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Changelog

Source: NEWS.md

glex 0.6.0.9000 (development version)

  • glex() now warns when x contains missing values (#41): splits are evaluated without the model’s learned missing-value direction, so the decomposition of rows with NAs is unreliable and does not sum to the model prediction. Proper missing value support needs more investigation; previously such input passed silently.

  • glex() on xgboost models fit with early stopping now decomposes only the trees up to best_iteration, matching what predict() evaluates by default. Previously all fitted trees were decomposed, so the components did not sum to the prediction. Closes #42.

  • randomPlantedForest (>= 0.3.0) is now required (in Suggests:): it fixes an out-of-bounds read in purify_3() that crashed R on Windows (PlantedML/randomPlantedForest#61), so rpf tests and examples run on all platforms.

  • $shap is now a scalar NA (with a warning) when the decomposition is constrained via max_interaction or features: a constrained decomposition does not sum to the full model prediction, so SHAP values cannot be reconstructed from it without violating the efficiency property. Previously, misleading values were returned. $m is unaffected. Supersedes #18, closes #13.

  • glex() objects gain a $constrained field naming the arguments that constrained the decomposition (character(0) if complete), so length(x$constrained) > 0 tells you whether $shap is usable.

  • A requested constraint only invalidates $shap if it actually drops something: a model can contain a high-order term whose value is zero, in which case dropping it leaves the decomposition (and the SHAP values) unchanged. glex() confirms the constraint against the model’s own predictions and, if the dropped terms were inert, keeps $shap and emits a message instead of a warning.

  • glex() on randomPlantedForest models now returns $shap as well, computed from the components like for the other model classes (for multiclass models, $shap columns are class-specific like those of $m). Previously the field was absent. Constraining the decomposition post-hoc via max_interaction or features is now detected for rpf models too, where it previously passed silently.

  • glex() objects gain a $remainder field: what the constraint’s dropped terms are collectively worth, per observation, on the scale of $m. It is present exactly when the decomposition is constrained, so intercept + rowSums(m) + remainder reconstructs the model prediction whether or not a constraint was applied. randomPlantedForest objects already carried a $remainder computed by predict_components(), but the other model classes did not; the two are now one field with one definition, computed in one place. Closes #11, supersedes #25. Unlike #25, this covers classification and other non-identity links: the remainder is taken on the scale the model is decomposed on, so for xgboost it is on the link scale (plogis(intercept + rowSums(m) + remainder) recovers a binary:logistic probability), while ranger probability forests and randomPlantedForest are decomposed on the response scale directly.

  • For randomPlantedForest classification models, glex() now confirms the constraint against predict(type = "numeric") rather than the default type = "prob". rpf decomposes the raw score, while type = "prob" applies rpf’s response function (a clamp to [0, 1] for loss = "L2", the inverse link for "logit" and "exponential") and for binary models returns the classes in an order whose first column is not the one being decomposed. The components reconstruct the raw score exactly, so $remainder now measures only what the dropped terms are worth, instead of silently absorbing the back-transformation and the class mix-up.

  • glex_explain() now reads SHAP values from $shap instead of recomputing them from the components, so the glex object is the single source of truth. The SHAP reference bar is omitted for constrained decompositions, where it previously showed a value reconstructed from the constrained components, and for objects created by earlier versions of glex, which have no $shap.

  • print() on a glex object reports when the decomposition is constrained.

glex 0.6.0

  • Extended compatibility with xgboost, now requiring xgboost (>= 3.0.0) in Suggests:
    • Updated tests and examples for the new API
  • Plot colors are now configurable via options() and documented in ?glex_options: glex.palette (diverging palette for continuous interaction effects; NULL for the default shap-style gradient, or the name of a scico palette), glex.palette_discrete (palette for categorical predictors: a color vector, "okabe-ito", a scico palette name, or a brewer palette name), glex.colors_sign (negative/positive colors in glex_explain() and gradient endpoints), and glex.color_line (main effect line/column color).
  • Default colors updated to follow the blue/red convention of the Python shap/shapiq packages: continuous interaction effects use a #008BFB → white → #FF0051 gradient (previously the cyclic scico palette "vikO"), and glex_explain() uses the same blue/red for negative/positive contributions.

glex 0.5.2

  • Fix newer xgboost R package compatibility:
    • Updated tree schema column name from Quality to Gain, matching xgboost commit 73713de ([R] rename Quality -> Gain (#9938), in upstream v2.1.0)
    • Implemented dynamic base_score extraction to replace hardcoded 0.5 intercept (modern xgboost auto-estimates base_score)
    • Fixed floating-point precision mismatch in C++ split comparators by casting to float, matching xgboost predictor behavior
    • Added node reindexing to ensure contiguous row ordering in tree matrices
    • For CRAN users, this schema change is observed in the later 3.x package line (for example 3.1.2.1+), which requires R >= 4.3.0
    • These changes ensure accurate model explanations for current CRAN xgboost releases

glex 0.5.1

  • Fix path-dependent algorithm by computing the proper covers manually
  • Allow glex() to accept data frames as input

glex 0.5.0

  • Optimize FastPD to be able to handle more features using bitmask represenation (#29)
  • Remove old probFuntion parameter to glex() in favor of weighting_method.
  • Add new progress bar when explaining many trees using glex()

glex 0.4.2

  • Optimize FastPD by only computing components up to max_interaction (#24)

glex 0.4.1

  • Added FastPD (arXiv) as default probFunction in glex.
  • Add rug plot to plot_*_effect[s] functions for continuous predictors, defaulting to showing a rug on the bottom side (rug_side = "b").

glex 0.4.0

  • Add support for ranger objects to glex() (PR#17).
  • Add new optional parameter probFunction to glex() which specifies the probability function for weighting/marginalization of the leaves (PR#17).
    By default, glex() now uses the empirical marginal probabilities to perform the weighting. Previously, the weighting of the leaves was done based on a path-dependent method.
  • Add theme_glex() as a default theme to all plots.
    This is almost identical to [ggplot2::theme_minimal()] aside from increased base font size and convenience flags to toggle vertical and horizontal grid lines.
  • Add subset_components() and subset_component_names() to make it easier to extract only components belonging to a given main term.
  • Add pre-processed version of Bikeshare data from ISLR2 to streamlined examples.
  • Add plot_pdp(), a version of plot_main_effect() with the intercept added.
  • Limit max_interaction in glex.xgb.Booster to max_depth parameter of xgboost model. If max_depth is not set during model fit, the default value of 6 is assumed. This prevents glex from returning spurious higher-order interactions containing values numerically close to 0.
  • Extend plot functions to multiclass classification. In most cases that means facetting by the target class.
  • Overhaul glex_explain to a waterfall plot showing the SHAP decomposition for given predictors.
  • autoplot.glex_vi gains a max_interaction argument in line with glex_explain, and now similarly aggregates terms that either fall below threshold or exceed max_interaction.
  • Add glex.print for a more compact output in case of large numbers of terms.

glex 0.3.0

  • Added plotting functions for main, 2- and 3-degree interaction terms
  • Added ggplot2::autoplot S3 method for glex objects.
  • Added pkgdown site
  • Added Bikesharing article
  • Added glex_vi() to compute variable importance scores including interaction terms, including a corresponding ggplot2::autoplot method.
  • Added glex_explain() to plot prediction components of a single observation.

glex 0.2.0

  • Convert glex() to an S3 generic function with methods for xgboost and randomPlantedForest models.
  • Fix bug in xgboost method that could lead to wrongly computed shap values in certain cases.
  • Added a NEWS.md file to track changes to the package.