High-Performance Open-Source Archive
Model tuning has been enabled for quantile regression models. (#1125)
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)
Tuning of models with submodel parameters now predicts all submodels at once (again) to speed up the tuning process (#1140).
tune_bayes() now uses an uncertainty sample if all
predictions from the Gaussian process model fail (#1167).
The Gaussian process model package was changed from to because the former is no longer actively maintained. There are some differences:
Fixed a bug where models with submodel parameters would train all calibration models on predictions from a single submodel value instead of the correct value for each submodel (#1144).
A bug was fixed where NULL results generated during
simulated annealing would cause errors when logging.
Fixed a bug for cases where we tune a grid without a model parameter but with a postprocessing parameter (#1119)
Fixed a bug in augment() when using
last_fit() objects (#1110)
In tune_grid(), a bug was fixed that caused
inefficiency where preprocessing steps were applied to data being
predicted several times (redundantly). We now execute that operation
once. (#1101)
The developer functions val_class_and_single() and
val_class_or_null() are now deprecated (#1161).
check_metrics() is deprecated in favor of
check_metrics_arg() (#1160).
finalize_workflow_preprocessor() is deprecated
(#1157).
Prepends a period to the name, and exports the following functions. This is to improve integration with other packages:
check_grid() (e.g., now named
.check_grid())determine_pred_types()get_config_key()loop_over_all_stages()update_parallel_over()get_data_subsets()int_pctl() wouldn’t work on
last_fit() outcomes when future parallelism was enabled.
(#1099)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.
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.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.
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).
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.
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).
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.
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 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.
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).
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".
Several functions gained an eval_time argument for
the evaluation time of dynamic metrics for censored regression. The
placement of the argument breaks passing-by-position for one or more
other arguments to autoplot.tune_results() and the
developer-focused check_initial() (#857).
Ellipses (…) are now used consistently in the package to require
optional arguments to be named. For functions that previously had
ellipses at the end of the function signature, they have been moved to
follow the last argument without a default value: this applies to
augment.tune_results(),
collect_predictions.tune_results(),
collect_metrics.tune_results(),
select_best.tune_results(),
show_best.tune_results(), and the developer-focused
estimate_tune_results(), load_pkgs(), and
encode_set(). Several other functions that previously did
not have ellipses in their signatures gained them: this applies to
conf_mat_resampled() and the developer-focused
check_workflow(). Optional arguments previously passed by
position will now error informatively prompting them to be named. These
changes don’t apply in cases when the ellipses are currently in use to
forward arguments to other functions (#863).
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)
collect_()
functions where the .iter column was dropped.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.
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)
Fixed bug in select_by_pct_loss() where the model
with the greatest loss within the limit was returned rather than the
most simple model whose loss was within the limit. (#543)
Fixed bug in tune_bayes() where
.Last.tune.result would return intermediate tuning results.
(#613)
Extended show_best(), select_best(),
select_by_one_std_error(),
select_by_pct_loss() to accommodate metrics with a target
value of zero (notably, yardstick::mpe() and
yardstick::msd()). (#243)
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.
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)
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)
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).
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)
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)
Fixed an issue in finalize_recipe() which failed
during tuning of recipe steps that contain multiple tune()
parameters in an single step.
Changed conf_mat_resampled() to return the same type
of object as yardstick::conf_mat() when
tidy = FALSE (#370).
The automatic parameter machinery for sample_size
with the C5.0 engine was changes to use
dials::sample_prop().
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.
collect_predictions() was made generic.
The default tuning parameter for the SVM polynomial degree was
switched from dials::degree() to
dials::prod_degree() since it must be an integer.
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.
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().
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.
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_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.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.
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).CRAN release.
Changed license to MIT
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()
NEWS.md file to track changes to the
package.
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