While augment.workflow() previously never returned a .resid column, the method will now return residuals under the same conditions that augment.model_fit() does (#201).
augment.workflow() gained an eval_time argument, enabling augmenting censored regression models (#200, #213).
The prediction columns are now appended to the LHS rather than RHS of new_data in augment.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).
generics::tune_args() and generics::tunable() are now registered unconditionally (#192).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).
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 parsnip::fit() and parsnip::fit_xy() (#160, tidymodels/parsnip#801).
broom::augment() now works correctly in the edge case where you had supplied a hardhat blueprint with composition 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 the template data 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).
New add_case_weights(), update_case_weights(), and 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.
Improved error message in workflow_variables() if either outcomes or predictors are missing (#144).
Removed ellipsis dependency in favor of equivalent functions in rlang.
New extract_parameter_set_dials() and extract_parameter_dials() methods to extract parameter sets and single parameters from workflow objects.
add_model() and update_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.
workflow() has gained new preprocessor and spec 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 existing pull_*() functions. This is part of a larger move across the tidymodels packages towards a family of generic extract_*() functions. The pull_*() functions have been soft-deprecated, and will eventually be removed (#106).
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).
New is_trained_workflow() for determining if a workflow has already been trained through a call to fit() (#91).
fit() now errors immediately if control is not created by control_workflow() (#89).
Added broom::augment() and broom::glance() methods for trained workflow objects (#76).
Added support for butchering a workflow using butcher::butcher().
Updated to testthat 3.0.0.
.fit_finalize() for internal usage by the tune package.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).
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 parsnip rand_forest() model is used with a ranger engine, dummy variables will not be created, because ranger can handle factors directly (#51, #53).NEWS.md file to track changes to the package.