Skip to content

Changelog

Source: NEWS.md

tune (development version)

  • When calculating resampling estimates, we can now use a weighted mean based on the number of rows in the assessment set. You can opt-in to this using the new add_resample_weights() function. See ?calculate_resample_weights (#990)

  • The warning threshold when check the size of a workflow is now a parameter to the control functions and has a new default of 100MB. (#914)

  • A bug was fixed where NULL results generated during simulated annealing would cause errors when logging.

Breaking Changes

  • The Gaussian process model package was changed from to because the former is no longer actively maintained. There are some differences:

    • Fit diagnostics are computed and reported. If the fit quality is poor, an “uncertainty sample” that is furthest away from the existing data is used as the new candidate.
    • The GP no longer uses binary indicators for qualitative predictors. Instead, a “categorical kernel” is used for those parameter columns. Fewer starting values are required with this change.
    • For numeric predictors, the Matern 3/2 kernel is always used.

tune 2.0.1

CRAN release: 2025-10-17

tune 2.0.0

CRAN release: 2025-09-01

Changes to tune_grid().

  • A major rewrite/refactor of the underlying code that runs tune_grid(). This was an upgrade to add postprocessing and to modernize our parallel processing infrastructure.

  • The pattern of .config values has changed.

    • For grid search, it changes from Preprocessor{num}_Model{num} to pre{num}_mod{num}_post{num}. The numbers include a zero when that element was static. For example, a value of pre0_mod3_post4 means no preprocessors were tuned and the model and postprocessor(s) had at least three and four candidates, respectively.
    • For iterative search, the pattern is not iter{num} instead of Iter{num} and the numbers are now zero padded to sort better. For example, if there between 10 and 99 iterations, the first .config value is now iter01 instead of Iter1.

  • The package will now log a backtrace for errors and warnings that occur during tuning. When a tuning process encounters issues, see the new trace column in the collect_notes(.Last.tune.result) output to find precisely where the error occurred (#873).

  • Postprocessors can now be tuned. Currently, we support the tailor package.

Parallel Processing

  • Introduced support for parallel processing with mirai in addition to the currently supported framework future. See ?parallelism to learn more (#1028).

  • Sequential and parallel processing all use the same L’Ecuyer-CMRG seeds (conditional on parallel_over) (#1033).

Breaking Changes

  • The foreach package is no longer supported. Instead, use the future or mirai packages.

  • The parallel backend(s) and the methods of constructing seeds for workers have changed. There will be a lack of reproducibility between objects created in this version of tune and previous versions.

Other Changes

  • int_pctl() now includes an option (keep_replicates) to retain the individual bootstrap estimates. It also processes the resamples more efficiently (#1000).

  • A min_grid() methods was added for proportional_hazards models so that their submodels are processed appropriately.

  • Post-processing: new schedule_grid() for scheduling a grid including post-processing (#988).

  • Removed functions deprecated since tune version 1.6.0 (circa 2021-07-21).

tune 1.3.0

CRAN release: 2025-02-21

  • The package will now warn when parallel processing has been enabled with foreach but not with future. See ?parallelism to learn more about transitioning your code to future (#878, #866). The next version of tune will move to a pure future implementation.

  • When automatic grids are used, dials::grid_space_filling() is now used (instead of dials::grid_latin_hypercube()). Overall, the new function produces optimized designs (not depending on random numbers). When using Bayesian models, we will use a Latin Hypercube since we produce 5,000 candidates, which is too slow to do with pre-optimized designs.

tune 1.2.1

CRAN release: 2024-04-18

  • Addressed issue in int_pctl() where the function would error when parallelized using makePSOCKcluster() (#885).

  • Addressed issue where tuning functions would raise the error object 'iteration' not found with plan(multisession) and the control option parallel_over = "everything" (#888).

tune 1.2.0

CRAN release: 2024-03-20

New Features

  • tune 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.

  • Introduced support for parallel processing using the future framework. The tune package previously supported parallelism with foreach, and users can use either framework for now. In a future release, tune will begin the deprecation cycle for parallelism with foreach, so we encourage users to begin migrating their code now. See the Parallel Processing section in the “Optimizations” article to learn more (#866).

  • Added a type argument to collect_metrics() to indicate the desired output format. The default, type = "long", returns output as before, while type = "wide" pivots the output such that each metric has its own column (#839).

  • Added a new function, compute_metrics(), that allows for computing new metrics after evaluating against resamples. The arguments and output formats are closely related to those from collect_metrics(), but this function requires that the input be generated with the control option save_pred = TRUE and additionally takes a metrics argument with a metric set for new metrics to compute. This allows for computing new performance metrics without requiring users to re-fit and re-predict from each model (#663).

  • A method for rsample’s int_pctl() function that will compute percentile confidence intervals on performance metrics for objects produced by fit_resamples(), tune_*(), and last_fit().

  • The Brier score is now part of the default metric set for classification models.

Bug Fixes

  • last_fit() will now error when supplied a fitted workflow (#678).

  • Fixes bug where .notes entries were sorted in the wrong order in tuning results for resampling schemes with IDs that aren’t already in alphabetical order (#728).

  • Fixes bug where .config entries in the .extracts column in tune_bayes() output didn’t align with the entries they ought to in the .metrics and .predictions columns (#715).

  • Metrics from apparent resamples are no longer included when estimating performance with estimate_tune_results() (and thus with collect_metrics(..., summarize = TRUE) and compute_metrics(..., summarize = TRUE), #714).

  • Handles edge cases for tune_bayes()’ iter argument more soundly. For iter = 0, the output of tune_bayes() should match tune_grid(), and tune_bayes() will now error when iter < 0. tune_bayes() will now alter the state of RNG slightly differently, resulting in changed Bayesian optimization search output (#720).

  • augment() methods to tune_results, resample_results, and last_fit objects now always return tibbles (#759).

Other Changes

  • Improved error message when needed packages aren’t installed (#727).

  • augment() methods to tune_results, resample_results, and last_fit objects now always returns tibbles (#759).

  • Improves documentation related to the hyperparameters associated with extracted objects that are generated from submodels. See the “Extracting with submodels” section of ?collect_extracts to learn more.

  • eval_time and eval_time_target attribute was added to tune objects. There are also .get_tune_eval_times() and .get_tune_eval_time_target() functions.

  • collect_predictions() now reorders the columns so that all prediction columns come first (#798).

  • augment() methods to tune_results, resample_results, and last_fit objects now return prediction results in the first columns (#761).

  • autoplot() will now meaningfully error if only 1 grid point is present, rather than producing a plot (#775).

  • Added notes on case weight usage to several functions (#805).

  • For iterative optimization routines, autoplot() will use integer breaks when type = "performance" or type = "parameters".

Breaking Changes

tune 1.1.2

CRAN release: 2023-08-23

  • last_fit() now works with the 3-way validation split objects from rsample::initial_validation_split(). last_fit() and fit_best() now have a new argument add_validation_set to include or exclude the validation set in the dataset used to fit the model (#701).

  • Disambiguates the verbose and verbose_iter control options to better align with documented functionality. The former controls logging for general progress updates, while the latter only does so for the Bayesian search process. (#682)

tune 1.1.1

CRAN release: 2023-04-11

  • Fixed a bug introduced in tune 1.1.0 in collect_() functions where the .iter column was dropped.

tune 1.1.0

CRAN release: 2023-04-04

tune 1.1.0 introduces a number of new features and bug fixes, accompanied by various optimizations that substantially decrease the total evaluation time to tune hyperparameters in the tidymodels.

New features

  • Introduced a new function fit_best() that provides a shorthand interface to fit a final model after parameter tuning. (#586)

  • Refined machinery for logging issues during tuning. Rather than printing out warnings and errors as they appear, the package will now only print unique tuning issues, updating a dynamic summary message that maintains counts of each unique issue. This feature is only enabled for tuning sequentially and can be manually toggled with the verbose option. (#588)

  • Introduced collect_extracts(), a function for collecting extracted objects from tuning results. The format of results closely mirrors collect_notes(), where the extracted objects are contained in a list-column alongside the resample ID and workflow .config. (#579)

Bug fixes

Other changes

  • Implemented various optimizations in tune’s backend that substantially decrease the total evaluation time to tune hyperparameters with the tidymodels. (#634, #635, #636, #637, #640, #641, #642, #648, #649, #653, #656, #657)

  • Allowed users to supply list-columns in grid arguments. This change allows for manually specifying grid values that must be contained in list-columns, like functions or lists. (#625)

  • Clarified error messages in select_by_* functions. Error messages now only note entries in ... that are likely candidates for failure to arrange(), and those error messages are no longer duplicated for each entry in ....

  • Improved condition handling for errors that occur during extraction from workflows. While messages and warnings were appropriately handled, errors occurring due to misspecified extract() functions being supplied to control_*() functions were silently caught. As with warnings, errors are now surfaced both during execution and at print() (#575).

  • Moved forward with the deprecation of parameters() methods for workflows, model_specs, and recipes. Each of these methods will now warn on every usage and will be defunct in a later release of the package. (#650)

  • Various bug fixes and improvements to documentation.

tune 1.0.1

CRAN release: 2022-10-09

  • last_fit(), fit_resamples(), tune_grid(), and tune_bayes() do not automatically error if the wrong type of control object is passed. If the passed control object is not a superset of the one that is needed, the function will still error. As an example, passing control_grid() to tune_bayes() will fail but passing control_bayes() to tune_grid() will not. (#449)

  • The collect_metrics() method for racing objects was removed (and is now in the finetune package).

  • Improved prompts related to parameter tuning. When tuning parameters are supplied that are not compatible with the given engine, tune_*() functions will now error. (#549)

  • The control_bayes() got a new argument verbose_iter that is used to control the verbosity of the Bayesian calculations. This change means that the verbose argument is being passed to tune_grid() to control its verbosity.

  • The control_last_fit() function gained an argument allow_par that defaults to FALSE. This change addresses failures after last_fit() using modeling engines that require native serialization, and we anticipate little to no increase in time-to-fit resulting from this change. (#539, tidymodels/bonsai#52)

  • show_notes() does a better jobs of… showing notes. (#558)

tune 1.0.0

CRAN release: 2022-07-07

  • show_notes() is a new function that can better help understand warnings and errors.

  • Logging that occurs using the tuning and resampling functions now show multi-line error messages and warnings in multiple lines.

  • When fit_resamples(), last_fit(), tune_grid(), or tune_bayes() complete without error (even if models fail), the results are also available via .Last.tune.result.

  • last_fit() now accepts a control argument to allow users to control aspects of the last fitting process via control_last_fit() (#399).

  • Case weights are enabled for models that can use them.

  • Some internal functions were exported for use by other packages.

  • A check was added to fit_resamples() and last_fit() to give a more informative error message when a preprocessor or model have parameters marked for tuning.

  • outcome_names() works correctly when recipe has NA roles. (#518)

tune 0.2.0

CRAN release: 2022-03-18

  • The .notes column now contains information on the type of note (error or warning), the location where it occurred, and the note. Printing a tune result has different output describing the notes.

  • collect_notes() can be used to gather any notes to a tibble. (#363)

  • Parallel processing with PSOCK clusters is now more efficient, due to carefully avoiding sending extraneous information to each worker (#384, #396).

  • The engine arguments for xgboost alpha, lambda, and scale_pos_weight are now tunable.

  • When the Bayesian optimization data contain missing values, these are removed before fitting the GP model. If all metrics are missing, no GP is fit and the current results are returned. (#432)

  • Moved tune() from tune to hardhat (#442).

  • The parameters() methods for recipe, model_spec, and workflow objects have been soft-deprecated in favor of extract_parameter_set_dials() methods (#428).

tune 0.1.6

CRAN release: 2021-07-21

  • When using load_pkgs(), packages that use random numbers on start-up do not affect the state of the RNG. We also added more control of the RNGkind to make it consistent with the user’s previous value (#389).

  • New extract_*() functions have been added that supersede many of the the existing pull_*() functions. This is part of a larger move across the tidymodels packages towards a family of generic extract_*() functions. Many pull_*() functions have been soft-deprecated, and will eventually be removed. (#378)

tune 0.1.5

CRAN release: 2021-04-23

  • Fixed a bug where the resampled confusion matrix is transposed when conf_mat_resamped(tidy = FALSE) (#372)

  • False positive warnings no longer occur when using the doFuture package for parallel processing (#377)

tune 0.1.4

CRAN release: 2021-04-20

tune 0.1.3

CRAN release: 2021-02-28

  • The rsample::pretty() methods were extended to tune_results objects.

  • Added pillar methods for formatting tune objects in list columns.

  • A method for .get_fingerprint() was added. This helps determine if tune objects used the same resamples.

tune 0.1.2

CRAN release: 2020-11-17

Bug Fixes

  • last_fit() and workflows::fit() will now give identical results for the same workflow when the underlying model uses random number generation (#300).

  • Fixed an issue where recipe tuning parameters could be randomly matched to the tuning grid incorrectly (#316).

  • last_fit() no longer accidentally adjusts the random seed (#264).

  • Fixed two bugs in the acquisition function calculations.

Other Changes

  • New parallel_over control argument to adjust the parallel processing method that tune uses.

  • The .config column that appears in the returned tibble from tuning and fitting resamples has changed slightly. It is now always of the form "Preprocessor<i>_Model<j>".

  • predict() can now be called on the workflow returned from last_fit() (#294, #295, #296).

  • tune now supports setting the event_level option from yardstick through the control objects (i.e. control_grid(event_level = "second")) (#240, #249).

  • tune now supports workflows created with the new workflows::add_variables() preprocessor.

  • Better control the random number streams in parallel for tune_grid() and fit_resamples() (#11)

  • Allow ... to pass options from tune_bayes() to GPfit::GP_fit().

  • Additional checks are done for the initial grid that is given to tune_bayes(). If the initial grid is small relative to the number of model terms, a warning is issued. If the grid is a single point, an error occurs. (#269)

  • Formatting of some messages created by tune_bayes() now respect the width and wrap lines using the new message_wrap() function.

  • tune functions (tune_grid(), tune_bayes(), etc) will now error if a model specification or model workflow are given as the first argument (the soft deprecation period is over).

  • An augment() method was added for objects generated by tune_*(), fit_resamples(), and last_fit().

tune 0.1.1

CRAN release: 2020-07-08

Breaking Changes

  • autoplot.tune_results() now requires objects made by version 0.1.0 or higher of tune.

  • tune objects no longer keep the rset class that they have from the resamples argument.

Other Changes

  • autoplot.tune_results() now produces a different plot when the tuning grid is a regular grid (i.e. factorial or nearly factorial in nature). If there are 5+ parameters, the standard plot is produced. Non-regular grids are plotted in the same way (although see next bullet point). See ?autoplot.tune_results for more information.

  • autoplot.tune_results() now transforms the parameter values for the plot. For example, if the penalty parameter was used for a regularized regression, the points are plotted on the log-10 scale (its default transformation). For non-regular grids, the facet labels show the transformation type (e.g. "penalty (log-10)" or "cost (log-2)"). For regular grid, the x-axis is scaled using scale_x_continuous().

  • Finally, autoplot.tune_results() now shows the parameter labels in a plot. For example, if a k-nearest neighbors model was used with neighbors = tune(), the parameter will be labeled as "# Nearest Neighbors". When an ID was used, such as neighbors = tune("K"), this is used to identify the parameter.

  • In other plotting news, coord_obs_pred() has been included for regression models. When plotting the observed and predicted values from a model, this forces the x- and y-axis to be the same range and uses an aspect ratio of 1.

  • The outcome names are saved in an attribute called outcomes to objects with class tune_results. Also, several accessor functions (named `.get_tune_*()) were added to more easily access such attributes.

  • conf_mat_resampled() computes the average confusion matrix across resampling statistics for a single model.

  • show_best(), and the select_*() functions will now use the first metric in the metric set if no metric is supplied.

  • filter_parameters() can trim the .metrics column of unwanted results (as well as columns .predictions and .extracts) from tune_* objects.

  • In concert with dials > 0.0.7, tuning engine-specific arguments is possible. Many known engine-specific tuning parameters and handled automatically.

  • If a grid is given, parameters do not need to be finalized to be used in the tune_*() functions.

  • Added a save_workflow argument to control_* functions that will result in the workflow object used to carry out tuning/fitting (regardless of whether a formula or recipe was given as input to the function) to be appended to the resulting tune_results object in a workflow attribute. The new .get_tune_workflow() function can be used to access the workflow.

  • Many of the output columns in a tune_results object have an additional column called .config. This is meant to be a unique, qualitative value that used for sorting and merging. These values also correspond to the messages in the logging produced when verbose = TRUE.

tune 0.1.0

CRAN release: 2020-04-02

Breaking Changes

  • The arguments to the main tuning/fitting functions (tune_grid(), tune_bayes(), etc) have been reordered to better align with parsnip’s fit(). The first argument to all these functions is now a model specification or model workflow. The previous versions are soft-deprecated as of 0.1.0 and will be deprecated as of 0.1.2.

Other Changes

  • Added more packages to be fully loaded in the workers when run in parallel using doParallel (#157), (#159), and (#160)

  • collect_predictions() gains two new arguments. parameters allows for pre-filtering of the hold-out predictions by tuning parameters values. If you are only interested in one sub-model, this makes things much faster. The other option is summarize and is used when the resampling method has training set rows that are predicted in multiple holdout sets.

  • select_best(), select_by_one_std_err(), and select_by_pct_loss() no longer have a redundant maximize argument (#176). Each metric set in yardstick now has a direction (maximize vs. minimize) built in.

Bug Fixes

  • tune_bayes() no longer errors with a recipe, which has tuning parameters, in combination with a parameter set, where the defaults contain unknown values (#168).

tune 0.0.1

CRAN release: 2020-02-11

  • CRAN release.

  • Changed license to MIT

tune 0.0.0.9002

  • The ... arguments of tune_grid() and tune_bayes() have been moved forward to force optional arguments to be named.

  • New fit_resamples() for fitting a set of resamples that don’t require any tuning.

  • Changed summarise.tune_results() back to estimate.tune_results()

tune 0.0.0.9000

  • Added a NEWS.md file to track changes to the package.