workflows 1.0.0
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 eitheroutcomesorpredictorsare 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 fromworkflowobjects.
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 newpreprocessorandspecarguments 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 ifcontrolis 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 - 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 parsniprand_forest()model is used with a ranger engine, dummy variables will not be created, because ranger can handle factors directly (#51, #53).
