Changelog

Source: NEWS.md

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

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