates$new(covariates = c("apgar1", "apgar5"))
learners <- list(glm_learner, glmnet_learner, subset_apgar)
sl1 <- make_learner(Lrnr_sl, learners, glm_learner)

sl1_fit <- sl1$train(task)

sl3 1.0.0
Please note the package is in early stages of development. Check often for updates and report bugs at http://github.com/jeremyrcoyle/sl3. 

> library(origami)
origami: Generalized Cross-Validation Framework
Version: 0.8.2
> library(SuperLearner)
Loading required package: nnls
Super Learner
Version: 2.0-22
Package created on 2017-07-18

> 
> data(cpp_imputed)
> covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
> outcome <- "haz"
> task <- sl3_Task$new(data.table::copy(cpp_imputed), covariates = covars, outcome = outcome)
> task2 <- sl3_Task$new(data.table::copy(cpp_imputed), covariates = covars, outcome = outcome)
> 
> glm_learner <- Lrnr_glm$new()
> glmnet_learner <- Lrnr_pkg_SuperLearner$new("SL.glmnet")
> subset_apgar <- Lrnr_subset_covariates$new(covariates = c("apgar1", "apgar5"))
> learners <- list(glm_learner, glmnet_learner, subset_apgar)
> sl1 <- make_learner(Lrnr_sl, learners, glm_learner)
> 
> sl1_fit <- sl1$train(task)
Error: `UQE()` can only be used within a quasiquoted argument

[nhejazi]

I tried investigating this and am unable to reproduce this issue. It may be something related to your particular machine/setup. In attempting to reproduce this, I did the following

remove.packages(c("sl3", "delayed", "origami", "SuperLearner"))
update.packages(ask=FALSE)
devtools::install_github(c("jeremyrcoyle/sl3", "jeremyrcoyle/origami", "jeremyrcoyle/delayed", "ecpolley/SuperLearner"))

Then, I ran the example given directly above

❯ library(sl3)
sl3 1.0.0
Please note the package is in early stages of development. Check often for updates and report bugs at http://github.com/jeremyrcoyle/sl3.

❯ library(origami)
  library(SuperLearner)

  data(cpp_imputed)
  covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
  outcome <- "haz"
  task <- sl3_Task$new(data.table::copy(cpp_imputed), covariates = covars, outcome = outco
  me)
  task2 <- sl3_Task$new(data.table::copy(cpp_imputed), covariates = covars, outcome = outc
  ome)

  glm_learner <- Lrnr_glm$new()
  glmnet_learner <- Lrnr_pkg_SuperLearner$new("SL.glmnet")
  subset_apgar <- Lrnr_subset_covariates$new(covariates = c("apgar1", "apgar5"))
  learners <- list(glm_learner, glmnet_learner, subset_apgar)
  sl1 <- make_learner(Lrnr_sl, learners, glm_learner)

  sl1_fit <- sl1$train(task)
origami: Generalized Cross-Validation Framework
Version: 0.8.2
Loading required package: nnls
Super Learner
Version: 2.0-23-9000
Package created on 2017-11-07

Loading required package: glmnet
Loading required package: Matrix

Attaching package: ‘Matrix’

The following object is masked from ‘package:tidyr’:

    expand

Loading required package: foreach
foreach: simple, scalable parallel programming from Revolution Analytics
Use Revolution R for scalability, fault tolerance and more.
http://www.revolutionanalytics.com

Attaching package: ‘foreach’

The following objects are masked from ‘package:purrr’:

    accumulate, when

Loaded glmnet 2.0-13

which yields the following

❯ sl1_fit
[1] "SuperLearner:"
List of 3
 $ : chr "Lrnr_glm"
 $ : chr "Lrnr_pkg_SuperLearner_SL.glmnet"
 $ : chr "Lrnr_subset_covariates_c(\"apgar1\", \"apgar5\")"
[1] "Lrnr_glm"
$coefficients
                                           Lrnr_glm
                                       -0.058447022
                    Lrnr_pkg_SuperLearner_SL.glmnet
                                        0.994056984
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar1
                                        0.007251873
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar5
                                       -0.001111357
                                          intercept
                                       -0.032777920

$R
                                                     Lrnr_glm
Lrnr_glm                                            -10.76306
Lrnr_pkg_SuperLearner_SL.glmnet                       0.00000
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar1   0.00000
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar5   0.00000
intercept                                             0.00000
                                                    Lrnr_pkg_SuperLearner_SL.glmnet
Lrnr_glm                                                                  -9.850734
Lrnr_pkg_SuperLearner_SL.glmnet                                           -1.275411
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar1                        0.000000
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar5                        0.000000
intercept                                                                  0.000000
                                                    Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar1
Lrnr_glm                                                                                     -177.20816
Lrnr_pkg_SuperLearner_SL.glmnet                                                               -99.51456
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar1                                          -215.59712
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar5                                             0.00000
intercept                                                                                       0.00000
                                                    Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar5
Lrnr_glm                                                                                      -187.2683
Lrnr_pkg_SuperLearner_SL.glmnet                                                               -118.3183
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar1                                           -210.2261
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar5                                           -104.4910
intercept                                                                                        0.0000
                                                     intercept
Lrnr_glm                                            -22.718235
Lrnr_pkg_SuperLearner_SL.glmnet                     -16.959897
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar1 -23.350899
Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar5  -4.689026
intercept                                             8.366137

$rank
[1] 5

$family

Family: gaussian
Link function: identity


$deviance
[1] 2302.752

$aic
[1] 4776.875

$null.deviance
[1] 2352.594

$iter
[1] 2

$df.residual
[1] 1436

$df.null
[1] 1440

$converged
[1] TRUE

$boundary
[1] FALSE

$linkinv_fun
function (eta)
eta
<environment: namespace:stats>

[1] "Cross-validated risk (MSE, squared error loss):"
                                               learner coefficients mean_risk   SE_risk
1:                                            Lrnr_glm -0.058447022  1.600699 0.1044375
2:                     Lrnr_pkg_SuperLearner_SL.glmnet  0.994056984  1.598497 0.1040675
3: Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar1  0.007251873 59.897271 0.8552774
4: Lrnr_subset_covariates_c("apgar1", "apgar5")_apgar5 -0.001111357 71.151577 0.8560152
5:                                        SuperLearner           NA  1.598082 0.1041452
     fold_SD fold_min_risk fold_max_risk
1: 0.2898641      1.117885      2.185334
2: 0.2897284      1.107970      2.177785
3: 2.0043825     58.100414     63.126207
4: 2.7441044     65.003838     74.550986
5: 0.2887530      1.111586      2.178588

For completeness, here's a call to sessionInfo:

❯ sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin17.2.0 (64-bit)
Running under: macOS High Sierra 10.13.1

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] glmnet_2.0-13            foreach_1.4.3            Matrix_1.2-12
 [4] SuperLearner_2.0-23-9000 nnls_1.4                 origami_0.8.2
 [7] sl3_1.0.0                forcats_0.2.0            stringr_1.3.0
[10] dplyr_0.7.4              purrr_0.2.4              readr_1.1.1
[13] tidyr_0.7.2              tibble_1.3.4             ggplot2_2.2.1
[16] tidyverse_1.2.1          devtools_1.13.4          nima_0.4.5
[19] fcuk_0.1.21              prettycode_1.0.0         colorout_1.1-2
[22] prompt_1.0.0

loaded via a namespace (and not attached):
 [1] httr_1.3.1          jsonlite_1.5        modelr_0.1.1        gtools_3.5.0
 [5] assertthat_0.2.0    cellranger_1.1.0    globals_0.10.3      backports_1.1.1
 [9] lattice_0.20-35     glue_1.2.0          uuid_0.1-2          digest_0.6.12
[13] checkmate_1.8.5     rvest_0.3.2         colorspace_1.3-2    htmltools_0.3.6
[17] plyr_1.8.4          psych_1.7.8         clisymbols_1.2.0    pkgconfig_2.0.1
[21] broom_0.4.3         listenv_0.6.0       haven_1.1.0         scales_0.5.0
[25] stringdist_0.9.4.6  withr_2.1.0         lazyeval_0.2.1      cli_1.0.0
[29] mnormt_1.5-5        magrittr_1.5        crayon_1.3.4        readxl_1.0.0
[33] memoise_1.1.0       future_1.6.2        delayed_0.2.1       nlme_3.1-131
[37] xml2_1.1.1          foreign_0.8-69      ggthemes_3.4.0      tools_3.4.3
[41] data.table_1.10.5   hms_0.4.0           BBmisc_1.11         ProjectTemplate_0.8
[45] munsell_0.4.3       bindrcpp_0.2        compiler_3.4.3      rlang_0.1.4
[49] grid_3.4.3          iterators_1.0.8     rstudioapi_0.7      htmlwidgets_0.9
[53] visNetwork_2.0.1    igraph_1.1.2        gtable_0.2.0        codetools_0.2-15
[57] abind_1.4-5         reshape2_1.4.2      R6_2.2.2            gridExtra_2.3
[61] lubridate_1.7.1     bindr_0.1           stringi_1.1.6       rstackdeque_1.1.1
[65] parallel_3.4.3      Rcpp_0.12.14

[jeremyrcoyle]

I also can't seem to reproduce this bug. To give you some idea where to look, UQE (defined in rlang). used in delayed to do nonstandard evalation. @osofr , can you post your sessionInfo()? I'd be curious about your version of rlang.

[osofr]

Thanks for this guys! Indeed, the problem was with rlang, reinstalling it completely took care of everything.