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name: Documentation | |
on: | |
push: | |
branches: | |
- main | |
- master | |
tags: '*' | |
pull_request: |
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#' Get SHAPs from a trained xgboost model | |
#' @param model trained xgboost model | |
#' @param data new data for calculating shaps | |
get_shaps <- function(model, data) { | |
p = predict(model, data, | |
predcontrib = T, approxcontrib = F) | |
p = p[, -ncol(p)] | |
shaps = reshape2::melt(p) | |
vars = reshape2::melt(data) |
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`%=%` <- function(l, r) { | |
l <- trimws(strsplit(l, ",", TRUE)[[1]]) | |
if (all(l == "USE.NAMES") || all(l == "*") || all(l == "?")) { | |
stopifnot(length(names(r)) > 0) | |
l <- names(r) | |
} else { | |
stopifnot(identical(length(l), length(r))) | |
which <- l == "?" | |
if (any(which)) { | |
stopifnot(length(names(r)) > 0) |
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raincloudplot <- function(data, y, fill, x = fill, cols = NULL, | |
boxplots = c("below", "in", "none")) { | |
require(dplyr) | |
require(ggplot2) | |
source("https://raw.githubusercontent.com/datavizpyr/data/master/half_flat_violinplot.R") | |
boxplots <- match.arg(boxplots) | |
p <- ggplot(data, aes_string(y = y, x = x, fill = fill)) + |
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## load ggplot2 for autoplots | |
library(ggplot2) | |
## critical difference diagrams for IGS | |
autoplot(bma, meas = "graf", type = "cd", ratio = 1/3, p.value = 0.1) |
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library(mlr3benchmark) | |
## create mlr3benchmark object | |
bma <- as.BenchmarkAggr(bm) | |
## run global Friedman test | |
bma$friedman_test() |
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## select holdout as the resampling strategy | |
resampling <- rsmp("cv", folds = 3) | |
## add KM and CPH | |
learners <- c(learners, lrns(c("surv.kaplan", "surv.coxph"))) | |
design <- benchmark_grid(tasks, learners, resampling) | |
bm <- benchmark(design) | |
## Aggreggate with Harrell's C and Integrated Graf Score | |
msrs <- msrs(c("surv.cindex", "surv.graf")) |
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library(mlr3pipelines) | |
create_pipeops <- function(learner) { | |
po("encode") %>>% po("scale") %>>% po("learner", learner) | |
} | |
## apply our function | |
learners <- lapply(learners, create_pipeops) |
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## learners are stored in mlr3extralearners | |
library(mlr3extralearners) | |
## load learners | |
learners <- lrns( | |
paste0("surv.", c("coxtime", "deephit", "deepsurv", "loghaz", "pchazard")), | |
frac = 0.3, early_stopping = TRUE, epochs = 10, optimizer = "adam" | |
) | |
# apply our function |
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library(mlr3tuning) | |
create_autotuner <- function(learner) { | |
AutoTuner$new( | |
learner = learner, | |
search_space = search_space, | |
resampling = rsmp("holdout"), | |
measure = msr("surv.cindex"), | |
terminator = trm("evals", n_evals = 2), | |
tuner = tnr("random_search") |
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