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Changelog

Source: NEWS.md

parsnip 1.0.0

Model Specification Changes

  • Enable the use of case weights for models that support them.

  • show_model_info() now indicates which models can utilize case weights.

  • Model type functions will now message informatively if a needed parsnip extension package is not loaded (#731).

  • Refactored internals of model specification printing functions. These changes are non-breaking for extension packages, but the new print_model_spec() helper is exported for use in extensions if desired (#739).

Bug fixes

  • Fixed bug where previously set engine arguments would propagate through update() methods despite fresh = TRUE (#704).

  • Fixed a bug where an error would be thrown if arguments to model functions were namespaced (#745).

  • predict(type = "prob") will now provide an error if the outcome variable has a level called "class" (#720).

  • An inconsistency for probability type predictions for two-class GAM models was fixed (#708)

  • Fixed translated printing for null_model() (#752)

Other changes

  • Added a glm_grouped() function to convert long data to the grouped format required by glm() for logistic regression.

  • xgb_train() now allows for case weights

  • Added ctree_train() and cforest_train() wrappers for the functions in the partykit package. Engines for these will be added to other parsnip extension packages.

  • Exported xgb_predict() which wraps xgboost’s predict() method for use with parsnip extension packages (#688).

  • Added a developer function, .model_param_name_key that translates names of tuning parameters.

parsnip 0.2.1

CRAN release: 2022-03-17

  • Fixed a major bug in spark models induced in the previous version (#671).

  • Updated the parsnip add-in with new models and engines.

  • Updated parameter ranges for some tunable() methods and added a missing engine argument for brulee models.

  • Added information about how to install the mixOmics package for PLS models (#680)

parsnip 0.2.0

CRAN release: 2022-03-09

Model Specification Changes

Bug fixes

  • A bug for class predictions of two-class GAM models was fixed (#541)

  • Fixed a bug for logistic_reg() with the LiblineaR engine (#552).

  • The list column produced when creating survival probability predictions is now always called .pred (with .pred_survival being used inside of the list column).

  • Fixed outcome type checking affecting a subset of regression models (#625).

  • Prediction using multinom_reg() with the nnet engine with a single row no longer fails (#612).

Other Changes

  • When the xy interface is used and the underlying model expects to use a matrix, a better warning is issued when predictors contain non-numeric columns (including dates).

  • The fit time is only calculated when the verbosity argument of control_parsnip() is 2L or greater. Also, the call to system.time() now uses gcFirst = FALSE. (#611)

  • fit_control() is soft-deprecated in favor of control_parsnip().

  • New extract_parameter_set_dials() method to extract parameter sets from model specs.

  • New extract_parameter_dials() method to extract a single parameter from model specs.

  • Argument interval was added for prediction: For types "survival" and "quantile", estimates for the confidence or prediction interval can be added if available (#615).

  • set_dependency() now allows developers to create package requirements that are specific to the model’s mode (#604).

  • varying() is soft-deprecated in favor of tune().

  • varying_args() is soft-deprecated in favor of tune_args().

  • An autoplot() method was added for glmnet objects, showing the coefficient paths versus the penalty values (#642).

  • parsnip is now more robust working with keras and tensorflow for a larger range of versions (#596).

  • xgboost engines now use the new iterationrange parameter instead of the deprecated ntreelimit (#656).

Developer

  • Models information can be re-registered as long as the information being registered is the same. This is helpful for packages that add new engines and use devtools::load_all() (#653).

parsnip 0.1.7

CRAN release: 2021-07-21

Model Specification Changes

  • A model function (gen_additive_mod()) was added for generalized additive models.

  • Each model now has a default engine that is used when the model is defined. The default for each model is listed in the help documents. This also adds functionality to declare an engine in the model specification function. set_engine() is still required if engine-specific arguments need to be added. (#513)

  • parsnip now checks for a valid combination of engine and mode (#529)

  • The default engine for multinom_reg() was changed to nnet.

Other Changes

parsnip 0.1.6

CRAN release: 2021-05-27

Model Specification Changes

  • A new linear SVM model svm_linear() is now available with the LiblineaR engine (#424) and the kernlab engine (#438), and the LiblineaR engine is available for logistic_reg() as well (#429). These models can use sparse matrices via fit_xy() (#447) and have a tidy method (#474).

  • For models with glmnet engines:

    • A single value is required for penalty (either a single numeric value or a value of tune()) (#481).
    • A special argument called path_values can be used to set the lambda path as a specific set of numbers (independent of the value of penalty). A pure ridge regression models (i.e., mixture = 1) will generate incorrect values if the path does not include zero. See issue #431 for discussion (#486).

  • The liquidSVM engine for svm_rbf() was deprecated due to that package’s removal from CRAN. (#425)

  • The xgboost engine for boosted trees was translating mtry to xgboost’s colsample_bytree. We now map mtry to colsample_bynode since that is more consistent with how random forest works. colsample_bytree can still be optimized by passing it in as an engine argument. colsample_bynode was added to xgboost after the parsnip package code was written. (#495)

  • For xgboost, mtry and colsample_bytree can be passed as integer counts or proportions, while subsample and validation should always be proportions. xgb_train() now has a new option counts (TRUE or FALSE) that states which scale for mtry and colsample_bytree is being used. (#461)

Other Changes

  • Re-licensed package from GPL-2 to MIT. See consent from copyright holders here.

  • set_mode() now checks if mode is compatible with the model class, similar to new_model_spec() (@jtlandis, #467). Both set_mode() and set_engine() now error for NULL or missing arguments (#503).

  • Re-organized model documentation:

    • update methods were moved out of the model help files (#479).
    • Each model/engine combination has its own help page.
    • The model help page has a dynamic bulleted list of the engines with links to the individual help pages.

  • generics::required_pkgs() was extended for parsnip objects.

  • Prediction functions now give a consistent error when a user uses an unavailable value of type (#489)

  • The augment() method was changed to avoid failing if the model does not enable class probabilities. The method now returns tibbles despite the input data class (#487) (#478)

  • xgboost engines now respect the event_level option for predictions (#460).

parsnip 0.1.5

CRAN release: 2021-01-19

  • An RStudio add-in is available that makes writing multiple parsnip model specifications to the source window. It can be accessed via the IDE addin menus or by calling parsnip_addin().

  • For xgboost models, users can now pass objective to set_engine("xgboost"). (#403)

  • Changes to test for cases when CRAN cannot get xgboost to work on their Solaris configuration.

  • There is now an augument() method for fitted models. See augment.model_fit. (#401)

  • Column names for x are now required when fit_xy() is used. (#398)

  • There is now an event_level argument for the xgboost engine. (#420)

  • New mode “censored regression” and new prediction types “linear_pred”, “time”, “survival”, “hazard”. (#396)

  • Censored regression models cannot use fit_xy() (use fit()). (#442)

parsnip 0.1.4

CRAN release: 2020-10-27

  • show_engines() will provide information on the current set for a model.

  • For three models (glmnet, xgboost, and ranger), enable sparse matrix use via fit_xy() (#373).

  • Some added protections were added for function arguments that are dependent on the data dimensions (e.g., mtry, neighbors, min_n, etc). (#184)

  • Infrastructure was improved for running parsnip models in parallel using PSOCK clusters on Windows.

parsnip 0.1.3

CRAN release: 2020-08-04

  • A glance() method for model_fit objects was added (#325)

  • Specific tidy() methods for glmnet models fit via parsnip were created so that the coefficients for the specific fitted parsnip model are returned.

Fixes

  • glmnet models were fitting two intercepts (#349)

  • The various update() methods now work with engine-specific parameters.

parsnip 0.1.2

CRAN release: 2020-07-03

Breaking Changes

  • parsnip now has options to set specific types of predictor encodings for different models. For example, ranger models run using parsnip and workflows do the same thing by not creating indicator variables. These encodings can be overridden using the blueprint options in workflows. As a consequence, it is possible to get a different model fit that previous versions of parsnip. More details about specific encoding changes are below. (#326)

Other Changes

  • tidyr >= 1.0.0 is now required.

  • SVM models produced by kernlab now use the formula method (see breaking change notice above). This change was due to how ksvm() made indicator variables for factor predictors (with one-hot encodings). Since the ordinary formula method did not do this, the data are passed as-is to ksvm() so that the results are closer to what one would get if ksmv() were called directly.

  • MARS models produced by earth now use the formula method.

  • For xgboost, a one-hot encoding is used when indicator variables are created.

  • Under-the-hood changes were made so that non-standard data arguments in the modeling packages can be accommodated. (#315)

New Features

  • A new main argument was added to boost_tree() called stop_iter for early stopping. The xgb_train() function gained arguments for early stopping and a percentage of data to leave out for a validation set.

  • If fit() is used and the underlying model uses a formula, the actual formula is pass to the model (instead of a placeholder). This makes the model call better.

  • A function named repair_call() was added. This can help change the underlying models call object to better reflect what they would have obtained if the model function had been used directly (instead of via parsnip). This is only useful when the user chooses a formula interface and the model uses a formula interface. It will also be of limited use when a recipes is used to construct the feature set in workflows or tune.

  • The predict() function now checks to see if required modeling packages are installed. The packages are loaded (but not attached). (#249) (#308) (tidymodels/workflows#45)

  • The function req_pkgs() is a user interface to determining the required packages. (#308)

parsnip 0.1.1

CRAN release: 2020-05-06

New Features

Fixes

  • The error message for missing packages was fixed (#289 and #292)

Other Changes

  • S3 dispatch for tidy() was broken on R 4.0.

parsnip 0.0.5

CRAN release: 2020-01-07

Fixes

  • A bug (#206 and #234) was fixed that caused an error when predicting with a multinomial glmnet model.

Other Changes

  • glmnet was removed as a dependency since the new version depends on 3.6.0 or greater. Keeping it would constrain parsnip to that same requirement. All glmnet tests are run locally.

  • A set of internal functions are now exported. These are helpful when creating a new package that registers new model specifications.

New Features

Breaking Changes

  • There were some mis-mapped parameters (going between parsnip and the underlying model function) for spark boosted trees and some keras models. See 897c927.

parsnip 0.0.4

CRAN release: 2019-11-02

New Features

  • The time elapsed during model fitting is stored in the $elapsed slot of the parsnip model object, and is printed when the model object is printed.

  • Some default parameter ranges were updated for SVM, KNN, and MARS models.

  • The model udpate() methods gained a parameters argument for cases when the parameters are contained in a tibble or list.

  • fit_control() is soft-deprecated in favor of control_parsnip().

Fixes

  • A bug was fixed standardizing the output column types of multi_predict and predict for multinom_reg.

  • A bug was fixed related to using data descriptors and fit_xy().

  • A bug was fixed related to the column names generated by multi_predict(). The top-level tibble will always have a column named .pred and this list column contains tibbles across sub-models. The column names for these sub-model tibbles will have names consistent with predict() (which was previously incorrect). See 43c15db.

  • A bug was fixed standardizing the column names of nnet class probability predictions.

parsnip 0.0.3.1

CRAN release: 2019-08-06

Test case update due to CRAN running extra tests (#202)

parsnip 0.0.3

CRAN release: 2019-07-31

Unplanned release based on CRAN requirements for Solaris.

Breaking Changes

  • The method that parsnip stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models. The methods are detailed in ?get_model_env and the package vignette for adding models.

  • The mode needs to be declared for models that can be used for more than one mode prior to fitting and/or translation.

  • For surv_reg(), the engine that uses the survival package is now called survival instead of survreg.

  • For glmnet models, the full regularization path is always fit regardless of the value given to penalty. Previously, the model was fit with passing penalty to glmnet’s lambda argument and the model could only make predictions at those specific values. (#195)

New Features

  • add_rowindex() can create a column called .row to a data frame.

  • If a computational engine is not explicitly set, a default will be used. Each default is documented on the corresponding model page. A warning is issued at fit time unless verbosity is zero.

  • nearest_neighbor() gained a multi_predict method. The multi_predict() documentation is a little better organized.

  • A suite of internal functions were added to help with upcoming model tuning features.

  • A parsnip object always saved the name(s) of the outcome variable(s) for proper naming of the predicted values.

parsnip 0.0.2

CRAN release: 2019-03-22

Small release driven by changes in sample() in the current r-devel.

New Features

  • A “null model” is now available that fits a predictor-free model (using the mean of the outcome for regression or the mode for classification).

  • fit_xy() can take a single column data frame or matrix for y without error

Other Changes

  • varying_args() now has a full argument to control whether the full set of possible varying arguments is returned (as opposed to only the arguments that are actually varying).

  • fit_control() not returns an S3 method.

  • For classification models, an error occurs if the outcome data are not encoded as factors (#115).

  • The prediction modules (e.g. predict_class, predict_numeric, etc) were de-exported. These were internal functions that were not to be used by the users and the users were using them.

  • An event time data set (check_times) was included that is the time (in seconds) to run R CMD check using the “r-devel-windows-ix86+x86_64` flavor. Packages that errored are censored.

Bug Fixes

  • varying_args() now uses the version from the generics package. This means that the first argument, x, has been renamed to object to align with generics.

  • For the recipes step method of varying_args(), there is now error checking to catch if a user tries to specify an argument that cannot be varying as varying (for example, the id) (#132).

  • find_varying(), the internal function for detecting varying arguments, now returns correct results when a size 0 argument is provided. It can also now detect varying arguments nested deeply into a call (#131, #134).

  • For multinomial regression, the .pred_ prefix is now only added to prediction column names once (#107).

  • For multinomial regression using glmnet, multi_predict() now pulls the correct default penalty (#108).

  • Confidence and prediction intervals for logistic regression were only computed the intervals for a single level. Both are now computed. (#156)

parsnip 0.0.1

CRAN release: 2018-11-12

First CRAN release

parsnip 0.0.0.9005

  • The engine, and any associated arguments, are now specified using set_engine(). There is no engine argument

parsnip 0.0.0.9004

  • Arguments to modeling functions are now captured as quosures.
  • others has been replaced by ...
  • Data descriptor names have been changed and are now functions. The descriptor definitions for “cols” and “preds” have been switched.

parsnip 0.0.0.9003

  • regularization was changed to penalty in a few models to be consistent with this change.
  • If a mode is not chosen in the model specification, it is assigned at the time of fit. 51
  • The underlying modeling packages now are loaded by namespace. There will be some exceptions noted in the documentation for each model. For example, in some predict methods, the earth package will need to be attached to be fully operational.

parsnip 0.0.0.9002

  • To be consistent with snake_case, newdata was changed to new_data.
  • A predict_raw method was added.

parsnip 0.0.0.9001

  • A package dependency suffered a new change.

parsnip 0.0.0.9000

  • The fit interface was previously used to cover both the x/y interface as well as the formula interface. Now, fit() is the formula interface and fit_xy() is for the x/y interface.
  • Added a NEWS.md file to track changes to the package.
  • predict methods were overhauled to be consistent.
  • MARS was added.