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These three functions can be used for model monitoring (such as in a monitoring dashboard):

  • vetiver_compute_metrics() computes metrics (such as accuracy for a classification model or RMSE for a regression model) at a chosen time aggregation period

  • vetiver_pin_metrics() updates an existing pin storing model metrics over time

  • vetiver_plot_metrics() creates a plot of metrics over time

Usage

vetiver_plot_metrics(
  df_metrics,
  .index = .index,
  .estimate = .estimate,
  .metric = .metric,
  .n = .n
)

Arguments

df_metrics

A tidy dataframe of metrics over time, such as created by

.index

The variable in df_metrics containing the aggregated dates or date-times (from time_var in data). Defaults to .index.

.estimate

The variable in df_metrics containing the metric estimate. Defaults to .estimate.

.metric

The variable in df_metrics containing the metric type. Defaults to .metric.

.n

The variable in df_metrics containing the number of observations used for estimating the metric.

Value

A ggplot2 object.

Examples

library(dplyr)
library(parsnip)
data(Chicago, package = "modeldata")
Chicago <- Chicago %>% select(ridership, date, all_of(stations))
training_data <- Chicago %>% filter(date < "2009-01-01")
testing_data <- Chicago %>% filter(date >= "2009-01-01", date < "2011-01-01")
monitoring <- Chicago %>% filter(date >= "2011-01-01", date < "2012-12-31")
lm_fit <- linear_reg() %>% fit(ridership ~ ., data = training_data)

library(pins)
b <- board_temp()

## before starting monitoring, initiate the metrics and pin
## (for example, with the testing data):
original_metrics <-
    augment(lm_fit, new_data = testing_data) %>%
    vetiver_compute_metrics(date, "week", ridership, .pred, every = 4L)
pin_write(b, original_metrics, "lm_fit_metrics", type = "arrow")
#> Creating new version '20220929T181229Z-b3d15'
#> Writing to pin 'lm_fit_metrics'

## to continue monitoring with new data, compute metrics and update pin:
new_metrics <-
    augment(lm_fit, new_data = monitoring) %>%
    vetiver_compute_metrics(date, "week", ridership, .pred, every = 4L)
vetiver_pin_metrics(b, new_metrics, "lm_fit_metrics")
#> Replacing version '20220929T181229Z-b3d15' with '20220929T181229Z-a5bef'
#> Writing to pin 'lm_fit_metrics'
#> # A tibble: 162 × 5
#>    .index        .n .metric .estimator .estimate
#>    <date>     <int> <chr>   <chr>          <dbl>
#>  1 2009-01-01     7 rmse    standard       6.78 
#>  2 2009-01-01     7 rsq     standard       0.154
#>  3 2009-01-01     7 mae     standard       5.25 
#>  4 2009-01-08    28 rmse    standard       4.61 
#>  5 2009-01-08    28 rsq     standard       0.576
#>  6 2009-01-08    28 mae     standard       2.98 
#>  7 2009-02-05    28 rmse    standard       1.90 
#>  8 2009-02-05    28 rsq     standard       0.916
#>  9 2009-02-05    28 mae     standard       1.17 
#> 10 2009-03-05    28 rmse    standard       1.24 
#> # … with 152 more rows

library(ggplot2)
vetiver_plot_metrics(new_metrics) +
    scale_size(range = c(2, 4))