modeling-package -> modeling-usethis at the
request of CRAN.New family of spruce_*_multiple() functions to support standardizing
multi-outcome predictions (#223, with contributions from @cregouby).
New fct_encode_one_hot() that encodes a factor as a one-hot indicator matrix
(#215).
default_recipe_blueprint() has gained a strings_as_factors argument, which
is passed on to recipes::prep() (#212).
Using a formula blueprint with indicators = "none" and character predictors
now works properly if you provide a character column that only contains a
single value (#213).
Using a formula blueprint with indicators = "traditional" or
indicators = "one_hot" and character predictors now properly enforces the
factor levels generated by those predictors on new_data during forge()
(#213).
Using a formula blueprint with indicators = "none" now works correctly if
there is a variable in the formula with a space in the name (#217).
mold() and forge() generally have less overhead (#235, #236).
Added more documentation about importance and frequency weights in
?importance_weights() and ?frequency_weights() (#214).
New internal recompose() helper (#220).
We have reverted the change made in hardhat 1.0.0 that caused recipe
preprocessors to drop non-standard roles by default when calling forge().
Determining what roles are required at bake() time is really something
that should be controlled within recipes, not hardhat. This results in the
following changes (#207):
The new argument, bake_dependent_roles, that was added to
default_recipe_blueprint() in 1.0.0 has been removed. It is no longer
needed with the new behavior.
By default, forge() will pass on all columns from new_data to bake()
except those with roles of "outcome" or "case_weights". With
outcomes = TRUE, it will also pass on the "outcome" role. This is
essentially the same as the pre-1.0.0 behavior, and means that, by default,
all non-standard roles are required at bake() time. This assumption is
now also enforced by recipes 1.0.0, even if you aren't using hardhat or
a workflow.
In the development version of recipes, which will become recipes 1.0.0,
there is a new update_role_requirements() function that can be used to
declare that a role is not required at bake() time. hardhat now knows how
to respect that feature, and in forge() it won't pass on columns of
new_data to bake() that have roles that aren't required at bake()
time.
Fixed a bug where the results from calling mold() using hardhat < 1.0.0 were
no longer compatible with calling forge() in hardhat >= 1.0.0. This could
occur if you save a workflow object after fitting it, then load it into an
R session that uses a newer version of hardhat (#200).
Internal details related to how blueprints work alongside mold() and
forge() were heavily re-factored to support the fix for #200. These changes
are mostly internal or developer focused. They include:
Blueprints no longer store the clean/process functions used when calling
mold() and forge(). These were stored in blueprint$mold$clean(),
blueprint$mold$process(), blueprint$forge$clean(), and
blueprint$forge$process() and were strictly for internal use. Storing
them in the blueprint caused problems because blueprints created with old
versions of hardhat were unlikely to be compatible with newer versions of
hardhat. This change means that new_blueprint() and the other blueprint
constructors no longer have mold or forge arguments.
run_mold() has been repurposed. Rather than calling the $clean() and
$process() functions (which, as mentioned above, are no longer in the
blueprint), the methods for this S3 generic have been rewritten to directly
call the current versions of the clean and process functions that live in
hardhat. This should result in less accidental breaking changes.
New run_forge() which is a forge() equivalent to run_mold(). It
handles the clean/process steps that were previously handled by the
$clean() and $process() functions stored directly in the blueprint.
Recipe preprocessors now ignore non-standard recipe roles (i.e. not
"outcome" or "predictor") by default when calling forge(). Previously,
it was assumed that all non-standard role columns present in the original
training data were also required in the test data when forge() is called.
It seems to be more often the case that those columns are actually not
required to bake() new data, and often won't even be present when making
predictions on new data. For example, a custom "case_weights" role might be
required for computing case-weighted estimates at prep() time, but won't
be necessary at bake() time (since the estimates have already been
pre-computed and stored). To account for the case when you do require a
specific non-standard role to be present at bake() time,
default_recipe_blueprint() has gained a new argument,
bake_dependent_roles, which can be set to a character vector of
non-standard roles that are required.
New weighted_table() for generating a weighted contingency table, similar to
table() (#191).
New experimental family of functions for working with case weights. In
particular, frequency_weights() and importance_weights() (#190).
use_modeling_files() and create_modeling_package() no longer open the
package documentation file in the current RStudio session (#192).
rlang >=1.0.2 and vctrs >=0.4.1 are now required.
Bumped required R version to >= 3.4.0 to reflect tidyverse standards.
Moved tune() from tune to hardhat (#181).
Added extract_parameter_dials() and extract_parameter_set_dials() generics
to extend the family of extract_*() generics.
mold() no longer misinterprets :: as an interaction term (#174).
When indicators = "none", mold() no longer misinterprets factor columns
as being part of an inline function if there is a similarly named non-factor
column also present (#182).
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 file to track changes to the package.