Skip to content

v1.0.0
Compare
Choose a tag to compare
@mikolajsp mikolajsp released this 20 Mar 19:30
· 318 commits to main since this release
v1.0.0
76993e0
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
  • breaking change: refactored the structure of model_performance_survival object - calculated metrics are now in a $result list.
  • added new calculation_method for surv_shap() called "kernelshap" that use kernelshap package and its implementation of improved Kernel SHAP (set as default) (#45)
  • rename old method "kernel" to "exact_kernel"
  • added new import (kernelshap package)
  • fixed invalid color palette order in plot feature importance
  • fixed predict_parts survshap running out of memory with more than 16 variables (#25)
  • added max_vars parameter for predict_parts explanations (#27)
  • set max_vars to 7 for every method
  • refactored survshap code (#29, #30, #43)
  • fixed survshap error when target columns named different than time and status (#44)
  • fixed survlime error when all variables are categorical (#46)
  • fixed subtitles in feature importance plots (#11)
  • added the possibility to set themes with set_theme_survex() (#32)
  • added the possibility of plotting multiple predict_parts() and model_parts() explanations in one plot (#12)
  • fixed the x axis of plots (it now starts from 0) (#37)
  • added geom_rug() to all time-dependent plots, marking event and censoring times (#35)
  • refactored surv_feature_importance.R - change auxiliary columns to include _ in their name. Necessary changes also done to plotting and printing functions. (#28)
  • changed default type argument of model_parts() to "difference" (#33)
  • refactored integration of metrics (#31)
  • changed behaviour of categorical_variables argument in model_parts() and predict_parts(). If it contains variable names not present in the variables argument, they will be added at the end. (#39)
  • added ROC AUC calculation and plotting for selected timepoints in model_performance() (#22)
  • added explanation_label parameter to predict_parts() function that can overwrite explainer label and thus, enable plotting multiple local SurvSHAP(t) explanations. (#47)
  • improved the printing of the explainer (#36)
  • reduced the default number of time points for evaluation when creating the explainer to 50
Assets 2
Loading