Last active
July 26, 2021 17:33
-
-
Save brshallo/4053df78265ab9d77f753d95f5faaf5b to your computer and use it in GitHub Desktop.
Prep interval and then produce prediction interval on a new data set. See thread: https://community.rstudio.com/t/prediction-intervals-with-tidymodels-best-practices/82594/15 also see prior set-up: https://gist.github.com/brshallo/3db2cd25172899f91b196a90d5980690 . The approach at this gist is similar but uses the bootstrapped residuals to produ…
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(tidymodels) | |
# Control function used as part of `prep_interval()` | |
ctrl_fit_recipe <- function(x){ | |
output <- list(fit = workflows::pull_workflow_fit(x), | |
recipe = workflows::pull_workflow_prepped_recipe(x)) | |
c(output, list(resids = | |
bind_cols( | |
pull_workflow_mold(x)$outcomes %>% set_names(".outcome"), | |
predict(output$fit, pull_workflow_mold(x)$predictors) | |
) | |
)) | |
} | |
#' 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(.extracts) | |
} | |
#' 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). | |
#' @retrun A named list of two tibbles named `model_uncertainty` and | |
#' `sample_uncertainty`. | |
prep_interval <- function(wf, train, n_boot = sqrt(nrow(train))){ | |
##### Uncertainty due to model specification ##### | |
ctrl <- control_resamples(extract = ctrl_fit_recipe, save_pred = TRUE) | |
# Could have where you input a resample specification... | |
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 ##### | |
# Am setting-up weighting here between residuals on analysis versus assessment sets | |
analysis_preds <- model_uncertainty %>% | |
select(resids) %>% | |
unnest() | |
no_info <- select(analysis_preds, .outcome) %>% | |
bind_cols(analysis_preds[sample(nrow(analysis_preds)), ".pred"]) %>% | |
transmute(.resid = .pred - .outcome) | |
analysis_resids <- analysis_preds %>% | |
transmute(.resid = .pred - .outcome) | |
assessment_resids <- wf_model_uncertainty %>% | |
select(.predictions) %>% | |
unnest(.predictions) %>% | |
mutate(.resid = .pred - cur_data()[[3]]) %>% | |
select(.resid) | |
R <- (mean(abs(assessment_resids$.resid)) - mean(abs(analysis_resids$.resid))) / (mean(abs(no_info$.resid)) - mean(abs(analysis_resids$.resid))) | |
W <- 0.632 / (1 - 0.368 * R) | |
quantiles <- seq(from = 0, to = 1, length.out = nrow(train)) | |
quantiles <- quantiles[c(-1, -length(quantiles))] | |
sample_uncertainty <- (1 - W) * quantile(analysis_resids$.resid, probs = quantiles) + W * quantile(assessment_resids$.resid, probs = quantiles) | |
sample_uncertainty <- tibble(.resid = sample_uncertainty) | |
# Outputs | |
model_uncertainty <- model_uncertainty %>% | |
select(-resids) | |
list( | |
model_uncertainty = model_uncertainty, | |
sample_uncertainty = sample_uncertainty | |
) | |
} | |
#' Predict Interval | |
#' | |
#' This function takes in the output from `predict_interval()` along with an | |
#' unprepped hold-out dataset and outputs a prediction interval. | |
#' | |
#' @param prepped_interval Object outputted by `prep_interval()`. | |
#' @param new_data Data to generate predictions on. | |
#' @param probs Quantiles of predictions to output. | |
#' @param cross If TRUE makes distribution for selecting quantiles from made up | |
#' of all possible combinations of samples of {model fitting uncertainty} and | |
#' {sample uncertainty}. If FALSE, create `n_sims` number of simulations for | |
#' each sample. | |
#' @param n_sims Number of simulations for each observation (if `cross` == TRUE, | |
#' is ignored). | |
#' @retrun A tibble containing columns `probs_*` at each quantile specified by `probs`. | |
predict_interval <- function(prepped_interval, new_data, probs = c(0.025, 0.50, 0.975), cross = TRUE, n_sims = 10000){ | |
model_uncertainty <- prepped_interval$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) | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
A prior version of this gist had a bug in
purrr::when()
step such that even ifcross = TRUE
the sim based approach was used.