Aggregate model metrics over time for monitoring

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_compute_metrics(
  data,
  date_var,
  period,
  truth,
  estimate,
  ...,
  metric_set = yardstick::metrics,
  every = 1L,
  origin = NULL,
  before = 0L,
  after = 0L,
  complete = FALSE
)

Arguments

data

A data.frame containing the columns specified by truth, estimate, and ....

date_var

The column in data containing dates or date-times for monitoring, to be aggregated with .period

period

[character(1)]

A string defining the period to group by. Valid inputs can be roughly broken into:

• "year", "quarter", "month", "week", "day"

• "hour", "minute", "second", "millisecond"

• "yweek", "mweek"

• "yday", "mday"

truth

The column identifier for the true results (that is numeric or factor). This should be an unquoted column name although this argument is passed by expression and support quasiquotation (you can unquote column names).

estimate

The column identifier for the predicted results (that is also numeric or factor). As with truth this can be specified different ways but the primary method is to use an unquoted variable name.

...

A set of unquoted column names or one or more dplyr selector functions to choose which variables contain the class probabilities. If truth is binary, only 1 column should be selected, and it should correspond to the value of event_level. Otherwise, there should be as many columns as factor levels of truth and the ordering of the columns should be the same as the factor levels of truth.

metric_set

A yardstick::metric_set() function for computing metrics. Defaults to yardstick::metrics().

every

[positive integer(1)]

The number of periods to group together.

For example, if the period was set to "year" with an every value of 2, then the years 1970 and 1971 would be placed in the same group.

origin

[Date(1) / POSIXct(1) / POSIXlt(1) / NULL]

The reference date time value. The default when left as NULL is the epoch time of 1970-01-01 00:00:00, in the time zone of the index.

This is generally used to define the anchor time to count from, which is relevant when the every value is > 1.

before, after

[integer(1) / Inf]

The number of values before or after the current element to include in the sliding window. Set to Inf to select all elements before or after the current element. Negative values are allowed, which allows you to "look forward" from the current element if used as the .before value, or "look backwards" if used as .after.

complete

[logical(1)]

Should the function be evaluated on complete windows only? If FALSE, the default, then partial computations will be allowed.

Value

A dataframe of metrics.

Details

For arguments used more than once in your monitoring dashboard, such as date_var, consider using R Markdown parameters to reduce repetition and/or errors.

See also

vetiver_pin_metrics(), vetiver_plot_metrics()

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
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()

original_metrics <-
    augment(lm_fit, new_data = testing_data) %>%
    vetiver_compute_metrics(date, "week", ridership, .pred, every = 4L)

new_metrics <-
    augment(lm_fit, new_data = monitoring) %>%
    vetiver_compute_metrics(date, "week", ridership, .pred, every = 4L)