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Added extract_postprocessor() generic (#247).
Added extract_fit_time() generic (#218).
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.
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They may not be fully stable and should be used with caution. We make no claims about them.
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