NEWS.md
N = NULL in partial_dependence() etc. #134
subtitle value in plot.fi changed to NULL from NA (unification)ceteris_paribus function one can specify how grid points shall be calculated, see variable_splits_type
ceteris_paribus and aggregates are now working with missing data, this solves #120
plot(ceteris_paribus) change default color to label or ids if more than one profile is detected, this solves #123
ceteris_paribus has now argument variable_splits_with_obs which included values from new_observations in the variable_splits, this solves #124
n_sample argument in feature_importance (now it’s N) #113
plot_profile now handles multilabel modelsDALEX is moved to Suggests as in #112
plot_categorical_ceteris_paribus can plot bars (again)bind_plots functionR v4.0 and depend on R v3.5 to comply with DALEX
title and subtitle in several plotsdependency to dependence #103
ceteris_paribus profiles are now working for categorical variablesshow_profiles, show_observations, show_residuals are now working for categorical variablesdesc_sorting in plot.variable_importance_explainer #94
feature_importance now does 15 permutations on each variable by default. Use the B argument to change this numberplot.feature_importance and plotD3.feature_importance that showcase the permutation dataaggregate_profiles: preserve _x_ column factor order and sort its values #82
aggregate_profiles use now gaussian kernel smoothing. Use the span argument for fine control over this parameter (#79)variable_type and variables arguments usage in the aggregate_profiles, plot.ceteris_paribus and plotD3.ceteris_paribus
variable_type argument from plotD3.aggregated_profiles (now the same as in plot.aggregated_profiles)DALEXtra as aspect_importance is moved to DALEXtra as well (See v0.3.12 changelog)plot.aspect_importance - it can plot more than single figuretriplot, plot.aspect_importance and plot_group_variables to add more clarity in plots and allow some parameterizationtriplot function that illustrates hierarchical aspect_importance() groupingsaspect_importance() functionsaspect_importance()
only_numerical parameter to variable_type in functions aggregated_profiles(), cluster_profiles(), plot() and others, as requested in #15describe() function for ceteris_paribus(), feature_importance() and aggregate_profiles() explanations.aggregated_profiles_conditional and aggregated_profiles_accumulated are rewritten with some code fixeslime is implemented in the lime()/aspect_importance() function.B that replicates permutations B times and calculates average from drop loss.plotD3 now supports Ceteris Paribus Profiles.feature_importance now can take variable_grouping argument that assess importance of group of featuresshow_profiles and show_residuals functions extend Ceteris Paribus Plots.show_aggreagated_profiles is renamed to show_aggregated_profiles
describe() and print.ceteris_paribus_descriptions() for text based descriptions of Ceteris Paribus explainersplot.ceteris_paribus_explainer works now also for categorical variables. Use the only_numerical = FALSE to force barspartial_profiles(), accumulated_profiles() and conditional_profiles for variable effectsceteris_paribus_2d extends classical ceteris paribus profilesceteris_paribus_oscillations calculates oscilations for ceteris paribus profiles