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ale_core.R
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ale_core.R
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##' ale_core.R
#'
#' Core functions for the ale package: ale, ale_ixn, and ale_core
#'
#' @title Create and return ALE data, statistics, and plots
#'
#' @description
#' `ale()` is the central function that manages the creation of ALE data and plots
#' for one-way ALE. For two-way interactions, see [ale_ixn()]. This function calls
#' `ale_core` (a non-exported function) that manages the ALE data and plot creation in detail. For details, see
#' the introductory vignette for this package or the details and examples below.
#'
#'
#' @section Custom predict function:
#' The calculation of ALE requires modifying several values of the original
#' `data`. Thus, `ale()` needs direct access to a `predict` function that work on
#' `model`. By default, `ale()` uses a generic default `predict` function of the form
#' `predict(object, newdata, type)` with the default prediction type of 'response'.
#' If, however, the desired prediction values are not generated with that format,
#' the user must specify what they want. Most of the time, the only modification needed is
#' to change the prediction type to some other value by setting the `pred_type` argument
#' (e.g., to 'prob' to generated classification probabilities). But if the desired
#' predictions need a different function signature, then the user must create a
#' custom prediction function and pass it to `pred_fun`. The requirements for this
#' custom function are:
#'
#' * It must take three required arguments and nothing else:
#' * `object`: a model
#' * `newdata`: a dataframe or compatible table type
#' * `type`: a string; it should usually be specified as `type = pred_type`
#' These argument names are according to the R convention for the
#' generic stats::predict function.
#' * It must return a vector of numeric values as the prediction.
#'
#' You can see an example below of a custom prediction function.
#'
#' **Note:** `survival` models probably do not need a custom prediction function
#' but `y_col` must be set to the name of the binary event column and
#' `pred_type` must be set to the desired prediction type.
#'
#'
#' @section ALE statistics:
#' For details about the ALE-based statistics (ALED, ALER, NALED, and NALER), see
#' `vignette('ale-statistics')`.
#'
#'
#' @section Parallel processing:
#' Parallel processing using the `{furrr}` library is enabled by default. By default,
#' it will use all the available physical
#' CPU cores (minus the core being used for the current R session) with the setting
#' `parallel = parallel::detectCores(logical = FALSE) - 1`. Note that only
#' physical cores are used (not logical cores or "hyperthreading") because
#' machine learning can only take advantage of the floating point processors on
#' physical cores, which are absent from logical cores. Trying to use logical
#' cores will not speed up processing and might actually slow it down with useless
#' data transfer. If you will dedicate
#' the entire computer to running this function (and you don't mind everything
#' else becoming very slow while it runs), you may use all cores by setting
#' `parallel = parallel::detectCores(logical = FALSE)`. To disable parallel
#' processing, set `parallel = 0`.
#'
#'
#' @section Progress bars:
#' Progress bars are implemented with the `{progressr}` package, which lets
#' the user fully control progress bars. **To disable progress bars, set `silent = TRUE`.**
#' The first time a function is called in
#' the `{ale}` package that requires progress bars, it checks if the user has
#' activated the necessary `{progressr}` settings. If not, the `{ale}` package
#' automatically enables `{progressr}` progress bars with the `cli` handler and
#' prints a message notifying the user.
#'
#' If you like the default progress bars and you want to make them permanent, then you
#' can add the following lines of code to your `.Rprofile` configuration file
#' and they will become your defaults for every R session; you will not see the
#' message again:
#' ```R
#' progressr::handlers(global = TRUE)
#' progressr::handlers('cli')
#' ```
#' For more details on formatting progress bars to your liking, see the introduction
#' to the [`{progressr}` package](https://progressr.futureverse.org/articles/progressr-intro.html).
#'
#'
#'
#'
#' @export
#'
#' @references Okoli, Chitu. 2023.
#' “Statistical Inference Using Machine Learning and Classical Techniques Based
#' on Accumulated Local Effects (ALE).” arXiv. <https://arxiv.org/abs/2310.09877>.
#'
#'
#' @param data dataframe. Dataset from which to create predictions for the ALE.
#' @param model model object. Model for which ALE should be calculated.
#' May be any kind of R object that can make predictions from data.
#' @param x_cols character. Vector of column names from `data` for which
#' one-way ALE data is to be calculated (that is, simple ALE without interactions).
#' If not provided, ALE will be created for all columns in `data` except `y_col`.
#' @param y_col character length 1. Name of the outcome target label (y) variable.
#' If not provided, `ale()` will try to detect it automatically. For non-standard
#' models, `y_col` should be provided. For survival models, set `y_col` to the
#' name of the binary event column; in that case, `pred_type` should also be specified.
#' @param ... not used. Inserted to require explicit naming of subsequent arguments.
#' @param parallel non-negative integer length 1. Number of parallel threads
#' (workers or tasks) for parallel execution of the function. See details.
#' @param model_packages character. Character vector of names of
#' packages that `model` depends on that might not be obvious.
#' The `{ale}` package should be able to automatically recognize and load most
#' packages that are needed, but with parallel processing enabled (which is the
#' default), some packages might not be properly loaded. If you get a strange error
#' message that mentions something somewhere about 'future', try adding the
#' package for your model to this vector, especially if you see such errors after
#' the progress bars begin displaying (assuming you did not disable progress bars
#' with `silent = TRUE`).
#' @param output character in c('plots', 'data', 'stats', 'conf_regions'). Vector of types of results to return.
#' 'plots' will return an ALE plot; 'data' will return the source ALE data;
#' 'stats' will return ALE statistics. Each option must be listed to return the
#' specified component. By default, all are returned.
#' @param pred_fun,pred_type function,character length 1. `pred_fun` is a function that
#' returns a vector of predicted values of type `pred_type` from `model` on `data`.
#' See details.
#' @param p_values instructions for calculating p-values and to determine the
#' median band. If `NULL` (default), no p-values are calculated and
#' `median_band_pct` is used to determine the median band.
#' To calculate p-values, an object generated by the
#' [create_p_funs()] function must be provided here. If `p_values` is set to 'auto',
#' this `ale()` function will try to automatically create the p-values function;
#' this only works with standard R model types. Any error message will be given
#' if p-values cannot be generated. Any other input provided to this argument
#' will result in an error. For more details about creating p-values,
#' see documentation for [create_p_funs()]. Note that p-values will not be
#' generated if 'stats' are not included as an option in the `output` argument.
#' @param p_alpha numeric length 2 from 0 to 1. Alpha for "confidence interval" ranges
#' for printing bands around the median for single-variable plots. These are the
#' default values used if `p_values` are provided. If `p_values` are not provided,
#' then `median_band_pct` is used instead.
#' The inner band range will be the median value of y ± `p_alpha[2]` of the relevant
#' ALE statistic (usually ALE range or normalized ALE range).
#' For plots with a second outer band, its range will be the median ± `p_alpha[1]`.
#' For example, in the ALE plots, for the default `p_alpha = c(0.01, 0.05)`,
#' the inner band will be the median ± ALE minimum or maximum at p = 0.05 and
#' the outer band will be the median ± ALE minimum or maximum at p = 0.01.
#' @param x_intervals positive integer length 1. Maximum number of intervals on the x-axis
#' for the ALE data for each column in `x_cols`. The number of intervals that the algorithm generates
#' might eventually be fewer than what the user specifies if the data values for
#' a given x value do not support that many intervals.
#' @param boot_it non-negative integer length 1. Number of bootstrap iterations for the
#' ALE values. If `boot_it = 0` (default), then ALE will be calculated on the entire dataset
#' with no bootstrapping.
#' @param seed integer length 1. Random seed. Supply this between runs to assure that
#' identical random ALE data is generated each time
#' @param boot_alpha numeric length 1 from 0 to 1. Alpha for percentile-based confidence
#' interval range for the bootstrap intervals; the bootstrap confidence intervals
#' will be the lowest and highest `(1 - 0.05) / 2` percentiles. For example,
#' if `boot_alpha = 0.05` (default), the intervals will be from the 2.5 and 97.5
#' percentiles.
#' @param boot_centre character length 1 in c('mean', 'median'). When bootstrapping, the
#' main estimate for `ale_y` is considered to be `boot_centre`. Regardless of the
#' value specified here, both the mean and median will be available.
#' @param relative_y character length 1 in c('median', 'mean', 'zero'). The ale_y values will
#' be adjusted relative to this value. 'median' is the default. 'zero' will maintain the
#' default of [ALEPlot::ALEPlot()], which is not shifted.
#' @param y_type character length 1. Datatype of the y (outcome) variable.
#' Must be one of c('binary', 'numeric', 'multinomial', 'ordinal'). Normally
#' determined automatically; only provide for complex non-standard models that
#' require it.
#' @param median_band_pct numeric length 2 from 0 to 1. Alpha for "confidence interval" ranges
#' for printing bands around the median for single-variable plots. These are the
#' default values used if `p_values` are not provided. If `p_values` are provided,
#' then `median_band_pct` is ignored.
#' The inner band range will be the median value of y ± `median_band_pct[1]/2`.
#' For plots with a second outer band, its range will be the median ± `median_band_pct[2]/2`.
#' For example, for the default `median_band_pct = c(0.05, 0.5)`, the inner band
#' will be the median ± 2.5% and the outer band will be the median ± 25%.
#' @param rug_sample_size,min_rug_per_interval single non-negative integer length 1.
#' Rug plots are normally
#' down-sampled otherwise they are too slow. `rug_sample_size` specifies the size
#' of this sample. To prevent down-sampling, set to `Inf`. To suppress rug plots,
#' set to 0. When down-sampling, the rug plots maintain representativeness of the
#' data by guaranteeing that each of the `x_intervals` intervals will retain at least
#' `min_rug_per_interval` elements; usually set to just 1 or 2.
#' @param ale_xs,ale_ns list of ale_x and ale_n vectors. If provided, these vectors will be used to
#' set the intervals of the ALE x axis for each variable. By default (NULL), the
#' function automatically calculates the ale_x intervals. `ale_xs` is normally used
#' in advanced analyses where the ale_x intervals from a previous analysis are
#' reused for subsequent analyses (for example, for full model bootstrapping;
#' see the [model_bootstrap()] function).
#' @param compact_plots logical length 1, default `FALSE`. When `output` includes
#' 'plots', the returned `ggplot` objects each include the environments of the plots.
#' This lets the user modify the plots with all the flexibility of `ggplot`, but it
#' can result in very large return objects (sometimes even hundreds of megabytes
#' large). To compact the plots to their bare minimum, set `compact_plots = TRUE`.
#' However, returned plots will not be easily modifiable, so this should only be
#' used if you do not want to subsequently modify the plots.
#' @param silent logical length 1, default `FALSE.` If `TRUE`, do not display any
#' non-essential messages during execution (such as progress bars).
#' Regardless, any warnings and errors will always display. See details for how
#' to enable progress bars.
#'
#'
#' @return list with the following elements:
#' * `data`: a list whose elements, named by each requested x variable, are each
#' a tibble with the following columns:
#' * `ale_x`: the values of each of the ALE x intervals or categories.
#' * `ale_n`: the number of rows of data in each `ale_x` interval or category.
#' * `ale_y`: the ALE function value calculated for that interval or category.
#' For bootstrapped ALE, this is the same as `ale_y_mean` by default
#' or `ale_y_median` if the `boot_centre = 'median'` argument is specified.
#' Regardless, both `ale_y_mean` and `ale_y_median` are returned as columns here.
#' * `ale_y_lo`, `ale_y_hi`: the lower and upper confidence intervals, respectively,
#' for the bootstrapped `ale_y` value.
#' Note: regardless what options are requested in the `output` argument, this
#' `data` element is always returned.
#' * `stats`: if `stats` are requested in the `output` argument (as is the default),
#' returns a list. If not requested, returns `NULL`. The returned list provides
#' ALE statistics of the `data` element duplicated and presented from various
#' perspectives in the following elements:
#' * `by_term`: a list named by each requested x variable, each of whose elements
#' is a tibble with the following columns:
#' * `statistic`: the ALE statistic specified in the row (see
#' the `by_statistic` element below).
#' * `estimate`: the bootstrapped `mean` or `median` of the `statistic`,
#' depending on the `boot_centre` argument to the [ale()] function.
#' Regardless, both `mean` and `median` are returned as columns here.
#' * `conf.low`, `conf.high`: the lower and upper confidence intervals,
#' respectively, for the bootstrapped `estimate`.
#' * `by_statistic`: list named by each of the following ALE statistics:
#' `aled`, `aler_min`, `aler_max`, `naled`, `naler_min`, `naler_max`. See
#' `vignette('ale-statistics')` for details.
#' * `estimate`: a tibble whose data consists of the `estimate` values from the
#' `by_term` element above. The columns are `term` (the variable name) and the
#' statistic for which the estimate is given:
#' `aled`, `aler_min`, `aler_max`, `naled`, `naler_min`, `naler_max`.
#' * `effects_plot`: a `ggplot` object which is the ALE effects plot for all the
#' x variables.
#' * `plots`: if `plots` are requested in the `output` argument (as is the default),
#' returns a list whose elements, named by each requested x variable, are each
#' a `ggplot` object of the ALE y values plotted against the x variable intervals.
#' If `plots` is not included in `output`, this element is `NULL`.
#' * `conf_regions`: if `conf_regions` are requested in the `output` argument (as is the default),
#' returns a list. If not requested, returns `NULL`. The returned list provides
#' summaries of the confidence regions of the relevant ALE statistics of the `data`
#' element.
#' The list has the following elements:
#' * `by_term`: a list named by each requested x variable, each of whose elements
#' is a tibble with the relevant data for the confidence regions.
#' (See `vignette('ale-statistics')` for details about confidence regions.)
#' * `significant`: a tibble that summarizes the `by_term` to only show confidence
#' regions that are statistically significant. Its columns are those from
#' `by_term` plus a `term` column to specify which x variable is indicated
#' by the respective row.
#' * `sig_criterion`: a length-one character vector that reports which values
#' were used to determine statistical significance: if `p_values` was
#' provided to the [ale()] function, it will be used; otherwise,
#' `median_band_pct` will be used.
#' * Various values echoed from the original call to the [ale()] function, provided
#' to document the key elements used to calculate the ALE data, statistics, and plots:
#' `y_col`, `x_cols`, `boot_it`, `seed`, `boot_alpha`, `boot_centre`, `relative_y`,
#' `y_type`, `median_band_pct`, `rug_sample_size`. These are either the values
#' provided by the user or used by default if the user did not change them.
#' * `y_summary`: summary statistics of y values used for the ALE calculation.
#' These statistics are based on the actual values of `y_col` unless if `y_type` is a
#' probability or other value that is constrained in the `[0, 1]` range. In that
#' case, `y_summary` is based on the predicted values of `y_col` by applying
#' `model` to the `data`. `y_summary` is a named numeric vector. Most of the
#' elements are the percentile of the y values. E.g., the '5%' element is the
#' 5th percentile of y values. The following elements have special meanings:
#' * The first element is named either `p` or `q` and its value is always 0.
#' The value is not used; only the name of the element is meaningful.
#' `p` means that the following special `y_summary` elements are based on
#' the provided `p_values` object. `q` means that quantiles were calculated
#' based on `median_band_pct` because `p_values` was not provided.
#' * `min`, `mean`, `max`: the minimum, mean, and maximum y values, respectively.
#' Note that the median is `50%`, the 50th percentile.
#' * `med_lo_2`, `med_lo`, `med_hi`, `med_hi_2`: `med_lo` and `med_hi` are the
#' inner lower and upper confidence intervals of y values with respect to
#' the median (`50%`); `med_lo_2` and `med_hi_2` are the outer confidence
#' intervals. See the documentation for the `p_alpha` and `median_band_pct`
#' arguments to understand how these are determined.
#'
#' @examples
# Sample 1000 rows from the ggplot2::diamonds dataset (for a simple example)
#' set.seed(0)
#' diamonds_sample <- ggplot2::diamonds[sample(nrow(ggplot2::diamonds), 1000), ]
#'
#' # Create a GAM model with flexible curves to predict diamond price
#' # Smooth all numeric variables and include all other variables
#' gam_diamonds <- mgcv::gam(
#' price ~ s(carat) + s(depth) + s(table) + s(x) + s(y) + s(z) +
#' cut + color + clarity,
#' data = diamonds_sample
#' )
#' summary(gam_diamonds)
#'
#'
#' \donttest{
#'
#' # Simple ALE without bootstrapping
#' ale_gam_diamonds <- ale(
#' diamonds_sample, gam_diamonds,
#' parallel = 2 # CRAN limit (delete this line on your own computer)
#' )
#'
#' # Plot the ALE data
#' ale_gam_diamonds$plots |>
#' patchwork::wrap_plots()
#'
#' # Bootstrapped ALE
#' # This can be slow, since bootstrapping runs the algorithm boot_it times
#'
#' # Create ALE with 100 bootstrap samples
#' ale_gam_diamonds_boot <- ale(
#' diamonds_sample, gam_diamonds, boot_it = 100,
#' parallel = 2 # CRAN limit (delete this line on your own computer)
#' )
#'
#' # Bootstrapped ALEs print with confidence intervals
#' ale_gam_diamonds_boot$plots |>
#' patchwork::wrap_plots()
#'
#'
#' # If the predict function you want is non-standard, you may define a
#' # custom predict function. It must return a single numeric vector.
#' custom_predict <- function(object, newdata, type = pred_type) {
#' predict(object, newdata, type = type, se.fit = TRUE)$fit
#' }
#'
#' ale_gam_diamonds_custom <- ale(
#' diamonds_sample, gam_diamonds,
#' pred_fun = custom_predict, pred_type = 'link',
#' parallel = 2 # CRAN limit (delete this line on your own computer)
#' )
#'
#' # Plot the ALE data
#' ale_gam_diamonds_custom$plots |>
#' patchwork::wrap_plots()
#'
#' }
#'
#'
#' @importFrom glue glue
#'
ale <- function (
data,
model,
x_cols = NULL,
y_col = NULL,
...,
parallel = parallel::detectCores(logical = FALSE) - 1,
model_packages = as.character(NA),
output = c('plots', 'data', 'stats', 'conf_regions'),
pred_fun = function(object, newdata, type = pred_type) {
stats::predict(object = object, newdata = newdata, type = type)
},
pred_type = "response",
p_values = NULL,
p_alpha = c(0.01, 0.05),
x_intervals = 100,
boot_it = 0,
seed = 0,
boot_alpha = 0.05,
boot_centre = 'mean',
relative_y = 'median',
y_type = NULL,
median_band_pct = c(0.05, 0.5),
rug_sample_size = 500,
min_rug_per_interval = 1,
ale_xs = NULL,
ale_ns = NULL,
compact_plots = FALSE,
silent = FALSE
) {
# capture all arguments passed into `ale()` (code thanks to ChatGPT)
args <- as.list(match.call())[-1]
args$ixn <- FALSE # when the user calls `ale()`, they want no interactions
do.call(ale_core, args, envir = parent.frame(1))
}
#' @title Create and return ALE interaction data, statistics, and plots
#'
#' @description
#' This is the central function that manages the creation of ALE data and plots
#' for two-way ALE interactions. For simple one-way ALE, see [ale()].
#' See documentation there for functionality shared between both functions.
#'
#' For details, see the introductory vignette for this package or the details and examples below.
#'
#' For the plots, `n_y_quant` is the number of quantiles into which to
#' divide the predicted variable (y). The middle quantiles are grouped specially:
#'
#' * The middle quantile is the first confidence interval of `median_band_pct`
#' (`median_band_pct[1]`) around the median.
#' This middle quantile is special because it generally represents no meaningful
#' interaction.
#' * The quantiles above and below the middle are extended from the borders of
#' the middle quantile to the regular borders of the other quantiles.
#'
#' There will always be an odd number of quantiles: the special middle quantile
#' plus an equal number of quantiles on each side of it. If n_y_quant is even,
#' then a middle quantile will be added to it. If n_y_quant is odd, then the
#' number specified will be used, including the middle quantile.
#'
#'
#' @param data See documentation for [ale()]
#' @param model See documentation for [ale()]
#' @param x1_cols,x2_cols character. Vectors of column names from `data` for which
#' two-way interaction ALE data is to be calculated. ALE data will be calculated
#' for each x1 column interacting with each x2 column. x1_cols can be of any standard
#' datatype (logical, factor, or numeric) but x2_cols can only be numeric. If
#' `ixn` is TRUE, then both values must be provided.
#' @param y_col See documentation for [ale()]
#' @param ... not used. Inserted to require explicit naming of subsequent arguments.
#' @param parallel See documentation for [ale()]
#' @param model_packages See documentation for [ale()]
#' @param output See documentation for [ale()]
#' @param pred_fun,pred_type See documentation for [ale()]
#' @param x_intervals See documentation for [ale()]
#' @param relative_y See documentation for [ale()]
#' @param y_type See documentation for [ale()]
#' @param median_band_pct See documentation for [ale()]
#' @param rug_sample_size,min_rug_per_interval See documentation for [ale()]
#' @param ale_xs See documentation for [ale()]
#' @param n_x1_int,n_x2_int positive scalar integer. Number of intervals
#' for the x1 or x2 axes respectively for interaction plot. These values are
#' ignored if x1 or x2 are not numeric (i.e, if they are logical or factors).
#' @param n_y_quant positive scalar integer. Number of intervals over which the range
#' of y values is divided for the colour bands of the interaction plot. See details.
#' @param compact_plots See documentation for [ale()]
#' @param silent See documentation for [ale()]
#'
#' @return list of ALE interaction data tibbles and plots.
#' The list has two levels of depth:
#' * The first level is named by the x1 variables.
#' * Within each x1 variable list, the second level is named by the x2 variables.
#' * Within each x1-x2 list element, the data or plot is returned as requested in
#' the `output` argument.
# * Within each list element, the data, plots, and stats are returned as requested in
# the `output` argument.
#'
#' @examples
#'
# Sample 1000 rows from the ggplot2::diamonds dataset (for a simple example)
#' set.seed(0)
#' diamonds_sample <- ggplot2::diamonds[sample(nrow(ggplot2::diamonds), 1000), ]
#'
#' # Create a GAM model with flexible curves to predict diamond price
#' # Smooth all numeric variables and include all other variables
#' gam_diamonds <- mgcv::gam(
#' price ~ s(carat) + s(depth) + s(table) + s(x) + s(y) + s(z) +
#' cut + color + clarity,
#' data = diamonds_sample
#' )
#' summary(gam_diamonds)
#'
#' \donttest{
#' # ALE two-way interactions
#' ale_ixn_gam_diamonds <- ale_ixn(
#' diamonds_sample, gam_diamonds,
#' parallel = 2 # CRAN limit (delete this line on your own computer)
#' )
#'
#' # Print interaction plots
#' ale_ixn_gam_diamonds$plots |>
#' # extract list of x1 ALE outputs
#' purrr::walk(\(.x1) {
#' # plot all x2 plots in each .x1 element
#' patchwork::wrap_plots(.x1) |>
#' print()
#' })
#' }
#'
#'
#' @export
#'
ale_ixn <- function (
data, model,
x1_cols = NULL, x2_cols = NULL,
y_col = NULL,
...,
parallel = parallel::detectCores(logical = FALSE) - 1,
model_packages = as.character(NA),
output = c('plots', 'data'),
pred_fun = function(object, newdata, type = pred_type) {
stats::predict(object = object, newdata = newdata, type = type)
},
pred_type = "response",
x_intervals = 100,
# boot_it = 0,
# boot_alpha = 0.05,
# boot_centre = 'mean',
relative_y = 'median',
y_type = NULL,
median_band_pct = c(0.05, 0.5),
rug_sample_size = 500,
min_rug_per_interval = 1,
ale_xs = NULL,
# ggplot_custom = NULL,
n_x1_int = 20,
n_x2_int = 20,
n_y_quant = 10,
compact_plots = FALSE,
silent = FALSE
) {
# capture all arguments passed into [ale_ixn()] (code thanks to ChatGPT)
args <- as.list(match.call())[-1]
args$ixn <- TRUE # when the user calls [ale_ixn()], they want interactions
# stats not yet enabled for ale_ixn
if (missing(output)) {
args$output = c('plots', 'data')
}
do.call(ale_core, args, envir = parent.frame(1))
}
# Create and return ALE data and plots
#
# Not exported. This is the central function that manages the creation of ALE data and plots,
# both for one-way ALE and two-way interactions. This function does not do the
# actual creation of the data or plots. It receives user inputs, preprocesses them,
# and then calls the appropriate functions to create the ALE data and plots.
# As such, this function is not normally directly called by users. So, the
# documentation details for each variable here is specified in the user-facing
# function that specifies it.
#
# @param data See documentation for [ale()]
# @param model See documentation for [ale()]
# @param ixn logical. If TRUE, `ale_core` will return interaction data between `x1_cols`
# and `x2_cols`; both must be provided; `x_cols` will be ignored.
# If FALSE (default), only one-way ALE data
# of `x_cols` (required in that case) will be created; `x1_cols` and `x2_cols` will
# be ignored.
# @param x_cols See documentation for [ale()]
# @param x1_cols,x2_cols See documentation for [ale_ixn()]
# @param y_col See documentation for [ale()]
# @param ... not used. See documentation for [ale()]
# @param parallel See documentation for [ale()]
# @param model_packages See documentation for [ale()]
# @param output See documentation for [ale()]
# @param pred_fun,pred_type See documentation for [ale()]
# @param p_values,p_alpha See documentation for [ale()]
# @param x_intervals See documentation for [ale()]
# @param boot_it See documentation for [ale()]
# @param seed See documentation for [ale()]
# @param boot_alpha See documentation for [ale()]
# @param boot_centre See documentation for [ale()]
# @param relative_y See documentation for [ale()]
# @param y_type See documentation for [ale()]
# @param median_band_pct See documentation for [ale()]
# @param rug_sample_size,min_rug_per_interval See documentation for [ale()]
# @param ale_xs See documentation for [ale()]
# @param ale_ns See documentation for [ale()]
# @param n_x1_int,n_x2_int See documentation for [ale_ixn()]
# @param n_y_quant See documentation for [ale_ixn()]
# @param compact_plots See documentation for [ale()]
# @param silent See documentation for [ale()]
#
# @import dplyr
# @import purrr
# @import assertthat
#
#
ale_core <- function (
data, model,
ixn,
x_cols = NULL, x1_cols = NULL, x2_cols = NULL,
y_col = NULL,
...,
parallel = parallel::detectCores(logical = FALSE) - 1,
model_packages = as.character(NA),
output = c('plots', 'data', 'stats', 'conf_regions'),
# pred_fun = function(object, newdata) {
pred_fun = function(object, newdata, type = pred_type) {
# stats::predict(object = object, newdata = newdata, type = pred_type)
stats::predict(object = object, newdata = newdata, type = type)
},
pred_type = "response",
p_values = NULL,
p_alpha = c(0.01, 0.05),
x_intervals = 100,
boot_it = 0,
seed = 0,
boot_alpha = 0.05,
boot_centre = 'mean',
relative_y = 'median',
y_type = NULL,
median_band_pct = c(0.05, 0.5),
rug_sample_size = 500,
min_rug_per_interval = 1,
ale_xs = NULL,
ale_ns = NULL,
# ggplot_custom = NULL,
n_x1_int = 20,
n_x2_int = 20,
n_y_quant = 10,
compact_plots = FALSE,
silent = FALSE
)
{
# Error if any unlisted argument is used (captured in ...).
# Never skip this validation step!
ellipsis::check_dots_empty()
# Validate arguments
# If model validation is done more rigorously, also validate that y_col is not
# contained in all_x__cols
# Validate the dataset
assert_that(data |> inherits('data.frame'))
# Validate the model:
# A valid model is one that, when passed to a predict function with a valid
# dataset, produces a numeric vector with length equal to the number of rows
# in the dataset.
# Note: y_preds will be used later in this function.
y_preds <- validate_y_preds(
pred_fun = pred_fun,
model = model,
data = data,
pred_type = pred_type
)
# Validate y_col.
# If y_col is NULL and model is a standard R model type, y_col can be automatically detected.
y_col <- validate_y_col(
y_col = y_col,
data = data,
model = model
)
model_packages <- validated_parallel_packages(parallel, model, model_packages)
assert_that(is.flag(ixn))
if (!is.null(x_cols)) assert_that(is.character(x_cols))
if (!is.null(x1_cols)) assert_that(is.character(x1_cols))
if (!is.null(x2_cols)) assert_that(is.character(x2_cols))
# If model validation is done more rigorously, also validate that y_col is not
# contained in all_x__cols
all_x_cols <- c(x_cols, x1_cols, x2_cols)
valid_x_cols <- all_x_cols %in% names(data)
if (!all(valid_x_cols)) {
stop(
'The following columns were not found in data: ',
paste0(all_x_cols[!valid_x_cols], collapse = ', ')
)
}
# #Later: Verify valid datatypes for all x_col
# "class(X[[x_col]]) must be logical, factor, ordered, integer, or numeric."
assert_that(
length(setdiff(output, c('plots', 'data', 'stats', 'conf_regions'))) == 0,
msg = paste0(
'The value in the output argument must be one or more of ',
'"plots", "data", "stats", or "conf_regions".'
)
)
if ('conf_regions' %in% output) {
assert_that(
'stats' %in% output,
msg = paste0(
'If "conf_regions" is requested in the output argument, ',
'then "stats" must also be requested.'
)
)
}
assert_that(is.string(pred_type))
if (!is.null(p_values)) {
# The user wants p-values
if (length(p_values) == 1 && p_values == 'auto') {
# Try to automatically obtain p-values
p_values <- create_p_funs(
data = data,
model = model,
pred_fun = pred_fun,
pred_type = pred_type
)
}
else { # a p_funs object should be provided
assert_that(
# Verify that p_values is a `p_funs` object (defined by the ale package).
p_values |> inherits('p_funs'),
# If the object structure changes in the future, verify the version number:
# e.g., numeric_version('0.2.0') <= numeric_version('0.2.20240111')
msg = glue(
'p_values is not a valid p-values model object.
See help(ale) for instructions for obtaining p-values.'
)
)
}
}
assert_that(is.natural(x_intervals) && (x_intervals > 1))
assert_that(is.whole(boot_it))
assert_that(is.number(seed))
assert_that(is.number(boot_alpha) && between(boot_alpha, 0, 1))
assert_that(
is.string(boot_centre) && (boot_centre %in% c('mean', 'median')),
msg = 'boot_centre must be one of "mean" or "median".'
)
assert_that(
is.string(relative_y) && (relative_y %in% c('median', 'mean', 'zero')),
msg = 'relative_y must be one of "median", "mean", or "zero".'
)
if (!is.null(y_type)) {
assert_that(is.string(y_type) &&
(y_type %in% c('binary', 'multinomial', 'ordinal', 'numeric')))
}
assert_that(is.string(pred_type))
if (!is.null(ale_xs)) {
map(
ale_xs,
\(.var) assert_that(
is.null(.var) || # if the variable is present, try the next two tests
is.numeric(.var) || is.factor(.var)
)
)
}
if (!is.null(ale_ns)) {
map(
ale_ns,
\(.var) assert_that(
is.null(.var) || # if the variable is present, try the next test
is.integer(.var)
)
)
}
# Validate plot-related arguments.
# If plots are not requested, then ignore these arguments.
if ('plots' %in% output) {
assert_that(
is.numeric(median_band_pct) &&
length(median_band_pct) == 2 &&
all(between(median_band_pct, 0, 1))
)
assert_that(
rug_sample_size == 0 || # 0 means no rug plots are desired
(is.natural(rug_sample_size) &&
# rug sample cannot be smaller than number of intervals
rug_sample_size > (x_intervals + 1)),
msg = 'rug_sample_size must be either 0 or
an integer larger than the number of x_intervals + 1.'
)
assert_that(is.whole(min_rug_per_interval))
assert_that(is.natural(n_x1_int))
assert_that(is.natural(n_x2_int))
assert_that(is.natural(n_y_quant))
}
validate_silent(silent)
# Determine datatype of y
if (is.null(y_type)) {
y_type <- var_type(data[[y_col]])
}
# Get list of y values depending on y_type
y_vals <-
if (y_type %in% c('numeric', 'ordinal')) {
data[[y_col]]
} else if (y_type == 'binary') {
y_preds
} else {
stop('Invalid datatype for y outcome variable: must be binary, ordinal, or numeric.')
}
# Generate summary statistics for y for plotting
y_summary <- var_summary(
var_vals = y_vals,
median_band_pct = median_band_pct,
p_funs = p_values,
p_alpha = p_alpha
)
# Calculate value to add to y to shift for requested relative_y
relative_y_shift <- case_when(
relative_y == 'median' ~ y_summary[['50%']],
relative_y == 'mean' ~ y_summary[['mean']],
relative_y == 'zero' ~ 0,
)
# Remove the Y target label; ALE calculation needs the X matrix as input;
# Y is obtained from the model predictions.
data_X <-
data |>
select(-any_of(y_col))
# If x_cols is default (NULL), set it to the names of all x variables
if (is.null(x_cols)) {
x_cols <-
data |>
names() |>
setdiff(y_col)
}
# If x1_cols is default (NULL), set it to the names of all x variables
if (is.null(x1_cols)) {
x1_cols <-
data |>
names() |>
setdiff(y_col)
}
# Eliminate all x1_cols that are not numeric
# This is only temporary, until calc_ale_ixn is rewritten to recognize
# binary and ordinal
x1_cols <-
data |>
select(any_of(x1_cols)) |>
select(where(is.numeric)) |>
names()
# If x2_cols is default (NULL), set it to the names of all x variables
if (is.null(x2_cols)) {
x2_cols <-
data |>
names() |>
setdiff(y_col)
}
# Eliminate all x2_cols that are not numeric
x2_cols <-
data |>
select(any_of(x2_cols)) |>
select(where(is.numeric)) |>
names()
# Prepare to create ALE statistics
ale_y_norm_fun <- NULL
# p_funs <- NULL
if ('stats' %in% output) {
ale_y_norm_fun <- create_ale_y_norm_function(y_vals)
}
# Enable parallel processing and set appropriate map function.
# Because furrr::future_map has an important .options argument absent from
# purrr::map, map_loop() is created to unify these two functions.
if (parallel > 0) {
future::plan(future::multisession, workers = parallel)
map_loop <- furrr::future_map
} else {
# If no parallel processing, do not set future::plan(future::sequential):
# this might interfere with other parallel processing in a larger workflow.
# Just do nothing parallel.
map_loop <- function(..., .options = NULL) {
# Ignore the .options argument and pass on everything else
purrr::map(...)
}
}
# Create list of ALE objects for all requested x variables
if (!ixn) {
# Create progress bar iterator only if not in an outer loop with ale_xs
if (!silent && is.null(ale_xs)) {
progress_iterator <- progressr::progressor(
steps = length(x_cols),
message = 'Calculating ALE'
)
}
ales <-
x_cols |>
# map(
map_loop(
.options = furrr::furrr_options(
# Enable parallel-processing random seed generation
seed = seed,
packages = model_packages
),
.f = \(x_col) {
# Increment progress bar iterator only if not in an outer loop with ale_xs
# Do not skip iterations (e.g., .it %% 10 == 0): inaccurate with parallelization
if (!silent && is.null(ale_xs)) {
progress_iterator()
}
# Calculate ale_data for single variables
ale_data_stats <-
calc_ale(
data_X, model, x_col,
pred_fun, pred_type, x_intervals,
boot_it, seed, boot_alpha, boot_centre,
ale_x = ale_xs[[x_col]],
ale_n = ale_ns[[x_col]],
ale_y_norm_fun = ale_y_norm_fun,
p_funs = p_values
)
ale_data <- ale_data_stats$summary
stats <- ale_data_stats$stats
# Shift ale_y by appropriate relative_y
ale_data <- ale_data |>
mutate(across(contains('ale_y'), \(.x) {
.x + relative_y_shift
}))
# Generate ALE plot
plot <- NULL # Start with a NULL plot
if ('plots' %in% output) { # user requested the plot
plot <- plot_ale(
ale_data, x_col, y_col, y_type,
y_summary,
relative_y = relative_y,
p_alpha = p_alpha,
median_band_pct = median_band_pct,
x_y = tibble(data[[x_col]], y_vals) |>
stats::setNames(c(x_col, y_col)),
rug_sample_size = rug_sample_size,
min_rug_per_interval = min_rug_per_interval,
compact_plots = compact_plots,
seed = seed
)
}
# Delete data if only plot was requested
if (identical(output, 'plots')) { # No data desired
ale_data <- NULL
}
list(
data = ale_data,
stats = stats,
plots = plot
)
}) |>
set_names(x_cols) |>
transpose()
}
else { # two-way interactions
# Create progress bar iterator only if not in an outer loop with ale_xs
if (!silent && is.null(ale_xs)) {
progress_iterator <- progressr::progressor(
steps = length(x1_cols) * length(x2_cols),
message = 'Calculating ALE interactions'
)
}
ales_by_var <-
x1_cols |>
# map(
map_loop(
.options = furrr::furrr_options(
# Enable parallel-processing random seed generation
seed = seed,
# Specify packages (parallel processing does not always see them easily)
packages = model_packages
),
.f = \(x1_col) {
# Calculate ale_data for two-way interactions
# Do not redo interactions that have already been done
x1_cols_before_current_x1_col <- x1_cols[1:which(x1_cols == x1_col)]
x2_cols_to_interact <- x2_cols |>
setdiff(x1_cols_before_current_x1_col)
x2_cols_to_interact |>
map(\(x2_col) {
# Increment progress bar iterator only if not in an outer loop with ale_xs
# Do not skip iterations (e.g., .it %% 10 == 0): inaccurate with parallelization
if (!silent && is.null(ale_xs)) {
progress_iterator()
}
ale_data <-
calc_ale_ixn(
data_X, model, x1_col, x2_col,
pred_fun, pred_type, x_intervals
)
# Shift ale_y by appropriate relative_y
ale_data$ale_y <- ale_data$ale_y + relative_y_shift
# Generate ALE plot
plot <- NULL # Start with a NULL plot
if ('plots' %in% output) { # user requested the plot
plot <- plot_ale_ixn(
ale_data, x1_col, x2_col, y_col, y_type,
y_summary,
y_vals,
relative_y = relative_y,
median_band_pct = median_band_pct,
n_x1_int = n_x1_int,
n_x2_int = n_x2_int,
n_y_quant = n_y_quant,
x1_x2_y = tibble(data[[x1_col]], data[[x2_col]], y_vals) |>
stats::setNames(c(x1_col, x2_col, y_col)),