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.