This package provides an R library to instrument prediction code that lets you capture inputs to the model, predictions, prediction properties, and other metadata. # Setup * Install R * You can use R dependency management to your projects to manage local r environments * Use below commands to get started

make deps
make test

Important make rules

Here are the available make rules that will help you in easing your development work

make all                 -> run check and clean
make clean               -> Remove intermediate files
make lint                -> Run lint
make test                -> Run test
make deps                -> Install dev dependencies
make install             -> Install package
make docs                -> Generate docs
make coverage            -> Run coverage
make check               -> Build as cran and run checks
make build               -> Run build

Releasing a package


How to create new environment in Domino

RUN R --no-save -e "install.packages(c('devtools'))"

RUN R --no-save -e "devtools::install_github('cerebrotech/r-prediction-logging', auth_token = '<github pat>')"

How to use

data_capture_client <- DataCaptureClient(feature_names=c("min","max"),predict_names=c("prediction"))
data_capture_client$capturePrediction(c(1,100), c("2"))


# This is a sample R model
# You can publish a model API by clicking on "Publish" and selecting
# "Model APIs" in your quick-start project.
# Load dependencies
data_capture_client <- DataCaptureClient(
# Define a function to create an API
# To call model use: {"data": {"min": 1, "max": 100}}
my_model <- function(min, max) {
  random_number <- runif(1, min, max)
  data_capture_client$capturePrediction(c(min,max), c(random_number))

Support policy