High-Performance Open-Source Archive
A bug was fixed where NULL results generated during
simulated annealing would cause errors when logging.
Input checks now provide more informative error messages for invalid arguments (#79).
When calculating resampling estimates, the tune package can now use a weighted mean based on the number of rows in the assessment set. finetune was updated to preserve those weights during racing (#135).
Maintenance release required by CRAN.
Transition from the magrittr pipe to the base R pipe.
finetune now fully supports models in the “censored regression” mode. These models can be fit, tuned, and evaluated like the regression and classification modes. tidymodels.org has more information and tutorials on how to work with survival analysis models.
Improved error message from tune_sim_anneal() when
values in the supplied param_info do not encompass all
values evaluated in the initial grid. This most often
happens when a user mistakenly supplies different parameter sets to the
function that generated the initial results and
tune_sim_anneal().
autoplot() methods for racing objects will now use
integers in x-axis breaks (#75).
Enabling the verbose_elim control option for
tune_race_anova() will now additionally introduce a message
confirming that the function is evaluating against the burn-in
resamples.
Updates based on the new version of tune, primarily for survival analysis models (#104).
Fixed bug where tune_sim_anneal() would fail when
supplied parameters needing finalization. The function will now finalize
needed parameter ranges internally (#39).
Fixed bug where packages specified in
control_race(pkgs) were not actually loaded in
tune_race_anova() (#74).
collect_predictions(),
collect_metrics() and show_best() methods
previously had ellipses at the end of the function signature that have
been moved to follow the last argument without a default value. Optional
arguments previously passed by position will now error informatively
prompting them to be named (#105).Various minor changes to keep up with developments in the tune and dplyr packages (#60) (#62) (#67) (#68).
Corrects .config output with
save_pred = TRUE in tune_sim_anneal(). The
function previously outputted a constant
Model1_Preprocessor1 in the .predictions slot,
and now provides .config values that align with those in
.metrics (#57).
An eval_time attribute was added to tune objects
produced by finetune.
For racing:
collect_metrics() and
collect_predictions() have a 'complete'
argument that only returns results for model configurations that were
fully resampled.
select_best() and show_best() now only
show results for model configurations that were fully
resampled.
tune_race_anova(),
tune_race_win_loss(), and tune_sim_anneal() no
longer error if control argument isn’t a the corresponding
control_*() object. Will work as long as the object passed
to control includes the same elements as the required
control_*() object.
The control_sim_anneal() got a new argument
verbose_iter that is used to control the verbosity of the
iterative calculations. This change means that the verbose
argument is being passed to tune_grid() to control its
verbosity.
An informative error is given when there are not enough resamples for racing (#33).
tune_sim_anneal() was not passing all arguments to
tune_grid() (#40).
Maintenance release for CRAN requirements.
Use extract_parameter_set_dials() instead of
parameters() to get parameter sets.
Removed some pillar-related S3 methods that currently live in tune.
tune_sim_anneal() only overwrites tuning parameter
information when they originally contain unknowns.A check was added to make sure that lme4 or
BradleyTerry2 are installed (#8)
Added pillar methods for formatting
tune objects in list columns.
Fixed bug in random_integer_neighbor_calc() to keep
values inside range (#10)
tune_sim_anneal() now retains a finalized parameter
set and replaces any existing parameter set that was not finalized
(#14)
A bug in win/loss racing was fixed for cases when one tuning parameter had results that were so bad that it broke the Bradley-Terry model (#7)
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