---
title: "Troubleshooting models that fail"
output: rmarkdown::html_vignette
#output: rmarkdown::pdf_document
vignette: >
  %\VignetteIndexEntry{Troubleshooting models that fail}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
# set options to limit threads used by imported libraries
options(cores=2)
options(mc.cores=2)
# xgboost uses data.table
data.table::setDTthreads(2)
```

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:
```{r}
library(tidysdm)
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
```{r}
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())
```

```{r error=TRUE}
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
  )
```

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:
```{r}
lacerta_prep <- lacerta_rec %>% prep(lacerta_thin)
lacerta_prep
```

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`:
```{r}
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:
```{r error=TRUE}
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
  )
```
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:

```{r}
lacerta_prep_sel <- lacerta_rec_sel %>% prep(lacerta_thin)
lacerta_prep_sel
```

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.

```{r}
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())
```


```{r}
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
  )
```

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:

```{r}
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:

```{r}
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
  )
```

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:

```{r}
gam_results <- extract_workflow_set_result(lacerta_models, id = "default_gam")
gam_results
```

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:

```{r}
gam_results$.notes[2]
```

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

```{r}
problem_split <- gam_results$splits[2][[1]]
summary(training(problem_split))
```

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:

```{r}
gam_workflow <- extract_workflow(lacerta_models, id = "default_gam")
faulty_gam <- fit(gam_workflow, training(problem_split))
```

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.
