README.md

In artichaud1/tidytune: A package for Tidy Hyperparameter Tuning

tidytune

Overview

tidytune performs hyperparameter tuning by leveraging the rsample and recipes packages. Adopting a tidy approach improves readability, reproducibility (I had to google that word) and allows the seamless usage of other tidying tools such as dplyr.

Currently, the following methods are implemented:

• Grid search
• Random search

Here's in a nutshell how you would perform a grid search in tidytune.

In the example below, xgboost_classif_score is a custom scoring function provided by the user.

xgboost_param_grid <- expand.grid(eta = c(0.1, 0.05), max_depth = c(3, 4))

grid_search(
    resamples = resamples, 
    recipe = rec, 
    param_grid = xgboost_param_grid, 
    score_func = xgboost_classif_score, 
    nrounds = 100,
    verbose = FALSE
  )

For grid and random search, there's also a batch version in case you need to save or see your results while the search is progressing (especially useful with big models that take a long time to fine tune).

• Model based optimization

Currently, model based optimization uses a random forest surrogate model under the hood to map parameter values to predicted model performance.

Installation

devtools::install_github('artichaud1/tidytune')

artichaud1/tidytune documentation built on May 20, 2019, 9:13 p.m.