Check results for 'mlr3fairness'

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checking running R code from vignettes ... [1s/2s] NONE ‘debiasing-vignette.Rmd’ using ‘UTF-8’... [0s/0s] OK ‘measures-vignette.Rmd’ using ‘UTF-8’... [0s/1s] OK ‘reports-vignette.Rmd’ using ‘UTF-8’... [0s/0s] OK ‘visualization-vignette.Rmd’ using ‘UTF-8’... [0s/0s] OK
checking re-building of vignette outputs ... [91s/181s] ERROR Error(s) in re-building vignettes: --- re-building ‘debiasing-vignette.Rmd’ using rmarkdown Pandoc is required to build R Markdown vignettes but not available. Please make sure it is installed. 2023-07-10 23:46:26.428 R[80044:2290430804] XType: Using static font registry. Warning in file.info(x, extra_cols = FALSE) : expanded path length 4561 would be too long for <h1 id="introduction-fairness-pipeline-operators">Introduction: Fairness Pipeline Operators</h1> <p>Given we detected some form of bias during bias auditing, we are often interested in obtaining fair(er) models. There are several ways to achieve this, such as collecting additional data or finding and fixing errors in the data. Assuming there are no biases in the data and labels, one other option is to debias models using either <strong>preprocessing</strong>, <strong>postprocessing</strong> and <strong>inprocessing</strong> methods. <code>mlr3fairness</code> provides some operators as <code>PipeOp</code>s for <code>mlr3pipelines</code>. If you are not familiar with <code>mlr3pipelines</code>, the <a href="https://mlr3book.mlr-org.com/pipelines.html">mlr3 book</a> contains an introduction.</p> <p>We again showcase debiasing using the <code>adult_train</code> task:</p> <pre><code class="language-r">library(mlr3) library(mlr3fairness) librar [... truncated] --- finished re-building ‘debiasing-vignette.Rmd’ --- re-building ‘measures-vignette.Rmd’ using rmarkdown Pandoc is required to build R Markdown vignettes but not available. Please make sure it is installed. Warning in file.info(x, extra_cols = FALSE) : expanded path length 17990 would be too long for <h1 id="fairness-measures">Fairness Measures</h1> <p>Fairness measures (or metrics) allow us to assess and audit for possible biases in a trained model. There are several types of metrics that are widely used in order to assess a model’s fairness. They can be coarsely classified into three groups:</p> <ul> <li> <p><strong>Statistical Group Fairness Metrics</strong>: Given a set of predictions from our model, we assess for differences in one or multiple metrics across groups given by a <em>protected attribute</em> [@fairmlbook; @hardt2016equality].</p> </li> <li> <p><strong>Individual Fairness</strong>: Basically requires that similar people are treated similar independent of the protected attribute [@dwork2012]. We will briefly introduce individual fairness in a dedicated section below.</p> </li> <li> <p><strong>Causal Fairness Notions</strong>: An important realization in the context of Fairness is, that whether a process is fair is o [... truncated] --- finished re-building ‘measures-vignette.Rmd’ --- re-building ‘reports-vignette.Rmd’ using rmarkdown Pandoc is required to build R Markdown vignettes but not available. Please make sure it is installed. Quitting from lines 52-54 [build_modelcard_example_for_vignette] (reports-vignette.Rmd) Error: processing vignette 'reports-vignette.Rmd' failed with diagnostics: pandoc version 1.12.3 or higher is required and was not found (see the help page ?rmarkdown::pandoc_available). --- failed re-building ‘reports-vignette.Rmd’ --- re-building ‘visualization-vignette.Rmd’ using rmarkdown Pandoc is required to build R Markdown vignettes but not available. Please make sure it is installed. 2023-07-10 23:48:42.299 R[18209:2290505996] XType: Using static font registry. Warning in file.info(x, extra_cols = FALSE) : expanded path length 8617 would be too long for <pre><code class="language-r">library(mlr3) library(mlr3fairness) library(mlr3learners) </code></pre> <h1 id="why-we-need-fairness-visualizations">Why we need fairness visualizations:</h1> <p>Through fairness visualizations allow for first investigations into possible fairness problems in a dataset. In this vignette we will showcase some of the pre-built fairness visualization functions. All the methods showcased below can be used together with objects of type <code>BenchmarkResult</code>, <code>ResampleResult</code> and <code>Prediction</code>.</p> <h1 id="the-scenario">The scenario</h1> <p>For this example, we use the <code>adult_train</code> dataset. Keep in mind all the datasets from <code>mlr3fairness</code> package already set protected attribute via the <code>col_role</code> “pta”, here the “sex” column.</p> <pre><code class="language-r">t = tsk("adult_train") t$col_roles$pta #> [1] "sex" </code></pre [... truncated] --- finished re-building ‘visualization-vignette.Rmd’ SUMMARY: processing the following file failed: ‘reports-vignette.Rmd’ Error: Vignette re-building failed. Execution halted
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