Changelog
Source:NEWS.md
workflows 1.1.4
CRAN release: 2024-02-19
While
augment.workflow()
previously never returned a.resid
column, the method will now return residuals under the same conditions thataugment.model_fit()
does (#201).augment.workflow()
gained aneval_time
argument, enabling augmenting censored regression models (#200, #213).The prediction columns are now appended to the LHS rather than RHS of
new_data
inaugment.workflow()
, following analogous changes in parsnip (#200).Each of the
pull_*()
functions soft-deprecated in workflows v0.2.3 now warn on every usage (#198).add_recipe()
will now error informatively when supplied a trained recipe (#179).
workflows 1.1.3
CRAN release: 2023-02-22
- The workflows methods for
generics::tune_args()
andgenerics::tunable()
are now registered unconditionally (#192).
workflows 1.1.2
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).
workflows 1.1.0
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 withparsnip::fit()
andparsnip::fit_xy()
(#160, tidymodels/parsnip#801).broom::augment()
now works correctly in the edge case where you had supplied a hardhat blueprint withcomposition
set to either"matrix"
or"dgCMatrix"
(#148).butcher::axe_fitted()
now axes the recipe preprocessor that is stored inside a workflow, which will reduce the size of thetemplate
data frame that is stored in the recipe (#147).add_formula()
no longer silently ignores offsets supplied withoffset()
. Instead, it now errors atfit()
time with a message that encourages you to use a model formula throughadd_model(formula = )
instead (#162).
workflows 1.0.0
CRAN release: 2022-07-05
New
add_case_weights()
,update_case_weights()
, andremove_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.
workflows 0.2.6
CRAN release: 2022-03-18
- Fixed tests that relied on an incorrect assumption about the version of tune that is installed.
workflows 0.2.5
CRAN release: 2022-03-16
Improved error message in
workflow_variables()
if eitheroutcomes
orpredictors
are missing (#144).Removed ellipsis dependency in favor of equivalent functions in rlang.
New
extract_parameter_set_dials()
andextract_parameter_dials()
methods to extract parameter sets and single parameters fromworkflow
objects.
workflows 0.2.4
CRAN release: 2021-10-12
add_model()
andupdate_model()
now use...
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
pull_*()
functions.
workflows 0.2.3
CRAN release: 2021-07-15
workflow()
has gained newpreprocessor
andspec
arguments 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).New
extract_*()
functions have been added that supersede the existingpull_*()
functions. This is part of a larger move across the tidymodels packages towards a family of genericextract_*()
functions. Thepull_*()
functions have been soft-deprecated, and will eventually be removed (#106).
workflows 0.2.2
CRAN release: 2021-03-10
add_variables()
now allows for specifying a bundle of model terms throughadd_variables(variables = )
, supplying a pre-created set of variables with the newworkflow_variables()
helper. This is useful for supplying a set of variables programmatically (#92).New
is_trained_workflow()
for determining if a workflow has already been trained through a call tofit()
(#91).fit()
now errors immediately ifcontrol
is not created bycontrol_workflow()
(#89).Added
broom::augment()
andbroom::glance()
methods for trained workflow objects (#76).Added support for butchering a workflow using
butcher::butcher()
.Updated to testthat 3.0.0.
workflows 0.2.0
CRAN release: 2020-09-15
-
New
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).
workflows 0.1.3
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).
workflows 0.1.2
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 - 1
levels), one-hot encoding (n
levels), 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 parsniprand_forest()
model is used with a ranger engine, dummy variables will not be created, because ranger can handle factors directly (#51, #53).