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In this vignette, we illustrate how to troubleshoot tuning errors. This is not a comprehensive list (yet), but rather an attempt to illustrate how an error can be approached.

NAs in the data

Most algorithms do not allow NAs. We can generate a problematic dataset by loading the Lacerta dataset, and manually add an NA:

library(tidysdm)
#> Loading required package: tidymodels
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
#>  broom        1.0.6       recipes      1.0.10
#>  dials        1.2.1       rsample      1.2.1 
#>  dplyr        1.1.4       tibble       3.2.1 
#>  ggplot2      3.5.1       tidyr        1.3.1 
#>  infer        1.0.7       tune         1.2.1 
#>  modeldata    1.3.0       workflows    1.1.4 
#>  parsnip      1.2.1       workflowsets 1.1.0 
#>  purrr        1.0.2       yardstick    1.3.1
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#>  purrr::discard() masks scales::discard()
#>  dplyr::filter()  masks stats::filter()
#>  dplyr::lag()     masks stats::lag()
#>  recipes::step()  masks stats::step()
#>  Use tidymodels_prefer() to resolve common conflicts.
#> Loading required package: spatialsample
lacerta_thin <- readRDS(system.file("extdata/lacerta_climate_sf.RDS",
  package = "tidysdm"
))
lacerta_thin$bio05[37] <- NA

Let us set up a recipe and fit workflow_set

lacerta_rec <- recipe(lacerta_thin, formula = class ~ .) %>%
  step_rm(all_of(c(
    "bio01", "bio02", "bio03", "bio04", "bio07", "bio08",
    "bio09", "bio10", "bio11", "bio12", "bio14", "bio16",
    "bio17", "bio18", "bio19", "altitude"
  )))

lacerta_models <-
  # create the workflow_set
  workflow_set(
    preproc = list(default = lacerta_rec),
    models = list(
      # the standard glm specs
      glm = sdm_spec_glm(),
      # rf specs with tuning
      rf = sdm_spec_rf()
    ),
    # make all combinations of preproc and models,
    cross = TRUE
  ) %>%
  # tweak controls to store information needed later to create the ensemble
  option_add(control = control_ensemble_grid())
set.seed(100)
lacerta_cv <- spatial_block_cv(lacerta_thin, v = 5)
lacerta_models <-
  lacerta_models %>%
  workflow_map("tune_grid",
    resamples = lacerta_cv, grid = 3,
    metrics = sdm_metric_set(), verbose = TRUE
  )
#> i  No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 2 resampling: default_glm
#>  1 of 2 resampling: default_glm (331ms)
#> i 2 of 2 tuning:     default_rf
#> i Creating pre-processing data to finalize unknown parameter: mtry
#> → A | error:   Missing data in columns: bio05.
#> There were issues with some computations   A: x1
#> There were issues with some computations   A: x15
#> 
#> Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
#> information.
#> Warning: Unknown or uninitialised column: `.notes`.
#>  2 of 2 tuning:     default_rf failed with 

We can see that the error is self-explanatory. Also, note that the error impacts all data splits (technically, rset objects): error A is repeated 15 times (5 splits for 3 hyperparameter values).

Prepping the recipe (which trains it on the dataset) can help diagnosing problems:

lacerta_prep <- lacerta_rec %>% prep(lacerta_thin)
lacerta_prep
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs
#> Number of variables by role
#> outcome:    1
#> predictor: 20
#> coords:     2
#> 
#> ── Training information
#> Training data contained 452 data points and 1 incomplete row.
#> 
#> ── Operations
#>  Variables removed: bio01, bio02, bio03, bio04, bio07, bio08, ... | Trained

Note that, in the training information, we were warned that 1 row was incomplete. You could use step_naomit to deal with this programmatically, or ascertain why you are generating missing data (we prefer the latter, as a good SDM pipeline should not generate observations, presences or pseudoabsences, with missing data).

Recipes and the response variable

The response variable is treated in a special way in recipes, and this can lead to problems. It is best not to manipulate (e.g. transform character into factor) the response variable in a recipe, since that response variable will only be available when we train and test models, but not when we make projections. If we hard-coded a step in a recipe that includes the response variable, the model will fit, but then it will fail when we start making predictions.

Another potential mistake is to remove the response variable when selecting variables of interest. This can happen if we use step_select to choose variables of interest, and the error is less than clear:

Let’s load the data and create a recipe with step_select:

lacerta_thin <- readRDS(system.file("extdata/lacerta_climate_sf.RDS",
  package = "tidysdm"
))
suggested_vars <- c("bio05", "bio06", "bio13", "bio14", "bio15")
lacerta_rec_sel <- recipe(lacerta_thin, formula = class ~ .) %>%
  step_select(all_of(suggested_vars))

Now we create the workflow set and fit it:

lacerta_models <-
  # create the workflow_set
  workflow_set(
    preproc = list(default = lacerta_rec_sel),
    models = list(
      # the standard glm specs
      glm = sdm_spec_glm(),
      # rf specs with tuning
      rf = sdm_spec_rf()
    ),
    # make all combinations of preproc and models,
    cross = TRUE
  ) %>%
  # tweak controls to store information needed later to create the ensemble
  option_add(control = control_ensemble_grid())

set.seed(100)
lacerta_cv <- spatial_block_cv(lacerta_thin, v = 5)
lacerta_models <-
  lacerta_models %>%
  workflow_map("tune_grid",
    resamples = lacerta_cv, grid = 3,
    metrics = sdm_metric_set(), verbose = TRUE
  )
#> i  No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 2 resampling: default_glm
#> → A | error:   ! `logistic_reg()` was unable to find an outcome.
#>                 Ensure that you have specified an outcome column and that it hasn't been
#>                  removed in pre-processing.
#> Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
#> information.
#> Warning: Unknown or uninitialised column: `.notes`.
#>  1 of 2 resampling: default_glm failed with 
#> i 2 of 2 tuning:     default_rf
#> i Creating pre-processing data to finalize unknown parameter: mtry
#> → A | error:   ! `rand_forest()` was unable to find an outcome.
#>                 Ensure that you have specified an outcome column and that it hasn't been
#>                  removed in pre-processing.
#> There were issues with some computations   A: x1
#> There were issues with some computations   A: x15
#> 
#> Warning: All models failed. Run `show_notes(.Last.tune.result)` for more information.
#> Unknown or uninitialised column: `.notes`.
#>  2 of 2 tuning:     default_rf failed with 

The errors are not very intuitive. However, all models have failed for all algorithms, which suggests that the problem lies with the data preparation side (either the data themselves, or what we did with the recipe).

Ideally, you should have already had a look at your data (with summary or glimpse). So, in this case, we know that the data are fine. Whilst prepping (and sometimes baking) the recipe is generally informative for predictor variables, it is hard to diagnose problems with the outcome variable in a recipe. Prepping will not show anything obvious:

lacerta_prep_sel <- lacerta_rec_sel %>% prep(lacerta_thin)
lacerta_prep_sel
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs
#> Number of variables by role
#> outcome:    1
#> predictor: 20
#> coords:     2
#> 
#> ── Training information
#> Training data contained 452 data points and no incomplete rows.
#> 
#> ── Operations
#>  Variables selected: bio05, bio06, bio13, bio14, bio15 | Trained

In this case, it is a process of exclusion. Everything seems fine, but the models don’t work. Then ask yourself if the outcome variable might be problematic. As a general rule, we have found it easier to rely on step_rm to remove variables (e.g. correlated variables).

Using the desired formula with GAM

General Additive Models have an unusual syntax, as the user has to define which variables are fitted with splines. tidysdm has some functions to simplify this process, assuming that the user just wants to fit a standard smooth to every continuous predictor.

lacerta_thin <- readRDS(system.file("extdata/lacerta_climate_sf.RDS",
  package = "tidysdm"
))

lacerta_rec <- recipe(lacerta_thin, formula = class ~ .) %>%
  step_rm(all_of(c(
    "bio01", "bio02", "bio03", "bio04", "bio07", "bio08",
    "bio09", "bio10", "bio11", "bio12", "bio14", "bio16",
    "bio17", "bio18", "bio19", "altitude"
  )))

lacerta_models <-
  # create the workflow_set
  workflow_set(
    preproc = list(default = lacerta_rec),
    models = list(
      # the standard glm specs
      glm = sdm_spec_glm(),
      # the standard gam specs
      gam = sdm_spec_gam()
    ),
    # make all combinations of preproc and models,
    cross = TRUE
  ) %>%
  # set formula for gams
  update_workflow_model("default_gam",
    spec = sdm_spec_gam(),
    formula = gam_formula(lacerta_rec)
  ) %>%
  # tweak controls to store information needed later to create the ensemble
  option_add(control = control_ensemble_grid())
set.seed(100)
lacerta_cv <- spatial_block_cv(lacerta_thin, v = 5)
lacerta_models <-
  lacerta_models %>%
  workflow_map("tune_grid",
    resamples = lacerta_cv, grid = 3,
    metrics = sdm_metric_set(), verbose = TRUE
  )
#> i  No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 2 resampling: default_glm
#>  1 of 2 resampling: default_glm (273ms)
#> i  No tuning parameters. `fit_resamples()` will be attempted
#> i 2 of 2 resampling: default_gam
#>  2 of 2 resampling: default_gam (1.4s)

Note that the step of defining a formula is incompatible with using step_cor in a recipe. step_cor removes correlated variables in recipes, using a similar algorithm to filter_collinear using method cor_caret. However, the algorithm is fitted to each data split when cross-validating. This means that different variables will eventually be presented to the model when it is fitted for each split, leading to an error as there will be a mismatch between the formula and the available variables. This is a known issue of how GAMs are implemented in tidymodels.

When only some splits fail

In the examples above, all the splits used for cross-validation of a given algorithms failed. However, it is also possible that failures occur only on some splits for certain algorithms (technically, a specific rsplit within certain workflows). When this type of problem occurs, it is best to extract the problematic workflow, and potentially investigate fitting it to the specific rsplit.

We generate a problematic dataset by subsampling the lacerta dataset:

lacerta_thin <- readRDS(system.file("extdata/lacerta_climate_sf.RDS",
  package = "tidysdm"
))
set.seed(123)
lacerta_thin <- lacerta_thin[sample(
  1:nrow(lacerta_thin),
  nrow(lacerta_thin) / 5
), ]

lacerta_rec <- recipe(lacerta_thin, formula = class ~ .) %>%
  step_rm(all_of(c(
    "bio01", "bio02", "bio03", "bio04", "bio07", "bio08",
    "bio09", "bio10", "bio11", "bio12", "bio14", "bio16",
    "bio17", "bio18", "bio19", "altitude"
  )))

lacerta_models <-
  # create the workflow_set
  workflow_set(
    preproc = list(default = lacerta_rec),
    models = list(
      # the standard glm specs
      glm = sdm_spec_glm(),
      # the standard gam specs
      gam = sdm_spec_gam(),
      # rf specs with tuning
      rf = sdm_spec_rf()
    ),
    # make all combinations of preproc and models,
    cross = TRUE
  ) %>%
  # set formula for gams
  update_workflow_model("default_gam",
    spec = sdm_spec_gam(),
    formula = gam_formula(lacerta_rec)
  ) %>%
  # tweak controls to store information needed later to create the ensemble
  option_add(control = control_ensemble_grid())

We then create 3 folds and attempt to fit the models:

set.seed(100)
lacerta_cv <- spatial_block_cv(lacerta_thin, v = 3)
lacerta_models <-
  lacerta_models %>%
  workflow_map("tune_grid",
    resamples = lacerta_cv, grid = 3,
    metrics = sdm_metric_set(), verbose = TRUE
  )
#> i  No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 3 resampling: default_glm
#>  1 of 3 resampling: default_glm (187ms)
#> i  No tuning parameters. `fit_resamples()` will be attempted
#> i 2 of 3 resampling: default_gam
#> → A | warning: Fitting terminated with step failure - check results carefully
#> There were issues with some computations   A: x1
#> There were issues with some computations   A: x1
#> 
#>  2 of 3 resampling: default_gam (1.6s)
#> i 3 of 3 tuning:     default_rf
#> i Creating pre-processing data to finalize unknown parameter: mtry
#>  3 of 3 tuning:     default_rf (466ms)

We see that one of the folds gives us an error when using GAMs. The error (“Fitting terminated with step failure - check results carefully”) comes from the gam function in the package mgcv. A quick google on StackOverflow[https://stats.stackexchange.com/questions/576273/gam-model-warning-message-step-failure-in-theta-estimation] gives us an idea of where this error comes from.

We start by extracting the results of the gam fits:

gam_results <- extract_workflow_set_result(lacerta_models, id = "default_gam")
gam_results
#> # Resampling results
#> # 3-fold spatial block cross-validation 
#> # A tibble: 3 × 5
#>   splits          id    .metrics         .notes           .predictions     
#>   <list>          <chr> <list>           <list>           <list>           
#> 1 <split [54/36]> Fold1 <tibble [3 × 4]> <tibble [0 × 3]> <tibble [36 × 5]>
#> 2 <split [63/27]> Fold2 <tibble [3 × 4]> <tibble [1 × 3]> <tibble [27 × 5]>
#> 3 <split [64/26]> Fold3 <tibble [3 × 4]> <tibble [0 × 3]> <tibble [26 × 5]>
#> 
#> There were issues with some computations:
#> 
#>   - Warning(s) x1: Fitting terminated with step failure - check results carefully
#> 
#> Run `show_notes(.Last.tune.result)` for more information.

We see that, in the .notes column, the second item is not empty (it does not have zero rows). We can check that it indeed contains the error that we wanted:

gam_results$.notes[2]
#> [[1]]
#> # A tibble: 1 × 3
#>   location                    type    note                                      
#>   <chr>                       <chr>   <chr>                                     
#> 1 preprocessor 1/1, model 1/1 warning Fitting terminated with step failure - ch…

We can now get the problematic data split, and extract the training data:

problem_split <- gam_results$splits[2][[1]]
summary(training(problem_split))
#>        class             geometry      bio01           bio02       
#>  presence :18   POINT        :63   Min.   : 4.74   Min.   : 6.737  
#>  pseudoabs:45   epsg:4326    : 0   1st Qu.:11.81   1st Qu.: 9.336  
#>                 +proj=long...: 0   Median :13.09   Median :10.937  
#>                                    Mean   :12.88   Mean   :11.052  
#>                                    3rd Qu.:14.82   3rd Qu.:12.649  
#>                                    Max.   :17.87   Max.   :14.037  
#>      bio03           bio04           bio05           bio06        
#>  Min.   :34.30   Min.   :341.2   Min.   :19.90   Min.   :-6.2732  
#>  1st Qu.:39.30   1st Qu.:500.8   1st Qu.:24.91   1st Qu.:-0.6787  
#>  Median :40.55   Median :610.8   Median :28.59   Median : 1.1918  
#>  Mean   :40.54   Mean   :584.6   Mean   :28.57   Mean   : 1.2175  
#>  3rd Qu.:42.19   3rd Qu.:656.1   3rd Qu.:32.31   3rd Qu.: 3.5664  
#>  Max.   :46.98   Max.   :756.7   Max.   :35.31   Max.   : 8.2344  
#>      bio07           bio08            bio09            bio10      
#>  Min.   :16.40   Min.   : 1.922   Min.   : 1.588   Min.   :12.86  
#>  1st Qu.:23.32   1st Qu.: 7.716   1st Qu.:16.995   1st Qu.:18.53  
#>  Median :27.88   Median : 9.668   Median :19.828   Median :20.51  
#>  Mean   :27.35   Mean   : 9.450   Mean   :18.938   Mean   :20.48  
#>  3rd Qu.:31.49   3rd Qu.:11.341   3rd Qu.:22.607   3rd Qu.:23.08  
#>  Max.   :35.27   Max.   :16.882   Max.   :25.470   Max.   :25.71  
#>      bio11            bio12            bio13           bio14      
#>  Min.   :-2.060   Min.   : 249.0   Min.   : 36.0   Min.   : 2.00  
#>  1st Qu.: 4.968   1st Qu.: 452.0   1st Qu.: 59.0   1st Qu.: 8.00  
#>  Median : 6.236   Median : 628.0   Median : 91.0   Median :17.00  
#>  Mean   : 6.268   Mean   : 757.8   Mean   :101.5   Mean   :21.97  
#>  3rd Qu.: 8.455   3rd Qu.:1016.5   3rd Qu.:119.0   3rd Qu.:30.50  
#>  Max.   :11.795   Max.   :1622.0   Max.   :248.0   Max.   :74.00  
#>      bio15           bio16           bio17            bio18      
#>  Min.   :13.44   Min.   : 96.0   Min.   : 17.00   Min.   : 22.0  
#>  1st Qu.:30.07   1st Qu.:157.0   1st Qu.: 43.00   1st Qu.: 47.0  
#>  Median :38.97   Median :249.0   Median : 71.00   Median : 78.0  
#>  Mean   :41.58   Mean   :280.3   Mean   : 88.08   Mean   : 96.0  
#>  3rd Qu.:54.30   3rd Qu.:334.0   3rd Qu.:109.50   3rd Qu.:117.5  
#>  Max.   :71.59   Max.   :714.0   Max.   :253.00   Max.   :253.0  
#>      bio19          altitude     
#>  Min.   : 68.0   Min.   :  38.0  
#>  1st Qu.:128.5   1st Qu.: 319.5  
#>  Median :225.0   Median : 689.0  
#>  Mean   :252.5   Mean   : 685.5  
#>  3rd Qu.:319.5   3rd Qu.: 855.0  
#>  Max.   :714.0   Max.   :1926.0

In this case, there is nothing too obvious that leads to the error (an important check is to make sure that you have enough presences in a split; too few presences will generally lead to errors).

We can now extract the workflow and refit it to the split to confirm that we have isolated the problem:

gam_workflow <- extract_workflow(lacerta_models, id = "default_gam")
faulty_gam <- fit(gam_workflow, training(problem_split))
#> Warning in newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L,
#> : Fitting terminated with step failure - check results carefully

The next step would be to dig deeper into the data, trying to understand whether there are some outliers that are problematic. The specific steps will depend on the algorithm that is giving problems.