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Prep interval and then produce prediction interval on a new data set. Not confident these are set-up correctly... see thread: https://community.rstudio.com/t/prediction-intervals-with-tidymodels-best-practices/82594/15
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library(tidyverse) | |
library(tidymodels) | |
# Control function used as part of `prep_interval()` | |
ctrl_fit_recipe <- function(x){ | |
list(fit = workflows::pull_workflow_fit(x), | |
recipe = workflows::pull_workflow_prepped_recipe(x) | |
) | |
} | |
#' extract parts of control function and output as dataframe with list-cols | |
#' @param wf_extracts A resample_results object with a column for .extracts | |
#' that was created using `ctrl_fit_recipe()` | |
extract_extracts <- function(wf_extracts){ | |
wf_extracts %>% | |
select(.extracts) %>% | |
transmute(.extracts = map(.extracts, ".extracts") %>% map(1)) %>% | |
unnest_wider(col = .extracts, strict = TRUE) | |
} | |
#' Prep Interval | |
#' | |
#' This function takes in a workflow and outputs a named list meant ot be passed | |
#' into `predict_interval()`. | |
#" | |
#' @param wf Workflow containing a recipe and a model. | |
#' @param train Training data. | |
#' @param n_boot Number of bootstrap samples used to simulate building the model | |
#' (used for uncertainty due to model). | |
#' @param n_cv Number of folds for k-fold cross-validation (used for uncertainty | |
#' due to sample). If is NULL, will use out-of-bag predictions from | |
#' bootstrapped samples used to build-models. | |
#' @retrun A named list of two tibbles named `model_uncertainty` and | |
#' `sample_uncertainty`. | |
prep_interval <- function(wf, train, n_boot = sqrt(nrow(train)), n_cv = 10){ | |
##### Uncertainty due to model specification ##### | |
ctrl <- control_resamples(extract = ctrl_fit_recipe, save_pred = TRUE) | |
resamples_boot <- rsample::bootstraps(train, n_boot) | |
wf_model_uncertainty <- wf %>% | |
fit_resamples(resamples_boot, control = ctrl) | |
model_uncertainty <- extract_extracts(wf_model_uncertainty) | |
##### Uncertainty due to sample ###### | |
if(is.null(n_cv)){ | |
wf_sample_uncertainty <- wf_model_uncertainty | |
} else{ | |
resamples_cv <- rsample::vfold_cv(train, 10) | |
ctrl_cv <- control_resamples(save_pred = TRUE) | |
wf_sample_uncertainty <- wf %>% | |
fit_resamples(resamples_cv, control = ctrl_cv) | |
} | |
sample_uncertainty <- wf_sample_uncertainty %>% | |
select(.predictions) %>% | |
unnest(.predictions) %>% | |
mutate(.resid = .pred - cur_data()[[3]]) %>% | |
select(.resid) | |
list( | |
model_uncertainty = model_uncertainty, | |
sample_uncertainty = sample_uncertainty | |
) | |
} | |
#' Predict Interval | |
#' | |
#' This function takes in the output from `predict_interval()` along with an | |
#' unprepped hold-out dataset and outputs a prediction interval. | |
#' | |
#' @param prepped_interval Object outputted by `prep_interval()`. | |
#' @param new_data Data to generate predictions on. | |
#' @param probs Quantiles of predictions to output. | |
#' @param cross If TRUE makes distribution for selecting quantiles from made up | |
#' of all possible combinations of samples of {model fitting uncertainty} and | |
#' {sample uncertainty}. If FALSE, create `n_sims` number of simulations for | |
#' each sample. | |
#' @param n_sims Number of simulations for each observation (if `cross` == TRUE, | |
#' is ignored). | |
#' @retrun A tibble containing columns `probs_*` at each quantile specified by `probs`. | |
predict_interval <- function(prepped_interval, new_data, probs = c(0.025, 0.50, 0.975), cross = TRUE, n_sims = 10000){ | |
model_uncertainty <- prepped_interval$model_uncertainty %>% | |
mutate(assessment = map2(fit, recipe, | |
~predict(.x, bake(.y, new_data = new_data)) %>% | |
mutate(.id = row_number())) | |
) %>% | |
select(assessment) %>% | |
unnest(assessment) %>% | |
group_by(.id) %>% | |
mutate(m = .pred - mean(.pred)) %>% | |
ungroup() | |
purrr::when(cross, | |
. ~ crossing(model_uncertainty, prepped_interval$sample_uncertainty), | |
~ bind_cols( | |
slice_sample(model_uncertainty, n = n_sims * nrow(new_data), replace = TRUE), | |
slice_sample(prepped_interval$sample_uncertainty, n = n_sims * nrow(new_data), replace = TRUE) | |
) | |
) %>% | |
mutate(c = m + .resid + .pred) %>% | |
group_by(.id) %>% | |
summarise(qs = quantile(c, probs), | |
probs = format(probs, nsmall = 2)) %>% | |
ungroup() %>% | |
pivot_wider(names_from = probs, | |
values_from = qs, | |
names_prefix = "probs_") %>% | |
select(-.id) | |
} |
A prior version of this gist had a bug in purrr::when()
step such that even if cross = TRUE
the sim based approach was used.
Updated this today so it should now work (had been a small change to unnest_wider() that caused it to error).
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Method based on 0.632+ adjustment rather than CV for sample uncertainty found here: https://gist.github.com/brshallo/4053df78265ab9d77f753d95f5faaf5b