From f0031a13f710a5d752e48cc84e6d9627e4c3fbc7 Mon Sep 17 00:00:00 2001 From: Emily Riederer Date: Thu, 19 Mar 2026 21:42:31 -0500 Subject: [PATCH 1/5] add core auroc quartet --- R/auroc_quartet.R | 20 + data-raw/auroc-quartet.csv | 400001 +++++++++++++++++++++++++++++++++ data-raw/generate-roctet.R | 6 + data-raw/generate-roctet.py | 31 + data/auroc_quartet.rda | Bin 0 -> 2919588 bytes 5 files changed, 400058 insertions(+) create mode 100644 R/auroc_quartet.R create mode 100644 data-raw/auroc-quartet.csv create mode 100644 data-raw/generate-roctet.R create mode 100644 data-raw/generate-roctet.py create mode 100644 data/auroc_quartet.rda diff --git a/R/auroc_quartet.R b/R/auroc_quartet.R new file mode 100644 index 0000000..fdd7c53 --- /dev/null +++ b/R/auroc_quartet.R @@ -0,0 +1,20 @@ +#' Area under the ROC Curve (AUROC) Quartet Data +#' +#' This dataset contains 4 datasets, each with equivalent +#' values for the AUROC, a popular model evaluation metric for +#' classification problems. Each curve features a substantially +#' different AUROC curve shape with implications for other evaluation +#' methods such as lift, precision, and recall. +#' +#' AUROC curves are parameterized as one of two functional forms +#' defined by `method` (piecewise linear or the Beta distribution CDF). +#' +#' @references Riederer E (2026). _roctet_: . Python package version 0.1.0. +#' +#' @format A dataframe with 400,000 rows and 4 variables: +#' +#' * `id`: The data id number within each method +#' * `method`: The data generating mechanism ("beta" or "piecewise") +#' * `score`: The numerical prediction score +#' * `target`: The true binary outcome being predicted +"auroc_quartet" diff --git a/data-raw/auroc-quartet.csv b/data-raw/auroc-quartet.csv new file mode 100644 index 0000000..7fd8b02 --- /dev/null +++ b/data-raw/auroc-quartet.csv @@ -0,0 +1,400001 @@ +score,target,id,method +0.9996820338971453,1,1,beta +0.9990528738926949,1,1,beta +0.9992195794522248,1,1,beta +0.9991835553660647,1,1,beta +0.9991750809820671,1,1,beta +0.9998119064130688,1,1,beta +0.9999232682662933,1,1,beta +0.9992758502480452,1,1,beta +0.9998195741357288,1,1,beta +0.9998897824755868,1,1,beta +0.9995124829381677,1,1,beta +0.9992442088098787,1,1,beta +0.9998240656617592,1,1,beta +0.9992129759393145,1,1,beta +0.9997412082604952,1,1,beta 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readr::read_csv('data-raw/auroc-quartet.csv') +usethis::use_data(auroc_quartet, overwrite = TRUE) diff --git a/data-raw/generate-roctet.py b/data-raw/generate-roctet.py new file mode 100644 index 0000000..6c39b10 --- /dev/null +++ b/data-raw/generate-roctet.py @@ -0,0 +1,31 @@ +#!/usr/bin/env -S uv run --script +# /// script +# requires-python = ">=3.14" +# dependencies = [ +# "polars>=1.39.2", +# "roctet", +# ] +# +# [tool.uv.sources] +# roctet = { git = "https://github.com/emilyriederer/roctet", +# rev = "4990dd2affccfc538ec3e27cad5b5197cca5ae4e" } +# /// + +# This python script is made reproducible with in-line `uv`` dependencies +# To run this script as an executable file, install `uv` and run in terminal: +# > cd data-raw +# > chmod +x generate-roctet.py +# > ./generate-roctet.py +# Then run generate-roctet.R to convert the resulting csv to rda + +from roctet import calc_roctet +import polars as pl + +dfs_beta = calc_roctet(0.67, method="beta", n_sets=2, n_obsv=1e5) +dfs_pcws = 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