ingredients 2.3.0

• breaking change: calculate_variable_splits() now treats integer variables as categorical. This change is propagated to ceteris_paribus(), partial_dependence(), accumulated_dependence(), conditional_dependence(), aggregate_profiles(), DALEX::predict_profile(), DALEX::model_profile()
• fix an error in ceteris_paribus / calculate_variable_splits when tidymodels uses integer variables #145
• fix an error in show_observations #148. This change is propagated to DALEX::plot.predict_profile() #540.
• fix #149 by replacing all class(x) = "y" with is(x, "y")

ingredients 2.2.1

• 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

ingredients 2.2.0

• fixes explanations when data has one column #137

ingredients 2.0.1

• code and documentation maintenance #130
• fixed an error when N = NULL in partial_dependence() etc. #134

ingredients 2.0

• 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

ingredients 1.3.1

• 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

ingredients 1.3.0

• deprecate n_sample argument in feature_importance (now it’s N) #113
• plot_profile now handles multilabel models

ingredients 1.2.0

• DALEX is moved to Suggests as in #112
• plot_categorical_ceteris_paribus can plot bars (again)
• add bind_plots function

ingredients 1.1.0

• support R v4.0 and depend on R v3.5 to comply with DALEX
• new arguments title and subtitle in several plots

ingredients 1.0.0

• change dependency to dependence #103

ingredients 0.5.2

• ceteris_paribus profiles are now working for categorical variables
• show_profiles, show_observations, show_residuals are now working for categorical variables

ingredients 0.5.1

• synchronisation with changes in DALEX 0.5
• new argument desc_sorting in plot.variable_importance_explainer #94

ingredients 0.5.0

• 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

ingredients 0.4.2

• 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

ingredients 0.4.1

• fixed rounding problem in the describe function (#76)

ingredients 0.4

• CRAN release

ingredients 0.3.12

• aspect_importance is moved to DALEXtra (#66)
• examples are updated in order to reflect changes in titanic_imputed from DALEX (#65)

ingredients 0.3.11

• 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

ingredients 0.3.10

• added triplot function that illustrates hierarchical aspect_importance() groupings
• changes in aspect_importance() functions
• added back the vigniette for aspect_importance()

ingredients 0.3.9

• change only_numerical parameter to variable_type in functions aggregated_profiles(), cluster_profiles(), plot() and others, as requested in #15

ingredients 0.3.8

• Natural language description generated with describe() function for ceteris_paribus(), feature_importance() and aggregate_profiles() explanations.

ingredients 0.3.7

• aggregated_profiles_conditional and aggregated_profiles_accumulated are rewritten with some code fixes

ingredients 0.3.6

• a new version of lime is implemented in the lime()/aspect_importance() function.
• Kasia Pekala and Huber Baniecki are added as contributors.

ingredients 0.3.5

• new feature #29. Feature importance now takes an argument B that replicates permutations B times and calculates average from drop loss.

ingredients 0.3.4

• 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

ingredients 0.3.3

• show_profiles and show_residuals functions extend Ceteris Paribus Plots.
• show_aggreagated_profiles is renamed to show_aggregated_profiles
• centering of ggplot2 title

ingredients 0.3.2

• 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

ingredients 0.3.1

• added references to PM VEE
• partial_profiles(), accumulated_profiles() and conditional_profiles for variable effects
• major changes in function names and file names

ingredients 0.3

• ceteris_paribus_2d extends classical ceteris paribus profiles
• ceteris_paribus_oscillations calculates oscilations for ceteris paribus profiles
• fixed examples and file names

ingredients 0.2

• cluster_profiles helps to identify interactions
• partial_dependency calculates partial dependency plots
• aggregate_profiles calculates partial dependency plots and much more

ingredients 0.1

• port of model_feature_importance and model_feature_response from DALEX to ingredients
• added tests