extract_parameter_dials()
and extract_parameter_set_dials()
generics
to extend the family of extract_*()
generics.Added a new family of extract_*()
S3 generics for extracting important
components from various tidymodels objects. S3 methods will be defined in
other tidymodels packages. For example, tune will register an
extract_workflow()
method to easily extract the workflow embedded within the
result of tune::last_fit()
.
A logical indicators
argument is no longer allowed in
default_formula_blueprint()
. This was soft-deprecated in hardhat 0.1.4,
but will now result in an error (#144).
use_modeling_files()
(and therefore, create_modeling_package()
) now
ensures that all generated functions are templated on the model name. This
makes it easier to add multiple models to the same package (#152).
All preprocessors can now mold()
and forge()
predictors to one of three
output formats (either tibble, matrix, or dgCMatrix
sparse matrix) via the
composition
argument of a blueprint (#100, #150).
Setting indicators = "none"
in default_formula_blueprint()
no longer
accidentally expands character columns into dummy variable columns. They
are now left completely untouched and pass through as characters. When
indicators = "traditional"
or indicators = "one_hot"
, character columns
are treated as unordered factors (#139).
The indicators
argument of default_formula_blueprint()
now takes character
input rather than logical. To update:
``` indicators = TRUE -> indicators = "traditional"
indicators = FALSE -> indicators = "none" ```
Logical input for indicators
will continue to work, with a warning, until
hardhat 0.1.6, where it will be formally deprecated.
There is also a new indicators = "one_hot"
option which expands all factor
columns into K
dummy variable columns corresponding to the K
levels of
that factor, rather than the more traditional K - 1
expansion.
Updated to stay current with the latest vctrs 0.3.0 conventions.
scream()
is now stricter when checking ordered factor levels in new data
against the ptype
used at training time. Ordered factors must now have
exactly the same set of levels at training and prediction time. See
?scream
for a new graphic outlining how factor levels are handled (#132).
The novel factor level check in scream()
no longer throws a novel level
warning on NA
values (#131).
default_recipe_blueprint()
now defaults to prepping recipes with
fresh = TRUE
. This is a safer default, and guards the user against
accidentally skipping this preprocessing step when tuning (#122).
model_matrix()
now correctly strips all attributes from the result of the
internal call to model.matrix()
.
forge()
now works correctly when used with a recipe that has a predictor
with multiple roles (#120).
Require recipes 0.1.8 to incorporate an important bug fix with juice()
and
0-column selections.
NEWS.md
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