add vignette on group-aware and -wise behavior

R/fair-aaa.R

@@ -12,7 +12,8 @@
 #' for each group, but those metric values are calculated by first additionally
 #' grouping by the variable passed to `by` and then summarizing the per-group
 #' metric estimates across groups using the function passed as the
-#' `aggregate` argument.
+#' `aggregate` argument. Learn more about grouping behavior in yardstick using
+#' `vignette("grouping", "yardstick")`.
 #'
 #' @param fn A yardstick metric function or metric set.
 #' @param name The name of the metric to place in the `.metric` column

vignettes/grouping.Rmd

@@ -0,0 +1,172 @@
+---
+title: "Grouping behavior in yardstick"
+author: "Simon Couch"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Grouping behavior in yardstick}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+The 1.3.0 release of yardstick introduced an implementation for _group-wise metrics_. The use case motivating the implementation of this functionality is _fairness metrics_, though group-wise metrics have applications beyond that domain. Fairness metrics quantify the degree of disparity in a metric value across groups. To learn more about carrying out fairness-oriented analyses with tidymodels, see the blog post on the tidymodels website. This vignette will instead focus on group-wise metrics generally, clarifying the meaning of "group-wise" and demonstrating functionality with an example dataset.
+
+<!-- TODO: link to forthcoming tidymodels blog post -->
+
+```{r pkgs, message = FALSE}
+library(yardstick)
+library(dplyr)
+
+data("hpc_cv")
+```
+
+# Group-awareness
+
+Even before the implementation of group-wise metrics, _all_ yardstick metrics had been _group-aware_. By group-aware, we mean that when passed grouped data, a metric will return metric values calculated for each group.
+
+To demonstrate, we'll make use of the `hpc_cv` data set, containing class probabilities and class predictions for a linear discriminant analysis fit to the HPC data set of Kuhn and Johnson (2013). The model is evaluated against a 10 fold cross-validation, and the predictions for all folds are included. 
+
+```{r hpc-cv}
+tibble(hpc_cv)
+```
+
+For the purposes of this vignette, we'll also add a column `batch` to the data and select off the columns for the class probabilities, which we don't need.
+
+```{r hpc-modify}
+set.seed(1)
+
+hpc <-
+  tibble(hpc_cv) %>%
+  mutate(batch = sample(c("a", "b"), nrow(.), replace = TRUE)) %>%
+  select(-c(VF, F, M, L))
+
+hpc
+```
+
+If we wanted to compute the accuracy of the first resampled model, we could write:
+
+```{r acc-1}
+hpc %>% 
+  filter(Resample == "Fold01") %>%
+  accuracy(obs, pred)
+```
+
+The metric function returns one row, giving the `.metric`, `.estimator`, and `.estimate` for the whole data set it is passed.
+
+If we instead group the data by fold, metric functions like `accuracy` will know to compute values for each group; in the input, each row will correspond to a Resample.
+
+```{r hpc-cv-2}
+hpc %>% 
+  group_by(Resample) %>%
+  accuracy(obs, pred)
+```
+
+Note that the first row, corresponding to `Resample`, gives the same value as manually filtering for the observations corresponding to the first resample and then computing the accuracy.
+
+This behavior is what we mean by group-awareness. When passed grouped data, metric functions will return values for each group.
+
+# Group-wise metrics
+
+Group-wise metrics are associated with a data-column such that, when passed data with that column, the metric will temporarily additionally group by that column, compute values for each of the groups defined by the column, and then aggregate the values computed for the temporary grouping back to the level of the inputted data's grouping.
+
+More concretely, let's turn to an example where there is no pre-existing grouping in the data. Consider the portion of the HPC data pertaining to the first resample:
+
+```{r res-1}
+hpc %>% 
+  filter(Resample == "Fold01")
+```
+
+Suppose that the `batch`es in the data represent two groups for which model performance ought not to differ. To quantify the degree to which model performance differs for these two groups, we could compute accuracy values for either group separately, and then take their difference. First, computing accuracies:
+
+```{r acc-by-group}
+acc_by_group <- 
+  hpc %>% 
+  filter(Resample == "Fold01") %>%
+  group_by(batch) %>%
+  accuracy(obs, pred)
+
+acc_by_group
+```
+
+Now, taking the difference:
+
+```{r diff-acc}
+diff(c(acc_by_group$.estimate[2], acc_by_group$.estimate[1]))
+```
+
+Group-wise metrics encode the `group_by()` and subtraction step shown above into a yardstick metric. We can define a new group-wise metric with the `new_groupwise_metric()` function:
+
+```{r}
+accuracy_diff <-
+  new_groupwise_metric(
+    fn = accuracy,
+    name = "accuracy_diff",
+    aggregate = function(acc_by_group) {
+      diff(c(acc_by_group$.estimate[2], acc_by_group$.estimate[1]))
+    }
+  )
+```
+
+* The `fn` argument is the yardstick metric that will be computed for each data group.
+* The `name` argument gives the name of the new metric we've created; we'll call ours "accuracy difference."
+* The `aggregate` argument is a function defining how to go from `fn` output by group to a single numeric.
+
+The output, `accuracy_diff`, is a function subclass called a `metric_factory()`:
+
+```{r acc-diff-class}
+class(accuracy_diff)
+```
+
+`accuracy_diff` now knows to take accuracy values for each group and then return the difference between the accuracy for the first and second result as output. The last thing we need to associate with the object is the name of the grouping variable to pass to `group_by()`; we can pass that variable name to `accuracy_diff` to do so:
+
+```{r acc-diff-by}
+accuracy_diff_by_batch <- accuracy_diff(batch)
+```
+
+The output, `accuracy_diff_by_batch`, is a yardstick metric function like any other:
+
+```{r metric-classes}
+class(accuracy)
+
+class(accuracy_diff_by_batch)
+```
+
+<!-- TODO: once a print method is added, we can print this out and the meaning of "this is just a yardstick metric" will be clearer -->
+
+We can use the `accuracy_diff_by_batch()` metric in the same way that we would use `accuracy()`. On it's own:
+
+```{r ex-acc-diff-by-batch}
+hpc %>% 
+  filter(Resample == "Fold01") %>%
+  accuracy_diff_by_batch(obs, pred)
+```
+
+We can also add `accuracy_diff_by_batch()` to metric sets:
+
+```{r ex-acc-diff-by-batch-ms}
+acc_ms <- metric_set(accuracy, accuracy_diff_by_batch)
+
+hpc %>% 
+  filter(Resample == "Fold01") %>%
+  acc_ms(truth = obs, estimate = pred)
+```
+
+_Group-wise metrics are group-aware._ When passed data with any grouping variables other than the column passed as the first argument to `accuracy_diff()`---in this case, `group`---`accuracy_diff_by_batch()` will behave like any other yardstick metric. For example:
+
+```{r ex-acc-diff-by-batch-2}
+hpc %>% 
+  group_by(Resample) %>%
+  accuracy_diff_by_batch(obs, pred)
+```
+
+Group-wise metrics form the backend of fairness metrics in tidymodels. To learn more about group-wise metrics and their applications in fairness problems, see `new_groupwise_metric()`.
+
+<!-- TODO: link to tidyverse blog post and tidymodels articles. -->
+