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bonsai 0.2.1

  • The most recent dials and parsnip releases introduced tuning integration for the lightgbm num_leaves engine argument! The num_leaves parameter sets the maximum number of nodes per tree, and is an important tuning parameter for lightgbm (tidymodels/dials#256, tidymodels/parsnip#838). With the newest version of each of dials, parsnip, and bonsai installed, tune this argument by marking the num_leaves engine argument for tuning when defining your model specification:

boost_tree() %>% set_engine("lightgbm", num_leaves = tune())
  • Fixed a bug where lightgbm’s parallelism argument num_threads was overridden when passed via param rather than as a main argument. By default, then, lightgbm will fit sequentially rather than with num_threads = foreach::getDoParWorkers(). The user can still set num_threads via engine arguments with engine = "lightgbm":

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.

  • The boost_tree argument stop_iter now maps to the lightgbm:::lgb.train() argument early_stopping_round rather than its alias early_stopping_rounds. This does not affect parsnip’s interface to lightgbm (i.e. via boost_tree() %>% set_engine("lightgbm")), though will introduce errors for code that uses the train_lightgbm() wrapper directly and sets the lightgbm::lgb.train() argument early_stopping_round by its alias early_stopping_rounds via train_lightgbm()’s ....

  • Disallowed passing main model arguments as engine arguments to set_engine("lightgbm", ...) via aliases. That is, if a main argument is marked for tuning and a lightgbm alias is supplied as an engine argument, bonsai will now error, rather than supplying both to lightgbm and allowing the package to handle aliases. Users can still interface with non-main boost_tree() arguments via their lightgbm aliases (#53).

bonsai 0.2.0

CRAN release: 2022-08-31

  • 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!

bonsai 0.1.0

CRAN release: 2022-06-23

Initial release!