NEWS.md

DALEX (development)

• @mayer79 fixed the use of the deprecated size argument in ggplot2 version 4, which is now called linewidth (#576)

DALEX 2.5.2

• fixed NOTE during devtools::document() such as S3 method plot.... needs @export or @exportS3method tag.

DALEX 2.5.1

• adding the support for calculating kernel SHAP values via predict_parts() function

DALEX 2.5.0

• breaking change: change the name of loss_yardstick() to get_loss_yardstick() and loss_default() to get_loss_default()
• add loss_one_minus_accuracy() and get_loss_one_minus_accuracy() (#535)

DALEX 2.4.3

• added implementation of aSHAP (aggregated SHAP) and waterfall plot (#519)
• adding a new system for default color schemes (#541)
• added cross_entropy as model performance measure to multilabel settings #542

DALEX 2.4.2

• removed the yardstick dependency
• new vignette added 'How to use DALEX with the yardstick package?'
• new datasets from World Happiness Report: happiness_test and happiness_train (#513)
• new datasets from COVID morality: covid_summer and covid_spring (#513)

DALEX 2.4.1

• changed URLs in the DESCRIPTION as requested in (#484)
• Fix model_info documentation (#498)
• Support for yardstic metrics (#495)

DALEX 2.4.0

• Changed default in explain(colorize=) according to (#473)
• Added explain/yhat support for partykit (#438)
• explain() warns if target has more than two values for classification (#418)

DALEX 2.3.0

• The plot.model_performance_roc, loss_one_minus_auc and model_performance_auc functions are rewritten to handle repeated predictions (#442)

DALEX 2.2.2

• The plot function works for list of explanations (if possible) (#424)

DALEX 2.2.1

• Order of explainer labels in different plots is the same. To get to this point, orders in plot.model_performance(..., geom = "histogram" & "boxplot") are reversed (#400)
• Fixed multiclass explainer when data has one column (#405)

DALEX 2.2

• Now explainer handles R functions (#396)
• predict_parts function handles the N argument natively (#394)

DALEX 2.1.1

• All encouters of nieghbour(s) (EN-spelling) were replaced with neighbor(s) (US-spelling) for the consistency and backword compatibility.
• Fixed bug when predict_diagnostics raised error if neighbor value was higer than nrow(explainer$data).

DALEX 2.1.0

• Added new parameter (predict_function_target_column) to explain function that allows specifying positive class in binary classification tasks (#250).
• Fixed model_diagnostics() returning an error when data is matrix (#355)

DALEX 2.0.1

• Fixed R package not working with Python Explainer (#318)
• Fixed model_diagnostics() returning an error when y_hat or residuals is of array class (#319)
• Fixed grid lines in theme_drwhy on Windows
• Fixed logical values in y rising unnecessery warnings for classification task (#336)

DALEX 2.0

• plot.predict_diagnostics now passess ellipsis to plot.ceteris_paribus_explainer
• This version requires iBreakDown v1.3.1 and ingredients v1.3.1
• Fixed plot.predict_parts and plot.model_profile (#277).
• Fixed plot.model_profile for multiple profiles (#237).
• External tests for not suggested packages added to gh-actions (#237).
• Extended and refreshed documentation (#237).
• All dontrun statements changed to donttest according to CRAN policy.
• Added value for s parameter in yhat.glmnet and yhat.cv.glmnet.
• Fixed model_diagnostics passing wrong arguments to residual_function.
• Fixed aesthetic for hist geometry in plot.model_performance using wrong arugments.
• model_performance will not work if model_info$type is NULL.
• Corrected description of N in model_parts (#287).
• New warning messages for y parameter in explain function.
• Solved bug in yhat.ranger causing predicts_parts not to plot correctly when task is multiclass.
• variable_effect is now deprecated

DALEX 1.3.1.1

• fixed typo in predict_parts_oscillations_emp
• rewrite tests

DALEX 1.3.1

• added predict_parts class to objects and plot.predict_parts function
• added model_parts class to objects and plot.model_parts function
• plot parameters added to the documentation
• Now in the predict_profile function one can specify how grid points shall be calculated, see variable_splits_type (#267).
• The predict_part function has two new options for type: oscillations_uni and oscillations_emp (#267).
• The plot.model_performance function has a new geom="prc" for Precision Recall curve (#273).

DALEX 1.3.0

• DALEX now fully supports multiclass classification.
• explain() will use new residual function (1 - true class probability) if multiclass classification is detected.
• model_performance() now support measures for multiclass classification.
• Remove ggpubr from suggests.
• lossFunction argument is now deprecated in plot.model_performance(). Use the loss_function argument.
• model_profile color changed to colors_discrete_drwhy(1) which impacts the color of the line in plot.model_profile
• loss_name attribute added to loss functions. It will be passed to plot function for objects created with model_parts.

DALEX 1.2.1

• fixed tests and WARNINGs on CRAN
• model_profile for Accumulated Local rofiles by default use centering (center = TRUE)
• deprecate n_sample argument in model_parts (now it's N) (#175)

DALEX 1.2.0

• ingredients and iBreakDown are now imported by DALEX

DALEX 1.1

• updated title for plot.model_performance (#160).
• in explain removed check related to duplicated target variable (#164).

DALEX 1.0

• variable_profile calls ingredients::ceteris_paribus (#131).
• variable_response and feature_response moved to variable_effect and now it calls ingredients::partial_dependency (#131).
• prediction_breakdown moved to variable_attribution and now it calls iBreakDown::break_down (#131).
• updated variable_importance, not it calls the ingredients::variable_importance (#131).
• updated model_performance (#130).
• added yhat for lrm models from rms package
• theme_drwhy has now left aligned title and subtitle.
• residuals_distribution calculates now diagnostic plots based on residuals (#143).
• model_performance calculates several metrics for classification and regression models (#146).
• plot.model_performance now supports ROC charts, LIFT charts, Cummulative Gain charts, histograms, boxplots and ecdf
• residuals_distributon is now individual_diagnostics and produces objects of the class individual_diagnostics_explainers
• plot.individual_diagnostics_explainers now plots objects of the class individual_diagnostics_explainers
• yhat for caret models now returns matrix instead of data.frame
• model_diagnostics new function that plots residuals againes selected variable
• names of functions are changed to be compliant with latest version of the XAI pyramide

DALEX 0.4.9

• updated titanic_imputed (#113).
• added weights to the explainer. Note that not all explanations know how to handle weights (#118).
• yhat() and model_info() now support models created with gbm package.

DALEX 0.4.8

• new argument colorize in the explain() as requested in (#112).
• new generic function model_info(). It will extract basic irnformation like model package nam version and task type. (#109, #110)
• new functions update_data() and update_label(). (#114))

DALEX 0.4.7

• new dataset titanic_imputed as requested in (#104).
• the explain() function now detects if target variable y is present in the data as requested in (#103).
• the DALEX GitHub repository is transfered from pbiecek/DALEX to ModelOriented/DALEX.

DALEX 0.4.6

• Examples updated. Now they use only datasets available from DALEX.
• yhat.H2ORegressionModel and yhat.H2OBinomialModel moved to (DALEXtra) and merged into explain_h2o() function.
• yhat.WrappedModelmoved to (DALEXtra) and merged as explain_mlr() function.
• Wrapper for scikit-learn models restored in (DALEXtra) package.
• loss_one_minus_auc function added to loss_functions.R. It uses 1-auc to compute loss. Function created by Alicja Gosiewska.
• Extension for DALEX avaiable at (DALEXtra)

DALEX 0.4.5

• the explain() function is more verbose. With verbose = TRUE (default) it prints detailed information about elements of an explainer (#95).

DALEX 0.4.4

• new color schemes: colors_breakdown_drwhy(), colors_discrete_drwhy() and colors_diverging_drwhy().
• in this version the scikitlearn_model() is removed as it is not working with python 2.7

DALEX 0.4.3

• New support for scikit-learn models via scikitlearn_model()

DALEX 0.4.2

• New yhat functions for mlr, h2o and caret packages (added by Szymon).

DALEX 0.4.1

• plot.variable_importance_explainer() has now desc_sorting argument. If FALSE then variable importance will be sorted in an increasing order (#41).

DALEX 0.4.0

• ingredients and iBreakDown are added to additional features (#72).
• feature_response() and variable_response() are marked as Deprecated. It is suggested to use ingredients::partial_dependency(), ingredients::accumulated_dependency() instead (#74).
• variable_importance() is marked as Deprecated. It is suggested to use ingredients::feature_importance() instead (#75).
• prediction_breakdown() is marked as Deprecated. It is suggested to use iBreakDown::break_down() or iBreakDown::shap() instead (#76).

DALEX 0.3.1

• updated filenames

DALEX 0.3

• pdp, factorMerger and ALEPlot are going to Suggested. (#60). In next releases they will be deprecated.
• added predict function that calls the predict_function hidden in the explainer object. (#58).

DALEX 0.2.9

• the titanic dataset is copied from stablelearner package. Some features are transformed (some NA replaced with 0, more numeric features).

DALEX 0.2.8

• DALEX is being prepared for tighter integration with iBreakDown and ingredients.
• temporally there is a duplicated single_variable and single_feature
• Added new theme_drwhy().
• New arguments in the plot.variable_importance_explainer(). Namely bar_width with widths of bars and show_baseline if baseline shall be included in these plots.
• New skin in the plot.variable_response_explainer().
• New skin in the plot.prediction_breakdown_explainer().

DALEX 0.2.7

• Test datasets are now named apartments_test and HR_test
• For binary classification we return just a second column. NOTE: this may cause some unexpected problems with code dependend on defaults for DALEX 0.2.6.

DALEX 0.2.6

• New versions of yhat for ranger and svm models.

DALEX 0.2.5

• Residual distribution plots for model performance are now more legible when multiple models are plotted. The styling of plot and axis titles have also been improved (@kevinykuo).
• The defaults of single_prediction() are now consistent with breakDown::broken(). Specifically, baseline is now 0 by default instead of "Intercept". The user can also specify the baseline and other arguments by passing them to single_prediction (@kevinykuo, #39). WARNING: Change in the default value of baseline.
• New yhat.* functions help to handle additional parameters to different predict() functions.
• Updated CITATION info

DALEX 0.2.4

• New dataset HR and HRTest. Target variable is a factor with three levels. Is used in examples for classification.
• The plot.model_performance() has now show_outliers parameter. Set it to anything >0 and observations with largest residuals will be presented in the plot. (#34)

DALEX 0.2.3

• Small fixes in variable_response() to better support of gbm models (c8393120ffb05e2f3c70b0143c4e92dc91f6c823).
• Better title for plot_model_performance() (e5e61d0398459b78ea38ccc980c4040fd853f449).
• Tested with breakDown v 0.1.6.

DALEX 0.2.2

• The single_variable() / variable_response() function uses predict_function from explainer (#17)

DALEX 0.2.1

• The explain() function converts tibbles to data.frame when specified as data argument (#15)
• The default generic explain.default() should help when explain() from dplyr is loaded after DALEX (#16)

DALEX 0.2.0

• New names for some functions: model_performance(), variable_importance(), variable_response(), outlier_detection(), prediction_breakdown(). Old names are now deprecated but still working. (#12)
• A new dataset apartments - will be used in examples
• variable_importance() allows work on full dataset if n_sample is negative
• plot_model_performance() uses ecdf or boxplots (depending on geom parameter).

DALEX 0.1.8

• Function single_variable() supports factor variables as well (with the use of factorMerger package). Remember to use type='factor' when playing with factors. (#10)
• Change in the function explain(). Old version has an argument predict.function, now it's predict_function. New name is more consistent with other arguments. (#7)
• New vigniette for xgboost model (#11)

DALEX 0.1.1

• Support for global model structure explainers with variable_dropout() function

DALEX 0.1

• DALEX package is now public
• explain() function implemented
• single_prediction() function implemented
• single_variable() function implemented