NEWS.md

In pdp: Partial Dependence Plots

pdp 0.8.1

Bug fixes

• Fixed an ICE plot issue for XGBoost models caused by the switch to foreach package.

Minor changes

• Fixed a couple of URLs in the package documentation.

Breaking changes

• partial()'s progress argument now only accepts a logical and defaults to FALSE.

pdp 0.8.0

New features

• New (experimental) function exemplar() for constructing an "exemplar" record from a data frame or matrix-like object. See ?pdp::exemplar for details (#91).

• partial() gained a new (experimental) feature via the new approx argument. If approx = TRUE, then partial() will compute predictions across the predictors specified in pred.var while holding the other predictors constant (a "poor man's partial dependence" function as Stephen Milborrow, the author of plotmo, puts it). See ?pdp::partial for details.

Breaking changes

• Bumped the R dependency to R (>= 3.6.0) to support the use of grDevices::hcl.colors() in plotPartial().

• Function grid.arrange() and the forward pipe operator %>% are no longer automatically imported from packages gridExtra and magrittr, respectively; users are encouraged to load them manually if needed.

• Removed the palette and alpha arguments from plotPartial() and autoplot(); the latter just got absorbed into the ... argument. By default, plotPartial()'s col.regions argument now corresponds to grDevices::hcl.colors(100), which defaults to the same viridis color palette as before, just without the dependency.

• topPredictors() is now deprecated and will be removed in the next update. Users are advised to use the vip package instead.

Minor changes

• Added support for gradient boosted Cox proportional hazards models in gbm.

• Removed dependency on the retired plyr package by relying directly on the foreach package. Consequently, ICE curves (ice = TRUE) are now slightly faster to compute (since the code refactoring avoids having to post-process ICE data from wide to long format) and the corresponding progress bar (progress = "text") is more honest.

• As a further consequence, the partial() function only supports a simple text-based progress bar (progress = "text"), but more options will possibly be added later.

• Removed dependency on viridis; consequently, to keep the 'viridis' color palettes in autoplot(), this required bumping the ggplot2 dependency to version 3.0.0, as well as some other code tweaks under the hood (#106).

• Removed dependency on mgcv by switching to an internal C implementation of mgcv's in.out() function (#107). (This is used behind the scenes whenever partial() is called with chull = TRUE.)

• "partial" is now a proper subclass of "data.frame" (#104); thanks to @RoelVerbelen for pointing this out.

• Fixed a bug where rug = TRUE would not work properly for xgboost models whenever calling partial() with plot = TRUE.

• Fixed a bug in partial() where the cats argument was never actually passed to pred_grid() (#86).

• Fixed a bug in partial() for "gbm" objects when recursive = TRUE that caused factors (including ordered factors) to be coerced to characters.

Miscellaneous

• Switched from Travis-CI to GitHub Actions for continuous integration.

• Added ICEbox and mlbench to the list of suggested packages.

• Refactored code for easier maintenance.

• Switched to tinytest framework and increased test coverage (#84).

• The internal function get_training_data(), which is used to (attempt to) extract a fitted model's training data whenever train is not specified, is (hopefully) a bit more flexible and robust in certain special cases(#90).

• Minor bug fixes in plotting functions (i.e., autoplot() and plotPartial()).

• Training data that inherits from class "tibble" is still not officially supported, but shouldn't cause as many errors from this point on.

• Using autoplot() with a factor followed by numeric in pred.var no longer seems to be an issue (#79).

pdp 0.7.0

• Added support for e1071::naiveBayes(), an implementation of the standard naive Bayes classifier (#42).

• Fixed a bug in plotPartial() that caused the col.regions argument to have no effect when levelplot = FALSE (#58).

• Fixed a bug with categorical variables in gbm models when recursive = TRUE (#63).

• More informative progress bars (with estimated time to completion!!) powered by the progress package. To use, simply call partial() with the option progress = "progress" (#66).

• Added ORCiD ID to the author field in the DESCRIPTION file.

• Way cooler logo?

• partial() gained several new plotting options: plot.engine, which controls the plotting engine used whenever plot = TRUE (current options include "lattice" (the default) and "ggplot2" (#71).

• The arguments to autoplot() and plotPartial() are now more consistent with each other.

• The names of (most) helper functions have changed from lowerCamelCase to snake_case.

• partial() now works (better) with tibbles (#59).

• partial() now treats "xgb.Booster" objects with objective = "reg:logistc" as regression (#68).

• Removed use of ggplot2::aes_string() in autoplot() (which is soft deprecated as of ggplot2 version 3.0.0) (#73).

pdp 0.6.0

• Properly registered native routines and disabled symbol search.

• Fixed a bug for gbm models using the multinomial distribution.

• Refactored code to improve structure.

• partial() gained three new options: inv.link (experimental), ice, and center. The latter two have to do with constructing individual conditional expectation (ICE) curves and centered ICE (c-ICE) curves. The inv.link option is for transforming predictions from models that can use non-Gaussian distributions (e.g., glm, gbm, and xgboost). Note that these options were added for convenience and the same results (plus much more) can still be obtained using the flexible pred.fun argument. (#36).

• plotPartial() gained five new options: center, plot.pdp, pdp.col, pdp.lwd, and pdp.lty; see ?plotPartial for details.

• Fixed default y-axis label for autoplot() with two numeric predictors (#48).

• Added CITATION file.

• Better support for neural networks from the nnet package.

• Fixed a bug for nnet::multinom() models with binary response.

pdp 0.5.2

• Fixed minor pandoc conversion issue with README.md.

• Added subdirectory called tools to hold figures for README.md.

pdp 0.5.1

• Registered native routines and disabled symbol search.

pdp 0.5.0

• Added support for MASS::lda(), MASS::qda(), and mda::mars().

• New arguments quantiles, probs, and trim.outliers in partial. These arguments make it easier to construct PDPs over the relevant range of a numeric predictor without having to specify pred.grid, especially when outliers are present in the predictors (which can distort the plotted relationship).

• The train argument can now accept matrices; in particular, object of class "matrix" or "dgCMatrix". This is useful, for example, when working with XGBoost models (i.e., objects of class "xgb.Booster").

• New logical argument prob indicating whether or not partial dependence values for classification problems should be returned on the original probability scale, rather than the centered logit; details for the centered logit can be found on page 370 in the second edition of The Elements of Statistical Learning.

• Fixed some typos in NEWS.md.

• New function autoplot for automatically creating ggplot2 graphics from "partial" objects.

pdp 0.4.0

• partial() is now much faster with "gbm" object due to a call to gbm::plot.gbm() whenever pred.grid is not explicitly given by the user. (gbm::plot.gbm() exploits a computational shortcut that does not involve any passes over the training data.)

• New (experimental) function topPredictors() for extracting the names of the most "important" predictors. This should make it one step easier (in most cases) to construct PDPs for the most "important"" features in a fitted model.

• A new argument, pred.fun, allows the user to supply their own prediction function. Hence, it is possible to obtain PDPs based on the median, rather than the mean. It is also possible to obtain PDPs for classification problems on the probability scale. See ?partial for examples.

• Minor bug fixes and documentation tweaks.

pdp 0.3.0

• The ... argument in the call to partial() now refers to additional arguments to be passed onto stats::predict() rather than plyr::aaply(). For example, using partial() with "gbm" objects will require specification of n.trees which can now simply be passed to partial() via the ... argument.

• Added the following arguments to partial(): progress (plyr-based progress bars), parallel (plyr/foreach-based parallel execution), and paropts (list of additional arguments passed onto foreach when parallel = TRUE).

• Various bug fixes.

• partial() now throws an informative error message when the pred.grid argument refers to predictors not in the original training data.

• The column name for the predicted value has been changed from "y" to "yhat".

pdp 0.2.0

• randomForest is no longer imported.

• Added support for the caret package (i.e., objects of class "train").

• Added example data sets: boston (corrected Boston housing data) and pima (corrected Pima Indians diabetes data).

• Fixed error that sometimes occurred when chull = TRUE causing the convex hull to not be computed.

• Refactored plotPartial() to be more modular.

• Added gbm support for most non-"binomial" families`.

pdp 0.1.0

• randomForest is now imported.

• Added examples.

pdp 0.0.6

• Fixed a non canonical CRAN URL in the README file.

pdp 0.0.5

• partial() now makes sure each column of pred.grid has the correct class, levels, etc.

• partial() gained a new option, levelplot, which defaults to TRUE. The original option, contour, has changed and now specifies whether or not to add contour lines whenever levelplot = TRUE.

pdp 0.0.4

• Fixed a number of URLs.

• More thorough documentation.

pdp 0.0.2

• Fixed a couple of URLs and typos.

• Added more thorough documentation.

• Added support for C5.0, Cubist, nonlinear least squares, and XGBoost models.

pdp 0.0.1

• Initial release.

pdp documentation built on June 8, 2022, 1:07 a.m.