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Changelog

Source: NEWS.md

pdp (development version)

Breaking changes

  • Removed topPredictors(), which had been deprecated since pdp 0.8.0; use the vip package to identify important predictors instead.

pdp 0.9.1

Deprecations

  • plotPartial() is deprecated in favor of the consolidated plot() interface: the plot() methods gained a lattice argument, so plot(..., lattice = TRUE) now produces the same lattice-based displays (including 3-D wireframe surfaces and paneled three-predictor plots). plotPartial() continues to work (with a warning) but will be removed in a future release.

Bug fixes

  • Plotting now works for non-syntactic predictor names (e.g., names containing dashes or spaces) in both the tinyplot and lattice engines (#113).

  • partial(chull = TRUE) no longer fails for sparse ("dgCMatrix") training data (#77).

  • Plotting multi-predictor output from pred.fun (i.e., objects with a yhat.id column and more than one predictor) now throws an informative error instead of producing a nonsense display (#111), (#112).

  • Failing to recover the training data (e.g., for recent xgboost models when train is not supplied) now always produces the informative “please supply the training data” error rather than a cryptic match.call() failure (#137).

New features

  • pred.var can now be omitted whenever pred.grid is supplied; it defaults to colnames(pred.grid) (#122).

  • New frac argument in partial() for randomly sampling a fraction of the training data before computing the partial dependence function; mostly useful for reducing the number of ICE curves and/or computation time (#74).

  • New color.by argument in the plot() methods for coloring ICE/c-ICE curves by another feature (#81).

  • New bars argument in plot.partial() for displaying factor predictors as bar plots instead of points (#132).

  • exemplar() gained a cats argument for matrix-like objects so categorical columns are summarized by their most frequent value (rather than a rounded median); used by partial(approx = TRUE) (#115).

Miscellaneous

  • Removed the (long stale) slowtests/ directory; its coverage is redundant with the tinytest suite and the new vignettes.

  • Pruned Suggests packages that were no longer used anywhere in the package (adabag, AmesHousing, Cubist, ipred, mda, partykit, and vip).

pdp 0.9.0

Breaking changes

  • The ggplot2-based autoplot() methods have been removed in favor of new, lightweight base R plot() methods built on tinyplot. Consequently, partial()’s plot.engine argument now accepts "tinyplot" in place of "ggplot2", and pdp no longer depends on ggplot2 or rlang.

  • "tinyplot" is now the default plotting engine for partial(..., plot = TRUE); note that it draws the plot directly (as a side effect) and returns the data frame of partial dependence values invisibly. Use plot.engine = "lattice" for the previous behavior (i.e., a returned "trellis" object).

Bug fixes

  • Fixed a serious bug in the recursive (i.e., weighted tree traversal) method for "gbm" objects that produced incorrect partial dependence values for factor predictors; the factor levels were passed to the underlying C++ routine using 1-based (rather than 0-based) integer codes, shifting each level’s value and reading out of bounds for the last level.

  • partial() no longer errors for "gbm" objects fit with list-valued distributions (e.g., distribution = list(name = "quantile", alpha = 0.5)).

  • The convex hull displayed by plotPartial(chull = TRUE) is now correct whenever the training data contain missing values.

  • Conflicting grid options (e.g., specifying both grid.resolution and quantiles) now always throw an error, even when all of the predictors listed in pred.var are factors.

  • Removed a stray "FALSE" that was appended to the message displayed when requesting a progress bar with the recursive method.

New features

  • New tinyplot-based plot() methods for "partial", "ice", and "cice" objects; see ?plot.partial for details and examples.

  • New batch.size argument in partial() for scoring multiple grid points per call to predict() (i.e., batching); this is often substantially faster than the default one-call-per-grid-point approach at the cost of additional memory. For example, try batch.size = 1e6 to score (roughly) one million rows per call.

Miscellaneous

  • foreach was moved from Imports to Suggests; it is now only required for parallel execution (i.e., whenever parallel = TRUE).

  • Removed unused packages from Suggests (keras, reticulate, and dplyr).

  • topPredictors() (which is deprecated) now throws an informative error when caret is not installed.

  • Internal cleanup: the brute force method (pardep()) was consolidated from four nearly identical loops into one, and the prediction wrappers in get_predictions.R now share a common helper.

  • Fixed failing tests due to changes in the xgboost package.

pdp 0.8.2

CRAN release: 2024-10-28

Minor changes

  • Set R_NO_REMAP per changes in CRAN policies.

  • Updated package documentation since @docType "package" is deprecated.

  • Removed unnecessary get_training_data.Rd file and cleaned up arguments.

  • Update citation file to use c() instead of personList() and bibentry() instead of citEntry().

pdp 0.8.1

CRAN release: 2022-06-07

Bug fixes

  • Fixed an ICE plot issue for XGBoost models caused by switching to the 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

CRAN release: 2022-05-11

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

CRAN release: 2018-08-27

  • 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

CRAN release: 2017-07-20

  • 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

CRAN release: 2017-03-13

  • 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

CRAN release: 2017-01-07

  • 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

CRAN release: 2016-12-18

  • 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

CRAN release: 2016-12-03

  • 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

CRAN release: 2016-10-06

  • 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

CRAN release: 2016-09-17

  • 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

CRAN release: 2016-09-02

  • Initial release.