- 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
- 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)
- changed URLs in the DESCRIPTION as requested in (#484)
- Fix model_info documentation (#498)
- Support for yardstic metrics (#495)
- 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)
- The
plot.model_performance_roc
, loss_one_minus_auc
and model_performance_auc
functions are rewritten to handle repeated predictions (#442)
- The
plot
function works for list of explanations (if possible) (#424)
- 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)
- Now explainer handles R functions (#396)
-
predict_parts
function handles the N
argument natively (#394)
- 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$data)
.
- Added new parameter (
predict_function_target_column
) to explain
function that allows specifying positive class in binary classification tasks (#250).
- Fixed
model_diagnostics()
returning an error when data
is matrix
(#355)
- Fixed R package not working with Python Explainer (#318)
- Fixed
model_diagnostics()
returning an error when y_hat
or residuals
is of array
class (#319)
- Fixed grid lines in
theme_drwhy
on Windows
- Fixed logical values in y rising unnecessery warnings for classification task (#336)
-
plot.predict_diagnostics
now passess ellipsis to plot.ceteris_paribus_explainer
- This version requires
iBreakDown v1.3.1
and ingredients v1.3.1
- Fixed
plot.predict_parts
and plot.model_profile
(#277).
- Fixed
plot.model_profile
for multiple profiles (#237).
- External tests for not suggested packages added to gh-actions (#237).
- Extended and refreshed documentation (#237).
- All dontrun statements changed to donttest according to CRAN policy.
- Added value for
s
parameter in yhat.glmnet
and yhat.cv.glmnet
.
- Fixed
model_diagnostics
passing wrong arguments to residual_function.
- Fixed aesthetic for
hist
geometry in plot.model_performance
using wrong arugments.
-
model_performance
will not work if model_info$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
- fixed typo in
predict_parts_oscillations_emp
- rewrite tests
- 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
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
.
- 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)
-
ingredients
and iBreakDown
are now imported by DALEX
- updated title for
plot.model_performance
(#160).
- in
explain
removed check related to duplicated target variable (#164).
-
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
- 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.
- 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.
- 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)
- the
explain()
function is more verbose. With verbose = TRUE
(default) it prints detailed information about elements of an explainer (#95).
- New support for scikit-learn models via
scikitlearn_model()
- New
yhat
functions for mlr
, h2o
and caret
packages (added by Szymon).
-
plot.variable_importance_explainer()
has now desc_sorting
argument. If FALSE then variable importance will be sorted in an increasing order (#41).
-
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).
- the
titanic
dataset is copied from stablelearner
package. Some features are transformed (some NA
replaced with 0
, more numeric features).
-
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()
.
- 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.
- New versions of
yhat
for ranger
and svm
models.
- 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
- 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)
- 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.
- The
single_variable() / variable_response()
function uses predict_function
from explainer
(#17)
- 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).
- 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)
- Support for global model structure explainers with
variable_dropout()
function
- DALEX package is now public
-
explain()
function implemented
-
single_prediction()
function implemented
-
single_variable()
function implemented