Model Step 3 - Model Metrics

Published

December 5, 2022

Background

This documents monitors the model performance. It is refreshed on a daily basis. The following metrics are monitored:

  • Root Mean Squared Error (RMSE),
  • R Squared (RSQ), and
  • Mean Absolute Error (MAE).

Setup

Load the required libraries and evaluation data. The table below is the first five rows of the evaluation data.

Code
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.5     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.0.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(vetiver)
library(pins)
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.
## 
## Attaching package: 'yardstick'
## The following object is masked from 'package:readr':
## 
##     spec
library(glue)
library(kableExtra)
## 
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
## 
##     group_rows

board <- pins::board_rsconnect()
## Connecting to RSC 2022.11.0 at <https://colorado.posit.co/rsc>
v <- vetiver_pin_read(board, params$name, version = params$version)
v_meta <- pin_meta(board, params$name)

con <- odbc::dbConnect(odbc::odbc(), "Content DB", timeout = 10)
bike_model_data <- tbl(con, "bike_model_data")

train_start_date <- lubridate::as_date(v$metadata$user$train_dates[1])
train_end_date <- lubridate::as_date(v$metadata$user$train_dates[2])
test_start_date <- lubridate::as_date(v$metadata$user$test_dates[1])
test_end_date <- lubridate::as_date(v$metadata$user$test_dates[2])

test_data <- bike_model_data %>%
  filter(
    date >= train_start_date,
    date <= train_end_date
  ) %>%
  collect()
  
test_data %>%
  head() %>%
  kable() %>%
  kable_material()
id hour date month dow n_bikes lat lon
298 10 2022-11-23 11 Wednesday 6 39.11469 -77.17149
298 10 2022-11-24 11 Thursday 7 39.11469 -77.17149
298 10 2022-11-25 11 Friday 6 39.11469 -77.17149
298 10 2022-11-26 11 Saturday 5 39.11469 -77.17149
298 10 2022-11-27 11 Sunday 7 39.11469 -77.17149
298 10 2022-11-28 11 Monday 7 39.11469 -77.17149

Compute metrics

Use vetiver to compute the latest evaluation metrics. The metrics are stored as a pin on RStudio Connect. The table below is the first 5 rows of the evaluation metrics

Code
## compute predictions for your evaluation data
## `handler_startup` is designed to get the R process ready to make predictions
suppressPackageStartupMessages(handler_startup(v))

# Specifically load the packages required by the model. Check 
# `v$metadata$required_pkgs` to see the required pacakges. These packages must
# be specicially defined so that RStudio Connect knows to install them when
# deploying this document.
library(parsnip)
library(ranger)
library(recipes)
## 
## Attaching package: 'recipes'
## The following object is masked from 'package:stringr':
## 
##     fixed
## The following object is masked from 'package:stats':
## 
##     step
library(workflows)
library(slider)

preds <- augment(v, test_data)

latest_metrics <- preds %>%
  arrange(date) %>%
  vetiver_compute_metrics(
    date_var = date,
    period = "day",
    truth = n_bikes,
    estimate = .pred
  )

pin_name <- "sam.edwardes/bike-predict-model-metrics"

if (pin_exists(board, pin_name)) {
  print("Pin already exists, updating existing pin...")
  vetiver_pin_metrics(board, latest_metrics, pin_name, overwrite = TRUE)
} else {
  print("Creating metrics pin for the first time...")
  pin_write(board, latest_metrics, pin_name)
}
## [1] "Pin already exists, updating existing pin..."
## Guessing `type = 'rds'`
## Writing to pin 'sam.edwardes/bike-predict-model-metrics'
## # A tibble: 510 × 5
##    .index        .n .metric .estimator .estimate
##    <date>     <int> <chr>   <chr>          <dbl>
##  1 2022-06-17  6872 rmse    standard       3.45 
##  2 2022-06-17  6872 rsq     standard       0.699
##  3 2022-06-17  6872 mae     standard       2.65 
##  4 2022-06-18  8172 rmse    standard       3.43 
##  5 2022-06-18  8172 rsq     standard       0.666
##  6 2022-06-18  8172 mae     standard       2.64 
##  7 2022-06-19  8172 rmse    standard       3.52 
##  8 2022-06-19  8172 rsq     standard       0.647
##  9 2022-06-19  8172 mae     standard       2.75 
## 10 2022-06-20  8172 rmse    standard       3.42 
## # … with 500 more rows

all_time_metrics <- pin_read(board, pin_name)

all_time_metrics %>%
  head() %>%
  kable() %>%
  kable_material()
.index .n .metric .estimator .estimate
2022-06-17 6872 rmse standard 3.4533047
2022-06-17 6872 rsq standard 0.6985257
2022-06-17 6872 mae standard 2.6482618
2022-06-18 8172 rmse standard 3.4253559
2022-06-18 8172 rsq standard 0.6655180
2022-06-18 8172 mae standard 2.6446323

Visualize metrics

Use vetiver to visualize the all time model metrics.

Code
vetiver_plot_metrics(all_time_metrics) +
  labs(
    title = "Model Metrics",
    size = "Number of\nObservations"
  )