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• numpy>=2.0.0 compatibility: replace instances of x.ptp() with np.ptp(x) and np.Inf with np.inf (#571)
• added a way to pass sample_weight to loss functions in model_parts() (variable importance) using weights from dx.Explainer (#563)
• fixed the visualization of shap_wrapper for shap==0.45.0

• increase the dependencies to python>=3.8, pandas>=1.5.0, numpy>=1.23.3 and add python==3.11 to CI
• added keras.src.models.sequential.Sequential to classes with a known predict_function; it should fix changes in keras==3.0.0 and tensorflow==2.16.0
• turn off verbose in the predict method of tensorflow/keras models that changed in tensorflow>=2.9.0
• update the warning occurring when specifying variable_splits (#558)
• fix an error occuring in predict_profile() when a DataFrame has MultiIndex in pandas>=1.3.0 (#550)
• fix gaussian norm() calculation in model_profile() from pi*sqrt(2) to sqrt(2*pi)
• fix a warning (future error) between prepare_numerical_categorical() and prepare_x() with pandas==2.1.0
• fix a warning (future error) concerning the default value of numeric_only in pandas.DataFrame.corr() in dalex.aspect.calculate_assoc_matrix()

• add ZeroDivisionError to precision and recall functions (#532)
• add a warning to calculate_depend_matrix() when there is a variable with only one value (#537)
• fix missing EDA plots in (Python) Arena (#544)
• fix baseline positions in the subplots of the predict parts explanations: BreakDown, Shap (#545)

This release consists of mostly maintenance updates and, after a year, marks the Beta -> Stable release.

• increase the dependency from python>=3.6 to python>=3.7 (at this moment, both numpy and pandas depend on python>=3.8), and add python==3.10 to CI
• increase the dependencies to pandas>=1.2.5, numpy>=1.20.3 (#526), scipy>=1.6.3, plotly>=5.1.0, and tqdm>=4.61.2 due to errors with pandas (see tqdm/#1199)
• remove the use of pd.Series.append() (#489)
• remove the use of np.isnan causing error in dalex.fairness (#491)
• fix iBreakDown plot y-axis labels (#493)
• stop the Arena's werkzeug server using a clearner and still supported API (#518)

• added fairness plot for regression models to Arena (dalex/#408)
• added new facet_scales parameter to AP.plot and CP.plot, which allows to free the y-axis with facet_scales="free" (dalex/#469); consistent with R (DALEX/#468, ingredients/#140)

• fixed AP and CP progress bars

• added new aspect module, which will focus on groups of dependent variables @krzyzinskim & @arturzolkowski
• added new scipy>=1.5.4 dependency

• improved the calculation of AUC, ROC plot (#459)

• wrong yaxis labels in VariableImportance.plot(split="variable") (#451)
• repr_html() didn't work for explanation objects before using the fit method (#449)

• added new Aspect object with the predict_triplot, model_triplot, predict_parts, model_parts, get_aspects methods
• added new PredictTriplot, ModelTriplot, PredictAspectImportance, ModelAspectImportance objects with the plot method

• added bias mitigation techniques (resample, reweight, roc_pivot) into the fairness module (#432)

• method set_options in Arena now takies option_category instead of plot_type (SHAPValues => ShapleyValues, FeatureImportance => VariableImportance) (#420)
• methods using the N parameter now properly sample rows from data

• fixed wrong error value when no predict_function is found in Explainer (77ca90d)
• set multiprocessing context to 'spawn' (#412)
• fixed bug in metric_scores plot that made only one subgroup appear on y-axis (#416)
• added support for older keras models (#415)

• added a resource mechanism to Arena (#419)
• added ShapleyValuesImportance and ShapleyValuesDependence plots to Arena (#420)
• return error instead of NaN when AUC is calculated on observations from one class only (#415)

• fixed concurrent random seeds when processes > 1 (#392), which means that the results of parallel computation will vary between v1.1.0 and previous versions

• GroupFairnessX.plot(type='fairness_check') generates ticks according to the x-axis range (#409)
• GroupFainressRegression.plot(type='density') has a more readable hover - only for outliers (#409)
• BreakDown.plot() wrongly displayed the "+all factors" bar when max_vars < p (#401)
• GroupFairnessClassification.plot(type='metric_scores') did not handle NaN's (#399)

• Experimental support for regression models in the fairness module. Added GroupFairnessRegression object, with the plot method having two types: fairness_check and density. Explainer.model_fairness method now depends on the model_type attribute. (#391)
• added N parameter to the predict_parts method which is None by default (#402)
• epsilon is now an argument of the GroupFairnessClassification object (#397)

• fixed broken range on yaxis in fairness_check plot (#376)
• warnings because np.float is depracated since numpy v1.20 (#384)

• added ipython to test dependencies

These are summed up in (#368):

• rename modules: dataset_level into model_explanations, instance_level into predict_explanations, _arena module into arena
• use __dir__ method to define autocompletion in IPython environment - show only ['Explainer', 'Arena', 'fairness', 'datasets']
• add plot method and result attribute to LimeExplanation (use lime.explanation.Explanation.as_pyplot_figure() and lime.explanation.Explanation.as_list())
• CeterisParibus.plot(variable_type='categorical') now has horizontal barplots - horizontal_spacing=None by default (varies on variable_type). Also, once again added the "dot" for observation value.
• predict_fn in predict_surrogate now uses predict_function (trying to make it work for more frameworks)

• fixed wrong verbose output when any value in y_hat/residuals was an int not float
• added proper "-" sign to negative dropout losses in VariableImportance.plot

• added geom='bars' to AggregateProfiles.plot to force the categorical plot
• added geom='roc' and geom='lift' to ModelPerformance.plot
• added Fairness plot to Arena

• remove colorize from Explainer
• updated the documentation, refactored code (import modules not functions, unify variable names in object.py, move utils funcitons from checks.py to utils.py, etc.)
• added license notice next to data

• added support for h2o.estimators.* (#332)
• added tensorflow.python.keras.engine.functional.Functional to the tensorflow list
• updated the plotly dependency to >=4.12.0
• code maintenance: yhat, check_data

• fixed check_if_empty_fields() used in loading the Explainer from a pickle file, since several checks were changed
• fixed plot() method in GroupFairnessClassification as it omitted plotting a metric when NaN was present in metric ratios (result)
• fixed dragons and HR datasets having , delimeter instead of ., which transformed numerical columns into categorical.
• fixed representation of the ShapWrapper class (removed _repr_html_ method)

• allow for y to be a pandas.DataFrame (converted)
• allow for data, y to be a H2OFrame (converted)
• added label parameter to all the relevant dx.Explainer methods, which overrides the default label in explanation's result
• now using GradientExplainer for tf.keras.engine.sequential.Sequential, added proper warning when shap_explainer_type is None (#366)

• unify verbose output of Explainer

• added new arena module, which adds the backend for Arena dashboard @piotrpiatyszek

• added new aliases to dx.Explainer methods (#350) in model_parts it is {'permutational': 'variable_importance', 'feature_importance': 'variable_importance'}, in model_profile it is {'pdp': 'partial', 'ale': 'accumulated'}
• added Arena object for dashboard backend. See https://github.com/ModelOriented/Arena
• new fairness plot types: stacked, radar, performance_and_fairness, heatmap, ceteris_paribus_cutoff
• upgraded fairness_check()

• added new fairness module, which will focus on bias detection, visualization and mitigation @jakwisn

• removed unnecessary warning when precalculate=False and verbose=False (#340)

• added model_fairness method to the Explainer, which performs fairness explanation
• added GroupFairnessClassification object, with the plot method having two types: fairness_check and metric_scores

• added the N=50000 argument to ResidualDiagnostics.plot, which samples observations from the result parameter to omit performance issues when smooth=True (#341)

• added support for tensorflow.python.keras.engine.sequential.Sequential and tensorflow.python.keras.engine.training.Model (#326)
• updated the tqdm dependency to >=4.48.2, pandas dependency to >=1.1.2 and numpy dependency to >=1.18.4

• fixed the wrong order of Explainer verbose messages
• fixed a bug that caused model_info parameter to be overwritten by the default values
• fixed a bug occurring when the variable from groups was not of str type (#327)
• fixed model_profile: variable_type='categorical' not working when user passed variables parameter (#329) + the reverse order of bars in 'categorical' plots + (again) added variable_splits_type parameter to model_profile to specify how grid points shall be calculated (#266) + allow for both 'quantile' and 'quantiles' types (alias)

• added informative error messages when importing optional dependencies (#316)
• allow for data and y to be None - added checks in Explainer methods

• wrong parameter name title_x changed to y_title in CeterisParibus.plot and AggregatedProfiles.plot (#317)
• now warning the user in Explainer when predict_function returns an error or doesn't return numpy.ndarray (1d) (#325)

• updated the pandas dependency to >=1.1.0

• ModelPerformance.plot now uses a drwhy color palette
• use unique method instead of np.unique in variable_splits (#293)
• v0.2.0 didn't export new datasets
• fixed a bug where predict_parts(type='shap') calculated wrong contributions (#300)
• model_profile uses observation mean instead of profile mean in _yhat_ centering
• fixed barplot baseline in categorical model_profile and predict_profile plots (#297)
• fixed model_profile(type='accumulated') giving wrong results (#302)
• vertical/horizontal lines in plots now end on the plot edges

• added new type='shap_wrapper' to predict_parts and model_parts methods, which returns a new ShapWrapper object. It contains the main result attribute (shapley_values) and the plot method (force_plot and summary_plot respectively). These come from the shap package
• Explainer.predict method now accepts numpy.ndarray
• added the ResidualDiagnostics object with a plot method
• added model_diagnostics method to the Explainer, which performs residual diagnostics
• added predict_surrogate method to the Explainer, which is a wrapper for the lime tabular explanation from the lime package
• added model_surrogate method to the Explainer, which creates a basic surrogate decision tree or linear model from the black-box model using the scikit-learn package
• added a _repr_html_ method to all of the explanation objects (it prints the result attribute)
• added dalex.__version__
• added informative error messages in Explainer methods when y is of wrong type (#294)
• CeterisParibus.plot(variable_type='categorical') now allows for multiple observations
• new verbose checks for model_type
• add type to model_info in dump and dumps for R compatibility (#303)
• ModelPerformance.result now has label as index

• removed _grid_ column in AggregatedProfiles.result and center only works with type=accumulated
• use Pipeline._final_estimator to extract model_class of the actual model
• use model._estimator_type to extract model_type if possible

• major documentation update (#270)
• unified the order of function parameters

• v0.1.9 had wrong _original_ column in predict_profile
• vertical_spacing acts as intended in VariableImportance.plot when split='variable'
• loss_function='auc' now uses loss_one_minus_auc as this should be a descending measure
• plots are now saved with the original height and width
• model_profile now properly passes the variables parameter to CeterisParibus
• variables parameter in predict_profile now can also be a string

• use px.express instead of core plotly to make model_profile and predict_profile plots; thus, enhance performance and scalability
• added verbose parameter where tqdm is used to verbose progress bar
• added loss_one_minus_auc function that can be used with loss_function='1-auc' in model_parts
• added new example data sets: apartments, dragons and hr
• added color, opacity, title_x parameters to model_profile and predict_profile plots (#236), changed tooltips and legends (#262)
• added geom='profiles' parameter to model_profile plot and raw_profiles attribute to AggregatedProfiles
• added variable_splits_type parameter to predict_profile to specify how grid points shall be calculated (#266)
• added variable_splits_with_obs parameter to predict_profile function to extend split points with observation variable values (#269)
• added variable_splits parameter to model_profile

• use different loss_function for classification and regression (#248)
• models that use proba yhats now get model_type='classification' if it's not specified
• use uniform way of grid points calculation in predict_profile and model_profile (see variable_splits_type parameter)
• add the variable values of new_observation to variable_splits in predict_profile (see variable_splits_with_obs parameter)
• use N=1000 in model_parts and N=300 in model_profile to comply with the R version
• keep_raw_permutation is now set to False instead of None in model_parts
• intercept parameter in model_profile is now named center

• feature: added random_state parameter for predict_parts(type='shap') and model_profile for reproducible calculations
• fix: fixed random_state parameter in model_parts
• feature: multiprocessing added for: model_profile, model_parts, predict_profile and predict_parts(type='shap'), through the processes parameter
• fix: significantly improved the speed of accumulated and conditional types in model_profile
• bugfix: use pd.api.types.is_numeric_dtype() instead of np.issubdtype() to cover more types; e.g. it caused errors with string type
• defaults: use pd.convert_dtypes() on the result of CeterisParibus to fix variable dtypes and later allow for a concatenation without the dtype conversion
• fix: variables parameter now can be a single str value
• fix: number rounding in predict_parts, model_parts (#245)
• fix: CP calculations for models that take only variables as an input

• bugfix: variable_splits parameter now works correctly in predict_profile
• bugfix: fix baseline for 3+ models in AggregatedProfiles.plot (#234)
• printing: now rounding numbers in Explainer messages
• fix: minor checks fixes in instance_level
• bugfix: AggregatedProfiles.plot now works with groups

• feature: parameter N in model_profile can be set to None, to select all observations
• input: groups and variable parameters in model_profile can be: str, list, numpy.ndarray, pandas.Series
• fix: check_label returned only a first letter
• bugfix: removed the conversion of all_variables to str in prepare_all_variables, which caused an error in model_profile (#214)
• defaults: change numpy data variable names from numbers to strings

• fix: change short_name encoding in fifa dataset (utf8->ascii)
• fix: remove scipy dependency
• defaults: default loss_root_mean_square in model parts changed to rmse
• bugfix: checks related to new_observation in BreakDown, Shap, CeterisParibus now work for multiple inputs (#207)
• bugfix: CeterisParibus.fit and CeterisParibus.plot now work for more types of new_observation.index, but won't work for a bolean type (#211)

• feature: add xgboost package compatibility (#188)
• feature: added model_class parameter to Explainer to handle wrapped models
• feature: Exaplainer attribute model_info remembers if parameters are default
• bugfix: variable_groups parameter now works correctly in model_parts
• fix: changed parameter order in Explainer: model_type, model_info, colorize
• documentation: model_parts documentation is updated
• feature: new show parameter in plot methods that (if False) returns plotly Figure (#190)
• feature: load_fifa() function which loads the preprocessed players_20 dataset
• fix: CeterisParibus.plot tooltip

• feature: new Explainer.residual method which uses residual_function to calculate residuals
• feature: new dump and dumps methods for saving Explainer in a binary form; load and loads methods for loading Explainer from binary form
• fix: Explainer constructor verbose text
• bugfix: B:=B+1 - Shap now stores average results as B=0 and path results as B=1,2,...
• bugfix: Explainer.model_performance method uses self.model_type when model_type is None
• bugfix: values in BreakDown and Shap are now rounded to 4 significant places (#180)
• bugfix: Shap by default uses path='average', sign column is properly updated and bars in plot are sorted by abs(contribution)

• release of the dalex package
• Explainer object with predict, predict_parts, predict_profile, model_performance, model_parts and model_profile methods
• BreakDown, Shap, CeterisParibus, ModelPerformance, VariableImportance and AggregatedProfiles objects with a plot method
• load_titanic() function which loads the titanic_imputed dataset