stacks 1.0.0
stacks 1.0.0 is the first production release of the package. While
this release includes only a few minor bug fixes, it’s accompanied by a
white paper recently published in the Journal of Open Source software.
You can read that paper here!
This release:
- Addresses re-introduction of a bug arising from outcome levels that
are not valid column names in the multinomial classification setting
(#133).
- Fixes bug where stacks will return incorrect predictions if an
elastic net meta-learner is used, the
type
argument to
predict
is set to "class"
, and the outcome
levels differ from alphabetical order.
- Transitions package internals from functions deprecated from the
recipes package.
stacks 0.2.4 (GitHub only)
This is a GitHub-only release and does not change package source
code. This update includes a data-raw/paper
subdirectory
containing source for a contributed paper to the Journal of Open Source
Software.
stacks 0.2.3
- Addressed deprecation warning in
add_candidates
(#99).
- Improved clarity of warnings/errors related to failed hyperparameter
tuning and resample fitting (#110).
- Reduced model stack object size and fixed bug where object size of
model stack inflated drastically after saving to file (#116). Also,
regenerated example objects with this change–saved model objects may
need to be regenerated in order to interface with newer versions of the
package.
- Introduced a
times
argument to
blend_predictions
that is passed on to
rsample::bootstraps
when fitting stacking coefficients.
Reducing this argument from its default (25
) greatly
reduces the run time of blend_predictions
(#94).
- The package will now load packages necessary for model fitting at
fit_members()
, if available, and fail informatively if not
(#118).
- Fixed bug where meta-learner tuning would fail with outcome names
and levels including the string
"class"
(#125).
stacks 0.2.2
- Fixed errors arising from outcome levels that are not valid column
names in the multinomial classification setting.
- Fixed
collect_parameters
failing to return stacking
coefficients in the two-class classification setting.
- Regenerated example objects with updated {rsample}
fingerprinting–saved model objects may need to be regenerated in order
to build stacks combining models generated before and after this
update.
stacks 0.2.1
- Updates for importing workflow sets that use the
add_variables()
preprocessor.
- Plot fixes for cases where coefficients are negative.
- Performance and member plots now show the effect of multiple mixture
values.
- Package diagrams now have alt text.
stacks 0.2.0
Breaking changes
This release of the package changes some elements of the internal
structure of model stacks. As such, model stacks stored as saved objects
will need to be regenerated before predicting, plotting, printing,
etc.
New features
- The package now supports elastic net models as a meta-learner via
the
mixture
argument to
blend_predictions
.
- The package can now add candidates from
workflow_map
objects from the new {workflowsets} package. The interface to
add_candidates
for doing so is the same as with
tune_results
objects, and add_candidates
is
now a generic function.
- Objects tuned with racing methods from the {finetune} package can
now be added as candidate members.
Bug fixes
- Fixed bug in determining member hyperparameters during member
fitting when using non-RMSE/ROC AUC metrics.
- Fixed bug arising from model definition names that are not valid
column names. The package will now message in the case that the provided
names are not valid column names and use
make.names
for
associated candidate members.
Miscellaneous improvements
- Drop {digest} dependency in favor of {tune}/{rsample}
“fingerprinting” to check consistency of resamples.
fit_members()
will now warn when supplied a model stack
whose members have already been fitted.
- Integrate with {tune} functionality for appropriately coloring
errors, warnings, and messages.
- Improved faceting and axis scales to make
autoplot
with
type = "members"
more informative.
- Various improvements to documentation.
stacks 0.1.0
Initial release!