bonsai 0.2.0

• Enabled bagging with lightgbm via the sample_size argument to boost_tree (#32 and tidymodels/parsnip#768). The following docs now available in ?details_boost_tree_lightgbm describe the interface in detail:

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.

• Corrected mapping of the mtry argument in boost_tree with the lightgbm engine. mtry previously mapped to the feature_fraction argument to lgb.train but was documented as mapping to an argument more closely resembling feature_fraction_bynode. mtry now maps to feature_fraction_bynode.

  This means that code that set feature_fraction_bynode as an argument to set_engine() will now error, and the user can now pass feature_fraction to set_engine() without raising an error.

• Fixed error in lightgbm with engine argument objective = "tweedie" and response values less than 1.

• A number of documentation improvements, increases in testing coverage, and changes to internals in anticipation of the 4.0.0 release of the lightgbm package. Thank you to @jameslamb for the effort and expertise!