- added
facet_scales parameter to plot.aggregated_profiles_explainer ('free_x' by default) #138 and plot.ceteris_paribus_explainer ('free_x' or 'free_y' by default, depending on plot type) #136
- fixes explanations when data has one column #137
-
plot.ceteris_paribus_explainer now by default for categorical variables plots profiles (not lines -prev default- nor bars)
- ALE plots are now centered around average y_hat #126
- colors from DrWhy color palette is used for CP #125
- default
subtitle value in plot.fi changed to NULL from NA (unification)
- now in the
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
- deprecate
n_sample argument in feature_importance (now it’s N) #113
-
plot_profile now handles multilabel models
-
DALEX is moved to Suggests as in #112
-
plot_categorical_ceteris_paribus can plot bars (again)
- add
bind_plots function
- support
R v4.0 and depend on R v3.5 to comply with DALEX
- new arguments
title and subtitle in several plots
- change
dependency to dependence #103
-
ceteris_paribus profiles are now working for categorical variables
-
show_profiles, show_observations, show_residuals are now working for categorical variables
- synchronisation with changes in DALEX 0.5
- new argument
desc_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 number
- added boxplots to
plot.feature_importance and plotD3.feature_importance that showcase the permutation data
- in
aggregate_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)
- change
variable_type and variables arguments usage in the aggregate_profiles, plot.ceteris_paribus and plotD3.ceteris_paribus
- remove
variable_type argument from plotD3.aggregated_profiles (now the same as in plot.aggregated_profiles)
- Kasia Pekala is moved as contributor to the
DALEXtra as aspect_importance is moved to DALEXtra as well (See v0.3.12 changelog)
- added Travis-CI for OSX
- fixed rounding problem in the describe function (#76)
-
aspect_importance is moved to DALEXtra (#66)
- examples are updated in order to reflect changes in
titanic_imputed from DALEX (#65)
- modified
plot.aspect_importance - it can plot more than single figure
- modified
triplot, plot.aspect_importance and plot_group_variables to add more clarity in plots and allow some parameterization
- added
triplot function that illustrates hierarchical aspect_importance() groupings
- changes in
aspect_importance() functions
- added back the vigniette for
aspect_importance()
- change
only_numerical parameter to variable_type in functions aggregated_profiles(), cluster_profiles(), plot() and others, as requested in #15
-
aggregated_profiles_conditional and aggregated_profiles_accumulated are rewritten with some code fixes
- a new version of
lime is implemented in the lime()/aspect_importance() function.
- Kasia Pekala and Huber Baniecki are added as contributors.
- new feature #29. Feature importance now takes an argument
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 features
- fix in ceteris_paribus, now it handles models with just one variable
- fix #27 for multiple rows
-
show_profiles and show_residuals functions extend Ceteris Paribus Plots.
-
show_aggreagated_profiles is renamed to show_aggregated_profiles
- centering of ggplot2 title
- added new functions
describe() and print.ceteris_paribus_descriptions() for text based descriptions of Ceteris Paribus explainers
-
plot.ceteris_paribus_explainer works now also for categorical variables. Use the only_numerical = FALSE to force bars
- added references to PM VEE
-
partial_profiles(), accumulated_profiles() and conditional_profiles for variable effects
- major changes in function names and file names
-
ceteris_paribus_2d extends classical ceteris paribus profiles
-
ceteris_paribus_oscillations calculates oscilations for ceteris paribus profiles
- fixed examples and file names
-
cluster_profiles helps to identify interactions
-
partial_dependency calculates partial dependency plots
-
aggregate_profiles calculates partial dependency plots and much more
- port of
model_feature_importance and model_feature_response from DALEX to ingredients
- added tests