Hi, @davis

Thanks for the follow-up! This is what I came with the custom metrics for weighted MAE. Is this the way that I can make the custom metric?

```
library(rlang)
library(yardstick)
#> For binary classification, the first factor level is assumed to be the event.
#> Use the argument `event_level = "second"` to alter this as needed.
wmae_vec <- function(truth, estimate, weights, na_rm = TRUE, ...) {
wmae_impl <- function(truth, estimate, weights) {
sum(weights * abs(truth - estimate)) / sum(weights)
}
metric_vec_template(
metric_impl = wmae_impl,
truth = truth,
estimate = estimate,
weights = weights,
na_rm = na_rm,
cls = "numeric",
...
)
}
wmae <- function(data, ...) {
UseMethod("wmae")
}
wmae <- new_numeric_metric(wmae, direction = "minimize")
wmae.data.frame <- function(data, truth, estimate, weights, na_rm = TRUE, ...) {
metric_summarizer(
metric_nm = "wmae",
metric_fn = wmae_vec,
data = data,
truth = !! enquo(truth),
estimate = !! enquo(estimate),
metric_fn_options = list(weights = weights),
na_rm = na_rm,
...
)
}
data("solubility_test")
wmae_vec(
truth = solubility_test$solubility,
estimate = solubility_test$prediction,
weights = 1
)
#> [1] 172.2424
solubility_test$weights <- 2
wmae(solubility_test,
truth = solubility,
estimate = prediction, weights = solubility_test$weights)
#> # A tibble: 1 x 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 wmae standard 0.545
```

It seems to work, but I wonder I can make the custom metric with function like;

```
wmae(solubility_test,
truth = solubility,
estimate = prediction,
weights = weights)
```

Moreover, how can I apply this metric in `tune_grid`

with an additional option (weights) ?