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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!

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.