CRAN release: 2023-02-22
- The workflows methods for
generics::tunable()are now registered unconditionally (#192).
CRAN release: 2022-11-16
Tightens integration with parsnip’s machinery for checking that needed parsnip extension packages are loaded.
add_model()will now error if a model specification is supplied that requires a missing extension package (#184).
Introduces support for unsupervised model specifications via the modelenv package (#180).
CRAN release: 2022-09-26
Simon Couch is now the maintainer (#170).
add_model()now errors if you try to add a model specification that contains an unknown mode. This is a breaking change, as previously in some cases it would successfully “guess” the mode. This change brings workflows more in line with
broom::augment()now works correctly in the edge case where you had supplied a hardhat blueprint with
compositionset to either
butcher::axe_fitted()now axes the recipe preprocessor that is stored inside a workflow, which will reduce the size of the
templatedata frame that is stored in the recipe (#147).
add_formula()no longer silently ignores offsets supplied with
offset(). Instead, it now errors at
fit()time with a message that encourages you to use a model formula through
add_model(formula = )instead (#162).
CRAN release: 2022-07-05
remove_case_weights()for specifying a column to use as case weights which will be passed on to the underlying parsnip model (#118).
R >=3.4.0 is now required, in line with the rest of the tidyverse.
CRAN release: 2022-03-18
- Fixed tests that relied on an incorrect assumption about the version of tune that is installed.
CRAN release: 2022-03-16
Improved error message in
predictorsare missing (#144).
Removed ellipsis dependency in favor of equivalent functions in rlang.
extract_parameter_dials()methods to extract parameter sets and single parameters from
CRAN release: 2021-10-12
...to separate the required arguments from the optional arguments, forcing optional arguments to be named. This change was made to make it easier for us to extend these functions with new arguments in the future.
The workflows method for
generics::required_pkgs()is now registered unconditionally (#121).
Internally cleaned up remaining usage of soft-deprecated
CRAN release: 2021-07-15
workflow()has gained new
specarguments for adding a preprocessor (such as a recipe or formula) and a parsnip model specification directly to a workflow upon creation. In many cases, this can reduce the lines of code required to construct a complete workflow (#108).
extract_*()functions have been added that supersede the existing
pull_*()functions. This is part of a larger move across the tidymodels packages towards a family of generic
pull_*()functions have been soft-deprecated, and will eventually be removed (#106).
CRAN release: 2021-03-10
add_variables()now allows for specifying a bundle of model terms through
add_variables(variables = ), supplying a pre-created set of variables with the new
workflow_variables()helper. This is useful for supplying a set of variables programmatically (#92).
is_trained_workflow()for determining if a workflow has already been trained through a call to
fit()now errors immediately if
controlis not created by
broom::glance()methods for trained workflow objects (#76).
Added support for butchering a workflow using
Updated to testthat 3.0.0.
CRAN release: 2020-10-08
.fit_finalize()for internal usage by the tune package.
CRAN release: 2020-09-15
add_variables()for specifying model terms using tidyselect expressions with no extra preprocessing. For example:
wf <- workflow() %>% add_variables(y, c(var1, start_with("x_"))) %>% add_model(spec_lm)
One benefit of specifying terms in this way over the formula method is to avoid preprocessing from
model.matrix(), which might strip the class of your predictor columns (as it does with Date columns) (#34).
CRAN release: 2020-08-10
- A test has been updated to reflect a change in parsnip 0.1.3 regarding how intercept columns are removed during prediction (#65).
CRAN release: 2020-07-07
- When using a formula preprocessor with
add_formula(), workflows now uses model-specific information from parsnip to decide whether to expand factors via dummy encoding (
n - 1levels), one-hot encoding (
nlevels), or no expansion at all. This should result in more intuitive behavior when working with models that don’t require dummy variables. For example, if a parsnip
rand_forest()model is used with a ranger engine, dummy variables will not be created, because ranger can handle factors directly (#51, #53).
CRAN release: 2020-03-17
- hardhat’s minimum required version has been bumped to 0.1.2, as it contains an important fix to how recipes are prepped by default.