2024-07-17 09:18:25.207 [info] [Positron] Connecting to R 4.3.3 kernel (pid 66302)
2024-07-17 09:18:25.207 [info] [Positron] Streaming log file: /var/folders/6c/w21prsj167b_x82q4_s45t340000gn/T/kernel-oQwqsE/kernel.log
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2024-07-17 09:18:25.207 [info] [Positron] Control socket created (count = 1)
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In preparing the most recent release of the broom package, I’ve run into
some headaches related to the large number of Suggests
that make it
very difficult to support older versions of R. I’m considering revising
the package’s approach so managing dependencies such that broom can
“support” a package’s output with including it in Suggests
.
For context on this document, see the fairness reading group summary.
The first component of fairness metrics functionality, assessment, is
completed and merged into the main
dev versions of tidymodels.
library(tidyverse)
library(parsnip)
x <- lapply(parsnip:::extensions(), require, character.only = TRUE)
#> Loading required package: baguette
#> Loading required package: censored
#> Loading required package: survival
#> Loading required package: discrim
#> Loading required package: multilevelmod
#> Loading required package: plsmod
library(tidymodels) | |
library(tidyverse) | |
library(stringr) | |
library(janitor) | |
library(doMC) | |
registerDoMC(cores = max(1, parallelly::availableCores() - 1)) | |
# data cleaning -------- | |
# we'd likely just do all this cleaning under the hood and supply | |
# the `chiburbs` result as the "initial" dataset |
# benchmarking the new parsnip release | |
library(tidymodels) | |
# with v1.0.2 ------------------------------------------------------------ | |
pak::pkg_install("tidymodels/parsnip@v1.0.2") | |
num_samples <- 10^(3:7) | |
num_resamples <- c(5, 10, 20) | |
nrow <- length(num_samples) * length(num_resamples) |
library(tidymodels)
library(cli)
The tune package has machinery to catch and log errors and warnings that occur while evaluating proposed models against resamples.
At the moment, we print those warnings/errors out one-by-one as they appear during evaluation.
Proposed modifications to the internals of tune:::check_grid
.
tune_tbl <- tune_args(workflow)
tune_params <- tune_tbl$id
if (nrow(pset) == 0L) {
msg <- c("!" = "No tuning parameters have been detected; performance will be
evaluated using the resamples with no tuning.")
library(tidymodels)
library(stacks)
library(bonsai)
tidymodels_prefer()
# regression ------------------------------------------------------------------
reg_bt <-
boost_tree(mtry = tune()) %>%