bonsai 0.3.1

bonsai 0.3.0

bonsai 0.2.1

boost_tree() %>% set_engine("lightgbm", num_leaves = tune())
boost_tree() %>% set_engine("lightgbm", num_threads = x)

Note that, when tuning hyperparameters with the tune package, detection of parallel backend will still work as usual.

bonsai 0.2.0

The sample_size argument is translated to the bagging_fraction parameter in the param argument of lgb.train. The argument is interpreted by lightgbm as a proportion rather than a count, so bonsai internally reparameterizes the sample_size argument with [dials::sample_prop()] during tuning.

To effectively enable bagging, the user would also need to set the bagging_freq argument to lightgbm. bagging_freq defaults to 0, which means bagging is disabled, and a bagging_freq argument of k means that the booster will perform bagging at every kth boosting iteration. Thus, by default, the sample_size argument would be ignored without setting this argument manually. Other boosting libraries, like xgboost, do not have an analogous argument to bagging_freq and use k = 1 when the analogue to bagging_fraction is in \((0, 1)\). bonsai will thus automatically set bagging_freq = 1 in set_engine("lightgbm", ...) if sample_size (i.e. bagging_fraction) is not equal to 1 and no bagging_freq value is supplied. This default can be overridden by setting the bagging_freq argument to set_engine() manually.

bonsai 0.1.0

Initial release!